From 07e4809aefbbae5aff9ab00a7880bc9200b5e828 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 16 Oct 2019 14:10:48 -0700
Subject: [PATCH 001/130] newlines

---
 engine.py |  3 ++-
 model.py  | 10 ++++------
 train.py  |  8 +++-----
 3 files changed, 9 insertions(+), 12 deletions(-)

diff --git a/engine.py b/engine.py
index 9fcd933..d56a103 100644
--- a/engine.py
+++ b/engine.py
@@ -2,7 +2,8 @@
 from model import *
 import util
 class trainer():
-    def __init__(self, scaler, in_dim, seq_length, num_nodes, nhid , dropout, lrate, wdecay, device, supports, gcn_bool, addaptadj, aptinit):
+    def __init__(self, scaler, in_dim, seq_length, num_nodes, nhid , dropout, lrate, wdecay, device,
+                 supports, gcn_bool, addaptadj, aptinit):
         self.model = gwnet(device, num_nodes, dropout, supports=supports, gcn_bool=gcn_bool, addaptadj=addaptadj, aptinit=aptinit, in_dim=in_dim, out_dim=seq_length, residual_channels=nhid, dilation_channels=nhid, skip_channels=nhid * 8, end_channels=nhid * 16)
         self.model.to(device)
         self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
diff --git a/model.py b/model.py
index 3710878..edd756c 100644
--- a/model.py
+++ b/model.py
@@ -47,7 +47,9 @@ def forward(self,x,support):
 
 
 class gwnet(nn.Module):
-    def __init__(self, device, num_nodes, dropout=0.3, supports=None, gcn_bool=True, addaptadj=True, aptinit=None, in_dim=2,out_dim=12,residual_channels=32,dilation_channels=32,skip_channels=256,end_channels=512,kernel_size=2,blocks=4,layers=2):
+    def __init__(self, device, num_nodes, dropout=0.3, supports=None, gcn_bool=True, addaptadj=True, aptinit=None,
+                 in_dim=2,out_dim=12,residual_channels=32,dilation_channels=32,skip_channels=256,
+                 end_channels=512,kernel_size=2,blocks=4,layers=2):
         super(gwnet, self).__init__()
         self.dropout = dropout
         self.blocks = blocks
@@ -90,9 +92,6 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, gcn_bool=True,
                 self.nodevec2 = nn.Parameter(initemb2, requires_grad=True).to(device)
                 self.supports_len += 1
 
-
-
-
         for b in range(blocks):
             additional_scope = kernel_size - 1
             new_dilation = 1
@@ -136,9 +135,8 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, gcn_bool=True,
 
         self.receptive_field = receptive_field
 
-
-
     def forward(self, input):
+        import ipdb; ipdb.set_trace()
         in_len = input.size(3)
         if in_len<self.receptive_field:
             x = nn.functional.pad(input,(self.receptive_field-in_len,0,0,0))
diff --git a/train.py b/train.py
index 9e1cf63..3c426a7 100644
--- a/train.py
+++ b/train.py
@@ -40,7 +40,7 @@ def main():
     #np.random.seed(args.seed)
     #load data
     device = torch.device(args.device)
-    sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata,args.adjtype)
+    sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
     dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size)
     scaler = dataloader['scaler']
     supports = [torch.tensor(i).to(device) for i in adj_mx]
@@ -55,11 +55,9 @@ def main():
     if args.aptonly:
         supports = None
 
-
-
     engine = trainer(scaler, args.in_dim, args.seq_length, args.num_nodes, args.nhid, args.dropout,
-                         args.learning_rate, args.weight_decay, device, supports, args.gcn_bool, args.addaptadj,
-                         adjinit)
+                     args.learning_rate, args.weight_decay, device, supports, args.gcn_bool,
+                     args.addaptadj, adjinit)
 
 
     print("start training...",flush=True)

From 906c9251b5cc301d105d551d4a9b6b38b8c00b71 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 17 Oct 2019 15:42:28 -0700
Subject: [PATCH 002/130] still working, some more cleanup

---
 engine.py |   6 ++-
 model.py  | 125 ++++++++++++++++++++++--------------------------------
 test.py   |   6 +--
 train.py  |   9 ++--
 4 files changed, 62 insertions(+), 84 deletions(-)

diff --git a/engine.py b/engine.py
index d56a103..570047e 100644
--- a/engine.py
+++ b/engine.py
@@ -1,10 +1,12 @@
 import torch.optim as optim
 from model import *
 import util
-class trainer():
+class Trainer():
     def __init__(self, scaler, in_dim, seq_length, num_nodes, nhid , dropout, lrate, wdecay, device,
                  supports, gcn_bool, addaptadj, aptinit):
-        self.model = gwnet(device, num_nodes, dropout, supports=supports, gcn_bool=gcn_bool, addaptadj=addaptadj, aptinit=aptinit, in_dim=in_dim, out_dim=seq_length, residual_channels=nhid, dilation_channels=nhid, skip_channels=nhid * 8, end_channels=nhid * 16)
+        # TODO(SS): pass model in.
+        self.model = GWNet(device, num_nodes, dropout, supports=supports, do_graph_conv=gcn_bool, addaptadj=addaptadj, aptinit=aptinit, in_dim=in_dim,
+                           out_dim=seq_length, residual_channels=nhid, dilation_channels=nhid, skip_channels=nhid * 8, end_channels=nhid * 16)
         self.model.to(device)
         self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
         self.loss = util.masked_mae
diff --git a/model.py b/model.py
index edd756c..a817959 100644
--- a/model.py
+++ b/model.py
@@ -4,65 +4,51 @@
 from torch.autograd import Variable
 import sys
 
-
-class nconv(nn.Module):
-    def __init__(self):
-        super(nconv,self).__init__()
-
-    def forward(self,x, A):
-        x = torch.einsum('ncvl,vw->ncwl',(x,A))
-        return x.contiguous()
-
-class linear(nn.Module):
-    def __init__(self,c_in,c_out):
-        super(linear,self).__init__()
+class GraphConvNet(nn.Module):
+    def __init__(self, c_in, c_out, dropout, support_len=3, order=2):
+        super().__init__()
+        c_in = (order * support_len + 1) * c_in
         self.mlp = torch.nn.Conv2d(c_in, c_out, kernel_size=(1, 1), padding=(0,0), stride=(1,1), bias=True)
-
-    def forward(self,x):
-        return self.mlp(x)
-
-class gcn(nn.Module):
-    def __init__(self,c_in,c_out,dropout,support_len=3,order=2):
-        super(gcn,self).__init__()
-        self.nconv = nconv()
-        c_in = (order*support_len+1)*c_in
-        self.mlp = linear(c_in,c_out)
         self.dropout = dropout
         self.order = order
 
-    def forward(self,x,support):
+    def nconv(self, x, A):
+        return torch.einsum('ncvl,vw->ncwl', (x, A)).contiguous()
+
+    def forward(self, x, support):
         out = [x]
         for a in support:
             x1 = self.nconv(x,a)
             out.append(x1)
             for k in range(2, self.order + 1):
-                x2 = self.nconv(x1,a)
+                x2 = self.nconv(x1, a)
                 out.append(x2)
                 x1 = x2
 
-        h = torch.cat(out,dim=1)
+        h = torch.cat(out, dim=1)
         h = self.mlp(h)
         h = F.dropout(h, self.dropout, training=self.training)
         return h
 
 
-class gwnet(nn.Module):
-    def __init__(self, device, num_nodes, dropout=0.3, supports=None, gcn_bool=True, addaptadj=True, aptinit=None,
-                 in_dim=2,out_dim=12,residual_channels=32,dilation_channels=32,skip_channels=256,
-                 end_channels=512,kernel_size=2,blocks=4,layers=2):
-        super(gwnet, self).__init__()
+class GWNet(nn.Module):
+    def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=True, addaptadj=True, aptinit=None,
+                 in_dim=2, out_dim=12, residual_channels=32, dilation_channels=32, skip_channels=256,
+                 end_channels=512, kernel_size=2, blocks=4, layers=2):
+        super().__init__()
         self.dropout = dropout
         self.blocks = blocks
         self.layers = layers
-        self.gcn_bool = gcn_bool
+        self.do_graph_conv = do_graph_conv
         self.addaptadj = addaptadj
 
+        # Each of these will
         self.filter_convs = nn.ModuleList()
         self.gate_convs = nn.ModuleList()
         self.residual_convs = nn.ModuleList()
         self.skip_convs = nn.ModuleList()
         self.bn = nn.ModuleList()
-        self.gconv = nn.ModuleList()
+        self.graph_convs = nn.ModuleList()
 
         self.start_conv = nn.Conv2d(in_channels=in_dim,
                                     out_channels=residual_channels,
@@ -75,16 +61,13 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, gcn_bool=True,
         if supports is not None:
             self.supports_len += len(supports)
 
-        if gcn_bool and addaptadj:
+        if do_graph_conv and addaptadj:
+            if supports is None: self.supports = []
             if aptinit is None:
-                if supports is None:
-                    self.supports = []
-                self.nodevec1 = nn.Parameter(torch.randn(num_nodes, 10).to(device), requires_grad=True).to(device)
-                self.nodevec2 = nn.Parameter(torch.randn(10, num_nodes).to(device), requires_grad=True).to(device)
+                self.nodevec1 = nn.Parameter(torch.randn(num_nodes, 10), requires_grad=True).to(device)
+                self.nodevec2 = nn.Parameter(torch.randn(10, num_nodes), requires_grad=True).to(device)
                 self.supports_len +=1
             else:
-                if supports is None:
-                    self.supports = []
                 m, p, n = torch.svd(aptinit)
                 initemb1 = torch.mm(m[:, :10], torch.diag(p[:10] ** 0.5))
                 initemb2 = torch.mm(torch.diag(p[:10] ** 0.5), n[:, :10].t())
@@ -99,7 +82,7 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, gcn_bool=True,
                 # dilated convolutions
                 self.filter_convs.append(nn.Conv2d(in_channels=residual_channels,
                                                    out_channels=dilation_channels,
-                                                   kernel_size=(1,kernel_size),dilation=new_dilation))
+                                                   kernel_size=(1,kernel_size), dilation=new_dilation))
 
                 self.gate_convs.append(nn.Conv1d(in_channels=residual_channels,
                                                  out_channels=dilation_channels,
@@ -118,84 +101,76 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, gcn_bool=True,
                 new_dilation *=2
                 receptive_field += additional_scope
                 additional_scope *= 2
-                if self.gcn_bool:
-                    self.gconv.append(gcn(dilation_channels,residual_channels,dropout,support_len=self.supports_len))
-
-
+                if self.do_graph_conv:
+                    self.graph_convs.append(GraphConvNet(dilation_channels, residual_channels, dropout, support_len=self.supports_len))
 
         self.end_conv_1 = nn.Conv2d(in_channels=skip_channels,
-                                  out_channels=end_channels,
-                                  kernel_size=(1,1),
-                                  bias=True)
+                                    out_channels=end_channels,
+                                    kernel_size=(1, 1),
+                                    bias=True)
 
         self.end_conv_2 = nn.Conv2d(in_channels=end_channels,
                                     out_channels=out_dim,
-                                    kernel_size=(1,1),
+                                    kernel_size=(1, 1),
                                     bias=True)
 
         self.receptive_field = receptive_field
 
     def forward(self, input):
-        import ipdb; ipdb.set_trace()
+        # Input shape is (bs, features, n_nodes, n_timesteps)
         in_len = input.size(3)
         if in_len<self.receptive_field:
-            x = nn.functional.pad(input,(self.receptive_field-in_len,0,0,0))
+            x = nn.functional.pad(input, (self.receptive_field - in_len,0,0,0))
         else:
             x = input
         x = self.start_conv(x)
         skip = 0
 
         # calculate the current adaptive adj matrix once per iteration
-        new_supports = None
-        if self.gcn_bool and self.addaptadj and self.supports is not None:
+        if self.do_graph_conv and self.addaptadj and self.supports is not None:
             adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)), dim=1)
-            new_supports = self.supports + [adp]
+            adjacency_matrices = self.supports + [adp]
+        else:
+            adjacency_matrices = self.supports
 
         # WaveNet layers
         for i in range(self.blocks * self.layers):
+            # EACH BLOCK
 
             #            |----------------------------------------|     *residual*
             #            |                                        |
-            #            |    |-- conv -- tanh --|                |
-            # -> dilate -|----|                  * ----|-- 1x1 -- + -->	*input*
-            #                 |-- conv -- sigm --|     |
+            #            |   |-dil_conv -- tanh --|                |
+            #         ---|                  * ----|-- 1x1 -- + -->	*input*
+            #                |-dil_conv -- sigm --|    |
             #                                         1x1
             #                                          |
             # ---------------------------------------> + ------------->	*skip*
-
-            #(dilation, init_dilation) = self.dilations[i]
-
-            #residual = dilation_func(x, dilation, init_dilation, i)
             residual = x
             # dilated convolution
-            filter = self.filter_convs[i](residual)
-            filter = torch.tanh(filter)
-            gate = self.gate_convs[i](residual)
-            gate = torch.sigmoid(gate)
+            filter = torch.tanh(self.filter_convs[i](residual))
+            gate = torch.sigmoid(self.gate_convs[i](residual))
             x = filter * gate
-
             # parametrized skip connection
-
             s = x
             s = self.skip_convs[i](s)
-            try:
-                skip = skip[:, :, :,  -s.size(3):]
+            try:  # if i > 0 this works
+                skip = skip[:, :, :,  -s.size(3):]  # Mean/Max Pool?
+                print(f'i={i}, SKIP={s.size()}')
             except:
+                print(f'i={i}, SKIP={s.size()}, setting skip=0')
                 skip = 0
             skip = s + skip
 
 
-            if self.gcn_bool and self.supports is not None:
+            if self.do_graph_conv and self.supports is not None:
                 if self.addaptadj:
-                    x = self.gconv[i](x, new_supports)
+                    x = self.graph_convs[i](x, adjacency_matrices)
                 else:
-                    x = self.gconv[i](x,self.supports)
+                    x = self.graph_convs[i](x, self.supports)
             else:
                 x = self.residual_convs[i](x)
-
-            x = x + residual[:, :, :, -x.size(3):]
-
-
+            print(f'x: {x.size()} residual: {residual.size()}')
+            x = x + residual[:, :, :, -x.size(3):] # Mean/Max Pool?
             x = self.bn[i](x)
 
         x = F.relu(skip)
diff --git a/test.py b/test.py
index 3d0d15e..f8f2784 100644
--- a/test.py
+++ b/test.py
@@ -11,7 +11,7 @@
 parser.add_argument('--data',type=str,default='data/METR-LA',help='data path')
 parser.add_argument('--adjdata',type=str,default='data/sensor_graph/adj_mx.pkl',help='adj data path')
 parser.add_argument('--adjtype',type=str,default='doubletransition',help='adj type')
-parser.add_argument('--gcn_bool',action='store_true',help='whether to add graph convolution layer')
+parser.add_argument('--do_graph_conv',action='store_true',help='whether to add graph convolution layer')
 parser.add_argument('--aptonly',action='store_true',help='whether only adaptive adj')
 parser.add_argument('--addaptadj',action='store_true',help='whether add adaptive adj')
 parser.add_argument('--randomadj',action='store_true',help='whether random initialize adaptive adj')
@@ -45,13 +45,13 @@ def main():
     if args.aptonly:
         supports = None
 
-    model =  gwnet(device, args.num_nodes, args.dropout, supports=supports, gcn_bool=args.gcn_bool, addaptadj=args.addaptadj, aptinit=adjinit)
+    model = GWNet(device, args.num_nodes, args.dropout, supports=supports, do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=adjinit)
     model.to(device)
     model.load_state_dict(torch.load(args.checkpoint))
     model.eval()
 
 
-    print('model load successfully')
+    print('model loaded successfully')
 
     dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size)
     scaler = dataloader['scaler']
diff --git a/train.py b/train.py
index 3c426a7..27b3873 100644
--- a/train.py
+++ b/train.py
@@ -4,14 +4,14 @@
 import time
 import util
 import matplotlib.pyplot as plt
-from engine import trainer
+from engine import Trainer
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--device',type=str,default='cuda:3',help='')
 parser.add_argument('--data',type=str,default='data/METR-LA',help='data path')
 parser.add_argument('--adjdata',type=str,default='data/sensor_graph/adj_mx.pkl',help='adj data path')
 parser.add_argument('--adjtype',type=str,default='doubletransition',help='adj type')
-parser.add_argument('--gcn_bool',action='store_true',help='whether to add graph convolution layer')
+parser.add_argument('--do_graph_conv',action='store_true',help='whether to add graph convolution layer')
 parser.add_argument('--aptonly',action='store_true',help='whether only adaptive adj')
 parser.add_argument('--addaptadj',action='store_true',help='whether add adaptive adj')
 parser.add_argument('--randomadj',action='store_true',help='whether random initialize adaptive adj')
@@ -39,6 +39,7 @@ def main():
     #torch.manual_seed(args.seed)
     #np.random.seed(args.seed)
     #load data
+    print("HERE")
     device = torch.device(args.device)
     sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
     dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size)
@@ -55,8 +56,8 @@ def main():
     if args.aptonly:
         supports = None
 
-    engine = trainer(scaler, args.in_dim, args.seq_length, args.num_nodes, args.nhid, args.dropout,
-                     args.learning_rate, args.weight_decay, device, supports, args.gcn_bool,
+    engine = Trainer(scaler, args.in_dim, args.seq_length, args.num_nodes, args.nhid, args.dropout,
+                     args.learning_rate, args.weight_decay, device, supports, args.do_graph_conv,
                      args.addaptadj, adjinit)
 
 

From 504e094b3127c4ee8de82ce9427bba5cd857cfbb Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 31 Oct 2019 14:09:56 -0700
Subject: [PATCH 003/130] little if simplification

---
 model.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/model.py b/model.py
index a817959..d11ea71 100644
--- a/model.py
+++ b/model.py
@@ -154,7 +154,7 @@ def forward(self, input):
             s = x
             s = self.skip_convs[i](s)
             try:  # if i > 0 this works
-                skip = skip[:, :, :,  -s.size(3):]  # Mean/Max Pool?
+                skip = skip[:, :, :,  -s.size(3):]  # TODO(SS): Mean/Max Pool?
                 print(f'i={i}, SKIP={s.size()}')
             except:
                 print(f'i={i}, SKIP={s.size()}, setting skip=0')
@@ -163,14 +163,12 @@ def forward(self, input):
 
 
             if self.do_graph_conv and self.supports is not None:
-                if self.addaptadj:
-                    x = self.graph_convs[i](x, adjacency_matrices)
-                else:
-                    x = self.graph_convs[i](x, self.supports)
+                support = adjacency_matrices if self.addaptadj else self.supports
+                x = self.graph_convs[i](x, support)
             else:
                 x = self.residual_convs[i](x)
             print(f'x: {x.size()} residual: {residual.size()}')
-            x = x + residual[:, :, :, -x.size(3):] # Mean/Max Pool?
+            x = x + residual[:, :, :, -x.size(3):] # TODO(SS): Mean/Max Pool?
             x = self.bn[i](x)
 
         x = F.relu(skip)

From ff9af4213b8f672cdeb019ef0d00aed0ca524996 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 31 Oct 2019 16:01:37 -0700
Subject: [PATCH 004/130] slow ass utest

---
 engine.py     |   9 ++-
 model.py      |   2 +-
 test_gwnet.py |  11 ++++
 train.py      | 169 +++++++++++++++++++++++++-------------------------
 4 files changed, 100 insertions(+), 91 deletions(-)
 create mode 100644 test_gwnet.py

diff --git a/engine.py b/engine.py
index 570047e..5dc4e39 100644
--- a/engine.py
+++ b/engine.py
@@ -3,7 +3,7 @@
 import util
 class Trainer():
     def __init__(self, scaler, in_dim, seq_length, num_nodes, nhid , dropout, lrate, wdecay, device,
-                 supports, gcn_bool, addaptadj, aptinit):
+                 supports, gcn_bool, addaptadj, aptinit, clip=5):
         # TODO(SS): pass model in.
         self.model = GWNet(device, num_nodes, dropout, supports=supports, do_graph_conv=gcn_bool, addaptadj=addaptadj, aptinit=aptinit, in_dim=in_dim,
                            out_dim=seq_length, residual_channels=nhid, dilation_channels=nhid, skip_channels=nhid * 8, end_channels=nhid * 16)
@@ -11,7 +11,7 @@ def __init__(self, scaler, in_dim, seq_length, num_nodes, nhid , dropout, lrate,
         self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
         self.loss = util.masked_mae
         self.scaler = scaler
-        self.clip = 5
+        self.clip = clip
 
     def train(self, input, real_val):
         self.model.train()
@@ -19,10 +19,9 @@ def train(self, input, real_val):
         input = nn.functional.pad(input,(1,0,0,0))
         output = self.model(input)
         output = output.transpose(1,3)
-        #output = [batch_size,12,num_nodes,1]
-        real = torch.unsqueeze(real_val,dim=1)
         predict = self.scaler.inverse_transform(output)
-
+        #output = [batch_size,12,num_nodes,1]
+        real = torch.unsqueeze(real_val, dim=1)
         loss = self.loss(predict, real, 0.0)
         loss.backward()
         if self.clip is not None:
diff --git a/model.py b/model.py
index d11ea71..e712ec8 100644
--- a/model.py
+++ b/model.py
@@ -171,7 +171,7 @@ def forward(self, input):
             x = x + residual[:, :, :, -x.size(3):] # TODO(SS): Mean/Max Pool?
             x = self.bn[i](x)
 
-        x = F.relu(skip)
+        x = F.relu(skip)  # ignore last X?
         x = F.relu(self.end_conv_1(x))
         x = self.end_conv_2(x)
         return x
diff --git a/test_gwnet.py b/test_gwnet.py
new file mode 100644
index 0000000..80edc38
--- /dev/null
+++ b/test_gwnet.py
@@ -0,0 +1,11 @@
+from train import main
+import unittest
+from durbango import pickle_load
+TEST_ARGS_PATH = 'test_args.pkl'
+
+class TestTrain(unittest.TestCase):
+
+    def test_1_epoch(self):
+        args = pickle_load(TEST_ARGS_PATH)
+        args.epochs = 1
+        main(args)
diff --git a/train.py b/train.py
index 27b3873..f1d14fb 100644
--- a/train.py
+++ b/train.py
@@ -5,46 +5,21 @@
 import util
 import matplotlib.pyplot as plt
 from engine import Trainer
+from durbango import pickle_save
 
-parser = argparse.ArgumentParser()
-parser.add_argument('--device',type=str,default='cuda:3',help='')
-parser.add_argument('--data',type=str,default='data/METR-LA',help='data path')
-parser.add_argument('--adjdata',type=str,default='data/sensor_graph/adj_mx.pkl',help='adj data path')
-parser.add_argument('--adjtype',type=str,default='doubletransition',help='adj type')
-parser.add_argument('--do_graph_conv',action='store_true',help='whether to add graph convolution layer')
-parser.add_argument('--aptonly',action='store_true',help='whether only adaptive adj')
-parser.add_argument('--addaptadj',action='store_true',help='whether add adaptive adj')
-parser.add_argument('--randomadj',action='store_true',help='whether random initialize adaptive adj')
-parser.add_argument('--seq_length',type=int,default=12,help='')
-parser.add_argument('--nhid',type=int,default=32,help='')
-parser.add_argument('--in_dim',type=int,default=2,help='inputs dimension')
-parser.add_argument('--num_nodes',type=int,default=207,help='number of nodes')
-parser.add_argument('--batch_size',type=int,default=64,help='batch size')
-parser.add_argument('--learning_rate',type=float,default=0.001,help='learning rate')
-parser.add_argument('--dropout',type=float,default=0.3,help='dropout rate')
-parser.add_argument('--weight_decay',type=float,default=0.0001,help='weight decay rate')
-parser.add_argument('--epochs',type=int,default=100,help='')
-parser.add_argument('--print_every',type=int,default=50,help='')
-#parser.add_argument('--seed',type=int,default=99,help='random seed')
-parser.add_argument('--save',type=str,default='./garage/metr',help='save path')
-parser.add_argument('--expid',type=int,default=1,help='experiment id')
-
-args = parser.parse_args()
-
-
-
-
-def main():
-    #set seed
-    #torch.manual_seed(args.seed)
-    #np.random.seed(args.seed)
-    #load data
+
+
+
+
+
+def main(args):
     print("HERE")
     device = torch.device(args.device)
     sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
+    supports = [torch.tensor(i).to(device) for i in adj_mx]
     dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size)
     scaler = dataloader['scaler']
-    supports = [torch.tensor(i).to(device) for i in adj_mx]
+
 
     print(args)
 
@@ -62,9 +37,7 @@ def main():
 
 
     print("start training...",flush=True)
-    his_loss =[]
-    val_time = []
-    train_time = []
+    his_loss, val_time, train_time =[], [], []
     for i in range(1,args.epochs+1):
         #if i % 10 == 0:
             #lr = max(0.000002,args.learning_rate * (0.1 ** (i // 10)))
@@ -76,39 +49,20 @@ def main():
         t1 = time.time()
         dataloader['train_loader'].shuffle()
         for iter, (x, y) in enumerate(dataloader['train_loader'].get_iterator()):
-            trainx = torch.Tensor(x).to(device)
-            trainx= trainx.transpose(1, 3)
-            trainy = torch.Tensor(y).to(device)
-            trainy = trainy.transpose(1, 3)
-            metrics = engine.train(trainx, trainy[:,0,:,:])
-            train_loss.append(metrics[0])
-            train_mape.append(metrics[1])
-            train_rmse.append(metrics[2])
+            trainx = torch.Tensor(x).to(device).transpose(1, 3)
+            trainy = torch.Tensor(y).to(device).transpose(1, 3)
+            loss, mape, rmse = engine.train(trainx, trainy[:,0,:,:])
+            train_loss.append(loss)
+            train_mape.append(mape)
+            train_rmse.append(rmse)
             if iter % args.print_every == 0 :
-                log = 'Iter: {:03d}, Train Loss: {:.4f}, Train MAPE: {:.4f}, Train RMSE: {:.4f}'
-                print(log.format(iter, train_loss[-1], train_mape[-1], train_rmse[-1]),flush=True)
-        t2 = time.time()
-        train_time.append(t2-t1)
-        #validation
-        valid_loss = []
-        valid_mape = []
-        valid_rmse = []
-
-
-        s1 = time.time()
-        for iter, (x, y) in enumerate(dataloader['val_loader'].get_iterator()):
-            testx = torch.Tensor(x).to(device)
-            testx = testx.transpose(1, 3)
-            testy = torch.Tensor(y).to(device)
-            testy = testy.transpose(1, 3)
-            metrics = engine.eval(testx, testy[:,0,:,:])
-            valid_loss.append(metrics[0])
-            valid_mape.append(metrics[1])
-            valid_rmse.append(metrics[2])
-        s2 = time.time()
-        log = 'Epoch: {:03d}, Inference Time: {:.4f} secs'
-        print(log.format(i,(s2-s1)))
-        val_time.append(s2-s1)
+                print(f'Iter: {iter:03d}, Train Loss: {loss:.4f}, Train MAPE: {mape:.4f}, Train RMSE: {rmse:.4f}', flush=True)
+        train_time.append(time.time()-t1)
+        total_time, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device, engine)
+        print(f'Epoch: {i:03d}, val time: {total_time} seconds.')
+        val_time.append(total_time)
+
+
         mtrain_loss = np.mean(train_loss)
         mtrain_mape = np.mean(train_mape)
         mtrain_rmse = np.mean(train_rmse)
@@ -134,8 +88,7 @@ def main():
     realy = realy.transpose(1,3)[:,0,:,:]
 
     for iter, (x, y) in enumerate(dataloader['test_loader'].get_iterator()):
-        testx = torch.Tensor(x).to(device)
-        testx = testx.transpose(1,3)
+        testx = torch.Tensor(x).to(device).transpose(1,3)
         with torch.no_grad():
             preds = engine.model(testx).transpose(1,3)
         outputs.append(preds.squeeze())
@@ -145,7 +98,7 @@ def main():
 
 
     print("Training finished")
-    print("The valid loss on best model is", str(round(his_loss[bestid],4)))
+    print(f'Valid loss on best model = {his_loss[bestid]:.4f}')
 
 
     amae = []
@@ -154,21 +107,67 @@ def main():
     for i in range(12):
         pred = scaler.inverse_transform(yhat[:,:,i])
         real = realy[:,:,i]
-        metrics = util.metric(pred,real)
-        log = 'Evaluate best model on test data for horizon {:d}, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}'
-        print(log.format(i+1, metrics[0], metrics[1], metrics[2]))
-        amae.append(metrics[0])
-        amape.append(metrics[1])
-        armse.append(metrics[2])
-
-    log = 'On average over 12 horizons, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}'
-    print(log.format(np.mean(amae),np.mean(amape),np.mean(armse)))
-    torch.save(engine.model.state_dict(), args.save+"_exp"+str(args.expid)+"_best_"+str(round(his_loss[bestid],2))+".pth")
-
+        mae,mape,rmse = util.metric(pred,real)
+        print(f'Best model on test data for horizon {i+1}, Test MAE: {mae:.4f}, Test MAPE: {mape:.4f}, Test RMSE: {rmse:.4f}')
+        amae.append(mae)
+        amape.append(mape)
+        armse.append(rmse)
+
+    print(
+        f'On average over 12 horizons, Test MAE: {np.mean(amae):.4f}, Test MAPE: {np.mean(amape):.4f}, Test RMSE: {np.mean(armse):.4f}'
+    )
+
+    save_path = f'{args.save}_exp{args.expid}_best_+{his_loss[bestid]:.2f}.pth'
+    torch.save(engine.model.state_dict(), save_path)
+
+
+def eval_(ds, device, engine):
+    valid_loss = []
+    valid_mape = []
+    valid_rmse = []
+    s1 = time.time()
+    for (x, y) in ds.get_iterator():
+        testx = torch.Tensor(x).to(device).transpose(1, 3)
+        testy = torch.Tensor(y).to(device).transpose(1, 3)
+        metrics = engine.eval(testx, testy[:, 0, :, :])
+        valid_loss.append(metrics[0])
+        valid_mape.append(metrics[1])
+        valid_rmse.append(metrics[2])
+    total_time = time.time() - s1
+    return total_time, valid_loss, valid_mape, valid_rmse
 
 
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cuda:0', help='')
+    parser.add_argument('--data', type=str, default='data/METR-LA', help='data path')
+    parser.add_argument('--adjdata', type=str, default='data/sensor_graph/adj_mx.pkl',
+                        help='adj data path')
+    parser.add_argument('--adjtype', type=str, default='doubletransition', help='adj type')
+    parser.add_argument('--do_graph_conv', action='store_true',
+                        help='whether to add graph convolution layer')
+    parser.add_argument('--aptonly', action='store_true', help='whether only adaptive adj')
+    parser.add_argument('--addaptadj', action='store_true', help='whether add adaptive adj')
+    parser.add_argument('--randomadj', action='store_true',
+                        help='whether random initialize adaptive adj')
+    parser.add_argument('--seq_length', type=int, default=12, help='')
+    parser.add_argument('--nhid', type=int, default=32, help='')
+    parser.add_argument('--in_dim', type=int, default=2, help='inputs dimension')
+    parser.add_argument('--num_nodes', type=int, default=207, help='number of nodes')
+    parser.add_argument('--batch_size', type=int, default=64, help='batch size')
+    parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
+    parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
+    parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
+    parser.add_argument('--epochs', type=int, default=100, help='')
+    parser.add_argument('--print_every', type=int, default=50, help='')
+    # parser.add_argument('--seed',type=int,default=99,help='random seed')
+    parser.add_argument('--save', type=str, default='experiment', help='save path')
+    parser.add_argument('--expid', default=1, help='experiment id')
+
+    args = parser.parse_args()
     t1 = time.time()
-    main()
+    pickle_save(args, 'args.pkl')
+    main(args)
     t2 = time.time()
-    print("Total time spent: {:.4f}".format(t2-t1))
+    mins = (t2 - t1)/60
+    print(f"Total time spent: {mins:.2f} seconds")

From e15e1397eb18ee494e6d967fe6837515a1020faa Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 14:24:56 -0700
Subject: [PATCH 005/130] metrics cleanup

---
 model.py      |  4 ---
 test_gwnet.py |  3 ++
 train.py      | 84 +++++++++++++++++++++++----------------------------
 util.py       |  7 ++++-
 4 files changed, 47 insertions(+), 51 deletions(-)

diff --git a/model.py b/model.py
index e712ec8..d4b5f09 100644
--- a/model.py
+++ b/model.py
@@ -155,19 +155,15 @@ def forward(self, input):
             s = self.skip_convs[i](s)
             try:  # if i > 0 this works
                 skip = skip[:, :, :,  -s.size(3):]  # TODO(SS): Mean/Max Pool?
-                print(f'i={i}, SKIP={s.size()}')
             except:
-                print(f'i={i}, SKIP={s.size()}, setting skip=0')
                 skip = 0
             skip = s + skip
 
-
             if self.do_graph_conv and self.supports is not None:
                 support = adjacency_matrices if self.addaptadj else self.supports
                 x = self.graph_convs[i](x, support)
             else:
                 x = self.residual_convs[i](x)
-            print(f'x: {x.size()} residual: {residual.size()}')
             x = x + residual[:, :, :, -x.size(3):] # TODO(SS): Mean/Max Pool?
             x = self.bn[i](x)
 
diff --git a/test_gwnet.py b/test_gwnet.py
index 80edc38..cbdb36e 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -8,4 +8,7 @@ class TestTrain(unittest.TestCase):
     def test_1_epoch(self):
         args = pickle_load(TEST_ARGS_PATH)
         args.epochs = 1
+        args.n_iters = 1
+        args.batch_size = 4
+        args.n_obs = 4
         main(args)
diff --git a/train.py b/train.py
index f1d14fb..00f966f 100644
--- a/train.py
+++ b/train.py
@@ -1,27 +1,24 @@
 import torch
 import numpy as np
+import pandas as pd
 import argparse
 import time
 import util
 import matplotlib.pyplot as plt
 from engine import Trainer
+import os
 from durbango import pickle_save
 
 
-
-
-
-
 def main(args):
     print("HERE")
     device = torch.device(args.device)
     sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
     supports = [torch.tensor(i).to(device) for i in adj_mx]
-    dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size)
+    dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
     scaler = dataloader['scaler']
-
-
     print(args)
+    best_model_save_path = os.path.join(args.save, 'best_model.pth')
 
     if args.randomadj:
         adjinit = None
@@ -37,7 +34,9 @@ def main(args):
 
 
     print("start training...",flush=True)
-    his_loss, val_time, train_time =[], [], []
+    his_loss, val_time, train_time = [], [], []
+    metrics = []
+    best_yet = 100
     for i in range(1,args.epochs+1):
         #if i % 10 == 0:
             #lr = max(0.000002,args.learning_rate * (0.1 ** (i // 10)))
@@ -57,30 +56,34 @@ def main(args):
             train_rmse.append(rmse)
             if iter % args.print_every == 0 :
                 print(f'Iter: {iter:03d}, Train Loss: {loss:.4f}, Train MAPE: {mape:.4f}, Train RMSE: {rmse:.4f}', flush=True)
+            if args.n_iters is not None and iter >= args.n_iters:
+                break
         train_time.append(time.time()-t1)
         total_time, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device, engine)
         print(f'Epoch: {i:03d}, val time: {total_time} seconds.')
         val_time.append(total_time)
 
-
-        mtrain_loss = np.mean(train_loss)
-        mtrain_mape = np.mean(train_mape)
-        mtrain_rmse = np.mean(train_rmse)
-
-        mvalid_loss = np.mean(valid_loss)
-        mvalid_mape = np.mean(valid_mape)
-        mvalid_rmse = np.mean(valid_rmse)
-        his_loss.append(mvalid_loss)
-
-        log = 'Epoch: {:03d}, Train Loss: {:.4f}, Train MAPE: {:.4f}, Train RMSE: {:.4f}, Valid Loss: {:.4f}, Valid MAPE: {:.4f}, Valid RMSE: {:.4f}, Training Time: {:.4f}/epoch'
-        print(log.format(i, mtrain_loss, mtrain_mape, mtrain_rmse, mvalid_loss, mvalid_mape, mvalid_rmse, (t2 - t1)),flush=True)
-        torch.save(engine.model.state_dict(), args.save+"_epoch_"+str(i)+"_"+str(round(mvalid_loss,2))+".pth")
-    print("Average Training Time: {:.4f} secs/epoch".format(np.mean(train_time)))
-    print("Average Inference Time: {:.4f} secs".format(np.mean(val_time)))
-
+        m = pd.Series(dict(train_loss=np.mean(train_loss),
+                           train_mape=np.mean(train_mape),
+                           train_rmse=np.mean(train_rmse),
+                           valid_loss=np.mean(valid_loss),
+                           valid_mape=np.mean(valid_mape),
+                           valid_rmse=np.mean(valid_rmse), ))
+        metrics.append(m)
+
+        his_loss.append(m.valid_loss)
+        print(f'Epoch {i}')
+        print(m.round(4))
+
+        #log = 'Epoch: {:03d}, Train Loss: {:.4f}, Train MAPE: {:.4f}, Train RMSE: {:.4f}, Valid Loss: {:.4f}, Valid MAPE: {:.4f}, Valid RMSE: {:.4f}, Training Time: {:.4f}/epoch'
+        #print(log.format(i, mtrain_loss, mtrain_mape, mtrain_rmse, mvalid_loss, mvalid_mape, mvalid_rmse, time.time() - t1),flush=True)
+        if m.valid_loss < best_yet:
+            torch.save(engine.model.state_dict(), best_model_save_path)
+            best_yet = m.valid_loss
+        met_df = pd.concat(metrics).T
+        met_df.to_csv(f'{args.save}/metrics.csv')
     #testing
-    bestid = np.argmin(his_loss)
-    engine.model.load_state_dict(torch.load(args.save+"_epoch_"+str(bestid+1)+"_"+str(round(his_loss[bestid],2))+".pth"))
+    engine.model.load_state_dict(torch.load(best_model_save_path))
 
 
     outputs = []
@@ -98,28 +101,15 @@ def main(args):
 
 
     print("Training finished")
-    print(f'Valid loss on best model = {his_loss[bestid]:.4f}')
-
-
-    amae = []
-    amape = []
-    armse = []
+    print(f'Valid loss on best model = {met_df.valid_loss.min():.4f}')
+    test_met = []
     for i in range(12):
         pred = scaler.inverse_transform(yhat[:,:,i])
         real = realy[:,:,i]
-        mae,mape,rmse = util.metric(pred,real)
-        print(f'Best model on test data for horizon {i+1}, Test MAE: {mae:.4f}, Test MAPE: {mape:.4f}, Test RMSE: {rmse:.4f}')
-        amae.append(mae)
-        amape.append(mape)
-        armse.append(rmse)
-
-    print(
-        f'On average over 12 horizons, Test MAE: {np.mean(amae):.4f}, Test MAPE: {np.mean(amape):.4f}, Test RMSE: {np.mean(armse):.4f}'
-    )
-
-    save_path = f'{args.save}_exp{args.expid}_best_+{his_loss[bestid]:.2f}.pth'
-    torch.save(engine.model.state_dict(), save_path)
-
+        test_met.append(util.metric(pred,real))
+    test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t').round(3)
+    test_met_df.to_csv(os.path.join(args.save, 'test_metrics.csv'))
+    print(test_met_df.mean().round(3))
 
 def eval_(ds, device, engine):
     valid_loss = []
@@ -163,10 +153,12 @@ def eval_(ds, device, engine):
     # parser.add_argument('--seed',type=int,default=99,help='random seed')
     parser.add_argument('--save', type=str, default='experiment', help='save path')
     parser.add_argument('--expid', default=1, help='experiment id')
+    parser.add_argument('--n_iters', default=None, help='quit after this many iterations')
+    parser.add_argument('--n_obs', default=None, help='Only use this many observations')
 
     args = parser.parse_args()
     t1 = time.time()
-    pickle_save(args, 'args.pkl')
+    pickle_save(args, f'{args.save}/args.pkl')
     main(args)
     t2 = time.time()
     mins = (t2 - t1)/60
diff --git a/util.py b/util.py
index d41afd7..ace05c9 100644
--- a/util.py
+++ b/util.py
@@ -141,16 +141,21 @@ def load_adj(pkl_filename, adjtype):
     return sensor_ids, sensor_id_to_ind, adj
 
 
-def load_dataset(dataset_dir, batch_size, valid_batch_size= None, test_batch_size=None):
+def load_dataset(dataset_dir, batch_size, valid_batch_size= None, test_batch_size=None, n_obs=None):
     data = {}
     for category in ['train', 'val', 'test']:
         cat_data = np.load(os.path.join(dataset_dir, category + '.npz'))
         data['x_' + category] = cat_data['x']
         data['y_' + category] = cat_data['y']
+        if n_obs is not None:
+            data['x_' + category] = data['x_' + category][:n_obs]
+            data['y_' + category] = data['y_' + category][:n_obs]
     scaler = StandardScaler(mean=data['x_train'][..., 0].mean(), std=data['x_train'][..., 0].std())
     # Data format
     for category in ['train', 'val', 'test']:
         data['x_' + category][..., 0] = scaler.transform(data['x_' + category][..., 0])
+
+
     data['train_loader'] = DataLoader(data['x_train'], data['y_train'], batch_size)
     data['val_loader'] = DataLoader(data['x_val'], data['y_val'], valid_batch_size)
     data['test_loader'] = DataLoader(data['x_test'], data['y_test'], test_batch_size)

From e1f9524a95615044c8457aad96698fa8e524c665 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 14:25:30 -0700
Subject: [PATCH 006/130] test args

---
 test_args.pkl | Bin 0 -> 491 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 test_args.pkl

diff --git a/test_args.pkl b/test_args.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..0f8dbb8abc1e816df550881f87576da07faee89b
GIT binary patch
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literal 0
HcmV?d00001


From 5a1b19cd95d597975eefbbe3eed5a3aebf84a5fe Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 14:28:55 -0700
Subject: [PATCH 007/130] no expid

---
 train.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/train.py b/train.py
index 00f966f..bdd0095 100644
--- a/train.py
+++ b/train.py
@@ -8,6 +8,7 @@
 from engine import Trainer
 import os
 from durbango import pickle_save
+from pathlib import Path
 
 
 def main(args):
@@ -152,12 +153,13 @@ def eval_(ds, device, engine):
     parser.add_argument('--print_every', type=int, default=50, help='')
     # parser.add_argument('--seed',type=int,default=99,help='random seed')
     parser.add_argument('--save', type=str, default='experiment', help='save path')
-    parser.add_argument('--expid', default=1, help='experiment id')
+    # parser.add_argument('--expid', default=1, help='experiment id')
     parser.add_argument('--n_iters', default=None, help='quit after this many iterations')
     parser.add_argument('--n_obs', default=None, help='Only use this many observations')
 
     args = parser.parse_args()
     t1 = time.time()
+
     pickle_save(args, f'{args.save}/args.pkl')
     main(args)
     t2 = time.time()

From a4baa863802fad98d8c3b52b576dbd002b212981 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 14:43:19 -0700
Subject: [PATCH 008/130] more cleanup

---
 train.py | 16 +++-------------
 1 file changed, 3 insertions(+), 13 deletions(-)

diff --git a/train.py b/train.py
index bdd0095..5604f54 100644
--- a/train.py
+++ b/train.py
@@ -12,7 +12,6 @@
 
 
 def main(args):
-    print("HERE")
     device = torch.device(args.device)
     sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
     supports = [torch.tensor(i).to(device) for i in adj_mx]
@@ -28,14 +27,11 @@ def main(args):
 
     if args.aptonly:
         supports = None
-
     engine = Trainer(scaler, args.in_dim, args.seq_length, args.num_nodes, args.nhid, args.dropout,
                      args.learning_rate, args.weight_decay, device, supports, args.do_graph_conv,
                      args.addaptadj, adjinit)
-
-
     print("start training...",flush=True)
-    his_loss, val_time, train_time = [], [], []
+    his_loss, train_time = [], [], []
     metrics = []
     best_yet = 100
     for i in range(1,args.epochs+1):
@@ -55,14 +51,11 @@ def main(args):
             train_loss.append(loss)
             train_mape.append(mape)
             train_rmse.append(rmse)
-            if iter % args.print_every == 0 :
-                print(f'Iter: {iter:03d}, Train Loss: {loss:.4f}, Train MAPE: {mape:.4f}, Train RMSE: {rmse:.4f}', flush=True)
             if args.n_iters is not None and iter >= args.n_iters:
                 break
         train_time.append(time.time()-t1)
         total_time, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device, engine)
-        print(f'Epoch: {i:03d}, val time: {total_time} seconds.')
-        val_time.append(total_time)
+        print(f'Epoch: {i:03d}')
 
         m = pd.Series(dict(train_loss=np.mean(train_loss),
                            train_mape=np.mean(train_mape),
@@ -75,9 +68,6 @@ def main(args):
         his_loss.append(m.valid_loss)
         print(f'Epoch {i}')
         print(m.round(4))
-
-        #log = 'Epoch: {:03d}, Train Loss: {:.4f}, Train MAPE: {:.4f}, Train RMSE: {:.4f}, Valid Loss: {:.4f}, Valid MAPE: {:.4f}, Valid RMSE: {:.4f}, Training Time: {:.4f}/epoch'
-        #print(log.format(i, mtrain_loss, mtrain_mape, mtrain_rmse, mvalid_loss, mvalid_mape, mvalid_rmse, time.time() - t1),flush=True)
         if m.valid_loss < best_yet:
             torch.save(engine.model.state_dict(), best_model_save_path)
             best_yet = m.valid_loss
@@ -150,7 +140,7 @@ def eval_(ds, device, engine):
     parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
     parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
     parser.add_argument('--epochs', type=int, default=100, help='')
-    parser.add_argument('--print_every', type=int, default=50, help='')
+    # parser.add_argument('--print_every', type=int, default=50, help='')
     # parser.add_argument('--seed',type=int,default=99,help='random seed')
     parser.add_argument('--save', type=str, default='experiment', help='save path')
     # parser.add_argument('--expid', default=1, help='experiment id')

From eafe6c6fc3340a58975cc64b43e606451a9cb752 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 14:47:33 -0700
Subject: [PATCH 009/130] more cleanup

---
 test_gwnet.py |  2 +-
 train.py      | 10 ++++------
 2 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/test_gwnet.py b/test_gwnet.py
index cbdb36e..c9705e1 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -7,7 +7,7 @@ class TestTrain(unittest.TestCase):
 
     def test_1_epoch(self):
         args = pickle_load(TEST_ARGS_PATH)
-        args.epochs = 1
+        args.epochs = 2
         args.n_iters = 1
         args.batch_size = 4
         args.n_obs = 4
diff --git a/train.py b/train.py
index 5604f54..497ae78 100644
--- a/train.py
+++ b/train.py
@@ -73,10 +73,10 @@ def main(args):
             best_yet = m.valid_loss
         met_df = pd.concat(metrics).T
         met_df.to_csv(f'{args.save}/metrics.csv')
-    #testing
+    print(f"Training finished. Best Valid Loss")
+    print(met_df.loc[met_df.valid_loss.idxmin()].round(4))
+    # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
-
-
     outputs = []
     realy = torch.Tensor(dataloader['y_test']).to(device)
     realy = realy.transpose(1,3)[:,0,:,:]
@@ -90,9 +90,6 @@ def main(args):
     yhat = torch.cat(outputs,dim=0)
     yhat = yhat[:realy.size(0),...]
 
-
-    print("Training finished")
-    print(f'Valid loss on best model = {met_df.valid_loss.min():.4f}')
     test_met = []
     for i in range(12):
         pred = scaler.inverse_transform(yhat[:,:,i])
@@ -103,6 +100,7 @@ def main(args):
     print(test_met_df.mean().round(3))
 
 def eval_(ds, device, engine):
+    """Run validation."""
     valid_loss = []
     valid_mape = []
     valid_rmse = []

From eda481f4d30f8772773f56df81caf50a5aa589a9 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 14:53:59 -0700
Subject: [PATCH 010/130] test metric shapes

---
 test_gwnet.py | 5 +++++
 train.py      | 7 ++-----
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/test_gwnet.py b/test_gwnet.py
index c9705e1..a493e55 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -1,6 +1,7 @@
 from train import main
 import unittest
 from durbango import pickle_load
+import pandas as pd
 TEST_ARGS_PATH = 'test_args.pkl'
 
 class TestTrain(unittest.TestCase):
@@ -12,3 +13,7 @@ def test_1_epoch(self):
         args.batch_size = 4
         args.n_obs = 4
         main(args)
+        df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
+        self.assertEqual(df.shape, (2,6))
+        test_df = pd.read_csv(f'{args.save}/test_metrics.csv', index_col=0)
+        self.assertEqual(test_df.shape, (12, 3))
diff --git a/train.py b/train.py
index 497ae78..63ee7b1 100644
--- a/train.py
+++ b/train.py
@@ -31,8 +31,7 @@ def main(args):
                      args.learning_rate, args.weight_decay, device, supports, args.do_graph_conv,
                      args.addaptadj, adjinit)
     print("start training...",flush=True)
-    his_loss, train_time = [], [], []
-    metrics = []
+    metrics, train_time = [], []
     best_yet = 100
     for i in range(1,args.epochs+1):
         #if i % 10 == 0:
@@ -64,14 +63,12 @@ def main(args):
                            valid_mape=np.mean(valid_mape),
                            valid_rmse=np.mean(valid_rmse), ))
         metrics.append(m)
-
-        his_loss.append(m.valid_loss)
         print(f'Epoch {i}')
         print(m.round(4))
         if m.valid_loss < best_yet:
             torch.save(engine.model.state_dict(), best_model_save_path)
             best_yet = m.valid_loss
-        met_df = pd.concat(metrics).T
+        met_df = pd.DataFrame(metrics)
         met_df.to_csv(f'{args.save}/metrics.csv')
     print(f"Training finished. Best Valid Loss")
     print(met_df.loc[met_df.valid_loss.idxmin()].round(4))

From bb977f04609ef8a41bc3e7f42e0ce7973bcf90c6 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 14:56:48 -0700
Subject: [PATCH 011/130] small requirements

---
 requirements.txt | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 requirements.txt

diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..fdad8a3
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,2 @@
+durbango
+tqdm

From fce44a2a1922660b06722fba118f778117afa2d2 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 15:02:47 -0700
Subject: [PATCH 012/130] README

---
 README.md | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 49f4525..8e65403 100644
--- a/README.md
+++ b/README.md
@@ -33,7 +33,14 @@ python -m scripts.generate_training_data --output_dir=data/METR-LA --traffic_df_
 python -m scripts.generate_training_data --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
 
 ```
-## Experiments
+## New Experiment Command
+```
+expid=bs_64
+mkdir $expid
+python train.py --batch_size 64 --learning_rate .004 --do_graph_conv --addaptadj  --randomadj   --save $expid | tee -a $expid.log
+```
+
+## Old Experiment Commands
 Train models configured in Table 3 of the paper.
 
 ```

From ee246b19782bb30f6133ff5a7f504cc467a8a27e Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 19:13:38 -0700
Subject: [PATCH 013/130] cheaper_metric fn

---
 engine.py | 21 +++++++--------------
 util.py   | 52 +++++++++++++++++++++++++++-------------------------
 2 files changed, 34 insertions(+), 39 deletions(-)

diff --git a/engine.py b/engine.py
index 5dc4e39..d457892 100644
--- a/engine.py
+++ b/engine.py
@@ -9,7 +9,6 @@ def __init__(self, scaler, in_dim, seq_length, num_nodes, nhid , dropout, lrate,
                            out_dim=seq_length, residual_channels=nhid, dilation_channels=nhid, skip_channels=nhid * 8, end_channels=nhid * 16)
         self.model.to(device)
         self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
-        self.loss = util.masked_mae
         self.scaler = scaler
         self.clip = clip
 
@@ -17,29 +16,23 @@ def train(self, input, real_val):
         self.model.train()
         self.optimizer.zero_grad()
         input = nn.functional.pad(input,(1,0,0,0))
-        output = self.model(input)
-        output = output.transpose(1,3)
+        output = self.model(input).transpose(1,3)
         predict = self.scaler.inverse_transform(output)
         #output = [batch_size,12,num_nodes,1]
         real = torch.unsqueeze(real_val, dim=1)
-        loss = self.loss(predict, real, 0.0)
-        loss.backward()
+        mae, mape, rmse = util.cheaper_metric(predict, real, null_val=0.0)
+        mae.backward()
         if self.clip is not None:
             torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
         self.optimizer.step()
-        mape = util.masked_mape(predict,real,0.0).item()
-        rmse = util.masked_rmse(predict,real,0.0).item()
-        return loss.item(),mape,rmse
+        return mae.item(),mape.item(),rmse.item()
 
     def eval(self, input, real_val):
         self.model.eval()
         input = nn.functional.pad(input,(1,0,0,0))
-        output = self.model(input)
-        output = output.transpose(1,3)
+        output = self.model(input).transpose(1,3)
         #output = [batch_size,12,num_nodes,1]
         real = torch.unsqueeze(real_val,dim=1)
         predict = self.scaler.inverse_transform(output)
-        loss = self.loss(predict, real, 0.0)
-        mape = util.masked_mape(predict,real,0.0).item()
-        rmse = util.masked_rmse(predict,real,0.0).item()
-        return loss.item(),mape,rmse
+        mae, mape, rmse = [x.item() for x in util.cheaper_metric(predict, real, null_val=0.0)]
+        return mae, mape, rmse
diff --git a/util.py b/util.py
index ace05c9..964c44a 100644
--- a/util.py
+++ b/util.py
@@ -162,55 +162,57 @@ def load_dataset(dataset_dir, batch_size, valid_batch_size= None, test_batch_siz
     data['scaler'] = scaler
     return data
 
-def masked_mse(preds, labels, null_val=np.nan):
-    if np.isnan(null_val):
-        mask = ~torch.isnan(labels)
-    else:
-        mask = (labels!=null_val)
-    mask = mask.float()
-    mask /= torch.mean((mask))
-    mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
-    loss = (preds-labels)**2
-    loss = loss * mask
-    loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
-    return torch.mean(loss)
 
 def masked_rmse(preds, labels, null_val=np.nan):
-    return torch.sqrt(masked_mse(preds=preds, labels=labels, null_val=null_val))
+    return torch.sqrt(masked_stat(preds, labels, null_val=null_val, stat='mse'))
+
+def masked_mape(preds, labels, null_val=np.nan):
+    return masked_stat(preds, labels, null_val=null_val, stat='mape')
 
 
-def masked_mae(preds, labels, null_val=np.nan):
+def masked_stat(preds, labels, null_val=np.nan, stat='mae'):
     if np.isnan(null_val):
         mask = ~torch.isnan(labels)
     else:
         mask = (labels!=null_val)
     mask = mask.float()
-    mask /=  torch.mean((mask))
+    mask /= torch.mean(mask)
     mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
-    loss = torch.abs(preds-labels)
+    if stat == 'mse':
+        loss = (preds - labels) ** 2
+    elif stat == 'mae':
+        loss = torch.abs(preds-labels)
+    elif stat == 'mape':
+        loss = torch.abs(preds - labels) / labels
     loss = loss * mask
     loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
     return torch.mean(loss)
 
 
-def masked_mape(preds, labels, null_val=np.nan):
+def cheaper_metric(preds, labels, null_val=np.nan):
     if np.isnan(null_val):
         mask = ~torch.isnan(labels)
     else:
         mask = (labels!=null_val)
     mask = mask.float()
-    mask /=  torch.mean((mask))
+    mask /= torch.mean(mask)
     mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
-    loss = torch.abs(preds-labels)/labels
+    mse = (preds - labels) ** 2
+    mae = torch.abs(preds-labels)
+    mape = torch.abs(preds - labels) / labels
+    mae, mape, mse =  [mask_and_fillna(l, mask) for l in [mae, mape, mse]]
+    rmse = torch.sqrt(mse)
+    return mae, mape, rmse
+
+
+def mask_and_fillna(loss, mask):
     loss = loss * mask
     loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
     return torch.mean(loss)
 
 
-def metric(pred, real):
-    mae = masked_mae(pred,real,0.0).item()
-    mape = masked_mape(pred,real,0.0).item()
-    rmse = masked_rmse(pred,real,0.0).item()
-    return mae,mape,rmse
-
 
+def metric(pred, real):
+    mae, mape = [masked_stat(pred, real, null_val=0.0, stat=s).item() for s in ['mae', 'mape']]
+    rmse = masked_rmse(pred, real, null_val=0.,)
+    return mae, mape, rmse

From c6799458742c56fb9dee933e5b743f5a7c680cfd Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 19:15:48 -0700
Subject: [PATCH 014/130] call cheaper_metric

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index 63ee7b1..2275113 100644
--- a/train.py
+++ b/train.py
@@ -91,7 +91,7 @@ def main(args):
     for i in range(12):
         pred = scaler.inverse_transform(yhat[:,:,i])
         real = realy[:,:,i]
-        test_met.append(util.metric(pred,real))
+        test_met.append(util.cheaper_metric(pred, real))
     test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t').round(3)
     test_met_df.to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(test_met_df.mean().round(3))

From 0d5a515930de7007036290986b6c631c86f94732 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 19:18:22 -0700
Subject: [PATCH 015/130] tests pass

---
 train.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/train.py b/train.py
index 2275113..1154c76 100644
--- a/train.py
+++ b/train.py
@@ -74,18 +74,15 @@ def main(args):
     print(met_df.loc[met_df.valid_loss.idxmin()].round(4))
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
-    outputs = []
-    realy = torch.Tensor(dataloader['y_test']).to(device)
-    realy = realy.transpose(1,3)[:,0,:,:]
 
+    realy = torch.Tensor(dataloader['y_test']).transpose(1,3)[:,0,:,:].to(device)
+    outputs = []
     for iter, (x, y) in enumerate(dataloader['test_loader'].get_iterator()):
         testx = torch.Tensor(x).to(device).transpose(1,3)
         with torch.no_grad():
             preds = engine.model(testx).transpose(1,3)
         outputs.append(preds.squeeze())
-
-    yhat = torch.cat(outputs,dim=0)
-    yhat = yhat[:realy.size(0),...]
+    yhat = torch.cat(outputs, dim=0)[:realy.size(0), ...]
 
     test_met = []
     for i in range(12):

From a9c2044bb7fab2a9f68cb357d37e387da7622a23 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 1 Nov 2019 19:21:44 -0700
Subject: [PATCH 016/130] delete other code

---
 test.py |  2 +-
 util.py | 35 +----------------------------------
 2 files changed, 2 insertions(+), 35 deletions(-)

diff --git a/test.py b/test.py
index f8f2784..0588149 100644
--- a/test.py
+++ b/test.py
@@ -76,7 +76,7 @@ def main():
     for i in range(12):
         pred = scaler.inverse_transform(yhat[:,:,i])
         real = realy[:,:,i]
-        metrics = util.metric(pred,real)
+        metrics = util.cheaper_metric(pred,real)
         log = 'Evaluate best model on test data for horizon {:d}, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}'
         print(log.format(i+1, metrics[0], metrics[1], metrics[2]))
         amae.append(metrics[0])
diff --git a/util.py b/util.py
index 964c44a..9594c33 100644
--- a/util.py
+++ b/util.py
@@ -163,32 +163,6 @@ def load_dataset(dataset_dir, batch_size, valid_batch_size= None, test_batch_siz
     return data
 
 
-def masked_rmse(preds, labels, null_val=np.nan):
-    return torch.sqrt(masked_stat(preds, labels, null_val=null_val, stat='mse'))
-
-def masked_mape(preds, labels, null_val=np.nan):
-    return masked_stat(preds, labels, null_val=null_val, stat='mape')
-
-
-def masked_stat(preds, labels, null_val=np.nan, stat='mae'):
-    if np.isnan(null_val):
-        mask = ~torch.isnan(labels)
-    else:
-        mask = (labels!=null_val)
-    mask = mask.float()
-    mask /= torch.mean(mask)
-    mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
-    if stat == 'mse':
-        loss = (preds - labels) ** 2
-    elif stat == 'mae':
-        loss = torch.abs(preds-labels)
-    elif stat == 'mape':
-        loss = torch.abs(preds - labels) / labels
-    loss = loss * mask
-    loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
-    return torch.mean(loss)
-
-
 def cheaper_metric(preds, labels, null_val=np.nan):
     if np.isnan(null_val):
         mask = ~torch.isnan(labels)
@@ -200,7 +174,7 @@ def cheaper_metric(preds, labels, null_val=np.nan):
     mse = (preds - labels) ** 2
     mae = torch.abs(preds-labels)
     mape = torch.abs(preds - labels) / labels
-    mae, mape, mse =  [mask_and_fillna(l, mask) for l in [mae, mape, mse]]
+    mae, mape, mse = [mask_and_fillna(l, mask) for l in [mae, mape, mse]]
     rmse = torch.sqrt(mse)
     return mae, mape, rmse
 
@@ -209,10 +183,3 @@ def mask_and_fillna(loss, mask):
     loss = loss * mask
     loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
     return torch.mean(loss)
-
-
-
-def metric(pred, real):
-    mae, mape = [masked_stat(pred, real, null_val=0.0, stat=s).item() for s in ['mae', 'mape']]
-    rmse = masked_rmse(pred, real, null_val=0.,)
-    return mae, mape, rmse

From 100e4d3c8ff16389c13bae2f47d76637be696526 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 3 Nov 2019 10:41:23 -0800
Subject: [PATCH 017/130] util update

---
 util.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/util.py b/util.py
index 9594c33..c81b081 100644
--- a/util.py
+++ b/util.py
@@ -163,11 +163,11 @@ def load_dataset(dataset_dir, batch_size, valid_batch_size= None, test_batch_siz
     return data
 
 
-def cheaper_metric(preds, labels, null_val=np.nan):
+def cheaper_metric(preds, labels, null_val=0.):
     if np.isnan(null_val):
         mask = ~torch.isnan(labels)
     else:
-        mask = (labels!=null_val)
+        mask = (labels != null_val)
     mask = mask.float()
     mask /= torch.mean(mask)
     mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)

From abc18aba257f69cc338b5cb07c25e49b6e2e538d Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 3 Nov 2019 10:42:39 -0800
Subject: [PATCH 018/130] item

---
 test.py  | 7 +------
 train.py | 2 +-
 2 files changed, 2 insertions(+), 7 deletions(-)

diff --git a/test.py b/test.py
index 0588149..db98655 100644
--- a/test.py
+++ b/test.py
@@ -25,13 +25,8 @@
 parser.add_argument('--weight_decay',type=float,default=0.0001,help='weight decay rate')
 parser.add_argument('--checkpoint',type=str,help='')
 parser.add_argument('--plotheatmap',type=str,default='True',help='')
-
-
 args = parser.parse_args()
 
-
-
-
 def main():
     device = torch.device(args.device)
 
@@ -76,7 +71,7 @@ def main():
     for i in range(12):
         pred = scaler.inverse_transform(yhat[:,:,i])
         real = realy[:,:,i]
-        metrics = util.cheaper_metric(pred,real)
+        metrics = [x.item() for x in util.cheaper_metric(pred,real)]
         log = 'Evaluate best model on test data for horizon {:d}, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}'
         print(log.format(i+1, metrics[0], metrics[1], metrics[2]))
         amae.append(metrics[0])
diff --git a/train.py b/train.py
index 1154c76..ff69a03 100644
--- a/train.py
+++ b/train.py
@@ -88,7 +88,7 @@ def main(args):
     for i in range(12):
         pred = scaler.inverse_transform(yhat[:,:,i])
         real = realy[:,:,i]
-        test_met.append(util.cheaper_metric(pred, real))
+        test_met.append([x.item() for x in util.cheaper_metric(pred, real)])
     test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t').round(3)
     test_met_df.to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(test_met_df.mean().round(3))

From d7b9be1d82b9b97e35fed211635dea6febfbcc64 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 3 Nov 2019 10:48:16 -0800
Subject: [PATCH 019/130] factor out test metrics

---
 test.py  |  1 +
 train.py | 23 +++++------------------
 util.py  | 19 +++++++++++++++++++
 3 files changed, 25 insertions(+), 18 deletions(-)

diff --git a/test.py b/test.py
index db98655..700fd1f 100644
--- a/test.py
+++ b/test.py
@@ -53,6 +53,7 @@ def main():
     outputs = []
     realy = torch.Tensor(dataloader['y_test']).to(device)
     realy = realy.transpose(1,3)[:,0,:,:]
+    util.calc_test_metrics(model, device, dataloader, scaler)
 
     for iter, (x, y) in enumerate(dataloader['test_loader'].get_iterator()):
         testx = torch.Tensor(x).to(device)
diff --git a/train.py b/train.py
index ff69a03..c334213 100644
--- a/train.py
+++ b/train.py
@@ -4,11 +4,11 @@
 import argparse
 import time
 import util
-import matplotlib.pyplot as plt
 from engine import Trainer
 import os
 from durbango import pickle_save
-from pathlib import Path
+
+from util import calc_test_metrics
 
 
 def main(args):
@@ -74,25 +74,12 @@ def main(args):
     print(met_df.loc[met_df.valid_loss.idxmin()].round(4))
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
-
-    realy = torch.Tensor(dataloader['y_test']).transpose(1,3)[:,0,:,:].to(device)
-    outputs = []
-    for iter, (x, y) in enumerate(dataloader['test_loader'].get_iterator()):
-        testx = torch.Tensor(x).to(device).transpose(1,3)
-        with torch.no_grad():
-            preds = engine.model(testx).transpose(1,3)
-        outputs.append(preds.squeeze())
-    yhat = torch.cat(outputs, dim=0)[:realy.size(0), ...]
-
-    test_met = []
-    for i in range(12):
-        pred = scaler.inverse_transform(yhat[:,:,i])
-        real = realy[:,:,i]
-        test_met.append([x.item() for x in util.cheaper_metric(pred, real)])
-    test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t').round(3)
+    realy = torch.Tensor(dataloader['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
+    test_met_df = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler, realy)
     test_met_df.to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(test_met_df.mean().round(3))
 
+
 def eval_(ds, device, engine):
     """Run validation."""
     valid_loss = []
diff --git a/util.py b/util.py
index c81b081..1f785c7 100644
--- a/util.py
+++ b/util.py
@@ -1,6 +1,8 @@
 import pickle
 import numpy as np
 import os
+
+import pandas as pd
 import scipy.sparse as sp
 import torch
 from scipy.sparse import linalg
@@ -183,3 +185,20 @@ def mask_and_fillna(loss, mask):
     loss = loss * mask
     loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
     return torch.mean(loss)
+
+
+def calc_test_metrics(model, device, test_loader, scaler, realy):
+    outputs = []
+    for iter, (x, y) in enumerate(test_loader.get_iterator()):
+        testx = torch.Tensor(x).to(device).transpose(1, 3)
+        with torch.no_grad():
+            preds = model(testx).transpose(1, 3)
+        outputs.append(preds.squeeze())
+    yhat = torch.cat(outputs, dim=0)[:realy.size(0), ...]
+    test_met = []
+    for i in range(12):
+        pred = scaler.inverse_transform(yhat[:, :, i])
+        real = realy[:, :, i]
+        test_met.append([x.item() for x in cheaper_metric(pred, real)])
+    test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t').round(3)
+    return test_met_df

From 5b8536606089cfe7a56f96202dfdfb510bd6060f Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 3 Nov 2019 10:59:59 -0800
Subject: [PATCH 020/130] utest for test script

---
 test.py       | 111 +++++++++++++++++++++-----------------------------
 test_gwnet.py |  18 ++++++--
 train.py      |   6 +--
 util.py       |   3 +-
 4 files changed, 65 insertions(+), 73 deletions(-)

diff --git a/test.py b/test.py
index 700fd1f..b7b69ef 100644
--- a/test.py
+++ b/test.py
@@ -5,29 +5,10 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
+from durbango import pickle_save
 
-parser = argparse.ArgumentParser()
-parser.add_argument('--device',type=str,default='cuda:3',help='')
-parser.add_argument('--data',type=str,default='data/METR-LA',help='data path')
-parser.add_argument('--adjdata',type=str,default='data/sensor_graph/adj_mx.pkl',help='adj data path')
-parser.add_argument('--adjtype',type=str,default='doubletransition',help='adj type')
-parser.add_argument('--do_graph_conv',action='store_true',help='whether to add graph convolution layer')
-parser.add_argument('--aptonly',action='store_true',help='whether only adaptive adj')
-parser.add_argument('--addaptadj',action='store_true',help='whether add adaptive adj')
-parser.add_argument('--randomadj',action='store_true',help='whether random initialize adaptive adj')
-parser.add_argument('--seq_length',type=int,default=12,help='')
-parser.add_argument('--nhid',type=int,default=32,help='')
-parser.add_argument('--in_dim',type=int,default=2,help='inputs dimension')
-parser.add_argument('--num_nodes',type=int,default=207,help='number of nodes')
-parser.add_argument('--batch_size',type=int,default=64,help='batch size')
-parser.add_argument('--learning_rate',type=float,default=0.001,help='learning rate')
-parser.add_argument('--dropout',type=float,default=0.3,help='dropout rate')
-parser.add_argument('--weight_decay',type=float,default=0.0001,help='weight decay rate')
-parser.add_argument('--checkpoint',type=str,help='')
-parser.add_argument('--plotheatmap',type=str,default='True',help='')
-args = parser.parse_args()
-
-def main():
+
+def main(args):
     device = torch.device(args.device)
 
     _, _, adj_mx = util.load_adj(args.adjdata,args.adjtype)
@@ -48,60 +29,62 @@ def main():
 
     print('model loaded successfully')
 
-    dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size)
+    dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
     scaler = dataloader['scaler']
-    outputs = []
     realy = torch.Tensor(dataloader['y_test']).to(device)
     realy = realy.transpose(1,3)[:,0,:,:]
-    util.calc_test_metrics(model, device, dataloader, scaler)
-
-    for iter, (x, y) in enumerate(dataloader['test_loader'].get_iterator()):
-        testx = torch.Tensor(x).to(device)
-        testx = testx.transpose(1,3)
-        with torch.no_grad():
-            preds = model(testx).transpose(1,3)
-        outputs.append(preds.squeeze())
-
-    yhat = torch.cat(outputs,dim=0)
-    yhat = yhat[:realy.size(0),...]
-
-
-    amae = []
-    amape = []
-    armse = []
-    for i in range(12):
-        pred = scaler.inverse_transform(yhat[:,:,i])
-        real = realy[:,:,i]
-        metrics = [x.item() for x in util.cheaper_metric(pred,real)]
-        log = 'Evaluate best model on test data for horizon {:d}, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}'
-        print(log.format(i+1, metrics[0], metrics[1], metrics[2]))
-        amae.append(metrics[0])
-        amape.append(metrics[1])
-        armse.append(metrics[2])
-
-    log = 'On average over 12 horizons, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}'
-    print(log.format(np.mean(amae),np.mean(amape),np.mean(armse)))
-
+    met_df, yhat = util.calc_test_metrics(model, device, dataloader['test_loader'], scaler, realy)
+    print(met_df)
+    print(met_df.mean().round(4))
 
     if args.plotheatmap == "True":
-        adp = F.softmax(F.relu(torch.mm(model.nodevec1, model.nodevec2)), dim=1)
-        device = torch.device('cpu')
-        adp.to(device)
-        adp = adp.cpu().detach().numpy()
-        adp = adp*(1/np.max(adp))
-        df = pd.DataFrame(adp)
-        sns.heatmap(df, cmap="RdYlBu")
-        plt.savefig("./emb"+ '.pdf')
+        plot_heatmap(model)
 
     y12 = realy[:,99,11].cpu().detach().numpy()
     yhat12 = scaler.inverse_transform(yhat[:,99,11]).cpu().detach().numpy()
 
     y3 = realy[:,99,2].cpu().detach().numpy()
     yhat3 = scaler.inverse_transform(yhat[:,99,2]).cpu().detach().numpy()
+    df2 = pd.DataFrame({'real12': y12, 'pred12': yhat12, 'real3': y3, 'pred3': yhat3})
+
+    df2.to_csv('./wave.csv', index=False)
+
 
-    df2 = pd.DataFrame({'real12':y12,'pred12':yhat12, 'real3': y3, 'pred3':yhat3})
-    df2.to_csv('./wave.csv',index=False)
+def plot_heatmap(model):
+    adp = F.softmax(F.relu(torch.mm(model.nodevec1, model.nodevec2)), dim=1)
+    device = torch.device('cpu')
+    adp.to(device)
+    adp = adp.cpu().detach().numpy()
+    adp = adp * (1 / np.max(adp))
+    df = pd.DataFrame(adp)
+    sns.heatmap(df, cmap="RdYlBu")
+    plt.savefig("./emb" + '.pdf')
 
 
 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cuda:3', help='')
+    parser.add_argument('--data', type=str, default='data/METR-LA', help='data path')
+    parser.add_argument('--adjdata', type=str, default='data/sensor_graph/adj_mx.pkl',
+                        help='adj data path')
+    parser.add_argument('--adjtype', type=str, default='doubletransition', help='adj type')
+    parser.add_argument('--do_graph_conv', action='store_true',
+                        help='whether to add graph convolution layer')
+    parser.add_argument('--aptonly', action='store_true', help='whether only adaptive adj')
+    parser.add_argument('--addaptadj', action='store_true', help='whether add adaptive adj')
+    parser.add_argument('--randomadj', action='store_true',
+                        help='whether random initialize adaptive adj')
+    parser.add_argument('--seq_length', type=int, default=12, help='')
+    parser.add_argument('--nhid', type=int, default=32, help='')
+    parser.add_argument('--in_dim', type=int, default=2, help='inputs dimension')
+    parser.add_argument('--num_nodes', type=int, default=207, help='number of nodes')
+    parser.add_argument('--batch_size', type=int, default=64, help='batch size')
+    parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
+    parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
+    parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
+    parser.add_argument('--checkpoint', type=str, help='')
+    parser.add_argument('--plotheatmap', type=str, default='True', help='')
+    parser.add_argument('--n_obs', default=None, help='Only use this many observations')
+    args = parser.parse_args()
+    pickle_save(args, 'test_script_args.pkl')
+    main(args)
diff --git a/test_gwnet.py b/test_gwnet.py
index a493e55..bb8a0ad 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -1,19 +1,29 @@
 from train import main
+import test
 import unittest
 from durbango import pickle_load
 import pandas as pd
-TEST_ARGS_PATH = 'test_args.pkl'
+TRAIN_ARGS = 'test_args.pkl'
+TEST_ARGS = 'test_script_args.pkl'
 
 class TestTrain(unittest.TestCase):
 
     def test_1_epoch(self):
-        args = pickle_load(TEST_ARGS_PATH)
+        args = pickle_load(TRAIN_ARGS)
         args.epochs = 2
         args.n_iters = 1
-        args.batch_size = 4
-        args.n_obs = 4
+        args.batch_size = 2
+        args.n_obs = 2
         main(args)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
         self.assertEqual(df.shape, (2,6))
         test_df = pd.read_csv(f'{args.save}/test_metrics.csv', index_col=0)
         self.assertEqual(test_df.shape, (12, 3))
+
+    def test_test_script(self):
+        train_args = pickle_load(TRAIN_ARGS)
+        test_args = pickle_load('test_script_args.pkl')
+        test_args.checkpoint = train_args.save + '/best_model.pth'
+        test_args.n_obs = 2
+        test.main(test_args)
+
diff --git a/train.py b/train.py
index c334213..22e4bf3 100644
--- a/train.py
+++ b/train.py
@@ -69,14 +69,14 @@ def main(args):
             torch.save(engine.model.state_dict(), best_model_save_path)
             best_yet = m.valid_loss
         met_df = pd.DataFrame(metrics)
-        met_df.to_csv(f'{args.save}/metrics.csv')
+        met_df.round(4).to_csv(f'{args.save}/metrics.csv')
     print(f"Training finished. Best Valid Loss")
     print(met_df.loc[met_df.valid_loss.idxmin()].round(4))
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
     realy = torch.Tensor(dataloader['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
-    test_met_df = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler, realy)
-    test_met_df.to_csv(os.path.join(args.save, 'test_metrics.csv'))
+    test_met_df, _ = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler, realy)
+    test_met_df.round(4).to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(test_met_df.mean().round(3))
 
 
diff --git a/util.py b/util.py
index 1f785c7..436d305 100644
--- a/util.py
+++ b/util.py
@@ -186,7 +186,6 @@ def mask_and_fillna(loss, mask):
     loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
     return torch.mean(loss)
 
-
 def calc_test_metrics(model, device, test_loader, scaler, realy):
     outputs = []
     for iter, (x, y) in enumerate(test_loader.get_iterator()):
@@ -201,4 +200,4 @@ def calc_test_metrics(model, device, test_loader, scaler, realy):
         real = realy[:, :, i]
         test_met.append([x.item() for x in cheaper_metric(pred, real)])
     test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t').round(3)
-    return test_met_df
+    return test_met_df, yhat

From e387cb9f200fe3ab1fbbde2520ebb1002d64b03f Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 3 Nov 2019 11:04:02 -0800
Subject: [PATCH 021/130] mkdir

---
 train.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/train.py b/train.py
index 22e4bf3..45306ba 100644
--- a/train.py
+++ b/train.py
@@ -128,6 +128,8 @@ def eval_(ds, device, engine):
 
     args = parser.parse_args()
     t1 = time.time()
+    if not os.path.exists(args.save):
+        os.mkdir(args.save)
 
     pickle_save(args, f'{args.save}/args.pkl')
     main(args)

From 5b820a68b6834732e63612d495e926b8783c1c35 Mon Sep 17 00:00:00 2001
From: DeepLearning VM <google-dl-platform@googlegroups.com>
Date: Mon, 4 Nov 2019 16:53:42 +0000
Subject: [PATCH 022/130] test push permissions

---
 clara_test.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 clara_test.py

diff --git a/clara_test.py b/clara_test.py
new file mode 100644
index 0000000..e69de29

From dce48c3efb474597193c26c08090a3619c2ace3b Mon Sep 17 00:00:00 2001
From: Vamsi Chitters <vamsi.kc@gmail.com>
Date: Mon, 4 Nov 2019 21:29:52 +0000
Subject: [PATCH 023/130] test push

---
 vamsi_test.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 vamsi_test.py

diff --git a/vamsi_test.py b/vamsi_test.py
new file mode 100644
index 0000000..e69de29

From d6cd0583b2fd155e38bb409de301fb9eda167153 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 4 Nov 2019 14:46:36 -0800
Subject: [PATCH 024/130] add test script args

---
 test_script_args.pkl | Bin 0 -> 486 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 test_script_args.pkl

diff --git a/test_script_args.pkl b/test_script_args.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..d276803bb2f5a2e9d8d410ca0fe7829601c3bcc1
GIT binary patch
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literal 0
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From 75d32805992744ceb95363da4bb90b9673c35c93 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 4 Nov 2019 15:18:50 -0800
Subject: [PATCH 025/130] utest stuff

---
 experiment/best_model.pth   | Bin 0 -> 1260605 bytes
 experiment/metrics.csv      |   3 +++
 experiment/test_metrics.csv |  13 +++++++++++++
 3 files changed, 16 insertions(+)
 create mode 100644 experiment/best_model.pth
 create mode 100644 experiment/metrics.csv
 create mode 100644 experiment/test_metrics.csv

diff --git a/experiment/best_model.pth b/experiment/best_model.pth
new file mode 100644
index 0000000000000000000000000000000000000000..88529ff1af0506549313fab15eb474e6e33edd9a
GIT binary patch
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HcmV?d00001

diff --git a/experiment/metrics.csv b/experiment/metrics.csv
new file mode 100644
index 0000000..ac5b308
--- /dev/null
+++ b/experiment/metrics.csv
@@ -0,0 +1,3 @@
+,train_loss,train_mape,train_rmse,valid_loss,valid_mape,valid_rmse
+0,10.4322,0.1704,11.7529,15.6946,0.6565,19.2591
+1,7.9076,0.1316,9.2204,14.6998,0.6639,19.4113
diff --git a/experiment/test_metrics.csv b/experiment/test_metrics.csv
new file mode 100644
index 0000000..59badb3
--- /dev/null
+++ b/experiment/test_metrics.csv
@@ -0,0 +1,13 @@
+t,mae,mape,rmse
+0,8.898,0.146,9.962
+1,8.152,0.133,9.18
+2,7.289,0.12,8.218
+3,5.441,0.092,6.442
+4,4.743,0.08,5.726
+5,5.338,0.089,6.375
+6,6.787,0.11,7.708
+7,6.109,0.1,7.004
+8,5.81,0.095,6.651
+9,5.02,0.084,6.016
+10,7.439,0.121,8.271
+11,7.919,0.128,8.65

From a7c4d553a9d352e073e0d2ea67a995fe631b4271 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 4 Nov 2019 15:48:28 -0800
Subject: [PATCH 026/130] dont overwrite script args

---
 test.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/test.py b/test.py
index b7b69ef..9292ac5 100644
--- a/test.py
+++ b/test.py
@@ -36,6 +36,7 @@ def main(args):
     met_df, yhat = util.calc_test_metrics(model, device, dataloader['test_loader'], scaler, realy)
     print(met_df)
     print(met_df.mean().round(4))
+    met_df.round(4).to_csv('last_test_metrics.csv')
 
     if args.plotheatmap == "True":
         plot_heatmap(model)
@@ -63,7 +64,7 @@ def plot_heatmap(model):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cuda:3', help='')
+    parser.add_argument('--device', type=str, default='cuda:0', help='')
     parser.add_argument('--data', type=str, default='data/METR-LA', help='data path')
     parser.add_argument('--adjdata', type=str, default='data/sensor_graph/adj_mx.pkl',
                         help='adj data path')
@@ -79,12 +80,10 @@ def plot_heatmap(model):
     parser.add_argument('--in_dim', type=int, default=2, help='inputs dimension')
     parser.add_argument('--num_nodes', type=int, default=207, help='number of nodes')
     parser.add_argument('--batch_size', type=int, default=64, help='batch size')
-    parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
     parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
-    parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
+    # parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
     parser.add_argument('--checkpoint', type=str, help='')
     parser.add_argument('--plotheatmap', type=str, default='True', help='')
     parser.add_argument('--n_obs', default=None, help='Only use this many observations')
     args = parser.parse_args()
-    pickle_save(args, 'test_script_args.pkl')
     main(args)

From 046cd2bce7ad1910d3155384114ff36e1d44c130 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 4 Nov 2019 23:50:15 +0000
Subject: [PATCH 027/130] checkin baseline_ckpt.pth

---
 baseline_ckpt.pth | Bin 0 -> 1245270 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 baseline_ckpt.pth

diff --git a/baseline_ckpt.pth b/baseline_ckpt.pth
new file mode 100644
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GIT binary patch
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From 14d20bd4b015db5e85078ae23f6b865e15012b7f Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 5 Nov 2019 07:43:07 +0000
Subject: [PATCH 028/130] Better baseline

---
 baseline_ckpt.pth | Bin 1245270 -> 1245270 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/baseline_ckpt.pth b/baseline_ckpt.pth
index 29aee7168e14b6d78586f74fc2868f9d6c1cf305..dcc6dd289fbf803ed4e77ef132df5a3b96d757a0 100644
GIT binary patch
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From 63724899957cf6e0a20016eeaabca8eba41a23e5 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 6 Nov 2019 15:58:23 -0800
Subject: [PATCH 029/130] Dont check in utest  side effects

---
 experiment/best_model.pth   | Bin 1260605 -> 0 bytes
 experiment/metrics.csv      |   3 ---
 experiment/test_metrics.csv |  13 -------------
 test_gwnet.py               |  11 +++++++++++
 4 files changed, 11 insertions(+), 16 deletions(-)
 delete mode 100644 experiment/best_model.pth
 delete mode 100644 experiment/metrics.csv
 delete mode 100644 experiment/test_metrics.csv

diff --git a/experiment/best_model.pth b/experiment/best_model.pth
deleted file mode 100644
index 88529ff1af0506549313fab15eb474e6e33edd9a..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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CIA00?

diff --git a/experiment/metrics.csv b/experiment/metrics.csv
deleted file mode 100644
index ac5b308..0000000
--- a/experiment/metrics.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-,train_loss,train_mape,train_rmse,valid_loss,valid_mape,valid_rmse
-0,10.4322,0.1704,11.7529,15.6946,0.6565,19.2591
-1,7.9076,0.1316,9.2204,14.6998,0.6639,19.4113
diff --git a/experiment/test_metrics.csv b/experiment/test_metrics.csv
deleted file mode 100644
index 59badb3..0000000
--- a/experiment/test_metrics.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-t,mae,mape,rmse
-0,8.898,0.146,9.962
-1,8.152,0.133,9.18
-2,7.289,0.12,8.218
-3,5.441,0.092,6.442
-4,4.743,0.08,5.726
-5,5.338,0.089,6.375
-6,6.787,0.11,7.708
-7,6.109,0.1,7.004
-8,5.81,0.095,6.651
-9,5.02,0.084,6.016
-10,7.439,0.121,8.271
-11,7.919,0.128,8.65
diff --git a/test_gwnet.py b/test_gwnet.py
index bb8a0ad..3d4a1d4 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -3,17 +3,28 @@
 import unittest
 from durbango import pickle_load
 import pandas as pd
+import os
+import shutil
+
 TRAIN_ARGS = 'test_args.pkl'
 TEST_ARGS = 'test_script_args.pkl'
+SAVE_DIR = 'utest_experiment/'
 
 class TestTrain(unittest.TestCase):
 
+    @classmethod
+    def setUpClass(cls):
+        if os.path.exists(SAVE_DIR):
+            shutil.rmtree(SAVE_DIR)
+        os.makedirs(SAVE_DIR)
+
     def test_1_epoch(self):
         args = pickle_load(TRAIN_ARGS)
         args.epochs = 2
         args.n_iters = 1
         args.batch_size = 2
         args.n_obs = 2
+        args.save = SAVE_DIR
         main(args)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
         self.assertEqual(df.shape, (2,6))

From 58a378a92abf938ebc102821369b9ecfac83b732 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 6 Nov 2019 15:59:15 -0800
Subject: [PATCH 030/130] gitignore

---
 .gitignore | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..424e51f
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,7 @@
+utest_experiment/*
+.ipynb_checkpoints/
+data/
+.DS_Store
+*.xlsx
+*.mp
+*.pkl

From db176e8bfd434295e312d8778c8a497753dd81d8 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 6 Nov 2019 16:47:08 -0800
Subject: [PATCH 031/130] utestfixes

---
 test_gwnet.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/test_gwnet.py b/test_gwnet.py
index 3d4a1d4..f6d864e 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -27,14 +27,14 @@ def test_1_epoch(self):
         args.save = SAVE_DIR
         main(args)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
-        self.assertEqual(df.shape, (2,6))
+        self.assertEqual(df.shape, (2, 6))
         test_df = pd.read_csv(f'{args.save}/test_metrics.csv', index_col=0)
         self.assertEqual(test_df.shape, (12, 3))
 
     def test_test_script(self):
-        train_args = pickle_load(TRAIN_ARGS)
-        test_args = pickle_load('test_script_args.pkl')
-        test_args.checkpoint = train_args.save + '/best_model.pth'
+        test_args = pickle_load(TEST_ARGS)
+        test_args.checkpoint = SAVE_DIR + '/best_model.pth'
+        self.assertTrue(os.path.exists(test_args.checkpoint))
         test_args.n_obs = 2
         test.main(test_args)
 

From edfbe913221ce24ad6bf8f0d438be4b576bf5163 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 6 Nov 2019 20:40:59 -0800
Subject: [PATCH 032/130] Hoist nconv

---
 model.py | 12 +++++++-----
 test.py  |  1 -
 train.py |  3 +--
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/model.py b/model.py
index d4b5f09..8870a67 100644
--- a/model.py
+++ b/model.py
@@ -4,6 +4,11 @@
 from torch.autograd import Variable
 import sys
 
+
+def nconv(x, A):
+    """Multiply along source node axis"""
+    return torch.einsum('ncvl,vw->ncwl', (x, A)).contiguous()
+
 class GraphConvNet(nn.Module):
     def __init__(self, c_in, c_out, dropout, support_len=3, order=2):
         super().__init__()
@@ -12,16 +17,13 @@ def __init__(self, c_in, c_out, dropout, support_len=3, order=2):
         self.dropout = dropout
         self.order = order
 
-    def nconv(self, x, A):
-        return torch.einsum('ncvl,vw->ncwl', (x, A)).contiguous()
-
     def forward(self, x, support):
         out = [x]
         for a in support:
-            x1 = self.nconv(x,a)
+            x1 = nconv(x, a)
             out.append(x1)
             for k in range(2, self.order + 1):
-                x2 = self.nconv(x1, a)
+                x2 = nconv(x1, a)
                 out.append(x2)
                 x1 = x2
 
diff --git a/test.py b/test.py
index 9292ac5..081a365 100644
--- a/test.py
+++ b/test.py
@@ -10,7 +10,6 @@
 
 def main(args):
     device = torch.device(args.device)
-
     _, _, adj_mx = util.load_adj(args.adjdata,args.adjtype)
     supports = [torch.tensor(i).to(device) for i in adj_mx]
     if args.randomadj:
diff --git a/train.py b/train.py
index 45306ba..abe0116 100644
--- a/train.py
+++ b/train.py
@@ -36,8 +36,7 @@ def main(args):
     for i in range(1,args.epochs+1):
         #if i % 10 == 0:
             #lr = max(0.000002,args.learning_rate * (0.1 ** (i // 10)))
-            #for g in engine.optimizer.param_groups:
-                #g['lr'] = lr
+            #for g in engine.optimizer.param_groups: g['lr'] = lr
         train_loss = []
         train_mape = []
         train_rmse = []

From a66878c698b5ee3aacec7c38de25da2dc2826cf5 Mon Sep 17 00:00:00 2001
From: Clara Mc <chmccreery@gmail.com>
Date: Thu, 7 Nov 2019 08:33:06 -0800
Subject: [PATCH 033/130] cleaning up test files

---
 clara_test.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 clara_test.py

diff --git a/clara_test.py b/clara_test.py
deleted file mode 100644
index e69de29..0000000

From 3d92651c0b490c1a1e8d202928c1f3df371718da Mon Sep 17 00:00:00 2001
From: Clara Mc <chmccreery@gmail.com>
Date: Thu, 7 Nov 2019 08:34:41 -0800
Subject: [PATCH 034/130] removing more test files

---
 vamsi_test.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 vamsi_test.py

diff --git a/vamsi_test.py b/vamsi_test.py
deleted file mode 100644
index e69de29..0000000

From fe96d62ec09929335ecc3dc58388e3edb7cd8edf Mon Sep 17 00:00:00 2001
From: Clara Mc <chmccreery@gmail.com>
Date: Thu, 7 Nov 2019 08:59:21 -0800
Subject: [PATCH 035/130] New adjacency matrices with less thresholding and
 script to analyze them

---
 adj_mx_stats.py                         |     21 +
 data/sensor_graph/adj_mat.pkl           |    Bin 0 -> 177424 bytes
 data/sensor_graph/adj_mat_0pt001.pkl    |    Bin 0 -> 182340 bytes
 data/sensor_graph/adj_mat_0pt01.pkl     |    Bin 0 -> 179762 bytes
 data/sensor_graph/adj_mat_no_thresh.pkl |    Bin 0 -> 192458 bytes
 data/sensor_graph/distances_la_2012.csv | 295375 +++++++++++++++++++++
 data/sensor_graph/graph_sensor_ids.txt  |      1 +
 gen_adj_mx.py                           |     63 +
 8 files changed, 295460 insertions(+)
 create mode 100644 adj_mx_stats.py
 create mode 100644 data/sensor_graph/adj_mat.pkl
 create mode 100644 data/sensor_graph/adj_mat_0pt001.pkl
 create mode 100644 data/sensor_graph/adj_mat_0pt01.pkl
 create mode 100644 data/sensor_graph/adj_mat_no_thresh.pkl
 create mode 100644 data/sensor_graph/distances_la_2012.csv
 create mode 100644 data/sensor_graph/graph_sensor_ids.txt
 create mode 100644 gen_adj_mx.py

diff --git a/adj_mx_stats.py b/adj_mx_stats.py
new file mode 100644
index 0000000..0d00774
--- /dev/null
+++ b/adj_mx_stats.py
@@ -0,0 +1,21 @@
+import argparse
+import pickle
+import numpy as np
+
+def print_stats(args):
+  with open('data/sensor_graph/' + args.adj_mx, 'rb') as f:
+    A = pickle.load(f)
+
+  adj_mx = A[2]
+  degrees = np.sum(adj_mx > 0, 1)
+
+  print(f"Minimum Degree: {np.min(degrees)}")
+  print(f"Maximum Degree: {np.max(degrees)}")
+  print(f"Average Degree: {np.mean(degrees)}")
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--adj_mx', type=str, default='adj_mat.pkl',
+    help='Pickle file in data/sensor_graph containing adjacency matrix')
+  args = parser.parse_args()
+  print_stats(args)
diff --git a/data/sensor_graph/adj_mat.pkl b/data/sensor_graph/adj_mat.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..efe1239d652edcef12f31a48a0ce9fdacd80a67d
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diff --git a/data/sensor_graph/adj_mat_no_thresh.pkl b/data/sensor_graph/adj_mat_no_thresh.pkl
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diff --git a/data/sensor_graph/distances_la_2012.csv b/data/sensor_graph/distances_la_2012.csv
new file mode 100644
index 0000000..759d2bc
--- /dev/null
+++ b/data/sensor_graph/distances_la_2012.csv
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\ No newline at end of file
diff --git a/data/sensor_graph/graph_sensor_ids.txt b/data/sensor_graph/graph_sensor_ids.txt
new file mode 100644
index 0000000..e2df022
--- /dev/null
+++ b/data/sensor_graph/graph_sensor_ids.txt
@@ -0,0 +1 @@
+773869,767541,767542,717447,717446,717445,773062,767620,737529,717816,765604,767471,716339,773906,765273,716331,771667,716337,769953,769402,769403,769819,769405,716941,717578,716960,717804,767572,767573,773012,773013,764424,769388,716328,717819,769941,760987,718204,718045,769418,768066,772140,773927,760024,774012,774011,767609,769359,760650,716956,769831,761604,717495,716554,773953,767470,716955,764949,773954,767366,769444,773939,774067,769443,767750,767751,767610,773880,764766,717497,717490,717491,717492,717493,765176,717498,717499,765171,718064,718066,765164,769431,769430,717610,767053,767621,772596,772597,767350,767351,716571,773023,767585,773024,717483,718379,717481,717480,717486,764120,772151,718371,717489,717488,717818,718076,718072,767455,767454,761599,717099,773916,716968,769467,717576,717573,717572,717571,717570,764760,718089,769847,717608,767523,716942,718090,769867,717472,717473,759591,764781,765099,762329,716953,716951,767509,765182,769358,772513,716958,718496,769346,773904,718499,764853,761003,717502,759602,717504,763995,717508,765265,773996,773995,717469,717468,764106,717465,764794,717466,717461,717460,717463,717462,769345,716943,772669,717582,717583,717580,716949,717587,772178,717585,716939,768469,764101,767554,773975,773974,717510,717513,717825,767495,767494,717821,717823,717458,717459,769926,764858,717450,717452,717453,759772,717456,771673,772167,769372,774204,769806,717590,717592,717595,772168,718141,769373
\ No newline at end of file
diff --git a/gen_adj_mx.py b/gen_adj_mx.py
new file mode 100644
index 0000000..39253d8
--- /dev/null
+++ b/gen_adj_mx.py
@@ -0,0 +1,63 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import numpy as np
+import pandas as pd
+import pickle
+
+
+def get_adjacency_matrix(distance_df, sensor_ids, normalized_k=0.1):
+    """
+
+    :param distance_df: data frame with three columns: [from, to, distance].
+    :param sensor_ids: list of sensor ids.
+    :param normalized_k: entries that become lower than normalized_k after normalization are set to zero for sparsity.
+    :return:
+    """
+    num_sensors = len(sensor_ids)
+    dist_mx = np.zeros((num_sensors, num_sensors), dtype=np.float32)
+    dist_mx[:] = np.inf
+    # Builds sensor id to index map.
+    sensor_id_to_ind = {}
+    for i, sensor_id in enumerate(sensor_ids):
+        sensor_id_to_ind[sensor_id] = i
+
+    # Fills cells in the matrix with distances.
+    for row in distance_df.values:
+        if row[0] not in sensor_id_to_ind or row[1] not in sensor_id_to_ind:
+            continue
+        dist_mx[sensor_id_to_ind[row[0]], sensor_id_to_ind[row[1]]] = row[2]
+
+    # Calculates the standard deviation as theta.
+    distances = dist_mx[~np.isinf(dist_mx)].flatten()
+    std = distances.std()
+    adj_mx = np.exp(-np.square(dist_mx / std))
+    # Make the adjacent matrix symmetric by taking the max.
+    # adj_mx = np.maximum.reduce([adj_mx, adj_mx.T])
+
+    # Sets entries that lower than a threshold, i.e., k, to zero for sparsity.
+    adj_mx[adj_mx < normalized_k] = 0
+    return sensor_ids, sensor_id_to_ind, adj_mx
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--sensor_ids_filename', type=str, default='data/sensor_graph/graph_sensor_ids.txt',
+                        help='File containing sensor ids separated by comma.')
+    parser.add_argument('--distances_filename', type=str, default='data/sensor_graph/distances_la_2012.csv',
+                        help='CSV file containing sensor distances with three columns: [from, to, distance].')
+    parser.add_argument('--normalized_k', type=float, default=0.1,
+                        help='Entries that become lower than normalized_k after normalization are set to zero for sparsity.')
+    parser.add_argument('--output_pkl_filename', type=str, default='data/sensor_graph/adj_mat.pkl',
+                        help='Path of the output file.')
+    args = parser.parse_args()
+
+    with open(args.sensor_ids_filename) as f:
+        sensor_ids = f.read().strip().split(',')
+    distance_df = pd.read_csv(args.distances_filename, dtype={'from': 'str', 'to': 'str'})
+    _, sensor_id_to_ind, adj_mx = get_adjacency_matrix(distance_df, sensor_ids, args.normalized_k)
+    # Save to pickle file.
+    with open(args.output_pkl_filename, 'wb') as f:
+        pickle.dump([sensor_ids, sensor_id_to_ind, adj_mx], f, protocol=2)

From 4a7330433311031e0fe4c166f1bc44b00e0683f7 Mon Sep 17 00:00:00 2001
From: Clara Mc <chmccreery@gmail.com>
Date: Thu, 7 Nov 2019 09:24:36 -0800
Subject: [PATCH 036/130] add day of the week feature

---
 generate_training_data.py | 129 ++++++++++++++++++++++++++++++++++++++
 requirements.txt          |   1 +
 2 files changed, 130 insertions(+)
 create mode 100644 generate_training_data.py

diff --git a/generate_training_data.py b/generate_training_data.py
new file mode 100644
index 0000000..f5eb739
--- /dev/null
+++ b/generate_training_data.py
@@ -0,0 +1,129 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import numpy as np
+import os
+import pandas as pd
+
+
+def generate_graph_seq2seq_io_data(
+        df, x_offsets, y_offsets, add_time_in_day=True, add_day_in_week=False, scaler=None
+):
+    """
+    Generate samples from
+    :param df:
+    :param x_offsets:
+    :param y_offsets:
+    :param add_time_in_day:
+    :param add_day_in_week:
+    :param scaler:
+    :return:
+    # x: (epoch_size, input_length, num_nodes, input_dim)
+    # y: (epoch_size, output_length, num_nodes, output_dim)
+    """
+
+    num_samples, num_nodes = df.shape
+    data = np.expand_dims(df.values, axis=-1)
+    data_list = [data]
+    if add_time_in_day:
+        time_ind = (df.index.values - df.index.values.astype("datetime64[D]")) / np.timedelta64(1, "D")
+        time_in_day = np.tile(time_ind, [1, num_nodes, 1]).transpose((2, 1, 0))
+        data_list.append(time_in_day)
+    if add_day_in_week:
+        day_in_week = np.zeros(shape=(num_samples, num_nodes, 7))
+        day_in_week[np.arange(num_samples), :, df.index.dayofweek] = 1
+        data_list.append(day_in_week)
+
+    data = np.concatenate(data_list, axis=-1)
+    # epoch_len = num_samples + min(x_offsets) - max(y_offsets)
+    x, y = [], []
+    # t is the index of the last observation.
+    min_t = abs(min(x_offsets))
+    max_t = abs(num_samples - abs(max(y_offsets)))  # Exclusive
+    for t in range(min_t, max_t):
+        x_t = data[t + x_offsets, ...]
+        y_t = data[t + y_offsets, ...]
+        x.append(x_t)
+        y.append(y_t)
+    x = np.stack(x, axis=0)
+    y = np.stack(y, axis=0)
+    return x, y
+
+
+def generate_train_val_test(args):
+    df = pd.read_hdf(args.traffic_df_filename)
+    # 0 is the latest observed sample.
+    x_offsets = np.sort(
+        # np.concatenate(([-week_size + 1, -day_size + 1], np.arange(-11, 1, 1)))
+        np.concatenate((np.arange(-11, 1, 1),))
+    )
+    # Predict the next one hour
+    y_offsets = np.sort(np.arange(1, 13, 1))
+    # x: (num_samples, input_length, num_nodes, input_dim)
+    # y: (num_samples, output_length, num_nodes, output_dim)
+    x, y = generate_graph_seq2seq_io_data(
+        df,
+        x_offsets=x_offsets,
+        y_offsets=y_offsets,
+        add_time_in_day=True,
+        add_day_in_week=False,
+    )
+
+    print("x shape: ", x.shape, ", y shape: ", y.shape)
+    # Write the data into npz file.
+    # num_test = 6831, using the last 6831 examples as testing.
+    # for the rest: 7/8 is used for training, and 1/8 is used for validation.
+    num_samples = x.shape[0]
+    num_test = round(num_samples * 0.2)
+    num_train = round(num_samples * 0.7)
+    num_val = num_samples - num_test - num_train
+
+    # train
+    x_train, y_train = x[:num_train], y[:num_train]
+    # val
+    x_val, y_val = (
+        x[num_train: num_train + num_val],
+        y[num_train: num_train + num_val],
+    )
+    # test
+    x_test, y_test = x[-num_test:], y[-num_test:]
+
+    for cat in ["train", "val", "test"]:
+        _x, _y = locals()["x_" + cat], locals()["y_" + cat]
+        print(cat, "x: ", _x.shape, "y:", _y.shape)
+        np.savez_compressed(
+            os.path.join(args.output_dir, "%s.npz" % cat),
+            x=_x,
+            y=_y,
+            x_offsets=x_offsets.reshape(list(x_offsets.shape) + [1]),
+            y_offsets=y_offsets.reshape(list(y_offsets.shape) + [1]),
+        )
+
+
+def main(args):
+    print("Generating training data")
+    generate_train_val_test(args)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--output_dir", type=str, default="data/METR-LA", help="Output directory."
+    )
+    parser.add_argument(
+        "--traffic_df_filename",
+        type=str,
+        default="data/metr-la.h5",
+        help="Raw traffic readings.",
+    )
+    args = parser.parse_args()
+    if not os.path.exists(args.output_dir):
+      reply = str(input(f'{args.output_dir} does not exist. Do you want to create it? (y/n)')).lower().strip()
+      if reply[0] == 'y':
+        os.mkdir(args.output_dir)
+      else:
+        exit
+    main(args)
diff --git a/requirements.txt b/requirements.txt
index fdad8a3..4710787 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,3 @@
 durbango
+tables
 tqdm

From 66409aff652325e4a4c5e6c3a4d49b64c8c5c1dc Mon Sep 17 00:00:00 2001
From: Clara Mc <chmccreery@gmail.com>
Date: Thu, 7 Nov 2019 09:25:11 -0800
Subject: [PATCH 037/130] Default to include day of week one-hot feature

---
 generate_training_data.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/generate_training_data.py b/generate_training_data.py
index f5eb739..e2cb614 100644
--- a/generate_training_data.py
+++ b/generate_training_data.py
@@ -69,7 +69,7 @@ def generate_train_val_test(args):
         x_offsets=x_offsets,
         y_offsets=y_offsets,
         add_time_in_day=True,
-        add_day_in_week=False,
+        add_day_in_week=True,
     )
 
     print("x shape: ", x.shape, ", y shape: ", y.shape)

From 02e97d85def55733f4cf06ad96778c6ee8398337 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 7 Nov 2019 15:12:31 -0800
Subject: [PATCH 038/130] Test metrics stuff

---
 model.py      |  6 ++++--
 test.py       | 16 +++++-----------
 test_gwnet.py |  5 +++--
 train.py      |  6 +++++-
 util.py       |  9 +++++++++
 5 files changed, 26 insertions(+), 16 deletions(-)

diff --git a/model.py b/model.py
index 8870a67..475d1d0 100644
--- a/model.py
+++ b/model.py
@@ -34,8 +34,10 @@ def forward(self, x, support):
 
 
 class GWNet(nn.Module):
-    def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=True, addaptadj=True, aptinit=None,
-                 in_dim=2, out_dim=12, residual_channels=32, dilation_channels=32, skip_channels=256,
+    def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=True,
+                 addaptadj=True, aptinit=None,
+                 in_dim=2, out_dim=12, residual_channels=32, dilation_channels=32,
+                 skip_channels=256,
                  end_channels=512, kernel_size=2, blocks=4, layers=2):
         super().__init__()
         self.dropout = dropout
diff --git a/test.py b/test.py
index 081a365..505be77 100644
--- a/test.py
+++ b/test.py
@@ -5,7 +5,6 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-from durbango import pickle_save
 
 
 def main(args):
@@ -20,14 +19,14 @@ def main(args):
     if args.aptonly:
         supports = None
 
-    model = GWNet(device, args.num_nodes, args.dropout, supports=supports, do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=adjinit)
+    model = GWNet(device, args.num_nodes, args.dropout,
+                  supports=supports, do_graph_conv=args.do_graph_conv,
+                  addaptadj=args.addaptadj, aptinit=adjinit
+                  )
     model.to(device)
     model.load_state_dict(torch.load(args.checkpoint))
     model.eval()
-
-
     print('model loaded successfully')
-
     dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
     scaler = dataloader['scaler']
     realy = torch.Tensor(dataloader['y_test']).to(device)
@@ -40,12 +39,7 @@ def main(args):
     if args.plotheatmap == "True":
         plot_heatmap(model)
 
-    y12 = realy[:,99,11].cpu().detach().numpy()
-    yhat12 = scaler.inverse_transform(yhat[:,99,11]).cpu().detach().numpy()
-
-    y3 = realy[:,99,2].cpu().detach().numpy()
-    yhat3 = scaler.inverse_transform(yhat[:,99,2]).cpu().detach().numpy()
-    df2 = pd.DataFrame({'real12': y12, 'pred12': yhat12, 'real3': y3, 'pred3': yhat3})
+    df2 = util.make_pred_df(realy, yhat, scaler)
 
     df2.to_csv('./wave.csv', index=False)
 
diff --git a/test_gwnet.py b/test_gwnet.py
index f6d864e..63cd513 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -30,11 +30,12 @@ def test_1_epoch(self):
         self.assertEqual(df.shape, (2, 6))
         test_df = pd.read_csv(f'{args.save}/test_metrics.csv', index_col=0)
         self.assertEqual(test_df.shape, (12, 3))
-
-    def test_test_script(self):
         test_args = pickle_load(TEST_ARGS)
         test_args.checkpoint = SAVE_DIR + '/best_model.pth'
         self.assertTrue(os.path.exists(test_args.checkpoint))
         test_args.n_obs = 2
         test.main(test_args)
+        new_met = pd.read_csv('last_test_metrics.csv', index_col=0)
+        deltas = test_df.mean() - new_met.mean()
+        self.assertGreaterEqual(.01, deltas.abs().max())
 
diff --git a/train.py b/train.py
index abe0116..d37ae34 100644
--- a/train.py
+++ b/train.py
@@ -74,9 +74,13 @@ def main(args):
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
     realy = torch.Tensor(dataloader['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
-    test_met_df, _ = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler, realy)
+    test_met_df, yhat = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler, realy)
     test_met_df.round(4).to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(test_met_df.mean().round(3))
+    pred_df = util.make_pred_df(realy, yhat, scaler)
+    pred_df.to_csv(os.path.join(args.save, 'preds.csv'))
+
+
 
 
 def eval_(ds, device, engine):
diff --git a/util.py b/util.py
index 436d305..430e789 100644
--- a/util.py
+++ b/util.py
@@ -201,3 +201,12 @@ def calc_test_metrics(model, device, test_loader, scaler, realy):
         test_met.append([x.item() for x in cheaper_metric(pred, real)])
     test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t').round(3)
     return test_met_df, yhat
+
+
+def make_pred_df(realy, yhat, scaler):
+    y12 = realy[:, 99, 11].cpu().detach().numpy()
+    yhat12 = scaler.inverse_transform(yhat[:, 99, 11]).cpu().detach().numpy()
+    y3 = realy[:, 99, 2].cpu().detach().numpy()
+    yhat3 = scaler.inverse_transform(yhat[:, 99, 2]).cpu().detach().numpy()
+    df2 = pd.DataFrame({'real12': y12, 'pred12': yhat12, 'real3': y3, 'pred3': yhat3})
+    return df2

From 209b6bc6d948409d22e3d31987edb7daf205c7fa Mon Sep 17 00:00:00 2001
From: Clara Mc <chmccreery@gmail.com>
Date: Thu, 7 Nov 2019 17:47:20 -0800
Subject: [PATCH 039/130] use utils to unpickle and add heatmap, histogram to
 adj_mx_stats

---
 adj_mx_stats.py  | 24 ++++++++++++++++++++----
 requirements.txt |  3 +++
 2 files changed, 23 insertions(+), 4 deletions(-)

diff --git a/adj_mx_stats.py b/adj_mx_stats.py
index 0d00774..c5c8308 100644
--- a/adj_mx_stats.py
+++ b/adj_mx_stats.py
@@ -1,18 +1,34 @@
 import argparse
 import pickle
 import numpy as np
+from util import *
+import matplotlib.pyplot as plt
 
 def print_stats(args):
-  with open('data/sensor_graph/' + args.adj_mx, 'rb') as f:
-    A = pickle.load(f)
-
+  if '/' not in args.adj_mx:
+    full_path = 'data/sensor_graph/' + args.adj_mx
+  else:
+    full_path = args.adj_mx
+  
+  A = load_pickle(full_path)
   adj_mx = A[2]
-  degrees = np.sum(adj_mx > 0, 1)
 
+  degrees = np.sum(adj_mx > 0, 1)
   print(f"Minimum Degree: {np.min(degrees)}")
   print(f"Maximum Degree: {np.max(degrees)}")
   print(f"Average Degree: {np.mean(degrees)}")
 
+  plt.imshow(adj_mx, cmap='hot', interpolation='nearest')
+  plt.title('Adjacency Matrix Heatmap')
+  plt.show()
+
+  plt.hist(degrees) 
+  plt.xticks(range(0,max(degrees) + 5,5))
+  plt.title('LA Degree Distribution')
+  plt.xlabel('Node Degree')
+  plt.ylabel('Number of Nodes')
+  plt.show()
+
 if __name__ == '__main__':
   parser = argparse.ArgumentParser()
   parser.add_argument('--adj_mx', type=str, default='adj_mat.pkl',
diff --git a/requirements.txt b/requirements.txt
index 4710787..bcf5eb8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,6 @@
 durbango
 tables
 tqdm
+matplotlib
+numpy
+argparse

From 15b62f49fdc6360fb5921225ff437a7669f6026e Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 7 Nov 2019 19:36:12 -0800
Subject: [PATCH 040/130] more cov

---
 test_gwnet.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/test_gwnet.py b/test_gwnet.py
index 63cd513..50432cb 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -4,6 +4,7 @@
 from durbango import pickle_load
 import pandas as pd
 import os
+import torch
 import shutil
 
 TRAIN_ARGS = 'test_args.pkl'
@@ -20,22 +21,23 @@ def setUpClass(cls):
 
     def test_1_epoch(self):
         args = pickle_load(TRAIN_ARGS)
-        args.epochs = 2
+        args.epochs = 1
         args.n_iters = 1
         args.batch_size = 2
         args.n_obs = 2
         args.save = SAVE_DIR
         main(args)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
-        self.assertEqual(df.shape, (2, 6))
+        self.assertEqual(df.shape, (args.epochs, 6))
         test_df = pd.read_csv(f'{args.save}/test_metrics.csv', index_col=0)
         self.assertEqual(test_df.shape, (12, 3))
         test_args = pickle_load(TEST_ARGS)
         test_args.checkpoint = SAVE_DIR + '/best_model.pth'
+        state_dict = torch.load(test_args.checkpoint)
+        assert 'nodevec1' in state_dict
         self.assertTrue(os.path.exists(test_args.checkpoint))
         test_args.n_obs = 2
         test.main(test_args)
         new_met = pd.read_csv('last_test_metrics.csv', index_col=0)
         deltas = test_df.mean() - new_met.mean()
         self.assertGreaterEqual(.01, deltas.abs().max())
-

From cfb72cf06361574fc8d261487cfd5077a3c59b5e Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 7 Nov 2019 19:45:17 -0800
Subject: [PATCH 041/130] register parameter

---
 model.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/model.py b/model.py
index 475d1d0..972f151 100644
--- a/model.py
+++ b/model.py
@@ -68,16 +68,17 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
         if do_graph_conv and addaptadj:
             if supports is None: self.supports = []
             if aptinit is None:
-                self.nodevec1 = nn.Parameter(torch.randn(num_nodes, 10), requires_grad=True).to(device)
-                self.nodevec2 = nn.Parameter(torch.randn(10, num_nodes), requires_grad=True).to(device)
+                nodevec1 = torch.randn(num_nodes, 10)
+                nodevec2 = torch.randn(10, num_nodes)
                 self.supports_len +=1
             else:
                 m, p, n = torch.svd(aptinit)
-                initemb1 = torch.mm(m[:, :10], torch.diag(p[:10] ** 0.5))
-                initemb2 = torch.mm(torch.diag(p[:10] ** 0.5), n[:, :10].t())
-                self.nodevec1 = nn.Parameter(initemb1, requires_grad=True).to(device)
-                self.nodevec2 = nn.Parameter(initemb2, requires_grad=True).to(device)
-                self.supports_len += 1
+                nodevec1 = torch.mm(m[:, :10], torch.diag(p[:10] ** 0.5))
+                nodevec2 = torch.mm(torch.diag(p[:10] ** 0.5), n[:, :10].t())
+            self.supports_len += 1
+
+        self.register_parameter('nodevec1', nn.Parameter(nodevec1, requires_grad=True))
+        self.register_parameter('nodevec2', nn.Parameter(nodevec2, requires_grad=True))
 
         for b in range(blocks):
             additional_scope = kernel_size - 1

From 0be46daf2193e012575a01d811159c988d49a3c2 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 7 Nov 2019 20:10:25 -0800
Subject: [PATCH 042/130] fix supports_len bug

---
 model.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/model.py b/model.py
index 972f151..05a3920 100644
--- a/model.py
+++ b/model.py
@@ -70,15 +70,14 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
             if aptinit is None:
                 nodevec1 = torch.randn(num_nodes, 10)
                 nodevec2 = torch.randn(10, num_nodes)
-                self.supports_len +=1
             else:
                 m, p, n = torch.svd(aptinit)
                 nodevec1 = torch.mm(m[:, :10], torch.diag(p[:10] ** 0.5))
                 nodevec2 = torch.mm(torch.diag(p[:10] ** 0.5), n[:, :10].t())
             self.supports_len += 1
 
-        self.register_parameter('nodevec1', nn.Parameter(nodevec1, requires_grad=True))
-        self.register_parameter('nodevec2', nn.Parameter(nodevec2, requires_grad=True))
+            self.register_parameter('nodevec1', nn.Parameter(nodevec1, requires_grad=True).to(device))
+            self.register_parameter('nodevec2', nn.Parameter(nodevec2, requires_grad=True).to(device))
 
         for b in range(blocks):
             additional_scope = kernel_size - 1
@@ -107,7 +106,9 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
                 receptive_field += additional_scope
                 additional_scope *= 2
                 if self.do_graph_conv:
-                    self.graph_convs.append(GraphConvNet(dilation_channels, residual_channels, dropout, support_len=self.supports_len))
+                    self.graph_convs.append(
+                        GraphConvNet(dilation_channels, residual_channels, dropout,
+                                     support_len=self.supports_len))
 
         self.end_conv_1 = nn.Conv2d(in_channels=skip_channels,
                                     out_channels=end_channels,

From cf8b066fd20c2cb48cce383d23353516a114e476 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 8 Nov 2019 06:45:45 +0000
Subject: [PATCH 043/130] Fix

---
 model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/model.py b/model.py
index 05a3920..77c4bc9 100644
--- a/model.py
+++ b/model.py
@@ -76,8 +76,8 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
                 nodevec2 = torch.mm(torch.diag(p[:10] ** 0.5), n[:, :10].t())
             self.supports_len += 1
 
-            self.register_parameter('nodevec1', nn.Parameter(nodevec1, requires_grad=True).to(device))
-            self.register_parameter('nodevec2', nn.Parameter(nodevec2, requires_grad=True).to(device))
+            self.register_parameter('nodevec1', nn.Parameter(nodevec1.to(device), requires_grad=True))
+            self.register_parameter('nodevec2', nn.Parameter(nodevec2.to(device), requires_grad=True))
 
         for b in range(blocks):
             additional_scope = kernel_size - 1

From 1d9d19d959e7ef55f5897c7f8a29dfc8d5badc3c Mon Sep 17 00:00:00 2001
From: Sam Shleifer <sshleifer@gmail.com>
Date: Fri, 22 Nov 2019 08:48:51 -0800
Subject: [PATCH 044/130] unit test on GPU (#3)

progress bar, apt_size param
---
 bug_example.py | 14 ++++++++++++++
 model.py       | 16 +++++++---------
 test_gwnet.py  |  3 +++
 train.py       |  8 +++++---
 util.py        |  1 +
 5 files changed, 30 insertions(+), 12 deletions(-)
 create mode 100644 bug_example.py

diff --git a/bug_example.py b/bug_example.py
new file mode 100644
index 0000000..e392a89
--- /dev/null
+++ b/bug_example.py
@@ -0,0 +1,14 @@
+import torch
+from torch import nn
+
+class Model(nn.Module):
+
+    def __init__(self, device):
+        super().__init__()
+        self.vector = nn.Parameter(torch.randn(5, 5).to(device), requires_grad=True)
+
+model_cpu = Model('cpu')
+assert 'vector' in model_cpu.state_dict()
+model_gpu = Model('cuda:0')
+assert 'vector' in model_gpu.state_dict() # Raise assertionerror
+
diff --git a/model.py b/model.py
index 77c4bc9..a089e4d 100644
--- a/model.py
+++ b/model.py
@@ -35,10 +35,8 @@ def forward(self, x, support):
 
 class GWNet(nn.Module):
     def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=True,
-                 addaptadj=True, aptinit=None,
-                 in_dim=2, out_dim=12, residual_channels=32, dilation_channels=32,
-                 skip_channels=256,
-                 end_channels=512, kernel_size=2, blocks=4, layers=2):
+                 addaptadj=True, aptinit=None, in_dim=2, out_dim=12, residual_channels=32, dilation_channels=32,
+                 skip_channels=256, end_channels=512, kernel_size=2, blocks=4, layers=2, apt_size=10):
         super().__init__()
         self.dropout = dropout
         self.blocks = blocks
@@ -68,14 +66,14 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
         if do_graph_conv and addaptadj:
             if supports is None: self.supports = []
             if aptinit is None:
-                nodevec1 = torch.randn(num_nodes, 10)
-                nodevec2 = torch.randn(10, num_nodes)
+                nodevec1 = torch.randn(num_nodes, apt_size)
+                nodevec2 = torch.randn(apt_size, num_nodes)
             else:
                 m, p, n = torch.svd(aptinit)
-                nodevec1 = torch.mm(m[:, :10], torch.diag(p[:10] ** 0.5))
-                nodevec2 = torch.mm(torch.diag(p[:10] ** 0.5), n[:, :10].t())
+                nodevec1 = torch.mm(m[:, :apt_size], torch.diag(p[:apt_size] ** 0.5))
+                nodevec2 = torch.mm(torch.diag(p[:apt_size] ** 0.5), n[:, :apt_size].t())
             self.supports_len += 1
-
+            
             self.register_parameter('nodevec1', nn.Parameter(nodevec1.to(device), requires_grad=True))
             self.register_parameter('nodevec2', nn.Parameter(nodevec2.to(device), requires_grad=True))
 
diff --git a/test_gwnet.py b/test_gwnet.py
index 50432cb..da750c5 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -5,6 +5,7 @@
 import pandas as pd
 import os
 import torch
+import util
 import shutil
 
 TRAIN_ARGS = 'test_args.pkl'
@@ -26,6 +27,8 @@ def test_1_epoch(self):
         args.batch_size = 2
         args.n_obs = 2
         args.save = SAVE_DIR
+        args.device = util.DEFAULT_DEVICE
+        args.addaptadj = True
         main(args)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
         self.assertEqual(df.shape, (args.epochs, 6))
diff --git a/train.py b/train.py
index d37ae34..122b9e0 100644
--- a/train.py
+++ b/train.py
@@ -9,6 +9,7 @@
 from durbango import pickle_save
 
 from util import calc_test_metrics
+from fastprogress import master_bar, progress_bar
 
 
 def main(args):
@@ -33,7 +34,8 @@ def main(args):
     print("start training...",flush=True)
     metrics, train_time = [], []
     best_yet = 100
-    for i in range(1,args.epochs+1):
+    mb = master_bar(list(range(1, args.epochs + 1)))
+    for i in mb:
         #if i % 10 == 0:
             #lr = max(0.000002,args.learning_rate * (0.1 ** (i // 10)))
             #for g in engine.optimizer.param_groups: g['lr'] = lr
@@ -53,7 +55,7 @@ def main(args):
                 break
         train_time.append(time.time()-t1)
         total_time, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device, engine)
-        print(f'Epoch: {i:03d}')
+
 
         m = pd.Series(dict(train_loss=np.mean(train_loss),
                            train_mape=np.mean(train_mape),
@@ -61,8 +63,8 @@ def main(args):
                            valid_loss=np.mean(valid_loss),
                            valid_mape=np.mean(valid_mape),
                            valid_rmse=np.mean(valid_rmse), ))
+        mb.first_bar.comment = f'valid_loss: {m.valid_loss: .3f}'
         metrics.append(m)
-        print(f'Epoch {i}')
         print(m.round(4))
         if m.valid_loss < best_yet:
             torch.save(engine.model.state_dict(), best_model_save_path)
diff --git a/util.py b/util.py
index 430e789..ba6f680 100644
--- a/util.py
+++ b/util.py
@@ -7,6 +7,7 @@
 import torch
 from scipy.sparse import linalg
 
+DEFAULT_DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
 
 class DataLoader(object):
     def __init__(self, xs, ys, batch_size, pad_with_last_sample=True):

From 00bfee0911957921381047e8be0e6373c8a807c6 Mon Sep 17 00:00:00 2001
From: Sam Shleifer <sshleifer@gmail.com>
Date: Fri, 22 Nov 2019 14:58:50 -0500
Subject: [PATCH 045/130] fix test.py (#4)

---
 engine.py     |  3 ++-
 test.py       |  2 +-
 test_gwnet.py |  2 ++
 train.py      | 29 +++++++++++++++--------------
 4 files changed, 20 insertions(+), 16 deletions(-)

diff --git a/engine.py b/engine.py
index d457892..2db3f07 100644
--- a/engine.py
+++ b/engine.py
@@ -3,9 +3,10 @@
 import util
 class Trainer():
     def __init__(self, scaler, in_dim, seq_length, num_nodes, nhid , dropout, lrate, wdecay, device,
-                 supports, gcn_bool, addaptadj, aptinit, clip=5):
+                 supports, gcn_bool, addaptadj, aptinit, clip=5, apt_size=10):
         # TODO(SS): pass model in.
         self.model = GWNet(device, num_nodes, dropout, supports=supports, do_graph_conv=gcn_bool, addaptadj=addaptadj, aptinit=aptinit, in_dim=in_dim,
+                           apt_size=apt_size,
                            out_dim=seq_length, residual_channels=nhid, dilation_channels=nhid, skip_channels=nhid * 8, end_channels=nhid * 16)
         self.model.to(device)
         self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
diff --git a/test.py b/test.py
index 505be77..2a926b8 100644
--- a/test.py
+++ b/test.py
@@ -21,7 +21,7 @@ def main(args):
 
     model = GWNet(device, args.num_nodes, args.dropout,
                   supports=supports, do_graph_conv=args.do_graph_conv,
-                  addaptadj=args.addaptadj, aptinit=adjinit
+                  addaptadj=args.addaptadj, aptinit=adjinit, apt_size=args.apt_size,
                   )
     model.to(device)
     model.load_state_dict(torch.load(args.checkpoint))
diff --git a/test_gwnet.py b/test_gwnet.py
index da750c5..a84f2cb 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -29,12 +29,14 @@ def test_1_epoch(self):
         args.save = SAVE_DIR
         args.device = util.DEFAULT_DEVICE
         args.addaptadj = True
+        args.apt_size = 20
         main(args)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
         self.assertEqual(df.shape, (args.epochs, 6))
         test_df = pd.read_csv(f'{args.save}/test_metrics.csv', index_col=0)
         self.assertEqual(test_df.shape, (12, 3))
         test_args = pickle_load(TEST_ARGS)
+        test_args.apt_size = 20
         test_args.checkpoint = SAVE_DIR + '/best_model.pth'
         state_dict = torch.load(test_args.checkpoint)
         assert 'nodevec1' in state_dict
diff --git a/train.py b/train.py
index 122b9e0..85bac0b 100644
--- a/train.py
+++ b/train.py
@@ -16,7 +16,8 @@ def main(args):
     device = torch.device(args.device)
     sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
     supports = [torch.tensor(i).to(device) for i in adj_mx]
-    dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
+    dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size,
+                                   n_obs=args.n_obs)
     scaler = dataloader['scaler']
     print(args)
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
@@ -30,15 +31,15 @@ def main(args):
         supports = None
     engine = Trainer(scaler, args.in_dim, args.seq_length, args.num_nodes, args.nhid, args.dropout,
                      args.learning_rate, args.weight_decay, device, supports, args.do_graph_conv,
-                     args.addaptadj, adjinit)
-    print("start training...",flush=True)
+                     args.addaptadj, adjinit, apt_size=args.apt_size)
+    print("start training...", flush=True)
     metrics, train_time = [], []
     best_yet = 100
     mb = master_bar(list(range(1, args.epochs + 1)))
     for i in mb:
-        #if i % 10 == 0:
-            #lr = max(0.000002,args.learning_rate * (0.1 ** (i // 10)))
-            #for g in engine.optimizer.param_groups: g['lr'] = lr
+        # if i % 10 == 0:
+        # lr = max(0.000002,args.learning_rate * (0.1 ** (i // 10)))
+        # for g in engine.optimizer.param_groups: g['lr'] = lr
         train_loss = []
         train_mape = []
         train_rmse = []
@@ -47,15 +48,15 @@ def main(args):
         for iter, (x, y) in enumerate(dataloader['train_loader'].get_iterator()):
             trainx = torch.Tensor(x).to(device).transpose(1, 3)
             trainy = torch.Tensor(y).to(device).transpose(1, 3)
-            loss, mape, rmse = engine.train(trainx, trainy[:,0,:,:])
+            loss, mape, rmse = engine.train(trainx, trainy[:, 0, :, :])
             train_loss.append(loss)
             train_mape.append(mape)
             train_rmse.append(rmse)
             if args.n_iters is not None and iter >= args.n_iters:
                 break
-        train_time.append(time.time()-t1)
-        total_time, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device, engine)
-
+        train_time.append(time.time() - t1)
+        total_time, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device,
+                                                               engine)
 
         m = pd.Series(dict(train_loss=np.mean(train_loss),
                            train_mape=np.mean(train_mape),
@@ -76,15 +77,14 @@ def main(args):
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
     realy = torch.Tensor(dataloader['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
-    test_met_df, yhat = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler, realy)
+    test_met_df, yhat = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler,
+                                          realy)
     test_met_df.round(4).to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(test_met_df.mean().round(3))
     pred_df = util.make_pred_df(realy, yhat, scaler)
     pred_df.to_csv(os.path.join(args.save, 'preds.csv'))
 
 
-
-
 def eval_(ds, device, engine):
     """Run validation."""
     valid_loss = []
@@ -130,6 +130,7 @@ def eval_(ds, device, engine):
     # parser.add_argument('--expid', default=1, help='experiment id')
     parser.add_argument('--n_iters', default=None, help='quit after this many iterations')
     parser.add_argument('--n_obs', default=None, help='Only use this many observations')
+    parser.add_argument('--apt_size', default=10)
 
     args = parser.parse_args()
     t1 = time.time()
@@ -139,5 +140,5 @@ def eval_(ds, device, engine):
     pickle_save(args, f'{args.save}/args.pkl')
     main(args)
     t2 = time.time()
-    mins = (t2 - t1)/60
+    mins = (t2 - t1) / 60
     print(f"Total time spent: {mins:.2f} seconds")

From cabed880fe926291115f3da5aab0fa15969c4596 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 22 Nov 2019 15:34:42 -0500
Subject: [PATCH 046/130] warning

---
 train.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/train.py b/train.py
index 85bac0b..c21f839 100644
--- a/train.py
+++ b/train.py
@@ -28,6 +28,7 @@ def main(args):
         adjinit = supports[0]
 
     if args.aptonly:
+        print('WARNING: not using adjacency matrix')
         supports = None
     engine = Trainer(scaler, args.in_dim, args.seq_length, args.num_nodes, args.nhid, args.dropout,
                      args.learning_rate, args.weight_decay, device, supports, args.do_graph_conv,
@@ -130,7 +131,7 @@ def eval_(ds, device, engine):
     # parser.add_argument('--expid', default=1, help='experiment id')
     parser.add_argument('--n_iters', default=None, help='quit after this many iterations')
     parser.add_argument('--n_obs', default=None, help='Only use this many observations')
-    parser.add_argument('--apt_size', default=10)
+    parser.add_argument('--apt_size', default=10, type=int)
 
     args = parser.parse_args()
     t1 = time.time()

From 35af7d1d95e5b0c80d1e4db3399a81fd4d80db68 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 22 Nov 2019 15:36:06 -0500
Subject: [PATCH 047/130] idiot

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index c21f839..60e5e4e 100644
--- a/train.py
+++ b/train.py
@@ -28,7 +28,7 @@ def main(args):
         adjinit = supports[0]
 
     if args.aptonly:
-        print('WARNING: not using adjacency matrix')
+        if not args.addaptadj: print('WARNING: not using adjacency matrix')
         supports = None
     engine = Trainer(scaler, args.in_dim, args.seq_length, args.num_nodes, args.nhid, args.dropout,
                      args.learning_rate, args.weight_decay, device, supports, args.do_graph_conv,

From e7b6d6ff6536e5263de62c999a965ea0dc4e5b3a Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 28 Nov 2019 18:42:32 -0800
Subject: [PATCH 048/130] Hoist model

---
 engine.py | 19 +++++++++----------
 train.py  | 22 ++++++++++++++++------
 2 files changed, 25 insertions(+), 16 deletions(-)

diff --git a/engine.py b/engine.py
index 2db3f07..adffddd 100644
--- a/engine.py
+++ b/engine.py
@@ -2,13 +2,13 @@
 from model import *
 import util
 class Trainer():
-    def __init__(self, scaler, in_dim, seq_length, num_nodes, nhid , dropout, lrate, wdecay, device,
-                 supports, gcn_bool, addaptadj, aptinit, clip=5, apt_size=10):
+    def __init__(self, model, scaler, lrate, wdecay, clip=5):
         # TODO(SS): pass model in.
-        self.model = GWNet(device, num_nodes, dropout, supports=supports, do_graph_conv=gcn_bool, addaptadj=addaptadj, aptinit=aptinit, in_dim=in_dim,
-                           apt_size=apt_size,
-                           out_dim=seq_length, residual_channels=nhid, dilation_channels=nhid, skip_channels=nhid * 8, end_channels=nhid * 16)
-        self.model.to(device)
+        # self.model = GWNet(device, num_nodes, dropout, supports=supports, do_graph_conv=gcn_bool, addaptadj=addaptadj, aptinit=aptinit, in_dim=in_dim,
+        #                    apt_size=apt_size,
+        #                    out_dim=seq_length, residual_channels=nhid, dilation_channels=nhid, skip_channels=nhid * 8, end_channels=nhid * 16)
+        self.model = model
+        #self.model.to(device)
         self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
         self.scaler = scaler
         self.clip = clip
@@ -17,9 +17,9 @@ def train(self, input, real_val):
         self.model.train()
         self.optimizer.zero_grad()
         input = nn.functional.pad(input,(1,0,0,0))
-        output = self.model(input).transpose(1,3)
+        output = self.model(input).transpose(1,3) # output = [batch_size,12,num_nodes,1]
         predict = self.scaler.inverse_transform(output)
-        #output = [batch_size,12,num_nodes,1]
+
         real = torch.unsqueeze(real_val, dim=1)
         mae, mape, rmse = util.cheaper_metric(predict, real, null_val=0.0)
         mae.backward()
@@ -31,8 +31,7 @@ def train(self, input, real_val):
     def eval(self, input, real_val):
         self.model.eval()
         input = nn.functional.pad(input,(1,0,0,0))
-        output = self.model(input).transpose(1,3)
-        #output = [batch_size,12,num_nodes,1]
+        output = self.model(input).transpose(1,3) #  [batch_size,12,num_nodes,1]
         real = torch.unsqueeze(real_val,dim=1)
         predict = self.scaler.inverse_transform(output)
         mae, mape, rmse = [x.item() for x in util.cheaper_metric(predict, real, null_val=0.0)]
diff --git a/train.py b/train.py
index 60e5e4e..03c750a 100644
--- a/train.py
+++ b/train.py
@@ -12,7 +12,7 @@
 from fastprogress import master_bar, progress_bar
 
 
-def main(args):
+def main(args, **model_kwargs):
     device = torch.device(args.device)
     sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
     supports = [torch.tensor(i).to(device) for i in adj_mx]
@@ -23,16 +23,26 @@ def main(args):
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
 
     if args.randomadj:
-        adjinit = None
+        assert args.addaptadj
+        aptinit = None
     else:
-        adjinit = supports[0]
+        aptinit = supports[0]
 
     if args.aptonly:
         if not args.addaptadj: print('WARNING: not using adjacency matrix')
         supports = None
-    engine = Trainer(scaler, args.in_dim, args.seq_length, args.num_nodes, args.nhid, args.dropout,
-                     args.learning_rate, args.weight_decay, device, supports, args.do_graph_conv,
-                     args.addaptadj, adjinit, apt_size=args.apt_size)
+    from model import GWNet
+    model = GWNet(device, args.num_nodes, args.dropout, supports=supports,
+                  do_graph_conv=args.do_graph_conv,
+                  addaptadj=args.addaptadj, aptinit=aptinit, in_dim=args.in_dim,
+                  apt_size=args.apt_size,
+                  out_dim=args.seq_length, residual_channels=args.nhid, dilation_channels=args.nhid,
+                  skip_channels=args.nhid * 8, end_channels=args.nhid * 16)
+    model.to(device)
+    engine = Trainer(model, scaler, args.learning_rate, args.weight_decay)
+    # engine = Trainer(scaler, args.in_dim, args.seq_length, args.num_nodes, args.nhid, args.dropout,
+    #                  args.learning_rate, args.weight_decay, device, supports, args.do_graph_conv,
+    #                  args.addaptadj, adjinit, apt_size=args.apt_size)
     print("start training...", flush=True)
     metrics, train_time = [], []
     best_yet = 100

From cc50db2c0dbdc9568d69523bfab811fb50d091e3 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 28 Nov 2019 18:51:04 -0800
Subject: [PATCH 049/130] early break

---
 engine.py | 5 -----
 model.py  | 2 ++
 train.py  | 3 ---
 3 files changed, 2 insertions(+), 8 deletions(-)

diff --git a/engine.py b/engine.py
index adffddd..9b41f45 100644
--- a/engine.py
+++ b/engine.py
@@ -3,12 +3,7 @@
 import util
 class Trainer():
     def __init__(self, model, scaler, lrate, wdecay, clip=5):
-        # TODO(SS): pass model in.
-        # self.model = GWNet(device, num_nodes, dropout, supports=supports, do_graph_conv=gcn_bool, addaptadj=addaptadj, aptinit=aptinit, in_dim=in_dim,
-        #                    apt_size=apt_size,
-        #                    out_dim=seq_length, residual_channels=nhid, dilation_channels=nhid, skip_channels=nhid * 8, end_channels=nhid * 16)
         self.model = model
-        #self.model.to(device)
         self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
         self.scaler = scaler
         self.clip = clip
diff --git a/model.py b/model.py
index a089e4d..d302354 100644
--- a/model.py
+++ b/model.py
@@ -162,6 +162,8 @@ def forward(self, input):
             except:
                 skip = 0
             skip = s + skip
+            if i == (self.blocks * self.layers -1): # last X getting ignored anyway
+                break
 
             if self.do_graph_conv and self.supports is not None:
                 support = adjacency_matrices if self.addaptadj else self.supports
diff --git a/train.py b/train.py
index 03c750a..f550b1a 100644
--- a/train.py
+++ b/train.py
@@ -40,9 +40,6 @@ def main(args, **model_kwargs):
                   skip_channels=args.nhid * 8, end_channels=args.nhid * 16)
     model.to(device)
     engine = Trainer(model, scaler, args.learning_rate, args.weight_decay)
-    # engine = Trainer(scaler, args.in_dim, args.seq_length, args.num_nodes, args.nhid, args.dropout,
-    #                  args.learning_rate, args.weight_decay, device, supports, args.do_graph_conv,
-    #                  args.addaptadj, adjinit, apt_size=args.apt_size)
     print("start training...", flush=True)
     metrics, train_time = [], []
     best_yet = 100

From ec24a8f79f8af288156488736b6f703d5f164e10 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 28 Nov 2019 19:22:47 -0800
Subject: [PATCH 050/130] remove assert

---
 train.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/train.py b/train.py
index f550b1a..636abf6 100644
--- a/train.py
+++ b/train.py
@@ -23,10 +23,9 @@ def main(args, **model_kwargs):
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
 
     if args.randomadj:
-        assert args.addaptadj
         aptinit = None
     else:
-        aptinit = supports[0]
+        aptinit = supports[0]  # ignored without do_graph_conv
 
     if args.aptonly:
         if not args.addaptadj: print('WARNING: not using adjacency matrix')

From 69ba1459faab44d5fc332a6e758a805556ded7df Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 28 Nov 2019 19:48:38 -0800
Subject: [PATCH 051/130] model kw

---
 train.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/train.py b/train.py
index 636abf6..af44a4b 100644
--- a/train.py
+++ b/train.py
@@ -20,6 +20,7 @@ def main(args, **model_kwargs):
                                    n_obs=args.n_obs)
     scaler = dataloader['scaler']
     print(args)
+
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
 
     if args.randomadj:
@@ -36,7 +37,7 @@ def main(args, **model_kwargs):
                   addaptadj=args.addaptadj, aptinit=aptinit, in_dim=args.in_dim,
                   apt_size=args.apt_size,
                   out_dim=args.seq_length, residual_channels=args.nhid, dilation_channels=args.nhid,
-                  skip_channels=args.nhid * 8, end_channels=args.nhid * 16)
+                  skip_channels=args.nhid * 8, end_channels=args.nhid * 16, **model_kwargs)
     model.to(device)
     engine = Trainer(model, scaler, args.learning_rate, args.weight_decay)
     print("start training...", flush=True)

From d093ef1eec2f13417f39e14aea7f73d4cbebec8d Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 28 Nov 2019 21:32:17 -0800
Subject: [PATCH 052/130] progbar

---
 train.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/train.py b/train.py
index af44a4b..2d33565 100644
--- a/train.py
+++ b/train.py
@@ -29,7 +29,7 @@ def main(args, **model_kwargs):
         aptinit = supports[0]  # ignored without do_graph_conv
 
     if args.aptonly:
-        if not args.addaptadj: print('WARNING: not using adjacency matrix')
+        if not args.addaptadj: raise ValueError('WARNING: not using adjacency matrix')
         supports = None
     from model import GWNet
     model = GWNet(device, args.num_nodes, args.dropout, supports=supports,
@@ -43,7 +43,7 @@ def main(args, **model_kwargs):
     print("start training...", flush=True)
     metrics, train_time = [], []
     best_yet = 100
-    mb = master_bar(list(range(1, args.epochs + 1)))
+    mb = progress_bar(list(range(1, args.epochs + 1)))
     for i in mb:
         # if i % 10 == 0:
         # lr = max(0.000002,args.learning_rate * (0.1 ** (i // 10)))
@@ -72,7 +72,7 @@ def main(args, **model_kwargs):
                            valid_loss=np.mean(valid_loss),
                            valid_mape=np.mean(valid_mape),
                            valid_rmse=np.mean(valid_rmse), ))
-        mb.first_bar.comment = f'valid_loss: {m.valid_loss: .3f}'
+        mb.comment = f'valid_loss: {m.valid_loss: .3f}'
         metrics.append(m)
         print(m.round(4))
         if m.valid_loss < best_yet:

From 63c049482262c07635effca36c5c7db702bb4bc5 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 28 Nov 2019 21:57:22 -0800
Subject: [PATCH 053/130] Major cleanup

---
 model.py | 79 +++++++++++++++++---------------------------------------
 1 file changed, 24 insertions(+), 55 deletions(-)

diff --git a/model.py b/model.py
index d302354..076416a 100644
--- a/model.py
+++ b/model.py
@@ -55,16 +55,11 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
         self.start_conv = nn.Conv2d(in_channels=in_dim,
                                     out_channels=residual_channels,
                                     kernel_size=(1,1))
-        self.supports = supports
-
+        self.fixed_supports = supports or []
         receptive_field = 1
 
-        self.supports_len = 0
-        if supports is not None:
-            self.supports_len += len(supports)
-
+        self.supports_len = len(self.fixed_supports)
         if do_graph_conv and addaptadj:
-            if supports is None: self.supports = []
             if aptinit is None:
                 nodevec1 = torch.randn(num_nodes, apt_size)
                 nodevec2 = torch.randn(apt_size, num_nodes)
@@ -76,66 +71,42 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
             
             self.register_parameter('nodevec1', nn.Parameter(nodevec1.to(device), requires_grad=True))
             self.register_parameter('nodevec2', nn.Parameter(nodevec2.to(device), requires_grad=True))
-
+        if do_graph_conv:
+            self.graph_convs = nn.ModuleList([GraphConvNet(dilation_channels, residual_channels, dropout, support_len=self.supports_len) for i in range(blocks*layers)])
         for b in range(blocks):
             additional_scope = kernel_size - 1
-            new_dilation = 1
+            D = 1 # dilation
             for i in range(layers):
                 # dilated convolutions
-                self.filter_convs.append(nn.Conv2d(in_channels=residual_channels,
-                                                   out_channels=dilation_channels,
-                                                   kernel_size=(1,kernel_size), dilation=new_dilation))
-
-                self.gate_convs.append(nn.Conv1d(in_channels=residual_channels,
-                                                 out_channels=dilation_channels,
-                                                 kernel_size=(1, kernel_size), dilation=new_dilation))
-
-                # 1x1 convolution for residual connection
-                self.residual_convs.append(nn.Conv1d(in_channels=dilation_channels,
-                                                     out_channels=residual_channels,
-                                                     kernel_size=(1, 1)))
-
-                # 1x1 convolution for skip connection
-                self.skip_convs.append(nn.Conv1d(in_channels=dilation_channels,
-                                                 out_channels=skip_channels,
-                                                 kernel_size=(1, 1)))
+                self.filter_convs.append(nn.Conv2d(residual_channels, dilation_channels, kernel_size=(1,kernel_size), dilation=D))
+                self.gate_convs.append(nn.Conv1d(residual_channels, dilation_channels, kernel_size=(1, kernel_size), dilation=D))
+
+                # 1x1 convolution for residual and skip connections (slightly different see docstring)
+                self.residual_convs.append(nn.Conv1d(dilation_channels, residual_channels, kernel_size=(1, 1)))
+                self.skip_convs.append(nn.Conv1d(dilation_channels, skip_channels, kernel_size=(1, 1)))
                 self.bn.append(nn.BatchNorm2d(residual_channels))
-                new_dilation *=2
+                D *= 2
                 receptive_field += additional_scope
                 additional_scope *= 2
-                if self.do_graph_conv:
-                    self.graph_convs.append(
-                        GraphConvNet(dilation_channels, residual_channels, dropout,
-                                     support_len=self.supports_len))
-
-        self.end_conv_1 = nn.Conv2d(in_channels=skip_channels,
-                                    out_channels=end_channels,
-                                    kernel_size=(1, 1),
-                                    bias=True)
 
-        self.end_conv_2 = nn.Conv2d(in_channels=end_channels,
-                                    out_channels=out_dim,
-                                    kernel_size=(1, 1),
-                                    bias=True)
+        self.end_conv_1 = nn.Conv2d(skip_channels, end_channels, kernel_size=(1, 1), bias=True)
+        self.end_conv_2 = nn.Conv2d(end_channels, out_dim, kernel_size=(1, 1), bias=True)
 
         self.receptive_field = receptive_field
 
     def forward(self, input):
         # Input shape is (bs, features, n_nodes, n_timesteps)
-        in_len = input.size(3)
-        if in_len<self.receptive_field:
-            x = nn.functional.pad(input, (self.receptive_field - in_len,0,0,0))
-        else:
-            x = input
-        x = self.start_conv(x)
+        if input.size(3) < self.receptive_field:
+            input = nn.functional.pad(input, (self.receptive_field - input.size(3), 0, 0, 0))
+        x = self.start_conv(input)
         skip = 0
 
         # calculate the current adaptive adj matrix once per iteration
-        if self.do_graph_conv and self.addaptadj and self.supports is not None:
+        if self.addaptadj:
             adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)), dim=1)
-            adjacency_matrices = self.supports + [adp]
+            adjacency_matrices = self.fixed_supports + [adp]
         else:
-            adjacency_matrices = self.supports
+            adjacency_matrices = self.fixed_supports
 
         # WaveNet layers
         for i in range(self.blocks * self.layers):
@@ -155,19 +126,17 @@ def forward(self, input):
             gate = torch.sigmoid(self.gate_convs[i](residual))
             x = filter * gate
             # parametrized skip connection
-            s = x
-            s = self.skip_convs[i](s)
+            s = self.skip_convs[i](x)  # what are we skipping??
             try:  # if i > 0 this works
                 skip = skip[:, :, :,  -s.size(3):]  # TODO(SS): Mean/Max Pool?
             except:
                 skip = 0
             skip = s + skip
-            if i == (self.blocks * self.layers -1): # last X getting ignored anyway
+            if i == (self.blocks * self.layers - 1):  # last X getting ignored anyway
                 break
 
-            if self.do_graph_conv and self.supports is not None:
-                support = adjacency_matrices if self.addaptadj else self.supports
-                x = self.graph_convs[i](x, support)
+            if self.do_graph_conv:
+                x = self.graph_convs[i](x, adjacency_matrices)
             else:
                 x = self.residual_convs[i](x)
             x = x + residual[:, :, :, -x.size(3):] # TODO(SS): Mean/Max Pool?

From 2c66c6a137af9a3899ec2de471392773ee5e10d2 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 28 Nov 2019 21:58:03 -0800
Subject: [PATCH 054/130] more cleanup

---
 model.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/model.py b/model.py
index 076416a..f454877 100644
--- a/model.py
+++ b/model.py
@@ -100,13 +100,11 @@ def forward(self, input):
             input = nn.functional.pad(input, (self.receptive_field - input.size(3), 0, 0, 0))
         x = self.start_conv(input)
         skip = 0
-
+        adjacency_matrices = self.fixed_supports
         # calculate the current adaptive adj matrix once per iteration
         if self.addaptadj:
             adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)), dim=1)
             adjacency_matrices = self.fixed_supports + [adp]
-        else:
-            adjacency_matrices = self.fixed_supports
 
         # WaveNet layers
         for i in range(self.blocks * self.layers):

From 5e1e8971cb00d5c1f93796f45e443231dd8d8ef5 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Fri, 29 Nov 2019 09:21:02 -0800
Subject: [PATCH 055/130] cleanup train.py a bit

---
 train.py | 45 ++++++++++++++++++++-------------------------
 1 file changed, 20 insertions(+), 25 deletions(-)

diff --git a/train.py b/train.py
index 2d33565..f3cb6fd 100644
--- a/train.py
+++ b/train.py
@@ -7,42 +7,40 @@
 from engine import Trainer
 import os
 from durbango import pickle_save
+from fastprogress import progress_bar
 
+from model import GWNet
 from util import calc_test_metrics
-from fastprogress import master_bar, progress_bar
 
 
 def main(args, **model_kwargs):
     device = torch.device(args.device)
-    sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
-    supports = [torch.tensor(i).to(device) for i in adj_mx]
     dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size,
                                    n_obs=args.n_obs)
     scaler = dataloader['scaler']
     print(args)
-
-    best_model_save_path = os.path.join(args.save, 'best_model.pth')
-
+    sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
+    supports = [torch.tensor(i).to(device) for i in adj_mx]
     if args.randomadj:
         aptinit = None
     else:
-        aptinit = supports[0]  # ignored without do_graph_conv
+        aptinit = supports[0]  # ignored without do_graph_conv and add_apt_adj
 
     if args.aptonly:
         if not args.addaptadj: raise ValueError('WARNING: not using adjacency matrix')
         supports = None
-    from model import GWNet
+
     model = GWNet(device, args.num_nodes, args.dropout, supports=supports,
-                  do_graph_conv=args.do_graph_conv,
-                  addaptadj=args.addaptadj, aptinit=aptinit, in_dim=args.in_dim,
-                  apt_size=args.apt_size,
-                  out_dim=args.seq_length, residual_channels=args.nhid, dilation_channels=args.nhid,
+                  do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=aptinit,
+                  in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
+                  residual_channels=args.nhid, dilation_channels=args.nhid,
                   skip_channels=args.nhid * 8, end_channels=args.nhid * 16, **model_kwargs)
     model.to(device)
     engine = Trainer(model, scaler, args.learning_rate, args.weight_decay)
     print("start training...", flush=True)
     metrics, train_time = [], []
-    best_yet = 100
+    best_model_save_path = os.path.join(args.save, 'best_model.pth')
+    lowest_mae_yet = 100  # high value in MPH will get overwritten
     mb = progress_bar(list(range(1, args.epochs + 1)))
     for i in mb:
         # if i % 10 == 0:
@@ -63,31 +61,28 @@ def main(args, **model_kwargs):
             if args.n_iters is not None and iter >= args.n_iters:
                 break
         train_time.append(time.time() - t1)
-        total_time, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device,
-                                                               engine)
+        _, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device, engine)
 
-        m = pd.Series(dict(train_loss=np.mean(train_loss),
-                           train_mape=np.mean(train_mape),
-                           train_rmse=np.mean(train_rmse),
-                           valid_loss=np.mean(valid_loss),
-                           valid_mape=np.mean(valid_mape),
-                           valid_rmse=np.mean(valid_rmse), ))
+        m = dict(train_loss=np.mean(train_loss), train_mape=np.mean(train_mape),
+                 train_rmse=np.mean(train_rmse), valid_loss=np.mean(valid_loss),
+                 valid_mape=np.mean(valid_mape), valid_rmse=np.mean(valid_rmse))
+        m = pd.Series(m)
         mb.comment = f'valid_loss: {m.valid_loss: .3f}'
         metrics.append(m)
         print(m.round(4))
-        if m.valid_loss < best_yet:
+        if m.valid_loss < lowest_mae_yet:
             torch.save(engine.model.state_dict(), best_model_save_path)
-            best_yet = m.valid_loss
+            lowest_mae_yet = m.valid_loss
         met_df = pd.DataFrame(metrics)
         met_df.round(4).to_csv(f'{args.save}/metrics.csv')
-    print(f"Training finished. Best Valid Loss")
+    print(f"Training finished. Best Valid Loss:")
     print(met_df.loc[met_df.valid_loss.idxmin()].round(4))
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
     realy = torch.Tensor(dataloader['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
     test_met_df, yhat = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler,
                                           realy)
-    test_met_df.round(4).to_csv(os.path.join(args.save, 'test_metrics.csv'))
+    test_met_df.round(6).to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(test_met_df.mean().round(3))
     pred_df = util.make_pred_df(realy, yhat, scaler)
     pred_df.to_csv(os.path.join(args.save, 'preds.csv'))

From d4b3ba5313a9f9db5b8664f2885fae6491f75756 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 11:34:27 -0800
Subject: [PATCH 056/130] Smaller nhid

---
 test.py       | 17 ++++++++++++-----
 test_gwnet.py | 10 ++++++++++
 train.py      |  1 -
 3 files changed, 22 insertions(+), 6 deletions(-)

diff --git a/test.py b/test.py
index 2a926b8..26e61ce 100644
--- a/test.py
+++ b/test.py
@@ -7,7 +7,7 @@
 import seaborn as sns
 
 
-def main(args):
+def main(args, **model_kwargs):
     device = torch.device(args.device)
     _, _, adj_mx = util.load_adj(args.adjdata,args.adjtype)
     supports = [torch.tensor(i).to(device) for i in adj_mx]
@@ -19,10 +19,17 @@ def main(args):
     if args.aptonly:
         supports = None
 
-    model = GWNet(device, args.num_nodes, args.dropout,
-                  supports=supports, do_graph_conv=args.do_graph_conv,
-                  addaptadj=args.addaptadj, aptinit=adjinit, apt_size=args.apt_size,
-                  )
+    # model = GWNet(device, args.num_nodes, args.dropout,
+    #               supports=supports, do_graph_conv=args.do_graph_conv,
+    #               addaptadj=args.addaptadj, aptinit=adjinit, apt_size=args.apt_size,
+    #
+    #
+    #               )
+    model = GWNet(device, args.num_nodes, args.dropout, supports=supports,
+                  do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=adjinit,
+                  in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
+                  residual_channels=args.nhid, dilation_channels=args.nhid,
+                  skip_channels=args.nhid * 8, end_channels=args.nhid * 16, **model_kwargs)
     model.to(device)
     model.load_state_dict(torch.load(args.checkpoint))
     model.eval()
diff --git a/test_gwnet.py b/test_gwnet.py
index a84f2cb..4ab4d68 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -12,6 +12,14 @@
 TEST_ARGS = 'test_script_args.pkl'
 SAVE_DIR = 'utest_experiment/'
 
+def modify_args(args, updates):
+    for k,v in updates.items():
+        setattr(args, k, v)
+    return args
+
+{'epochs': 1, 'n_iters': 1, 'batch_size':2, 'n_obs': 2, 'device': util.DEFAULT_DEVICE,
+'save': SAVE_DIR, 'addaptadj': True, 'apt_size': 20, 'nhid': 2}
+
 class TestTrain(unittest.TestCase):
 
     @classmethod
@@ -30,6 +38,7 @@ def test_1_epoch(self):
         args.device = util.DEFAULT_DEVICE
         args.addaptadj = True
         args.apt_size = 20
+        args.nhid = 2
         main(args)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
         self.assertEqual(df.shape, (args.epochs, 6))
@@ -42,6 +51,7 @@ def test_1_epoch(self):
         assert 'nodevec1' in state_dict
         self.assertTrue(os.path.exists(test_args.checkpoint))
         test_args.n_obs = 2
+        test_args.nhid = 2
         test.main(test_args)
         new_met = pd.read_csv('last_test_metrics.csv', index_col=0)
         deltas = test_df.mean() - new_met.mean()
diff --git a/train.py b/train.py
index f3cb6fd..8dd01da 100644
--- a/train.py
+++ b/train.py
@@ -18,7 +18,6 @@ def main(args, **model_kwargs):
     dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size,
                                    n_obs=args.n_obs)
     scaler = dataloader['scaler']
-    print(args)
     sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
     supports = [torch.tensor(i).to(device) for i in adj_mx]
     if args.randomadj:

From 64bed791d4bb8bb2d9c6c839937c9d93e2b5367a Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 11:40:26 -0800
Subject: [PATCH 057/130] Unit-test speedup

---
 test_gwnet.py | 24 ++++++------------------
 1 file changed, 6 insertions(+), 18 deletions(-)

diff --git a/test_gwnet.py b/test_gwnet.py
index 4ab4d68..6049a03 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -11,15 +11,15 @@
 TRAIN_ARGS = 'test_args.pkl'
 TEST_ARGS = 'test_script_args.pkl'
 SAVE_DIR = 'utest_experiment/'
+ARG_UPDATES = {'epochs': 1, 'n_iters': 1, 'batch_size': 2, 'n_obs': 2,
+               'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
+               'apt_size': 2, 'nhid': 1, }
 
 def modify_args(args, updates):
     for k,v in updates.items():
         setattr(args, k, v)
     return args
 
-{'epochs': 1, 'n_iters': 1, 'batch_size':2, 'n_obs': 2, 'device': util.DEFAULT_DEVICE,
-'save': SAVE_DIR, 'addaptadj': True, 'apt_size': 20, 'nhid': 2}
-
 class TestTrain(unittest.TestCase):
 
     @classmethod
@@ -29,29 +29,17 @@ def setUpClass(cls):
         os.makedirs(SAVE_DIR)
 
     def test_1_epoch(self):
-        args = pickle_load(TRAIN_ARGS)
-        args.epochs = 1
-        args.n_iters = 1
-        args.batch_size = 2
-        args.n_obs = 2
-        args.save = SAVE_DIR
-        args.device = util.DEFAULT_DEVICE
-        args.addaptadj = True
-        args.apt_size = 20
-        args.nhid = 2
+        args = modify_args(pickle_load(TRAIN_ARGS), ARG_UPDATES)
         main(args)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
         self.assertEqual(df.shape, (args.epochs, 6))
         test_df = pd.read_csv(f'{args.save}/test_metrics.csv', index_col=0)
         self.assertEqual(test_df.shape, (12, 3))
-        test_args = pickle_load(TEST_ARGS)
-        test_args.apt_size = 20
+        test_args = modify_args(pickle_load(TEST_ARGS), ARG_UPDATES)
         test_args.checkpoint = SAVE_DIR + '/best_model.pth'
         state_dict = torch.load(test_args.checkpoint)
-        assert 'nodevec1' in state_dict
+        self.assertTrue('nodevec1' in state_dict)
         self.assertTrue(os.path.exists(test_args.checkpoint))
-        test_args.n_obs = 2
-        test_args.nhid = 2
         test.main(test_args)
         new_met = pd.read_csv('last_test_metrics.csv', index_col=0)
         deltas = test_df.mean() - new_met.mean()

From 6799d6199aa0c8b4ed88bd2861560b84e1c1967a Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 11:50:03 -0800
Subject: [PATCH 058/130] better comment

---
 engine.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/engine.py b/engine.py
index 9b41f45..bc8a3cf 100644
--- a/engine.py
+++ b/engine.py
@@ -12,9 +12,8 @@ def train(self, input, real_val):
         self.model.train()
         self.optimizer.zero_grad()
         input = nn.functional.pad(input,(1,0,0,0))
-        output = self.model(input).transpose(1,3) # output = [batch_size,12,num_nodes,1]
+        output = self.model(input).transpose(1,3) # now, output = [batch_size,1,num_nodes,12]
         predict = self.scaler.inverse_transform(output)
-
         real = torch.unsqueeze(real_val, dim=1)
         mae, mape, rmse = util.cheaper_metric(predict, real, null_val=0.0)
         mae.backward()

From 2a957238b54a7c230a0b325a70019b3940734127 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 15:14:09 -0800
Subject: [PATCH 059/130] more test.py improvements

---
 util.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/util.py b/util.py
index ba6f680..d64d60f 100644
--- a/util.py
+++ b/util.py
@@ -204,10 +204,12 @@ def calc_test_metrics(model, device, test_loader, scaler, realy):
     return test_met_df, yhat
 
 
+def _to_ser(arr):
+    return pd.DataFrame(arr.cpu().detach().numpy()).stack().rename_axis(['obs', 'sensor_id'])
+
+
 def make_pred_df(realy, yhat, scaler):
-    y12 = realy[:, 99, 11].cpu().detach().numpy()
-    yhat12 = scaler.inverse_transform(yhat[:, 99, 11]).cpu().detach().numpy()
-    y3 = realy[:, 99, 2].cpu().detach().numpy()
-    yhat3 = scaler.inverse_transform(yhat[:, 99, 2]).cpu().detach().numpy()
-    df2 = pd.DataFrame({'real12': y12, 'pred12': yhat12, 'real3': y3, 'pred3': yhat3})
-    return df2
+    return pd.DataFrame(dict(y12=_to_ser(realy[:, :, 11]),
+                             yhat12=_to_ser(scaler.inverse_transform(yhat[:, :, 11])),
+                             y3=_to_ser(realy[:, :, 2]),
+                             yhat3=_to_ser(scaler.inverse_transform(yhat[:, :, 2]))))

From 3610e9daba88dc6726364fd956403d52fde37e04 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 15:18:35 -0800
Subject: [PATCH 060/130] more test.py improvements

---
 test.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/test.py b/test.py
index 26e61ce..414c85f 100644
--- a/test.py
+++ b/test.py
@@ -43,23 +43,22 @@ def main(args, **model_kwargs):
     print(met_df.mean().round(4))
     met_df.round(4).to_csv('last_test_metrics.csv')
 
-    if args.plotheatmap == "True":
-        plot_heatmap(model)
 
-    df2 = util.make_pred_df(realy, yhat, scaler)
 
+    df2 = util.make_pred_df(realy, yhat, scaler)
     df2.to_csv('./wave.csv', index=False)
 
+    if args.plotheatmap == "True":
+        plot_learned_adj_matrix(model)
+
 
-def plot_heatmap(model):
+def plot_learned_adj_matrix(model):
     adp = F.softmax(F.relu(torch.mm(model.nodevec1, model.nodevec2)), dim=1)
-    device = torch.device('cpu')
-    adp.to(device)
     adp = adp.cpu().detach().numpy()
-    adp = adp * (1 / np.max(adp))
+    adp = adp / np.max(adp)
     df = pd.DataFrame(adp)
     sns.heatmap(df, cmap="RdYlBu")
-    plt.savefig("./emb" + '.pdf')
+    plt.savefig("heatmap.png")
 
 
 if __name__ == "__main__":

From 0be74bc4c54fceba8018be3078eabeb295f05636 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 15:35:30 -0800
Subject: [PATCH 061/130] Clamp

---
 engine.py     |  3 +++
 test.py       | 10 +++-------
 test_gwnet.py |  5 +++--
 util.py       |  9 +++++----
 4 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/engine.py b/engine.py
index bc8a3cf..3a20890 100644
--- a/engine.py
+++ b/engine.py
@@ -13,6 +13,8 @@ def train(self, input, real_val):
         self.optimizer.zero_grad()
         input = nn.functional.pad(input,(1,0,0,0))
         output = self.model(input).transpose(1,3) # now, output = [batch_size,1,num_nodes,12]
+        #torch.clamp(output, 0, 70)
+
         predict = self.scaler.inverse_transform(output)
         real = torch.unsqueeze(real_val, dim=1)
         mae, mape, rmse = util.cheaper_metric(predict, real, null_val=0.0)
@@ -28,5 +30,6 @@ def eval(self, input, real_val):
         output = self.model(input).transpose(1,3) #  [batch_size,12,num_nodes,1]
         real = torch.unsqueeze(real_val,dim=1)
         predict = self.scaler.inverse_transform(output)
+        predict = torch.clamp(predict, min=0., max=70.)
         mae, mape, rmse = [x.item() for x in util.cheaper_metric(predict, real, null_val=0.0)]
         return mae, mape, rmse
diff --git a/test.py b/test.py
index 414c85f..0957938 100644
--- a/test.py
+++ b/test.py
@@ -39,18 +39,14 @@ def main(args, **model_kwargs):
     realy = torch.Tensor(dataloader['y_test']).to(device)
     realy = realy.transpose(1,3)[:,0,:,:]
     met_df, yhat = util.calc_test_metrics(model, device, dataloader['test_loader'], scaler, realy)
-    print(met_df)
-    print(met_df.mean().round(4))
-    met_df.round(4).to_csv('last_test_metrics.csv')
-
-
-
     df2 = util.make_pred_df(realy, yhat, scaler)
+    met_df.to_csv('last_test_metrics.csv')
     df2.to_csv('./wave.csv', index=False)
 
+
     if args.plotheatmap == "True":
         plot_learned_adj_matrix(model)
-
+    return met_df, df2
 
 def plot_learned_adj_matrix(model):
     adp = F.softmax(F.relu(torch.mm(model.nodevec1, model.nodevec2)), dim=1)
diff --git a/test_gwnet.py b/test_gwnet.py
index 6049a03..cdac9c8 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -40,7 +40,8 @@ def test_1_epoch(self):
         state_dict = torch.load(test_args.checkpoint)
         self.assertTrue('nodevec1' in state_dict)
         self.assertTrue(os.path.exists(test_args.checkpoint))
-        test.main(test_args)
-        new_met = pd.read_csv('last_test_metrics.csv', index_col=0)
+        new_met, new_preds = test.main(test_args)
+        #import ipdb; ipdb.set_trace()
+        #new_met = pd.read_csv('last_test_metrics.csv', index_col=0)
         deltas = test_df.mean() - new_met.mean()
         self.assertGreaterEqual(.01, deltas.abs().max())
diff --git a/util.py b/util.py
index d64d60f..d37eb48 100644
--- a/util.py
+++ b/util.py
@@ -209,7 +209,8 @@ def _to_ser(arr):
 
 
 def make_pred_df(realy, yhat, scaler):
-    return pd.DataFrame(dict(y12=_to_ser(realy[:, :, 11]),
-                             yhat12=_to_ser(scaler.inverse_transform(yhat[:, :, 11])),
-                             y3=_to_ser(realy[:, :, 2]),
-                             yhat3=_to_ser(scaler.inverse_transform(yhat[:, :, 2]))))
+    df = pd.DataFrame(dict(y12=_to_ser(realy[:, :, 11]),
+                           yhat12=_to_ser(scaler.inverse_transform(yhat[:, :, 11])).clip(0, 70),
+                           y3=_to_ser(realy[:, :, 2]),
+                           yhat3=_to_ser(scaler.inverse_transform(yhat[:, :, 2])).clip(0, 70)))
+    return df

From 25045b3fccff728edd568b989532711e28cc1e7e Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 15:40:39 -0800
Subject: [PATCH 062/130] C

---
 util.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/util.py b/util.py
index d37eb48..beeebb0 100644
--- a/util.py
+++ b/util.py
@@ -198,6 +198,7 @@ def calc_test_metrics(model, device, test_loader, scaler, realy):
     test_met = []
     for i in range(12):
         pred = scaler.inverse_transform(yhat[:, :, i])
+        pred = torch.clamp(pred, min=0., max=70.)
         real = realy[:, :, i]
         test_met.append([x.item() for x in cheaper_metric(pred, real)])
     test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t').round(3)

From 0b433dbf2a797389e0722afbfacc11f5ace368b2 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 15:50:55 -0800
Subject: [PATCH 063/130] bango

---
 util.py | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/util.py b/util.py
index beeebb0..d6b627f 100644
--- a/util.py
+++ b/util.py
@@ -9,6 +9,12 @@
 
 DEFAULT_DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
 
+
+def calc_trainable_params(model):
+    model_parameters = filter(lambda p: p.requires_grad, model.parameters())
+    params = sum([np.prod(p.size()) for p in model_parameters])
+    return params
+
 class DataLoader(object):
     def __init__(self, xs, ys, batch_size, pad_with_last_sample=True):
         """
@@ -198,20 +204,16 @@ def calc_test_metrics(model, device, test_loader, scaler, realy):
     test_met = []
     for i in range(12):
         pred = scaler.inverse_transform(yhat[:, :, i])
-        pred = torch.clamp(pred, min=0., max=70.)
         real = realy[:, :, i]
         test_met.append([x.item() for x in cheaper_metric(pred, real)])
     test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t').round(3)
     return test_met_df, yhat
 
 
-def _to_ser(arr):
-    return pd.DataFrame(arr.cpu().detach().numpy()).stack().rename_axis(['obs', 'sensor_id'])
-
-
 def make_pred_df(realy, yhat, scaler):
-    df = pd.DataFrame(dict(y12=_to_ser(realy[:, :, 11]),
-                           yhat12=_to_ser(scaler.inverse_transform(yhat[:, :, 11])).clip(0, 70),
-                           y3=_to_ser(realy[:, :, 2]),
-                           yhat3=_to_ser(scaler.inverse_transform(yhat[:, :, 2])).clip(0, 70)))
-    return df
+    y12 = realy[:, 99, 11].cpu().detach().numpy()
+    yhat12 = scaler.inverse_transform(yhat[:, 99, 11]).cpu().detach().numpy()
+    y3 = realy[:, 99, 2].cpu().detach().numpy()
+    yhat3 = scaler.inverse_transform(yhat[:, 99, 2]).cpu().detach().numpy()
+    df2 = pd.DataFrame({'real12': y12, 'pred12': yhat12, 'real3': y3, 'pred3': yhat3})
+    return df2

From a8f21a93df36e00f24e1b2445ddc566786d44aa4 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 15:51:52 -0800
Subject: [PATCH 064/130] new util

---
 util.py | 24 +++++++++++-------------
 1 file changed, 11 insertions(+), 13 deletions(-)

diff --git a/util.py b/util.py
index d6b627f..9a2a12f 100644
--- a/util.py
+++ b/util.py
@@ -9,12 +9,6 @@
 
 DEFAULT_DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
 
-
-def calc_trainable_params(model):
-    model_parameters = filter(lambda p: p.requires_grad, model.parameters())
-    params = sum([np.prod(p.size()) for p in model_parameters])
-    return params
-
 class DataLoader(object):
     def __init__(self, xs, ys, batch_size, pad_with_last_sample=True):
         """
@@ -204,16 +198,20 @@ def calc_test_metrics(model, device, test_loader, scaler, realy):
     test_met = []
     for i in range(12):
         pred = scaler.inverse_transform(yhat[:, :, i])
+        pred = torch.clamp(pred, min=0., max=70.)
         real = realy[:, :, i]
         test_met.append([x.item() for x in cheaper_metric(pred, real)])
-    test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t').round(3)
+    test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t')
     return test_met_df, yhat
 
 
+def _to_ser(arr):
+    return pd.DataFrame(arr.cpu().detach().numpy()).stack().rename_axis(['obs', 'sensor_id'])
+
+
 def make_pred_df(realy, yhat, scaler):
-    y12 = realy[:, 99, 11].cpu().detach().numpy()
-    yhat12 = scaler.inverse_transform(yhat[:, 99, 11]).cpu().detach().numpy()
-    y3 = realy[:, 99, 2].cpu().detach().numpy()
-    yhat3 = scaler.inverse_transform(yhat[:, 99, 2]).cpu().detach().numpy()
-    df2 = pd.DataFrame({'real12': y12, 'pred12': yhat12, 'real3': y3, 'pred3': yhat3})
-    return df2
+    df = pd.DataFrame(dict(y12=_to_ser(realy[:, :, 11]),
+                           yhat12=_to_ser(scaler.inverse_transform(yhat[:, :, 11])),
+                           y3=_to_ser(realy[:, :, 2]),
+                           yhat3=_to_ser(scaler.inverse_transform(yhat[:, :, 2]))))
+    return df

From da29d41dd5b70b3a447d55e5ed3730ec950412f2 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 18:57:48 -0800
Subject: [PATCH 065/130] small sensible changes

---
 test.py | 2 +-
 util.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/test.py b/test.py
index 0957938..eec3f2c 100644
--- a/test.py
+++ b/test.py
@@ -78,7 +78,7 @@ def plot_learned_adj_matrix(model):
     parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
     # parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
     parser.add_argument('--checkpoint', type=str, help='')
-    parser.add_argument('--plotheatmap', type=str, default='True', help='')
+    parser.add_argument('--plotheatmap', action='store_true')
     parser.add_argument('--n_obs', default=None, help='Only use this many observations')
     args = parser.parse_args()
     main(args)
diff --git a/util.py b/util.py
index 9a2a12f..34ea890 100644
--- a/util.py
+++ b/util.py
@@ -176,7 +176,7 @@ def cheaper_metric(preds, labels, null_val=0.):
     mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
     mse = (preds - labels) ** 2
     mae = torch.abs(preds-labels)
-    mape = torch.abs(preds - labels) / labels
+    mape = mae / labels
     mae, mape, mse = [mask_and_fillna(l, mask) for l in [mae, mape, mse]]
     rmse = torch.sqrt(mse)
     return mae, mape, rmse

From 1474035ba1e57b9d4ab0f714a847341616baa04d Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 30 Nov 2019 19:03:10 -0800
Subject: [PATCH 066/130] lr scheduling

---
 engine.py | 4 +++-
 train.py  | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/engine.py b/engine.py
index 3a20890..e694756 100644
--- a/engine.py
+++ b/engine.py
@@ -2,11 +2,13 @@
 from model import *
 import util
 class Trainer():
-    def __init__(self, model, scaler, lrate, wdecay, clip=5):
+    def __init__(self, model, scaler, lrate, wdecay, clip=5, lr_decay_rate=.97):
         self.model = model
         self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
         self.scaler = scaler
         self.clip = clip
+        l1 = lambda epoch: lr_decay_rate ** epoch
+        self.scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=l1)
 
     def train(self, input, real_val):
         self.model.train()
diff --git a/train.py b/train.py
index 8dd01da..a6cf37e 100644
--- a/train.py
+++ b/train.py
@@ -60,6 +60,7 @@ def main(args, **model_kwargs):
             if args.n_iters is not None and iter >= args.n_iters:
                 break
         train_time.append(time.time() - t1)
+        engine.scheduler.step()
         _, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device, engine)
 
         m = dict(train_loss=np.mean(train_loss), train_mape=np.mean(train_mape),

From 19b1bb46ea0a54cb0c46da0084ac0739fda656b6 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 14:48:50 -0800
Subject: [PATCH 067/130] style

---
 engine.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/engine.py b/engine.py
index e694756..f5543fc 100644
--- a/engine.py
+++ b/engine.py
@@ -14,7 +14,7 @@ def train(self, input, real_val):
         self.model.train()
         self.optimizer.zero_grad()
         input = nn.functional.pad(input,(1,0,0,0))
-        output = self.model(input).transpose(1,3) # now, output = [batch_size,1,num_nodes,12]
+        output = self.model(input).transpose(1,3)  # now, output = [batch_size,1,num_nodes,12]
         #torch.clamp(output, 0, 70)
 
         predict = self.scaler.inverse_transform(output)

From 932bb6ade756964b9ca59231f6bbc9ce2079e23b Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 15:01:40 -0800
Subject: [PATCH 068/130] exp result analyzer

---
 exp_results.py | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)
 create mode 100644 exp_results.py

diff --git a/exp_results.py b/exp_results.py
new file mode 100644
index 0000000..1034aa4
--- /dev/null
+++ b/exp_results.py
@@ -0,0 +1,37 @@
+import pandas as pd
+import os
+from glob import glob
+import matplotlib.pyplot as plt
+
+
+def summary(d):
+    try:
+        tr_val = pd.read_csv(f'{d}/metrics.csv', index_col=0)
+        tr_ser = tr_val.loc[tr_val.valid_loss.idxmin()]
+        tr_ser['best_epoch'] = tr_val.valid_loss.idxmin()
+        tr_ser['min_train_loss'] = tr_val.train_loss.min()
+    except FileNotFoundError:
+        tr_ser = pd.Series()
+    try:
+        tmet = pd.read_csv(f'{d}/test_metrics.csv', index_col=0)
+        tmean = tmet.add_prefix('test_').mean()
+
+    except FileNotFoundError:
+        tmean = pd.Series()
+    tab = pd.concat([tr_ser, tmean]).round(3)
+    return tab
+
+def loss_curve(d):
+    if 'logs' not in d: d =  f'logs/{d}'
+    tr_val = pd.read_csv(f'{d}/metrics.csv', index_col=0)
+    return tr_val[['train_loss', 'valid_loss']]
+
+
+def plot_loss_curve(log_dir):
+    d = loss_curve(log_dir)
+    ax = d.plot()
+    plt.axhline(d.valid_loss.min())
+    print(d.valid_loss.idxmin())
+
+def make_table():
+    return pd.DataFrame({os.path.basename(c): summary(c) for c in glob('logs/*')}).T.sort_values('valid_loss')

From 78dc46531caf93bff03622019fb3fc169cd7d18e Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 19:47:23 -0800
Subject: [PATCH 069/130] update requirements

---
 requirements.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index bcf5eb8..53ddb3e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,3 +4,6 @@ tqdm
 matplotlib
 numpy
 argparse
+fastprogress
+pandas
+funcy

From 954ac457037222747a30c85050ac0b4c190a6174 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 20:51:54 -0800
Subject: [PATCH 070/130] Shared args

---
 model.py |  4 +++-
 test.py  | 25 ++++---------------------
 train.py | 28 +---------------------------
 util.py  | 27 +++++++++++++++++++++++++++
 4 files changed, 35 insertions(+), 49 deletions(-)

diff --git a/model.py b/model.py
index f454877..53ed185 100644
--- a/model.py
+++ b/model.py
@@ -35,10 +35,12 @@ def forward(self, x, support):
 
 class GWNet(nn.Module):
     def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=True,
+                 softmax_eps=0.,
                  addaptadj=True, aptinit=None, in_dim=2, out_dim=12, residual_channels=32, dilation_channels=32,
                  skip_channels=256, end_channels=512, kernel_size=2, blocks=4, layers=2, apt_size=10):
         super().__init__()
         self.dropout = dropout
+        self.softmax_eps = softmax_eps
         self.blocks = blocks
         self.layers = layers
         self.do_graph_conv = do_graph_conv
@@ -103,7 +105,7 @@ def forward(self, input):
         adjacency_matrices = self.fixed_supports
         # calculate the current adaptive adj matrix once per iteration
         if self.addaptadj:
-            adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)), dim=1)
+            adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)) + self.softmax_eps, dim=1)
             adjacency_matrices = self.fixed_supports + [adp]
 
         # WaveNet layers
diff --git a/test.py b/test.py
index eec3f2c..cc969c2 100644
--- a/test.py
+++ b/test.py
@@ -1,11 +1,12 @@
 import util
-import argparse
 from model import *
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
 
+from util import get_shared_arg_parser
+
 
 def main(args, **model_kwargs):
     device = torch.device(args.device)
@@ -58,27 +59,9 @@ def plot_learned_adj_matrix(model):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cuda:0', help='')
-    parser.add_argument('--data', type=str, default='data/METR-LA', help='data path')
-    parser.add_argument('--adjdata', type=str, default='data/sensor_graph/adj_mx.pkl',
-                        help='adj data path')
-    parser.add_argument('--adjtype', type=str, default='doubletransition', help='adj type')
-    parser.add_argument('--do_graph_conv', action='store_true',
-                        help='whether to add graph convolution layer')
-    parser.add_argument('--aptonly', action='store_true', help='whether only adaptive adj')
-    parser.add_argument('--addaptadj', action='store_true', help='whether add adaptive adj')
-    parser.add_argument('--randomadj', action='store_true',
-                        help='whether random initialize adaptive adj')
-    parser.add_argument('--seq_length', type=int, default=12, help='')
-    parser.add_argument('--nhid', type=int, default=32, help='')
-    parser.add_argument('--in_dim', type=int, default=2, help='inputs dimension')
-    parser.add_argument('--num_nodes', type=int, default=207, help='number of nodes')
-    parser.add_argument('--batch_size', type=int, default=64, help='batch size')
-    parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
-    # parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
+    parser = get_shared_arg_parser()
     parser.add_argument('--checkpoint', type=str, help='')
     parser.add_argument('--plotheatmap', action='store_true')
-    parser.add_argument('--n_obs', default=None, help='Only use this many observations')
+
     args = parser.parse_args()
     main(args)
diff --git a/train.py b/train.py
index a6cf37e..46eb11f 100644
--- a/train.py
+++ b/train.py
@@ -106,40 +106,14 @@ def eval_(ds, device, engine):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cuda:0', help='')
-    parser.add_argument('--data', type=str, default='data/METR-LA', help='data path')
-    parser.add_argument('--adjdata', type=str, default='data/sensor_graph/adj_mx.pkl',
-                        help='adj data path')
-    parser.add_argument('--adjtype', type=str, default='doubletransition', help='adj type')
-    parser.add_argument('--do_graph_conv', action='store_true',
-                        help='whether to add graph convolution layer')
-    parser.add_argument('--aptonly', action='store_true', help='whether only adaptive adj')
-    parser.add_argument('--addaptadj', action='store_true', help='whether add adaptive adj')
-    parser.add_argument('--randomadj', action='store_true',
-                        help='whether random initialize adaptive adj')
-    parser.add_argument('--seq_length', type=int, default=12, help='')
-    parser.add_argument('--nhid', type=int, default=32, help='')
-    parser.add_argument('--in_dim', type=int, default=2, help='inputs dimension')
-    parser.add_argument('--num_nodes', type=int, default=207, help='number of nodes')
-    parser.add_argument('--batch_size', type=int, default=64, help='batch size')
-    parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
-    parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
-    parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
+    parser = util.get_shared_arg_parser()
     parser.add_argument('--epochs', type=int, default=100, help='')
-    # parser.add_argument('--print_every', type=int, default=50, help='')
-    # parser.add_argument('--seed',type=int,default=99,help='random seed')
     parser.add_argument('--save', type=str, default='experiment', help='save path')
-    # parser.add_argument('--expid', default=1, help='experiment id')
     parser.add_argument('--n_iters', default=None, help='quit after this many iterations')
-    parser.add_argument('--n_obs', default=None, help='Only use this many observations')
-    parser.add_argument('--apt_size', default=10, type=int)
-
     args = parser.parse_args()
     t1 = time.time()
     if not os.path.exists(args.save):
         os.mkdir(args.save)
-
     pickle_save(args, f'{args.save}/args.pkl')
     main(args)
     t2 = time.time()
diff --git a/util.py b/util.py
index 34ea890..a194a91 100644
--- a/util.py
+++ b/util.py
@@ -1,3 +1,4 @@
+import argparse
 import pickle
 import numpy as np
 import os
@@ -215,3 +216,29 @@ def make_pred_df(realy, yhat, scaler):
                            y3=_to_ser(realy[:, :, 2]),
                            yhat3=_to_ser(scaler.inverse_transform(yhat[:, :, 2]))))
     return df
+
+
+def get_shared_arg_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cuda:0', help='')
+    parser.add_argument('--data', type=str, default='data/METR-LA', help='data path')
+    parser.add_argument('--adjdata', type=str, default='data/sensor_graph/adj_mx.pkl',
+                        help='adj data path')
+    parser.add_argument('--adjtype', type=str, default='doubletransition', help='adj type')
+    parser.add_argument('--do_graph_conv', action='store_true',
+                        help='whether to add graph convolution layer')
+    parser.add_argument('--aptonly', action='store_true', help='whether only adaptive adj')
+    parser.add_argument('--addaptadj', action='store_true', help='whether add adaptive adj')
+    parser.add_argument('--randomadj', action='store_true',
+                        help='whether random initialize adaptive adj')
+    parser.add_argument('--seq_length', type=int, default=12, help='')
+    parser.add_argument('--nhid', type=int, default=32, help='')
+    parser.add_argument('--in_dim', type=int, default=2, help='inputs dimension')
+    parser.add_argument('--num_nodes', type=int, default=207, help='number of nodes')
+    parser.add_argument('--batch_size', type=int, default=64, help='batch size')
+    parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
+    parser.add_argument('--n_obs', default=None, help='Only use this many observations')
+    parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
+    parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
+    parser.add_argument('--apt_size', default=10, type=int)
+    return parser

From 96a72829986507590ad4b07aeaeca08515842cc8 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 20:53:38 -0800
Subject: [PATCH 071/130] save import

---
 test.py | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/test.py b/test.py
index cc969c2..0bee409 100644
--- a/test.py
+++ b/test.py
@@ -5,8 +5,6 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 
-from util import get_shared_arg_parser
-
 
 def main(args, **model_kwargs):
     device = torch.device(args.device)
@@ -19,13 +17,6 @@ def main(args, **model_kwargs):
 
     if args.aptonly:
         supports = None
-
-    # model = GWNet(device, args.num_nodes, args.dropout,
-    #               supports=supports, do_graph_conv=args.do_graph_conv,
-    #               addaptadj=args.addaptadj, aptinit=adjinit, apt_size=args.apt_size,
-    #
-    #
-    #               )
     model = GWNet(device, args.num_nodes, args.dropout, supports=supports,
                   do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=adjinit,
                   in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
@@ -59,7 +50,7 @@ def plot_learned_adj_matrix(model):
 
 
 if __name__ == "__main__":
-    parser = get_shared_arg_parser()
+    parser = util.get_shared_arg_parser()
     parser.add_argument('--checkpoint', type=str, help='')
     parser.add_argument('--plotheatmap', action='store_true')
 

From f4bf38a338605b31b67fec3be2c2dcbe6e3a5d06 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 21:02:19 -0800
Subject: [PATCH 072/130] Softmax-temp

---
 model.py      | 18 +++++++++++++++---
 test.py       | 14 +++++++-------
 test_gwnet.py |  2 +-
 train.py      |  6 +-----
 util.py       |  1 +
 5 files changed, 25 insertions(+), 16 deletions(-)

diff --git a/model.py b/model.py
index 53ed185..05b2d69 100644
--- a/model.py
+++ b/model.py
@@ -35,12 +35,12 @@ def forward(self, x, support):
 
 class GWNet(nn.Module):
     def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=True,
-                 softmax_eps=0.,
+                 softmax_temp=1.,
                  addaptadj=True, aptinit=None, in_dim=2, out_dim=12, residual_channels=32, dilation_channels=32,
                  skip_channels=256, end_channels=512, kernel_size=2, blocks=4, layers=2, apt_size=10):
         super().__init__()
         self.dropout = dropout
-        self.softmax_eps = softmax_eps
+        self.softmax_temp = softmax_temp
         self.blocks = blocks
         self.layers = layers
         self.do_graph_conv = do_graph_conv
@@ -96,6 +96,18 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
 
         self.receptive_field = receptive_field
 
+
+    @classmethod
+    def from_args(cls, args, device, supports, aptinit, **kwargs):
+        model = cls(device, args.num_nodes, dropout=args.dropout, supports=supports,
+                    do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=aptinit,
+                    in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
+                    softmax_temp=args.softmax_temp,
+                    residual_channels=args.nhid, dilation_channels=args.nhid,
+                    skip_channels=args.nhid * 8, end_channels=args.nhid * 16, **kwargs)
+        return model
+
+
     def forward(self, input):
         # Input shape is (bs, features, n_nodes, n_timesteps)
         if input.size(3) < self.receptive_field:
@@ -105,7 +117,7 @@ def forward(self, input):
         adjacency_matrices = self.fixed_supports
         # calculate the current adaptive adj matrix once per iteration
         if self.addaptadj:
-            adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)) + self.softmax_eps, dim=1)
+            adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)) / self.softmax_temp, dim=1)
             adjacency_matrices = self.fixed_supports + [adp]
 
         # WaveNet layers
diff --git a/test.py b/test.py
index 0bee409..0a838df 100644
--- a/test.py
+++ b/test.py
@@ -17,11 +17,12 @@ def main(args, **model_kwargs):
 
     if args.aptonly:
         supports = None
-    model = GWNet(device, args.num_nodes, args.dropout, supports=supports,
-                  do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=adjinit,
-                  in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
-                  residual_channels=args.nhid, dilation_channels=args.nhid,
-                  skip_channels=args.nhid * 8, end_channels=args.nhid * 16, **model_kwargs)
+    model = GWNet.from_args(args, device, supports, adjinit, **model_kwargs)
+    # model = GWNet(device, args.num_nodes, args.dropout, supports=supports,
+    #               do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=adjinit,
+    #               in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
+    #               residual_channels=args.nhid, dilation_channels=args.nhid,
+    #               skip_channels=args.nhid * 8, end_channels=args.nhid * 16, **model_kwargs)
     model.to(device)
     model.load_state_dict(torch.load(args.checkpoint))
     model.eval()
@@ -36,8 +37,7 @@ def main(args, **model_kwargs):
     df2.to_csv('./wave.csv', index=False)
 
 
-    if args.plotheatmap == "True":
-        plot_learned_adj_matrix(model)
+    if args.plotheatmap: plot_learned_adj_matrix(model)
     return met_df, df2
 
 def plot_learned_adj_matrix(model):
diff --git a/test_gwnet.py b/test_gwnet.py
index cdac9c8..ca916d0 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -13,7 +13,7 @@
 SAVE_DIR = 'utest_experiment/'
 ARG_UPDATES = {'epochs': 1, 'n_iters': 1, 'batch_size': 2, 'n_obs': 2,
                'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
-               'apt_size': 2, 'nhid': 1, }
+               'apt_size': 2, 'nhid': 1, 'softmax_temp': 1.}
 
 def modify_args(args, updates):
     for k,v in updates.items():
diff --git a/train.py b/train.py
index 46eb11f..0283cf9 100644
--- a/train.py
+++ b/train.py
@@ -29,11 +29,7 @@ def main(args, **model_kwargs):
         if not args.addaptadj: raise ValueError('WARNING: not using adjacency matrix')
         supports = None
 
-    model = GWNet(device, args.num_nodes, args.dropout, supports=supports,
-                  do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=aptinit,
-                  in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
-                  residual_channels=args.nhid, dilation_channels=args.nhid,
-                  skip_channels=args.nhid * 8, end_channels=args.nhid * 16, **model_kwargs)
+    model = GWNet.from_args(args, device, supports, aptinit, **model_kwargs)
     model.to(device)
     engine = Trainer(model, scaler, args.learning_rate, args.weight_decay)
     print("start training...", flush=True)
diff --git a/util.py b/util.py
index a194a91..a858533 100644
--- a/util.py
+++ b/util.py
@@ -241,4 +241,5 @@ def get_shared_arg_parser():
     parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
     parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
     parser.add_argument('--apt_size', default=10, type=int)
+    parser.add_argument('--softmax_temp', default=1., type=float)
     return parser

From aeb22ca14d362983c9ae68d91182d08fad74bf15 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 21:57:03 -0800
Subject: [PATCH 073/130] style model.py

---
 model.py | 75 +++++++++++++++++++++++++++-----------------------------
 1 file changed, 36 insertions(+), 39 deletions(-)

diff --git a/model.py b/model.py
index 05b2d69..4798b89 100644
--- a/model.py
+++ b/model.py
@@ -1,23 +1,22 @@
 import torch
 import torch.nn as nn
+from torch.nn import BatchNorm2d, Conv1d, Conv2d, ModuleList, Parameter
 import torch.nn.functional as F
-from torch.autograd import Variable
-import sys
 
 
 def nconv(x, A):
-    """Multiply along source node axis"""
+    """Multiply x by adjacency matrix along source node axis"""
     return torch.einsum('ncvl,vw->ncwl', (x, A)).contiguous()
 
 class GraphConvNet(nn.Module):
     def __init__(self, c_in, c_out, dropout, support_len=3, order=2):
         super().__init__()
         c_in = (order * support_len + 1) * c_in
-        self.mlp = torch.nn.Conv2d(c_in, c_out, kernel_size=(1, 1), padding=(0,0), stride=(1,1), bias=True)
+        self.final_conv = Conv2d(c_in, c_out, (1, 1), padding=(0, 0), stride=(1, 1), bias=True)
         self.dropout = dropout
         self.order = order
 
-    def forward(self, x, support):
+    def forward(self, x, support: list):
         out = [x]
         for a in support:
             x1 = nconv(x, a)
@@ -28,16 +27,17 @@ def forward(self, x, support):
                 x1 = x2
 
         h = torch.cat(out, dim=1)
-        h = self.mlp(h)
+        h = self.final_conv(h)
         h = F.dropout(h, self.dropout, training=self.training)
         return h
 
 
 class GWNet(nn.Module):
     def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=True,
-                 softmax_temp=1.,
-                 addaptadj=True, aptinit=None, in_dim=2, out_dim=12, residual_channels=32, dilation_channels=32,
-                 skip_channels=256, end_channels=512, kernel_size=2, blocks=4, layers=2, apt_size=10):
+                 softmax_temp=1., addaptadj=True, aptinit=None, in_dim=2, out_dim=12,
+                 residual_channels=32, dilation_channels=32,
+                 skip_channels=256, end_channels=512, kernel_size=2, blocks=4, layers=2,
+                 apt_size=10):
         super().__init__()
         self.dropout = dropout
         self.softmax_temp = softmax_temp
@@ -46,14 +46,6 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
         self.do_graph_conv = do_graph_conv
         self.addaptadj = addaptadj
 
-        # Each of these will
-        self.filter_convs = nn.ModuleList()
-        self.gate_convs = nn.ModuleList()
-        self.residual_convs = nn.ModuleList()
-        self.skip_convs = nn.ModuleList()
-        self.bn = nn.ModuleList()
-        self.graph_convs = nn.ModuleList()
-
         self.start_conv = nn.Conv2d(in_channels=in_dim,
                                     out_channels=residual_channels,
                                     kernel_size=(1,1))
@@ -63,39 +55,45 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
         self.supports_len = len(self.fixed_supports)
         if do_graph_conv and addaptadj:
             if aptinit is None:
-                nodevec1 = torch.randn(num_nodes, apt_size)
-                nodevec2 = torch.randn(apt_size, num_nodes)
+                nodevec1, nodevec2 = torch.randn(num_nodes, apt_size), torch.randn(apt_size, num_nodes)
             else:
-                m, p, n = torch.svd(aptinit)
-                nodevec1 = torch.mm(m[:, :apt_size], torch.diag(p[:apt_size] ** 0.5))
-                nodevec2 = torch.mm(torch.diag(p[:apt_size] ** 0.5), n[:, :apt_size].t())
+                nodevec1, nodevec2 = self.svd_init(apt_size, aptinit)
             self.supports_len += 1
-            
-            self.register_parameter('nodevec1', nn.Parameter(nodevec1.to(device), requires_grad=True))
-            self.register_parameter('nodevec2', nn.Parameter(nodevec2.to(device), requires_grad=True))
-        if do_graph_conv:
-            self.graph_convs = nn.ModuleList([GraphConvNet(dilation_channels, residual_channels, dropout, support_len=self.supports_len) for i in range(blocks*layers)])
+            self.register_parameter('nodevec1', Parameter(nodevec1.to(device), requires_grad=True))
+            self.register_parameter('nodevec2', Parameter(nodevec2.to(device), requires_grad=True))
+
+        depth = blocks * layers
+
+        # 1x1 convolution for residual and skip connections (slightly different see docstring)
+        self.residual_convs = ModuleList([Conv1d(dilation_channels, residual_channels, (1, 1)) for _ in range(depth)])
+        self.skip_convs = ModuleList([Conv1d(dilation_channels, skip_channels, (1, 1)) for _ in range(depth)])
+        self.bn = ModuleList([BatchNorm2d(residual_channels) for _ in range(depth)])
+        self.graph_convs = ModuleList([GraphConvNet(dilation_channels, residual_channels, dropout, support_len=self.supports_len)
+                                              for _ in range(depth)])
+
+        self.filter_convs = ModuleList()
+        self.gate_convs = ModuleList()
         for b in range(blocks):
             additional_scope = kernel_size - 1
             D = 1 # dilation
             for i in range(layers):
                 # dilated convolutions
-                self.filter_convs.append(nn.Conv2d(residual_channels, dilation_channels, kernel_size=(1,kernel_size), dilation=D))
-                self.gate_convs.append(nn.Conv1d(residual_channels, dilation_channels, kernel_size=(1, kernel_size), dilation=D))
-
-                # 1x1 convolution for residual and skip connections (slightly different see docstring)
-                self.residual_convs.append(nn.Conv1d(dilation_channels, residual_channels, kernel_size=(1, 1)))
-                self.skip_convs.append(nn.Conv1d(dilation_channels, skip_channels, kernel_size=(1, 1)))
-                self.bn.append(nn.BatchNorm2d(residual_channels))
+                self.filter_convs.append(Conv2d(residual_channels, dilation_channels, (1,kernel_size), dilation=D))
+                self.gate_convs.append(Conv1d(residual_channels, dilation_channels, (1, kernel_size), dilation=D))
                 D *= 2
                 receptive_field += additional_scope
                 additional_scope *= 2
-
-        self.end_conv_1 = nn.Conv2d(skip_channels, end_channels, kernel_size=(1, 1), bias=True)
-        self.end_conv_2 = nn.Conv2d(end_channels, out_dim, kernel_size=(1, 1), bias=True)
-
         self.receptive_field = receptive_field
 
+        self.end_conv_1 = Conv2d(skip_channels, end_channels, (1, 1), bias=True)
+        self.end_conv_2 = Conv2d(end_channels, out_dim, (1, 1), bias=True)
+
+    @staticmethod
+    def svd_init(apt_size, aptinit):
+        m, p, n = torch.svd(aptinit)
+        nodevec1 = torch.mm(m[:, :apt_size], torch.diag(p[:apt_size] ** 0.5))
+        nodevec2 = torch.mm(torch.diag(p[:apt_size] ** 0.5), n[:, :apt_size].t())
+        return nodevec1, nodevec2
 
     @classmethod
     def from_args(cls, args, device, supports, aptinit, **kwargs):
@@ -107,7 +105,6 @@ def from_args(cls, args, device, supports, aptinit, **kwargs):
                     skip_channels=args.nhid * 8, end_channels=args.nhid * 16, **kwargs)
         return model
 
-
     def forward(self, input):
         # Input shape is (bs, features, n_nodes, n_timesteps)
         if input.size(3) < self.receptive_field:

From b1ec325a462bfb8851f07313f09c14c99416086d Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 22:08:54 -0800
Subject: [PATCH 074/130] lr_decay_rate_hoist

---
 test_gwnet.py | 2 +-
 train.py      | 9 +++++++--
 util.py       | 2 --
 3 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/test_gwnet.py b/test_gwnet.py
index ca916d0..d7d0d6c 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -13,7 +13,7 @@
 SAVE_DIR = 'utest_experiment/'
 ARG_UPDATES = {'epochs': 1, 'n_iters': 1, 'batch_size': 2, 'n_obs': 2,
                'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
-               'apt_size': 2, 'nhid': 1, 'softmax_temp': 1.}
+               'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.}
 
 def modify_args(args, updates):
     for k,v in updates.items():
diff --git a/train.py b/train.py
index 0283cf9..0621425 100644
--- a/train.py
+++ b/train.py
@@ -31,7 +31,7 @@ def main(args, **model_kwargs):
 
     model = GWNet.from_args(args, device, supports, aptinit, **model_kwargs)
     model.to(device)
-    engine = Trainer(model, scaler, args.learning_rate, args.weight_decay)
+    engine = Trainer(model, scaler, args.learning_rate, args.weight_decay, args.lr_decay_rate)
     print("start training...", flush=True)
     metrics, train_time = [], []
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
@@ -63,13 +63,14 @@ def main(args, **model_kwargs):
                  train_rmse=np.mean(train_rmse), valid_loss=np.mean(valid_loss),
                  valid_mape=np.mean(valid_mape), valid_rmse=np.mean(valid_rmse))
         m = pd.Series(m)
-        mb.comment = f'valid_loss: {m.valid_loss: .3f}'
+
         metrics.append(m)
         print(m.round(4))
         if m.valid_loss < lowest_mae_yet:
             torch.save(engine.model.state_dict(), best_model_save_path)
             lowest_mae_yet = m.valid_loss
         met_df = pd.DataFrame(metrics)
+        mb.comment = f'valid_loss: {met_df.valid_loss.min(): .3f}'
         met_df.round(4).to_csv(f'{args.save}/metrics.csv')
     print(f"Training finished. Best Valid Loss:")
     print(met_df.loc[met_df.valid_loss.idxmin()].round(4))
@@ -104,6 +105,10 @@ def eval_(ds, device, engine):
 if __name__ == "__main__":
     parser = util.get_shared_arg_parser()
     parser.add_argument('--epochs', type=int, default=100, help='')
+    parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
+    parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
+    parser.add_argument('--lr_decay_rate', type=float, default=0.97, help='learning rate')
+
     parser.add_argument('--save', type=str, default='experiment', help='save path')
     parser.add_argument('--n_iters', default=None, help='quit after this many iterations')
     args = parser.parse_args()
diff --git a/util.py b/util.py
index a858533..c04cb20 100644
--- a/util.py
+++ b/util.py
@@ -238,8 +238,6 @@ def get_shared_arg_parser():
     parser.add_argument('--batch_size', type=int, default=64, help='batch size')
     parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
     parser.add_argument('--n_obs', default=None, help='Only use this many observations')
-    parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
-    parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
     parser.add_argument('--apt_size', default=10, type=int)
     parser.add_argument('--softmax_temp', default=1., type=float)
     return parser

From bccf70da24375cb0c0ecdec6715f15c2a4607a11 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 22:16:18 -0800
Subject: [PATCH 075/130] better train ux

---
 train.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/train.py b/train.py
index 0621425..b65415f 100644
--- a/train.py
+++ b/train.py
@@ -63,15 +63,13 @@ def main(args, **model_kwargs):
                  train_rmse=np.mean(train_rmse), valid_loss=np.mean(valid_loss),
                  valid_mape=np.mean(valid_mape), valid_rmse=np.mean(valid_rmse))
         m = pd.Series(m)
-
         metrics.append(m)
-        print(m.round(4))
         if m.valid_loss < lowest_mae_yet:
             torch.save(engine.model.state_dict(), best_model_save_path)
             lowest_mae_yet = m.valid_loss
         met_df = pd.DataFrame(metrics)
-        mb.comment = f'valid_loss: {met_df.valid_loss.min(): .3f}'
-        met_df.round(4).to_csv(f'{args.save}/metrics.csv')
+        mb.comment = f'best valid_loss: {met_df.valid_loss.min(): .3f}, current valid_loss: {m.valid_loss:.3f}'
+        met_df.round(6).to_csv(f'{args.save}/metrics.csv')
     print(f"Training finished. Best Valid Loss:")
     print(met_df.loc[met_df.valid_loss.idxmin()].round(4))
     # Metrics on test data

From 5972db3c65a84a4cd4d9946b5594d8bcc4badd9f Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 22:21:25 -0800
Subject: [PATCH 076/130] More cleanup

---
 train.py | 27 ++++++++++-----------------
 1 file changed, 10 insertions(+), 17 deletions(-)

diff --git a/train.py b/train.py
index b65415f..120189e 100644
--- a/train.py
+++ b/train.py
@@ -11,6 +11,8 @@
 
 from model import GWNet
 from util import calc_test_metrics
+from exp_results import summary
+
 
 
 def main(args, **model_kwargs):
@@ -33,29 +35,22 @@ def main(args, **model_kwargs):
     model.to(device)
     engine = Trainer(model, scaler, args.learning_rate, args.weight_decay, args.lr_decay_rate)
     print("start training...", flush=True)
-    metrics, train_time = [], []
+    metrics = []
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
-    lowest_mae_yet = 100  # high value in MPH will get overwritten
+    lowest_mae_yet = 100  # high value, will get overwritten
     mb = progress_bar(list(range(1, args.epochs + 1)))
-    for i in mb:
-        # if i % 10 == 0:
-        # lr = max(0.000002,args.learning_rate * (0.1 ** (i // 10)))
-        # for g in engine.optimizer.param_groups: g['lr'] = lr
-        train_loss = []
-        train_mape = []
-        train_rmse = []
-        t1 = time.time()
+    for _ in mb:
+        train_loss, train_mape, train_rmse = [], [], []
         dataloader['train_loader'].shuffle()
         for iter, (x, y) in enumerate(dataloader['train_loader'].get_iterator()):
             trainx = torch.Tensor(x).to(device).transpose(1, 3)
             trainy = torch.Tensor(y).to(device).transpose(1, 3)
-            loss, mape, rmse = engine.train(trainx, trainy[:, 0, :, :])
-            train_loss.append(loss)
+            mae, mape, rmse = engine.train(trainx, trainy[:, 0, :, :])
+            train_loss.append(mae)
             train_mape.append(mape)
             train_rmse.append(rmse)
             if args.n_iters is not None and iter >= args.n_iters:
                 break
-        train_time.append(time.time() - t1)
         engine.scheduler.step()
         _, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device, engine)
 
@@ -70,17 +65,15 @@ def main(args, **model_kwargs):
         met_df = pd.DataFrame(metrics)
         mb.comment = f'best valid_loss: {met_df.valid_loss.min(): .3f}, current valid_loss: {m.valid_loss:.3f}'
         met_df.round(6).to_csv(f'{args.save}/metrics.csv')
-    print(f"Training finished. Best Valid Loss:")
-    print(met_df.loc[met_df.valid_loss.idxmin()].round(4))
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
     realy = torch.Tensor(dataloader['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
     test_met_df, yhat = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler,
                                           realy)
     test_met_df.round(6).to_csv(os.path.join(args.save, 'test_metrics.csv'))
-    print(test_met_df.mean().round(3))
+    print(summary(args.save))
     pred_df = util.make_pred_df(realy, yhat, scaler)
-    pred_df.to_csv(os.path.join(args.save, 'preds.csv'))
+    #pred_df.to_csv(os.path.join(args.save, 'preds.csv'))
 
 
 def eval_(ds, device, engine):

From 452cbb7bf45159eb23d3882ed3d2a3300ff71e25 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 22:22:20 -0800
Subject: [PATCH 077/130] dont save preds

---
 train.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/train.py b/train.py
index 120189e..1c016d7 100644
--- a/train.py
+++ b/train.py
@@ -72,8 +72,6 @@ def main(args, **model_kwargs):
                                           realy)
     test_met_df.round(6).to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(summary(args.save))
-    pred_df = util.make_pred_df(realy, yhat, scaler)
-    #pred_df.to_csv(os.path.join(args.save, 'preds.csv'))
 
 
 def eval_(ds, device, engine):

From 2db9f5b1ca2deea137e709086e9bffec13ef72af Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 22:23:47 -0800
Subject: [PATCH 078/130] more cleanup

---
 test.py  | 5 -----
 train.py | 3 +--
 2 files changed, 1 insertion(+), 7 deletions(-)

diff --git a/test.py b/test.py
index 0a838df..c6961d9 100644
--- a/test.py
+++ b/test.py
@@ -18,11 +18,6 @@ def main(args, **model_kwargs):
     if args.aptonly:
         supports = None
     model = GWNet.from_args(args, device, supports, adjinit, **model_kwargs)
-    # model = GWNet(device, args.num_nodes, args.dropout, supports=supports,
-    #               do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=adjinit,
-    #               in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
-    #               residual_channels=args.nhid, dilation_channels=args.nhid,
-    #               skip_channels=args.nhid * 8, end_channels=args.nhid * 16, **model_kwargs)
     model.to(device)
     model.load_state_dict(torch.load(args.checkpoint))
     model.eval()
diff --git a/train.py b/train.py
index 1c016d7..bb80a3f 100644
--- a/train.py
+++ b/train.py
@@ -68,8 +68,7 @@ def main(args, **model_kwargs):
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
     realy = torch.Tensor(dataloader['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
-    test_met_df, yhat = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler,
-                                          realy)
+    test_met_df, yhat = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler, realy)
     test_met_df.round(6).to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(summary(args.save))
 

From effada8ba1c7852630c7bf4ef4dcf77e62eac1f9 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 22:26:59 -0800
Subject: [PATCH 079/130] more cleanup

---
 train.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/train.py b/train.py
index bb80a3f..a9ee15f 100644
--- a/train.py
+++ b/train.py
@@ -1,7 +1,6 @@
 import torch
 import numpy as np
 import pandas as pd
-import argparse
 import time
 import util
 from engine import Trainer
@@ -28,7 +27,7 @@ def main(args, **model_kwargs):
         aptinit = supports[0]  # ignored without do_graph_conv and add_apt_adj
 
     if args.aptonly:
-        if not args.addaptadj: raise ValueError('WARNING: not using adjacency matrix')
+        if not args.addaptadj and args.do_graph_conv: raise ValueError('WARNING: not using adjacency matrix')
         supports = None
 
     model = GWNet.from_args(args, device, supports, aptinit, **model_kwargs)

From 2e2379bacea84df1d78881f0d003c4fcc7aaf959 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sun, 1 Dec 2019 22:32:42 -0800
Subject: [PATCH 080/130] more cleanup

---
 test.py  | 18 ++++--------------
 train.py | 27 ++++++++-------------------
 util.py  | 11 +++++++++++
 3 files changed, 23 insertions(+), 33 deletions(-)

diff --git a/test.py b/test.py
index c6961d9..7bfe8d0 100644
--- a/test.py
+++ b/test.py
@@ -6,17 +6,9 @@
 import seaborn as sns
 
 
-def main(args, **model_kwargs):
+def main(args, save_pred_path='preds.csv', save_metrics_path='last_test_metrics.csv', **model_kwargs):
     device = torch.device(args.device)
-    _, _, adj_mx = util.load_adj(args.adjdata,args.adjtype)
-    supports = [torch.tensor(i).to(device) for i in adj_mx]
-    if args.randomadj:
-        adjinit = None
-    else:
-        adjinit = supports[0]
-
-    if args.aptonly:
-        supports = None
+    adjinit, supports = util.make_graph_inputs(args, device)
     model = GWNet.from_args(args, device, supports, adjinit, **model_kwargs)
     model.to(device)
     model.load_state_dict(torch.load(args.checkpoint))
@@ -28,10 +20,8 @@ def main(args, **model_kwargs):
     realy = realy.transpose(1,3)[:,0,:,:]
     met_df, yhat = util.calc_test_metrics(model, device, dataloader['test_loader'], scaler, realy)
     df2 = util.make_pred_df(realy, yhat, scaler)
-    met_df.to_csv('last_test_metrics.csv')
-    df2.to_csv('./wave.csv', index=False)
-
-
+    met_df.to_csv(save_metrics_path)
+    df2.to_csv(save_pred_path, index=False)
     if args.plotheatmap: plot_learned_adj_matrix(model)
     return met_df, df2
 
diff --git a/train.py b/train.py
index a9ee15f..c024acd 100644
--- a/train.py
+++ b/train.py
@@ -16,19 +16,9 @@
 
 def main(args, **model_kwargs):
     device = torch.device(args.device)
-    dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size,
-                                   n_obs=args.n_obs)
-    scaler = dataloader['scaler']
-    sensor_ids, sensor_id_to_ind, adj_mx = util.load_adj(args.adjdata, args.adjtype)
-    supports = [torch.tensor(i).to(device) for i in adj_mx]
-    if args.randomadj:
-        aptinit = None
-    else:
-        aptinit = supports[0]  # ignored without do_graph_conv and add_apt_adj
-
-    if args.aptonly:
-        if not args.addaptadj and args.do_graph_conv: raise ValueError('WARNING: not using adjacency matrix')
-        supports = None
+    data = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
+    scaler = data['scaler']
+    aptinit, supports = util.make_graph_inputs(args, device)
 
     model = GWNet.from_args(args, device, supports, aptinit, **model_kwargs)
     model.to(device)
@@ -40,8 +30,8 @@ def main(args, **model_kwargs):
     mb = progress_bar(list(range(1, args.epochs + 1)))
     for _ in mb:
         train_loss, train_mape, train_rmse = [], [], []
-        dataloader['train_loader'].shuffle()
-        for iter, (x, y) in enumerate(dataloader['train_loader'].get_iterator()):
+        data['train_loader'].shuffle()
+        for iter, (x, y) in enumerate(data['train_loader'].get_iterator()):
             trainx = torch.Tensor(x).to(device).transpose(1, 3)
             trainy = torch.Tensor(y).to(device).transpose(1, 3)
             mae, mape, rmse = engine.train(trainx, trainy[:, 0, :, :])
@@ -51,7 +41,7 @@ def main(args, **model_kwargs):
             if args.n_iters is not None and iter >= args.n_iters:
                 break
         engine.scheduler.step()
-        _, valid_loss, valid_mape, valid_rmse = eval_(dataloader['val_loader'], device, engine)
+        _, valid_loss, valid_mape, valid_rmse = eval_(data['val_loader'], device, engine)
 
         m = dict(train_loss=np.mean(train_loss), train_mape=np.mean(train_mape),
                  train_rmse=np.mean(train_rmse), valid_loss=np.mean(valid_loss),
@@ -66,12 +56,11 @@ def main(args, **model_kwargs):
         met_df.round(6).to_csv(f'{args.save}/metrics.csv')
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
-    realy = torch.Tensor(dataloader['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
-    test_met_df, yhat = calc_test_metrics(engine.model, device, dataloader['test_loader'], scaler, realy)
+    realy = torch.Tensor(data['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
+    test_met_df, yhat = calc_test_metrics(engine.model, device, data['test_loader'], scaler, realy)
     test_met_df.round(6).to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(summary(args.save))
 
-
 def eval_(ds, device, engine):
     """Run validation."""
     valid_loss = []
diff --git a/util.py b/util.py
index c04cb20..a097266 100644
--- a/util.py
+++ b/util.py
@@ -218,6 +218,17 @@ def make_pred_df(realy, yhat, scaler):
     return df
 
 
+def make_graph_inputs(args, device):
+    sensor_ids, sensor_id_to_ind, adj_mx = load_adj(args.adjdata, args.adjtype)
+    supports = [torch.tensor(i).to(device) for i in adj_mx]
+    aptinit = None if args.randomadj else supports[0]  # ignored without do_graph_conv and add_apt_adj
+    if args.aptonly:
+        if not args.addaptadj and args.do_graph_conv: raise ValueError(
+            'WARNING: not using adjacency matrix')
+        supports = None
+    return aptinit, supports
+
+
 def get_shared_arg_parser():
     parser = argparse.ArgumentParser()
     parser.add_argument('--device', type=str, default='cuda:0', help='')

From 6fa65ddba161d978d545f257c230c042d65b4ca1 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 2 Dec 2019 11:09:47 -0800
Subject: [PATCH 081/130] More cleanup

---
 model.py      | 12 ++++++------
 test_gwnet.py |  1 +
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/model.py b/model.py
index 4798b89..8357f0e 100644
--- a/model.py
+++ b/model.py
@@ -62,14 +62,14 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
             self.register_parameter('nodevec1', Parameter(nodevec1.to(device), requires_grad=True))
             self.register_parameter('nodevec2', Parameter(nodevec2.to(device), requires_grad=True))
 
-        depth = blocks * layers
+        depth = list(range(blocks * layers))
 
         # 1x1 convolution for residual and skip connections (slightly different see docstring)
-        self.residual_convs = ModuleList([Conv1d(dilation_channels, residual_channels, (1, 1)) for _ in range(depth)])
-        self.skip_convs = ModuleList([Conv1d(dilation_channels, skip_channels, (1, 1)) for _ in range(depth)])
-        self.bn = ModuleList([BatchNorm2d(residual_channels) for _ in range(depth)])
+        self.residual_convs = ModuleList([Conv1d(dilation_channels, residual_channels, (1, 1)) for _ in depth])
+        self.skip_convs = ModuleList([Conv1d(dilation_channels, skip_channels, (1, 1)) for _ in depth])
+        self.bn = ModuleList([BatchNorm2d(residual_channels) for _ in depth])
         self.graph_convs = ModuleList([GraphConvNet(dilation_channels, residual_channels, dropout, support_len=self.supports_len)
-                                              for _ in range(depth)])
+                                              for _ in depth])
 
         self.filter_convs = ModuleList()
         self.gate_convs = ModuleList()
@@ -78,7 +78,7 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
             D = 1 # dilation
             for i in range(layers):
                 # dilated convolutions
-                self.filter_convs.append(Conv2d(residual_channels, dilation_channels, (1,kernel_size), dilation=D))
+                self.filter_convs.append(Conv2d(residual_channels, dilation_channels, (1, kernel_size), dilation=D))
                 self.gate_convs.append(Conv1d(residual_channels, dilation_channels, (1, kernel_size), dilation=D))
                 D *= 2
                 receptive_field += additional_scope
diff --git a/test_gwnet.py b/test_gwnet.py
index d7d0d6c..41beeb8 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -15,6 +15,7 @@
                'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
                'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.}
 
+
 def modify_args(args, updates):
     for k,v in updates.items():
         setattr(args, k, v)

From 3f283f44407b842b210c3bee05e410f317363976 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 2 Dec 2019 13:25:33 -0800
Subject: [PATCH 082/130] Dont register parameter

---
 model.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/model.py b/model.py
index 8357f0e..7780c3b 100644
--- a/model.py
+++ b/model.py
@@ -55,12 +55,11 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
         self.supports_len = len(self.fixed_supports)
         if do_graph_conv and addaptadj:
             if aptinit is None:
-                nodevec1, nodevec2 = torch.randn(num_nodes, apt_size), torch.randn(apt_size, num_nodes)
+                nodevecs = torch.randn(num_nodes, apt_size), torch.randn(apt_size, num_nodes)
             else:
-                nodevec1, nodevec2 = self.svd_init(apt_size, aptinit)
+                nodevecs = self.svd_init(apt_size, aptinit)
             self.supports_len += 1
-            self.register_parameter('nodevec1', Parameter(nodevec1.to(device), requires_grad=True))
-            self.register_parameter('nodevec2', Parameter(nodevec2.to(device), requires_grad=True))
+            self.nodevec1, self.nodevec2 = [Parameter(n.to(device), requires_grad=True) for n in nodevecs]
 
         depth = list(range(blocks * layers))
 

From a4bc70c4fc7bc3ada2fc282fb8bcdd52fbb416bd Mon Sep 17 00:00:00 2001
From: Sam Shleifer <sshleifer@gmail.com>
Date: Mon, 2 Dec 2019 16:19:37 -0800
Subject: [PATCH 083/130] Cat feat plane gc (#5)

* += graph conv
* process time feature at beginning
* add cat_feat_gc arg
---
 model.py      | 46 +++++++++++++++++++++++++++++++++-------------
 test.py       |  1 -
 test_gwnet.py |  5 ++---
 util.py       |  2 ++
 4 files changed, 37 insertions(+), 17 deletions(-)

diff --git a/model.py b/model.py
index 7780c3b..7606aee 100644
--- a/model.py
+++ b/model.py
@@ -35,7 +35,7 @@ def forward(self, x, support: list):
 class GWNet(nn.Module):
     def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=True,
                  softmax_temp=1., addaptadj=True, aptinit=None, in_dim=2, out_dim=12,
-                 residual_channels=32, dilation_channels=32,
+                 residual_channels=32, dilation_channels=32, cat_feat_gc=False,
                  skip_channels=256, end_channels=512, kernel_size=2, blocks=4, layers=2,
                  apt_size=10):
         super().__init__()
@@ -44,11 +44,22 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
         self.blocks = blocks
         self.layers = layers
         self.do_graph_conv = do_graph_conv
+        self.cat_feat_gc = cat_feat_gc
         self.addaptadj = addaptadj
 
-        self.start_conv = nn.Conv2d(in_channels=in_dim,
-                                    out_channels=residual_channels,
-                                    kernel_size=(1,1))
+
+        if self.cat_feat_gc:
+            self.start_conv = nn.Conv2d(in_channels=1,  # hard code to avoid errors
+                                        out_channels=residual_channels,
+                                        kernel_size=(1, 1))
+            self.cat_feature_conv = nn.Conv2d(in_channels=1,
+                                              out_channels=residual_channels,
+                                              kernel_size=(1, 1))
+        else:
+            self.start_conv = nn.Conv2d(in_channels=in_dim,
+                                        out_channels=residual_channels,
+                                        kernel_size=(1, 1))
+
         self.fixed_supports = supports or []
         receptive_field = 1
 
@@ -101,14 +112,22 @@ def from_args(cls, args, device, supports, aptinit, **kwargs):
                     in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
                     softmax_temp=args.softmax_temp,
                     residual_channels=args.nhid, dilation_channels=args.nhid,
-                    skip_channels=args.nhid * 8, end_channels=args.nhid * 16, **kwargs)
+                    skip_channels=args.nhid * 8, end_channels=args.nhid * 16,
+                    cat_feat_gc=args.cat_feat_gc, **kwargs)
         return model
 
-    def forward(self, input):
+    def forward(self, x):
         # Input shape is (bs, features, n_nodes, n_timesteps)
-        if input.size(3) < self.receptive_field:
-            input = nn.functional.pad(input, (self.receptive_field - input.size(3), 0, 0, 0))
-        x = self.start_conv(input)
+        in_len = x.size(3)
+        if in_len < self.receptive_field:
+            x = nn.functional.pad(x, (self.receptive_field - in_len, 0, 0, 0))
+        if self.cat_feat_gc:
+            f1, f2 = x[:,[0]], x[:,[1]]
+            x1 = self.start_conv(f1)
+            x2 = F.leaky_relu(self.cat_feature_conv(f2))
+            x = x1 + x2
+        else:
+            x = self.start_conv(x)
         skip = 0
         adjacency_matrices = self.fixed_supports
         # calculate the current adaptive adj matrix once per iteration
@@ -123,7 +142,7 @@ def forward(self, input):
             #            |----------------------------------------|     *residual*
             #            |                                        |
             #            |   |-dil_conv -- tanh --|                |
-            #         ---|                  * ----|-- 1x1 -- + -->	*input*
+            #         ---|                  * ----|-- 1x1 -- + -->	*x_in*
             #                |-dil_conv -- sigm --|    |
             #                                         1x1
             #                                          |
@@ -144,15 +163,16 @@ def forward(self, input):
                 break
 
             if self.do_graph_conv:
-                x = self.graph_convs[i](x, adjacency_matrices)
+                graph_out = self.graph_convs[i](x, adjacency_matrices)
+                x = x + graph_out if self.cat_feat_gc else graph_out
             else:
                 x = self.residual_convs[i](x)
-            x = x + residual[:, :, :, -x.size(3):] # TODO(SS): Mean/Max Pool?
+            x = x + residual[:, :, :, -x.size(3):]  # TODO(SS): Mean/Max Pool?
             x = self.bn[i](x)
 
         x = F.relu(skip)  # ignore last X?
         x = F.relu(self.end_conv_1(x))
-        x = self.end_conv_2(x)
+        x = self.end_conv_2(x)  # downsample to (bs, 12, 207, nfeatures)
         return x
 
 
diff --git a/test.py b/test.py
index 7bfe8d0..a96a898 100644
--- a/test.py
+++ b/test.py
@@ -38,6 +38,5 @@ def plot_learned_adj_matrix(model):
     parser = util.get_shared_arg_parser()
     parser.add_argument('--checkpoint', type=str, help='')
     parser.add_argument('--plotheatmap', action='store_true')
-
     args = parser.parse_args()
     main(args)
diff --git a/test_gwnet.py b/test_gwnet.py
index 41beeb8..417eb2b 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -13,7 +13,8 @@
 SAVE_DIR = 'utest_experiment/'
 ARG_UPDATES = {'epochs': 1, 'n_iters': 1, 'batch_size': 2, 'n_obs': 2,
                'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
-               'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.}
+               'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.,
+               'in_dim': 1, 'cat_feat_gc': True}
 
 
 def modify_args(args, updates):
@@ -42,7 +43,5 @@ def test_1_epoch(self):
         self.assertTrue('nodevec1' in state_dict)
         self.assertTrue(os.path.exists(test_args.checkpoint))
         new_met, new_preds = test.main(test_args)
-        #import ipdb; ipdb.set_trace()
-        #new_met = pd.read_csv('last_test_metrics.csv', index_col=0)
         deltas = test_df.mean() - new_met.mean()
         self.assertGreaterEqual(.01, deltas.abs().max())
diff --git a/util.py b/util.py
index a097266..a3b22e5 100644
--- a/util.py
+++ b/util.py
@@ -251,4 +251,6 @@ def get_shared_arg_parser():
     parser.add_argument('--n_obs', default=None, help='Only use this many observations')
     parser.add_argument('--apt_size', default=10, type=int)
     parser.add_argument('--softmax_temp', default=1., type=float)
+    parser.add_argument('--cat_feat_gc', action='store_true')
+
     return parser

From f991c298b975e46c5ff4ec590e90891415823f21 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 3 Dec 2019 01:12:14 +0000
Subject: [PATCH 084/130] check in baseline args

---
 baseline_args.pkl | Bin 0 -> 579 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 baseline_args.pkl

diff --git a/baseline_args.pkl b/baseline_args.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..c13dc8a911cd86305505132ce05a37b8655b808b
GIT binary patch
literal 579
zcmXv~J#Q2-5Iypp%_W3y2nV4gMRG_;iG+j}gwPgCP6$!Bv%L0X*JRiB*xpMHDH3#-
z-$BK%L4tywmICN0@OjA;Bm2#~H*e;{JZH8v%sNO$tN~{jhXi{&AMw%|&OIbcy<Shi
zSaQJm_~ET!Pafd{S(sIrXShiE|DVIdmv7(fzIu*JWH~@gyo(LXWN^{oKsjw`X_={p
zp-!9AJ#$>+$_2qs4B#r+oDp<e)Zi^sPI{>oCS*Or9i4Km#<(`^n8s^WPq2SZR^w#B
zj1T&_K8-*S5xSWH8&i^m4hKkUP^GW%a^8>2s7eal%pb%xrJ_PMcqN~UeAPBoX#oze
z=3k;f!91_1lkWkq<<FwC8kkj5mDDm1cs)yB7l&W}eEu693agE7J>JODZ;xZdvz>q}
zD^CS*Ho==&y0!n~=itR|^Z-Wl%HgeCM1PKr0dJFy;J((S8y3t#EfpN#oj5^J>A^bO
z8j;1&qo^(%-W`#Z*+Go&ybkj2inZ-#c3$sg>96Fe`Sd-Brkp<V4LhYC8iV(<^mkAQ
ULDoXz%+n(XspPnQ-WGfR0O`4_DF6Tf

literal 0
HcmV?d00001


From 5c8fc475c7e2a761a5859c9673b835cd256c9dd4 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 2 Dec 2019 22:12:15 -0800
Subject: [PATCH 085/130] plot grad flow

---
 util.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/util.py b/util.py
index a3b22e5..4e6482f 100644
--- a/util.py
+++ b/util.py
@@ -254,3 +254,21 @@ def get_shared_arg_parser():
     parser.add_argument('--cat_feat_gc', action='store_true')
 
     return parser
+
+
+def plot_grad_flow(named_parameters):
+    import matplotlib.pyplot as plt
+    ave_grads = []
+    layers = []
+    for n, p in named_parameters:
+        if (p.requires_grad) and ("bias" not in n):
+            layers.append(n)
+            ave_grads.append(p.grad.abs().mean())
+    plt.plot(ave_grads, alpha=0.3, color="b")
+    plt.hlines(0, 0, len(ave_grads) + 1, linewidth=1, color="k")
+    plt.xticks(range(0, len(ave_grads), 1), layers, rotation="vertical")
+    plt.xlim(xmin=0, xmax=len(ave_grads))
+    plt.xlabel("Layers")
+    plt.ylabel("Average Gradient")
+    plt.title("Gradient flow")
+    plt.grid(True)

From 932b865a59e4ef4bcadd80e36e7a5c322aaa9ed5 Mon Sep 17 00:00:00 2001
From: Sam Shleifer <sshleifer@gmail.com>
Date: Mon, 2 Dec 2019 22:23:14 -0800
Subject: [PATCH 086/130] Apex (#6)

* register func

* err msg
---
 engine.py     | 26 ++++++++++++++++++++++----
 model.py      |  2 +-
 test_gwnet.py |  1 +
 train.py      |  6 +++---
 util.py       |  1 -
 5 files changed, 27 insertions(+), 9 deletions(-)

diff --git a/engine.py b/engine.py
index f5543fc..85cbf6a 100644
--- a/engine.py
+++ b/engine.py
@@ -1,28 +1,46 @@
 import torch.optim as optim
 from model import *
 import util
+
 class Trainer():
-    def __init__(self, model, scaler, lrate, wdecay, clip=5, lr_decay_rate=.97):
+    def __init__(self, model, scaler, lrate, wdecay, clip=5, lr_decay_rate=.97, fp16=''):
         self.model = model
         self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
         self.scaler = scaler
         self.clip = clip
+        self.fp16 = fp16
         l1 = lambda epoch: lr_decay_rate ** epoch
         self.scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=l1)
+        if self.fp16:
+            try:
+                from apex import amp  # Apex is only required if we use fp16 training
+            except ImportError:
+                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
+            amp.register_half_function(torch, 'einsum')
+            self.model, self.optimizer = amp.initialize(self.model, self.optimizer,
+                                                            opt_level=self.fp16)
 
     def train(self, input, real_val):
         self.model.train()
         self.optimizer.zero_grad()
         input = nn.functional.pad(input,(1,0,0,0))
         output = self.model(input).transpose(1,3)  # now, output = [batch_size,1,num_nodes,12]
+
         #torch.clamp(output, 0, 70)
 
         predict = self.scaler.inverse_transform(output)
         real = torch.unsqueeze(real_val, dim=1)
         mae, mape, rmse = util.cheaper_metric(predict, real, null_val=0.0)
-        mae.backward()
-        if self.clip is not None:
-            torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
+
+        if self.fp16:
+            from apex import amp
+            with amp.scale_loss(mae, self.optimizer) as scaled_loss:
+                scaled_loss.backward()
+            torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.clip)
+        else:
+            mae.backward()
+            if self.clip is not None:
+                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
         self.optimizer.step()
         return mae.item(),mape.item(),rmse.item()
 
diff --git a/model.py b/model.py
index 7606aee..0f76913 100644
--- a/model.py
+++ b/model.py
@@ -132,7 +132,7 @@ def forward(self, x):
         adjacency_matrices = self.fixed_supports
         # calculate the current adaptive adj matrix once per iteration
         if self.addaptadj:
-            adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)) / self.softmax_temp, dim=1)
+            adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)) / self.softmax_temp, dim=1).half()
             adjacency_matrices = self.fixed_supports + [adp]
 
         # WaveNet layers
diff --git a/test_gwnet.py b/test_gwnet.py
index 417eb2b..e45fd2c 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -32,6 +32,7 @@ def setUpClass(cls):
 
     def test_1_epoch(self):
         args = modify_args(pickle_load(TRAIN_ARGS), ARG_UPDATES)
+        args.fp16 = 'O1'
         main(args)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
         self.assertEqual(df.shape, (args.epochs, 6))
diff --git a/train.py b/train.py
index c024acd..c57545a 100644
--- a/train.py
+++ b/train.py
@@ -22,8 +22,7 @@ def main(args, **model_kwargs):
 
     model = GWNet.from_args(args, device, supports, aptinit, **model_kwargs)
     model.to(device)
-    engine = Trainer(model, scaler, args.learning_rate, args.weight_decay, args.lr_decay_rate)
-    print("start training...", flush=True)
+    engine = Trainer(model, scaler, args.learning_rate, args.weight_decay, lr_decay_rate=args.lr_decay_rate, fp16=args.fp16)
     metrics = []
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
     lowest_mae_yet = 100  # high value, will get overwritten
@@ -84,7 +83,8 @@ def eval_(ds, device, engine):
     parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
     parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
     parser.add_argument('--lr_decay_rate', type=float, default=0.97, help='learning rate')
-
+    parser.add_argument("--fp16", type=str, default="",
+                        help="Set to O0, O1, O2 or O3 for fp16 training (see apex documentation)")
     parser.add_argument('--save', type=str, default='experiment', help='save path')
     parser.add_argument('--n_iters', default=None, help='quit after this many iterations')
     args = parser.parse_args()
diff --git a/util.py b/util.py
index 4e6482f..b49791d 100644
--- a/util.py
+++ b/util.py
@@ -252,7 +252,6 @@ def get_shared_arg_parser():
     parser.add_argument('--apt_size', default=10, type=int)
     parser.add_argument('--softmax_temp', default=1., type=float)
     parser.add_argument('--cat_feat_gc', action='store_true')
-
     return parser
 
 

From b248ba639bf74c9421edda9d3f2324d1f2a5a328 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 2 Dec 2019 22:25:12 -0800
Subject: [PATCH 087/130] fix fp16 compat

---
 model.py      | 2 +-
 test_gwnet.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/model.py b/model.py
index 0f76913..7606aee 100644
--- a/model.py
+++ b/model.py
@@ -132,7 +132,7 @@ def forward(self, x):
         adjacency_matrices = self.fixed_supports
         # calculate the current adaptive adj matrix once per iteration
         if self.addaptadj:
-            adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)) / self.softmax_temp, dim=1).half()
+            adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)) / self.softmax_temp, dim=1)
             adjacency_matrices = self.fixed_supports + [adp]
 
         # WaveNet layers
diff --git a/test_gwnet.py b/test_gwnet.py
index e45fd2c..949f4e4 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -32,7 +32,7 @@ def setUpClass(cls):
 
     def test_1_epoch(self):
         args = modify_args(pickle_load(TRAIN_ARGS), ARG_UPDATES)
-        args.fp16 = 'O1'
+        args.fp16 = ''
         main(args)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
         self.assertEqual(df.shape, (args.epochs, 6))

From 7fff6e7e1c8d5816a3b1b30a2b9bd6c2ebfc6697 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 3 Dec 2019 11:13:55 -0800
Subject: [PATCH 088/130] hoist clip arg

---
 test_gwnet.py | 2 +-
 train.py      | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/test_gwnet.py b/test_gwnet.py
index 949f4e4..23611e3 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -14,7 +14,7 @@
 ARG_UPDATES = {'epochs': 1, 'n_iters': 1, 'batch_size': 2, 'n_obs': 2,
                'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
                'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.,
-               'in_dim': 1, 'cat_feat_gc': True}
+               'in_dim': 1, 'cat_feat_gc': True, 'clip': 1}
 
 
 def modify_args(args, updates):
diff --git a/train.py b/train.py
index c57545a..ab04d4b 100644
--- a/train.py
+++ b/train.py
@@ -22,7 +22,7 @@ def main(args, **model_kwargs):
 
     model = GWNet.from_args(args, device, supports, aptinit, **model_kwargs)
     model.to(device)
-    engine = Trainer(model, scaler, args.learning_rate, args.weight_decay, lr_decay_rate=args.lr_decay_rate, fp16=args.fp16)
+    engine = Trainer(model, scaler, args.learning_rate, args.weight_decay, clip=args.clip, lr_decay_rate=args.lr_decay_rate, fp16=args.fp16)
     metrics = []
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
     lowest_mae_yet = 100  # high value, will get overwritten
@@ -80,6 +80,7 @@ def eval_(ds, device, engine):
 if __name__ == "__main__":
     parser = util.get_shared_arg_parser()
     parser.add_argument('--epochs', type=int, default=100, help='')
+    parser.add_argument('--clip', type=int, default=5, help='Gradient Clipping')
     parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
     parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
     parser.add_argument('--lr_decay_rate', type=float, default=0.97, help='learning rate')

From a8bb661179102c68195feb417eae6ccff884b019 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 3 Dec 2019 11:15:33 -0800
Subject: [PATCH 089/130] cleanup

---
 engine.py | 4 ++--
 util.py   | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/engine.py b/engine.py
index 85cbf6a..a191779 100644
--- a/engine.py
+++ b/engine.py
@@ -30,7 +30,7 @@ def train(self, input, real_val):
 
         predict = self.scaler.inverse_transform(output)
         real = torch.unsqueeze(real_val, dim=1)
-        mae, mape, rmse = util.cheaper_metric(predict, real, null_val=0.0)
+        mae, mape, rmse = util.calc_metrics(predict, real, null_val=0.0)
 
         if self.fp16:
             from apex import amp
@@ -51,5 +51,5 @@ def eval(self, input, real_val):
         real = torch.unsqueeze(real_val,dim=1)
         predict = self.scaler.inverse_transform(output)
         predict = torch.clamp(predict, min=0., max=70.)
-        mae, mape, rmse = [x.item() for x in util.cheaper_metric(predict, real, null_val=0.0)]
+        mae, mape, rmse = [x.item() for x in util.calc_metrics(predict, real, null_val=0.0)]
         return mae, mape, rmse
diff --git a/util.py b/util.py
index b49791d..3ce53ba 100644
--- a/util.py
+++ b/util.py
@@ -167,7 +167,7 @@ def load_dataset(dataset_dir, batch_size, valid_batch_size= None, test_batch_siz
     return data
 
 
-def cheaper_metric(preds, labels, null_val=0.):
+def calc_metrics(preds, labels, null_val=0.):
     if np.isnan(null_val):
         mask = ~torch.isnan(labels)
     else:
@@ -188,9 +188,9 @@ def mask_and_fillna(loss, mask):
     loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
     return torch.mean(loss)
 
-def calc_test_metrics(model, device, test_loader, scaler, realy):
+def calc_test_metrics(model, device, test_loader, scaler, realy) -> pd.DataFrame:
     outputs = []
-    for iter, (x, y) in enumerate(test_loader.get_iterator()):
+    for _, (x, y) in enumerate(test_loader.get_iterator()):
         testx = torch.Tensor(x).to(device).transpose(1, 3)
         with torch.no_grad():
             preds = model(testx).transpose(1, 3)
@@ -201,7 +201,7 @@ def calc_test_metrics(model, device, test_loader, scaler, realy):
         pred = scaler.inverse_transform(yhat[:, :, i])
         pred = torch.clamp(pred, min=0., max=70.)
         real = realy[:, :, i]
-        test_met.append([x.item() for x in cheaper_metric(pred, real)])
+        test_met.append([x.item() for x in calc_metrics(pred, real)])
     test_met_df = pd.DataFrame(test_met, columns=['mae', 'mape', 'rmse']).rename_axis('t')
     return test_met_df, yhat
 

From 00010317fdd4df6095f0ba48424388dbb383f59d Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 3 Dec 2019 11:29:35 -0800
Subject: [PATCH 090/130] es patience 20

---
 test_gwnet.py | 2 +-
 train.py      | 6 ++++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/test_gwnet.py b/test_gwnet.py
index 23611e3..517a744 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -14,7 +14,7 @@
 ARG_UPDATES = {'epochs': 1, 'n_iters': 1, 'batch_size': 2, 'n_obs': 2,
                'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
                'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.,
-               'in_dim': 1, 'cat_feat_gc': True, 'clip': 1}
+               'in_dim': 1, 'cat_feat_gc': True, 'clip': 1, 'es_patience': 10}
 
 
 def modify_args(args, updates):
diff --git a/train.py b/train.py
index ab04d4b..09540c1 100644
--- a/train.py
+++ b/train.py
@@ -27,6 +27,7 @@ def main(args, **model_kwargs):
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
     lowest_mae_yet = 100  # high value, will get overwritten
     mb = progress_bar(list(range(1, args.epochs + 1)))
+    since_best = 0
     for _ in mb:
         train_loss, train_mape, train_rmse = [], [], []
         data['train_loader'].shuffle()
@@ -50,9 +51,13 @@ def main(args, **model_kwargs):
         if m.valid_loss < lowest_mae_yet:
             torch.save(engine.model.state_dict(), best_model_save_path)
             lowest_mae_yet = m.valid_loss
+            since_best = 0
+        else:
+            since_best += 1
         met_df = pd.DataFrame(metrics)
         mb.comment = f'best valid_loss: {met_df.valid_loss.min(): .3f}, current valid_loss: {m.valid_loss:.3f}'
         met_df.round(6).to_csv(f'{args.save}/metrics.csv')
+        if since_best >= args.es_patience: break  #
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
     realy = torch.Tensor(data['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
@@ -88,6 +93,7 @@ def eval_(ds, device, engine):
                         help="Set to O0, O1, O2 or O3 for fp16 training (see apex documentation)")
     parser.add_argument('--save', type=str, default='experiment', help='save path')
     parser.add_argument('--n_iters', default=None, help='quit after this many iterations')
+    parser.add_argument('--es_patience', type=int, default=20, help='quit if no improvement after this many iterations')
     args = parser.parse_args()
     t1 = time.time()
     if not os.path.exists(args.save):

From 3088d06ee282205ae72220602d48397fd0478102 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 3 Dec 2019 17:04:23 -0800
Subject: [PATCH 091/130] allows model kwarg passing

---
 model.py      | 6 ++++--
 test_gwnet.py | 6 +++---
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/model.py b/model.py
index 7606aee..48ee4fa 100644
--- a/model.py
+++ b/model.py
@@ -107,13 +107,15 @@ def svd_init(apt_size, aptinit):
 
     @classmethod
     def from_args(cls, args, device, supports, aptinit, **kwargs):
-        model = cls(device, args.num_nodes, dropout=args.dropout, supports=supports,
+        defaults =  dict(dropout=args.dropout, supports=supports,
                     do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=aptinit,
                     in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
                     softmax_temp=args.softmax_temp,
                     residual_channels=args.nhid, dilation_channels=args.nhid,
                     skip_channels=args.nhid * 8, end_channels=args.nhid * 16,
-                    cat_feat_gc=args.cat_feat_gc, **kwargs)
+                    cat_feat_gc=args.cat_feat_gc)
+        defaults.update(**kwargs)
+        model = cls(device, args.num_nodes, **defaults)
         return model
 
     def forward(self, x):
diff --git a/test_gwnet.py b/test_gwnet.py
index 517a744..cf08a52 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -16,7 +16,7 @@
                'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.,
                'in_dim': 1, 'cat_feat_gc': True, 'clip': 1, 'es_patience': 10}
 
-
+MODEL_KWARGS = {'end_channels': 4, 'skip_channels': 2}
 def modify_args(args, updates):
     for k,v in updates.items():
         setattr(args, k, v)
@@ -33,7 +33,7 @@ def setUpClass(cls):
     def test_1_epoch(self):
         args = modify_args(pickle_load(TRAIN_ARGS), ARG_UPDATES)
         args.fp16 = ''
-        main(args)
+        engine = main(args, **MODEL_KWARGS)
         df = pd.read_csv(f'{args.save}/metrics.csv', index_col=0)
         self.assertEqual(df.shape, (args.epochs, 6))
         test_df = pd.read_csv(f'{args.save}/test_metrics.csv', index_col=0)
@@ -43,6 +43,6 @@ def test_1_epoch(self):
         state_dict = torch.load(test_args.checkpoint)
         self.assertTrue('nodevec1' in state_dict)
         self.assertTrue(os.path.exists(test_args.checkpoint))
-        new_met, new_preds = test.main(test_args)
+        new_met, new_preds = test.main(test_args, **MODEL_KWARGS)
         deltas = test_df.mean() - new_met.mean()
         self.assertGreaterEqual(.01, deltas.abs().max())

From 65c2d230c997e9b0ac66d2c5fbe686f01ae9fe6a Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 3 Dec 2019 18:15:49 -0800
Subject: [PATCH 092/130] bigger eval batch sizes

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index 09540c1..e5d597f 100644
--- a/train.py
+++ b/train.py
@@ -16,7 +16,7 @@
 
 def main(args, **model_kwargs):
     device = torch.device(args.device)
-    data = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
+    data = util.load_dataset(args.data, args.batch_size, args.batch_size*2, args.batch_size*2, n_obs=args.n_obs)
     scaler = data['scaler']
     aptinit, supports = util.make_graph_inputs(args, device)
 

From 99e29ee95828cadb3c79548cf05e6d14b8992ed8 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 3 Dec 2019 21:22:05 -0800
Subject: [PATCH 093/130] lrfinder

---
 lr_finder.py | 319 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 319 insertions(+)
 create mode 100644 lr_finder.py

diff --git a/lr_finder.py b/lr_finder.py
new file mode 100644
index 0000000..73816bb
--- /dev/null
+++ b/lr_finder.py
@@ -0,0 +1,319 @@
+from __future__ import print_function, with_statement, division
+import copy
+import os
+import torch
+from tqdm.autonotebook import tqdm
+from torch.optim.lr_scheduler import _LRScheduler
+import matplotlib.pyplot as plt
+
+
+class LRFinder(object):
+    """Learning rate range test.
+    The learning rate range test increases the learning rate in a pre-training run
+    between two boundaries in a linear or exponential manner. It provides valuable
+    information on how well the network can be trained over a range of learning rates
+    and what is the optimal learning rate.
+    Arguments:
+        model (torch.nn.Module): wrapped model.
+        optimizer (torch.optim.Optimizer): wrapped optimizer where the defined learning
+            is assumed to be the lower boundary of the range test.
+        criterion (torch.nn.Module): wrapped loss function.
+        device (str or torch.device, optional): a string ("cpu" or "cuda") with an
+            optional ordinal for the device type (e.g. "cuda:X", where is the ordinal).
+            Alternatively, can be an object representing the device on which the
+            computation will take place. Default: None, uses the same device as `model`.
+        memory_cache (boolean): if this flag is set to True, `state_dict` of model and
+            optimizer will be cached in memory. Otherwise, they will be saved to files
+            under the `cache_dir`.
+        cache_dir (string): path for storing temporary files. If no path is specified,
+            system-wide temporary directory is used.
+            Notice that this parameter will be ignored if `memory_cache` is True.
+    Example:
+        >>> lr_finder = LRFinder(net, optimizer, criterion, device="cuda")
+        >>> lr_finder.range_test(dataloader, end_lr=100, num_iter=100)
+    Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
+    fastai/lr_find: https://github.com/fastai/fastai
+    """
+
+    def __init__(self, engine, device=None, memory_cache=True, cache_dir=None):
+        self.engine = engine
+        self.model = engine.model
+        self.optimizer = engine.optimizer
+        self.history = {"lr": [], "loss": []}
+        self.best_loss = None
+        self.memory_cache = memory_cache
+        self.cache_dir = cache_dir
+
+        # Save the original state of the model and optimizer so they can be restored if
+        # needed
+        self.model_device = next(self.model.parameters()).device
+        self.state_cacher = StateCacher(memory_cache, cache_dir=cache_dir)
+        self.state_cacher.store('model', self.model.state_dict())
+        self.state_cacher.store('optimizer', self.optimizer.state_dict())
+
+        # If device is None, use the same as the model
+        if device:
+            self.device = device
+        else:
+            self.device = self.model_device
+
+    def reset(self):
+        """Restores the model and optimizer to their initial states."""
+        self.model.load_state_dict(self.state_cacher.retrieve('model'))
+        self.optimizer.load_state_dict(self.state_cacher.retrieve('optimizer'))
+        self.model.to(self.model_device)
+
+    def range_test(
+            self,
+            train_loader,
+            val_loader=None,
+            end_lr=10,
+            num_iter=100,
+            step_mode="exp",
+            smooth_f=0.05,
+            diverge_th=5,
+    ):
+        """Performs the learning rate range test.
+        Arguments:
+            train_loader (torch.utils.data.DataLoader): the training set data laoder.
+            val_loader (torch.utils.data.DataLoader, optional): if `None` the range test
+                will only use the training loss. When given a data loader, the model is
+                evaluated after each iteration on that dataset and the evaluation loss
+                is used. Note that in this mode the test takes significantly longer but
+                generally produces more precise results. Default: None.
+            end_lr (float, optional): the maximum learning rate to test. Default: 10.
+            num_iter (int, optional): the number of iterations over which the test
+                occurs. Default: 100.
+            step_mode (str, optional): one of the available learning rate policies,
+                linear or exponential ("linear", "exp"). Default: "exp".
+            smooth_f (float, optional): the loss smoothing factor within the [0, 1[
+                interval. Disabled if set to 0, otherwise the loss is smoothed using
+                exponential smoothing. Default: 0.05.
+            diverge_th (int, optional): the test is stopped when the loss surpasses the
+                threshold:  diverge_th * best_loss. Default: 5.
+        """
+        # Reset test results
+        self.history = {"lr": [], "loss": []}
+        self.best_loss = None
+
+        # Move the model to the proper device
+        self.model.to(self.device)
+
+        # Initialize the proper learning rate policy
+        if step_mode.lower() == "exp":
+            lr_schedule = ExponentialLR(self.optimizer, end_lr, num_iter)
+        elif step_mode.lower() == "linear":
+            lr_schedule = LinearLR(self.optimizer, end_lr, num_iter)
+        else:
+            raise ValueError("expected one of (exp, linear), got {}".format(step_mode))
+
+        if smooth_f < 0 or smooth_f >= 1:
+            raise ValueError("smooth_f is outside the range [0, 1[")
+
+        # Create an iterator to get data batch by batch
+        iterator = iter(train_loader)
+        for iteration in tqdm(range(num_iter)):
+            # Get a new set of inputs and labels
+            try:
+                inputs, labels = next(iterator)
+            except StopIteration:
+                iterator = iter(train_loader)
+                inputs, labels = next(iterator)
+
+            # Train on batch and retrieve loss
+            loss = self._train_batch(inputs, labels)
+            if val_loader:
+                loss = self._validate(val_loader)
+
+            # Update the learning rate
+            lr_schedule.step()
+            self.history["lr"].append(lr_schedule.get_lr()[0])
+
+            # Track the best loss and smooth it if smooth_f is specified
+            if iteration == 0:
+                self.best_loss = loss
+            else:
+                if smooth_f > 0:
+                    loss = smooth_f * loss + (1 - smooth_f) * self.history["loss"][-1]
+                if loss < self.best_loss:
+                    self.best_loss = loss
+
+            # Check if the loss has diverged; if it has, stop the test
+            self.history["loss"].append(loss)
+            if loss > diverge_th * self.best_loss:
+                print("Stopping early, the loss has diverged")
+                break
+
+        print("Learning rate search finished. See the graph with {finder_name}.plot()")
+
+    def _train_batch(self, x, y):
+        # Set model to training mode
+        self.model.train()
+        trainx = torch.Tensor(x).to(self.device).transpose(1, 3)
+        trainy = torch.Tensor(y).to(self.device).transpose(1, 3)
+        mae, mape, rmse = self.engine.train(trainx, trainy[:, 0, :, :])
+        return mae
+
+    def _move_to_device(self, inputs, labels):
+        def move(obj, device):
+            if isinstance(obj, tuple):
+                return tuple(move(o, device) for o in obj)
+            elif torch.is_tensor(obj):
+                return obj.to(device)
+            else:
+                return obj
+
+        inputs = move(inputs, self.device)
+        labels = move(labels, self.device)
+        return inputs, labels
+
+    def _validate(self, dataloader):
+        # Set model to evaluation mode and disable gradient computation
+        running_loss = 0
+        self.model.eval()
+        with torch.no_grad():
+            for inputs, labels in dataloader:
+                # Move data to the correct device
+                inputs, labels = self._move_to_device(inputs, labels)
+
+                # Forward pass and loss computation
+                outputs = self.model(inputs)
+                loss = self.criterion(outputs, labels)
+                running_loss += loss.item() * inputs.size(0)
+
+        return running_loss / len(dataloader.dataset)
+
+    def plot(self, skip_start=10, skip_end=5, log_lr=True, show_lr=None):
+        """Plots the learning rate range test.
+        Arguments:
+            skip_start (int, optional): number of batches to trim from the start.
+                Default: 10.
+            skip_end (int, optional): number of batches to trim from the start.
+                Default: 5.
+            log_lr (bool, optional): True to plot the learning rate in a logarithmic
+                scale; otherwise, plotted in a linear scale. Default: True.
+            show_lr (float, optional): is set, will add vertical line to visualize
+                specified learning rate; Default: None
+        """
+
+        if skip_start < 0:
+            raise ValueError("skip_start cannot be negative")
+        if skip_end < 0:
+            raise ValueError("skip_end cannot be negative")
+        if show_lr is not None and not isinstance(show_lr, float):
+            raise ValueError("show_lr must be float")
+
+        # Get the data to plot from the history dictionary. Also, handle skip_end=0
+        # properly so the behaviour is the expected
+        lrs = self.history["lr"]
+        losses = self.history["loss"]
+        if skip_end == 0:
+            lrs = lrs[skip_start:]
+            losses = losses[skip_start:]
+        else:
+            lrs = lrs[skip_start:-skip_end]
+            losses = losses[skip_start:-skip_end]
+
+        # Plot loss as a function of the learning rate
+        plt.plot(lrs, losses)
+        if log_lr:
+            plt.xscale("log")
+        plt.xlabel("Learning rate")
+        plt.ylabel("Loss")
+
+        if show_lr is not None:
+            plt.axvline(x=show_lr, color="red")
+        plt.show()
+
+
+class LinearLR(_LRScheduler):
+    """Linearly increases the learning rate between two boundaries over a number of
+    iterations.
+    Arguments:
+        optimizer (torch.optim.Optimizer): wrapped optimizer.
+        end_lr (float, optional): the initial learning rate which is the lower
+            boundary of the test. Default: 10.
+        num_iter (int, optional): the number of iterations over which the test
+            occurs. Default: 100.
+        last_epoch (int): the index of last epoch. Default: -1.
+    """
+
+    def __init__(self, optimizer, end_lr, num_iter, last_epoch=-1):
+        self.end_lr = end_lr
+        self.num_iter = num_iter
+        super(LinearLR, self).__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        curr_iter = self.last_epoch + 1
+        r = curr_iter / self.num_iter
+        return [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]
+
+
+class ExponentialLR(_LRScheduler):
+    """Exponentially increases the learning rate between two boundaries over a number of
+    iterations.
+    Arguments:
+        optimizer (torch.optim.Optimizer): wrapped optimizer.
+        end_lr (float, optional): the initial learning rate which is the lower
+            boundary of the test. Default: 10.
+        num_iter (int, optional): the number of iterations over which the test
+            occurs. Default: 100.
+        last_epoch (int): the index of last epoch. Default: -1.
+    """
+
+    def __init__(self, optimizer, end_lr, num_iter, last_epoch=-1):
+        self.end_lr = end_lr
+        self.num_iter = num_iter
+        super(ExponentialLR, self).__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        curr_iter = self.last_epoch + 1
+        r = curr_iter / self.num_iter
+        return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]
+
+
+class StateCacher(object):
+    def __init__(self, in_memory, cache_dir=None):
+        self.in_memory = in_memory
+        self.cache_dir = cache_dir
+
+        if self.cache_dir is None:
+            import tempfile
+            self.cache_dir = tempfile.gettempdir()
+        else:
+            if not os.path.isdir(self.cache_dir):
+                raise ValueError('Given `cache_dir` is not a valid directory.')
+
+        self.cached = {}
+
+    def store(self, key, state_dict):
+        if self.in_memory:
+            self.cached.update({key: copy.deepcopy(state_dict)})
+        else:
+            fn = os.path.join(self.cache_dir, 'state_{}_{}.pt'.format(key, id(self)))
+            self.cached.update({key: fn})
+            torch.save(state_dict, fn)
+
+    def retrieve(self, key):
+        if key not in self.cached:
+            raise KeyError('Target {} was not cached.'.format(key))
+
+        if self.in_memory:
+            return self.cached.get(key)
+        else:
+            fn = self.cached.get(key)
+            if not os.path.exists(fn):
+                raise RuntimeError(
+                    'Failed to load state in {}. File does not exist anymore.'.format(fn))
+            state_dict = torch.load(fn, map_location=lambda storage, location: storage)
+            return state_dict
+
+    def __del__(self):
+        """Check whether there are unused cached files existing in `cache_dir` before
+        this instance being destroyed."""
+        if self.in_memory:
+            return
+
+        for k in self.cached:
+            if os.path.exists(self.cached[k]):
+                os.remove(self.cached[k])

From 5766a99f1a7dc2bef6c9fd74e7465ff4bb8ba3c5 Mon Sep 17 00:00:00 2001
From: Sam Shleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 00:18:29 -0800
Subject: [PATCH 094/130] (VAMSI) Seq length (#7)

* [seq-length] Support variable length prediction windows.
---
 engine.py                 |  4 ++--
 generate_training_data.py | 20 +++++++++++++++++---
 model.py                  |  2 +-
 test.py                   |  4 ++--
 train.py                  |  2 +-
 util.py                   | 15 ++++++++-------
 6 files changed, 31 insertions(+), 16 deletions(-)

diff --git a/engine.py b/engine.py
index a191779..82e6cc7 100644
--- a/engine.py
+++ b/engine.py
@@ -24,7 +24,7 @@ def train(self, input, real_val):
         self.model.train()
         self.optimizer.zero_grad()
         input = nn.functional.pad(input,(1,0,0,0))
-        output = self.model(input).transpose(1,3)  # now, output = [batch_size,1,num_nodes,12]
+        output = self.model(input).transpose(1,3)  # now, output = [batch_size,1,num_nodes,seq_length]
 
         #torch.clamp(output, 0, 70)
 
@@ -47,7 +47,7 @@ def train(self, input, real_val):
     def eval(self, input, real_val):
         self.model.eval()
         input = nn.functional.pad(input,(1,0,0,0))
-        output = self.model(input).transpose(1,3) #  [batch_size,12,num_nodes,1]
+        output = self.model(input).transpose(1,3) #  [batch_size,seq_length,num_nodes,1]
         real = torch.unsqueeze(real_val,dim=1)
         predict = self.scaler.inverse_transform(output)
         predict = torch.clamp(predict, min=0., max=70.)
diff --git a/generate_training_data.py b/generate_training_data.py
index e2cb614..1154933 100644
--- a/generate_training_data.py
+++ b/generate_training_data.py
@@ -54,14 +54,16 @@ def generate_graph_seq2seq_io_data(
 
 
 def generate_train_val_test(args):
+    seq_length = args.seq_length
+
     df = pd.read_hdf(args.traffic_df_filename)
     # 0 is the latest observed sample.
     x_offsets = np.sort(
         # np.concatenate(([-week_size + 1, -day_size + 1], np.arange(-11, 1, 1)))
-        np.concatenate((np.arange(-11, 1, 1),))
+        np.concatenate((np.arange(-(seq_length - 1), 1, 1),))
     )
     # Predict the next one hour
-    y_offsets = np.sort(np.arange(1, 13, 1))
+    y_offsets = np.sort(np.arange(1, (seq_length + 1), 1))
     # x: (num_samples, input_length, num_nodes, input_dim)
     # y: (num_samples, output_length, num_nodes, output_dim)
     x, y = generate_graph_seq2seq_io_data(
@@ -69,7 +71,7 @@ def generate_train_val_test(args):
         x_offsets=x_offsets,
         y_offsets=y_offsets,
         add_time_in_day=True,
-        add_day_in_week=True,
+        add_day_in_week=args.dow,
     )
 
     print("x shape: ", x.shape, ", y shape: ", y.shape)
@@ -119,6 +121,18 @@ def main(args):
         default="data/metr-la.h5",
         help="Raw traffic readings.",
     )
+    parser.add_argument(
+        "--seq_length",
+        type=int,
+        default=12,
+        help="Sequence Length.",
+    )
+    parser.add_argument(
+        "--dow",
+        action='store_true',
+        #type=bool,
+    )
+
     args = parser.parse_args()
     if not os.path.exists(args.output_dir):
       reply = str(input(f'{args.output_dir} does not exist. Do you want to create it? (y/n)')).lower().strip()
diff --git a/model.py b/model.py
index 48ee4fa..ffe0077 100644
--- a/model.py
+++ b/model.py
@@ -174,7 +174,7 @@ def forward(self, x):
 
         x = F.relu(skip)  # ignore last X?
         x = F.relu(self.end_conv_1(x))
-        x = self.end_conv_2(x)  # downsample to (bs, 12, 207, nfeatures)
+        x = self.end_conv_2(x)  # downsample to (bs, seq_length, 207, nfeatures)
         return x
 
 
diff --git a/test.py b/test.py
index a96a898..3cfee7c 100644
--- a/test.py
+++ b/test.py
@@ -18,8 +18,8 @@ def main(args, save_pred_path='preds.csv', save_metrics_path='last_test_metrics.
     scaler = dataloader['scaler']
     realy = torch.Tensor(dataloader['y_test']).to(device)
     realy = realy.transpose(1,3)[:,0,:,:]
-    met_df, yhat = util.calc_test_metrics(model, device, dataloader['test_loader'], scaler, realy)
-    df2 = util.make_pred_df(realy, yhat, scaler)
+    met_df, yhat = util.calc_test_metrics(model, device, dataloader['test_loader'], scaler, realy, args.seq_length)
+    df2 = util.make_pred_df(realy, yhat, scaler, args.seq_length)
     met_df.to_csv(save_metrics_path)
     df2.to_csv(save_pred_path, index=False)
     if args.plotheatmap: plot_learned_adj_matrix(model)
diff --git a/train.py b/train.py
index e5d597f..0c4b969 100644
--- a/train.py
+++ b/train.py
@@ -61,7 +61,7 @@ def main(args, **model_kwargs):
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
     realy = torch.Tensor(data['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
-    test_met_df, yhat = calc_test_metrics(engine.model, device, data['test_loader'], scaler, realy)
+    test_met_df, yhat = calc_test_metrics(engine.model, device, data['test_loader'], scaler, realy, args.seq_length)
     test_met_df.round(6).to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(summary(args.save))
 
diff --git a/util.py b/util.py
index 3ce53ba..88c9683 100644
--- a/util.py
+++ b/util.py
@@ -188,7 +188,7 @@ def mask_and_fillna(loss, mask):
     loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
     return torch.mean(loss)
 
-def calc_test_metrics(model, device, test_loader, scaler, realy) -> pd.DataFrame:
+def calc_test_metrics(model, device, test_loader, scaler, realy, seq_length) -> pd.DataFrame:
     outputs = []
     for _, (x, y) in enumerate(test_loader.get_iterator()):
         testx = torch.Tensor(x).to(device).transpose(1, 3)
@@ -197,7 +197,8 @@ def calc_test_metrics(model, device, test_loader, scaler, realy) -> pd.DataFrame
         outputs.append(preds.squeeze())
     yhat = torch.cat(outputs, dim=0)[:realy.size(0), ...]
     test_met = []
-    for i in range(12):
+
+    for i in range(seq_length):
         pred = scaler.inverse_transform(yhat[:, :, i])
         pred = torch.clamp(pred, min=0., max=70.)
         real = realy[:, :, i]
@@ -210,11 +211,11 @@ def _to_ser(arr):
     return pd.DataFrame(arr.cpu().detach().numpy()).stack().rename_axis(['obs', 'sensor_id'])
 
 
-def make_pred_df(realy, yhat, scaler):
-    df = pd.DataFrame(dict(y12=_to_ser(realy[:, :, 11]),
-                           yhat12=_to_ser(scaler.inverse_transform(yhat[:, :, 11])),
-                           y3=_to_ser(realy[:, :, 2]),
-                           yhat3=_to_ser(scaler.inverse_transform(yhat[:, :, 2]))))
+def make_pred_df(realy, yhat, scaler, seq_length):
+    df = pd.DataFrame(dict(y_last=_to_ser(realy[:, :, seq_length - 1]),
+                           yhat_last=_to_ser(scaler.inverse_transform(yhat[:, :, seq_length - 1])),
+                           y_3=_to_ser(realy[:, :, 2]),
+                           yhat_3=_to_ser(scaler.inverse_transform(yhat[:, :, 2]))))
     return df
 
 

From bed47e040b50a145a9c4698eb788353a492b433e Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 14:58:37 -0800
Subject: [PATCH 095/130] surgery func

---
 test_gwnet.py |  3 ++-
 train.py      | 15 +++++++++++++++
 util.py       |  3 ++-
 3 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/test_gwnet.py b/test_gwnet.py
index cf08a52..d8e09c7 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -14,7 +14,8 @@
 ARG_UPDATES = {'epochs': 1, 'n_iters': 1, 'batch_size': 2, 'n_obs': 2,
                'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
                'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.,
-               'in_dim': 1, 'cat_feat_gc': True, 'clip': 1, 'es_patience': 10}
+               'in_dim': 1, 'cat_feat_gc': True, 'clip': 1, 'es_patience': 10,
+               'checkpoint': ''}
 
 MODEL_KWARGS = {'end_channels': 4, 'skip_channels': 2}
 def modify_args(args, updates):
diff --git a/train.py b/train.py
index 0c4b969..defc3bc 100644
--- a/train.py
+++ b/train.py
@@ -12,6 +12,19 @@
 from util import calc_test_metrics
 from exp_results import summary
 
+bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']
+
+
+def surgery(model, surg_checkpoint):
+    state_dict = torch.load(surg_checkpoint)
+    b,w = state_dict.pop(bk), state_dict.pop(wk)
+    model.load_state_dict(state_dict, strict=False)
+    cur_state_dict = model.state_dict()
+    cur_state_dict[bk][:b.shape[0]] = b
+    cur_state_dict[wk][:w.shape[0]] = w
+    model.load_state_dict(cur_state_dict)
+    return model
+
 
 
 def main(args, **model_kwargs):
@@ -21,6 +34,8 @@ def main(args, **model_kwargs):
     aptinit, supports = util.make_graph_inputs(args, device)
 
     model = GWNet.from_args(args, device, supports, aptinit, **model_kwargs)
+    if args.checkpoint:
+        model = surgery(model, args.checkpoint)
     model.to(device)
     engine = Trainer(model, scaler, args.learning_rate, args.weight_decay, clip=args.clip, lr_decay_rate=args.lr_decay_rate, fp16=args.fp16)
     metrics = []
diff --git a/util.py b/util.py
index 88c9683..39e57cb 100644
--- a/util.py
+++ b/util.py
@@ -194,7 +194,7 @@ def calc_test_metrics(model, device, test_loader, scaler, realy, seq_length) ->
         testx = torch.Tensor(x).to(device).transpose(1, 3)
         with torch.no_grad():
             preds = model(testx).transpose(1, 3)
-        outputs.append(preds.squeeze())
+        outputs.append(preds.squeeze(1))
     yhat = torch.cat(outputs, dim=0)[:realy.size(0), ...]
     test_met = []
 
@@ -253,6 +253,7 @@ def get_shared_arg_parser():
     parser.add_argument('--apt_size', default=10, type=int)
     parser.add_argument('--softmax_temp', default=1., type=float)
     parser.add_argument('--cat_feat_gc', action='store_true')
+    parser.add_argument('--checkpoint', type=str, help='')
     return parser
 
 

From 8b74eecd7c1e2f78073c5beacf48fed84aa18a08 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 14:59:04 -0800
Subject: [PATCH 096/130] squeeze like master I guess

---
 util.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/util.py b/util.py
index 39e57cb..abe7550 100644
--- a/util.py
+++ b/util.py
@@ -194,7 +194,7 @@ def calc_test_metrics(model, device, test_loader, scaler, realy, seq_length) ->
         testx = torch.Tensor(x).to(device).transpose(1, 3)
         with torch.no_grad():
             preds = model(testx).transpose(1, 3)
-        outputs.append(preds.squeeze(1))
+        outputs.append(preds.squeeze())
     yhat = torch.cat(outputs, dim=0)[:realy.size(0), ...]
     test_met = []
 

From 0dee3928390599d2abb64305f22c3ddb022daf3e Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 14:59:30 -0800
Subject: [PATCH 097/130] jk

---
 util.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/util.py b/util.py
index abe7550..39e57cb 100644
--- a/util.py
+++ b/util.py
@@ -194,7 +194,7 @@ def calc_test_metrics(model, device, test_loader, scaler, realy, seq_length) ->
         testx = torch.Tensor(x).to(device).transpose(1, 3)
         with torch.no_grad():
             preds = model(testx).transpose(1, 3)
-        outputs.append(preds.squeeze())
+        outputs.append(preds.squeeze(1))
     yhat = torch.cat(outputs, dim=0)[:realy.size(0), ...]
     test_met = []
 

From 7fb7ae28a17c593e7e3757e57f0c2affb5a59478 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 15:01:19 -0800
Subject: [PATCH 098/130] force squeeze1

---
 util.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/util.py b/util.py
index 88c9683..4b36a8e 100644
--- a/util.py
+++ b/util.py
@@ -188,13 +188,14 @@ def mask_and_fillna(loss, mask):
     loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
     return torch.mean(loss)
 
+
 def calc_test_metrics(model, device, test_loader, scaler, realy, seq_length) -> pd.DataFrame:
     outputs = []
     for _, (x, y) in enumerate(test_loader.get_iterator()):
         testx = torch.Tensor(x).to(device).transpose(1, 3)
         with torch.no_grad():
             preds = model(testx).transpose(1, 3)
-        outputs.append(preds.squeeze())
+        outputs.append(preds.squeeze(1))
     yhat = torch.cat(outputs, dim=0)[:realy.size(0), ...]
     test_met = []
 

From 11c83de54d8df1546715548e80aa9c79052968b4 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 15:30:23 -0800
Subject: [PATCH 099/130] end_conv_lr arg

---
 engine.py     | 23 ++++++++++++++++-------
 model.py      | 10 ++++++++++
 test_gwnet.py |  2 +-
 train.py      |  4 +++-
 4 files changed, 30 insertions(+), 9 deletions(-)

diff --git a/engine.py b/engine.py
index 82e6cc7..a7dc3a3 100644
--- a/engine.py
+++ b/engine.py
@@ -3,9 +3,15 @@
 import util
 
 class Trainer():
-    def __init__(self, model, scaler, lrate, wdecay, clip=5, lr_decay_rate=.97, fp16=''):
+    def __init__(self, model: GWNet, scaler, lrate, wdecay, clip=5, lr_decay_rate=.97, fp16='', end_conv_lr=None):
         self.model = model
-        self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
+        if end_conv_lr:
+            end_conv2, other_params = model.conv_group
+            self.optimizer = optim.Adam([{'params': end_conv2, 'lr': end_conv_lr},
+                                         {'params': other_params}]
+                                        , lr=lrate, weight_decay=wdecay)
+        else:
+            self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
         self.scaler = scaler
         self.clip = clip
         self.fp16 = fp16
@@ -17,17 +23,20 @@ def __init__(self, model, scaler, lrate, wdecay, clip=5, lr_decay_rate=.97, fp16
             except ImportError:
                 raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
             amp.register_half_function(torch, 'einsum')
-            self.model, self.optimizer = amp.initialize(self.model, self.optimizer,
-                                                            opt_level=self.fp16)
+            self.model, self.optimizer = amp.initialize(self.model, self.optimizer, opt_level=self.fp16)
+
+
+    @classmethod
+    def from_args(cls, model, scaler, args):
+        end_conv_lr = getattr(args, 'end_conv_lr', None)
+        return cls(model, scaler, args.learning_rate, args.weight_decay, clip=args.clip,
+                   lr_decay_rate=args.lr_decay_rate, fp16=args.fp16, end_conv_lr=end_conv_lr)
 
     def train(self, input, real_val):
         self.model.train()
         self.optimizer.zero_grad()
         input = nn.functional.pad(input,(1,0,0,0))
         output = self.model(input).transpose(1,3)  # now, output = [batch_size,1,num_nodes,seq_length]
-
-        #torch.clamp(output, 0, 70)
-
         predict = self.scaler.inverse_transform(output)
         real = torch.unsqueeze(real_val, dim=1)
         mae, mape, rmse = util.calc_metrics(predict, real, null_val=0.0)
diff --git a/model.py b/model.py
index ffe0077..6728056 100644
--- a/model.py
+++ b/model.py
@@ -118,6 +118,16 @@ def from_args(cls, args, device, supports, aptinit, **kwargs):
         model = cls(device, args.num_nodes, **defaults)
         return model
 
+    @property
+    def conv_group(self):
+        bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']
+        grpb = [v for k, v in self.named_parameters() if k in [bk, wk]]
+        grpa = [v for k, v in self.named_parameters() if k not in [bk, wk]]
+        return grpa, grpb
+
+
+
+
     def forward(self, x):
         # Input shape is (bs, features, n_nodes, n_timesteps)
         in_len = x.size(3)
diff --git a/test_gwnet.py b/test_gwnet.py
index d8e09c7..784a4e2 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -15,7 +15,7 @@
                'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
                'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.,
                'in_dim': 1, 'cat_feat_gc': True, 'clip': 1, 'es_patience': 10,
-               'checkpoint': ''}
+               'checkpoint': '', 'end_conv_lr': None}
 
 MODEL_KWARGS = {'end_channels': 4, 'skip_channels': 2}
 def modify_args(args, updates):
diff --git a/train.py b/train.py
index defc3bc..e8226ba 100644
--- a/train.py
+++ b/train.py
@@ -37,7 +37,7 @@ def main(args, **model_kwargs):
     if args.checkpoint:
         model = surgery(model, args.checkpoint)
     model.to(device)
-    engine = Trainer(model, scaler, args.learning_rate, args.weight_decay, clip=args.clip, lr_decay_rate=args.lr_decay_rate, fp16=args.fp16)
+    engine = Trainer.from_args(model, scaler, args)
     metrics = []
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
     lowest_mae_yet = 100  # high value, will get overwritten
@@ -109,6 +109,8 @@ def eval_(ds, device, engine):
     parser.add_argument('--save', type=str, default='experiment', help='save path')
     parser.add_argument('--n_iters', default=None, help='quit after this many iterations')
     parser.add_argument('--es_patience', type=int, default=20, help='quit if no improvement after this many iterations')
+    parser.add_argument('--end_conv_lr', type=float, default=None)
+
     args = parser.parse_args()
     t1 = time.time()
     if not os.path.exists(args.save):

From df4c895aa700ad7570a0fb4bc1e44d60a274fa33 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 23:41:41 +0000
Subject: [PATCH 100/130] args update

---
 baseline_args.pkl | Bin 579 -> 722 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/baseline_args.pkl b/baseline_args.pkl
index c13dc8a911cd86305505132ce05a37b8655b808b..064e0db863873041c660eb2c8f78231a74f964a5 100644
GIT binary patch
delta 197
zcmY++OA3Ne7zN-iC`1iw0llCo(1>skAvkvSqt`#Zbh~eZTtRa;(jKA>w4#ifbY|zk
z`5xZOzmEu6wPSMV_kL-SB%5VdxGZf*GgnfuwJFhDBRCqw1|j1d^4bB?UD!TJ)7;Ww
z_dp9Oa|{JVJ&B8S&~3CrdQy@Jr>E)qpA{7=_FJ&1fun#Z%+WeH%K~|zT%~7URTMy_
UxBxeLo}}If<xJ^C?ALkl2IT)eCjbBd

delta 54
zcmcb_dYEN{Fe9VFWD!PB4*P&4-%B4?{+XQ0=qJR-z`&4`pI)q=lvtdalbM%lKKUMF
K6-#Gnk{$r*`VwXU


From 41fa3bf36c0c3a090cd2dd36f5924a223d89b43a Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 15:56:06 -0800
Subject: [PATCH 101/130] freeze group b code

---
 engine.py | 1 -
 model.py  | 5 +++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/engine.py b/engine.py
index a7dc3a3..2aef5a8 100644
--- a/engine.py
+++ b/engine.py
@@ -25,7 +25,6 @@ def __init__(self, model: GWNet, scaler, lrate, wdecay, clip=5, lr_decay_rate=.9
             amp.register_half_function(torch, 'einsum')
             self.model, self.optimizer = amp.initialize(self.model, self.optimizer, opt_level=self.fp16)
 
-
     @classmethod
     def from_args(cls, model, scaler, args):
         end_conv_lr = getattr(args, 'end_conv_lr', None)
diff --git a/model.py b/model.py
index 6728056..ccb8e60 100644
--- a/model.py
+++ b/model.py
@@ -125,8 +125,9 @@ def conv_group(self):
         grpa = [v for k, v in self.named_parameters() if k not in [bk, wk]]
         return grpa, grpb
 
-
-
+    def freeze_group_b(self):
+        for param in self.conv_group[1]:
+            param.requires_grad = False
 
     def forward(self, x):
         # Input shape is (bs, features, n_nodes, n_timesteps)

From 0be119180f42bb00a4fef13acc1add61cea600ee Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 15:57:36 -0800
Subject: [PATCH 102/130] Freeze if lrate=0

---
 engine.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/engine.py b/engine.py
index 2aef5a8..988f21a 100644
--- a/engine.py
+++ b/engine.py
@@ -7,9 +7,10 @@ def __init__(self, model: GWNet, scaler, lrate, wdecay, clip=5, lr_decay_rate=.9
         self.model = model
         if end_conv_lr:
             end_conv2, other_params = model.conv_group
-            self.optimizer = optim.Adam([{'params': end_conv2, 'lr': end_conv_lr},
-                                         {'params': other_params}]
-                                        , lr=lrate, weight_decay=wdecay)
+            groups = [{'params': end_conv2, 'lr': end_conv_lr}]
+            if lrate > 0:
+                groups.append({'params': other_params})
+            self.optimizer = optim.Adam(groups, lr=lrate, weight_decay=wdecay)
         else:
             self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
         self.scaler = scaler

From b777b96b05caba716627704143e095d116d861d3 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 15:59:50 -0800
Subject: [PATCH 103/130] call freezer

---
 engine.py | 1 +
 model.py  | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/engine.py b/engine.py
index 988f21a..9ff381d 100644
--- a/engine.py
+++ b/engine.py
@@ -10,6 +10,7 @@ def __init__(self, model: GWNet, scaler, lrate, wdecay, clip=5, lr_decay_rate=.9
             groups = [{'params': end_conv2, 'lr': end_conv_lr}]
             if lrate > 0:
                 groups.append({'params': other_params})
+            self.model.freeze_group_b()
             self.optimizer = optim.Adam(groups, lr=lrate, weight_decay=wdecay)
         else:
             self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
diff --git a/model.py b/model.py
index ccb8e60..28642eb 100644
--- a/model.py
+++ b/model.py
@@ -121,8 +121,8 @@ def from_args(cls, args, device, supports, aptinit, **kwargs):
     @property
     def conv_group(self):
         bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']
-        grpb = [v for k, v in self.named_parameters() if k in [bk, wk]]
         grpa = [v for k, v in self.named_parameters() if k not in [bk, wk]]
+        grpb = [v for k, v in self.named_parameters() if k in [bk, wk]]
         return grpa, grpb
 
     def freeze_group_b(self):

From 102a21e8d30a87b30950d937a1097f8b1b507fd6 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 16:32:10 -0800
Subject: [PATCH 104/130] Revert batch size

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index e8226ba..7298fbd 100644
--- a/train.py
+++ b/train.py
@@ -29,7 +29,7 @@ def surgery(model, surg_checkpoint):
 
 def main(args, **model_kwargs):
     device = torch.device(args.device)
-    data = util.load_dataset(args.data, args.batch_size, args.batch_size*2, args.batch_size*2, n_obs=args.n_obs)
+    data = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
     scaler = data['scaler']
     aptinit, supports = util.make_graph_inputs(args, device)
 

From 2678b901fa108939832d986ca2423301013bda28 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 16:46:38 -0800
Subject: [PATCH 105/130] Train loss progbar

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index 7298fbd..a6ef21d 100644
--- a/train.py
+++ b/train.py
@@ -70,7 +70,7 @@ def main(args, **model_kwargs):
         else:
             since_best += 1
         met_df = pd.DataFrame(metrics)
-        mb.comment = f'best valid_loss: {met_df.valid_loss.min(): .3f}, current valid_loss: {m.valid_loss:.3f}'
+        mb.comment = f'best val_loss: {met_df.valid_loss.min(): .3f}, current val_loss: {m.valid_loss:.3f}, current train loss: {m.train_loss: .3f}'
         met_df.round(6).to_csv(f'{args.save}/metrics.csv')
         if since_best >= args.es_patience: break  #
     # Metrics on test data

From 9cc8923a58b485a2cb80f9df3dc900e4e52d0f06 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 16:50:30 -0800
Subject: [PATCH 106/130] log test flag

---
 train.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/train.py b/train.py
index a6ef21d..c36e9c7 100644
--- a/train.py
+++ b/train.py
@@ -28,6 +28,7 @@ def surgery(model, surg_checkpoint):
 
 
 def main(args, **model_kwargs):
+    log_test = getattr(args, 'log_test', False)
     device = torch.device(args.device)
     data = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
     scaler = data['scaler']
@@ -57,10 +58,15 @@ def main(args, **model_kwargs):
                 break
         engine.scheduler.step()
         _, valid_loss, valid_mape, valid_rmse = eval_(data['val_loader'], device, engine)
-
         m = dict(train_loss=np.mean(train_loss), train_mape=np.mean(train_mape),
                  train_rmse=np.mean(train_rmse), valid_loss=np.mean(valid_loss),
                  valid_mape=np.mean(valid_mape), valid_rmse=np.mean(valid_rmse))
+        if log_test:
+            _, test_loss, test_mape, test_rmse = eval_(data['test_loader'], device, engine)
+            mn = np.mean
+            test_m = dict(test_loss=mn(test_loss), test_mape=mn(test_mape), test_rmse=mn(test_rmse))
+            m.update(test_m)
+
         m = pd.Series(m)
         metrics.append(m)
         if m.valid_loss < lowest_mae_yet:

From 804428e3d2243899940c84082524a4f73145a55d Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 18:13:57 -0800
Subject: [PATCH 107/130] Uneven datasets

---
 generate_training_data.py | 35 ++++++++++++++++-------------------
 1 file changed, 16 insertions(+), 19 deletions(-)

diff --git a/generate_training_data.py b/generate_training_data.py
index 1154933..e59ab24 100644
--- a/generate_training_data.py
+++ b/generate_training_data.py
@@ -54,16 +54,12 @@ def generate_graph_seq2seq_io_data(
 
 
 def generate_train_val_test(args):
-    seq_length = args.seq_length
-
+    seq_length_x, seq_length_y = args.seq_length_x, args.seq_length_y
     df = pd.read_hdf(args.traffic_df_filename)
     # 0 is the latest observed sample.
-    x_offsets = np.sort(
-        # np.concatenate(([-week_size + 1, -day_size + 1], np.arange(-11, 1, 1)))
-        np.concatenate((np.arange(-(seq_length - 1), 1, 1),))
-    )
+    x_offsets = np.sort(np.concatenate((np.arange(-(seq_length_x - 1), 1, 1),)))
     # Predict the next one hour
-    y_offsets = np.sort(np.arange(1, (seq_length + 1), 1))
+    y_offsets = np.sort(np.arange(1, (seq_length_y + 1), 1))
     # x: (num_samples, input_length, num_nodes, input_dim)
     # y: (num_samples, output_length, num_nodes, output_dim)
     x, y = generate_graph_seq2seq_io_data(
@@ -105,10 +101,6 @@ def generate_train_val_test(args):
         )
 
 
-def main(args):
-    print("Generating training data")
-    generate_train_val_test(args)
-
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
@@ -122,7 +114,13 @@ def main(args):
         help="Raw traffic readings.",
     )
     parser.add_argument(
-        "--seq_length",
+        "--seq_length_x",
+        type=int,
+        default=12,
+        help="Sequence Length.",
+    )
+    parser.add_argument(
+        "--seq_length_y",
         type=int,
         default=12,
         help="Sequence Length.",
@@ -134,10 +132,9 @@ def main(args):
     )
 
     args = parser.parse_args()
-    if not os.path.exists(args.output_dir):
-      reply = str(input(f'{args.output_dir} does not exist. Do you want to create it? (y/n)')).lower().strip()
-      if reply[0] == 'y':
-        os.mkdir(args.output_dir)
-      else:
-        exit
-    main(args)
+    if os.path.exists(args.output_dir):
+      reply = str(input(f'{args.output_dir} exists. Do you want to overwrite it? (y/n)')).lower().strip()
+      if reply[0] == 'n': exit
+    else:
+        os.makedirs(args.output_dir)
+    generate_train_val_test(args)

From 48a0eaecfdb2235efd06f8a645fbd6d7a35ae9ba Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 20:41:03 -0800
Subject: [PATCH 108/130] y_start clarg

---
 generate_training_data.py | 16 ++++------------
 1 file changed, 4 insertions(+), 12 deletions(-)

diff --git a/generate_training_data.py b/generate_training_data.py
index e59ab24..dae7cb2 100644
--- a/generate_training_data.py
+++ b/generate_training_data.py
@@ -59,7 +59,7 @@ def generate_train_val_test(args):
     # 0 is the latest observed sample.
     x_offsets = np.sort(np.concatenate((np.arange(-(seq_length_x - 1), 1, 1),)))
     # Predict the next one hour
-    y_offsets = np.sort(np.arange(1, (seq_length_y + 1), 1))
+    y_offsets = np.sort(np.arange(args.y_start, (seq_length_y + 1), 1))
     # x: (num_samples, input_length, num_nodes, input_dim)
     # y: (num_samples, output_length, num_nodes, output_dim)
     x, y = generate_graph_seq2seq_io_data(
@@ -119,17 +119,9 @@ def generate_train_val_test(args):
         default=12,
         help="Sequence Length.",
     )
-    parser.add_argument(
-        "--seq_length_y",
-        type=int,
-        default=12,
-        help="Sequence Length.",
-    )
-    parser.add_argument(
-        "--dow",
-        action='store_true',
-        #type=bool,
-    )
+    parser.add_argument("--seq_length_y", type=int, default=12, help="Sequence Length.",)
+    parser.add_argument("--y_start", type=int, default=1, help="Y pred start", )
+    parser.add_argument("--dow", action='store_true',)
 
     args = parser.parse_args()
     if os.path.exists(args.output_dir):

From 29c9f3d7be02a716ddb8a88e6408749ba180ef0c Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 21:24:27 -0800
Subject: [PATCH 109/130] assert and squeeze

---
 engine.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/engine.py b/engine.py
index 9ff381d..2bb1d6d 100644
--- a/engine.py
+++ b/engine.py
@@ -37,10 +37,10 @@ def train(self, input, real_val):
         self.model.train()
         self.optimizer.zero_grad()
         input = nn.functional.pad(input,(1,0,0,0))
-        output = self.model(input).transpose(1,3)  # now, output = [batch_size,1,num_nodes,seq_length]
+        output = self.model(input).transpose(1,3)  # now, output = [batch_size,1,num_nodes, seq_length]
         predict = self.scaler.inverse_transform(output)
-        real = torch.unsqueeze(real_val, dim=1)
-        mae, mape, rmse = util.calc_metrics(predict, real, null_val=0.0)
+        assert predict.shape[1] == 1
+        mae, mape, rmse = util.calc_metrics(predict.squeeze(1), real_val, null_val=0.0)
 
         if self.fp16:
             from apex import amp

From c664dd79bf1ea6495a092f440477fd60e6fac1cb Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 4 Dec 2019 22:55:05 -0800
Subject: [PATCH 110/130] gitignore

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index 424e51f..57db9b8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,7 @@
 utest_experiment/*
+heatmap.png
+last_test_metrics.csv
+preds.csv
 .ipynb_checkpoints/
 data/
 .DS_Store

From ae06854905e543fd46a63f26a70664910a67e542 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 5 Dec 2019 08:35:28 -0800
Subject: [PATCH 111/130] Some cleanup

---
 generate_training_data.py | 26 ++++++++------------------
 1 file changed, 8 insertions(+), 18 deletions(-)

diff --git a/generate_training_data.py b/generate_training_data.py
index dae7cb2..5a6bd7c 100644
--- a/generate_training_data.py
+++ b/generate_training_data.py
@@ -27,27 +27,23 @@ def generate_graph_seq2seq_io_data(
 
     num_samples, num_nodes = df.shape
     data = np.expand_dims(df.values, axis=-1)
-    data_list = [data]
+    feature_list = [data]
     if add_time_in_day:
         time_ind = (df.index.values - df.index.values.astype("datetime64[D]")) / np.timedelta64(1, "D")
         time_in_day = np.tile(time_ind, [1, num_nodes, 1]).transpose((2, 1, 0))
-        data_list.append(time_in_day)
+        feature_list.append(time_in_day)
     if add_day_in_week:
         day_in_week = np.zeros(shape=(num_samples, num_nodes, 7))
         day_in_week[np.arange(num_samples), :, df.index.dayofweek] = 1
-        data_list.append(day_in_week)
+        feature_list.append(day_in_week)
 
-    data = np.concatenate(data_list, axis=-1)
-    # epoch_len = num_samples + min(x_offsets) - max(y_offsets)
+    data = np.concatenate(feature_list, axis=-1)
     x, y = [], []
-    # t is the index of the last observation.
     min_t = abs(min(x_offsets))
     max_t = abs(num_samples - abs(max(y_offsets)))  # Exclusive
-    for t in range(min_t, max_t):
-        x_t = data[t + x_offsets, ...]
-        y_t = data[t + y_offsets, ...]
-        x.append(x_t)
-        y.append(y_t)
+    for t in range(min_t, max_t):  # t is the index of the last observation.
+        x.append(data[t + x_offsets, ...])
+        y.append(data[t + y_offsets, ...])
     x = np.stack(x, axis=0)
     y = np.stack(y, axis=0)
     return x, y
@@ -72,28 +68,22 @@ def generate_train_val_test(args):
 
     print("x shape: ", x.shape, ", y shape: ", y.shape)
     # Write the data into npz file.
-    # num_test = 6831, using the last 6831 examples as testing.
-    # for the rest: 7/8 is used for training, and 1/8 is used for validation.
     num_samples = x.shape[0]
     num_test = round(num_samples * 0.2)
     num_train = round(num_samples * 0.7)
     num_val = num_samples - num_test - num_train
-
-    # train
     x_train, y_train = x[:num_train], y[:num_train]
-    # val
     x_val, y_val = (
         x[num_train: num_train + num_val],
         y[num_train: num_train + num_val],
     )
-    # test
     x_test, y_test = x[-num_test:], y[-num_test:]
 
     for cat in ["train", "val", "test"]:
         _x, _y = locals()["x_" + cat], locals()["y_" + cat]
         print(cat, "x: ", _x.shape, "y:", _y.shape)
         np.savez_compressed(
-            os.path.join(args.output_dir, "%s.npz" % cat),
+            os.path.join(args.output_dir, f"cat.npz"),
             x=_x,
             y=_y,
             x_offsets=x_offsets.reshape(list(x_offsets.shape) + [1]),

From 1cb00bf0edc33281d85403eadebef32013de53c7 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 5 Dec 2019 08:41:46 -0800
Subject: [PATCH 112/130] bug fix

---
 generate_training_data.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/generate_training_data.py b/generate_training_data.py
index 5a6bd7c..37826fc 100644
--- a/generate_training_data.py
+++ b/generate_training_data.py
@@ -33,9 +33,9 @@ def generate_graph_seq2seq_io_data(
         time_in_day = np.tile(time_ind, [1, num_nodes, 1]).transpose((2, 1, 0))
         feature_list.append(time_in_day)
     if add_day_in_week:
-        day_in_week = np.zeros(shape=(num_samples, num_nodes, 7))
-        day_in_week[np.arange(num_samples), :, df.index.dayofweek] = 1
-        feature_list.append(day_in_week)
+        dow = df.index.dayofweek
+        dow_tiled = np.tile(dow, [1, num_nodes, 1]).transpose((2, 1, 0))
+        feature_list.append(dow_tiled)
 
     data = np.concatenate(feature_list, axis=-1)
     x, y = [], []
@@ -83,7 +83,7 @@ def generate_train_val_test(args):
         _x, _y = locals()["x_" + cat], locals()["y_" + cat]
         print(cat, "x: ", _x.shape, "y:", _y.shape)
         np.savez_compressed(
-            os.path.join(args.output_dir, f"cat.npz"),
+            os.path.join(args.output_dir, f"{cat}.npz"),
             x=_x,
             y=_y,
             x_offsets=x_offsets.reshape(list(x_offsets.shape) + [1]),
@@ -91,7 +91,6 @@ def generate_train_val_test(args):
         )
 
 
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument(

From cb6736bd75f899777481eb80033e35d6247315c8 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 5 Dec 2019 08:58:01 -0800
Subject: [PATCH 113/130] replace 0 feat with 58.435

---
 util.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/util.py b/util.py
index 7c06478..774361c 100644
--- a/util.py
+++ b/util.py
@@ -51,16 +51,17 @@ def _wrapper():
 
         return _wrapper()
 
+
 class StandardScaler():
-    """
-    Standard the input
-    """
 
     def __init__(self, mean, std):
         self.mean = mean
         self.std = std
+        self.fill_val = 58.435
 
     def transform(self, data):
+        mask = (data == 0)
+        data[mask] = self.fill_val
         return (data - self.mean) / self.std
 
     def inverse_transform(self, data):

From 03063d0af563a6cb0fd719d1e69fad852e24d9e7 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 5 Dec 2019 16:21:35 -0800
Subject: [PATCH 114/130] Skip zero batches

---
 train.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/train.py b/train.py
index c36e9c7..68502db 100644
--- a/train.py
+++ b/train.py
@@ -50,7 +50,9 @@ def main(args, **model_kwargs):
         for iter, (x, y) in enumerate(data['train_loader'].get_iterator()):
             trainx = torch.Tensor(x).to(device).transpose(1, 3)
             trainy = torch.Tensor(y).to(device).transpose(1, 3)
-            mae, mape, rmse = engine.train(trainx, trainy[:, 0, :, :])
+            yspeed = trainy[:, 0, :, :]
+            if yspeed.max() == 0: continue
+            mae, mape, rmse = engine.train(trainx, yspeed)
             train_loss.append(mae)
             train_mape.append(mape)
             train_rmse.append(rmse)

From 3e85bde54cfdd8a6d61942b9fb3c3f494c604202 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 5 Dec 2019 16:26:01 -0800
Subject: [PATCH 115/130] test.main supports loader=val

---
 test.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/test.py b/test.py
index 3cfee7c..64fc33a 100644
--- a/test.py
+++ b/test.py
@@ -6,7 +6,7 @@
 import seaborn as sns
 
 
-def main(args, save_pred_path='preds.csv', save_metrics_path='last_test_metrics.csv', **model_kwargs):
+def main(args, save_pred_path='preds.csv', save_metrics_path='last_test_metrics.csv', loader='test', **model_kwargs):
     device = torch.device(args.device)
     adjinit, supports = util.make_graph_inputs(args, device)
     model = GWNet.from_args(args, device, supports, adjinit, **model_kwargs)
@@ -16,9 +16,9 @@ def main(args, save_pred_path='preds.csv', save_metrics_path='last_test_metrics.
     print('model loaded successfully')
     dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
     scaler = dataloader['scaler']
-    realy = torch.Tensor(dataloader['y_test']).to(device)
+    realy = torch.Tensor(dataloader[f'y_{loader}']).to(device)
     realy = realy.transpose(1,3)[:,0,:,:]
-    met_df, yhat = util.calc_test_metrics(model, device, dataloader['test_loader'], scaler, realy, args.seq_length)
+    met_df, yhat = util.calc_test_metrics(model, device, dataloader[f'{loader}_loader'], scaler, realy, args.seq_length)
     df2 = util.make_pred_df(realy, yhat, scaler, args.seq_length)
     met_df.to_csv(save_metrics_path)
     df2.to_csv(save_pred_path, index=False)

From bc103a1f51e86286fb401f2123bf96ac8e265c89 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 5 Dec 2019 16:47:19 -0800
Subject: [PATCH 116/130] scaler prints

---
 util.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/util.py b/util.py
index 7c06478..81653da 100644
--- a/util.py
+++ b/util.py
@@ -59,6 +59,7 @@ class StandardScaler():
     def __init__(self, mean, std):
         self.mean = mean
         self.std = std
+        print(f'mean:{self.mean}, scaler: {self.std}')
 
     def transform(self, data):
         return (data - self.mean) / self.std

From 6f7a92617eb4f599e2aee5994eb4579d9c4e7a34 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 5 Dec 2019 16:47:28 -0800
Subject: [PATCH 117/130] scaler prints

---
 util.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/util.py b/util.py
index 81653da..9e7fda5 100644
--- a/util.py
+++ b/util.py
@@ -59,7 +59,7 @@ class StandardScaler():
     def __init__(self, mean, std):
         self.mean = mean
         self.std = std
-        print(f'mean:{self.mean}, scaler: {self.std}')
+        print(f'mean:{self.mean:.3f}, scaler: {self.std:.3f}')
 
     def transform(self, data):
         return (data - self.mean) / self.std

From 64253c5dc487fc00a7bd83f9f75162feddb51443 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 5 Dec 2019 17:37:55 -0800
Subject: [PATCH 118/130] call .eval

---
 util.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/util.py b/util.py
index 9e7fda5..d1c9035 100644
--- a/util.py
+++ b/util.py
@@ -59,7 +59,6 @@ class StandardScaler():
     def __init__(self, mean, std):
         self.mean = mean
         self.std = std
-        print(f'mean:{self.mean:.3f}, scaler: {self.std:.3f}')
 
     def transform(self, data):
         return (data - self.mean) / self.std
@@ -190,9 +189,10 @@ def mask_and_fillna(loss, mask):
     return torch.mean(loss)
 
 
-def calc_test_metrics(model, device, test_loader, scaler, realy, seq_length) -> pd.DataFrame:
+def calc_tstep_metrics(model, device, test_loader, scaler, realy, seq_length) -> pd.DataFrame:
+    model.eval()
     outputs = []
-    for _, (x, y) in enumerate(test_loader.get_iterator()):
+    for _, (x, __) in enumerate(test_loader.get_iterator()):
         testx = torch.Tensor(x).to(device).transpose(1, 3)
         with torch.no_grad():
             preds = model(testx).transpose(1, 3)

From 2e0778dadb61cb9411d21abe1585082fedbb35f0 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Thu, 5 Dec 2019 20:58:37 -0800
Subject: [PATCH 119/130] fix imports

---
 test.py  | 2 +-
 train.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/test.py b/test.py
index 64fc33a..9d3d0a5 100644
--- a/test.py
+++ b/test.py
@@ -18,7 +18,7 @@ def main(args, save_pred_path='preds.csv', save_metrics_path='last_test_metrics.
     scaler = dataloader['scaler']
     realy = torch.Tensor(dataloader[f'y_{loader}']).to(device)
     realy = realy.transpose(1,3)[:,0,:,:]
-    met_df, yhat = util.calc_test_metrics(model, device, dataloader[f'{loader}_loader'], scaler, realy, args.seq_length)
+    met_df, yhat = util.calc_tstep_metrics(model, device, dataloader[f'{loader}_loader'], scaler, realy, args.seq_length)
     df2 = util.make_pred_df(realy, yhat, scaler, args.seq_length)
     met_df.to_csv(save_metrics_path)
     df2.to_csv(save_pred_path, index=False)
diff --git a/train.py b/train.py
index 68502db..7531d38 100644
--- a/train.py
+++ b/train.py
@@ -9,7 +9,7 @@
 from fastprogress import progress_bar
 
 from model import GWNet
-from util import calc_test_metrics
+from util import calc_tstep_metrics
 from exp_results import summary
 
 bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']
@@ -84,7 +84,7 @@ def main(args, **model_kwargs):
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
     realy = torch.Tensor(data['y_test']).transpose(1, 3)[:, 0, :, :].to(device)
-    test_met_df, yhat = calc_test_metrics(engine.model, device, data['test_loader'], scaler, realy, args.seq_length)
+    test_met_df, yhat = calc_tstep_metrics(engine.model, device, data['test_loader'], scaler, realy, args.seq_length)
     test_met_df.round(6).to_csv(os.path.join(args.save, 'test_metrics.csv'))
     print(summary(args.save))
 

From 371a784ac49ab3ed3f5895247faada8fd7908cea Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Sat, 7 Dec 2019 11:44:24 -0800
Subject: [PATCH 120/130] copy of nb

---
 metrics.ipynb | 6303 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 6303 insertions(+)
 create mode 100644 metrics.ipynb

diff --git a/metrics.ipynb b/metrics.ipynb
new file mode 100644
index 0000000..fe64daf
--- /dev/null
+++ b/metrics.ipynb
@@ -0,0 +1,6303 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-07T04:08:59.602372Z",
+     "start_time": "2019-12-07T04:08:39.678031Z"
+    },
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:90% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created `%h` as an alias for `%history -l 20 -u -g`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %load /home/shleifer/durbango/imports.py\n",
+    "#IMPORTS at top of notebook\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "ISO = \"ISO-8859-1\"\n",
+    "\n",
+    "import os\n",
+    "import pickle\n",
+    "from tqdm import tqdm, tqdm_notebook, tnrange\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import sys\n",
+    "from sklearn.metrics import *\n",
+    "import itertools\n",
+    "from collections import *\n",
+    "import funcy\n",
+    "from scipy.spatial.distance import cosine as cosine_distance\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import *\n",
+    "from sklearn.model_selection import StratifiedKFold, KFold, ParameterGrid\n",
+    "from sklearn.utils import shuffle\n",
+    "from numpy.testing import assert_array_equal\n",
+    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
+    "from pathlib import Path\n",
+    "import re\n",
+    "from glob import glob\n",
+    "import pickle\n",
+    "import socket\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "import torch.nn.functional as F\n",
+    "from IPython.lib.display import FileLink\n",
+    "\n",
+    "from durbango import *\n",
+    "\n",
+    "HOSTNAME = socket.gethostname()\n",
+    "mock_arr = np.array(range(25)).reshape(5,5)\n",
+    "mock_df = pd.DataFrame(np.ones((5,5)), columns=['a', 'b', 'c', 'd', 'e'])\n",
+    "mock_df['color'] = ['red', 'blue', 'red', 'blue', 'green']\n",
+    "mock_df['bool_col'] = [True, False, True, False, True]\n",
+    "np.set_printoptions(precision=5, linewidth=110, suppress=True)\n",
+    "\n",
+    "from ipykernel.kernelapp import IPKernelApp\n",
+    "from IPython.display import Markdown, display, HTML\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "def in_notebook(): return IPKernelApp.initialized()\n",
+    "def printmd(string): display(Markdown(string))\n",
+    "import warnings\n",
+    "\n",
+    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
+    "\n",
+    "# pretty print only the last output of the cell\n",
+    "# InteractiveShell.ast_node_interactivity = \"last_expr\" # \"all\" for all\n",
+    "try:\n",
+    "    with warnings.catch_warnings():\n",
+    "        import eli5\n",
+    "        from lightgbm.sklearn import LGBMClassifier, LGBMRegressor\n",
+    "        import seaborn as sns\n",
+    "        from PIL import Image\n",
+    "except ImportError:\n",
+    "    pass\n",
+    "\n",
+    "\n",
+    "Path.ls =  lambda self: sorted(list(self.iterdir()))\n",
+    "ParameterGrid.l = property(lambda self: list(self))\n",
+    "pd.Series.flipped_dict = property(lambda ser: funcy.flip(ser.to_dict()))\n",
+    "\n",
+    "pd.DataFrame.dsort = descending_sort\n",
+    "pd.Series.dsort = descending_sort\n",
+    "pd.DataFrame.asort = ascending_sort\n",
+    "pd.Series.asort = ascending_sort\n",
+    "\n",
+    "%alias_magic h history -p \"-l 20 -u -g\"\n",
+    "pd.set_option('display.max_rows', 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-07T04:09:00.534094Z",
+     "start_time": "2019-12-07T04:09:00.509346Z"
+    },
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "#FOLD \n",
+    "import datetime\n",
+    "import pytz\n",
+    "d = 'cheaper-metric-fixed'\n",
+    "def get_modtime(path):\n",
+    "    timestamp = os.path.getmtime(path)\n",
+    "    dt_object = datetime.datetime.fromtimestamp(timestamp)\n",
+    "    dt_object = dt_object.astimezone(pytz.timezone(\"America/Los_Angeles\"))\n",
+    "    \n",
+    "    return dt_object.strftime('%m-%d-%H:%M')\n",
+    "    return str(dt_object)\n",
+    "\n",
+    "def es_score(d, patience=10):\n",
+    "    best_yet = 100\n",
+    "    since_best = 0\n",
+    "    lc = loss_curve(d)\n",
+    "    for i in lc.index:\n",
+    "        k = lc.loc[i]\n",
+    "        if k.valid_loss < best_yet:\n",
+    "            best_yet = k.valid_loss\n",
+    "            since_best = 0\n",
+    "        else:\n",
+    "            since_best +=1\n",
+    "        if since_best == patience:\n",
+    "            break\n",
+    "    return best_yet,i\n",
+    "\n",
+    "def summary(d):\n",
+    "    try:\n",
+    "        pth = f'{d}/metrics.csv'\n",
+    "        tr_val = pd.read_csv(pth, index_col=0)\n",
+    "        tr_ser = tr_val.loc[tr_val.valid_loss.idxmin()]\n",
+    "        tr_ser['best_epoch'] = tr_val.valid_loss.idxmin()\n",
+    "        tr_ser['min_train_loss'] = tr_val.train_loss.min()\n",
+    "        tr_ser['date'] = get_modtime(f'{d}/best_model.pth')\n",
+    "        tr_ser['es20_score'], tr_ser['es20_i'] = es_score(d, patience=20)\n",
+    "        tr_ser['es10_score'], tr_ser['es10_i'] = es_score(d, patience=10)\n",
+    "        \n",
+    "    except FileNotFoundError:\n",
+    "        tr_ser = pd.Series()\n",
+    "    try:\n",
+    "        tmet = pd.read_csv(f'{d}/test_metrics.csv', index_col=0)\n",
+    "\n",
+    "        tmean = tmet.add_prefix('test_').mean()\n",
+    "        #tmean['test_ratio'] = tmet.mae.loc[11] / tmet.mae.mean()\n",
+    "        #tmean['test_mae12'] = tmet.mae.loc[11]\n",
+    "    except FileNotFoundError:\n",
+    "        tmean = pd.Series()\n",
+    "    tab = pd.concat([tr_ser, tmean ])\n",
+    "    return tab\n",
+    "\n",
+    "def loss_curve(d):\n",
+    "    if 'logs' not in d: d =  f'logs/{d}'\n",
+    "    tr_val = pd.read_csv(f'{d}/metrics.csv', index_col=0)\n",
+    "    return tr_val[['train_loss', 'valid_loss']]    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-07T04:10:29.414488Z",
+     "start_time": "2019-12-07T04:10:29.104994Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f785587e910>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 261,
+       "width": 386
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bl = pd.read_csv('logs/baseline7/test_metrics.csv', index_col=0).mae\n",
+    "\n",
+    "short = pd.read_csv('logs/catgc_clip_3_t_12_6/test_metrics.csv', index_col=0)\n",
+    "short3 = pd.read_csv('logs/front_3/test_metrics.csv', index_col=0).mae - bl.head(3)\n",
+    "short9 = (pd.read_csv('logs/front_9/test_metrics.csv', index_col=0).mae - bl.head(9)).to_frame('front_9')\n",
+    "\n",
+    "long = pd.read_csv('logs/replace-0-feat_v2/test_metrics.csv', index_col=0).mae -bl\n",
+    "\n",
+    "surg = pd.read_csv('logs/surgery_t12_6/test_metrics.csv', index_col=0).mae - bl\n",
+    "surg3 = pd.read_csv('logs/surgery_front_3/test_metrics.csv', index_col=0).mae - bl\n",
+    "\n",
+    "back =  pd.read_csv('logs/catgc_clip_3_offset__7_12/test_metrics.csv', index_col=0).mae \n",
+    "back.index = back.index+6 \n",
+    "\n",
+    "back =  pd.read_csv('logs/catgc_clip_3_offset__7_12/test_metrics.csv', index_col=0).mae\n",
+    "\n",
+    "back.index = back.index+6 \n",
+    "\n",
+    "#pd.read_csv('logs/catgc_clip_3_offset__7_12/test_metrics.csv', index_col=0)\n",
+    "\n",
+    "ax = long.to_frame('long').plot(style='-o')\n",
+    "(short.mae - bl.head(6)).to_frame('front_6').plot(ax=ax, style='-o')\n",
+    "short3.to_frame('front_3').plot(ax=ax, style='-o')\n",
+    "short9.plot(ax=ax, style='-o')\n",
+    "(back- bl.tail(6)).to_frame('back').plot(ax=ax, style='-o')\n",
+    "surg.to_frame('surg_6').plot(ax=ax, style='-o')\n",
+    "surg3.to_frame('surg_3').plot(ax=ax, style='-o')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-06T19:16:01.743847Z",
+     "start_time": "2019-12-06T19:16:01.712563Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mae     2.981355\n",
+       "mape    0.081183\n",
+       "rmse    5.935189\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "long = pd.read_csv('logs/replace-0-feat_v2/test_metrics.csv', index_col=0).tail(6)\n",
+    "catted = pd.concat([pd.read_csv('logs/catgc_clip_3_t_12_6/test_metrics.csv', index_col=0), long])\n",
+    "catted.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-07T04:09:39.648977Z",
+     "start_time": "2019-12-07T04:09:39.627609Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train_loss            2.80489\n",
+       "train_mape           0.074919\n",
+       "train_rmse            5.60668\n",
+       "valid_loss            2.72805\n",
+       "valid_mape           0.074973\n",
+       "valid_rmse            5.27131\n",
+       "best_epoch                 32\n",
+       "min_train_loss         2.7365\n",
+       "date              12-06-15:24\n",
+       "es20_score            2.72805\n",
+       "es20_i                     52\n",
+       "es10_score            2.72805\n",
+       "es10_i                     42\n",
+       "test_mae              3.01317\n",
+       "test_mape           0.0818051\n",
+       "test_rmse             5.97758\n",
+       "test_loss                 NaN\n",
+       "Name: surgery_front_3, dtype: object"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab.loc['surgery_front_3']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "back"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-07T04:09:20.284410Z",
+     "start_time": "2019-12-07T04:09:14.365957Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/shleifer/.conda/envs/nb/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: to_msgpack is deprecated and will be removed in a future version.\n",
+      "It is recommended to use pyarrow for on-the-wire transmission of pandas objects.\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>train_mape</th>\n",
+       "      <th>train_rmse</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>valid_mape</th>\n",
+       "      <th>valid_rmse</th>\n",
+       "      <th>best_epoch</th>\n",
+       "      <th>min_train_loss</th>\n",
+       "      <th>es20_score</th>\n",
+       "      <th>es20_i</th>\n",
+       "      <th>es10_score</th>\n",
+       "      <th>es10_i</th>\n",
+       "      <th>test_mae</th>\n",
+       "      <th>test_mape</th>\n",
+       "      <th>test_rmse</th>\n",
+       "      <th>test_loss</th>\n",
+       "      <th>date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>front_1</td>\n",
+       "      <td>2.0282</td>\n",
+       "      <td>0.0457</td>\n",
+       "      <td>3.4831</td>\n",
+       "      <td>1.9568</td>\n",
+       "      <td>0.0448</td>\n",
+       "      <td>3.3350</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>2.0276</td>\n",
+       "      <td>1.9568</td>\n",
+       "      <td>99</td>\n",
+       "      <td>1.9568</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.1357</td>\n",
+       "      <td>0.0503</td>\n",
+       "      <td>3.7082</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-06-14:54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>front_3</td>\n",
+       "      <td>2.2550</td>\n",
+       "      <td>0.0533</td>\n",
+       "      <td>4.1214</td>\n",
+       "      <td>2.1794</td>\n",
+       "      <td>0.0528</td>\n",
+       "      <td>3.9365</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>2.2539</td>\n",
+       "      <td>2.1794</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.1794</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.3972</td>\n",
+       "      <td>0.0592</td>\n",
+       "      <td>4.3965</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-06-13:23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_clip_3_t_12_6</td>\n",
+       "      <td>2.4717</td>\n",
+       "      <td>0.0618</td>\n",
+       "      <td>4.7332</td>\n",
+       "      <td>2.4084</td>\n",
+       "      <td>0.0624</td>\n",
+       "      <td>4.5413</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2.4690</td>\n",
+       "      <td>2.4084</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2.4152</td>\n",
+       "      <td>65</td>\n",
+       "      <td>2.6597</td>\n",
+       "      <td>0.0689</td>\n",
+       "      <td>5.0939</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-04-22:29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid40_seq_len_6</td>\n",
+       "      <td>2.4961</td>\n",
+       "      <td>0.0625</td>\n",
+       "      <td>4.7963</td>\n",
+       "      <td>2.4941</td>\n",
+       "      <td>0.0646</td>\n",
+       "      <td>4.7549</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2.4822</td>\n",
+       "      <td>2.4941</td>\n",
+       "      <td>95</td>\n",
+       "      <td>2.5025</td>\n",
+       "      <td>64</td>\n",
+       "      <td>2.6731</td>\n",
+       "      <td>0.0689</td>\n",
+       "      <td>5.1211</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-04-12:56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>front_9</td>\n",
+       "      <td>2.6732</td>\n",
+       "      <td>0.0696</td>\n",
+       "      <td>5.2682</td>\n",
+       "      <td>2.5941</td>\n",
+       "      <td>0.0709</td>\n",
+       "      <td>4.9901</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>2.6623</td>\n",
+       "      <td>2.5941</td>\n",
+       "      <td>41</td>\n",
+       "      <td>2.5941</td>\n",
+       "      <td>41</td>\n",
+       "      <td>2.8758</td>\n",
+       "      <td>0.0769</td>\n",
+       "      <td>5.6416</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-06-13:56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>surgery_t12_6</td>\n",
+       "      <td>2.6984</td>\n",
+       "      <td>0.0711</td>\n",
+       "      <td>5.3523</td>\n",
+       "      <td>2.6909</td>\n",
+       "      <td>0.0746</td>\n",
+       "      <td>5.2014</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>2.6486</td>\n",
+       "      <td>2.6909</td>\n",
+       "      <td>49</td>\n",
+       "      <td>2.6909</td>\n",
+       "      <td>39</td>\n",
+       "      <td>2.9952</td>\n",
+       "      <td>0.0817</td>\n",
+       "      <td>5.9641</td>\n",
+       "      <td>2.8771</td>\n",
+       "      <td>12-04-23:25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_clip_3_lrd_97_nhid40_v2</td>\n",
+       "      <td>2.7168</td>\n",
+       "      <td>0.0720</td>\n",
+       "      <td>5.4021</td>\n",
+       "      <td>2.7114</td>\n",
+       "      <td>0.0752</td>\n",
+       "      <td>5.2668</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>2.6861</td>\n",
+       "      <td>2.7114</td>\n",
+       "      <td>87</td>\n",
+       "      <td>2.7386</td>\n",
+       "      <td>39</td>\n",
+       "      <td>2.9981</td>\n",
+       "      <td>0.0817</td>\n",
+       "      <td>5.9847</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-04-17:29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>replace-0-feat_v2</td>\n",
+       "      <td>2.7361</td>\n",
+       "      <td>0.0727</td>\n",
+       "      <td>5.4589</td>\n",
+       "      <td>2.7203</td>\n",
+       "      <td>0.0752</td>\n",
+       "      <td>5.2630</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>2.7040</td>\n",
+       "      <td>2.7203</td>\n",
+       "      <td>75</td>\n",
+       "      <td>2.7282</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.0002</td>\n",
+       "      <td>0.0818</td>\n",
+       "      <td>5.9746</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-23:47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>skip_channels_400</td>\n",
+       "      <td>2.7581</td>\n",
+       "      <td>0.0734</td>\n",
+       "      <td>5.5062</td>\n",
+       "      <td>2.7234</td>\n",
+       "      <td>0.0757</td>\n",
+       "      <td>5.2921</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>2.7171</td>\n",
+       "      <td>2.7234</td>\n",
+       "      <td>69</td>\n",
+       "      <td>2.7234</td>\n",
+       "      <td>59</td>\n",
+       "      <td>3.0020</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>5.9804</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-16:04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid40_lrd_0.99</td>\n",
+       "      <td>2.8849</td>\n",
+       "      <td>0.0777</td>\n",
+       "      <td>5.7723</td>\n",
+       "      <td>2.7371</td>\n",
+       "      <td>0.0763</td>\n",
+       "      <td>5.2582</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>2.8675</td>\n",
+       "      <td>2.7371</td>\n",
+       "      <td>31</td>\n",
+       "      <td>2.7371</td>\n",
+       "      <td>31</td>\n",
+       "      <td>3.0040</td>\n",
+       "      <td>0.0817</td>\n",
+       "      <td>5.9994</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-11:24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>replace_0_feat</td>\n",
+       "      <td>2.7287</td>\n",
+       "      <td>0.0723</td>\n",
+       "      <td>5.4418</td>\n",
+       "      <td>2.7167</td>\n",
+       "      <td>0.0753</td>\n",
+       "      <td>5.2939</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>2.7010</td>\n",
+       "      <td>2.7167</td>\n",
+       "      <td>79</td>\n",
+       "      <td>2.7282</td>\n",
+       "      <td>52</td>\n",
+       "      <td>3.0070</td>\n",
+       "      <td>0.0825</td>\n",
+       "      <td>6.0179</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-10:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_clip_4_lrd_97_nhid40</td>\n",
+       "      <td>2.7034</td>\n",
+       "      <td>0.0714</td>\n",
+       "      <td>5.3699</td>\n",
+       "      <td>2.8135</td>\n",
+       "      <td>0.0782</td>\n",
+       "      <td>5.5668</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>2.6908</td>\n",
+       "      <td>2.8172</td>\n",
+       "      <td>77</td>\n",
+       "      <td>2.8172</td>\n",
+       "      <td>67</td>\n",
+       "      <td>3.0099</td>\n",
+       "      <td>0.0811</td>\n",
+       "      <td>6.0230</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-03-19:33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid40_lrd_0.95</td>\n",
+       "      <td>2.7601</td>\n",
+       "      <td>0.0735</td>\n",
+       "      <td>5.4993</td>\n",
+       "      <td>2.7101</td>\n",
+       "      <td>0.0749</td>\n",
+       "      <td>5.2541</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>2.7527</td>\n",
+       "      <td>2.7128</td>\n",
+       "      <td>77</td>\n",
+       "      <td>2.7128</td>\n",
+       "      <td>67</td>\n",
+       "      <td>3.0106</td>\n",
+       "      <td>0.0817</td>\n",
+       "      <td>6.0009</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-13:39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid_42</td>\n",
+       "      <td>2.7327</td>\n",
+       "      <td>0.0723</td>\n",
+       "      <td>5.4273</td>\n",
+       "      <td>2.7160</td>\n",
+       "      <td>0.0739</td>\n",
+       "      <td>5.2762</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>2.7327</td>\n",
+       "      <td>2.7160</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7222</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3.0114</td>\n",
+       "      <td>0.0809</td>\n",
+       "      <td>6.0094</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-03-00:09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>logs_nhid_36</td>\n",
+       "      <td>2.7866</td>\n",
+       "      <td>0.0744</td>\n",
+       "      <td>5.5750</td>\n",
+       "      <td>2.7273</td>\n",
+       "      <td>0.0753</td>\n",
+       "      <td>5.2803</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>2.7587</td>\n",
+       "      <td>2.7273</td>\n",
+       "      <td>89</td>\n",
+       "      <td>2.7293</td>\n",
+       "      <td>67</td>\n",
+       "      <td>3.0117</td>\n",
+       "      <td>0.0816</td>\n",
+       "      <td>5.9907</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-04:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>end_channels_360</td>\n",
+       "      <td>2.7596</td>\n",
+       "      <td>0.0734</td>\n",
+       "      <td>5.5119</td>\n",
+       "      <td>2.7203</td>\n",
+       "      <td>0.0744</td>\n",
+       "      <td>5.2728</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>2.7235</td>\n",
+       "      <td>2.7203</td>\n",
+       "      <td>70</td>\n",
+       "      <td>2.7203</td>\n",
+       "      <td>60</td>\n",
+       "      <td>3.0119</td>\n",
+       "      <td>0.0807</td>\n",
+       "      <td>5.9924</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-12:21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_clip_4_lrd_97_nhid40_v2</td>\n",
+       "      <td>2.7016</td>\n",
+       "      <td>0.0715</td>\n",
+       "      <td>5.3795</td>\n",
+       "      <td>2.7189</td>\n",
+       "      <td>0.0750</td>\n",
+       "      <td>5.2804</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2.6898</td>\n",
+       "      <td>2.7189</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7225</td>\n",
+       "      <td>78</td>\n",
+       "      <td>3.0125</td>\n",
+       "      <td>0.0818</td>\n",
+       "      <td>6.0135</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-04-19:06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>surgery_front_3</td>\n",
+       "      <td>2.8049</td>\n",
+       "      <td>0.0749</td>\n",
+       "      <td>5.6067</td>\n",
+       "      <td>2.7280</td>\n",
+       "      <td>0.0750</td>\n",
+       "      <td>5.2713</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>2.7365</td>\n",
+       "      <td>2.7280</td>\n",
+       "      <td>52</td>\n",
+       "      <td>2.7280</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.0132</td>\n",
+       "      <td>0.0818</td>\n",
+       "      <td>5.9776</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-06-15:24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>logs_0.001</td>\n",
+       "      <td>2.7978</td>\n",
+       "      <td>0.0749</td>\n",
+       "      <td>5.5934</td>\n",
+       "      <td>2.7301</td>\n",
+       "      <td>0.0757</td>\n",
+       "      <td>5.3018</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>2.7850</td>\n",
+       "      <td>2.7301</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7326</td>\n",
+       "      <td>76</td>\n",
+       "      <td>3.0133</td>\n",
+       "      <td>0.0832</td>\n",
+       "      <td>6.0157</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-01:05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_clip_2_lrd_97_nhid40</td>\n",
+       "      <td>2.7128</td>\n",
+       "      <td>0.0718</td>\n",
+       "      <td>5.3965</td>\n",
+       "      <td>2.7117</td>\n",
+       "      <td>0.0746</td>\n",
+       "      <td>5.2571</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>2.6956</td>\n",
+       "      <td>2.7117</td>\n",
+       "      <td>97</td>\n",
+       "      <td>2.7127</td>\n",
+       "      <td>75</td>\n",
+       "      <td>3.0134</td>\n",
+       "      <td>0.0814</td>\n",
+       "      <td>6.0110</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-03-12:29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>logs_nhid_40</td>\n",
+       "      <td>2.7485</td>\n",
+       "      <td>0.0730</td>\n",
+       "      <td>5.4782</td>\n",
+       "      <td>2.7211</td>\n",
+       "      <td>0.0754</td>\n",
+       "      <td>5.2888</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>2.7220</td>\n",
+       "      <td>2.7211</td>\n",
+       "      <td>92</td>\n",
+       "      <td>2.7232</td>\n",
+       "      <td>64</td>\n",
+       "      <td>3.0146</td>\n",
+       "      <td>0.0816</td>\n",
+       "      <td>6.0093</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-05:49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>hist_6</td>\n",
+       "      <td>2.7159</td>\n",
+       "      <td>0.0718</td>\n",
+       "      <td>5.4117</td>\n",
+       "      <td>2.7335</td>\n",
+       "      <td>0.0754</td>\n",
+       "      <td>5.3293</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>2.7038</td>\n",
+       "      <td>2.7335</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7500</td>\n",
+       "      <td>49</td>\n",
+       "      <td>3.0148</td>\n",
+       "      <td>0.0821</td>\n",
+       "      <td>6.0289</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-06-17:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline_lr_decay</td>\n",
+       "      <td>2.7915</td>\n",
+       "      <td>0.0747</td>\n",
+       "      <td>5.5931</td>\n",
+       "      <td>2.7222</td>\n",
+       "      <td>0.0757</td>\n",
+       "      <td>5.2941</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>2.7891</td>\n",
+       "      <td>2.7222</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7222</td>\n",
+       "      <td>99</td>\n",
+       "      <td>3.0159</td>\n",
+       "      <td>0.0816</td>\n",
+       "      <td>6.0115</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-20:18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid40_lrd_0.98</td>\n",
+       "      <td>2.7587</td>\n",
+       "      <td>0.0733</td>\n",
+       "      <td>5.4941</td>\n",
+       "      <td>2.7258</td>\n",
+       "      <td>0.0755</td>\n",
+       "      <td>5.2913</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>2.7478</td>\n",
+       "      <td>2.7258</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7290</td>\n",
+       "      <td>71</td>\n",
+       "      <td>3.0166</td>\n",
+       "      <td>0.0821</td>\n",
+       "      <td>6.0022</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-15:03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>end_channels_400</td>\n",
+       "      <td>2.7151</td>\n",
+       "      <td>0.0718</td>\n",
+       "      <td>5.3992</td>\n",
+       "      <td>2.7065</td>\n",
+       "      <td>0.0753</td>\n",
+       "      <td>5.2468</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>2.6965</td>\n",
+       "      <td>2.7065</td>\n",
+       "      <td>93</td>\n",
+       "      <td>2.7098</td>\n",
+       "      <td>68</td>\n",
+       "      <td>3.0168</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>6.0190</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-13:40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline_lrd_nhid_40_cat_feat_early_gc</td>\n",
+       "      <td>2.7387</td>\n",
+       "      <td>0.0727</td>\n",
+       "      <td>5.4660</td>\n",
+       "      <td>2.7216</td>\n",
+       "      <td>0.0758</td>\n",
+       "      <td>5.2958</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>2.6887</td>\n",
+       "      <td>2.7216</td>\n",
+       "      <td>76</td>\n",
+       "      <td>2.7216</td>\n",
+       "      <td>66</td>\n",
+       "      <td>3.0179</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>6.0495</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-15:41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>dow_one_hot</td>\n",
+       "      <td>2.7594</td>\n",
+       "      <td>0.0733</td>\n",
+       "      <td>5.5027</td>\n",
+       "      <td>2.7331</td>\n",
+       "      <td>0.0749</td>\n",
+       "      <td>5.2938</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>2.7028</td>\n",
+       "      <td>2.7331</td>\n",
+       "      <td>56</td>\n",
+       "      <td>2.7331</td>\n",
+       "      <td>46</td>\n",
+       "      <td>3.0183</td>\n",
+       "      <td>0.0830</td>\n",
+       "      <td>6.0433</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-06-11:43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>skip_channels_240</td>\n",
+       "      <td>2.7536</td>\n",
+       "      <td>0.0731</td>\n",
+       "      <td>5.4940</td>\n",
+       "      <td>2.7203</td>\n",
+       "      <td>0.0748</td>\n",
+       "      <td>5.2934</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>2.7193</td>\n",
+       "      <td>2.7203</td>\n",
+       "      <td>75</td>\n",
+       "      <td>2.7458</td>\n",
+       "      <td>39</td>\n",
+       "      <td>3.0184</td>\n",
+       "      <td>0.0817</td>\n",
+       "      <td>6.0187</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-15:03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline_temp_2</td>\n",
+       "      <td>2.8174</td>\n",
+       "      <td>0.0756</td>\n",
+       "      <td>5.6405</td>\n",
+       "      <td>2.7261</td>\n",
+       "      <td>0.0752</td>\n",
+       "      <td>5.2852</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>2.8093</td>\n",
+       "      <td>2.7261</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7566</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.0198</td>\n",
+       "      <td>0.0819</td>\n",
+       "      <td>5.9979</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-22:08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_clip_8_lrd_97_nhid_40</td>\n",
+       "      <td>2.7095</td>\n",
+       "      <td>0.0717</td>\n",
+       "      <td>5.3899</td>\n",
+       "      <td>2.7224</td>\n",
+       "      <td>0.0759</td>\n",
+       "      <td>5.2885</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2.6897</td>\n",
+       "      <td>2.7224</td>\n",
+       "      <td>96</td>\n",
+       "      <td>2.7555</td>\n",
+       "      <td>31</td>\n",
+       "      <td>3.0211</td>\n",
+       "      <td>0.0824</td>\n",
+       "      <td>6.0427</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-03-14:47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>sm_1.5</td>\n",
+       "      <td>2.7333</td>\n",
+       "      <td>0.0724</td>\n",
+       "      <td>5.4382</td>\n",
+       "      <td>2.7321</td>\n",
+       "      <td>0.0747</td>\n",
+       "      <td>5.2941</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>2.7054</td>\n",
+       "      <td>2.7321</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2.7348</td>\n",
+       "      <td>55</td>\n",
+       "      <td>3.0213</td>\n",
+       "      <td>0.0806</td>\n",
+       "      <td>5.9997</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-06-01:22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cfg_node_bias_nhid_36_lrd_99_lr_0.002</td>\n",
+       "      <td>2.7662</td>\n",
+       "      <td>0.0736</td>\n",
+       "      <td>5.5168</td>\n",
+       "      <td>2.7245</td>\n",
+       "      <td>0.0754</td>\n",
+       "      <td>5.3007</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2.7076</td>\n",
+       "      <td>2.7245</td>\n",
+       "      <td>72</td>\n",
+       "      <td>2.7269</td>\n",
+       "      <td>46</td>\n",
+       "      <td>3.0214</td>\n",
+       "      <td>0.0828</td>\n",
+       "      <td>6.0458</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-19:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_clip_1_lrd_97_nhid40</td>\n",
+       "      <td>2.7689</td>\n",
+       "      <td>0.0737</td>\n",
+       "      <td>5.5271</td>\n",
+       "      <td>2.7155</td>\n",
+       "      <td>0.0742</td>\n",
+       "      <td>5.2606</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>2.7483</td>\n",
+       "      <td>2.7155</td>\n",
+       "      <td>94</td>\n",
+       "      <td>2.7306</td>\n",
+       "      <td>52</td>\n",
+       "      <td>3.0220</td>\n",
+       "      <td>0.0811</td>\n",
+       "      <td>6.0025</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-03-16:25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_clip_3_lrd_97_nhid40</td>\n",
+       "      <td>2.7134</td>\n",
+       "      <td>0.0718</td>\n",
+       "      <td>5.3979</td>\n",
+       "      <td>2.7035</td>\n",
+       "      <td>0.0754</td>\n",
+       "      <td>5.2344</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>2.6835</td>\n",
+       "      <td>2.7035</td>\n",
+       "      <td>88</td>\n",
+       "      <td>2.7193</td>\n",
+       "      <td>58</td>\n",
+       "      <td>3.0223</td>\n",
+       "      <td>0.0832</td>\n",
+       "      <td>6.0249</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-03-17:50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline_lrd_nhid_40_cat_feat_early_gc_wd5</td>\n",
+       "      <td>2.8244</td>\n",
+       "      <td>0.0756</td>\n",
+       "      <td>5.6375</td>\n",
+       "      <td>2.7318</td>\n",
+       "      <td>0.0756</td>\n",
+       "      <td>5.2679</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>2.7967</td>\n",
+       "      <td>2.7318</td>\n",
+       "      <td>92</td>\n",
+       "      <td>2.7318</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3.0242</td>\n",
+       "      <td>0.0828</td>\n",
+       "      <td>5.9898</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-17:19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline_temp_0.5</td>\n",
+       "      <td>2.8096</td>\n",
+       "      <td>0.0753</td>\n",
+       "      <td>5.6231</td>\n",
+       "      <td>2.7331</td>\n",
+       "      <td>0.0758</td>\n",
+       "      <td>5.3033</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.8073</td>\n",
+       "      <td>2.7331</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7416</td>\n",
+       "      <td>78</td>\n",
+       "      <td>3.0244</td>\n",
+       "      <td>0.0821</td>\n",
+       "      <td>6.0311</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-23:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cfg_nhid_40_lrd_0.94</td>\n",
+       "      <td>2.7714</td>\n",
+       "      <td>0.0738</td>\n",
+       "      <td>5.5301</td>\n",
+       "      <td>2.7218</td>\n",
+       "      <td>0.0758</td>\n",
+       "      <td>5.2801</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>2.7549</td>\n",
+       "      <td>2.7218</td>\n",
+       "      <td>98</td>\n",
+       "      <td>2.7487</td>\n",
+       "      <td>46</td>\n",
+       "      <td>3.0260</td>\n",
+       "      <td>0.0830</td>\n",
+       "      <td>6.0273</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-23:50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid_38</td>\n",
+       "      <td>2.7633</td>\n",
+       "      <td>0.0737</td>\n",
+       "      <td>5.5157</td>\n",
+       "      <td>2.7159</td>\n",
+       "      <td>0.0748</td>\n",
+       "      <td>5.2437</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>2.7602</td>\n",
+       "      <td>2.7159</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7189</td>\n",
+       "      <td>86</td>\n",
+       "      <td>3.0262</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>6.0221</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-22:37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cfg_node_bias_nhid_40_lrd_99</td>\n",
+       "      <td>2.7930</td>\n",
+       "      <td>0.0747</td>\n",
+       "      <td>5.5820</td>\n",
+       "      <td>2.7268</td>\n",
+       "      <td>0.0775</td>\n",
+       "      <td>5.2685</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>2.7493</td>\n",
+       "      <td>2.7268</td>\n",
+       "      <td>71</td>\n",
+       "      <td>2.7450</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3.0268</td>\n",
+       "      <td>0.0843</td>\n",
+       "      <td>5.9900</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-17:58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>replace_0_scale-no-mask</td>\n",
+       "      <td>2.7472</td>\n",
+       "      <td>0.0729</td>\n",
+       "      <td>5.4767</td>\n",
+       "      <td>2.7473</td>\n",
+       "      <td>0.0764</td>\n",
+       "      <td>5.3935</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>2.7073</td>\n",
+       "      <td>2.7473</td>\n",
+       "      <td>66</td>\n",
+       "      <td>2.7473</td>\n",
+       "      <td>56</td>\n",
+       "      <td>3.0271</td>\n",
+       "      <td>0.0832</td>\n",
+       "      <td>6.0864</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-11:23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid_34</td>\n",
+       "      <td>2.8601</td>\n",
+       "      <td>0.0768</td>\n",
+       "      <td>5.7332</td>\n",
+       "      <td>2.7357</td>\n",
+       "      <td>0.0762</td>\n",
+       "      <td>5.2769</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>2.8537</td>\n",
+       "      <td>2.7357</td>\n",
+       "      <td>47</td>\n",
+       "      <td>2.7357</td>\n",
+       "      <td>47</td>\n",
+       "      <td>3.0273</td>\n",
+       "      <td>0.0830</td>\n",
+       "      <td>6.0413</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-20:38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline_leaky_relu</td>\n",
+       "      <td>2.7963</td>\n",
+       "      <td>0.0747</td>\n",
+       "      <td>5.5884</td>\n",
+       "      <td>2.7296</td>\n",
+       "      <td>0.0762</td>\n",
+       "      <td>5.3019</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>2.7902</td>\n",
+       "      <td>2.7359</td>\n",
+       "      <td>84</td>\n",
+       "      <td>2.7430</td>\n",
+       "      <td>57</td>\n",
+       "      <td>3.0282</td>\n",
+       "      <td>0.0824</td>\n",
+       "      <td>6.0221</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-20:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>gc_bn</td>\n",
+       "      <td>2.7406</td>\n",
+       "      <td>0.0725</td>\n",
+       "      <td>5.4394</td>\n",
+       "      <td>2.7322</td>\n",
+       "      <td>0.0750</td>\n",
+       "      <td>5.3037</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>2.6920</td>\n",
+       "      <td>2.7322</td>\n",
+       "      <td>63</td>\n",
+       "      <td>2.7347</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3.0289</td>\n",
+       "      <td>0.0817</td>\n",
+       "      <td>6.0406</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-21:40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid40_lrd_0.99_v2</td>\n",
+       "      <td>2.7391</td>\n",
+       "      <td>0.0726</td>\n",
+       "      <td>5.4394</td>\n",
+       "      <td>2.8334</td>\n",
+       "      <td>0.0785</td>\n",
+       "      <td>5.6040</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>2.6550</td>\n",
+       "      <td>2.8334</td>\n",
+       "      <td>74</td>\n",
+       "      <td>2.8334</td>\n",
+       "      <td>64</td>\n",
+       "      <td>3.0313</td>\n",
+       "      <td>0.0817</td>\n",
+       "      <td>6.0403</td>\n",
+       "      <td>3.0113</td>\n",
+       "      <td>12-03-21:47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline_nhid_48</td>\n",
+       "      <td>2.7942</td>\n",
+       "      <td>0.0746</td>\n",
+       "      <td>5.5748</td>\n",
+       "      <td>2.7294</td>\n",
+       "      <td>0.0749</td>\n",
+       "      <td>5.2692</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>2.6489</td>\n",
+       "      <td>2.7294</td>\n",
+       "      <td>53</td>\n",
+       "      <td>2.7294</td>\n",
+       "      <td>43</td>\n",
+       "      <td>3.0332</td>\n",
+       "      <td>0.0816</td>\n",
+       "      <td>6.0327</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-21:38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>end_channels_280</td>\n",
+       "      <td>2.7711</td>\n",
+       "      <td>0.0738</td>\n",
+       "      <td>5.5316</td>\n",
+       "      <td>2.7139</td>\n",
+       "      <td>0.0749</td>\n",
+       "      <td>5.2543</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>2.7304</td>\n",
+       "      <td>2.7139</td>\n",
+       "      <td>67</td>\n",
+       "      <td>2.7139</td>\n",
+       "      <td>57</td>\n",
+       "      <td>3.0332</td>\n",
+       "      <td>0.0821</td>\n",
+       "      <td>6.0429</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-12:19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline_no_weight_decay</td>\n",
+       "      <td>2.7690</td>\n",
+       "      <td>0.0738</td>\n",
+       "      <td>5.5304</td>\n",
+       "      <td>2.7324</td>\n",
+       "      <td>0.0752</td>\n",
+       "      <td>5.3048</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>2.7605</td>\n",
+       "      <td>2.7324</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7420</td>\n",
+       "      <td>55</td>\n",
+       "      <td>3.0333</td>\n",
+       "      <td>0.0818</td>\n",
+       "      <td>6.0438</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-21:24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid_28</td>\n",
+       "      <td>2.8604</td>\n",
+       "      <td>0.0770</td>\n",
+       "      <td>5.7362</td>\n",
+       "      <td>2.7440</td>\n",
+       "      <td>0.0759</td>\n",
+       "      <td>5.2986</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>2.8418</td>\n",
+       "      <td>2.7440</td>\n",
+       "      <td>92</td>\n",
+       "      <td>2.7626</td>\n",
+       "      <td>60</td>\n",
+       "      <td>3.0336</td>\n",
+       "      <td>0.0828</td>\n",
+       "      <td>6.0115</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-19:45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cat_feat_early_gc_10_rand</td>\n",
+       "      <td>2.8320</td>\n",
+       "      <td>0.0759</td>\n",
+       "      <td>5.6565</td>\n",
+       "      <td>2.7309</td>\n",
+       "      <td>0.0760</td>\n",
+       "      <td>5.2880</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>2.7231</td>\n",
+       "      <td>2.7309</td>\n",
+       "      <td>58</td>\n",
+       "      <td>2.7309</td>\n",
+       "      <td>48</td>\n",
+       "      <td>3.0342</td>\n",
+       "      <td>0.0828</td>\n",
+       "      <td>6.0358</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-19:29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>replace_0_mean_hax</td>\n",
+       "      <td>2.7035</td>\n",
+       "      <td>0.0714</td>\n",
+       "      <td>5.3743</td>\n",
+       "      <td>2.7308</td>\n",
+       "      <td>0.0747</td>\n",
+       "      <td>5.3098</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>2.6854</td>\n",
+       "      <td>2.7308</td>\n",
+       "      <td>94</td>\n",
+       "      <td>2.7559</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0359</td>\n",
+       "      <td>0.0824</td>\n",
+       "      <td>6.0808</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-18:16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cat_feat_early</td>\n",
+       "      <td>2.7591</td>\n",
+       "      <td>0.0732</td>\n",
+       "      <td>5.4826</td>\n",
+       "      <td>2.7437</td>\n",
+       "      <td>0.0764</td>\n",
+       "      <td>5.3119</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>2.7557</td>\n",
+       "      <td>2.7471</td>\n",
+       "      <td>77</td>\n",
+       "      <td>2.7471</td>\n",
+       "      <td>67</td>\n",
+       "      <td>3.0378</td>\n",
+       "      <td>0.0827</td>\n",
+       "      <td>6.0372</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-17:33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cat_feat_early_gc_10_nhid_48_v2</td>\n",
+       "      <td>2.7225</td>\n",
+       "      <td>0.0719</td>\n",
+       "      <td>5.3839</td>\n",
+       "      <td>2.7312</td>\n",
+       "      <td>0.0750</td>\n",
+       "      <td>5.3134</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>2.6586</td>\n",
+       "      <td>2.7312</td>\n",
+       "      <td>65</td>\n",
+       "      <td>2.7577</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3.0384</td>\n",
+       "      <td>0.0819</td>\n",
+       "      <td>6.0953</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-11:34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>add_gc_baseline</td>\n",
+       "      <td>2.8039</td>\n",
+       "      <td>0.0749</td>\n",
+       "      <td>5.5808</td>\n",
+       "      <td>2.7337</td>\n",
+       "      <td>0.0751</td>\n",
+       "      <td>5.2846</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>2.7182</td>\n",
+       "      <td>2.7337</td>\n",
+       "      <td>68</td>\n",
+       "      <td>2.7421</td>\n",
+       "      <td>46</td>\n",
+       "      <td>3.0388</td>\n",
+       "      <td>0.0823</td>\n",
+       "      <td>6.0412</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-00:01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cfg_nhid_40_lrd_0.96</td>\n",
+       "      <td>2.8415</td>\n",
+       "      <td>0.0763</td>\n",
+       "      <td>5.6596</td>\n",
+       "      <td>2.7496</td>\n",
+       "      <td>0.0771</td>\n",
+       "      <td>5.3007</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>2.8379</td>\n",
+       "      <td>2.7496</td>\n",
+       "      <td>74</td>\n",
+       "      <td>2.7497</td>\n",
+       "      <td>52</td>\n",
+       "      <td>3.0390</td>\n",
+       "      <td>0.0836</td>\n",
+       "      <td>6.0135</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-22:04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>logs_nhid_44</td>\n",
+       "      <td>2.7390</td>\n",
+       "      <td>0.0727</td>\n",
+       "      <td>5.4474</td>\n",
+       "      <td>2.7332</td>\n",
+       "      <td>0.0760</td>\n",
+       "      <td>5.2928</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>2.6949</td>\n",
+       "      <td>2.7332</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2.7332</td>\n",
+       "      <td>70</td>\n",
+       "      <td>3.0392</td>\n",
+       "      <td>0.0826</td>\n",
+       "      <td>6.0374</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-07:06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>dow_raw</td>\n",
+       "      <td>2.7850</td>\n",
+       "      <td>0.0744</td>\n",
+       "      <td>5.5603</td>\n",
+       "      <td>2.7453</td>\n",
+       "      <td>0.0766</td>\n",
+       "      <td>5.2849</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2.7229</td>\n",
+       "      <td>2.7453</td>\n",
+       "      <td>55</td>\n",
+       "      <td>2.7453</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.0397</td>\n",
+       "      <td>0.0840</td>\n",
+       "      <td>6.0908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-11:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline_gelu</td>\n",
+       "      <td>2.8467</td>\n",
+       "      <td>0.0762</td>\n",
+       "      <td>5.6851</td>\n",
+       "      <td>2.7417</td>\n",
+       "      <td>0.0760</td>\n",
+       "      <td>5.3045</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>2.8401</td>\n",
+       "      <td>2.7417</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7445</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3.0418</td>\n",
+       "      <td>0.0823</td>\n",
+       "      <td>6.0493</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-19:36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>layers3</td>\n",
+       "      <td>2.8352</td>\n",
+       "      <td>0.0761</td>\n",
+       "      <td>5.6685</td>\n",
+       "      <td>2.7381</td>\n",
+       "      <td>0.0770</td>\n",
+       "      <td>5.3013</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>2.7606</td>\n",
+       "      <td>2.7381</td>\n",
+       "      <td>66</td>\n",
+       "      <td>2.7381</td>\n",
+       "      <td>56</td>\n",
+       "      <td>3.0428</td>\n",
+       "      <td>0.0842</td>\n",
+       "      <td>6.0639</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-14:33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>apt_only_256_rand_gc</td>\n",
+       "      <td>2.7976</td>\n",
+       "      <td>0.0749</td>\n",
+       "      <td>5.5994</td>\n",
+       "      <td>2.7528</td>\n",
+       "      <td>0.0762</td>\n",
+       "      <td>5.3495</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2.7592</td>\n",
+       "      <td>2.7528</td>\n",
+       "      <td>96</td>\n",
+       "      <td>2.7704</td>\n",
+       "      <td>54</td>\n",
+       "      <td>3.0432</td>\n",
+       "      <td>0.0835</td>\n",
+       "      <td>6.0788</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-12:30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>end_channels_200</td>\n",
+       "      <td>2.7597</td>\n",
+       "      <td>0.0733</td>\n",
+       "      <td>5.4977</td>\n",
+       "      <td>2.7160</td>\n",
+       "      <td>0.0731</td>\n",
+       "      <td>5.2789</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2.7244</td>\n",
+       "      <td>2.7160</td>\n",
+       "      <td>72</td>\n",
+       "      <td>2.7160</td>\n",
+       "      <td>62</td>\n",
+       "      <td>3.0435</td>\n",
+       "      <td>0.0805</td>\n",
+       "      <td>6.0801</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-13:19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cat_feat_early_gc_10_nhid_48_dropout_40</td>\n",
+       "      <td>2.7499</td>\n",
+       "      <td>0.0729</td>\n",
+       "      <td>5.4627</td>\n",
+       "      <td>2.7462</td>\n",
+       "      <td>0.0760</td>\n",
+       "      <td>5.3425</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>2.6842</td>\n",
+       "      <td>2.7482</td>\n",
+       "      <td>50</td>\n",
+       "      <td>2.7482</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.0445</td>\n",
+       "      <td>0.0820</td>\n",
+       "      <td>6.0604</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-03:19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cat_feat_early_gc_10_nhid_64</td>\n",
+       "      <td>2.7712</td>\n",
+       "      <td>0.0737</td>\n",
+       "      <td>5.5109</td>\n",
+       "      <td>2.7309</td>\n",
+       "      <td>0.0754</td>\n",
+       "      <td>5.2928</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>2.5892</td>\n",
+       "      <td>2.7309</td>\n",
+       "      <td>47</td>\n",
+       "      <td>2.7309</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0459</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>6.0568</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-00:56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cat_feat_early_gc_10_nhid_48</td>\n",
+       "      <td>2.7492</td>\n",
+       "      <td>0.0729</td>\n",
+       "      <td>5.4579</td>\n",
+       "      <td>2.7392</td>\n",
+       "      <td>0.0753</td>\n",
+       "      <td>5.3257</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>2.6500</td>\n",
+       "      <td>2.7392</td>\n",
+       "      <td>66</td>\n",
+       "      <td>2.7574</td>\n",
+       "      <td>28</td>\n",
+       "      <td>3.0468</td>\n",
+       "      <td>0.0816</td>\n",
+       "      <td>6.0635</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-23:30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cat_feat_early_gc_6</td>\n",
+       "      <td>2.8353</td>\n",
+       "      <td>0.0760</td>\n",
+       "      <td>5.6567</td>\n",
+       "      <td>2.7416</td>\n",
+       "      <td>0.0762</td>\n",
+       "      <td>5.3152</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>2.7510</td>\n",
+       "      <td>2.7416</td>\n",
+       "      <td>67</td>\n",
+       "      <td>2.7750</td>\n",
+       "      <td>36</td>\n",
+       "      <td>3.0477</td>\n",
+       "      <td>0.0831</td>\n",
+       "      <td>6.0719</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-06:10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cat_feat_early_gc_10_nhid_48_dropout_20</td>\n",
+       "      <td>2.7246</td>\n",
+       "      <td>0.0719</td>\n",
+       "      <td>5.3885</td>\n",
+       "      <td>2.7457</td>\n",
+       "      <td>0.0760</td>\n",
+       "      <td>5.3114</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>2.6156</td>\n",
+       "      <td>2.7457</td>\n",
+       "      <td>67</td>\n",
+       "      <td>2.7467</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.0479</td>\n",
+       "      <td>0.0825</td>\n",
+       "      <td>6.0810</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-04:46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_surgery6_finetune</td>\n",
+       "      <td>2.7106</td>\n",
+       "      <td>0.0713</td>\n",
+       "      <td>5.3516</td>\n",
+       "      <td>2.7356</td>\n",
+       "      <td>0.0742</td>\n",
+       "      <td>5.2844</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>2.6593</td>\n",
+       "      <td>2.7356</td>\n",
+       "      <td>68</td>\n",
+       "      <td>2.7356</td>\n",
+       "      <td>58</td>\n",
+       "      <td>3.0483</td>\n",
+       "      <td>0.0816</td>\n",
+       "      <td>6.0711</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-04-18:13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid_24</td>\n",
+       "      <td>2.8841</td>\n",
+       "      <td>0.0779</td>\n",
+       "      <td>5.7705</td>\n",
+       "      <td>2.7462</td>\n",
+       "      <td>0.0761</td>\n",
+       "      <td>5.3066</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>2.8743</td>\n",
+       "      <td>2.7462</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7959</td>\n",
+       "      <td>47</td>\n",
+       "      <td>3.0500</td>\n",
+       "      <td>0.0830</td>\n",
+       "      <td>6.0435</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-18:45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>init_extra_adj</td>\n",
+       "      <td>2.7733</td>\n",
+       "      <td>0.0738</td>\n",
+       "      <td>5.5150</td>\n",
+       "      <td>2.7376</td>\n",
+       "      <td>0.0762</td>\n",
+       "      <td>5.3351</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>2.7272</td>\n",
+       "      <td>2.7376</td>\n",
+       "      <td>87</td>\n",
+       "      <td>2.7462</td>\n",
+       "      <td>65</td>\n",
+       "      <td>3.0505</td>\n",
+       "      <td>0.0833</td>\n",
+       "      <td>6.1143</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-12:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline7</td>\n",
+       "      <td>2.7886</td>\n",
+       "      <td>0.0743</td>\n",
+       "      <td>5.5527</td>\n",
+       "      <td>2.7351</td>\n",
+       "      <td>0.0750</td>\n",
+       "      <td>5.2642</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>2.7523</td>\n",
+       "      <td>2.7351</td>\n",
+       "      <td>90</td>\n",
+       "      <td>2.7484</td>\n",
+       "      <td>55</td>\n",
+       "      <td>3.0544</td>\n",
+       "      <td>0.0823</td>\n",
+       "      <td>6.0684</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-02:42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>hist_1</td>\n",
+       "      <td>2.7840</td>\n",
+       "      <td>0.0740</td>\n",
+       "      <td>5.5698</td>\n",
+       "      <td>2.7797</td>\n",
+       "      <td>0.0774</td>\n",
+       "      <td>5.4092</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>2.7712</td>\n",
+       "      <td>2.7797</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.8059</td>\n",
+       "      <td>48</td>\n",
+       "      <td>3.0584</td>\n",
+       "      <td>0.0849</td>\n",
+       "      <td>6.1150</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-06-16:04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cat_feat_early_gc_256_rand</td>\n",
+       "      <td>2.7940</td>\n",
+       "      <td>0.0745</td>\n",
+       "      <td>5.5731</td>\n",
+       "      <td>2.7451</td>\n",
+       "      <td>0.0748</td>\n",
+       "      <td>5.3086</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2.7249</td>\n",
+       "      <td>2.7451</td>\n",
+       "      <td>72</td>\n",
+       "      <td>2.7598</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3.0586</td>\n",
+       "      <td>0.0820</td>\n",
+       "      <td>6.0972</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-17:28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cat_feat_early_gc_14</td>\n",
+       "      <td>2.7488</td>\n",
+       "      <td>0.0728</td>\n",
+       "      <td>5.4508</td>\n",
+       "      <td>2.7434</td>\n",
+       "      <td>0.0766</td>\n",
+       "      <td>5.3150</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>2.7377</td>\n",
+       "      <td>2.7483</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2.7522</td>\n",
+       "      <td>55</td>\n",
+       "      <td>3.0587</td>\n",
+       "      <td>0.0837</td>\n",
+       "      <td>6.1258</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-07:51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid40_cyclic_v2</td>\n",
+       "      <td>2.7066</td>\n",
+       "      <td>0.0713</td>\n",
+       "      <td>5.3531</td>\n",
+       "      <td>2.8332</td>\n",
+       "      <td>0.0784</td>\n",
+       "      <td>5.5971</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2.6860</td>\n",
+       "      <td>2.8332</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.8408</td>\n",
+       "      <td>57</td>\n",
+       "      <td>3.0606</td>\n",
+       "      <td>0.0821</td>\n",
+       "      <td>6.0994</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-04-00:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>logs_0.0005</td>\n",
+       "      <td>2.9029</td>\n",
+       "      <td>0.0787</td>\n",
+       "      <td>5.8291</td>\n",
+       "      <td>2.7572</td>\n",
+       "      <td>0.0774</td>\n",
+       "      <td>5.3246</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>2.8972</td>\n",
+       "      <td>2.7572</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7572</td>\n",
+       "      <td>99</td>\n",
+       "      <td>3.0624</td>\n",
+       "      <td>0.0838</td>\n",
+       "      <td>6.0522</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-02:15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cheaper-metric-fixed</td>\n",
+       "      <td>2.8063</td>\n",
+       "      <td>0.0746</td>\n",
+       "      <td>5.5674</td>\n",
+       "      <td>2.7439</td>\n",
+       "      <td>0.0751</td>\n",
+       "      <td>5.2858</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>2.7783</td>\n",
+       "      <td>2.7439</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7566</td>\n",
+       "      <td>58</td>\n",
+       "      <td>3.0638</td>\n",
+       "      <td>0.0824</td>\n",
+       "      <td>6.0895</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-19:38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>gc_leaky_relu</td>\n",
+       "      <td>2.8233</td>\n",
+       "      <td>0.0753</td>\n",
+       "      <td>5.6229</td>\n",
+       "      <td>2.7589</td>\n",
+       "      <td>0.0767</td>\n",
+       "      <td>5.3333</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>2.7836</td>\n",
+       "      <td>2.7589</td>\n",
+       "      <td>65</td>\n",
+       "      <td>2.7589</td>\n",
+       "      <td>55</td>\n",
+       "      <td>3.0642</td>\n",
+       "      <td>0.0844</td>\n",
+       "      <td>6.0940</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-05-22:43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>actual_matrix</td>\n",
+       "      <td>2.8702</td>\n",
+       "      <td>0.0772</td>\n",
+       "      <td>5.7285</td>\n",
+       "      <td>2.7673</td>\n",
+       "      <td>0.0764</td>\n",
+       "      <td>5.3261</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>2.8613</td>\n",
+       "      <td>2.7673</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7897</td>\n",
+       "      <td>57</td>\n",
+       "      <td>3.0710</td>\n",
+       "      <td>0.0837</td>\n",
+       "      <td>6.0774</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-02-12:50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>no_add_apt_adj</td>\n",
+       "      <td>2.8291</td>\n",
+       "      <td>0.0754</td>\n",
+       "      <td>5.6288</td>\n",
+       "      <td>2.7831</td>\n",
+       "      <td>0.0752</td>\n",
+       "      <td>5.4045</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>2.8291</td>\n",
+       "      <td>2.7948</td>\n",
+       "      <td>71</td>\n",
+       "      <td>2.7948</td>\n",
+       "      <td>61</td>\n",
+       "      <td>3.0717</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>6.1247</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-21:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline5</td>\n",
+       "      <td>2.8013</td>\n",
+       "      <td>0.0745</td>\n",
+       "      <td>5.5596</td>\n",
+       "      <td>2.7586</td>\n",
+       "      <td>0.0759</td>\n",
+       "      <td>5.3295</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2.7759</td>\n",
+       "      <td>2.7586</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7607</td>\n",
+       "      <td>78</td>\n",
+       "      <td>3.0727</td>\n",
+       "      <td>0.0832</td>\n",
+       "      <td>6.1174</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-19:38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>no_apt_adj</td>\n",
+       "      <td>2.8718</td>\n",
+       "      <td>0.0768</td>\n",
+       "      <td>5.7116</td>\n",
+       "      <td>2.7791</td>\n",
+       "      <td>0.0791</td>\n",
+       "      <td>5.3618</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>2.8266</td>\n",
+       "      <td>2.7791</td>\n",
+       "      <td>92</td>\n",
+       "      <td>2.7983</td>\n",
+       "      <td>55</td>\n",
+       "      <td>3.0783</td>\n",
+       "      <td>0.0862</td>\n",
+       "      <td>6.1080</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-17:07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>bs_512_fixed_lr</td>\n",
+       "      <td>2.8607</td>\n",
+       "      <td>0.0765</td>\n",
+       "      <td>5.6819</td>\n",
+       "      <td>2.7907</td>\n",
+       "      <td>0.0768</td>\n",
+       "      <td>5.6190</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>2.8530</td>\n",
+       "      <td>2.7907</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.8385</td>\n",
+       "      <td>50</td>\n",
+       "      <td>3.0822</td>\n",
+       "      <td>0.0848</td>\n",
+       "      <td>6.1115</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-19:38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>apt_only_512_rand_gc</td>\n",
+       "      <td>2.8395</td>\n",
+       "      <td>0.0763</td>\n",
+       "      <td>5.6848</td>\n",
+       "      <td>2.7665</td>\n",
+       "      <td>0.0778</td>\n",
+       "      <td>5.3448</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>2.6733</td>\n",
+       "      <td>2.7665</td>\n",
+       "      <td>71</td>\n",
+       "      <td>2.7665</td>\n",
+       "      <td>61</td>\n",
+       "      <td>3.0859</td>\n",
+       "      <td>0.0855</td>\n",
+       "      <td>6.1623</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-10:15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>logs_0.005</td>\n",
+       "      <td>2.8102</td>\n",
+       "      <td>0.0749</td>\n",
+       "      <td>5.5830</td>\n",
+       "      <td>2.7563</td>\n",
+       "      <td>0.0760</td>\n",
+       "      <td>5.3101</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>2.6882</td>\n",
+       "      <td>2.7563</td>\n",
+       "      <td>59</td>\n",
+       "      <td>2.7563</td>\n",
+       "      <td>49</td>\n",
+       "      <td>3.0860</td>\n",
+       "      <td>0.0832</td>\n",
+       "      <td>6.1543</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-23:14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>apt_only_aptsize256</td>\n",
+       "      <td>2.8174</td>\n",
+       "      <td>0.0757</td>\n",
+       "      <td>5.6415</td>\n",
+       "      <td>2.7656</td>\n",
+       "      <td>0.0766</td>\n",
+       "      <td>5.3806</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>2.7572</td>\n",
+       "      <td>2.7656</td>\n",
+       "      <td>94</td>\n",
+       "      <td>2.7836</td>\n",
+       "      <td>57</td>\n",
+       "      <td>3.0907</td>\n",
+       "      <td>0.0832</td>\n",
+       "      <td>6.1898</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-19:38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>baseline_nhid_16</td>\n",
+       "      <td>2.9070</td>\n",
+       "      <td>0.0785</td>\n",
+       "      <td>5.8052</td>\n",
+       "      <td>2.7890</td>\n",
+       "      <td>0.0785</td>\n",
+       "      <td>5.3828</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>2.9003</td>\n",
+       "      <td>2.7890</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.7959</td>\n",
+       "      <td>75</td>\n",
+       "      <td>3.0941</td>\n",
+       "      <td>0.0852</td>\n",
+       "      <td>6.1398</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-21:03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1feat</td>\n",
+       "      <td>2.8542</td>\n",
+       "      <td>0.0762</td>\n",
+       "      <td>5.6696</td>\n",
+       "      <td>2.7812</td>\n",
+       "      <td>0.0777</td>\n",
+       "      <td>5.3344</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>2.8082</td>\n",
+       "      <td>2.7812</td>\n",
+       "      <td>84</td>\n",
+       "      <td>2.7914</td>\n",
+       "      <td>52</td>\n",
+       "      <td>3.0958</td>\n",
+       "      <td>0.0847</td>\n",
+       "      <td>6.1218</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-19:38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid_40_fp16O1_lrd_99</td>\n",
+       "      <td>2.7017</td>\n",
+       "      <td>0.0711</td>\n",
+       "      <td>5.3355</td>\n",
+       "      <td>2.7434</td>\n",
+       "      <td>0.0751</td>\n",
+       "      <td>5.2842</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>2.6312</td>\n",
+       "      <td>2.7565</td>\n",
+       "      <td>49</td>\n",
+       "      <td>2.7565</td>\n",
+       "      <td>39</td>\n",
+       "      <td>3.1043</td>\n",
+       "      <td>0.0869</td>\n",
+       "      <td>6.1828</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-03-01:30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_nhid_40_fp16O1_lrd_99_bs32</td>\n",
+       "      <td>2.7017</td>\n",
+       "      <td>0.0711</td>\n",
+       "      <td>5.3355</td>\n",
+       "      <td>2.7434</td>\n",
+       "      <td>0.0751</td>\n",
+       "      <td>5.2842</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>2.6312</td>\n",
+       "      <td>2.7565</td>\n",
+       "      <td>49</td>\n",
+       "      <td>2.7565</td>\n",
+       "      <td>39</td>\n",
+       "      <td>3.1043</td>\n",
+       "      <td>0.0869</td>\n",
+       "      <td>6.1828</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-03-01:30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>logs_0.01</td>\n",
+       "      <td>2.9456</td>\n",
+       "      <td>0.0797</td>\n",
+       "      <td>5.8723</td>\n",
+       "      <td>2.7841</td>\n",
+       "      <td>0.0776</td>\n",
+       "      <td>5.3534</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2.7083</td>\n",
+       "      <td>2.7841</td>\n",
+       "      <td>41</td>\n",
+       "      <td>2.7841</td>\n",
+       "      <td>31</td>\n",
+       "      <td>3.1112</td>\n",
+       "      <td>0.0847</td>\n",
+       "      <td>6.1789</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-21:50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>apt_only_512_rand</td>\n",
+       "      <td>2.8642</td>\n",
+       "      <td>0.0774</td>\n",
+       "      <td>5.7511</td>\n",
+       "      <td>2.8058</td>\n",
+       "      <td>0.0790</td>\n",
+       "      <td>5.4436</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>2.6414</td>\n",
+       "      <td>2.8058</td>\n",
+       "      <td>65</td>\n",
+       "      <td>2.8058</td>\n",
+       "      <td>55</td>\n",
+       "      <td>3.1172</td>\n",
+       "      <td>0.0862</td>\n",
+       "      <td>6.2545</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-00:05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_surgery6_freeze_all_but_ec2</td>\n",
+       "      <td>2.8671</td>\n",
+       "      <td>0.0770</td>\n",
+       "      <td>5.6898</td>\n",
+       "      <td>2.7880</td>\n",
+       "      <td>0.0768</td>\n",
+       "      <td>5.4042</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>2.7745</td>\n",
+       "      <td>2.7880</td>\n",
+       "      <td>53</td>\n",
+       "      <td>2.8205</td>\n",
+       "      <td>23</td>\n",
+       "      <td>3.1241</td>\n",
+       "      <td>0.0843</td>\n",
+       "      <td>6.2294</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-04-17:02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>apt_only_apt_size_10</td>\n",
+       "      <td>2.8888</td>\n",
+       "      <td>0.0784</td>\n",
+       "      <td>5.8103</td>\n",
+       "      <td>2.8290</td>\n",
+       "      <td>0.0810</td>\n",
+       "      <td>5.4849</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2.8593</td>\n",
+       "      <td>2.8290</td>\n",
+       "      <td>99</td>\n",
+       "      <td>2.8541</td>\n",
+       "      <td>50</td>\n",
+       "      <td>3.1377</td>\n",
+       "      <td>0.0874</td>\n",
+       "      <td>6.2904</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-29-11:38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>apt_only_20_fixed</td>\n",
+       "      <td>2.8749</td>\n",
+       "      <td>0.0779</td>\n",
+       "      <td>5.7954</td>\n",
+       "      <td>2.8250</td>\n",
+       "      <td>0.0805</td>\n",
+       "      <td>5.5060</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>2.8236</td>\n",
+       "      <td>2.8250</td>\n",
+       "      <td>89</td>\n",
+       "      <td>2.8250</td>\n",
+       "      <td>79</td>\n",
+       "      <td>3.1607</td>\n",
+       "      <td>0.0888</td>\n",
+       "      <td>6.3773</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-21:35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_seq_len_15</td>\n",
+       "      <td>3.0252</td>\n",
+       "      <td>0.0840</td>\n",
+       "      <td>6.1593</td>\n",
+       "      <td>3.1211</td>\n",
+       "      <td>0.0913</td>\n",
+       "      <td>6.2274</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>2.9733</td>\n",
+       "      <td>3.1211</td>\n",
+       "      <td>62</td>\n",
+       "      <td>3.1211</td>\n",
+       "      <td>52</td>\n",
+       "      <td>3.2132</td>\n",
+       "      <td>0.0892</td>\n",
+       "      <td>6.4515</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-04-14:08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>rmse_backward</td>\n",
+       "      <td>2.9858</td>\n",
+       "      <td>0.0769</td>\n",
+       "      <td>5.2855</td>\n",
+       "      <td>2.9295</td>\n",
+       "      <td>0.0786</td>\n",
+       "      <td>5.2941</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>2.8752</td>\n",
+       "      <td>2.9295</td>\n",
+       "      <td>65</td>\n",
+       "      <td>2.9295</td>\n",
+       "      <td>55</td>\n",
+       "      <td>3.2543</td>\n",
+       "      <td>0.0860</td>\n",
+       "      <td>6.0148</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-30-13:49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>catgc_clip_3_offset__7_12</td>\n",
+       "      <td>3.0457</td>\n",
+       "      <td>0.0852</td>\n",
+       "      <td>6.2246</td>\n",
+       "      <td>3.0011</td>\n",
+       "      <td>0.0863</td>\n",
+       "      <td>5.8707</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>2.9424</td>\n",
+       "      <td>3.0011</td>\n",
+       "      <td>50</td>\n",
+       "      <td>3.0011</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.3582</td>\n",
+       "      <td>0.0929</td>\n",
+       "      <td>6.8876</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-04-21:08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>logs_0.0001</td>\n",
+       "      <td>3.2691</td>\n",
+       "      <td>0.0907</td>\n",
+       "      <td>6.5459</td>\n",
+       "      <td>3.0283</td>\n",
+       "      <td>0.0867</td>\n",
+       "      <td>5.7707</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>3.2691</td>\n",
+       "      <td>3.0283</td>\n",
+       "      <td>99</td>\n",
+       "      <td>3.0283</td>\n",
+       "      <td>99</td>\n",
+       "      <td>3.3754</td>\n",
+       "      <td>0.0953</td>\n",
+       "      <td>6.6263</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12-01-03:33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>no_graph_conv3</td>\n",
+       "      <td>3.4046</td>\n",
+       "      <td>0.0947</td>\n",
+       "      <td>6.9631</td>\n",
+       "      <td>3.2221</td>\n",
+       "      <td>0.0920</td>\n",
+       "      <td>6.2695</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>3.3764</td>\n",
+       "      <td>3.2221</td>\n",
+       "      <td>71</td>\n",
+       "      <td>3.2435</td>\n",
+       "      <td>24</td>\n",
+       "      <td>3.5817</td>\n",
+       "      <td>0.1019</td>\n",
+       "      <td>7.1858</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-19:38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>apt_only</td>\n",
+       "      <td>3.4046</td>\n",
+       "      <td>0.0947</td>\n",
+       "      <td>6.9631</td>\n",
+       "      <td>3.2221</td>\n",
+       "      <td>0.0920</td>\n",
+       "      <td>6.2695</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>3.3764</td>\n",
+       "      <td>3.2221</td>\n",
+       "      <td>71</td>\n",
+       "      <td>3.2435</td>\n",
+       "      <td>24</td>\n",
+       "      <td>3.5817</td>\n",
+       "      <td>0.1019</td>\n",
+       "      <td>7.1858</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-19:38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>apt_only_aptsize20</td>\n",
+       "      <td>3.4044</td>\n",
+       "      <td>0.0946</td>\n",
+       "      <td>6.9589</td>\n",
+       "      <td>3.2225</td>\n",
+       "      <td>0.0931</td>\n",
+       "      <td>6.2525</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>3.3808</td>\n",
+       "      <td>3.2225</td>\n",
+       "      <td>71</td>\n",
+       "      <td>3.2266</td>\n",
+       "      <td>44</td>\n",
+       "      <td>3.5821</td>\n",
+       "      <td>0.1033</td>\n",
+       "      <td>7.1754</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11-28-19:38</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            train_loss  train_mape  \\\n",
+       "front_1                                         2.0282      0.0457   \n",
+       "front_3                                         2.2550      0.0533   \n",
+       "catgc_clip_3_t_12_6                             2.4717      0.0618   \n",
+       "catgc_nhid40_seq_len_6                          2.4961      0.0625   \n",
+       "front_9                                         2.6732      0.0696   \n",
+       "surgery_t12_6                                   2.6984      0.0711   \n",
+       "catgc_clip_3_lrd_97_nhid40_v2                   2.7168      0.0720   \n",
+       "replace-0-feat_v2                               2.7361      0.0727   \n",
+       "skip_channels_400                               2.7581      0.0734   \n",
+       "catgc_nhid40_lrd_0.99                           2.8849      0.0777   \n",
+       "replace_0_feat                                  2.7287      0.0723   \n",
+       "catgc_clip_4_lrd_97_nhid40                      2.7034      0.0714   \n",
+       "catgc_nhid40_lrd_0.95                           2.7601      0.0735   \n",
+       "catgc_nhid_42                                   2.7327      0.0723   \n",
+       "logs_nhid_36                                    2.7866      0.0744   \n",
+       "end_channels_360                                2.7596      0.0734   \n",
+       "catgc_clip_4_lrd_97_nhid40_v2                   2.7016      0.0715   \n",
+       "surgery_front_3                                 2.8049      0.0749   \n",
+       "logs_0.001                                      2.7978      0.0749   \n",
+       "catgc_clip_2_lrd_97_nhid40                      2.7128      0.0718   \n",
+       "logs_nhid_40                                    2.7485      0.0730   \n",
+       "hist_6                                          2.7159      0.0718   \n",
+       "baseline_lr_decay                               2.7915      0.0747   \n",
+       "catgc_nhid40_lrd_0.98                           2.7587      0.0733   \n",
+       "end_channels_400                                2.7151      0.0718   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc          2.7387      0.0727   \n",
+       "dow_one_hot                                     2.7594      0.0733   \n",
+       "skip_channels_240                               2.7536      0.0731   \n",
+       "baseline_temp_2                                 2.8174      0.0756   \n",
+       "catgc_clip_8_lrd_97_nhid_40                     2.7095      0.0717   \n",
+       "sm_1.5                                          2.7333      0.0724   \n",
+       "cfg_node_bias_nhid_36_lrd_99_lr_0.002           2.7662      0.0736   \n",
+       "catgc_clip_1_lrd_97_nhid40                      2.7689      0.0737   \n",
+       "catgc_clip_3_lrd_97_nhid40                      2.7134      0.0718   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc_wd5      2.8244      0.0756   \n",
+       "baseline_temp_0.5                               2.8096      0.0753   \n",
+       "cfg_nhid_40_lrd_0.94                            2.7714      0.0738   \n",
+       "catgc_nhid_38                                   2.7633      0.0737   \n",
+       "cfg_node_bias_nhid_40_lrd_99                    2.7930      0.0747   \n",
+       "replace_0_scale-no-mask                         2.7472      0.0729   \n",
+       "catgc_nhid_34                                   2.8601      0.0768   \n",
+       "baseline_leaky_relu                             2.7963      0.0747   \n",
+       "gc_bn                                           2.7406      0.0725   \n",
+       "catgc_nhid40_lrd_0.99_v2                        2.7391      0.0726   \n",
+       "baseline_nhid_48                                2.7942      0.0746   \n",
+       "end_channels_280                                2.7711      0.0738   \n",
+       "baseline_no_weight_decay                        2.7690      0.0738   \n",
+       "catgc_nhid_28                                   2.8604      0.0770   \n",
+       "cat_feat_early_gc_10_rand                       2.8320      0.0759   \n",
+       "replace_0_mean_hax                              2.7035      0.0714   \n",
+       "cat_feat_early                                  2.7591      0.0732   \n",
+       "cat_feat_early_gc_10_nhid_48_v2                 2.7225      0.0719   \n",
+       "add_gc_baseline                                 2.8039      0.0749   \n",
+       "cfg_nhid_40_lrd_0.96                            2.8415      0.0763   \n",
+       "logs_nhid_44                                    2.7390      0.0727   \n",
+       "dow_raw                                         2.7850      0.0744   \n",
+       "baseline_gelu                                   2.8467      0.0762   \n",
+       "layers3                                         2.8352      0.0761   \n",
+       "apt_only_256_rand_gc                            2.7976      0.0749   \n",
+       "end_channels_200                                2.7597      0.0733   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_40         2.7499      0.0729   \n",
+       "cat_feat_early_gc_10_nhid_64                    2.7712      0.0737   \n",
+       "cat_feat_early_gc_10_nhid_48                    2.7492      0.0729   \n",
+       "cat_feat_early_gc_6                             2.8353      0.0760   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_20         2.7246      0.0719   \n",
+       "catgc_surgery6_finetune                         2.7106      0.0713   \n",
+       "catgc_nhid_24                                   2.8841      0.0779   \n",
+       "init_extra_adj                                  2.7733      0.0738   \n",
+       "baseline7                                       2.7886      0.0743   \n",
+       "hist_1                                          2.7840      0.0740   \n",
+       "cat_feat_early_gc_256_rand                      2.7940      0.0745   \n",
+       "cat_feat_early_gc_14                            2.7488      0.0728   \n",
+       "catgc_nhid40_cyclic_v2                          2.7066      0.0713   \n",
+       "logs_0.0005                                     2.9029      0.0787   \n",
+       "cheaper-metric-fixed                            2.8063      0.0746   \n",
+       "gc_leaky_relu                                   2.8233      0.0753   \n",
+       "actual_matrix                                   2.8702      0.0772   \n",
+       "no_add_apt_adj                                  2.8291      0.0754   \n",
+       "baseline5                                       2.8013      0.0745   \n",
+       "no_apt_adj                                      2.8718      0.0768   \n",
+       "bs_512_fixed_lr                                 2.8607      0.0765   \n",
+       "apt_only_512_rand_gc                            2.8395      0.0763   \n",
+       "logs_0.005                                      2.8102      0.0749   \n",
+       "apt_only_aptsize256                             2.8174      0.0757   \n",
+       "baseline_nhid_16                                2.9070      0.0785   \n",
+       "1feat                                           2.8542      0.0762   \n",
+       "catgc_nhid_40_fp16O1_lrd_99                     2.7017      0.0711   \n",
+       "catgc_nhid_40_fp16O1_lrd_99_bs32                2.7017      0.0711   \n",
+       "logs_0.01                                       2.9456      0.0797   \n",
+       "apt_only_512_rand                               2.8642      0.0774   \n",
+       "catgc_surgery6_freeze_all_but_ec2               2.8671      0.0770   \n",
+       "apt_only_apt_size_10                            2.8888      0.0784   \n",
+       "apt_only_20_fixed                               2.8749      0.0779   \n",
+       "catgc_seq_len_15                                3.0252      0.0840   \n",
+       "rmse_backward                                   2.9858      0.0769   \n",
+       "catgc_clip_3_offset__7_12                       3.0457      0.0852   \n",
+       "logs_0.0001                                     3.2691      0.0907   \n",
+       "no_graph_conv3                                  3.4046      0.0947   \n",
+       "apt_only                                        3.4046      0.0947   \n",
+       "apt_only_aptsize20                              3.4044      0.0946   \n",
+       "\n",
+       "                                            train_rmse  valid_loss  \\\n",
+       "front_1                                         3.4831      1.9568   \n",
+       "front_3                                         4.1214      2.1794   \n",
+       "catgc_clip_3_t_12_6                             4.7332      2.4084   \n",
+       "catgc_nhid40_seq_len_6                          4.7963      2.4941   \n",
+       "front_9                                         5.2682      2.5941   \n",
+       "surgery_t12_6                                   5.3523      2.6909   \n",
+       "catgc_clip_3_lrd_97_nhid40_v2                   5.4021      2.7114   \n",
+       "replace-0-feat_v2                               5.4589      2.7203   \n",
+       "skip_channels_400                               5.5062      2.7234   \n",
+       "catgc_nhid40_lrd_0.99                           5.7723      2.7371   \n",
+       "replace_0_feat                                  5.4418      2.7167   \n",
+       "catgc_clip_4_lrd_97_nhid40                      5.3699      2.8135   \n",
+       "catgc_nhid40_lrd_0.95                           5.4993      2.7101   \n",
+       "catgc_nhid_42                                   5.4273      2.7160   \n",
+       "logs_nhid_36                                    5.5750      2.7273   \n",
+       "end_channels_360                                5.5119      2.7203   \n",
+       "catgc_clip_4_lrd_97_nhid40_v2                   5.3795      2.7189   \n",
+       "surgery_front_3                                 5.6067      2.7280   \n",
+       "logs_0.001                                      5.5934      2.7301   \n",
+       "catgc_clip_2_lrd_97_nhid40                      5.3965      2.7117   \n",
+       "logs_nhid_40                                    5.4782      2.7211   \n",
+       "hist_6                                          5.4117      2.7335   \n",
+       "baseline_lr_decay                               5.5931      2.7222   \n",
+       "catgc_nhid40_lrd_0.98                           5.4941      2.7258   \n",
+       "end_channels_400                                5.3992      2.7065   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc          5.4660      2.7216   \n",
+       "dow_one_hot                                     5.5027      2.7331   \n",
+       "skip_channels_240                               5.4940      2.7203   \n",
+       "baseline_temp_2                                 5.6405      2.7261   \n",
+       "catgc_clip_8_lrd_97_nhid_40                     5.3899      2.7224   \n",
+       "sm_1.5                                          5.4382      2.7321   \n",
+       "cfg_node_bias_nhid_36_lrd_99_lr_0.002           5.5168      2.7245   \n",
+       "catgc_clip_1_lrd_97_nhid40                      5.5271      2.7155   \n",
+       "catgc_clip_3_lrd_97_nhid40                      5.3979      2.7035   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc_wd5      5.6375      2.7318   \n",
+       "baseline_temp_0.5                               5.6231      2.7331   \n",
+       "cfg_nhid_40_lrd_0.94                            5.5301      2.7218   \n",
+       "catgc_nhid_38                                   5.5157      2.7159   \n",
+       "cfg_node_bias_nhid_40_lrd_99                    5.5820      2.7268   \n",
+       "replace_0_scale-no-mask                         5.4767      2.7473   \n",
+       "catgc_nhid_34                                   5.7332      2.7357   \n",
+       "baseline_leaky_relu                             5.5884      2.7296   \n",
+       "gc_bn                                           5.4394      2.7322   \n",
+       "catgc_nhid40_lrd_0.99_v2                        5.4394      2.8334   \n",
+       "baseline_nhid_48                                5.5748      2.7294   \n",
+       "end_channels_280                                5.5316      2.7139   \n",
+       "baseline_no_weight_decay                        5.5304      2.7324   \n",
+       "catgc_nhid_28                                   5.7362      2.7440   \n",
+       "cat_feat_early_gc_10_rand                       5.6565      2.7309   \n",
+       "replace_0_mean_hax                              5.3743      2.7308   \n",
+       "cat_feat_early                                  5.4826      2.7437   \n",
+       "cat_feat_early_gc_10_nhid_48_v2                 5.3839      2.7312   \n",
+       "add_gc_baseline                                 5.5808      2.7337   \n",
+       "cfg_nhid_40_lrd_0.96                            5.6596      2.7496   \n",
+       "logs_nhid_44                                    5.4474      2.7332   \n",
+       "dow_raw                                         5.5603      2.7453   \n",
+       "baseline_gelu                                   5.6851      2.7417   \n",
+       "layers3                                         5.6685      2.7381   \n",
+       "apt_only_256_rand_gc                            5.5994      2.7528   \n",
+       "end_channels_200                                5.4977      2.7160   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_40         5.4627      2.7462   \n",
+       "cat_feat_early_gc_10_nhid_64                    5.5109      2.7309   \n",
+       "cat_feat_early_gc_10_nhid_48                    5.4579      2.7392   \n",
+       "cat_feat_early_gc_6                             5.6567      2.7416   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_20         5.3885      2.7457   \n",
+       "catgc_surgery6_finetune                         5.3516      2.7356   \n",
+       "catgc_nhid_24                                   5.7705      2.7462   \n",
+       "init_extra_adj                                  5.5150      2.7376   \n",
+       "baseline7                                       5.5527      2.7351   \n",
+       "hist_1                                          5.5698      2.7797   \n",
+       "cat_feat_early_gc_256_rand                      5.5731      2.7451   \n",
+       "cat_feat_early_gc_14                            5.4508      2.7434   \n",
+       "catgc_nhid40_cyclic_v2                          5.3531      2.8332   \n",
+       "logs_0.0005                                     5.8291      2.7572   \n",
+       "cheaper-metric-fixed                            5.5674      2.7439   \n",
+       "gc_leaky_relu                                   5.6229      2.7589   \n",
+       "actual_matrix                                   5.7285      2.7673   \n",
+       "no_add_apt_adj                                  5.6288      2.7831   \n",
+       "baseline5                                       5.5596      2.7586   \n",
+       "no_apt_adj                                      5.7116      2.7791   \n",
+       "bs_512_fixed_lr                                 5.6819      2.7907   \n",
+       "apt_only_512_rand_gc                            5.6848      2.7665   \n",
+       "logs_0.005                                      5.5830      2.7563   \n",
+       "apt_only_aptsize256                             5.6415      2.7656   \n",
+       "baseline_nhid_16                                5.8052      2.7890   \n",
+       "1feat                                           5.6696      2.7812   \n",
+       "catgc_nhid_40_fp16O1_lrd_99                     5.3355      2.7434   \n",
+       "catgc_nhid_40_fp16O1_lrd_99_bs32                5.3355      2.7434   \n",
+       "logs_0.01                                       5.8723      2.7841   \n",
+       "apt_only_512_rand                               5.7511      2.8058   \n",
+       "catgc_surgery6_freeze_all_but_ec2               5.6898      2.7880   \n",
+       "apt_only_apt_size_10                            5.8103      2.8290   \n",
+       "apt_only_20_fixed                               5.7954      2.8250   \n",
+       "catgc_seq_len_15                                6.1593      3.1211   \n",
+       "rmse_backward                                   5.2855      2.9295   \n",
+       "catgc_clip_3_offset__7_12                       6.2246      3.0011   \n",
+       "logs_0.0001                                     6.5459      3.0283   \n",
+       "no_graph_conv3                                  6.9631      3.2221   \n",
+       "apt_only                                        6.9631      3.2221   \n",
+       "apt_only_aptsize20                              6.9589      3.2225   \n",
+       "\n",
+       "                                            valid_mape  valid_rmse  \\\n",
+       "front_1                                         0.0448      3.3350   \n",
+       "front_3                                         0.0528      3.9365   \n",
+       "catgc_clip_3_t_12_6                             0.0624      4.5413   \n",
+       "catgc_nhid40_seq_len_6                          0.0646      4.7549   \n",
+       "front_9                                         0.0709      4.9901   \n",
+       "surgery_t12_6                                   0.0746      5.2014   \n",
+       "catgc_clip_3_lrd_97_nhid40_v2                   0.0752      5.2668   \n",
+       "replace-0-feat_v2                               0.0752      5.2630   \n",
+       "skip_channels_400                               0.0757      5.2921   \n",
+       "catgc_nhid40_lrd_0.99                           0.0763      5.2582   \n",
+       "replace_0_feat                                  0.0753      5.2939   \n",
+       "catgc_clip_4_lrd_97_nhid40                      0.0782      5.5668   \n",
+       "catgc_nhid40_lrd_0.95                           0.0749      5.2541   \n",
+       "catgc_nhid_42                                   0.0739      5.2762   \n",
+       "logs_nhid_36                                    0.0753      5.2803   \n",
+       "end_channels_360                                0.0744      5.2728   \n",
+       "catgc_clip_4_lrd_97_nhid40_v2                   0.0750      5.2804   \n",
+       "surgery_front_3                                 0.0750      5.2713   \n",
+       "logs_0.001                                      0.0757      5.3018   \n",
+       "catgc_clip_2_lrd_97_nhid40                      0.0746      5.2571   \n",
+       "logs_nhid_40                                    0.0754      5.2888   \n",
+       "hist_6                                          0.0754      5.3293   \n",
+       "baseline_lr_decay                               0.0757      5.2941   \n",
+       "catgc_nhid40_lrd_0.98                           0.0755      5.2913   \n",
+       "end_channels_400                                0.0753      5.2468   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc          0.0758      5.2958   \n",
+       "dow_one_hot                                     0.0749      5.2938   \n",
+       "skip_channels_240                               0.0748      5.2934   \n",
+       "baseline_temp_2                                 0.0752      5.2852   \n",
+       "catgc_clip_8_lrd_97_nhid_40                     0.0759      5.2885   \n",
+       "sm_1.5                                          0.0747      5.2941   \n",
+       "cfg_node_bias_nhid_36_lrd_99_lr_0.002           0.0754      5.3007   \n",
+       "catgc_clip_1_lrd_97_nhid40                      0.0742      5.2606   \n",
+       "catgc_clip_3_lrd_97_nhid40                      0.0754      5.2344   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc_wd5      0.0756      5.2679   \n",
+       "baseline_temp_0.5                               0.0758      5.3033   \n",
+       "cfg_nhid_40_lrd_0.94                            0.0758      5.2801   \n",
+       "catgc_nhid_38                                   0.0748      5.2437   \n",
+       "cfg_node_bias_nhid_40_lrd_99                    0.0775      5.2685   \n",
+       "replace_0_scale-no-mask                         0.0764      5.3935   \n",
+       "catgc_nhid_34                                   0.0762      5.2769   \n",
+       "baseline_leaky_relu                             0.0762      5.3019   \n",
+       "gc_bn                                           0.0750      5.3037   \n",
+       "catgc_nhid40_lrd_0.99_v2                        0.0785      5.6040   \n",
+       "baseline_nhid_48                                0.0749      5.2692   \n",
+       "end_channels_280                                0.0749      5.2543   \n",
+       "baseline_no_weight_decay                        0.0752      5.3048   \n",
+       "catgc_nhid_28                                   0.0759      5.2986   \n",
+       "cat_feat_early_gc_10_rand                       0.0760      5.2880   \n",
+       "replace_0_mean_hax                              0.0747      5.3098   \n",
+       "cat_feat_early                                  0.0764      5.3119   \n",
+       "cat_feat_early_gc_10_nhid_48_v2                 0.0750      5.3134   \n",
+       "add_gc_baseline                                 0.0751      5.2846   \n",
+       "cfg_nhid_40_lrd_0.96                            0.0771      5.3007   \n",
+       "logs_nhid_44                                    0.0760      5.2928   \n",
+       "dow_raw                                         0.0766      5.2849   \n",
+       "baseline_gelu                                   0.0760      5.3045   \n",
+       "layers3                                         0.0770      5.3013   \n",
+       "apt_only_256_rand_gc                            0.0762      5.3495   \n",
+       "end_channels_200                                0.0731      5.2789   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_40         0.0760      5.3425   \n",
+       "cat_feat_early_gc_10_nhid_64                    0.0754      5.2928   \n",
+       "cat_feat_early_gc_10_nhid_48                    0.0753      5.3257   \n",
+       "cat_feat_early_gc_6                             0.0762      5.3152   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_20         0.0760      5.3114   \n",
+       "catgc_surgery6_finetune                         0.0742      5.2844   \n",
+       "catgc_nhid_24                                   0.0761      5.3066   \n",
+       "init_extra_adj                                  0.0762      5.3351   \n",
+       "baseline7                                       0.0750      5.2642   \n",
+       "hist_1                                          0.0774      5.4092   \n",
+       "cat_feat_early_gc_256_rand                      0.0748      5.3086   \n",
+       "cat_feat_early_gc_14                            0.0766      5.3150   \n",
+       "catgc_nhid40_cyclic_v2                          0.0784      5.5971   \n",
+       "logs_0.0005                                     0.0774      5.3246   \n",
+       "cheaper-metric-fixed                            0.0751      5.2858   \n",
+       "gc_leaky_relu                                   0.0767      5.3333   \n",
+       "actual_matrix                                   0.0764      5.3261   \n",
+       "no_add_apt_adj                                  0.0752      5.4045   \n",
+       "baseline5                                       0.0759      5.3295   \n",
+       "no_apt_adj                                      0.0791      5.3618   \n",
+       "bs_512_fixed_lr                                 0.0768      5.6190   \n",
+       "apt_only_512_rand_gc                            0.0778      5.3448   \n",
+       "logs_0.005                                      0.0760      5.3101   \n",
+       "apt_only_aptsize256                             0.0766      5.3806   \n",
+       "baseline_nhid_16                                0.0785      5.3828   \n",
+       "1feat                                           0.0777      5.3344   \n",
+       "catgc_nhid_40_fp16O1_lrd_99                     0.0751      5.2842   \n",
+       "catgc_nhid_40_fp16O1_lrd_99_bs32                0.0751      5.2842   \n",
+       "logs_0.01                                       0.0776      5.3534   \n",
+       "apt_only_512_rand                               0.0790      5.4436   \n",
+       "catgc_surgery6_freeze_all_but_ec2               0.0768      5.4042   \n",
+       "apt_only_apt_size_10                            0.0810      5.4849   \n",
+       "apt_only_20_fixed                               0.0805      5.5060   \n",
+       "catgc_seq_len_15                                0.0913      6.2274   \n",
+       "rmse_backward                                   0.0786      5.2941   \n",
+       "catgc_clip_3_offset__7_12                       0.0863      5.8707   \n",
+       "logs_0.0001                                     0.0867      5.7707   \n",
+       "no_graph_conv3                                  0.0920      6.2695   \n",
+       "apt_only                                        0.0920      6.2695   \n",
+       "apt_only_aptsize20                              0.0931      6.2525   \n",
+       "\n",
+       "                                            best_epoch  min_train_loss  \\\n",
+       "front_1                                           97.0          2.0276   \n",
+       "front_3                                           98.0          2.2539   \n",
+       "catgc_clip_3_t_12_6                               76.0          2.4690   \n",
+       "catgc_nhid40_seq_len_6                            75.0          2.4822   \n",
+       "front_9                                           38.0          2.6623   \n",
+       "surgery_t12_6                                     29.0          2.6486   \n",
+       "catgc_clip_3_lrd_97_nhid40_v2                     67.0          2.6861   \n",
+       "replace-0-feat_v2                                 55.0          2.7040   \n",
+       "skip_channels_400                                 49.0          2.7171   \n",
+       "catgc_nhid40_lrd_0.99                             27.0          2.8675   \n",
+       "replace_0_feat                                    59.0          2.7010   \n",
+       "catgc_clip_4_lrd_97_nhid40                        83.0          2.6908   \n",
+       "catgc_nhid40_lrd_0.95                             87.0          2.7527   \n",
+       "catgc_nhid_42                                     99.0          2.7327   \n",
+       "logs_nhid_36                                      69.0          2.7587   \n",
+       "end_channels_360                                  50.0          2.7235   \n",
+       "catgc_clip_4_lrd_97_nhid40_v2                     84.0          2.6898   \n",
+       "surgery_front_3                                   32.0          2.7365   \n",
+       "logs_0.001                                        82.0          2.7850   \n",
+       "catgc_clip_2_lrd_97_nhid40                        77.0          2.6956   \n",
+       "logs_nhid_40                                      72.0          2.7220   \n",
+       "hist_6                                            82.0          2.7038   \n",
+       "baseline_lr_decay                                 93.0          2.7891   \n",
+       "catgc_nhid40_lrd_0.98                             85.0          2.7478   \n",
+       "end_channels_400                                  73.0          2.6965   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc            56.0          2.6887   \n",
+       "dow_one_hot                                       36.0          2.7028   \n",
+       "skip_channels_240                                 55.0          2.7193   \n",
+       "baseline_temp_2                                   87.0          2.8093   \n",
+       "catgc_clip_8_lrd_97_nhid_40                       76.0          2.6897   \n",
+       "sm_1.5                                            60.0          2.7054   \n",
+       "cfg_node_bias_nhid_36_lrd_99_lr_0.002             52.0          2.7076   \n",
+       "catgc_clip_1_lrd_97_nhid40                        74.0          2.7483   \n",
+       "catgc_clip_3_lrd_97_nhid40                        68.0          2.6835   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc_wd5        72.0          2.7967   \n",
+       "baseline_temp_0.5                                 96.0          2.8073   \n",
+       "cfg_nhid_40_lrd_0.94                              78.0          2.7549   \n",
+       "catgc_nhid_38                                     93.0          2.7602   \n",
+       "cfg_node_bias_nhid_40_lrd_99                      51.0          2.7493   \n",
+       "replace_0_scale-no-mask                           46.0          2.7073   \n",
+       "catgc_nhid_34                                     45.0          2.8537   \n",
+       "baseline_leaky_relu                               90.0          2.7902   \n",
+       "gc_bn                                             43.0          2.6920   \n",
+       "catgc_nhid40_lrd_0.99_v2                          54.0          2.6550   \n",
+       "baseline_nhid_48                                  33.0          2.6489   \n",
+       "end_channels_280                                  47.0          2.7304   \n",
+       "baseline_no_weight_decay                          85.0          2.7605   \n",
+       "catgc_nhid_28                                     72.0          2.8418   \n",
+       "cat_feat_early_gc_10_rand                         38.0          2.7231   \n",
+       "replace_0_mean_hax                                74.0          2.6854   \n",
+       "cat_feat_early                                    94.0          2.7557   \n",
+       "cat_feat_early_gc_10_nhid_48_v2                   45.0          2.6586   \n",
+       "add_gc_baseline                                   48.0          2.7182   \n",
+       "cfg_nhid_40_lrd_0.96                              54.0          2.8379   \n",
+       "logs_nhid_44                                      60.0          2.6949   \n",
+       "dow_raw                                           35.0          2.7229   \n",
+       "baseline_gelu                                     91.0          2.8401   \n",
+       "layers3                                           46.0          2.7606   \n",
+       "apt_only_256_rand_gc                              76.0          2.7592   \n",
+       "end_channels_200                                  52.0          2.7244   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_40           56.0          2.6842   \n",
+       "cat_feat_early_gc_10_nhid_64                      27.0          2.5892   \n",
+       "cat_feat_early_gc_10_nhid_48                      46.0          2.6500   \n",
+       "cat_feat_early_gc_6                               47.0          2.7510   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_20           47.0          2.6156   \n",
+       "catgc_surgery6_finetune                           48.0          2.6593   \n",
+       "catgc_nhid_24                                     82.0          2.8743   \n",
+       "init_extra_adj                                    67.0          2.7272   \n",
+       "baseline7                                         70.0          2.7523   \n",
+       "hist_1                                            82.0          2.7712   \n",
+       "cat_feat_early_gc_256_rand                        52.0          2.7249   \n",
+       "cat_feat_early_gc_14                              90.0          2.7377   \n",
+       "catgc_nhid40_cyclic_v2                            84.0          2.6860   \n",
+       "logs_0.0005                                       91.0          2.8972   \n",
+       "cheaper-metric-fixed                              82.0          2.7783   \n",
+       "gc_leaky_relu                                     45.0          2.7836   \n",
+       "actual_matrix                                     87.0          2.8613   \n",
+       "no_add_apt_adj                                    99.0          2.8291   \n",
+       "baseline5                                         80.0          2.7759   \n",
+       "no_apt_adj                                        72.0          2.8266   \n",
+       "bs_512_fixed_lr                                   98.0          2.8530   \n",
+       "apt_only_512_rand_gc                              51.0          2.6733   \n",
+       "logs_0.005                                        39.0          2.6882   \n",
+       "apt_only_aptsize256                               74.0          2.7572   \n",
+       "baseline_nhid_16                                  94.0          2.9003   \n",
+       "1feat                                             64.0          2.8082   \n",
+       "catgc_nhid_40_fp16O1_lrd_99                       59.0          2.6312   \n",
+       "catgc_nhid_40_fp16O1_lrd_99_bs32                  59.0          2.6312   \n",
+       "logs_0.01                                         21.0          2.7083   \n",
+       "apt_only_512_rand                                 45.0          2.6414   \n",
+       "catgc_surgery6_freeze_all_but_ec2                 33.0          2.7745   \n",
+       "apt_only_apt_size_10                              80.0          2.8593   \n",
+       "apt_only_20_fixed                                 69.0          2.8236   \n",
+       "catgc_seq_len_15                                  42.0          2.9733   \n",
+       "rmse_backward                                     45.0          2.8752   \n",
+       "catgc_clip_3_offset__7_12                         30.0          2.9424   \n",
+       "logs_0.0001                                       99.0          3.2691   \n",
+       "no_graph_conv3                                    51.0          3.3764   \n",
+       "apt_only                                          51.0          3.3764   \n",
+       "apt_only_aptsize20                                51.0          3.3808   \n",
+       "\n",
+       "                                            es20_score  es20_i  es10_score  \\\n",
+       "front_1                                         1.9568      99      1.9568   \n",
+       "front_3                                         2.1794      99      2.1794   \n",
+       "catgc_clip_3_t_12_6                             2.4084      78      2.4152   \n",
+       "catgc_nhid40_seq_len_6                          2.4941      95      2.5025   \n",
+       "front_9                                         2.5941      41      2.5941   \n",
+       "surgery_t12_6                                   2.6909      49      2.6909   \n",
+       "catgc_clip_3_lrd_97_nhid40_v2                   2.7114      87      2.7386   \n",
+       "replace-0-feat_v2                               2.7203      75      2.7282   \n",
+       "skip_channels_400                               2.7234      69      2.7234   \n",
+       "catgc_nhid40_lrd_0.99                           2.7371      31      2.7371   \n",
+       "replace_0_feat                                  2.7167      79      2.7282   \n",
+       "catgc_clip_4_lrd_97_nhid40                      2.8172      77      2.8172   \n",
+       "catgc_nhid40_lrd_0.95                           2.7128      77      2.7128   \n",
+       "catgc_nhid_42                                   2.7160      99      2.7222   \n",
+       "logs_nhid_36                                    2.7273      89      2.7293   \n",
+       "end_channels_360                                2.7203      70      2.7203   \n",
+       "catgc_clip_4_lrd_97_nhid40_v2                   2.7189      99      2.7225   \n",
+       "surgery_front_3                                 2.7280      52      2.7280   \n",
+       "logs_0.001                                      2.7301      99      2.7326   \n",
+       "catgc_clip_2_lrd_97_nhid40                      2.7117      97      2.7127   \n",
+       "logs_nhid_40                                    2.7211      92      2.7232   \n",
+       "hist_6                                          2.7335      99      2.7500   \n",
+       "baseline_lr_decay                               2.7222      99      2.7222   \n",
+       "catgc_nhid40_lrd_0.98                           2.7258      99      2.7290   \n",
+       "end_channels_400                                2.7065      93      2.7098   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc          2.7216      76      2.7216   \n",
+       "dow_one_hot                                     2.7331      56      2.7331   \n",
+       "skip_channels_240                               2.7203      75      2.7458   \n",
+       "baseline_temp_2                                 2.7261      99      2.7566   \n",
+       "catgc_clip_8_lrd_97_nhid_40                     2.7224      96      2.7555   \n",
+       "sm_1.5                                          2.7321      80      2.7348   \n",
+       "cfg_node_bias_nhid_36_lrd_99_lr_0.002           2.7245      72      2.7269   \n",
+       "catgc_clip_1_lrd_97_nhid40                      2.7155      94      2.7306   \n",
+       "catgc_clip_3_lrd_97_nhid40                      2.7035      88      2.7193   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc_wd5      2.7318      92      2.7318   \n",
+       "baseline_temp_0.5                               2.7331      99      2.7416   \n",
+       "cfg_nhid_40_lrd_0.94                            2.7218      98      2.7487   \n",
+       "catgc_nhid_38                                   2.7159      99      2.7189   \n",
+       "cfg_node_bias_nhid_40_lrd_99                    2.7268      71      2.7450   \n",
+       "replace_0_scale-no-mask                         2.7473      66      2.7473   \n",
+       "catgc_nhid_34                                   2.7357      47      2.7357   \n",
+       "baseline_leaky_relu                             2.7359      84      2.7430   \n",
+       "gc_bn                                           2.7322      63      2.7347   \n",
+       "catgc_nhid40_lrd_0.99_v2                        2.8334      74      2.8334   \n",
+       "baseline_nhid_48                                2.7294      53      2.7294   \n",
+       "end_channels_280                                2.7139      67      2.7139   \n",
+       "baseline_no_weight_decay                        2.7324      99      2.7420   \n",
+       "catgc_nhid_28                                   2.7440      92      2.7626   \n",
+       "cat_feat_early_gc_10_rand                       2.7309      58      2.7309   \n",
+       "replace_0_mean_hax                              2.7308      94      2.7559   \n",
+       "cat_feat_early                                  2.7471      77      2.7471   \n",
+       "cat_feat_early_gc_10_nhid_48_v2                 2.7312      65      2.7577   \n",
+       "add_gc_baseline                                 2.7337      68      2.7421   \n",
+       "cfg_nhid_40_lrd_0.96                            2.7496      74      2.7497   \n",
+       "logs_nhid_44                                    2.7332      80      2.7332   \n",
+       "dow_raw                                         2.7453      55      2.7453   \n",
+       "baseline_gelu                                   2.7417      99      2.7445   \n",
+       "layers3                                         2.7381      66      2.7381   \n",
+       "apt_only_256_rand_gc                            2.7528      96      2.7704   \n",
+       "end_channels_200                                2.7160      72      2.7160   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_40         2.7482      50      2.7482   \n",
+       "cat_feat_early_gc_10_nhid_64                    2.7309      47      2.7309   \n",
+       "cat_feat_early_gc_10_nhid_48                    2.7392      66      2.7574   \n",
+       "cat_feat_early_gc_6                             2.7416      67      2.7750   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_20         2.7457      67      2.7467   \n",
+       "catgc_surgery6_finetune                         2.7356      68      2.7356   \n",
+       "catgc_nhid_24                                   2.7462      99      2.7959   \n",
+       "init_extra_adj                                  2.7376      87      2.7462   \n",
+       "baseline7                                       2.7351      90      2.7484   \n",
+       "hist_1                                          2.7797      99      2.8059   \n",
+       "cat_feat_early_gc_256_rand                      2.7451      72      2.7598   \n",
+       "cat_feat_early_gc_14                            2.7483      80      2.7522   \n",
+       "catgc_nhid40_cyclic_v2                          2.8332      99      2.8408   \n",
+       "logs_0.0005                                     2.7572      99      2.7572   \n",
+       "cheaper-metric-fixed                            2.7439      99      2.7566   \n",
+       "gc_leaky_relu                                   2.7589      65      2.7589   \n",
+       "actual_matrix                                   2.7673      99      2.7897   \n",
+       "no_add_apt_adj                                  2.7948      71      2.7948   \n",
+       "baseline5                                       2.7586      99      2.7607   \n",
+       "no_apt_adj                                      2.7791      92      2.7983   \n",
+       "bs_512_fixed_lr                                 2.7907      99      2.8385   \n",
+       "apt_only_512_rand_gc                            2.7665      71      2.7665   \n",
+       "logs_0.005                                      2.7563      59      2.7563   \n",
+       "apt_only_aptsize256                             2.7656      94      2.7836   \n",
+       "baseline_nhid_16                                2.7890      99      2.7959   \n",
+       "1feat                                           2.7812      84      2.7914   \n",
+       "catgc_nhid_40_fp16O1_lrd_99                     2.7565      49      2.7565   \n",
+       "catgc_nhid_40_fp16O1_lrd_99_bs32                2.7565      49      2.7565   \n",
+       "logs_0.01                                       2.7841      41      2.7841   \n",
+       "apt_only_512_rand                               2.8058      65      2.8058   \n",
+       "catgc_surgery6_freeze_all_but_ec2               2.7880      53      2.8205   \n",
+       "apt_only_apt_size_10                            2.8290      99      2.8541   \n",
+       "apt_only_20_fixed                               2.8250      89      2.8250   \n",
+       "catgc_seq_len_15                                3.1211      62      3.1211   \n",
+       "rmse_backward                                   2.9295      65      2.9295   \n",
+       "catgc_clip_3_offset__7_12                       3.0011      50      3.0011   \n",
+       "logs_0.0001                                     3.0283      99      3.0283   \n",
+       "no_graph_conv3                                  3.2221      71      3.2435   \n",
+       "apt_only                                        3.2221      71      3.2435   \n",
+       "apt_only_aptsize20                              3.2225      71      3.2266   \n",
+       "\n",
+       "                                            es10_i  test_mae  test_mape  \\\n",
+       "front_1                                         99    2.1357     0.0503   \n",
+       "front_3                                         99    2.3972     0.0592   \n",
+       "catgc_clip_3_t_12_6                             65    2.6597     0.0689   \n",
+       "catgc_nhid40_seq_len_6                          64    2.6731     0.0689   \n",
+       "front_9                                         41    2.8758     0.0769   \n",
+       "surgery_t12_6                                   39    2.9952     0.0817   \n",
+       "catgc_clip_3_lrd_97_nhid40_v2                   39    2.9981     0.0817   \n",
+       "replace-0-feat_v2                               45    3.0002     0.0818   \n",
+       "skip_channels_400                               59    3.0020     0.0822   \n",
+       "catgc_nhid40_lrd_0.99                           31    3.0040     0.0817   \n",
+       "replace_0_feat                                  52    3.0070     0.0825   \n",
+       "catgc_clip_4_lrd_97_nhid40                      67    3.0099     0.0811   \n",
+       "catgc_nhid40_lrd_0.95                           67    3.0106     0.0817   \n",
+       "catgc_nhid_42                                   80    3.0114     0.0809   \n",
+       "logs_nhid_36                                    67    3.0117     0.0816   \n",
+       "end_channels_360                                60    3.0119     0.0807   \n",
+       "catgc_clip_4_lrd_97_nhid40_v2                   78    3.0125     0.0818   \n",
+       "surgery_front_3                                 42    3.0132     0.0818   \n",
+       "logs_0.001                                      76    3.0133     0.0832   \n",
+       "catgc_clip_2_lrd_97_nhid40                      75    3.0134     0.0814   \n",
+       "logs_nhid_40                                    64    3.0146     0.0816   \n",
+       "hist_6                                          49    3.0148     0.0821   \n",
+       "baseline_lr_decay                               99    3.0159     0.0816   \n",
+       "catgc_nhid40_lrd_0.98                           71    3.0166     0.0821   \n",
+       "end_channels_400                                68    3.0168     0.0822   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc          66    3.0179     0.0829   \n",
+       "dow_one_hot                                     46    3.0183     0.0830   \n",
+       "skip_channels_240                               39    3.0184     0.0817   \n",
+       "baseline_temp_2                                 45    3.0198     0.0819   \n",
+       "catgc_clip_8_lrd_97_nhid_40                     31    3.0211     0.0824   \n",
+       "sm_1.5                                          55    3.0213     0.0806   \n",
+       "cfg_node_bias_nhid_36_lrd_99_lr_0.002           46    3.0214     0.0828   \n",
+       "catgc_clip_1_lrd_97_nhid40                      52    3.0220     0.0811   \n",
+       "catgc_clip_3_lrd_97_nhid40                      58    3.0223     0.0832   \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc_wd5      82    3.0242     0.0828   \n",
+       "baseline_temp_0.5                               78    3.0244     0.0821   \n",
+       "cfg_nhid_40_lrd_0.94                            46    3.0260     0.0830   \n",
+       "catgc_nhid_38                                   86    3.0262     0.0822   \n",
+       "cfg_node_bias_nhid_40_lrd_99                    34    3.0268     0.0843   \n",
+       "replace_0_scale-no-mask                         56    3.0271     0.0832   \n",
+       "catgc_nhid_34                                   47    3.0273     0.0830   \n",
+       "baseline_leaky_relu                             57    3.0282     0.0824   \n",
+       "gc_bn                                           42    3.0289     0.0817   \n",
+       "catgc_nhid40_lrd_0.99_v2                        64    3.0313     0.0817   \n",
+       "baseline_nhid_48                                43    3.0332     0.0816   \n",
+       "end_channels_280                                57    3.0332     0.0821   \n",
+       "baseline_no_weight_decay                        55    3.0333     0.0818   \n",
+       "catgc_nhid_28                                   60    3.0336     0.0828   \n",
+       "cat_feat_early_gc_10_rand                       48    3.0342     0.0828   \n",
+       "replace_0_mean_hax                              37    3.0359     0.0824   \n",
+       "cat_feat_early                                  67    3.0378     0.0827   \n",
+       "cat_feat_early_gc_10_nhid_48_v2                 30    3.0384     0.0819   \n",
+       "add_gc_baseline                                 46    3.0388     0.0823   \n",
+       "cfg_nhid_40_lrd_0.96                            52    3.0390     0.0836   \n",
+       "logs_nhid_44                                    70    3.0392     0.0826   \n",
+       "dow_raw                                         45    3.0397     0.0840   \n",
+       "baseline_gelu                                   82    3.0418     0.0823   \n",
+       "layers3                                         56    3.0428     0.0842   \n",
+       "apt_only_256_rand_gc                            54    3.0432     0.0835   \n",
+       "end_channels_200                                62    3.0435     0.0805   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_40         40    3.0445     0.0820   \n",
+       "cat_feat_early_gc_10_nhid_64                    37    3.0459     0.0829   \n",
+       "cat_feat_early_gc_10_nhid_48                    28    3.0468     0.0816   \n",
+       "cat_feat_early_gc_6                             36    3.0477     0.0831   \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_20         45    3.0479     0.0825   \n",
+       "catgc_surgery6_finetune                         58    3.0483     0.0816   \n",
+       "catgc_nhid_24                                   47    3.0500     0.0830   \n",
+       "init_extra_adj                                  65    3.0505     0.0833   \n",
+       "baseline7                                       55    3.0544     0.0823   \n",
+       "hist_1                                          48    3.0584     0.0849   \n",
+       "cat_feat_early_gc_256_rand                      30    3.0586     0.0820   \n",
+       "cat_feat_early_gc_14                            55    3.0587     0.0837   \n",
+       "catgc_nhid40_cyclic_v2                          57    3.0606     0.0821   \n",
+       "logs_0.0005                                     99    3.0624     0.0838   \n",
+       "cheaper-metric-fixed                            58    3.0638     0.0824   \n",
+       "gc_leaky_relu                                   55    3.0642     0.0844   \n",
+       "actual_matrix                                   57    3.0710     0.0837   \n",
+       "no_add_apt_adj                                  61    3.0717     0.0822   \n",
+       "baseline5                                       78    3.0727     0.0832   \n",
+       "no_apt_adj                                      55    3.0783     0.0862   \n",
+       "bs_512_fixed_lr                                 50    3.0822     0.0848   \n",
+       "apt_only_512_rand_gc                            61    3.0859     0.0855   \n",
+       "logs_0.005                                      49    3.0860     0.0832   \n",
+       "apt_only_aptsize256                             57    3.0907     0.0832   \n",
+       "baseline_nhid_16                                75    3.0941     0.0852   \n",
+       "1feat                                           52    3.0958     0.0847   \n",
+       "catgc_nhid_40_fp16O1_lrd_99                     39    3.1043     0.0869   \n",
+       "catgc_nhid_40_fp16O1_lrd_99_bs32                39    3.1043     0.0869   \n",
+       "logs_0.01                                       31    3.1112     0.0847   \n",
+       "apt_only_512_rand                               55    3.1172     0.0862   \n",
+       "catgc_surgery6_freeze_all_but_ec2               23    3.1241     0.0843   \n",
+       "apt_only_apt_size_10                            50    3.1377     0.0874   \n",
+       "apt_only_20_fixed                               79    3.1607     0.0888   \n",
+       "catgc_seq_len_15                                52    3.2132     0.0892   \n",
+       "rmse_backward                                   55    3.2543     0.0860   \n",
+       "catgc_clip_3_offset__7_12                       40    3.3582     0.0929   \n",
+       "logs_0.0001                                     99    3.3754     0.0953   \n",
+       "no_graph_conv3                                  24    3.5817     0.1019   \n",
+       "apt_only                                        24    3.5817     0.1019   \n",
+       "apt_only_aptsize20                              44    3.5821     0.1033   \n",
+       "\n",
+       "                                            test_rmse  test_loss         date  \n",
+       "front_1                                        3.7082        NaN  12-06-14:54  \n",
+       "front_3                                        4.3965        NaN  12-06-13:23  \n",
+       "catgc_clip_3_t_12_6                            5.0939        NaN  12-04-22:29  \n",
+       "catgc_nhid40_seq_len_6                         5.1211        NaN  12-04-12:56  \n",
+       "front_9                                        5.6416        NaN  12-06-13:56  \n",
+       "surgery_t12_6                                  5.9641     2.8771  12-04-23:25  \n",
+       "catgc_clip_3_lrd_97_nhid40_v2                  5.9847        NaN  12-04-17:29  \n",
+       "replace-0-feat_v2                              5.9746        NaN  12-05-23:47  \n",
+       "skip_channels_400                              5.9804        NaN  12-05-16:04  \n",
+       "catgc_nhid40_lrd_0.99                          5.9994        NaN  12-02-11:24  \n",
+       "replace_0_feat                                 6.0179        NaN  12-05-10:00  \n",
+       "catgc_clip_4_lrd_97_nhid40                     6.0230        NaN  12-03-19:33  \n",
+       "catgc_nhid40_lrd_0.95                          6.0009        NaN  12-02-13:39  \n",
+       "catgc_nhid_42                                  6.0094        NaN  12-03-00:09  \n",
+       "logs_nhid_36                                   5.9907        NaN  12-01-04:26  \n",
+       "end_channels_360                               5.9924        NaN  12-05-12:21  \n",
+       "catgc_clip_4_lrd_97_nhid40_v2                  6.0135        NaN  12-04-19:06  \n",
+       "surgery_front_3                                5.9776        NaN  12-06-15:24  \n",
+       "logs_0.001                                     6.0157        NaN  12-01-01:05  \n",
+       "catgc_clip_2_lrd_97_nhid40                     6.0110        NaN  12-03-12:29  \n",
+       "logs_nhid_40                                   6.0093        NaN  12-01-05:49  \n",
+       "hist_6                                         6.0289        NaN  12-06-17:26  \n",
+       "baseline_lr_decay                              6.0115        NaN  11-30-20:18  \n",
+       "catgc_nhid40_lrd_0.98                          6.0022        NaN  12-02-15:03  \n",
+       "end_channels_400                               6.0190        NaN  12-05-13:40  \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc         6.0495        NaN  12-01-15:41  \n",
+       "dow_one_hot                                    6.0433        NaN  12-06-11:43  \n",
+       "skip_channels_240                              6.0187        NaN  12-05-15:03  \n",
+       "baseline_temp_2                                5.9979        NaN  12-01-22:08  \n",
+       "catgc_clip_8_lrd_97_nhid_40                    6.0427        NaN  12-03-14:47  \n",
+       "sm_1.5                                         5.9997        NaN  12-06-01:22  \n",
+       "cfg_node_bias_nhid_36_lrd_99_lr_0.002          6.0458        NaN  12-02-19:20  \n",
+       "catgc_clip_1_lrd_97_nhid40                     6.0025        NaN  12-03-16:25  \n",
+       "catgc_clip_3_lrd_97_nhid40                     6.0249        NaN  12-03-17:50  \n",
+       "baseline_lrd_nhid_40_cat_feat_early_gc_wd5     5.9898        NaN  12-01-17:19  \n",
+       "baseline_temp_0.5                              6.0311        NaN  12-01-23:26  \n",
+       "cfg_nhid_40_lrd_0.94                           6.0273        NaN  12-02-23:50  \n",
+       "catgc_nhid_38                                  6.0221        NaN  12-02-22:37  \n",
+       "cfg_node_bias_nhid_40_lrd_99                   5.9900        NaN  12-02-17:58  \n",
+       "replace_0_scale-no-mask                        6.0864        NaN  12-05-11:23  \n",
+       "catgc_nhid_34                                  6.0413        NaN  12-02-20:38  \n",
+       "baseline_leaky_relu                            6.0221        NaN  12-01-20:53  \n",
+       "gc_bn                                          6.0406        NaN  12-05-21:40  \n",
+       "catgc_nhid40_lrd_0.99_v2                       6.0403     3.0113  12-03-21:47  \n",
+       "baseline_nhid_48                               6.0327        NaN  11-29-21:38  \n",
+       "end_channels_280                               6.0429        NaN  12-05-12:19  \n",
+       "baseline_no_weight_decay                       6.0438        NaN  11-30-21:24  \n",
+       "catgc_nhid_28                                  6.0115        NaN  12-02-19:45  \n",
+       "cat_feat_early_gc_10_rand                      6.0358        NaN  11-29-19:29  \n",
+       "replace_0_mean_hax                             6.0808        NaN  12-05-18:16  \n",
+       "cat_feat_early                                 6.0372        NaN  11-28-17:33  \n",
+       "cat_feat_early_gc_10_nhid_48_v2                6.0953        NaN  11-30-11:34  \n",
+       "add_gc_baseline                                6.0412        NaN  11-29-00:01  \n",
+       "cfg_nhid_40_lrd_0.96                           6.0135        NaN  12-02-22:04  \n",
+       "logs_nhid_44                                   6.0374        NaN  12-01-07:06  \n",
+       "dow_raw                                        6.0908        NaN  12-05-11:20  \n",
+       "baseline_gelu                                  6.0493        NaN  12-01-19:36  \n",
+       "layers3                                        6.0639        NaN  12-02-14:33  \n",
+       "apt_only_256_rand_gc                           6.0788        NaN  11-29-12:30  \n",
+       "end_channels_200                               6.0801        NaN  12-05-13:19  \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_40        6.0604        NaN  11-30-03:19  \n",
+       "cat_feat_early_gc_10_nhid_64                   6.0568        NaN  11-30-00:56  \n",
+       "cat_feat_early_gc_10_nhid_48                   6.0635        NaN  11-29-23:30  \n",
+       "cat_feat_early_gc_6                            6.0719        NaN  11-30-06:10  \n",
+       "cat_feat_early_gc_10_nhid_48_dropout_20        6.0810        NaN  11-30-04:46  \n",
+       "catgc_surgery6_finetune                        6.0711        NaN  12-04-18:13  \n",
+       "catgc_nhid_24                                  6.0435        NaN  12-02-18:45  \n",
+       "init_extra_adj                                 6.1143        NaN  11-30-12:53  \n",
+       "baseline7                                      6.0684        NaN  11-29-02:42  \n",
+       "hist_1                                         6.1150        NaN  12-06-16:04  \n",
+       "cat_feat_early_gc_256_rand                     6.0972        NaN  11-29-17:28  \n",
+       "cat_feat_early_gc_14                           6.1258        NaN  11-30-07:51  \n",
+       "catgc_nhid40_cyclic_v2                         6.0994        NaN  12-04-00:20  \n",
+       "logs_0.0005                                    6.0522        NaN  12-01-02:15  \n",
+       "cheaper-metric-fixed                           6.0895        NaN  11-28-19:38  \n",
+       "gc_leaky_relu                                  6.0940        NaN  12-05-22:43  \n",
+       "actual_matrix                                  6.0774        NaN  12-02-12:50  \n",
+       "no_add_apt_adj                                 6.1247        NaN  11-28-21:00  \n",
+       "baseline5                                      6.1174        NaN  11-28-19:38  \n",
+       "no_apt_adj                                     6.1080        NaN  11-28-17:07  \n",
+       "bs_512_fixed_lr                                6.1115        NaN  11-28-19:38  \n",
+       "apt_only_512_rand_gc                           6.1623        NaN  11-29-10:15  \n",
+       "logs_0.005                                     6.1543        NaN  11-30-23:14  \n",
+       "apt_only_aptsize256                            6.1898        NaN  11-28-19:38  \n",
+       "baseline_nhid_16                               6.1398        NaN  11-29-21:03  \n",
+       "1feat                                          6.1218        NaN  11-28-19:38  \n",
+       "catgc_nhid_40_fp16O1_lrd_99                    6.1828        NaN  12-03-01:30  \n",
+       "catgc_nhid_40_fp16O1_lrd_99_bs32               6.1828        NaN  12-03-01:30  \n",
+       "logs_0.01                                      6.1789        NaN  11-30-21:50  \n",
+       "apt_only_512_rand                              6.2545        NaN  11-29-00:05  \n",
+       "catgc_surgery6_freeze_all_but_ec2              6.2294        NaN  12-04-17:02  \n",
+       "apt_only_apt_size_10                           6.2904        NaN  11-29-11:38  \n",
+       "apt_only_20_fixed                              6.3773        NaN  11-28-21:35  \n",
+       "catgc_seq_len_15                               6.4515        NaN  12-04-14:08  \n",
+       "rmse_backward                                  6.0148        NaN  11-30-13:49  \n",
+       "catgc_clip_3_offset__7_12                      6.8876        NaN  12-04-21:08  \n",
+       "logs_0.0001                                    6.6263        NaN  12-01-03:33  \n",
+       "no_graph_conv3                                 7.1858        NaN  11-28-19:38  \n",
+       "apt_only                                       7.1858        NaN  11-28-19:38  \n",
+       "apt_only_aptsize20                             7.1754        NaN  11-28-19:38  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summaries = {os.path.basename(c): summary(c) for c in glob('logs/*')}\n",
+    "tab = pd.DataFrame.from_dict(summaries, orient='index')\n",
+    "nc = get_num_cols(tab)\n",
+    "tab.to_msgpack('latest_metrics.mp')\n",
+    "tab.round({c: 4 for c in nc})[nc.tolist() + ['date']].sort_values('test_mae').head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-06T04:47:37.422487Z",
+     "start_time": "2019-12-06T04:47:32.861Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "args = pickle_load('baseline_args.pkl')\n",
+    "\n",
+    "data = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)\n",
+    "\n",
+    "data['scaler'].mean, data['scaler'].std\n",
+    "\n",
+    "ax = loss_curve('dow_raw').plot(figsize=(6,6))\n",
+    "loss_curve('catgc_clip_3_lrd_97_nhid40').add_prefix('base_').plot(figsize=(6,6), ax=ax)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "t712 = pd.read_csv('logs/catgc_clip_3_offset__7_12/test_metrics.csv', index_col=0)\n",
+    "t712\n",
+    "\n",
+    "pd.read_csv('logs/catgc_clip_3_lrd_97_nhid40/test_metrics.csv', index_col=0).tail(6)\n",
+    "\n",
+    "pickle_load('logs/skip_channels_400/metadata.pkl')\n",
+    "\n",
+    "tab.loc['dow_raw']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-06T02:16:02.920299Z",
+     "start_time": "2019-12-06T02:16:02.849625Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train_loss             2.8607\n",
+       "train_mape          0.0765048\n",
+       "train_rmse            5.68195\n",
+       "valid_loss            2.79065\n",
+       "valid_mape          0.0768377\n",
+       "valid_rmse            5.61896\n",
+       "best_epoch                 98\n",
+       "min_train_loss        2.85301\n",
+       "date              11-28-19:38\n",
+       "es20_score            2.79065\n",
+       "es20_i                     99\n",
+       "es10_score            2.83846\n",
+       "es10_i                     50\n",
+       "test_mae              3.08225\n",
+       "test_mape           0.0848333\n",
+       "test_rmse              6.1115\n",
+       "test_loss                 NaN\n",
+       "Name: bs_512_fixed_lr, dtype: object"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab.loc['bs_512_fixed_lr']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-06T02:16:14.056007Z",
+     "start_time": "2019-12-06T02:16:14.018468Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train_loss             2.7886\n",
+       "train_mape             0.0743\n",
+       "train_rmse             5.5527\n",
+       "valid_loss             2.7351\n",
+       "valid_mape              0.075\n",
+       "valid_rmse             5.2642\n",
+       "best_epoch                 70\n",
+       "min_train_loss         2.7523\n",
+       "date              11-29-02:42\n",
+       "es20_score             2.7351\n",
+       "es20_i                     90\n",
+       "es10_score             2.7484\n",
+       "es10_i                     55\n",
+       "test_mae              3.05442\n",
+       "test_mape           0.0823333\n",
+       "test_rmse             6.06842\n",
+       "test_loss                 NaN\n",
+       "Name: baseline7, dtype: object"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab.loc['baseline7']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T23:42:35.109745Z",
+     "start_time": "2019-12-04T23:42:34.824607Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "27\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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b5j4HoEWLFmzdupX27dtXOu/gwYMMGjSIe+65h3HjxtGsWbNjruPw4cPccMMNBAQEsHDhQvr27es+dscdd/Dss88ec59Hc++997Ju3Tquv/56XnnlFYwx7va+ffty2223cc4557hXofnnP/9Ju3btWLNmDeHh4ZX62rdvn3t72rRp5OXl8d///pcLL7yw0nkHDhyodq1TPDUG/Q5gJfAVsAeIAAYAU4DrjTEDrLXbj9ZJadC/B9iPK9zvAzoBFwKXGGPGW2une6hmADq1Kn+CvlFroYuIiPi2KcfR+OgpBxt0+dVXX81DDz3EtGnTKgX0goIC3n33XVq3bs15550HQGhoaLVwDq4x5ddccw133XUXP/zwA2ecccYx1/Hxxx+TkZHB+PHjK4VzgClTpvDGG29w8GDDvmtFhYWFTJ8+ncjISJ544gl3OAdITk7mtttu47HHHuPNN9/kz3/+s/tYcHAwgYHV5xLGxcVVa6vpF5UWLVp46Bs0nKeGuERbawdYa6+x1t5nrf2jtfY04HHgBOD+o3VgjIkH7gZ2A92stdeW9nUpcA5ggEc9VK9bcpsKa6FrqUURERHxEe3bt+ess85i+fLlrFu3zt3+6aefkpGRwbhx4wgKKn/WunbtWiZOnEhSUhLNmjXDGIMxhrvuuguAnTt31quOlStXAjB06NBqx2JiYujVq1e9+q3Nhg0byMnJoWfPnsTGxlY7PmzYMABWrVrlbhs3bhxpaWl0796d+++/n9mzZ9f4S8PYsWMJDAzkoosuYvz48bz55pts2rTJo/V7gkcCurU2r5ZD75d+Jtehmw6l9Sy11u6p0v88IAtoVe8ia1FxqcUt+7IpLC7x9C1ERERE6mXixIlA+ZCWittlw1sAvv/+e0477TRmzJhB586dueGGG3j44YeZPHmyeyhHfcdXlwXdNm3a1Hg8Pj6+Xv0e7X61LfVY1p6Zmelu+/vf/86zzz5LREQETz75JOeddx5xcXFceOGFbNy40X1ev379WLhwIcOGDeODDz5gwoQJdOrUiS5duvDOO+949Hs0hLeXWRxd+rm6Duf+DygA+hlj4qy17gFDxpgzgChcw16OyhizopZDXao2RIYGcUJMGL8dzKOoxLJ1f06l0C4iIiI+pIHDRo43F198MdHR0UyfPp3HH3+cjIwMvvjiC3r27EnPnj3d5z322GPk5uYyb968am/lfOKJJ/j444/rXUPZsou7d++u8Xh6enq9+z7S/Wrrd9euXZXOAwgMDOT222/n9ttvZ8+ePSxatIh3332XmTNnsnbtWtauXUtoaCjgmoA7a9Ys8vPzWbFiBbNnz+aFF17gqquuolWrVgwfPtyj36c+PLqKizHmbmPMFGPM340xC4G/4ArnTx7tWmttBnAv0AZYZ4x51RjzhDHmfeBLXOPbb/BkvWU6ttY4dBEREfE9zZo14/LLL+e3335j7ty5vP322xQVFVV6eg6wceNGYmNjq4VzgG+//bZBNaSkpNTaz8GDB/nxxx8b1H9VnTt3Jjw8nB9//JEDBw5UOz5v3rxKdVXVunVrxowZw/vvv8+wYcPYtGkTa9asqXZeaGgoAwcO5NFHH+X5558HaNAvMp7k6WUW7wYmA5OAwcBsYIS1dm9dLrbWPguMwfVk/zrgPuAyYDswterQlyP006emP0CNi3Umt9Y4dBEREfFNZcNc3nzzTd58802CgoIYN25cpXMSExPJyMhg9erKgxZef/115syZ06D7X3jhhbRo0YIZM2awfPnySsemTJni0QmiACEhIYwbN47Dhw9XmgQKsGnTJp5//nmCg4O5+uqrAdfQna+//hprbaVzCwsLycjIAHCvzrJw4cIa6y3714GmtooLANbaeABjTBtgIK4n56uMMaOstSuPdr0x5h5cE0ufB14E0nENS3kCeNsY08tae48nawZIbqO10EVERMQ3DRo0iE6dOjFz5kwKCwsZPXo0rVu3rnTOpEmTmDNnDoMHD+byyy8nJiaG5cuXs2jRIi699FI++OCDet8/MjKSV199lbFjxzJkyBDGjh1L27ZtWbRoEWvWrOGMM85gwYIFDf2alTz55JMsXLiQF198kR9++IEzzzyTffv28f7775OVlcWLL77ISSedBEBubi7Dhw8nMTGR/v3706FDB/Ly8vjqq69Yv349F1xwAV27dgXgb3/7G19++SWpqakkJSURGRnJ2rVr+eKLL2jRogXXX3+9R79HfXnlRUXW2t3W2o+AEbjWMn/zaNcYY1KBp4BPrLV3Wms3W2tzSoP9xcBO4C5jTJKn602uNMRFAV1ERER8y4QJE9xvuaw6vAXg3HPP5dNPP6Vbt2689957vP7664SGhjJv3jxGjhzZ4PtfeumlzJ49mz59+vD+++/zyiuvEBsby5IlS9xB2ZPK+r7nnnvYv38/zzzzDDNnzqRfv37Mnj2bm2++2X1uREQETz31FF26dGHx4sU899xzzJgxg+joaP7xj39UelHRzTffzNixY0lLS+Ott97ihRde4Ndff+Xmm29m1apVdOzY0ePfpT5M1X8O8PgNjFkF9AJaVZz4WcN5/wfcBdxmrX2hhuP/wRXUL7XWfljPWlakpKSkrFhReQ5pZk4BvR79CoDQoADWPXougQGmpi5ERETEi9avXw/gfuIp4mvq+jPap08fVq5cubJ0mPUx8coT9CpOKP0sPsp5oaWftS2lWNZe0OCKqmgeHkJcpOv2+UUl7DiQ4+lbiIiIiIjUSYMDujGmS+lLhqq2Bxhj/gq0BhZbaw+UtgeXXlP13xAWln5eb4xpV6Wv84BBQB6wuKE116TiMBdNFBURERERp3hikui5wNPGmAXAJmA/rqUShwJJuCZ6Xlfh/HbAemArkFih/QNgLjAcWG+M+aj02q7AKFxvEr3PWrvfAzVXk9wmkiWbXV1v3HuY4dS8GL+IiIiI1CwzM5Nnn322TudOnDiRxMRE7xZ0nPJEQJ8LvIrrCXdPoDmQDfwKvAU8X7rG+RFZa0uMMecDtwBX4BpvHg5kAJ+X9vOlB+qtUSc9QRcRERFpkMzMTB555JE6nZuamqqAXosGB3Rr7Rpcobqu56fhehpe07FC4NnSP42qk15WJCIiItIgiYmJ1dYjl2PXGJNEjwsVX1a0cc9h/XCJiIiIiCMU0EvFRYbQPDwYgOyCYnYdzHO4IhERERHxRwropYwxdGqlN4qKiIiISM0aa4SFAnoFyW0qThTVOHQREZHGZoxrmlpJSYnDlYhUVxbQy35OvUUBvYJOVcahi4iISOMKDXW9ODA7O9vhSkSqK/u5LPs59RYF9AqSK63kooAuIiLS2KKiXA/L0tPTycrKoqSkRAs3iKOstZSUlJCVlUV6ejpQ/nPqLZ5YB73JqLQWeulKLt7+JwwREREpFxsbS3Z2Njk5OezYscPpckSqCQ8PJzY21qv30BP0CtrGhBEREgjAwdxC9h7Od7giERER/xIQEEBCQgKtWrUiLCxMD8rEJxhjCAsLo1WrViQkJBAQ4N0IrSfoFRhj6NQmip+2ZwKwcfdhWkeFOVyViIiIfwkICCAuLo64uDinSxFxhJ6gV1FpHPpejUMXERERkcalgF5FpXHouxXQRURERKRxKaBXkVxpoqjWQhcRERGRxqWAXkWy1kIXEREREQcpoFfRrkUzwoJd/1n2HS7gQHaBwxWJiIiIiD9RQK8iMMCQFKeJoiIiIiLiDAX0GiS30URREREREXGGAnoNNFFURERERJyigF6DTpooKiIiIiIOUUCvQcW10BXQRURERKQxKaDXoEPLcIIDDQC7DuaRlVfocEUiIiIi4i8U0GsQHBjASXER7n09RRcRERGRxqKAXgu9sEhEREREnKCAXouOGocuIiIiIg5QQK9F5aUWFdBFREREpHEooNei0suKtBa6iIiIiDQSBfRanBQXQYBrIRd2HMglt6DY2YJERERExC8ooNciNCiQDi1dK7lYC5v2apiLiIiIiHifAvoR6IVFIiIiItLYFNCPoPJEUY1DFxERERHvU0A/gooTRfUEXUREREQagwL6EXRqVf6yIi21KCIiIiKNQQH9CDq2jnBvb92fQ36RVnIREREREe9SQD+C8JAg2rdoBkBxiSVtX47DFYmIiIhIU6eAfhTJWslFRERERBqRAvpRdNJKLiIiIiLSiBTQjyK5tSaKioiIiEjjUUA/ik4Vl1rcrYAuIiIiIt6lgH4UFYe4bNmXTVFxiYPViIiIiEhTp4B+FNFhwbSJDgWgoLiEbRlayUVEREREvEcBvQ40Dl1EREREGosCeh100lKLIiIiItJIFNDrILmNArqIiIiINA4F9DqoPMRFa6GLiIiIiPcooNdB1SEuJSXWwWpEREREpClTQK+D2IgQWkaEAJBXWMLOzFyHKxIRERGRpkoBvY40UVREREREGoMCeh1VnCiqcegiIiIi4i0K6HXUqVWFgL5bT9BFRERExDsU0OsouY1eViQiIiIi3qeAXkfJFcagb9pzGGu1kouIiIiIeJ4Ceh21igolOiwIgKz8InYfyne4IhERERFpijwS0I0xTxljvjbGbDfG5BpjMowxq4wxk40xLevR3xBjzIfGmF3GmPzSzy+NMed7ot76MMZUWslFE0VFRERExBs89QT9DiAC+Ap4DngbKAKmAKuNMQl17cgY8xCwADgDmA38DfgUaAGkeqjeeqn0RlFNFBURERERLwjyUD/R1tq8qo3GmL8CDwD3AzcfrRNjzGXAX4C5wBhrbVaV48GeKbd+Ki61uHGvArqIiIiIeJ5HnqDXFM5LvV/6mXy0PowxAcBTQA5wVdVwXnqfwnoX6QGVXlakJ+giIiIi4gWeeoJem9Gln6vrcO5A4CTgA+CAMWYk0APIA5ZZa5d4p8S6qxjQf92ThbUWY4yDFYmIiIhIU+PRgG6MuRuIBGKAvsBgXOH8yTpcflrp525gJXBKlb4XAJdaa/fWoY4VtRzqUoc6anVCTDPCQwLJKSgmM6eQ/dkFxEWGNqRLEREREZFKPL3M4t3AZGASrnA+GxhRl1ANtC79vBFoBgwHonA9RZ+Da9LoTA/Xe0wCAiqv5LJRLywSEREREQ/zaEC31sZbaw0QD4wBkoBVxpiUOlweWPppcD0p/9pae9hauxa4GNgBDDXGnF6HOvrU9AfYUK8vVkHlpRYV0EVERETEs7zyoiJr7W5r7UfACKAl8GYdLjtQ+rnZWvtTlf5ycT1FB+jnsULrofJEUa2FLiIiIiKe5dU3iVprtwLrgO7GmLijnP5L6WdmLcfLAnwzT9RWX5XWQtcTdBERERHxMK8G9FInlH4WH+W8BbhebpRsjAmp4XiP0s80D9VVL8kagy4iIiIiXtTggG6M6WKMia+hPaD0RUWtgcXW2gOl7cGl13SseL61dh/wHq4VYP5cpa+zgXOAg7gmnjomITackCDXf7Y9WfkczHF0aXYRERERaWI8scziucDTpcsgbgL2A22AobgmiaYD11U4vx2wHtgKJFbp606gP/CgMeYMYBnQAdck0WLgOmttbUNgGkVggCEpLoIN6a7x5xv3ZtGnQ6yTJYmIiIhIE+KJIS5zgVdxTQYdA/wJuATIAB4Bultr19WlI2vtHlwB/e9AAnAbMAz4DBhirXV0mcUyyW0qjEPXG0VFRERExIMa/ATdWrsGuOUYzk/DtZRibcczcD1Jv7OhtXlLspZaFBEREREvaYxJok2OJoqKiIiIiLcooNeD3iYqIiIiIt6igF4PHVpGEBTgGqWzMzOXw/lFDlckIiIiIk2FAno9hAQFkBgX4d7fpKfoIiIiIuIhCuj1pHHoIiIiIuINCuj11EkruYiIiIiIFyig11PliaJZDlYiIiIiIk2JAno9Jbeu8LIiPUEXEREREQ9RQK+npFYRlC7kwvaMHPIKi50tSERERESaBAX0egoLDiQhNhyAEgub92Y7XJGIiIiINAUK6A2QXGmiqMahi4iIiEjDKaA3QKcK49C11KKIiIiIeIICegNoLXQRERER8TQF9AbQWugiIiIi4mkK6A3QsUJAT9uXTUFRiYPViIiIiEhToIDeAJGhQbRr3gyAohLL1v1ayUVEREREGkYBvYEqDnOZu36Pg5WIiP9qYKEAACAASURBVIiISFOggN5AAzu2dG8/89Uv/LQ908FqREREROR4p4DeQBMHJXJKuxgACostt8xYycHcQoerEhEREZHjlQJ6A4UGBfLiVb2JCg0CYMeBXO79YDXWWocrExEREZHjkQK6B3RoGcFTl57q3p+9Np03l2x1sCIREREROV4poHvI+ae05eoBHdz7f/1sPT/vOOhgRSIiIiJyPFJA96AHR3al+wnRABQUl3DLjJUcytN4dBERERGpOwV0DwoLDuSlq1KILB2Pvi0jh/s+1Hh0EREREak7BXQPS4yL4Ikxp7j3P/85nenfazy6iIiIiNSNAroXjO55AuP6n+je/8us9azZqfHoIiIiInJ0Cuhe8vCobnRtW3k8epbGo4uIiIjIUSige4lrPHpvIkICAdi6P4f7//OzxqOLiIiIyBEpoHtRUqtIHq8wHn3W6l3MWLbNwYpERERExNcpoHvZhb3acWW/8vHoj3y6jrW/aTy6iIiIiNRMAb0RTB7djS7xUQAUFJVw64xVHM4vcrgqEREREfFFCuiNICw4kJfGpRBeOh59y75sHtB4dBERERGpgQJ6I+nYKpLHLy4fj/7JT7/x7g/bHaxIRERERHyRAnojuqh3O8b2TXDvT/lkLet3HXKwIhERERHxNQrojWzKBd3p3MY1Hj2/yLU+erbGo4uIiIhIKQX0RtYsJJCXxvWmWbBrPPrmvdk89N81Go8uIiIiIoACuiM6tY7isYt6uPc/WrWTmct3OFiRiIiIiPgKBXSHXNKnPZf1ae/e//Mna/glPcvBikRERETEFyigO+iRC7uT3DoSgLxC13j0nAKNRxcRERHxZwroDgoPCeKlcSmEBbv+N2zcc5iH/7vW4apERERExEkK6A47uU0Uf7mwfDz6hyt3MHO51kcXERER8VcK6D7gsr4JjElp595/+OM1/Lpb49FFRERE/JECuo947KIedKo4Hv1tjUcXERER8UcK6D4iPCSIl64qH4/+vz2HmfyxxqOLiIiI+BsFdB/SOT6KRy7o7t6fuWIHH67Q+ugiIiIi/kQB3cdc3jeBi3uXj0e//z8/s/B/ex2sSEREREQakwK6jzHG8NhFPejYKgKAguISrntzOT+kZThcmYiIiIg0BgV0HxQRGsS0a/pxQkwY4Jo0es0bP/DzjoMOVyYiIiIi3qaA7qPatwjn7esGEBcZCkBWfhHj/71Uyy+KiIiINHEK6D7spLgIpl/bj+bhwQAcyClk3GtLSduX7XBlIiIiIuItCug+rkt8NNN+34/I0CAA9mblM+61pfyWmetwZSIiIiLiDQrox4GeCc15fUJf9xrpOzNz+d1rS9mble9wZSIiIiLiaR4J6MaYp4wxXxtjthtjco0xGcaYVcaYycaYlg3o92pjjC39c60naj1e9U9qySu/60NwoAFg875srn59KZk5BQ5XJiIiIiKe5Kkn6HcAEcBXwHPA20ARMAVYbYxJONYOS695ATjsoRqPe6mdW/P8Fb0JcGV0NqRnMeGNHzicX+RsYSIiIiLiMZ4K6NHW2gHW2mustfdZa/9orT0NeBw4Abj/WDozxhjgDWA/8IqHamwSzjulLU9f2tO9/9P2TK6d9gN5hcUOViUiIiIinuKRgG6tzavl0Puln8nH2OVtwDDg94CWLKnikj7t+cuF3d3732/O4MbpKygoKnGwKhERERHxBG9PEh1d+rm6rhcYY7oCTwLPWWsXeKWqJuDq0xO577wu7v35v+xl0nurKCpWSBcRERE5ngV5sjNjzN1AJBAD9AUG4wrnT9bx+iDgLWAb8EAD6lhRy6EutbT7nj3rYcMs6HYxxHWq8ZQbh3YkO7+IF77ZCMDnP6fTLPhnnr70VALKBqqLiIiIyHHFowEduBtoU2F/NjDRWru3jtf/GegNDLbW+u9C34W58OZFcDgdfnoXbl0OpubAfefZJ5OVV8TUxWkAfLhyB5GhgUy5oDumlmtERERExHd5dIiLtTbeWmuAeGAMkASsMsakHO1aY0w/XE/N/2atXdLAOvrU9AfY0JB+G82Gz1zhHGD/RtizrtZTjTH8eVQ3Lu/b3t02bclWnp7zi7erFBEREREv8MoYdGvtbmvtR8AIoCXw5pHOrzC05VfgYW/UdFxZ9Vbl/U3zjnh6QIDhiTGnMurUtu62l+dv4qV5G71RnYiIiIh4kVcniVprtwLrgO7GmLgjnBoJnAx0BfIqvJzIApNLz/lXaduz3qzZcQe2wuZvK7dtnn/UywIDDH8f24uzurR2tz095xemfrfFwwWKiIiIiDd5egx6TU4o/TzSQt35wOu1HEvBNS59EfAL0KDhLz7vp3cAW7lt63dQlA9BoUe8NDgwgJfGpXDN1B9YvGk/AFM+XUd4aBCX9z3md0WJiIiIiAMaHNCNMV2ATGttepX2AOAvQGtgsbX2QGl7MNARKLTWbgIonRB6bS39T8EV0KdZa19raL0+raQEVr1dvm8CwJZAYQ7s+AESBx+1i7DgQP41vi+/e30pq7ZlAnDfh6uJCAliZIUhMCIiIiLimzwxxOVcYLsx5mtjzKvGmCeMMf8G/odr0mc6cF2F89sB64GvPXDvpiVtARzc5tpu1gJ6Xll+rA7DXMpEhAYxdWI/urWNBqDEwu3vrmLehj0eLFZEREREvMETAX0u8CquyaBjgD8BlwAZwCNAd2tt7cuQSLlV08u3T7kcTj6nfP8oE0WrigkP5s0/9KNjqwgAikosN05fwZLSoS8iIiIi4psaHNCttWustbdYa3tZa+OstUHW2hhr7WnW2inW2owq56dZa421NrGO/U8pPb9pD2/JPQDrPinf7/07SBwClK5l/ttKyM08pi7jIkOZfm1/2rdoBkB+UQnXTvuBVdsOeKhoEREREfE0r67iIsdgzYdQnO/ajj8V2p4K4bFwQi9Xmy2BtIXH3G3bmGbMuHYAraNcE0yzC4qZ8O9lrN5xbGFfRERERBqHArqvqDi8pffV5dtJZ5ZvH8M49IpObBnO29f2JzYiBIBDeUVc8er3fPtrXV/wKiIiIiKNRQHdF6Svgd9WubYDQ+CUS8uPJaWWb9czoAMkt4nizWv6EdMsGICcgmL+MPUHPlyxo959ioiIiIjnKaD7gh8rLK3YZZRraEuZhP4Q5BpDzv6NkLm93rfp0S6GD286nXbNXf0VlVjumvkTL83biLX2KFeLiIiISGNQQHdaUQH89G75fu/fVT4eHAYdTi/fb8BTdIBOraP48KaBdImPcrc9PecXJn+yluIShXQRERERpymgO+3XLyC3dKGb6PaVh7SUqdi2+diWW6xJfEwY791wOgOSyp/Uv7lkK7fOWEle4ZFe+CoiIiIi3qaA7rSKk0N7XQUBgdXPqTRR9FvXG0cbKKZZMNOu6Vfp7aJfrEln/L+XcTCnsMH9i4iIiEj9KKA76dBvsHFu+X6vq2o+r00PCI9zbefsg91rPHL70KBAXriiN78flOhuW7Ylg8v+uZhdB3M9cg8REREROTYK6E766R3X+ubgeilR7Ek1nxcQAElDy/cbOA69cteGP4/qxgPnd3G3/br7MGNeXsyvu7M8dh8RERERqRsFdKdYW/va5zVJSi3f9mBABzDGcP0ZHXl2bC+CAlxvLt11MI9L/7GYZVsyjnK1iIiIiHiSArpTti2BjM2u7dBo6Dr6yOcnpZZvb10MhXkeL+mi3u144/enERHiGgd/KK+I372+lC9+3uXxe4mIiIhIzfwvoOdnuZ5eO63i0/Mel0BI+JHPb34ixHZ0bRflwo5lXilrSHIr3rvhdOIiQwEoKCrh5hkrmbY4zSv3ExEREZHK/C+gZ2xyjf12Un4WrP2ofP9ow1vKJKWWb29q+HKLtenRLoaPbh7ISXERgOv3mcmfrOX/zd6gFxqJiIiIeJn/BXSALx+CHAfHVq/9CApzXNutukK7lLpd17HicovzPV5WRQmx4Xx400B6JTR3t708fxN3z1xNYXHDl3kUERERkZr5Z0DP2Q9fPezc/StNDv0dGFO36xKHgCn9X/bbKsg94PnaKoiNCGHGdf0Z1qW1u+3DlTu4dtpysvOLvHpvEREREX/lnwEdXCF56+LGv+/eX2H7Utd2QBCcOrbu1zZrDieUPW23sGWBx8urKjwkiFev7sPlfdu72779dS9X/ut79h3O9/r9RURERPyN/wX0sJjy7U8nQVFB497/xwpPz08+FyJbHdv1Sanl214e5lImKDCApy45ldvOSna3rd5xkEv+sZit+7MbpQYRERERf+F/AT26PYREurb3/QKLn2+8excXwo8VJqjWdXJoRUmp5dtenChalTGGO88+mb9e3IPSpdLZuj+HMS8vZvWOzEarQ0RERKSp87+AHhgMZz5Yvr/g6fL1yL1t41zI3uPajmwDnYYfex8J/SC4dEnGA1vgQJrHyquLcf078Mrv+hAa5PrR2Z9dwBWvfs/8X/Y0ah0iIiIiTZX/BXSAftdD/Kmu7aI8+OzuxlkbveLk0J5XQmDQsfcRFAodBpbvb/624XUdoxHd45lxXX+ahwcDkFNQzLXTlvPOsm1ahlFERESkgfwzoAcGwehngdKxGpu+hrX/8e49D++BX2eX7/f+Xf37Sqq43GLjDXOpqE+HWD648XTaNW8GQFGJ5f7//MwVr37PL+lZjtQkIiIi0hT4Z0AHaNcH+l1Xvj/7fsj14ljq1e9BSenShAkDIC75yOcfSVJq+fbmb6HEmXXJO7WO4j83D6RLfJS7bemWDM5/fiGPfLqWg7mFjtQlIiIicjzz34AOMOwhiIx3bR/eDd/8xTv3sbb62ucN0bobRJSu/pKbAemrG9ZfA7SJDuODmwZy7eCTCCydPVpcYnnjuzTO+tt8Plixg5ISDXsRERERqSv/DuhhMXDek+X7P7wOO1Z4/j47V8DeDa7t4AjoflHD+gsIcGS5xdpEhgbx0KhufHH7EAZ2bOlu33e4gLtn/sSlryxmzc6DDlYoIiIicvzw74AO0O0i6HR26Y6FWbdDsYffkrnqrfLt7hdDaFTt59ZVUmr5tkPj0Ks6uU0Ub1/bn5euSqFtTJi7feW2TEa/uIgHP/qZA9mNvO68iIiIyHFGAd0YGPl/EOSa7Ej6z7D0Fc/1X5ADP39Yvt/Q4S1lklLLt7cugcJcz/TbQMYYRp7alq/vGsotZ3YkJND1I2YtvL10G2f+bT5vL91KsYa9iIiIiNRIAR2gRSIMvad8f97jkLndM32v/wQKSlc1ie0IJw7wTL8x7aFl6UTT4nzYvtQz/XpIeEgQfzqnC3PuOIPUzuVvS83MKeTBj9Zw0UvfsXLbAQcrFBEREfFNCuhlBv4RWnV1bRdmwxf3eqbfqpNDjfFMv+DYW0WPxUlxEbwx8TT+Nb4vCbHN3O0/7zzImJcX86eZP7HvcL6DFYqIiIj4FgX0MoHBpWujl/rlM9jwWcP6zNgMaQtd2yYQel3VsP6q6lhxPfT5nu3bg4wxnN2tDV/dMZQ7hp/sfgspwMwVOzjz/+Yz9bstFBU7s1ykiIiIiC9RQK/oxAGQMr58//N7IP9w/fv7cUb5dvLZEBVf/75qkjjYFfwBdv0EORme7d/DwoIDuX14MnPvHMqIbm3c7Vl5RUz5dB2jXljE0s37HaxQRERExHkK6FUNfwTC41zbh3bA/Cfq109JceWA7qnJoRWFxbheuASAhS3fev4eXpAQG86r4/sy9fencVJchLt9Q3oWY1/9ntvfXcXuQ3kOVigiIiLiHAX0qsJj4Zy/lu9//w/YVY8XAW2eB4d2lvYZB8nneKa+qpJSK9xzvnfu4SWpnVsze9IQ7j23C+Ehge72j3/8jWH/N59XF2yioEjDXkRERMS/KKDX5NSxcNIZrm1bDLMmuZ6IH4uKk0N7XgFBIZ6rr6KK49B9dKLokYQGBXJTake+vmsoo05t627PLijm8c83cN5zC1ie5ttDd0REREQ8SQG9JsbAyGcgsDRU71wBy/9d9+tzMipPMO01zrP1VdSur+vtpACZWyFji/fu5UVtY5rx4lUpzLiuPye3iXS3b9qbzeX/XMJzc/+nSaQiIiLiFxTQaxOXDIPvLN//+lHISq/btT/PhOLSN2a26wNtunm+vjJBIZA4qHzfR94qWl8DO8bx2W1DeHhUN6JCgwAosfD3ub9y5b++Z8eBHIcrFBEREfEuBfQjGXyH6+VCAPmHYPb9dbtu1Vvl296YHFpV0vGx3GJdBQcG8IfBJzHnjjPof1Ksu/2HtAOc99xCPlu9y8HqRERERLxLAf1IgsNg1N/L99f+BzbOPfI1u36C9J9d20Fh0OMS79VXJim1fHvLgmMfL++jTmjejBnXDeDuEScTGOB6wVNWXhG3zFjJPR/8RE5BkcMVioiIiHieAvrRJA11TRot89ldUJhb+/kVJ4d2u9C1FKK3te4KkaXriucecP2S0EQEBhhuHZbM+zecTvsW5W8ifX/5DkY9v4g1Ow86WJ2IiIiI5ymg18WIv0JYc9f2gTRY8HTN5xXmwer3y/cbY3gLuCa1JqWW7zeBYS5V9enQgs9vH8KFvU5wt23el83FL3/HvxZspqTEOlidiIiIiOcooNdFZCs4+5Hy/e+ehz0bqp/3y2eQl+nabt4BOgxunPqgSkA/vieK1iY6LJhnx/bib5f1JKJ03fTCYstfP1/PhDeWsSdLLzcSERGR458Cel31Hg8JA1zbJYUw6w4oqbLsX8XhLb1/BwGN+J83KbV8e9v3UNA0VzsxxnBJn/Z8dtsQerYvHz608H/7OO/ZhczbsMfB6kREREQaTgG9rgICXBNGA1xL/7FtMfz4dvnxzO0VXhRkoOeVjVtf9AkQ19m1XVwA25Y07v0bWWJcBDNvHMiNQztiXPNH2Z9dwO+n/sAjn64lr7BpTJQVERER/6OAfizadIPTby3f/+phyN7n2v7pHaB0HHTHM6F5QqOXV+mtok1wHHpVIUEB3HdeF6b/oT+to0Ld7W98l8bFLy9m454sB6sTERERqR8F9GM19F5ofqJrO/cAfPmwa6hL1eEtTkhKLd/2g4BeZlCnOGZPOoPhXVu729bvOsSoFxbx9tKtWKsJpCIiInL8UEA/ViHhcP7fyvd/mgHfPgmZW137Yc2h80hnauswCIxr8iTpq8uf7vuB2IgQ/jW+L49e2J2QINePdV5hCQ9+tIYbp6/gQHaBwxWKiIiI1I0Cen2cPMK1xnmZb58q3z71ctcLjpwQFg3tTyvf3/KtM3U4xBjD+NMT+fTWwZzcJtLdPmftbs57biFLNu13sDoRERGRulFAr69zn4KQqOrtTg1vKZOUWr69qWkut3g0neOj+OTWwYw/vYO7Lf1QHle99j1Pz9lAYXHJEa4WERERcZYCen1Ft4WzHq7cFn8KtO3pTD1lqk4U9dPx12HBgTx6YQ/+Nb4vLcKDAdd/ipfmbeKyV5awbX/TXIZSREREjn8K6A1x2rVwQu/y/d5XO1dLmXZ9IKR0eMfB7ZCx2dl6HHZ2tzZ8cfsZDOzY0t324/ZMzntuAW8tSdMbSEVERMTnKKA3REAgXDYVkkdAygToe43TFUFgMCRWeINpE32r6LGIjwlj+h/6c++5XQgKcC2anl1QzMMfr2Xsq0vYuOewwxWKiIiIlFNAb6gWiTBuJlzwvCsc+4Ik/1oPvS4CAgw3pXbkw5sGktQqwt3+Q9oBzn9uIS9+8z+NTRcRERGfoIDeFCWllm9vWQAleqtmmZ4Jzfn8tiHcemYn99P0guIS/u/LXxn9wiJW78h0uEIRERHxdx4J6MaYp4wxXxtjthtjco0xGcaYVcaYycaYlkfvAYwxLY0x1xpjPjLGbCzt56AxZpEx5g/GGP0yUVetOkNUW9d23kH47Udn6/ExYcGB3H1OZz65dTCntItxt29Iz+Kil77j8c/Xk1ugX2pERETEGZ4KvXcAEcBXwHPA20ARMAVYbYypy3vvLwP+BfQHlgLPAh8CPYDXgPeNMcZD9TZtxlR5q6jGodek2wnRfHTzQB44vwthwa6/CiUWXl2wmXOfW8Dijf7zoicRERHxHZ4K6NHW2gHW2mustfdZa/9orT0NeBw4Abi/Dn38ClwAtLfWjrPW3m+tvQboAmwHLgHGeKjepk/j0OskKDCA68/oyJxJZ3B6Uvk/9mzdn8NVry3l3g9WczC30MEKRURExN94JKBba/NqOfR+6WdyHfr4xlr7qbW2pEp7OvBK6W5qvYv0N0lDy7e3L4WCbOdqOQ50aBnBjOv689QlpxAVFuRuf2/5ds5+5ltmr0l3sDoRERHxJ94e1z269HN1A/spe4RZ1MB+/EdUPLTq6touLoCtS5yt5zhgjGHsaScy986hnNO9jbt9T1Y+N05fwU3TV7Anq7bfRUVEREQ8I+jop9SdMeZuIBKIAfoCg3GF8ycb0GcQML50d3Ydr1lRy6Eu9a3juNTxTNi73rW9eR4kD3e2nuNEm+gw/nl1X774eRcPf7yWfYfzAfhiTTrfbdzHQyO7cVnf9mhKhIiIiHiDp5+g3w1MBibhCuezgRHW2r0N6PNJXBNFP7fWzml4iX4kKbV8W+PQj9l5p7Tl6zuHcnnf9u62Q3lF3PPhan73+lK27c9xsDoRERFpqjwa0K218dZaA8TjmtCZBKwyxqTUpz9jzG3AXcAG4OpjqKNPTX9K+/EfHQZBQOk/kuxeA4f3OFvPcSgmPJj/d2lP3r62PyfGhrvbv9u4nxHPfstrCzdTXGIdrFBERESaGq+MQbfW7rbWfgSMAFoCbx5rH8aYW3At2bgOONNam+HZKv1AaCS071e+v2WBc7Uc5wZ1imP2pCFcO/gkSt9vRF5hCY99tp4xL3/HhvRDzhYoIiIiTYZXJ4laa7fiCtjdjTFxdb3OGDMJeBFYgyucawmN+kpKLd/epPXQGyI8JIiHRnXjo5sH0SU+yt3+046DjHp+EX/78hfyi/SCIxEREWmYxng75wmln3VKLsaYe4G/Az/iCucal9EQHaush241HKOheiY055NbB3PX2ScTEuj6K1RUYnnhm42c99xCvt+83+EKRURE5HjW4IBujOlijImvoT3AGPNXoDWw2Fp7oLQ9uPSajjVc8zCuSaErgLOstXqVY0OdkAKh0a7tQztg/0Zn62kiQoIC+ONZyXx++2D6dGjhbt+8N5srXv2eez74icycAgcrFBERkeOVJ5ZZPBd42hizANgE7AfaAENxTRJNB66rcH47YD2wFUgsazTGTAAexfWkfSFwWw3L2KVZa6d6oGb/ERgEiUPgl89c+5vnQ9xR3xslddSpdRQzbzidt77fytNzfuFwvmup/veX7+Dr9Xt4aFRXLurVTksyioiISJ15IqDPBV4FBgE9geZANvAr8BbwfB0neJ5U+hmIa5nGmnwLTG1IsX4pKbVyQO933RFOlmMVEGCYMDCREd3bMOWTtcxZuxuA/dkF3PHeT3y4YiePXdSDxLgIhysVERGR44GxfjQm2RizIiUlJWXFitreY9RE7f0VXjrNtR0aDfdscT1ZF6/4cm06kz9Zy66D5W8dDQ0K4LazkrluSBIhQY0x9UNERESc1KdPH1auXLmydKnvY6Kk4A/ikiG6nWs7/xD8tsrZepq4Ed3j+erOofx+UKJ7Scb8ohKenvMLo15YyIqtWjFUREREaqeA7g+MqfJWUS236G2RoUFMHt2d/94yiO4nRLvbf919mEv+sYQHP/qZg7mFDlYoIiIivkoB3V8kVVluURrFqe2b8/Etg3hoZFfCQwLd7W8v3cbwZ75l1urf8KdhZiIiInJ0Cuj+Imlo+fb2ZZB/2Lla/ExQYADXDkniyzvO4Kwurd3te7PyuXXGKq6Z+gPbM3IcrFBERER8iQK6v4hsDW16uLZLCmHtR3ppUSNr3yKc1yb05R/jUmgdFepun/fLXkb8fQGvLthEUXGJgxWKiIiIL1BA9ydJqeXbn9wKr53lCuolej19YzHGcN4pbZl711CuHtCBsuXRcwuLefzzDYx+8Tt+3J7pbJEiIiLiKAV0f3Lq5RAYUr6/cwXMnAgvpMCyf0FBtmOl+ZvosGD+clEPPrhxIF3io9zt63cd4uKXv2PKJ2vJytMkUhEREX+kgO5P2vaEm5ZAygQILB9iwYE0+Pxu+Ht3+OavcHivYyX6mz4dWvDpHwdz77ldCAt2/XW0FqYuTuPsZxYwe026wxWKiIhIY1NA9zdxneCC5+GONXDGnyCsefmx3AOw4P/Bsz3g00mwb6NzdfqR4MAAbkrtyJeThjIkOc7dnn4ojxunr+DaacvZkH7IwQpFRESkMelNov6uIBtWTYclL0LmtioHDXQZCQP/CCcOcKQ8f2Ot5ZOffuMvs9ax73BBpWNndWnNTakd6ZsY61B1IiIiUlcNeZOoArq4FBfB+k9g8fM1v2m0fT8YdBt0Ph8CAqsfF4/KzCngqdkbeGfZ9mrH+iXGctOZHUk9uRWmbJapiIiI+BQF9DpSQK8Da2Hrd/Dd8/C/OdWPx3aE02+BXldBcLPGr8/PrN6RycvzNjFnXXq1VTG7to3mptSOnN8jnqBAjVYTERHxJQrodaSAfoz2bIAlL8Dq96G48nALwltCv+vhtOsgoqUz9fmRjXuy+Oe3m/lo1U6KSir/ne3QMpzrz0jikpT2hAXrXzdERER8gQJ6HSmg11NWOix9BX74N+QfrHwsqBn0HgcDboaWHZ2pz4/8lpnLvxZu5t1l28ktrLx+fauoUP4w+CTG9T+RqLBghyoUERERUECvMwX0BsrPgpX/n737jo+qyv8//jqT3kmBUEIvIkUQECkWxN57713X77rurtt/W3T7uqtrW9e1l1XcFcuuveDaAFGKgIDSIUAS0nud8/vjTJgEkjAhycwkeT8fj3nM3HPvzJwwXHjPybmf8wws/huU7D032sCUK+DUv0CEwmFXK6yoo5TsQQAAIABJREFU5cmFW3hq4RZKqprXS0+OjeSKmcO4avYwMhJjWnkFERER6UoK6AFSQO8kDfWw5hX49F7IWdl834Tz4Jx/6ELSICmvqWfekm088vEmcktrmu2LifRw0WGDue7IEQxOiw9RD0VERHonBfQAKaB3Mmth80fwyT2w6QN/++TL4Iz7waMLF4Olpr6BV5bv4O8fbmJzfvMVYSM8hjMnDeSmOSMZk5nUyiuIiIhIZ+pIQFeCkgNnDIw4Gi5/GQ67zt++4ll48wfsU3ZEukxMZAQXHjaE9753NA9eMoXxA5P37GvwWl5avoMT7vmI6576gmXbikLYUxEREdmfyFB3QHoAY+Dku6Cu2oVzgM8fhchYOOE3br8ERYTHcOohAzhlYn8+Xp/PQ//byKJNBXv2v7c2l/fW5nL48DSunj2MOQf1U+UXERGRMKOALp3D44Ez7oP6Klg937UtegCiE+CYn4a2b72QMYajxvTlqDF9WbatiIf+t5F31+Tu2f/Z5kI+21xIfHQEx4ztxykTBnDM2L7ER+ufBBERkVDT/8bSeTwRcPbDUF8D615zbR/+0Y2kH/m90PatF5syJJVHrpjG+twyHvpwI6+u2EmDr5Z6ZW0Dr6/cxesrdxEb5WHOmH6cPLE/c8f2U6lGERGRENFFotL56mtg3qWw4V1/20l/hBk3ha5Pskd2USXzlmznjdW72LS7osVjoiM9HDU6g5MnDOC4cZmkxCmsi4iItIequARIAT2I6qrgn+fDlo/9baffC1OvClmXpDlrLevzynlj1S7eXJXD17llLR4XFWGYPSqDUyYM4PhxmaQmRAe5pyIiIt2PAnqAFNCDrKYcnj0Htn/mazBuCsykC0PaLWnZhrxy3lq9izdW5bBmV2mLx0R4DLNGpnPyhAGcMD5TCyGJiIi0QgE9QAroIVBdAk+dAbtWuG3jgfOegPFnhbZf0qYt+RW8uTqHN1fvYmV2SYvHeAwcPjydUyb258Tx/emXHBvkXoqIiIQvBfQAKaCHSGUhPHka5H3ltj2RcOE/4aCTQtsvCcj2wkreWp3DG6t3sXxbcYvHGAPThqZyysQBnHNoFinxmrMuIiK9mwJ6gBTQQ6g8D548FfK/cdsR0XDJCzBybmj7Je2ys7iKt3wj619sLWpxLar46AgumDaYa2YPZ0h6fPA7KSIiEgYU0AOkgB5ipTvhiZOhaIvbjoyDy+bDsNkh7ZYcmLzSat76Koc3Vu1iyeZCvHv9U+IxcPKEAVx35HAOHZIamk6KiIiEiAJ6gBTQw0DRVnjiFCjNdtvRiXDFq5A1LbT9kg7JL6/hrdU5PLNoa4vVYA4blsp1R47guIMzifBoZVkREen5OhLQPV3RIZFWpQ6FK/8DiZluu9ZX6WXXytD2SzokIzGGy2YM5a3bjuSpa6Zz5OiMZvs/31LEjc8s5bi7P+SZxVupqm0IUU9FRETCnwK6BF/6SLjiPxCf7rarS+CZsyBvXWj7JR1mjOHoMX155trDeePWIzlnyiCiIvwj5pvzK/j5K6uZ9Yf3ufvdb8gvrwlhb0VERMKTArqERr+xcPnLEJvitisL4OkzoGBjaPslnWbcwGTuvmAyH/9wLjcdPZKk2Mg9+4oq67jv/fXM+sMCfvLSSjbklYewpyIiIuFFAV1CZ8AkuOwliE5y2+W5rmZ68bbQ9ks6Vf+UWH588lgW/eRYfnHaOAb1iduzr7bey/NLtnPc3R9y7ZOfs2hjAb3puhgREZGWKKBLaGVNg0v/5Sq6gLt49KnTXcUX6VESYyK55ojhfPiDOTxwyaFMykpptv/9dXlc/MhiznjgU15dsYO6Bm+IeioiIhJaCugSekNnwcXPQ4Rv2fiiLfD0mVC+O6Tdkq4RGeHhtEMG8sots/nXjTM57uBMTJPCLqt2lPCdeSuYc9f/ePTjTZRV14WusyIiIiGgMosSPr5+C164FLz1brvfeLjqNYhPC22/pMtt3F3OY59sZv7SbGrqm4+cJ8VEcsbkgUwfnsaUIalkpcZhjEo1iohIeFMd9AApoHcDX70CL14N1hfSBh7q6qTHprT9POkRCspreGbxVp5ZtJWCitoWj+mbFMOUIX2YMiSVqUNTmTAohdioiCD3VEREpG0K6AFSQO8mvnwBXr4R8P3dTBsJ5z0OAyeHtFsSPNV1Dby0bAePfrKJTbsr2jw2KsIwbmAKU4b0YerQVKYMSWVgkwtRRUREQkEBPUAK6N3I0ifhv9/xb3ui4Pg7YMa3QNMbeg2v17J4UwGfbS5k2bYiVmwrpqymfr/P658cy9ShqRw6pA9ThqYyfmAyMZEaZRcRkeDpSECP3P8hIiEw9SqITnQhvbYcvHXw9k9h0//gzL9BYt9Q91CCwOMxzBqVwaxRbmXSBq9lQ145S7cWsWybu7U0wp5TWs3rq3bx+qpdAERHepg4KKXZ1Jh+ybFB/VlEREQCpRF0CW8FG2H+tbBzub8tMRPO+QeMmBOqXkkYKaqoZfn2IpZtLWbp1iK+zC6msrZhv8/LSo3juIMzOW9qFuMHJuvCUxER6VSa4hIgBfRuqr4WFtwJC+9v0mjgiNvgmJ9BRFTIuibhp77By9e5ZSzbVswy30j71oLKNp9zUGYS504dxFmTB2lkXUREOoUCeoAU0Lu5De/ByzdBRZP66IOmwXmPQeqwkHVLwl9+eY0vrBezbFsRK7OLqa7bdyEkj4GjxvTlnClZnDAuU9VhRETkgCmgB0gBvQcoy4VXboKNC/xtMclw2j0w8bzQ9Uu6lboGL4s3FTB/aTZvfZXTYlhPio3ktEMGcO6ULKYOTdUUGBERaRcF9AApoPcQXi8suh/ev9O/qBHA5MvglD9BdELo+ibdTll1HW+uyuHFZdks2VzY4jHD0uM5Z0oWZx86iMFp8UHuoYiIdEcK6AFSQO9hdiyFF6+Fos3+tvTRrmb6gENC1y/ptrYXVjJ/WTYvLdvBtsKW563PGJHGuVOyOHniABJjVAhLRERapoAeIAX0Hqi6FF7/Pqz6l78tIhqO/zUcfqNqpssBsdbyxdYi5i/N5vWVu1qsvR4XFcHJE/pzzpQsZo5MJ8Kjv2siIuKngB4gBfQeylr4cp4L6nVNamKPOcnVTE9ID13fpNurrmvg7a9ymL9sB5+s3423hX8yB6TEcvahgzh3ahYj+yYGv5MiIhJ2FNADpIDew+VvgBevhpyV/rbE/nDuIzD8qND1S3qM3NJqXl6+g/lLs1mfV97iMYdkpXDEqAxmjkxn2tA04qJVCUZEpDdSQA+QAnovUF/jLh5d9ECTRgNHfh/m/Fg106VTWGtZvaOU+cuyeXXFDooq61o8LirCMHlwH2aOSGfGyHSmDElV6UYRkV5CAT1ACui9yDfvwCs3Q2W+vy1rOpz7KKQODV2/pMeprffywdd5zF+azYJ1edS3NAfGJzrSw5QhfZg5IoNZo9KZlNWH6EhPEHsrIiLBooAeIAX0XqYsB166ATZ/6G+LSYEz7oXxZ4euX9JjFVfWsmhjAYs2FbBoY0Gr02AaxUVFMG1YKjNGpDNzZDqHDEohMkKBXUSkJwh5QDfG/BGYBowBMoAqYCvwCvCAtbagHa+VBdwJnASkA7t8r3OHtbaog/1UQO9tvF5YeC8s+E3zmumHXg4n/Abi+oSub9Lj7S6rYfEmF9gXbyxgU35Fm8cnREdw2PA0ZvoC+/iBKaoOIyLSTYVDQK8FlgFrgDwgAZiBC+07gRnW2u0BvM5IYCHQD3gVWAdMB44BvgZmtyfst/D6Cui91fbPYf61ULzV35bQD078LUw8X+UYJShySqpdYPeNsrdWa71RUmwkhw9PY8aIdGaPyuCgzCQ8CuwiIt1COAT0WGttdQvtvwV+Cjxkrf1WAK/zNnACcKu19v4m7XcD3wUettbe1IF+KqD3ZtUl8Np3YfX85u3Dj4JT/gJ9x4SmX9Jr7SiucmF9YwGLNxWwo7iqzePTE6KZNSqD2SNdYNeqpiIi4SvkAb3VFzdmErACeM9ae/x+jh0BbAS2ACOttd4m+5JwU10M0M9a2/bviVt/DwX03s5aWPsfePPHULbT3+6Jgtm3wpG3Q7RCjwSftZbthVUs2pS/Z4Q9t7SmzecMSYtn9qgMZo9KZ9bIDNISooPUWxER2Z+OBPSuXqf6dN/9yjaPcub67t9pGs4BrLVlxphPcaPrM4D3O6+L0qsYA+POhJFz4X9/gMUPgW0Abx18/BdY9W845c8w5sRQ91R6GWMMQ9LjGZI+hAsPG4K1ls35FSzaVMBC3yh7YUVts+dsK6xk25JtPL9kGwDjBiRzxOgMZo1MZ/rwNOKju/qfeBER6Qqd+q+3MeZ2IBFIwc0/PwIXzv8QwNMP8t1/08r+9biAPob9BHRjTGtD5GMD6If0BjFJbv75pIvdtJfsJa69eBs8dwGMPQ1O/iOkZIW2n9JrGWMY0TeREX0TufTwoXi9lnU5ZXy6IZ9PN+bz2aZCquoamj1nza5S1uwq5R8fbSIqwnDokFRmj8zgiNHpHJLVhyhViBER6RY6e3jldiCzyfZbwFXW2t0BPDfFd1/Syv7GdpXdkM7TfwJc8zaseBbe/QVU+QoFrXsNNn4Ac34EM76lBY4k5Dwew7iByYwbmMz1R42gtt7Liu3FfLIhn4Ub8lm+vZiGJjXY6xosSzYXsmRzIfe85yrEzBiRzqxRGRwxKoMxmYkYXRwtIhKWOjWgW2v7AxhjMoFZuJHz5caY06y1yzr48o3/k+x30nxrc318I+tTOtgP6Wk8HphyBRx0Krz3C1j+rGuvq3Ch/ct5cOrdMHRmaPsp0kR0pIfpw9OYPjyN7x0/hvKaepZsLuCT9QUs3JjPupyyZsdX1Dbw/ro83l+XB0BGYgwzR6YzYWAyYwckc3D/JPomxSi0i4iEgS6ZoGitzQVeNsYsw01ZeRqYsJ+nNY6Qp7SyP3mv40Q6V0I6nPmgq5H+2vcg7yvXnrcGnjgJJl8Gx98BCRmh7adICxJjIpk7NpO5Y90vMXeX1bBwY76bErNh3wox+eU1/PfLnfz3S//F0mkJ0Yztn8TY/smMHZDEwf2TGZ2ZSGxURFB/FhGR3q5LryCy1m41xqwBJhtjMqy1+W0c/rXvvrVad6N9963NURfpHENmwI0fwmd/hw9+70bSwU2DWfeaC+mHXuFG3kXCVN+kGM6cPIgzJw/CWsvWgko+9QX2hRsLKK6s2+c5hRW1LNzoLkpt5DEwLCOBg/snu/A+wN1npcZptF1EpIt0aZlFAGNMLm7hobS2VgL1LVK0gbbLLHqAviqzKEFTkg1v/RjW/rd5e9ZhbtrLgENC06+OqMiHZU9DQx1ExUKk7xYV1+RxLETGtb4/optXB/E2QOlOdxFwLwyZXq9lza5Slm8vZt2uUtbllPF1ThnlNfX7f7JPUkwkB/VPYuwAN+J+8IAkxmQmkRSr6zVERCDEZRaNMWOBYmttzl7tHuDXuHC+sDGcG2OigJFAnbV2Y+Px1tqNxph3cJVabgHub/Jyd+BWJ334QMO5yAFJyYILn4Vv3oE3bvevRJr9OfzjaDj8Jjjmp64qTHdQuhOePBUKN3XsdTyRLQT4WEjMhON+Bf0ndkZvu4bXC89fDOvfhjEnwQXPQGTvqh/u8RgmDEphwiD/jEKv17KjuIp1OWV7QvvanFK25FfgbWEcp6ymni+2FvHF1ubjLlmpcYzul8jQ9ASGpMUzNN3dslLjNVVGRCRAHR5BN8bcBtwFfIRbaKgAV8nlaGAEkAMca61d4zt+GLAZ2GqtHbbXa40EFuJC/avAWuBw4Bjc1JZZ1toCDpBG0KVD6qpcrfRP/urqpjdKGgAn/R7GnRXeo7Glu3zhfOP+j+2I9FHwrcXhW/lm9Uvw4tX+7UMuhLMfDu/PLoSqahtYn1fGul0usDfetzRFpi3GwIDkWIakxzM0LcHdN3mcEhemf19ERA5QSFcSNcZMAG4GZgNZuDKIFbhA/Tpwn7W2sMnxw2gloPv2DwbuBE4C0nFTW14B7mj6OgfYVwV06bj89fD692DzR83bRx4Lp90NqcNC0q02leXAk6dBwXq37YmE6TeA8UB9NdRVQ30V1Ne4LyL11b77GtdeV+3aGtv3V0zp1LvhsGu7/Mdqt4Y6eOAwKNrcvP3I2+HYn4emT92QtZa8shrWNpkes3ZXKRt3l1PXcGD/p6TGRzEkPYGhvlF3N/qewND0ePqpuoyIdEMhDejdiQK6dBprYfV8ePunUJ7rb49LhYvnuQtNw0VZLjx1GuT7rq/2RML5T8LBp7f5tFZZ64LunuDuC/JfzoNP7nbHJPSDW5dDTGKn/AidZskjbqpSS06/F6ZeFdTu9DS19V425ZezJb+CrQWVbCmoZFuhe7yzuKrFqTKBiI3yMDQtgYP6JzHeVwt+/MAU0hJ619QkEeleFNADpIAuna66BBb8Bj5/FBqva46IgbMfggnnhrZvAOV5buQ831ckyUTA+U/AuDM7/73qquC+KVDmK9s356duoadwUVMO902GCt+6aXN/Dts/g/XvuG0TAZe8AKOPD10fe7Daei87iqvYUlDBtoJKtjYJ71sLK6mt9+7/RfbSPzm2SWBPZtyAFAanqbqMiIQHBfQAKaBLl9m+BOZd4g9/4C6WnH1b6OY2l++Gp06H3WvdtomA8x6D8Wd33Xsuewb+83/ucXQi3LoCEvt23fu1x4d/gg9+6x4nDYRbl7lqLk+eCrtWuPaoBLj6DRg4OXT97IW8XktuWbUL7QWVbC2scKPvBZVsLaigtLp91WUOHpjMuAHJe8L76H5JREeqLKqIBJcCeoAU0KVLFW2Bf57vn0oCMPVqOOXPwS9LWJHvwnneGrdtIuDcR2HCOV37vt4GeGi2/0vBYdfDqX/u2vcMREU+3DsJasvd9hkPwJTL3eOyXHj0OCjZ5rYTM+HadyF1aGj6Kvsorqxl4+5y1uwqY83OEtbsdHPfawIcdY+KMIzul9RkpD2Zgwcmk6ySkCLShRTQA6SALl2uqgjmXQZbP/G3jTreTSsJVinGigJ4+gzIXe22jQfOeQQmnhec9//mbXjuAvfYEwm3LIH0kcF579a8+SO38BRA37Fw06fNvzTt/hoeOwGqi912xkFw7dvumgIJS/UNXjblV7BmZylf7Sxhza5SvtrZvuoyQ9LimTAomUlZfZg8uA8Ts1KIj+7mNf5FJGwooAdIAV2Cor4G/vNtWPmCv63/RLjkX5A8sGvfu7IQnjoDcle5beOBs/8Bh5zfte/blLVu3nvjl5RxZ8EFTwXv/fdWtAXun+YvjXnRczD21H2P2/IpPHMWNNS67aFHwOUvQWRM0LoqHWOtZVdJtS+0l7Jmlwvu2wurAnq+x8CYzCQOHeIC++TBqYzql0iER3PaRaT9FNADpIAuQWOtm+/80V3+tuRBLqT3n9A171lZCE+fCTkrfQ3G1feedGHXvF9bspfCo3P929e9D1nTgt8PgPnXw6p/uceDD4dr3m79uoBVL8L8JuUhJ5wL5zwKHs1f7s5KqupY6xthbxxx35BXTn0AZWUSoiOYmJXC5MGpTB7ch0OH9CEzOTYIvRaR7i6kK4mKSAuMgbn/D/oMhdduA289lO6Ax09yo8mjju3c96sqcqO/TcP5WX8LTTgHyJrqRs7XvOK23/0FXPV68C+Y3bXSH84Bjruj7T5MPA9KsuG9X7rt1fMhZTAcf0fX9lO6VEpcFDNGpDNjRPqetpr6BtbnlvNldjFfbi9mxfZi1ueVs/eYVUVtA4s3FbJ4k38ZjgEpsW5ajG+kfeKgFBJi9N+piHQejaCLdLWNC+CFK6C2zG17IuG0v/ovUuyoqmIXzncu9zUYOPMBOPSyznn9A1WwER6c7r6cAFz8Ahx0UnD78Oy5sOE993jMyXDJvP0/x1pXK/3zR/1tp/4FDruua/ooYaOsuo5V2SUs9wX2FduL2V1Ws9/nNZ0a0xjch6UnEBsVEYRei0i40hSXACmgS8jkfuUqvJTu8LcdebsbZe/IqHJ1CTx9Fuxc5m87436YcsWBv2Znev12+PwR97jvwXDzp+AJUmjZ9KG7WBbcXPybPoXMcYE9t6EeXrgMvnnT//yLng/+FwwJqcY57Y1hfcW2YlbtKKGqriGg5yfFRJKRFEPfxBgykqLdfWIMfZOa3CfFkJEYTUykwrxIT6OAHiAFdAmp0l3w3PmQs8rfNvF8OPPBA7sQsboUnjkbdnzhbwu31TDLd7vFgVoqb9iVrIVH5vq/uEy+DM56sH2vUVvhLnZtfI2oeLjqNRjU7n9npQepb/DyTW65L7QXtTo1pr2SY5uGeXfft1m4jyUz2bVpISaR7kEBPUAK6BJyNWXw76thw7v+tqGz4cJnIT4t8NepLnXTN7KX+NtOuwemXdN5fe0sey8Q9O2lEB3fte/51Svw7yvd44gYtyhRSlb7X6c8z9VIL97qthP6wnXvQeqwTuuqdH/lNfWszPaPsn+1s5Tc0uqALkJtr6SYSEZlJjK6XyKj+yXteTwwJQ6Pqs2IhBUF9AApoEtYaKh3c5yXPuFvyxgDl/47sOBXU+bC+fbP/G3hPEe6tgLuOxTKc932sb+EI7/Xde/XUAcPHg6FG932rG/DCb858NfLXw+PHe8uxAVIHw3XvtO+L1TS63i9lpKqOvLLa9hdVsNu331+ee2etsb7gopaGjoY5uOjIxjVL5FRvuA+ul8iozMTyUqNV5lIkRBRQA+QArqEDWvh03v91ULAjc5e/IKrgNKamnL453mwbZG/7ZQ/w/Tru66vneGLx+G177rHMclw6wpISG/7OQfq88fgdd8XgJgU+M6KjofprYtcCcsG3wWDQ2bC5a9AlMrtScd5vZaiytoWw3vTYJ9dVElZdX27Xjs2ysPIvr4R98wkX4BPZEhaPJERKh8q0pUU0AOkgC5hZ/V8ePlmf/CLjINzH4WDT9v32NoKd6Hp1k/9bSf9EWbcFJy+dkRDPfxtBhSsd9szvgUn/b7z32fv0frjfgVHfLdzXvurl930JHz/Zo4/G859XDXSJWistewuq2F9Xjnrc8t89+V8k1fWrhVUAaIjPIzom8CofokMz0ggKzWOQX3iyUqNY0CfWF20KtIJVAddpLuacK6blz3vYjeFor7KVQ856fcw42b/cbUV8NyFzcP5ib/vHuEcICISjvul+9kAljwCh9/Y+XO5F//NH86TBsD0GzvvtcefDSU74J2fue2vXnbz2jsyfUakHYwx9EuOpV9yLLNHZexpt9ZSUFHL+txyNuT5g/v6vHLyy1suE1nb4GVdThnrcspaeB/olxRDVmo8g/rEufCeGkdWarwvyMephKRIF1NAFwm1oTPh2vfc1JWizYCFt37slqg/8XdQX+PC+ZaP/c854bcw81uh6vGBGXuaW8lz+2fgrYMFv3G/LegsFQXwyb3+7Tk/6fyLUWfeAsXbYMnDbnvh/ZAyBA6/oXPfR6QdjDFk+Eo4zhzZfOpYUUUtG3Y3BvYyNuSV801uGbmlrdd3txZyS2vILa1h6daiFo/JSIzxhfY4snwhPis1nkG+AK+Fm0Q6RlNcRMJFRT48f3HzyiwHnQp1FbDpf/624++E2d8Jevc6xbbF8PiJ/u0bPoSBkzvntd/6iRtBB3fR7c2L3Mh9Z/M2wL+ugHWv+RoMXPRPGHtq57+XSBcpqapjQ54bcd9eWEV2USU7iqvILqoip7S6w2UjU+OjWhyBH9THPU6Ji+qcH0QkjGkOeoAU0CXs1VXBSzfA2v+0vP+4X3XenOpQmXepP9yOmANXvNrx1yzaCg9Mg4Zat33hs3Dw6R1/3dbUVrpFkLI/d9uRca5Geta0rntPkSCprfeSU1JNdnEl2UVV7Chywb0xxO8qqe5w1Zmk2EhfeI/3jb7H7dkelBpHanyU6r1Lt6c56CI9RVQcnP8UvPcLN32iqWN/0f3DObgyi1+/CbbB/WZgw/sw6tiOveYHv/OH86zD3HSarhQdDxfPczXSiza7aweeu8DVSE8b0bXvLdLFoiM9DEmPZ0h6y1PE6hu85JbVkF3oH3XfUVRFdnElO4qq2FFcRV1D2wG+rLq+1Tnw4MpGtjT6npkcS2JMpLvFRpIQE6ELWqVHUkAXCTcej7vwsM9QePNHYL0w92dw5PdD3bPO0XeMW0106ZNu+91fwohjDrwaSs5qWPmCf/u4O9xVbl0tIQMum+9CelUhVBbAs+fBte92XQlJkTAQGeFxU1X6xLW43+u15JXVsMM3Ap/tC+0uyLu2mnpvm+9RWdvgLnbNK99vf6IjPCTERLjAHh1JUmwkCY0h3ndLiGmhPdb/uG9SjC58lbCigC4SrqZfD+POdBVc0oaHujeda85PYOW/oK4SclfBqn/BpIsO7LXev4M9pQ9HnwjDZndaN/crfSRc8gI8dTrUV7vFkZ6/CK78j/ttiEgv5PEY+qfE0j8llqlD993fWHVmx57w3nwqzY7iKsprAq/3XtvgpbbSS1E7S002ZQxkJsUyJC2ewWnxDEmLZ0h6HINT3eO+STGaciNBpYAuEs4S+4W6B10jqb+riPLRXW57wW9g3FntX/hn88ew/h3fhnGlHINt8HQ45xF34SjWXeT75g/hjPv3+1SR3qhp1ZlJg/vss99atwpr85F3Nwe+sKKW8pp6/626nvoOzod37wk5pdXklFazZEvhPvtjozwMTvWH9z0hPi2ewWlxxEcrTknn0t8oEQmNWbe6FUYrC6BkO3z+CMz6duDPt7b5SqyTLobM8Z3fz0CMO8PVrn/rx2572dMwcq6rnS4i7WKMoU98NH3io5kwKKXNY6211NR7Ka+pp6KmnrJqd793iK+oqaespum+Bsqr6yivqae0qp68smrayvnVdd42p9xkJEb7w7tv1D0rLY6BKXH0T4nV9BlpNwV0EQmN2GQ4+kdutBngoz/DoZendPQRAAAgAElEQVRBXGpgz1/7X9jhq8gUEQPH/LRr+hmow2+C7C9g9Ytu+z/fgUFToc+Q0PZLpAczxhAbFUFsVAQZiTEH/Dq19V52FlexrbCS7UWV7r6w8b6Kkqq2p8/kl9eSX17L8m3FLe5PjY+if0ocA3xTfwYk++59AX5ASqxqx0sz+tsgIqEz9WpY/JCrhFJdDJ/c4+q8709DvW/uuc/066HP4K7rZyCMgdPudlNcirdBTQnMvw6ueqNr6rGLSKeJjvQwLCOBYRkJLe4vqaxje5E/tDfeGstP7q9qTVFlHUWVdazdVdrqMUmxkb4AH9ckwDcP8smxkZoL30vofw0RCZ3IaFc+8sWr3fbiv8P0GyAlq+3nLX8GCja4xzHJ4VPhJjYFzn3cLcZkG9yqqR/9KfSj+yLSISnxUaTEp7Q45abBa8kprWZbQWWzEJ9dVEVOSTW5pdUBzZMvq66nrLqcb3Jbr1wTHx3BwD5xvqk0cQxOaz4vPlGj8D2GPkkRCa3xZ7ua7zuXQUONq2l+1t9aP762Ev73B//2EbdBfFrX9zNQgw9zgXzBr932R3fB8KODW11GRIImwmP2lJ2cyb4lVr1eS35FDTkl1ewqqW5y7xZ9yil127X7KT0JrvykWwG25RCflhDN4NQ4svaaDz84LY6BfeKIijjAcrYSdAroIhJaxrhpLU/5Fhda8RzM+Bb0n9Dy8Z89BOU57nFifzj85uD0sz2O+K5bhGnLx66O/UvXw02fhNcXCREJCo/H0C8pln5JsRzSyi8HrbUUVdaxq6Rq3yBf6oL8ruJqquoa2nyvwopaCitq+TK7ZN9+GBiQErcnsDeOug9OiyerTxzxMZFER3iIijCaRhMGFNBFJPSGHwmjT/CVTLTw3q/gshf3Pa6yED75q397zo/dqp7hxhMB5/wDHprtFjEq3QH/+TZc+GxwFlHqKayFncvdl7acVTDtGph0Yah7JdLpjDGkJUSTlhDN+IEtV66x1lJaVc/2okqyi/wXsDZe2JpdWEVtQ+uj8F4LO4pd6cpFm9ruT3Skh5gID9GR7hYT6X8cvac9gugIt6/l/R4SYyLJ9M2nz0yKpV+yFoQKlAK6iISH434F698FLGx4FzZ/BMOPan7Mx3+BGt9FVumj4NDLg9zJdkgeCGc+CPMudtvrXoOlT7iQKW0ry3Grw654Dnav87dvX+x+IzH54tD1TSREjDFtzoX3ei25ZdX+0N5484X53NKagN+rtt7rptwE/pSA9YmP2hPW+yfHkpkcS2ZyjO/ehfn0hGgie/l0HAV0EQkPmeNh8iWw4p9u+91fwHULwOP7R7p4Gyz5h//4Y38R/tVRxp4Ch13varwDvPUTGDIT+h0c2n6Fo7pq+Pp1F8o3LnBBvCWv3gJxfeCgk4PbP5Ew5/EYBqTEMSAljunD951OV13XQHZRlf9C1oLG8F5FTkkV1XVeahu8NHTCwk9tKa6so7iyjq9zy1o9xmMgIzGG/ilualBmkzDfLzmG9IQYUhOiSI2PJj46okdOyQnz/91EpFc55qewej7UV7upDWtehgnnun0f/B4aat3jQVPh4DNC18/2OOHXsHUh5H3lfq4Xr4Xr34eouFD3LPSshezPXShf/ZIrTbm3qAS3EFTOKshd7arj/PsquPxlGDor6F0W6a5ioyIY1S+RUf0S2zyuwWv3jKDXNDTseVzb4Gtr3G583ODfrq1vaHZcaVUdOaXV5JbWkFtaTV5ZTUBfALwW8spqyCurAVr4d6GJ6AgPfeJdWG8M7X3io0nd0+YeN21LiYvC4wnvUK+ALiLhIyXLLfjzqW+e+ft3wtjToWA9fPm8/7jj7+w+c7mj4uC8x+Efc6C+ygX1d34Op/451D0LnZJs+HKe+0wby2XubdiR7jcqB58BMYlQlguPnwBFW9wXnecugqtfh/4Tg9p1kZ4uwmOIi44gLjoCiOrU1/Z6LQUVteSWVvtuNeSUVpPn284prSGvtJqCitqAX7O2wdskzAfGYyAlrjHM+0N9SlwUyXGR7j42iuS4qH3agjVir4AuIuHliO/CsqegqsiFsaVPuCkP+EZdRp8Aw44IZQ/br99YOOl38Np33fbnj8DIuW4KTG9RWwFrX4Mvn4NNH7Ln82wqdRhMugQmXQSpQ5vvS8p0o+aPnQgVeW60/Zlz4Nq3IW1EMH4CEekgj8fQNymGvkkxLc6jb1Rb7yWvzAX4vNLqPaPweaXV5JZVU1hRR3Glq1hTE0B5yr15rX/xqPaK9BiS46JIjvWF9sZbbPMgnxIXRXlNfbtfv5GxtmvnGoUTY8zSKVOmTFm6dGmouyIibVn4ALzzM/c4KgHqKnw7jCtX2FoJxnBmLfzrclj7X7cdlwo3L3QXk/ZU1rrpPV8+B1+9ArUt1G6OToLxZ7nR8iEz9/+bkZxV8MSp/ukwqcPgmrchqX+nd19Ewl9VbQNFlbUUVdZSXFlHYUUtxZW1vgDecltZ9YEH5/bY9eR3qM3duMxaO7W9z9UIuoiEn+nXw2cPQ8m2JuEcOOTC7hnOwQXP0++DHctc2cWqInjpBrjiVVeWsScp2uKfwlK0pYUDDIyY40L52NPaVyqz/0S4ZB48c7ab6lK0BZ49F6563V08KiK9ipuO4xZiClRdg5eSqjqKKpoGeRfcS6rqKK2qc/fV9U0eu/vquvaP2B8IBXQRCT+RMTD3/8HLN/jbIqLdRaTdWXwanPOIW5TJet1CRp/cA0fdHvy+bPofbP7YXXhrveBtcBdgehvAW+977PW37dnXsG9b0+fXVkLuqpbfM320K5F4yIXueoMDNXQWnP8UzLvEvWfuanj+IrjspfCsiy8iYSUqwkNGYgwZiTHtfm5NfQOlVfWUVjcP8o3BvrG9tKqeeTGRFB5gHxXQRSQ8TTwfFt3vpjQAHHbdvvOSu6Nhs+GoH8CHf3TbH/zO1XsfPD0471+WA2/8ANb+JzjvF5viKvFMugSypnXexb0HnQRn/Q1evtFtb1sEL17tFoOK6NwL20REGsVERtA3KYK+SfsP94vvTqBw24G9T++uAi8i4cvjgbMfhswJMOp4OPpHoe5R5znqhzB4hntsG2D+tVDddimxDvN64Ysn4IHpXR/OjcddzHveE/D9b+C0e2DwYZ1feWfSRXDi7/zb37wFr/6f+1lFRLoxjaCLSPjKHA83fxrqXnS+iEg49xH4+xEumBdvcxVezn2sa8pH5q+H/9wK2xY2b594AWSOAxPh5sHvufeAJ7KFtgjX3ux4j+++yfFpwyEho/N/jpbMvAUqC9wqswAr57mpRCf+rvuU4hQR2YsCuohIKPQZ4i4a/feVbnv1fBh5LBx6aee9R32tqyn/0V3+RZ7AlSU8/V43taYnmPtzF9KXPum2F/8N4tNDM7dfRKQTaIqLiEiojD8Lplzp337jB5DfysI97bV9CTx8FHzwW384NxFwxPdceceeEs7BjZSfenfz1WUX/NpN6RER6YYU0EVEQumk30PGGPe4rsJd6Fgf+Ip4+6gpc0H/sRNg91p/+8ApcOOHcNwv3eqmPY0nAs59FIYf7W97/Xuu/nqwVBbCx3e7P/v3fgUN7V8ERUQEFNBFREIrOgHOe9yVkQTIWQnv33lgr/X1m/Dg4bDkH+xZqTMqAU78PVz3nqsh3pNFxsBF/4SBh7pt64WXrnclJbvS7m/cNQR3j4P374Dtn7nymc9dANWlXfveItIjKaCLiIRa/4lw/K/924segPXvBf78slz415WuFnjpDn/7qOPhlsUw81s9bzGk1sQkwaUvuprr4Kb3zLvULRDVmayFjQvg2fPgwcPgi8ehvqr5MRsXwBMnQ8mOll9DRKQVCugiIuHg8Bth9In+7VducsG7LdbC0qdcQFzTZCpHfIarCHPpv93FqL1NQgZc/jIkD3LbteXwz/PcSHdH1VW5P/O/zXSrmW54t/n+zIkw5Qr/du5qePQ4fz1/EZEAKKCLiIQDY9zCO4n93XbFbhfSW6vpnb8BnjwN/ntr8xrqky+F//scJp7Xu8sM9hnsQnpcqtuuLHCBuiT7wF6vLAcW/AbuGe/+zJvO78fAQafAla/BTR/DGffDWQ+50pMAZTvh8ZNhQzt+KyIivZoCuohIuEjIgHMeBnzBeuMCWPxg82Pqa13ZxIdmwdZP/O2pw+DyV1zIj08LVo/DW9+D3HSXqAS3XZrtQnpFQeCvsetLePkmuGeC+3OvbPLcqASYfiN8eylc/DwMP9L/pWjyJXDZfIhJdtu1ZfDPC2DZ053zs4lIj6aALiISTkbMgSNu82+/dwfsXO4eZ38B/zjajeQ2+Cq9mAiY/R24eRGMPCbYvQ1/WdPgomfBE+W287+B586HmvLWn+NtgLWvwROnulKVXz4P3iYVWVIGwwm/ge+tgVP+BOkjW36dEXPgmrchOctt2wb4z7fh/V+76UkiIq0wthf9I2GMWTplypQpS5cuDXVXRERa11AHj58IO3z/VqWNcBd8Nq3OAjBgMpxxHwyYFJJudiurX4IXr2HPn9+IY+CSF1zll0Y1ZbD8Wfjs71C0Zd/XGDwDZtwMY09zq8EGqnSXq+iSs9LfNvF8OPPB5u8vIj3K1KlTWbZs2TJr7dT2Plcj6CIi4SYiytX0jk5y24WbYMnD+EsnxsMJv4Xr3lc4D9SEc+DUv/i3N30AL9/oRsuLtsJbP3VlEt/6cfNw7omECefBdQvg2rfd4lLtCecAyQPg6jfcl6xGq/4Nz5wDVUUd+rFEpGdq578yIiISFGkj4LS7XR3vpkYe69pTh4WkW93aYde6OeQf/NZtf/Wyq+yye62rmd5UbB+YdjUcdj2kDOr4e8ckwcXz4I3bYalvhdOtn7hFjS79tz5PEWmmwyPoxph0Y8x1xpiXjTEbjDFVxpgSY8wnxphrjTHteg9jzKnGmHeMMdm+19pkjPm3MWZmR/sqItKtHHKBuwgRID4dznnEXXioMHfgjvoBHH6Tfzvvq+bhPH00nHq3m19+3K86J5w3ioiE0+5xr9so/xtXhnGHpl6KiF9njKCfDzwE7AI+ALYBmcA5wKPAycaY820Ak92NMX8EfggUAK8A+cAo4EzgXGPMFdbaZzuhzyIi3cMpf3LznpMHar5yZzDGraxaWQir/uVvH3EMzLzF/YbC04WzP42BI77rLjR95Wa3kFLFblcy89zHYOwpXffeItJtdPgiUWPMXCABeN1a/zCEMaY/sAQYDJxnrZ2/n9fpD+wAdgOHWGvzmuw7BlgAbLbWjuhAX3WRqIiIuAtxF//NBfVDLoTMccHvw9aF8PzFUF3sto0HTvojHH5D8PsiIp0upBeJWmsXWGv/2zSc+9pzgL/7NucE8FJDff35rGk4973WB0AZ0Lej/RURESEiypWnPP6O0IRzgKGz4Lr3oM9Qt2298OYP4O2ftb5AlYj0Cl1dxaWxcGx9AMeuB2qB6caYjKY7jDFHAUmAlmETEZGeI2O0q8YzqMkA26IH4MWroK4qZN0SkdDqsiouxphI4Arf5lv7O95aW2iM+RFwN7DGGPMKbi76SOAM4F3gxgDfu7U5LGMDeb6IiEjQJPaFK19zFXvWveba1rzq6qdf/LxbYVY6h7cByna50prF26B4q7sGYOChMPECiIwOdQ9FgK4ts/gHYALwhrX27UCeYK39qzFmC/A40LS22Abgyb2nvoiIiPQI0fFwwdNuestnD7m27CXw2PFw6Yutr1YqzXm9UJ7rD9/FW5uH8ZJs8LbyS/0Pfu9W8T30coiKDW6/JfgqC93iYbtWwq4v3UJlw2a7hcjC4HzrkpVEjTG3AvcC64DZ1trCAJ/3Q+B3wH3AA0AObtT798AJwF3W2h92oF+6SFRERMLb4ofgrZ+wZ2GquDRXQ33I4SHtVliwFiryWwjfjYF8OzTUdOw9kgbA7Ntg6pUQFdc5/ZbQKstxIXzPbSWUbGv9+L5jYeyp7jbg0AOu7NSRi0Q7PaAbY27Bhes1wLG+i0UDed4cXJnGl6215+y1Lx74BhgAjLbWbjrAvimgi4hI+Fv7Gsy/Dup989AjYuCch2H82aHtVyjUVrpFpZY/48JVXWXHXi8+A1KHQp8h7gLdiCj44gmozG9+XEI/mH0rTLsGohM69p4SHNa6lYB3fekbHfeF8YoOTMBIGujKn449FYYe0a5pUGET0I0xtwH3AKtx4TzgPxFjzJ+B7wO3Wmvvb2H/S8DZBFCysY33UEAXEZHuIfsLeO7C5sFx6BEwai6MnAv9J3VtzfZQy1vrgvOX86CmJPDnxaX6w3fj/Z5APqTlsF1b6VZ4/fReN0Wmqfh0mPVtOOw6tyKshAdvA+Sv3zeMB/p3JSIa+o2DAZNgwCFu++u3YOMC/xfjvcWkwJgTXFgfddx+/z6ERUD3XeD5B2AFcLy1Nn8/T9n7+fcD/wf82lr7ixb2fwwcAZxhrf3vAfZRAV1ERLqPws3wz/OhYP2+++LT3QJLo45198kDgt+/zlZX5S6Q/eIJ2L645WNikv3hu2nw7jMU+gyG2JSOvf+yp+GTv0LZzub74lLdYlbTb+jYe0j71ZRB7hq38m/OahfIc1a3HqT3FpXgQnj/Q/yBvO9Y99uTvdVWupC+7nX45k2oKmr5NSOiYcQcF9bHnAxJmfscEvKAboz5OXAnsBQ4oa0558aYKFxlljpr7cYm7RcALwC5wFRr7Y4m+04GXgdqgCxrbcEB9lMBXUREupfKQrfq6Df7KYjWbzyM9AX2ITO71/zp3d/A0idhxT/9Czc1lTocpl4Fky6CxEy3ImtXqq+B5c/CJ/dAyfbm+2JTYMa34PAbXWiXzuNtcF9Kc1dD7le+22p3fUGg4lJdCN8TxidD2ogD+21TQz1sW+TC+rrX25i3bmDwdN+8df9FpiEN6MaYK4EngQbgfqCl3y1ssdY+6Tt+GLAZ2GqtHdbkdTzA28BxuEWJXsZdJHowcBpggNustfd2oK8K6CIi0j2V5cCm/8GG990I395zppuKjIWhs91UmFHHutHCrg617VVfA2v/60bLt36y735PpAs8U6+G4UeHZjpPfS18+Tx8/Jd9Q2JMshtNn3kLxKcFv2/dXWWhL4iv8QfyvLWBj4qDmx8+4JDmgTwlq2v+rlsLOav8YT13VevH+i4ynfrd51m2al3IAvqvgF/u57APrbVzfMcPo4WA7tsXBdwCXASMA+KBQmAJcJ+19p0O9lUBXUREuj+v1wWEjQtcYN+2GLx1rR+fNNCF9ZHHuOkwCenB6+veCja6+d4rnoPKFn4h3meIGy2ffFmL0wZCoqEOVv0bProLCveqUxGd6Oanz/w/V9O+o6x18+ALN0PRZt/9Fve4JBvSRsKcH8PwIzv+XsFQX+umaDWOhjeOjJftCvw1PJGQMQYyx/vmjR/irsHojD/vA1W0Bda94cL6toVuJeC9TP1HOct2eUN/kWi4U0AXEZEeqbYCtnziD+wtzVnfw8DAyTDyWBfaB03p+ukw9bXw9etutHzzhy10KQIOOtmNlo+cG74XvzbUw1cvuaCe/03zfVHxruLLrFv3/8WivtZNnWkWwhuD+JbAKtWMPQ2OvzMsanbvo3gbLLwfti6E3V+3/eVxb4n9XRBvessYA5ExXdffjqoocFPQ1r3e7CJTBfQAKaCLiEivULwNNn4AG99302Kq91PZIjrJrVia2A8S+vpvif1ce0Jje4ab4xvoFILCzbDsKTefu2L3vvuTs1y98UMvg+SB7f4xQ8bbAGtegQ/vgt1rm++LjHW/AZh+g7u4sTF47wnjW6A0u8UR13bzRMGMm+CoH4THhaslO+DjP8OyZ/YfyiNj3VSQzAnNw3h3Xzm3yUWmU7/9OMt21Cqg748CuoiI9DreBtixzIWGje+78o224cBfzxPlC+2thPjEvi6YLnvGvSd75QzjgdEnwrSrXak6T0SHfryQ8nph3X9dUG9rTnJ7xaa4C2NTh0HacPc4bbj7crTwAVg5r/nx8elwzM9gypUQ0ZWLxLeiLBc+udv9hqSlhaL6DPEH8X7j3OO0EaHpaxBNnTqFZcuWK6DvjwK6iIj0elXFsOVjNxVm84du9c32TEE4UEkDYMoV7paS1fXvF0zWwtdvwod/hF0rAniCcb8xSB0OacP2DeP7u+g0eym89WPIXtK8ve/BcNLv3DShYKjIh0//Ckse3ffiziEz4cjvu+om4TC6HwIhL7PYXSigi4iI7MVaV9qwfLebhlKR54JXeZ5vu8mtfDfUlrXjxY2rIjPtGjdq3sNHTLEW1r/ryjPmrnZfStJ84btxFDx1uBtRjort+Ht99RK8+8t9S0GOPhFO+A30HdOx92hNZSEsegAW/x3qKprvGzTVjeaPnBt+lYOCrCMBvYefKSIiItImY9zUibjUwAJdXVXzwL5PiM9zxww/0k25SB3a9T9DuDDGrTQ55oTgvNeEc+GgU2DRg/Dx3f6wvP5tN53psOvg6B91XhnI6hJY/JB7v5rS5vv6H+KC+ZgTe30w7wwK6CIiIhK4qDj/6p0SelFxcNTt7kLbBb+G5f8ELHjr4bO/w5fz4Jifut9itLRyZiBqymHJw/DpffsuJNVvHMz5CRx8uoJ5JwrTOkYiIiIiErCk/nDmg3DD/2DoEf726mJ484fw0Cz45m03NSZQtZWuXOK9k+D9O5uH8/TRcO5jcNOnMO4MhfNOphF0ERERkZ5i4GS46jW3Suu7P3clHsHVbX/uAjc3/MTfQb+DW3+NumpY+qSrzFKe23xf6nC3UNKE83r+NQUhpD9ZERERkZ7EGDeqPeZEN83lw7v8F/duXOBG06de7aa+NK07Xl8Ly5+Bj/8CpTuav2bKYDj6hzDp4gOfKiMBU0AXERER6YkiY2D2d2DSJfDBb92iUdbrbl88BqtedKF72jWwej589Ce3yFVTSQPhqO/DoVdAZHRofo5eSAFdREREpCdL7Aun/xWmXw9v/9StLgtQUwLv/AzevwMaaps/J6EfHPk9N9Le0ZKQ0m69rg56dObIKQOuujfUXRERERGRHmzXk9+hNnfjAdVBVxUXEREREZEwooAuIiIiIhJGet0c9AmDUlj6h1ND3Q0RERER6cGmvvsLluXu/7iWaARdRERERCSMKKCLiIiIiIQRBXQRERERkTCigC4iIiIiEkYU0EVEREREwogCuoiIiIhIGFFAFxEREREJIwroIiIiIiJhRAFdRERERCSMKKCLiIiIiIQRBXQRERERkTCigC4iIiIiEkYU0EVEREREwogCuoiIiIhIGFFAFxEREREJIwroIiIiIiJhxFhrQ92HoDHGFMTFxaUdfPDBoe6KiIiIiPRga9eupaqqqtBam97e5/a2gF4DRABfhrovEpCxvvt1Ie2FBEqfV/eiz6t70efVvejz6l666vMaBpRaa4e394mRndyRcLcawFo7NdQdkf0zxiwFfV7dhT6v7kWfV/eiz6t70efVvYTj56U56CIiIiIiYUQBXUREREQkjCigi4iIiIiEEQV0EREREZEwooAuIiIiIhJGelWZRRERERGRcKcRdBERERGRMKKALiIiIiISRhTQRURERETCiAK6iIiIiEgYUUAXEREREQkjCugiIiIiImFEAV1EREREJIz0ioBujMkyxjxujNlpjKkxxmwxxvzVGJMa6r5Jc77PxrZyywl1/3orY8x5xpj7jTEfG2NKfZ/Hs/t5zixjzBvGmEJjTKUxZqUx5jZjTESw+t1btefzMsYMa+Ocs8aYecHuf29ijEk3xlxnjHnZGLPBGFNljCkxxnxijLnWGNPi/9M6v0KjvZ+Xzq/QM8b80RjzvjFmu+/zKjTGLDfG/NIYk97Kc0J+fkUG641CxRgzElgI9ANeBdYB04HvACcZY2ZbawtC2EXZVwnw1xbay4PdEdnj/wGTcJ9BNjC2rYONMWcC84Fq4AWgEDgduAeYDZzflZ2V9n1ePl8Cr7TQvroT+yX7Oh94CNgFfABsAzKBc4BHgZONMefbJqsK6vwKqXZ/Xj46v0Lnu8Ay4F0gD0gAZgC/Am4wxsyw1m5vPDhszi9rbY++AW8DFvj2Xu13+9r/Huo+6tbsc9kCbAl1P3Tb53M5BhgNGGCO79x5tpVjk3H/CNYA05q0x+K+LFvgolD/TD351s7Pa5hv/5Oh7ndvvAFzcf/5e/Zq748LfxY4t0m7zq/u9Xnp/Ar9ZxbbSvtvfZ/N35q0hc351aOnuBhjRgAn4ELfg3vt/iVQAVxujEkIctdEuhVr7QfW2vXW9y/VfpwH9AXmWWu/aPIa1biRXYCbu6Cb4tPOz0tCyFq7wFr7X2utd6/2HODvvs05TXbp/AqhA/i8JMR850ZL/uW7H92kLWzOr54+xWWu7/6dFk6mMmPMp7gAPwN4P9idk1bFGGMuA4bgvkStBD6y1jaEtlsSoMbz7q0W9n0EVAKzjDEx1tqa4HVL9mOgMeZGIB0oABZZa1eGuE+9XZ3vvr5Jm86v8NXS59VI51f4Od133/RzCJvzq6cH9IN899+0sn89LqCPQQE9nPQHntmrbbMx5mpr7Yeh6JC0S6vnnbW23hizGRgPjADWBrNj0qbjfbc9jDH/A6601m4LSY96MWNMJHCFb7NpWND5FYba+Lwa6fwKMWPM7UAikAJMA47AhfM/NDksbM6vHj3FBfchgLvosCWN7X2C0BcJzBPAsbiQngBMBB7GzeN70xgzKXRdkwDpvOteKoFfA1OBVN/taNwFcHOA9zUNMCT+AEwA3rDWvt2kXedXeGrt89L5FT5ux01vvg0Xzt8CTrDW7m5yTNicXz09oO+P8d1rnmaYsNbe4Zvjl2utrbTWrrbW3oS7qDcOd9W1dG8678KItTbPWvsLa+0ya22x7/YR7reLnwGjgOtC28vexRhzK/B9XNWxy9v7dN+9zq8gaevz0vkVPqy1/a21BjcAeA5uFHy5MWZKO14maDZKWHkAAANxSURBVOdXTw/ojd90UlrZn7zXcRK+Gi++OSqkvZBA6LzrAay19biycaDzLmiMMbcA9wJrgGOstYV7HaLzK4wE8Hm1SOdX6PgGAF/GfUlKB55usjtszq+eHtC/9t2PaWV/45W7rc1Rl/CR57vXrwLDX6vnnW+e5nDcRVSbgtkpOSCNv/rVeRcExpjbgAdwtbGP8VUG2ZvOrzAR4OfVFp1fIWSt3Yr7YjXeGJPhaw6b86unB/QPfPcntLC6VxKu4HwVsDjYHZN2m+m713864W+B7/6kFvYdBcQDC1VholuY4bvXedfFjDE/wi2EsgIX9vJaOVTnVxhox+fVFp1foTfQd99YJS5szq8eHdCttRuBd3AXGN6y1+47cN9an7bWVgS5a9ICY8x4Y0xaC+1DcaMUAG0uLy9h4UUgH7jIGDOtsdEYEwv8xrf5UCg6JvsyxhxujIluoX0ubgU+0HnXpYwxP8ddZLgUONZam9/G4Tq/Qqw9n5fOr9Ayxow1xvRvod1jjPktbpX5hdbaIt+usDm/TE9fx8IYMxK3+lM/4FVcWZzDcSvtfQPMstYWhK6H0sgY8yvgx7jffGwGyoCRwKm4VbzeAM621taGqo+9lTHmLOAs32Z/4ETcqM/HvrZ8a+3tex3/Im6p5Hm4pZLPwJWwehG4QIvodJ32fF6+Um/jgf8B2b79h+CvB/xza23jf0zSyYwxVwJP4kbw7qflua1brLVPNnmOzq8Qae/npfMrtHzTkO7C1TDfiKtBn4mrpDMCyMF9yVrT5DlhcX71+IAOYIwZDNyJ+5VFOrALeAW4I9ALOqTrGWOOBm4CDsVfZrEY9yvEZ4Bn9J9OaPi+PP2yjUO2WmuH7fWc2cDPcNOTYoENwOPAfVp0qmu15/MyxlwLnI0rEZcBRAG5wCLgAWvtx629iHRcAJ8VwIfW2jl7PU/nVwi09/PS+RVaxpgJuJU/ZwNZuPKIFbgB2tdx58s+OTAczq9eEdBFRERERLqLHj0HXURERESku1FAFxEREREJIwroIiIiIiJhRAFdRERERCSMKKCLiIiIiIQRBXQRERERkTCigC4iIiIiEkYU0EXk/7dbxwIAAAAAg/ytp7GjKAIARgQdAABGBB0AAEYEHQAARgQdAABGBB0AAEYEHQAARgQdAABGBB0AAEYEHQAARgKdSqLtnD6oHgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 248,
+       "width": 372
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "d = loss_curve('catgc_nhid40_lrd_0.99').head(35)\n",
+    "ax = d.plot()\n",
+    "plt.axhline(d.valid_loss.min())\n",
+    "#plt.axvline(14)\n",
+    "print(d.valid_loss.idxmin())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T23:42:35.639406Z",
+     "start_time": "2019-12-04T23:42:35.334499Z"
+    },
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa160ae2f10>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 248,
+       "width": 372
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "loss_curve('catgc_nhid40_lrd_0.99').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T23:42:40.310718Z",
+     "start_time": "2019-12-04T23:42:40.030971Z"
+    },
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa160a600d0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 248,
+       "width": 372
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "loss_curve('catgc_nhid40_lrd_0.95').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T04:35:40.376222Z",
+     "start_time": "2019-12-05T04:35:40.353435Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-03T19:44:02.144321Z",
+     "start_time": "2019-12-03T19:44:02.117690Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#pd.read_csv('logs/add_gc_baseline/preds.csv').min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-03T21:33:57.971208Z",
+     "start_time": "2019-12-03T21:33:57.825977Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "args.pkl  best_model.pth  metadata.pkl  metrics.csv  test_metrics.csv\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls logs/catgc_clip_2_lrd_97"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-03T21:40:50.279288Z",
+     "start_time": "2019-12-03T21:40:50.099053Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mv logs/catgc_clip_2_lrd_97 logs/catgc_clip_2_lrd_97_nhid40"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T07:15:07.449001Z",
+     "start_time": "2019-12-04T07:15:07.422419Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pd.read_csv('logs/catgc_nhid40_lrd_0.99_v2/metrics.csv').pipe(subset, ['loss']).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T01:06:43.758494Z",
+     "start_time": "2019-12-04T01:06:43.727906Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21, 18)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab[tab.valid_loss < 2.75].shap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T18:19:04.722750Z",
+     "start_time": "2019-12-05T18:19:04.506326Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd943cd8110>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Aq4F9wE3Adf3rSSn9MiKWAivJVn36fWAy8DPg/wB/nVL69qg/oSRJktTEShUgUkrf49ghR4OV38NTS7IOdP0h4NJh1PdLssBxfd57JEmSpFZSqjkQkiRJksaXAUKSJElSbgYISZIkSbkZICRJkiTlZoCQJEmSlJsBQpIkSVJuBghJkiRJuRkgJEmSJOVmgJAkSZKUmwFCkiRJUm4GCEmSJEm5GSAkSZIk5WaAkCRJkpSbAUKSJElSbgYISZIkSbkZICRJkiTlZoCQJEmSlJsBQpIkSVJuBghJkiRJuRkgJEmSJOVmgJAkSZKUmwFCkiRJUm4GCEmSJEm5GSAkSZIk5WaAkCRJkpSbAUKSJElSbgYISZIkSbkZICRJkiTlZoCQJEmSlJsBQpIkSVJuBghJkiRJuRkgJEmSJOVmgJAkSZKUmwFCkiRJUm4GCEmSJEm5HT/eDZAkSVKxenr7WN+1h4079nHg4GFmTp3MsvbZdC6Zx5wZbePdPJWcAUKSJKmFbN21n8vW38Ohw0eOnuvp7WPtlt1s6OpmXedizj1z1ji2UGXnECZJkqQW0dPbd0x4qHXo8BEuW38PPb19Y9wytRIDhCRJUotY37WnbnioOnT4CBu6usemQWpJBghJkqQWsXHHvlzlbtmxt8EtUSszQEiSJLWIAwcPF1pOGkihASIilkfEzRGxIyJ21Zw/OyL+LCLmFPk8SZIkPWXm1MmFlpMGUkiAiMw/AV8C3gjMB55bU+RR4K+ANxfxPEmSJB1rWfvsXOWWt5/W4JaolRXVA3EFsAL4R2Am8Ne1F1NKDwN3A/+toOdJkiSpn84l85gyedKgZaZMnsSKJXPHqEVqRUUFiD8EdgBvTSk9BqQByvyIp/dKSJIkqUBzZrSxrnNx3RAxZfIk1nUudjM5jUpRG8k9H1ibUhooOFT9FPiNgp4nSZKkAZx75iw2rexgQ1c3t+zYe3Qn6uXtp7FiyVzDg0atqADxJPDMIcrMAX452gdFxPuBxcBZwCygD+gmm3/xdymlR4ZR1+nA9cCFwEnAvko916WUHh3kvtcAf1xpxzSycHQv8FcppW+N4GNJkiQVZs6MNlZdtIBVFy0Y76aoBRUVIO4HXh4RMVAvREQ8EziP7CV7tN4JbAc2kb24TwVeAlwL/FFEvCSl9NBQlUTEfGArcDLwZeAHwO8C7wAujIil/cNIRBwH/D3wVuAh4AvAI8AplTYsAgwQkiRJallFBYgNwN8BH4yIlbUXImISsAY4DVhVwLOmpZQe738yIm4A3g28i2xS91A+ShYe3p5S+nBNPWvIQsoNwOX97vlTsvCwAbgspfS0RZQj4oRhfA5JkiSpdIqaRL0W+BrwdrK/zP8PgIj4V7LhRZcD/5ZS+sxoHzRQeKj4XOX4vKHqiIgzgAuAPcBH+l2+BjgIrIiIqTX3TAPeC/yYbLL4MTuwpJSeGOrZkiRJUpkVEiBSSkeAZWTzCSaTzU8I4GJgCvA+sv0hGml55XhfjrLnVY5fSyn9uvZCSukXZEvOTiEbllT1GuBE4GbguIh4Q0SsiogrI6J9dE2XJEmSyqGoIUyklJ4Ero2I68gCxEnAY8APKgGjUBFxFdkL/XSyycwvJQsPq3Pc/vzK8YE6139E1kNxFnBH5dyLKscngJ3A0xZQjojPA50ppUM52r6tziVnOkmSpGHp6e1jfdceNu7Yd3TFpWXts+lcMm/IFZdGc68mrsICRFVlEvUPi653AFeRTV6uug24JKX0sxz3Tq8cH6tzvXp+Rs25kyvHPyObDP7fySaP/xbZMKjXk60ydUmO50uSJI3a1l37uWz9PRw6/NTfant6+1i7ZTcburpZ17mYc8+cVfi9mtiKmgMx5lJKp6aUAjiVbKjUGcC9EbGwgOqj+piac9UdWfqA5Smlb6eUfplS+jbZ8KZfks2bmJOj7YsG+iJbCUqSJGlIPb19xwSAWocOH+Gy9ffQ09tX6L1SYQEiMm+MiHURcWtEbB7g646haxqelNJPUkpfJBtydBKwPsdt1R6G6XWuT+tXDqC6L8S3UkoP92vDPuA/yP57Ls7TbkmSpNFY37WnbgCoOnT4CBu6ugu9VypkCFNEPAP4KvBysr/eJ576Kz413w+2U/WopJS6I+J+4IURMSultH+Q4tUhVmfVuV5dyal2jkT1nt4691QDhgMGJUlSw23csS9XuVt27D1mQ7nR3CsV1QPx58ArgL8EfoMsLFxLtvfD75Mt7Xoz2QpNjXRa5TjUpO07K8cLKpvDHRURzwKWkg1Vqt0Urtp7ck6dOqvn9+RqqSRJ0igcOHjMivK5y43mXqmoAPFGYHtK6Zra3ZtTSg+nlG4mWzZ1GfAno3lIRCyIiFMHOH9cZSO5k4GtKaVHK+dPqNwzv7Z8SulBsn0r5gFX9qvuOrLdrdenlA7W3LODbHnXsyPisn7Pvww4G3gQ+M/RfEZJkqQ8Zk7N93fZgcqN5l6pqAAxn+zluioBR3dlTintBr7C6FcouhB4KCLuiIiPR8SNEfFJsmVX3w08TLZTdNUcsiVXB5p7cQXwU+CmiPhSpa7NZLtQPwC8Z4B7/hDYD3wiIr4aER+IiK8AnwAOka0CVfiStZIkSf0ta5+dq9zy9tOOOTeae6WiAsQTQO0O0b8gG8pUq5tspaTRuB34ONlk6YuB/0W2fOoBsp6Dc1JK9+epqNILsRj4FPBi4E/JgtBNwJLanpSae34ILAT+AWgH3gEsAv4FWJxS+uYoPpskSVJunUvmMWXypEHLTJk8iRVL5h5zfjT3SkXtA/Fjsr/2Vz0ALOlX5v8he9EfsZTS9zh2yNFg5ffw9Mnc/a8/BFw6zDY8BFw2ZEFJkqQGmjOjjXWdi+suxzpl8iTWdS4ecEO40dwrFRUg7gZeVfP9l4C/jIh/AL5AtjrTq4B/Luh5kiRJE965Z85i08oONnR1c8uOvUd3k17efhorlswdNACM5l5NbJFtHD3KSiJeTrYS0x+nlPZExBSylY5exFNLuO4CXpFS6hn1A1tURGxbuHDhwm3bto13UyRJktTCFi1axPbt27dXNjMelkJ6IFJKXwe+XvP9oYhYCrwWOJNsadNbUkqHinieJEmSpPFR1BCmY6SUngQ+36j6JUnSxNLT28f6rj1s3LHv6HCbZe2z6Vwyz+E20hhqWICQJEkqytZd+4+Z8NvT28faLbvZ0NXNus7FnHvmrHFsoTRxFBogIuJ3yJY3PZ2afSBqpJTS+4p8piRJam09vX11VwsCOHT4CJetv4dNKzvsiZDGQCEBIiJmAhvINnqD+kunJsAAIUmSclvftadueKg6dPgIG7q6WXXRgrFplDSBFdUD8bfARWQbvX0a6AGeLKhuSZI0gW3csS9XuVt27DVASGOgqACxDNiaUrqgoPokSZIAOHDwcKHlJI3OcQXVMwnYWlBdkiRJR82cOrnQcpJGp6gAsR04o6C6JEmSjlrWPjtXueXtpzW4JZKguADxPmBZRLy0oPokSZIA6FwyjymTJw1aZsrkSaxYMneMWiRNbEXtRL05It4EfDEiNpL1SDxWp+z6Ip4pSZImhjkz2ljXubjuUq5TJk9iXedil3CVxkhRy7hOBl4LPBt4S+Ur9S9WOWeAkCRJw3LumbPYtLKDDV3d3LJj79GdqJe3n8aKJXMND9IYKmoVphvJQsP9wGeBvbiMqyRJKtCcGW2sumiBS7VK46yoAPEm4LvAi1JKrqEmSZJKo6e3j/Vde9i4Y9/Rno1l7bPpXDLPng1pAEUFiBnAPxseJElSmWzdtf+YuRU9vX2s3bKbDV3drOtczLlnzhrHFo4/A5b6K2oVpp1AvjXWJEmSmkBPb1/didkAhw4f4bL199DT2zfGLWseW3ft5/w1W1i7ZTc9vX30PXHkaMA6f80Wtu7aP95N1DgoKkD8DfC6iDiroPokSZIaan3XnrrhoerQ4SNs6OoemwY1GQOW6ikqQPQAtwH/ERHXRsTyiHjZQF8FPU+SJGlUNu7Yl6vcLTv2NrglzcmApXqKmgPxdbIlWgN4L8cu4Vpr8J1gJEmSxsCBg/mmbuYt12qGE7BcGWtiKSpAXM/goUGSJKmpzJw6Odfwm5lTJ49Ba5qPAUv1FLUT9bVF1CNJkpTXaFcHWtY+m7Vbdg9Zbnn7aQW0tnwMWKqnqDkQIxIRb4mIzePZBkmSVD5FrA7UuWQeUyYPPrJ6yuRJrFgyt6hml0JPbx833rqTx/qeyFV+ogasiayoIUwjNQ/oGOc2SJKkEsm7OtCmlR2D9kTMmdHGus7FdeuaMnkS6zoXj8leB82y18JA+2IMZiIGLI1zD4QkSdJwFbk60LlnzmLTyg4u75jPnBlttJ0wiTkz2ri8Yz6bVnaMySZyzbLXwlDBrL+xDFhqLuPdAyFJkjQsRa8ONGdGG6suWjCslYSK6jEoqjelCHmCGcCJzzieN79kLiuWzDU8TFD2QEiSpFIZ79WBiuwxaKa9FvIGs+ltJ7DqogWGhwnMACFJkkol76o/jVgdqOjdmZtpM7vxDmYqDwOEJEmqq7oiz9LVmzn76ttYunozN966M/cLciMsa5+dq1wjVgcqusegmV7axzOYqVwMEJIkaUDNMrm3v/FcfrXoHoNmemkfz2CmcjFASJKkYxQ9VKdI1eVX64WIRq4OVHSPQTO9tLsvhvIyQEiSpGM00+TegYzX8qtF9xg000v7eAYzlct4L+P6HWD9OLdBkiT1U/RSqY0wkuVXR2tZ+2zWbtk9ZLm8PQbNtJkdPBXMNnR1c8uOvRw4eJhpbccze3ob+x7r4w//6Z5x2+ROzaOQHoiIOBIRVw9R5j0R8WTtuZTSl1NKlxbRBkmSVJxmmtzbTBrRY9AMm9nVqgazu1edxz+8ZTG/ePxJvvNQLz/5+a+aZh6MxldRPRBR+cpTTpIkNbmZUyfnmt8w0VbkaVSPwXj0pgylmTa5U3MZyzkQzwYeH8PnSZKkEWqmyb3Nptl6DBql2efBaPyMuAciIl7W79S8Ac4BTAJ+E/gD4IcjfZ4kSRo7nUvmsaGre9AXyIm8Ik8z9hgUrQzzYDQ+RjOE6etAqvw7AW+pfA0kgF8DfzqK50mSpDHSbJN7x1JPbx/ru/awccc+Dhw8PGEnDTsPRvWMJkBcTxYcAngvWaDYMkC5I8AjwJ0ppR+M4nmSJGkMDbQiz8ypk1nefhorlsxtyZfprbv2HxOaqpOGN3R1s65zccsMURqK82BUz4gDRErp2uq/I+ItwJdSSjcV0ShJktQcJsJQnSonDT9d0UvWqnUUsgpTSum5RdQjSZI0XoYzabgRgarZhk45D0b1FLUPxKSImDLA+fMi4kMRcWNEGDIkSVLTGs6k4aJt3bWf89dsYe2W3fT09jXFfgvuTK16ilrG9a+BAxExvXoiIt4EbALeBvw58O2IeE5Bz5MkSSrUeE0azjt0Ks98hKJNlCVrNTxFbST3MrJJ0o/VnLsG6AXeAZwK3AisBN5Z0DMlSZIKM16Thsd76NRQJtI8GOVTVA/Ec4Bd1W8i4gzg+cCHU0qfTin9NXArcGFBz5MkSSrUeG2eN55Dp6SRKCpATAN+XvP9UrIlXm+rOfd94PTRPigi3h8Rd0TEQxHRFxEHIuLeiLgmIk4aZl2nR8QnI2JvRPwqIvZExN9GxLNz3n91RKTK16tG9okkSVIz6Fwyr+54/6pGTBp2vwWVTVEBYh9QO0n6VUAfsK3m3InAkwU8653AVLL5FR8CPlOp91rgvrzzLCJifqV9lwLfBj4I7CYbctU1VBiJiIXA1cAvR/QpJElSUxmvScN5h0S534KaRVFzIL4FvCYilgGPA28A7kgpPVFT5gygp4BnTUspPd7/ZETcALwbeBdwRY56PgqcDLw9pfThmnrWkIWUG4DLB7oxIp4JbADuIRu6tWKYn0GSJDWh8dg8z/0WVDZFBYi/Al4LfLny/a/JXsABiIhpwMuBm0f7oIHCQ8XnyALE84aqozJH4wJgD/CRfpevAf4IWBERf5pSOjhAFTeS9bi8sPJMSZI0hGbb56CesZ407H4LKpuiNpL7bkS8GHhL5dRnU0r/WVPkd4CvAf9SxPPqWF453pej7HmV49dSSr+uvZBS+kVE3IDRsVMAACAASURBVE0WMF4C3FF7PSJeQTbM6Z0ppQciYnStliRpAti6a/8xS5VW9zn45Df/i+nPPIGDh480bahopOrQqXpLuU7E/RbKEjYnqqJ6IEgpfRe4qs61bwLfLOpZABFxFdm8iunAYuClZOFhdY7bn185PlDn+o/IAsRZ1ASIyj4XnwK+Adw0knZX6tlW55Lro0mSWs5Q+xw8cSSxvzJBuBoqNnR1s65z8YTZZ2A8hk41q8HC5kT7vWhWhQWIWpVVjE5MKT3UiPorrgJOqfn+NuCSlNLPctxb3fDusTrXq+dn9Dv/YeAk4BUppZS3oZIkld1o/iKcZ5+D/qqbp21a2TFhXp7dbyH/pnoT6feiGRW1ChMRcWJE/E1EPAzsB/6r5tqLI+KrlZWLCpFSOjWlFGSb1F1MNkn73oKeUR2XdDQkRMTFZJOl/yylNPRMp0GklBYN9AX8YDT1SpLUCFt37ef8NVtYu2U3Pb199D1x5OhfhM9fs4Wtu/YPen/efQ76q26epoljOJvqafwUEiAqQ3u6yFYv2gvs5KmXcIDvAr8H/I8inlcrpfSTlNIXyYYcnQSsz3FbtYdhep3r02rLRcRMYC2wGfjYyFsrSVK55P2L8GA7OI9m/wI3Txuent4+brx1J0tXb+bsq29j6erN3Hjrzlw7bDcDN9Urh6J6IN4DnEM2hGgh8H9qL6aUDgFbgFcW9LxjpJS6gfuBcyJiqIFxP6wcz6pzvbqSU3WOxG8Cs8gmX/+6ZvO4xFMTxzdVzv3JyD6BJEnNp4i/CI9m/wI3T8tvtD1FzcBN9cqhqABxMfDvKaXB/vrfDcwp6Hn1VBdIHmqg5Z2V4wUR8bT/BhHxLLKdtPvI9rcAeAT4hzpfP6qUubXy/fdG0X5JkppKEX8RXtY+e8TPd/O0fIroKWoGbqpXDkUFiNMZevnUX1J/yFAuEbEgIk4d4PxxlY3kTga2ppQerZw/oXLP/NryKaUHyZaVnQdc2a+668h2ul5f3QMipfRQSumygb6ArZX71lTO3T6azyhJUjMp4i/CnUvm1d3deShunpZPq8wdyBs2/b0YX0UFiF+QvbwP5rlkk6tH40LgoYi4IyI+HhE3RsQnyXoB3g08DLy1pvwcsvkYdxxbFVcAPwVuiogvVeraTDaP4wGyYVmSJE1oRfxFuLrPwXBDhJun5dcqcwfyhE1/L8ZfUQHiP4FlleE/x4iI2cCrGf1eELcDHyebLH0x8L+A1wMHyHoOzkkp3Z+nokovxGKyfR1eDPwpMJ9sf4clKaVHRtlWSZJKr6i/CFf3Obi8Yz5zZrTRdsIkZp04meOPG3hD1om4edpotMrcgaHCpr8XzaGofSA+RDYH4KsR8Ue1FyLibOATwDMZxeZrACml73HskKPByu/h6atB9b/+EHDpKNt0CXDJaOqQJKlZdS6Zx4au7kGHx+T9i/BA+xz09Pa5eVoBZk6dnGt+QxnmDripXvOLovZDi4j3AteS7Z3wBHAC8CjwbLKX+FUppf9dyMNaVERsW7hw4cJt2+ptVC1J0tgbaGfgqupfhN0ZeHzdeOtO1m4ZepuqyzvmT+iN6vSURYsWsX379u2VvciGpbCN5FJK15Mt0/pvZMHhCFmY+CrwKsODJEnlNNDwozkz2ri8Yz6bVnYYHpqAcwc0lgoZwhQRncB3Ukp38tQSqf3LvABYOMRSr5IkqQkNNPxIzaM6d2ConiKH/6gIRc2B+BTZ8KXBlnJ9LXA9+XaKliRJTaCnt4/1XXvYuGPf0bHoy9pn07lkni+jTca5AxorRQWIPCaRDWmSJEklMNDch+rOxhu6up370ITsKdJYKGwORA5nkc2NkCRJTa5VdjaWVLwR90BUNnCr9bqImDdA0UnAbwK/B3xlpM+TJEljZzg7G/f/a7fDnsafPwM10oiXcY2IX9d8mxhkv4XK9f8A3pxSGnqNsQnKZVwlSY00nJfKpas35+pdmDOjjbtXnXf0e5d8HX/+DJTHeC3j+tzK1xlk4eFva87Vfv0mMC2ldK7hQZKk8bF1137OX7OFtVt209PbR98TR47OZzh/zRa27tr/tPIj2dnYYU/jz5+BxsKIA0RKqbvytQe4DvhSzbnarx+nlA4W1mJJkjQsI3mpzLtjcW254Qx7UmP4M9BYKGQSdUrpupTSXUXUJUmSijWSl8pl7bNz1b28/bSj/964Y1+ue27ZsTdXOQ2fPwONhbFchUmSJI2DkbxUjmRn45EMe1Kx/BloLBggJElqcSN5qazubFwvRAy0s/FIhj2pWP4MNBYMEJIktbiRvlRWdza+vGM+c2a00XbCJObMaOPyjvlsWtlxzEo+Ixn2pGL5M9BYGMudqCVJ0jhY1j6btVuGXghxoJfK4exs3LlkHhu6ugedb9F/2JOK5c9AY8EeCEmSWtxI5jOMxEiGPalY/gw0FgwQkiS1uLF8qRzusCcVz5+BGm3EO1GreO5ELUlqpJ7ePjZ0dXPLjr1Hd6Je3n4aK5bM9S/S0gQzmp2onQMhSdIEMZz5DJJUj0OYJEmSJOVmgJAkSZKUmwFCkiRJUm7OgZAkaYLp6e1jfdceNu7Yd3Qy9bL22XQumedkaklDMkBIkjSBbN21n8vW3/O0jcZ6evtYu2U3G7q6Wde52GU+JQ3KIUySJE0QPb19x4SHWocOH+Gy9ffQ09s3xi2TVCb2QEiSNEGs79pTNzxUHTp8hA1d3aVZ6tXhWNLYM0BIktSEGvFivHHHvlzlbtmxtxQBwuFY0vhwCJMkSU1m6679nL9mC2u37Kant4++J44cfTE+f80Wtu7aP6J6Dxw8XGi58eRwLGn8GCAkSUPq6e3jxlt3snT1Zs6++jaWrt7Mjbfu9OWsARr5Yjxz6uRCy42n4QzHklQsA4QkaVCN+mv4eChDEGrki/Gy9tm5yi1vP23YdY+14QzHklQsA4Qkqa5WGiZSliDUyBfjziXzmDJ50qBlpkyexIolc4dd91hrpeFYUtkYICRJdbXKMJEyBaFGvhjPmdHGus7FdUPElMmTWNe5uBSrF7XScCypbAwQkqS6WmWYSJmCUKNfjM89cxabVnZwecd85sxoo+2EScyZ0cblHfPZtLKjNKsWtdJwLKlsXMZVklRXqwwTKdPypcvaZ7N2y+4hy43mxXjOjDZWXbRg3D/raHQumceGru5Bg2FZhmNJZWOAkCTVNXPq5FzDepp9mEiZglDZXoyHs19FkXtbVIdj1RuaVqbhWFLZOIRJklRXqwwTKdN4+TLNUxjOxPRGTGJvleFYUtlESmm826CKiNi2cOHChdu2bRvvpkgSkP3F+Pw1W4b8a/imlR1N8UJbz4237sw1LOjyjvlNM6ynp7ePDV3d3LJj79G/1i9vP40VS+Y2xX/r4fxuAC3xeyS1kkWLFrF9+/btKaVFw73XIUySpLpaZZhI2YYFQfPPUxjOxPREyl22WT+vpKc4hEmSNKhWGCZSpmFBZTGciemtspqXpIw9EJKkITX7X8PzqAahZh4WVCaNmJjeDJPYJQ3NACGpKRS5OotUTysEoWYx3BW6WmE1L0kZA4Skcbd11/5jxthXV2fZ0NXNus7FpRgm02oMdRrMcParSKSG720haew4B0LSuOrp7as7QReyiZWXrb8n118vVZxGLLmp1tK5ZF7dOSVV1YnpwykrqfkZICSNq+Gs5KKxYagbmZ7ePm68dSdLV2/m7KtvY+nqzdx4686W/e80nInpTmKXWotDmCSNq+GszuK49bExnFDXCj+TIoZqTdRheMOZmO4kdql1uJFcE3EjOU1EZ199G31PDP6yCtB2wiR2vu/CMWiRlq7enOuv5nNmtHH3qvPGoEWNM9CLf1X1r+JDvfi3ymZ7kiaW0WwkV7ohTBHx/oi4IyIeioi+iDgQEfdGxDURcdIw6zo9Ij4ZEXsj4lcRsSci/jYinj1A2TkR8baIuLVS7lcR8UhEbIqIi4v7hNLEknfVFVdnGTuNWJ6zGRU1VMtheJImmtIFCOCdwFRgE/Ah4DPAk8C1wH0R8Zw8lUTEfGAbcCnwbeCDwG7gHUDXAGHkbcBNwPOBO4E1wL8Dvwd8PiLWjOpTSRPUsvbZucq5OsvYmSihrqgXfzdJkzTRlHEOxLSU0uP9T0bEDcC7gXcBV+So56PAycDbU0ofrqlnDVlIuQG4vKb8t4GXp5S29Hvu2cC3gHdGxGdSSo4/koahc8k8NnR1Dzn8w9VZxs5wlucss6Lm34ymx8alciWVUel6IAYKDxWfqxyfN1QdEXEGcAGwB/hIv8vXAAeBFRExtea5X+gfHirndwKfrXz78qGeLenpXJ2l+UyUJTeLGqo10h4bl8qVVFalCxCDWF453pejbHXW39dSSr+uvZBS+gVwNzAFeEnOZz9ROT6Zs7ykGtXVWS7vmM+cGW20nTCJOTPauLxjPptWdrTk6jXNbKKEuqKGao1kGJ5L5UoqszIOYQIgIq4CTgSmA4uBl5KFh9U5bn9+5fhAnes/IuuhOAu4Y4h2TANeDyTgazmeTUTUG+ZU/vUQpRGaM6ONVRctaIllQVvBRFhys6ihWiMZhjfRlsqV1FpKGyCAq4BTar6/DbgkpfSzHPdOrxwfq3O9en7GYJVERADrKu34aGU4kyS1hFYPdUXNv6n22Ay1HGxt6HL/E0llVtoAkVI6FSAiTgHOJet5uDcilqWUto+y+qg+ZohyfwO8EfgGsDJv5fXW2630TCzMW48kKZ96k5VXX/zbrPrCd3O/+Ncz3B6bibJUrqTWVNoAUZVS+gnwxYjYTjYkaT3wgiFuq/YwTK9zfVq/cseIiA+QrdZ0F/DfUkq/yt1oSdKYGWyX6CmTJ7H64t/m/n2/GPVQreH02MycOjnX/IayL5UrqTWVPkBUpZS6I+J+4IURMSulNNjyFT+sHM+qc726ktOAcyQi4oPAn5DtB7EspXRoJG2WJDVWnsnKq77wXTat7BjToUITZalcSa2plVZhAqj+L+3gM9OyF3+ACyLiaf8NIuJZwFKgj2x/h9prEREfIQsPm8h6HgwPktSkmnWX6ImyVK6k1lSqABERCyLi1AHOH1fZSO5kYGtK6dHK+RMq98yvLZ9SepBsxaR5wJX9qruObKfr9SmlgzXPCODjZJvU3Qq8JqXk+nqS1MSacZfo6nyMKSfUDxCtslSupNZUtiFMFwIfiIi7gAeBR8hWQOoAzgAeBt5aU34OsBPoJgsLta4AtgI3RcQrK+VeDLyCbOjSe/qVfy9wGVnPxHeAVVmmeJrvpJS+NPKPJ0kqUrNNVh5oPkatWSdO5g2LntMyS+VKak1lCxC3k/UCLAXayZZZPUj2wr8BuCmldCBPRSmlByNiMXA9WTB5NbAPuAm4boB6nls5tgHvqlPtPwEGCElqEnknKz/+xBGWrt7MsvbZdC6Z15CX96HmY0A2nMrwIKnZlWoIU0rpeymlK1NKL0wpzUopHZ9Smp5SelFK6dr+L/0ppT0ppUgpzatT30MppUtTSrNTSpNTSnNTSu8YKISklC6p1DXY1yWN+eSSpJHIu0t04qmVmc5fs4WtuwZbh2NkmnU+hiQNV6kChCRJw5FnsnJ/hw4f4bL19+TquRiOZpyPIUkjYYCQJLWs6i7RIwkRRfcENNt8DEkaqbLNgZAkaUj9d56e1nY8Z51yIvsee5yf/vxXpBx13LJjb6F7Q7h5nKRWYYCQJLWUgVY66nviCD/5+a+YMnkSJ0w6jsNHfj1kPUX3BLh5nKRW4RAmSVLLyLPz9BM5wgMU3xPg5nGSWoUBQpLUMvKsdJRn+BIU3xMw1HwMN4+TVBYOYZKkJtV/HP/MqZMbuk9BK8i70lEweJBoVE/AuWfOYtPKDjZ0dXPLjr1Hf67L208b1/0f/F2TNByRUt6/xajRImLbwoULF27btm28myJpnA22Y3H1L9XnnjlrHFrW3M6++jb6nhi8BwLgGccfx6Tjwv+++LsmTVSLFi1i+/bt21NKi4Z7r0OYJKnJ5BnH34h9ClpB3nkLs058BptWdnB5x3zmzGij7YRJzJnRxuUd89m0smPCvDD7uyZpJBzCJElNZjg7Fhe5zGgzGO1QmuGsdDRnRhurLlrQcv8Nh2Mi/65JGjl7ICSpyUzUHYu37trP+Wu2sHbLbnp6++h74gg9vX2s3bKb89dsYeuu/UPW4UpHwzNRf9ckjY4BQpKazETcsbiooTStvNJRT28fN966k6WrN3P21bexdPVmbrx156iGF03E3zVJo+cQJklqMs26Y3EjV+opcihNs650NBoDTXSu9s5s6Ooe8UTnZv1dk9Tc7IGQpCazrH12rnJjuWNxEcOLBlP0UJrq/Ia7V53HzvddyN2rzmPVRQtKGR4aOdG5GX/XJDU/A4QkNZlmG8c/Fiv1OJSmvuH0zgxXs/2uSSoHA4QkNdhwx6432zj+Rr7AVuUdIjMRh9I0cqJzs/2uSSoHA4QkNdBIh/5Ux/E3wz4FY7FSj0Np6mt070wz/a5JKgcnUUtSg+Qd+rNpZceAf+Ftln0KxmJ4UeeSeWzo6h60p2OiDqUZi4nOzfK7Jqkc7IGQpAYZi6E/Y2Eshhc5lKY+e2ckNRsDhCQ1SKts0jVWL7AOpRmYE50lNRuHMElSg7TKykJjObzIoTTHqvbO1BsON5F7ZySND3sgJKlBWmVlIYcXjT97ZyQ1E3sgJKlBlrXPZu2W3UOWK8PY9Vbc3bls7J2R1CwMEJLUIK22spAvsJIkcAiTJDWMQ38kSa3IHghJaiCH/kiSWo0BQpIarNWH/vT09rG+aw8bd+w7GpCWtc+mc8k8A5IktSADhCRpxLbu2n/M8qI9vX2s3bKbDV3drOtc7ApBktRinAMhSRqRnt6+unsTQLbL9mXr76Gnt2+MWyZJaiQDhCRpRNZ37Rl0hSnIQsSGru6xaZAkaUwYICRJI7Jxx75c5W7ZsbfBLZEkjSUDhCRpRA4cPFxoOUlSORggJEkjMnPq5ELLSZLKwQAhSRqRZe2zc5Vb3n5ag1siSRpLBghJ0oh0LplXd5ftqimTJ7FiydwxapEkaSy4D4QkaUTmzGhjXefiuku5Tpk8iXWdi91MTk3FjQ+l0TNASJJG7NwzZ7FpZQcburq5Zcfeoy9ky9tPY8WSub6Qqam48aFUDAOEJGlU5sxoY9VFC1h10YLxbopUV96NDzet7DD4SkNwDoQkSWp5bnwoFccAIUmSWp4bH0rFMUBIkqSW58aHUnEMEJIkqeW58aFUHAOEJElqeW58KBXHACFJklqeGx9KxTFASJKkllfd+LBeiHDjQyk/94GQJEkTghsfSsUoXYCIiPcDi4GzgFlAH9ANfAn4u5TSI8Oo63TgeuBC4CRgX6We61JKj9a557eAa4GXA9Mqz74ZWJ1S6hvRh5IkSWPCjQ+l0SvjEKZ3AlOBTcCHgM8AT5K91N8XEc/JU0lEzAe2AZcC3wY+COwG3gF0RcRJA9zzYuA/gdcBt1ee/3PgvcCmiHjGaD6YJEmS1OxK1wMBTEspPd7/ZETcALwbeBdwRY56PgqcDLw9pfThmnrWkIWUG4DLa85PAv4RmAK8NqX0b5XzxwGfA15fuW/1yD6WJEmS1PxK1wMxUHio+Fzl+Lyh6oiIM4ALgD3AR/pdvgY4CKyIiKk15zuAs4G7quGh0p5fA39W+fbyiIihni9JkiSVVekCxCCWV4735Sh7XuX4tUoAOCql9AvgbrKehpcMcM9t/StLKe0GHgDmAmcMo82SJElSqZRxCBMAEXEVcCIwnWxS9UvJwkOeIUTPrxwfqHP9R2Q9FGcBdwzjnrMqXw8O0fZtdS45o0uSJElNrbQBArgKOKXm+9uAS1JKP8tx7/TK8bE616vnZ4zyHkmSJKmllDZApJROBYiIU4BzyXoe7o2IZSml7aOsvjqPITXinpTSogEryHomFg7jmZJaTE9vH+u79rBxx76ja9Qva59N55J5rlEvSWoKpQ0QVSmlnwBfjIjtZMOL1gMvGOK2am/B9DrXp/UrN9J7JCm3rbv2c9n6ezh0+MjRcz29fazdspsNXd2s61zMuWfOGscWSpLUQpOoU0rdwP3AOREx1P/D/rByPKvO9epKTrXzHUZyjyTl0tPbd0x4qHXo8BEuW38PPb3uVylJGl8tEyAqTqscB/5/4KfcWTleUNnH4aiIeBawlGyH62/VXNpcOV7Yv7LKsrBnke1KvXuYbZYk1nftqRseqg4dPsKGru6xaZAkSXWUKkBExIKIOHWA88dVNpI7GdiaUnq0cv6Eyj3za8unlB4EvgbMA67sV911ZDtdr08pHaw5vwXYCbwsIl5T+2zg/ZVv/z6lNJx5E5IEwMYd+3KVu2XH3ga3RJKkwZVtDsSFwAci4i6ypVIfIVuJqYNs/4WHgbfWlJ9D9tLfTRYWal0BbAVuiohXVsq9GHgF2TCk99QWTikdiYhLyXoi/jUi/hX4v8AryZaRvRv4YFEfVNLEcuDg4ULLSZLUKGULELcDHycbYtROtmTqQbIX/g3ATSmlA3kqSik9GBGLgevJgsmrgX3ATcB1A9WTUvqPiHgRWS/FBcCzyMLJ9cDqlNKvRvfxJE1UM6dOzjW/YebUyWPQGkmS6itVgEgpfY9jhxwNVn4PTy2vOtD1h4BLh9mG+4E3DuceSRrKsvbZrN0y9BSq5e2nDVlGkqRGKlWAkIbLNfVVFp1L5rGhq3vQidRTJk9ixZK5Y9gqSZKOVapJ1NJwbN21n/PXbGHtlt309PbR98SRo2vqn79mC1t37R/vJkpHzZnRxrrOxUyZPGnA61MmT2Jd52KDryRp3Bkg1JJcU19ldO6Zs9i0soPLO+YzZ0YbbSdMYs6MNi7vmM+mlR1uIidJagoOYVJLGs6a+qsuWjA2jZJymDOjjVUXLfD3UpLUtOyBUEtyTX1JkqTGMECoJbmmviRJUmMYINSS8q6V75r6kiRJw2OAUEta1j47VznX1JckSRoeA4RaUueSeXWXw6xyTX1JkqThM0CoJbmmviRJUmO4jKtaVnVN/Q1d3dyyY+/RnaiXt5/GiiVzDQ+SJEkjYIBQS3NNfUmSpGI5hEmSJElSbgYISZIkSbkZICRJkiTlZoCQJEmSlJsBQpIkSVJuBghJkiRJuRkgJEmSJOVmgJAkSZKUmwFCkiRJUm4GCEmSJEm5GSAkSZIk5WaAkCRJkpSbAUKSJElSbgYISZIkSbkZICRJkiTlZoCQJEmSlJsBQpIkSVJuBghJkiRJuRkgJEmSJOVmgJAkSZKUmwFCkiRJUm4GCEmSJEm5GSAkSZIk5WaAkCRJkpSbAUKSJElSbgYISZIkSbkZICRJkiTlZoCQJEmSlJsBQpIkSVJuBghJkiRJuRkgJEmSJOVmgJAkSZKUmwFCkiRJUm4GCEmSJEm5lSpARMRJEXFZRHwxInZFRF9EPBYR34yIP4yI3J8nMv8zIr4VEb+IiEMRcW9EvD0iJtW55xkRcWVEfDsi9kfELyNiZ0TcFBFzi/ukkiRJUnM6frwbMExvBD4G7APuBP4vcApwMbAOuCgi3phSSjnq+idgBfBT4LPAQeBVwIeAl/WvJyKOB+4AlgI/AP4F+BXwIuBtQGdEnJtSur+IDypJkiQ1o7IFiAeA1wBfSSn9unoyIt4NfBt4PVmY+PxglUTE68jCw38Bv5tS2l85fwLwuUo9bwE+VXPb/0sWHu4ALuj3/OuA9wJXAf9zVJ9QkiRJamKlGsKUUtqcUrql9uW9cv5h4O8r3748R1UXV45/Uw0PlXqeAK6ufPu2fvecUTl+pf/zgS9Xjr+R49mSJElSaZUqQAzhicrxyRxlT60cdw9wrXpuYUTMqDn//crxogHmWiyrHG/P8WxJkiSptMo2hGlAlfkJnZVvb8txS7XX4bkDXDuj5t8LgG9V/v0V4AtkvRffjYjbgcPAIuClwIeBv8vZ3m11Li3Ic78kSZI0XlqlB2I18ALgqymlf89RfmPluDIiZlZPVoLIdTXlnl39R2VC9RuAa4HnA28nm/PwCuAu4J9TSkdG8RkkSZKkplf6HoiIeDvwp2QrI63IedvNwJuBi4D7I+LfgENkqzDNB34EPA84Gggi4pnA+so9V5LNezhENrH6JuCuyspN/3979x5uR1Wfcfz7AkmIRoiAAlLLqQRFQhHkUqCGi0BFELyUmyi3goqVAurzFLBoo2grPLWC9aGVBzSoIGhQIhVEuRwIEEFEoEhEbgcBgQCJiUBywuXXP9baZmfYO2fOPvuc2XPyfp5nPcOeWWvNzI85k732rLVmDkOIiO3anMuvgLeXPAczMzMzszFX6ycQkj5Bmnb1HmCPiFhYplweBH0A6QnCE6SGxz8Aj5K6Iz2Tsy5oKnYKaRrZf4mIb0TEExGxJCKuJD2ZmJCPxczMzMxs3KrtEwhJJwFfBe4G9oyIBUMUWUlEvAh8JafmeicD2wBLWTFwGlYMlL6uRV13SloIbCpp/Yh4ppjHzMzMzGw8qGUDQtLJpHEPdwB7N0/F2gWHA2sDF+RpXRsm5eUrpmqVNAlYJ39cPoJ9982fP5/ttmvZw8nMzMzMrCvmz58P0NdJ2do1ICR9FvgC8CvSC93adlvKL4bbDHghIh4obFsnIpYU1u1Aapg8m/fRbC5poPZnJN0UEYNN22aSYvnLiPhTRyeWLFm6dCm33377wAjqWJ01ZrH6baVHMX44nt3leHafY9pdjmf3Oabd5Xh2Vx+wZKhMrShNLlQPkhpvh36JNG3q4hbZBiJiVs7fR3rb9MMR0Veo6xZSN6W7gT8B04F9gUHgA8XZnCRtQprS9S+AAdJ0sUtJg6h3zP+9Z0TMG+FpWoca0+O2G6Ruw+N4dpfj2X2OaXc5nt3nmHaX49k76vYEovHehjWBk9rkuZ7UyBjKbOBQ0mxMk4E/AOcBX46IgWLmiHhM0tuBk4H9gKNJg9Afz/s7p4QRKgAAEA5JREFUIyLcIjYzMzOzca1WTyDMVsW/THSX49ldjmf3Oabd5Xh2n2PaXY5n76j1NK5mZmZmZja23IAwMzMzM7PS3IAwMzMzM7PSPAbCzMzMzMxK8xMIMzMzMzMrzQ0IMzMzMzMrzQ0IMzMzMzMrzQ0IMzMzMzMrzQ0IMzMzMzMrzQ0IMzMzMzMrzQ0IMzMzMzMrzQ0IMzMzMzMrzQ0Iq4yk9SUdK+lHku6XtFTSYkk3SjpG0rCvT0kzJF0q6XFJg3n5M0n7tsg7RdLpkuZLWibpj5KuaZW3DroVT0lHSYoh0kttyu4i6QpJCyU9L+kuSSdJWrO7Zzs2qoyppAmSTpT0LUl3SFqe8x07Omc7+iqO5+aSTpZ0raRHcjyflDRH0h6jc8ajr+KYvlHSOZJukfREvuf+QdJcSUdLmjA6Zz16euE+Wqjn/Kb800Z+hmOr4uuzb4j8F4/OWa8e/CZqq4yk44D/Bh4HrgN+D2wIfABYF7gUOChKXqSSTgNOB54G/jfXuwGwLXBdRPxzU96pwFxgK+A3wDXAq4EDgNcBJ0bE10Z+lmOnW/GUtA3wvjabZwDvBH4SEe8plHtv3scy4BJgIbA/8BZgdkQc1NmZVafKmOZrdFH++CSwHHgj8JGIOK/Tc6pSxfG8GDgEuAe4kXR9voX0N78mNfybh8pjujswB7gFeJAU0/WBd5Ou1X5g74h4sbOzG3tV30cLdewP/Bh4FpgCbB4R9w/rhCpW8fXZBzwE3Alc1qLc3RExexinY80iwsmpkkT6g98fWKOwfiPSTSaAvy9Z10E5/8+B17TYPqHw+ayc/1Jgrab1ryPdcJaTbtaVx6mKeK5iH/NyPQcU1q8DLAAGge2b1q8N3JzLHFp1jGoW04mkL2Ib588zc75jq45LTeN5FLBti/y75b/3wUas65R64Bpdo0X+CaQviwEcXHWM6hLPQp7XAU8AF5MaYgFMqzo+dYon0JfXz6o6DuMxuQuTVSYiro2IyyPi5cL6J4D/yR93H6qe/Aj0DOB54LCI+FOLfb1QWPWBvPxcNP06FhFPAV8h/QN4XMlT6Qndimc7krYCdgIeA35S2Hwg6R+8iyPitqZ9LwNOyx8/3um+q1JlTCNieURcGRGPd1p/r6k4nrMi4tctjul60he0icAune67Kj1wjb5cLJPvt41ffDfvdN9VqPg+2uzcvPxEp/vqBT0UT+uytao+ALM2Gl/4yzz63gX4K2A2sEjSfqSuScuAWyNiXosyG+Xlgy22NdbtWf5we95w4tnOx/Ly/Igo9t19Z17+tEW5G0iNu10kTYqIwREcQy8Z7ZiubqqMZzf23YsqianSmKfGWLK7RrDvXjMm8ZR0FKm7zvsj4hlJI9hdTxur6/MNkj5G6l73DDAvIsbTdVkJNyCs50haCzgif2z1hbRoh7x8Ergd+OtCfTcAB+anCw1PAxuTGh73FOp7U15uMYzD7lkdxLNVHZOBDwMvA636378lL39X3BARL0p6CJhOiu38To6hl4xRTFcbVcZT0qakHwueJzV2x4WxjKmkDYDjAZGeRO4NTAMuIo1Hq72xime+Hs8GvhsRrfrtjwtj/De/d07NZfuBIyPi953s2zwLk/WmL5OeIFwREVeVyP/6vDwOmAzsBbwm13EVsCvwg0KZxj9qM9U0Q5Ck9YFP5Y+T8g2q7oYbz1YOBqYCV0bEIy22r5uXi9uUb6yf2uH+e81YxHR1Ukk8JU0CLgQmATMjYtEQRepkLGO6AfCvwOdIXRU3A/4DOCoixstMLaMez9wd9wLSoOkTOj3QmhiL6/N50sQq2wGvzWk30vic3YFrJL26w31b1YMwnJyaE+mmGaRfqdcrWebMXOYl4G2FbZOBR/L2nZvWbwQM5PX/RxpUfS7pKcb9wHN528SqYzLW8WxTz025nv3bbP8dqxjkx4qB1DtVHZO6xLRF/pnUfBB1j8VzTeD7uczF5FkJx0OqOKZ/CZxI+tFg3kj23ytpDO+jn87b9y2s71/V/bVuqarrs6ncWsAvctkTq45HXVPlB+Dk1EikwWJBmlZ1o2GUOzWXu6/N9vNa3ShIj9rPJo15WJ4bD+eTZm54Gfhj1TGpIp4t6tky1/MIsGabPL/MebZrs/3uvP2tVcelLjFtUWbcNSCqimf+ovu9XOYSmmZiq3uq8hotlD80l/961TGpQzxJg82XAd9ssW3cNCB66Po8Npe/tOqY1DW5C5P1BEknAV8nfdHcI9IMDWXdm5d/bLO90S1hpe5IEfFURJwYEW+KiIkRsWFEHEMaFyHSl+JaGmE8i8oMUmv8P3hzi2NZixTTF2k9aL0WKojpuFZVPPP1+D3SF9yLSDO3jYvB0z12jV6Zl7uP4BgqNcbxnE7qSnd08YVnpG43APflde3eh9DTeuz6bIyJdBemDnkQtVVO0smk/pB3kF469PQwq7iB9OV0c0kTI2J5YftWeTlQsr6P5OWFwzyOntCFeDbXtTZwOOmJzPmryHot8CFgH9KXs2a7Aq8CboiazsBUUUzHrariKWkiqdvSe4FvA0dHi2lI66gHr9FN8rKWjbMK4jmwim37kbrd/gBYQvl/y3pGD16fO+VlbX/UqlzVj0CcVu8EfJb0GPE2hugLSXo3wxbAZi22fTfX88XC+r3J3ZGAqU3r1wCmtKin8Vjz1xRePleH1K14NuU5PNd3+RB1rUP6RWdcvUiuypi2KDeTcdCFqcJrdBJpnvggdWt8xQvQ6poqjOnfAK9qsX4K6aWeAXyp6vjUJZ6rKN9PjbswVXx9vmIcI2na8WW5jl2qjk9dk3IwzcacpCOBWaTBz/9F6xl8BiJiVs7fR3pL9MMR0Veo6/WkAVXTgLnArcCmwPtJN4nDIuIHTfmnkMY8/Jw0aBpgBrAj8ACwV0QMjPQcx1I349lU51zgHaQ3fF4+xP7fR3oXxzLSoNSFwAGkKV5nk95IW6sbTg/E9BRWTCe8DfA2UoPsvrzuxoiozRSwVcZT0rdIb6N+GjiHdF8o6o+I/hKn0jMqjullpC5K15PeKvw88EbSG9Snkq7Vd0XEs8M9r6pU/Tffpnw/qRvT5hFx/xDZe0rF12c/qWtYP/BoXr01K95b9NmI+GL5s7GVVN2CcVp9Eyt+UV1V6m/K35fXDbSpbz3gP0k3n+WkF8bMocXMP6RfOc4n9d1/Lqe7SNMQvuLJRB3SKMTzrQx/oO/fAleQxp0sJc1w9cmy5XstVR1TVvzy2C7NqjpGdYlniVgGaSrXyuNUo5juR+rq+TvSF8MXgAXA1cBHqeHg9Kr/5oe4dmv3BKLi6/MY0pTtA6SpcQdJDd1LgBlVx6buyU8gzMzMzMysNM/CZGZmZmZmpbkBYWZmZmZmpbkBYWZmZmZmpbkBYWZmZmZmpbkBYWZmZmZmpbkBYWZmZmZmpbkBYWZmZmZmpbkBYWZmZmZmpbkBYWZmZmZmpbkBYWZmZmZmpbkBYWZmZmZmpbkBYWZmY0LSgKSBwrqjJIWko4ZRz6xcpq/D4whJ/Z2UNTMzNyDMzMzMzGwY1qr6AMzMbLX2I+AXwONVH4iZmZXjBoSZmVUmIhYDi6s+DjMzK89dmMzMDEk757EBP1xFnvmSBiWtJ2mipOMlXSHp4bx+oaSrJb17GPttOwZC0l6S5kp6Ltd9maQtOjzFMseyrqR/l3SvpGWSFkm6StJeLfJK0pGSbpb0VM7/SM5/SCHv1pK+l8eADOb8t0s6S9KE0TofM7PR4icQZmZGRMyTdC/wHknrR8Qzzdsl7QhsAVwaEQslbQScDdwM/Bx4CtgY2B+4QtJHIuK8To9H0oHAJcDyvHwceAcwD7ir03pXsb+pwE3AlsAvgbOADYCDgZ9J+nhEfKOpyJeAU4GHgO+TnqJsDOwAHJSPGUlbA7cAAfw4518HmAb8I3Aa8EK3z8fMbDS5AWFmZg0XAP8GfBD4emHbkU15ABYBm0bEo82ZJK1L+iJ+pqQLI2LpcA9C0hTgG8DLwIyIuK1p21eBk4ZbZwlnkBoP5wLHRUTk/Z0B3AZ8TdJVETGQ838MeAzYKiKeLxz/Bk0fjwTWBt4XEXMK+V4LrFTWzKwO3IXJzMwavkP60n5k80pJE4FDgQXAlQARMVhsPOT1i4FvAq8l/RrfifcC6wEXNTcespl0ecxE7kb0YeBZ4NRG4wEgIu4DvgZMBI4oFH0BeKlYX0Q83WI3r2hIRcSiiHh5BIduZlYJNyDMzAyA3CC4Bthe0pZNm/YnfaG/MCJebKyUND2/k+FBSUvzWIYAvpKzbNLhobw9L69vcYyLgTs6rLedLYBXAXdGxMIW26/Ny22b1l0I9AG/yeMm9slPX4ouITUyLpP0bUlHSNqsi8duZjbm3IAwM7Nms/Ky+SlEsfsSknYijRU4DLiX1OXodODzQKOrzqQOj6HxRfzJNtuf6LDeofbXbirZxvqpTes+SepK9RxwCunJzNOS5kia1sgUEbcCM0iNkANJMbxf0m8lfbB7p2BmNnY8BsLMzJr9CFgCfFjSZ0hPHt5N+nX+zqZ8pwGTgT0ior+5AkmnkrohdarRRWnDNts3GkHdq9pfu3o3LuQjIl4iDSI/W9LrSQO8DyUNoJ4uaXpEDOa880iD0ycB2wH7AP8EXCTpqYi4usvnY2Y2qvwEwszM/iwPev4+8AZgL+BDpB+bLihknQYsLDYest1GeBi3t6sndxPaZoT1F91LGsy8TR7YXLRH4bhWEhELIuKHEXEw6UnDZsBWLfINRsTNEfE54IS8eiQNLTOzSrgBYWZmRbPy8oicXiT1+W82AKyXpyn9M0nHAO8a4f7nkGZ5OkzS9oVtM1nR5agrImI56fymAF9o3pbHK5xAGjD9nbxukqQ9JamQdwLpiQ3k2ZUkzWgzNmLD5nxmZnXiLkxmZraSiLhJ0v2k7jgTgMsjYkEh21mkhsKNkhrvQdie1JVnNqm/f6f7f1bSR0kDkOdKan4PxFbADcCundbfximksQrHS9oBuI4V74F4DXB8RDyU804GrgYGJN0CPEyaqnVv4K3AjyNifs77aeDvJPUDD5JmeppO6ha2iDRtrJlZrfgJhJmZtXIBqfHQ+O+VRMRPSbMz3QMcAhwDDJK6+/xkpDuPiNmksQK/In2JPw5YCOxMehlbV+XZl3YGzgTWBz5FakDdCuwTEec0ZX8OOBn4LbALcCJpMPkS4OO5XMM5pIZQH3A4aezDm/P6bSPigW6fi5nZaFPTdNdmZmZmZmar5CcQZmZmZmZWmhsQZmZmZmZWmgdRm5lZbUmaSnqhWxmzImJgFA/HzGy14DEQZmZWW5L6KD+o+hUvvTMzs+FzA8LMzMzMzErzGAgzMzMzMyvNDQgzMzMzMyvNDQgzMzMzMyvNDQgzMzMzMyvNDQgzMzMzMyvNDQgzMzMzMyvNDQgzMzMzMyvNDQgzMzMzMyvNDQgzMzMzMyvNDQgzMzMzMyvNDQgzMzMzMyvNDQgzMzMzMyvt/wHfH2iqZvDS/gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 264,
+       "width": 392
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tab[(tab.valid_loss < 2.75) & (tab.test_mae < 3.2) & (tab.valid_loss > 2.55)].plot.scatter(x='valid_loss', y='test_mae')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T21:23:20.652512Z",
+     "start_time": "2019-12-04T21:23:20.631368Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "s6 = pd.read_csv('logs/catgc_nhid40_seq_len_6/test_metrics.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T21:23:23.475725Z",
+     "start_time": "2019-12-04T21:23:23.455418Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "s12 = pd.read_csv('logs/catgc_clip_4_lrd_97_nhid40/test_metrics.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T21:25:06.770106Z",
+     "start_time": "2019-12-04T21:25:06.747588Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mae</th>\n",
+       "      <th>mape</th>\n",
+       "      <th>rmse</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0.037871</td>\n",
+       "      <td>0.001241</td>\n",
+       "      <td>0.090233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.025943</td>\n",
+       "      <td>0.000731</td>\n",
+       "      <td>0.076548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.027989</td>\n",
+       "      <td>0.000834</td>\n",
+       "      <td>0.071236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.027029</td>\n",
+       "      <td>0.000744</td>\n",
+       "      <td>0.074011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>0.019894</td>\n",
+       "      <td>0.000449</td>\n",
+       "      <td>0.067328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>0.011825</td>\n",
+       "      <td>0.000092</td>\n",
+       "      <td>0.056449</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mae      mape      rmse\n",
+       "t                              \n",
+       "0  0.037871  0.001241  0.090233\n",
+       "1  0.025943  0.000731  0.076548\n",
+       "2  0.027989  0.000834  0.071236\n",
+       "3  0.027029  0.000744  0.074011\n",
+       "4  0.019894  0.000449  0.067328\n",
+       "5  0.011825  0.000092  0.056449"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "improvement = s12.head(6) - s6\n",
+    "improvement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T00:31:05.196840Z",
+     "start_time": "2019-12-05T00:31:04.732857Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa15f62de10>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 250,
+       "width": 372
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = loss_curve('logs/catgc_clip_3_lrd_97_nhid40_v2/').plot()\n",
+    "loss_curve('logs/catgc_clip_3_lrd_97_nhid40/').head(42).add_prefix('v1_').plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T01:36:26.360151Z",
+     "start_time": "2019-12-05T01:36:26.066428Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa15f4c10d0>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 248,
+       "width": 372
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = loss_curve('logs/catgc_clip_3_lrd_97_nhid40_v2/').plot()\n",
+    "loss_curve('logs/catgc_clip_3_lrd_97_nhid40/').add_prefix('v1_').plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T02:07:58.485445Z",
+     "start_time": "2019-12-05T02:07:58.461519Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "seq_length = 12\n",
+    "x_offsets = np.sort(np.concatenate((np.arange(-(seq_length - 1), 1, 1),)))\n",
+    "\n",
+    "y_offsets = np.sort(np.arange(1, (seq_length + 1), 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T02:08:06.712675Z",
+     "start_time": "2019-12-05T02:08:06.689180Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-11, -10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_offsets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T02:08:02.103614Z",
+     "start_time": "2019-12-05T02:08:02.078979Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_offsets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T22:00:05.051671Z",
+     "start_time": "2019-12-04T22:00:05.028752Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'date': '2019-12-03-18:17:15',\n",
+       " 'branch': 'master',\n",
+       " 'runtime': 84.40258767604828}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pickle_load('logs/catgc_clip_3_lrd_97_nhid40/metadata.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T18:14:49.765180Z",
+     "start_time": "2019-12-05T18:14:49.717133Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>t</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "      <th>11</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>epoch</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>2.238773</td>\n",
+       "      <td>2.540319</td>\n",
+       "      <td>2.766938</td>\n",
+       "      <td>2.960691</td>\n",
+       "      <td>3.133506</td>\n",
+       "      <td>3.290497</td>\n",
+       "      <td>12.075896</td>\n",
+       "      <td>11.846109</td>\n",
+       "      <td>11.134290</td>\n",
+       "      <td>11.792063</td>\n",
+       "      <td>10.730303</td>\n",
+       "      <td>11.021757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.062324</td>\n",
+       "      <td>2.313159</td>\n",
+       "      <td>2.487053</td>\n",
+       "      <td>2.628664</td>\n",
+       "      <td>2.741528</td>\n",
+       "      <td>2.839528</td>\n",
+       "      <td>2.922224</td>\n",
+       "      <td>3.001096</td>\n",
+       "      <td>3.071752</td>\n",
+       "      <td>3.133916</td>\n",
+       "      <td>3.196247</td>\n",
+       "      <td>3.261839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2.065176</td>\n",
+       "      <td>2.310613</td>\n",
+       "      <td>2.479302</td>\n",
+       "      <td>2.612068</td>\n",
+       "      <td>2.726832</td>\n",
+       "      <td>2.824262</td>\n",
+       "      <td>2.912179</td>\n",
+       "      <td>2.988843</td>\n",
+       "      <td>3.061409</td>\n",
+       "      <td>3.125404</td>\n",
+       "      <td>3.183621</td>\n",
+       "      <td>3.251594</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2.057276</td>\n",
+       "      <td>2.303777</td>\n",
+       "      <td>2.470208</td>\n",
+       "      <td>2.602266</td>\n",
+       "      <td>2.723184</td>\n",
+       "      <td>2.817395</td>\n",
+       "      <td>2.905609</td>\n",
+       "      <td>2.985737</td>\n",
+       "      <td>3.052621</td>\n",
+       "      <td>3.113910</td>\n",
+       "      <td>3.170520</td>\n",
+       "      <td>3.229811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2.061854</td>\n",
+       "      <td>2.305904</td>\n",
+       "      <td>2.478938</td>\n",
+       "      <td>2.619848</td>\n",
+       "      <td>2.737687</td>\n",
+       "      <td>2.837034</td>\n",
+       "      <td>2.910292</td>\n",
+       "      <td>2.987834</td>\n",
+       "      <td>3.060872</td>\n",
+       "      <td>3.122151</td>\n",
+       "      <td>3.178870</td>\n",
+       "      <td>3.236028</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t            0         1         2         3         4         5          6   \\\n",
+       "epoch                                                                          \n",
+       "0      2.238773  2.540319  2.766938  2.960691  3.133506  3.290497  12.075896   \n",
+       "2      2.062324  2.313159  2.487053  2.628664  2.741528  2.839528   2.922224   \n",
+       "3      2.065176  2.310613  2.479302  2.612068  2.726832  2.824262   2.912179   \n",
+       "4      2.057276  2.303777  2.470208  2.602266  2.723184  2.817395   2.905609   \n",
+       "5      2.061854  2.305904  2.478938  2.619848  2.737687  2.837034   2.910292   \n",
+       "\n",
+       "t             7          8          9          10         11  \n",
+       "epoch                                                         \n",
+       "0      11.846109  11.134290  11.792063  10.730303  11.021757  \n",
+       "2       3.001096   3.071752   3.133916   3.196247   3.261839  \n",
+       "3       2.988843   3.061409   3.125404   3.183621   3.251594  \n",
+       "4       2.985737   3.052621   3.113910   3.170520   3.229811  \n",
+       "5       2.987834   3.060872   3.122151   3.178870   3.236028  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val_ts.head()[[:6]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T18:14:57.680246Z",
+     "start_time": "2019-12-05T18:14:57.644865Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tm6 = pd.read_csv('logs/catgc_clip_3_t_12_6/test_metrics.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T18:15:15.783208Z",
+     "start_time": "2019-12-05T18:15:15.745449Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mae</th>\n",
+       "      <th>mape</th>\n",
+       "      <th>rmse</th>\n",
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+       "    <tr>\n",
+       "      <th>t</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>2.192615</td>\n",
+       "      <td>0.052006</td>\n",
+       "      <td>3.775220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2.473688</td>\n",
+       "      <td>0.061570</td>\n",
+       "      <td>4.555334</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.660313</td>\n",
+       "      <td>0.068608</td>\n",
+       "      <td>5.084322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2.808197</td>\n",
+       "      <td>0.074444</td>\n",
+       "      <td>5.494185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2.930484</td>\n",
+       "      <td>0.079439</td>\n",
+       "      <td>5.825542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>3.034795</td>\n",
+       "      <td>0.083473</td>\n",
+       "      <td>6.112247</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mae      mape      rmse\n",
+       "t                              \n",
+       "0  2.192615  0.052006  3.775220\n",
+       "1  2.473688  0.061570  4.555334\n",
+       "2  2.660313  0.068608  5.084322\n",
+       "3  2.808197  0.074444  5.494185\n",
+       "4  2.930484  0.079439  5.825542\n",
+       "5  3.034795  0.083473  6.112247"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('logs/surgery_t12_6/test_metrics.csv', index_col=0).head(6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T18:15:36.565746Z",
+     "start_time": "2019-12-05T18:15:36.526727Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mae</th>\n",
+       "      <th>mape</th>\n",
+       "      <th>rmse</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>2.159525</td>\n",
+       "      <td>0.051048</td>\n",
+       "      <td>3.716178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2.446493</td>\n",
+       "      <td>0.060767</td>\n",
+       "      <td>4.502765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.633458</td>\n",
+       "      <td>0.067655</td>\n",
+       "      <td>5.031873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2.784979</td>\n",
+       "      <td>0.073334</td>\n",
+       "      <td>5.451159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2.911976</td>\n",
+       "      <td>0.078185</td>\n",
+       "      <td>5.788602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>3.021536</td>\n",
+       "      <td>0.082300</td>\n",
+       "      <td>6.072630</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mae      mape      rmse\n",
+       "t                              \n",
+       "0  2.159525  0.051048  3.716178\n",
+       "1  2.446493  0.060767  4.502765\n",
+       "2  2.633458  0.067655  5.031873\n",
+       "3  2.784979  0.073334  5.451159\n",
+       "4  2.911976  0.078185  5.788602\n",
+       "5  3.021536  0.082300  6.072630"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tm6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T17:04:57.368372Z",
+     "start_time": "2019-12-05T17:04:57.344314Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "val_met = pd.read_msgpack('logs/surgery_t12_6/val_ts_metrics.mp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T17:05:36.620894Z",
+     "start_time": "2019-12-05T17:05:36.592199Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mae</th>\n",
+       "      <th>mape</th>\n",
+       "      <th>rmse</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>epoch</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>7.127595</td>\n",
+       "      <td>0.189563</td>\n",
+       "      <td>9.652049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.804944</td>\n",
+       "      <td>0.077025</td>\n",
+       "      <td>5.677625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2.795109</td>\n",
+       "      <td>0.078577</td>\n",
+       "      <td>5.699111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2.786026</td>\n",
+       "      <td>0.079258</td>\n",
+       "      <td>5.681844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2.794776</td>\n",
+       "      <td>0.081313</td>\n",
+       "      <td>5.701449</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            mae      mape      rmse\n",
+       "epoch                              \n",
+       "0      7.127595  0.189563  9.652049\n",
+       "2      2.804944  0.077025  5.677625\n",
+       "3      2.795109  0.078577  5.699111\n",
+       "4      2.786026  0.079258  5.681844\n",
+       "5      2.794776  0.081313  5.701449"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val_met.groupby('epoch').mean().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T18:12:27.411830Z",
+     "start_time": "2019-12-05T18:12:27.380912Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "val_ts = val_met.set_index('epoch', append=True).mae.unstack('t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T18:12:55.780527Z",
+     "start_time": "2019-12-05T18:12:55.749537Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "epoch\n",
+       "2    2.804944\n",
+       "3    2.795109\n",
+       "4    2.786026\n",
+       "5    2.794776\n",
+       "6    2.833704\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val_ts.mean(1).head(6).tail(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T18:13:03.784936Z",
+     "start_time": "2019-12-05T18:13:03.750746Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2.763850\n",
+       "1    2.759017\n",
+       "2    2.748445\n",
+       "3    2.756769\n",
+       "4    2.793560\n",
+       "Name: valid_loss, dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('logs/surgery_t12_6/metrics.csv', index_col=0).valid_loss.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T17:06:58.260277Z",
+     "start_time": "2019-12-05T17:06:58.209678Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train_loss            2.69839\n",
+       "train_mape           0.071131\n",
+       "train_rmse            5.35232\n",
+       "valid_loss            2.69088\n",
+       "valid_mape           0.074577\n",
+       "valid_rmse            5.20142\n",
+       "test_loss             2.87712\n",
+       "test_mape            0.078454\n",
+       "test_rmse             5.58229\n",
+       "best_epoch                 29\n",
+       "min_train_loss        2.64864\n",
+       "date              12-04-23:25\n",
+       "es20_score            2.69088\n",
+       "es20_i                     49\n",
+       "es10_score            2.69088\n",
+       "es10_i                     39\n",
+       "test_mae              2.99524\n",
+       "test_mape           0.0817155\n",
+       "test_rmse             5.96411\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary('logs/surgery_t12_6')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-05T17:04:14.732265Z",
+     "start_time": "2019-12-05T17:04:14.688598Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>train_mape</th>\n",
+       "      <th>train_rmse</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>valid_mape</th>\n",
+       "      <th>valid_rmse</th>\n",
+       "      <th>test_loss</th>\n",
+       "      <th>test_mape</th>\n",
+       "      <th>test_rmse</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>3.015238</td>\n",
+       "      <td>0.081299</td>\n",
+       "      <td>5.905416</td>\n",
+       "      <td>2.763850</td>\n",
+       "      <td>0.075851</td>\n",
+       "      <td>5.301300</td>\n",
+       "      <td>2.964000</td>\n",
+       "      <td>0.079803</td>\n",
+       "      <td>5.681182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2.878663</td>\n",
+       "      <td>0.077395</td>\n",
+       "      <td>5.736143</td>\n",
+       "      <td>2.759017</td>\n",
+       "      <td>0.077593</td>\n",
+       "      <td>5.319852</td>\n",
+       "      <td>2.939792</td>\n",
+       "      <td>0.081759</td>\n",
+       "      <td>5.695280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.855020</td>\n",
+       "      <td>0.076603</td>\n",
+       "      <td>5.700277</td>\n",
+       "      <td>2.748445</td>\n",
+       "      <td>0.078143</td>\n",
+       "      <td>5.301726</td>\n",
+       "      <td>2.925142</td>\n",
+       "      <td>0.081511</td>\n",
+       "      <td>5.633650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2.845773</td>\n",
+       "      <td>0.076285</td>\n",
+       "      <td>5.686201</td>\n",
+       "      <td>2.756769</td>\n",
+       "      <td>0.080107</td>\n",
+       "      <td>5.304424</td>\n",
+       "      <td>2.925313</td>\n",
+       "      <td>0.083246</td>\n",
+       "      <td>5.630223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2.833188</td>\n",
+       "      <td>0.075857</td>\n",
+       "      <td>5.659127</td>\n",
+       "      <td>2.793560</td>\n",
+       "      <td>0.079724</td>\n",
+       "      <td>5.347400</td>\n",
+       "      <td>2.973964</td>\n",
+       "      <td>0.082975</td>\n",
+       "      <td>5.694450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2.822660</td>\n",
+       "      <td>0.075456</td>\n",
+       "      <td>5.635772</td>\n",
+       "      <td>2.733875</td>\n",
+       "      <td>0.075189</td>\n",
+       "      <td>5.243294</td>\n",
+       "      <td>2.912289</td>\n",
+       "      <td>0.078403</td>\n",
+       "      <td>5.588139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>2.812965</td>\n",
+       "      <td>0.075122</td>\n",
+       "      <td>5.613942</td>\n",
+       "      <td>2.744125</td>\n",
+       "      <td>0.078774</td>\n",
+       "      <td>5.265176</td>\n",
+       "      <td>2.933120</td>\n",
+       "      <td>0.082986</td>\n",
+       "      <td>5.643149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>2.806497</td>\n",
+       "      <td>0.074926</td>\n",
+       "      <td>5.601945</td>\n",
+       "      <td>2.721878</td>\n",
+       "      <td>0.075483</td>\n",
+       "      <td>5.199852</td>\n",
+       "      <td>2.905680</td>\n",
+       "      <td>0.078748</td>\n",
+       "      <td>5.550199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>2.797231</td>\n",
+       "      <td>0.074550</td>\n",
+       "      <td>5.580240</td>\n",
+       "      <td>2.769696</td>\n",
+       "      <td>0.079436</td>\n",
+       "      <td>5.343644</td>\n",
+       "      <td>2.923900</td>\n",
+       "      <td>0.081994</td>\n",
+       "      <td>5.638175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>2.793452</td>\n",
+       "      <td>0.074429</td>\n",
+       "      <td>5.572321</td>\n",
+       "      <td>2.739749</td>\n",
+       "      <td>0.078516</td>\n",
+       "      <td>5.277908</td>\n",
+       "      <td>2.911309</td>\n",
+       "      <td>0.082028</td>\n",
+       "      <td>5.628092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>2.786675</td>\n",
+       "      <td>0.074253</td>\n",
+       "      <td>5.558450</td>\n",
+       "      <td>2.784132</td>\n",
+       "      <td>0.080520</td>\n",
+       "      <td>5.359370</td>\n",
+       "      <td>2.961336</td>\n",
+       "      <td>0.084558</td>\n",
+       "      <td>5.723536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>2.777624</td>\n",
+       "      <td>0.073927</td>\n",
+       "      <td>5.540169</td>\n",
+       "      <td>2.776885</td>\n",
+       "      <td>0.074610</td>\n",
+       "      <td>5.410938</td>\n",
+       "      <td>2.958202</td>\n",
+       "      <td>0.077901</td>\n",
+       "      <td>5.747124</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>2.775612</td>\n",
+       "      <td>0.073829</td>\n",
+       "      <td>5.532881</td>\n",
+       "      <td>2.769046</td>\n",
+       "      <td>0.079336</td>\n",
+       "      <td>5.413217</td>\n",
+       "      <td>2.927833</td>\n",
+       "      <td>0.083003</td>\n",
+       "      <td>5.752684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>2.766773</td>\n",
+       "      <td>0.073440</td>\n",
+       "      <td>5.513285</td>\n",
+       "      <td>2.746747</td>\n",
+       "      <td>0.075394</td>\n",
+       "      <td>5.353852</td>\n",
+       "      <td>2.912329</td>\n",
+       "      <td>0.079060</td>\n",
+       "      <td>5.669528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>2.763540</td>\n",
+       "      <td>0.073338</td>\n",
+       "      <td>5.506307</td>\n",
+       "      <td>2.723890</td>\n",
+       "      <td>0.074809</td>\n",
+       "      <td>5.290591</td>\n",
+       "      <td>2.893322</td>\n",
+       "      <td>0.078049</td>\n",
+       "      <td>5.630763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>2.760132</td>\n",
+       "      <td>0.073325</td>\n",
+       "      <td>5.496188</td>\n",
+       "      <td>2.741411</td>\n",
+       "      <td>0.075785</td>\n",
+       "      <td>5.317901</td>\n",
+       "      <td>2.908101</td>\n",
+       "      <td>0.079337</td>\n",
+       "      <td>5.654812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>2.751319</td>\n",
+       "      <td>0.072971</td>\n",
+       "      <td>5.475735</td>\n",
+       "      <td>2.774490</td>\n",
+       "      <td>0.080613</td>\n",
+       "      <td>5.368562</td>\n",
+       "      <td>2.955025</td>\n",
+       "      <td>0.084583</td>\n",
+       "      <td>5.754783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>2.747433</td>\n",
+       "      <td>0.072877</td>\n",
+       "      <td>5.470366</td>\n",
+       "      <td>2.715501</td>\n",
+       "      <td>0.075824</td>\n",
+       "      <td>5.275898</td>\n",
+       "      <td>2.886395</td>\n",
+       "      <td>0.079102</td>\n",
+       "      <td>5.625157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td>2.742455</td>\n",
+       "      <td>0.072636</td>\n",
+       "      <td>5.457284</td>\n",
+       "      <td>2.716192</td>\n",
+       "      <td>0.074233</td>\n",
+       "      <td>5.269914</td>\n",
+       "      <td>2.898089</td>\n",
+       "      <td>0.078067</td>\n",
+       "      <td>5.626609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td>2.737459</td>\n",
+       "      <td>0.072504</td>\n",
+       "      <td>5.445713</td>\n",
+       "      <td>2.728917</td>\n",
+       "      <td>0.073995</td>\n",
+       "      <td>5.259087</td>\n",
+       "      <td>2.898954</td>\n",
+       "      <td>0.077453</td>\n",
+       "      <td>5.611806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>20</td>\n",
+       "      <td>2.732890</td>\n",
+       "      <td>0.072320</td>\n",
+       "      <td>5.434697</td>\n",
+       "      <td>2.711882</td>\n",
+       "      <td>0.076398</td>\n",
+       "      <td>5.261203</td>\n",
+       "      <td>2.891528</td>\n",
+       "      <td>0.080203</td>\n",
+       "      <td>5.636918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>21</td>\n",
+       "      <td>2.728321</td>\n",
+       "      <td>0.072149</td>\n",
+       "      <td>5.418555</td>\n",
+       "      <td>2.724890</td>\n",
+       "      <td>0.073479</td>\n",
+       "      <td>5.271303</td>\n",
+       "      <td>2.911381</td>\n",
+       "      <td>0.077315</td>\n",
+       "      <td>5.642815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>22</td>\n",
+       "      <td>2.722630</td>\n",
+       "      <td>0.071923</td>\n",
+       "      <td>5.408564</td>\n",
+       "      <td>2.724854</td>\n",
+       "      <td>0.073840</td>\n",
+       "      <td>5.254061</td>\n",
+       "      <td>2.910983</td>\n",
+       "      <td>0.077527</td>\n",
+       "      <td>5.603779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>23</td>\n",
+       "      <td>2.718412</td>\n",
+       "      <td>0.071771</td>\n",
+       "      <td>5.398759</td>\n",
+       "      <td>2.718729</td>\n",
+       "      <td>0.075438</td>\n",
+       "      <td>5.271355</td>\n",
+       "      <td>2.903010</td>\n",
+       "      <td>0.079159</td>\n",
+       "      <td>5.643558</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>24</td>\n",
+       "      <td>2.714758</td>\n",
+       "      <td>0.071658</td>\n",
+       "      <td>5.389404</td>\n",
+       "      <td>2.720055</td>\n",
+       "      <td>0.074273</td>\n",
+       "      <td>5.236692</td>\n",
+       "      <td>2.908485</td>\n",
+       "      <td>0.078486</td>\n",
+       "      <td>5.624601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>25</td>\n",
+       "      <td>2.713580</td>\n",
+       "      <td>0.071650</td>\n",
+       "      <td>5.385610</td>\n",
+       "      <td>2.774897</td>\n",
+       "      <td>0.078653</td>\n",
+       "      <td>5.291463</td>\n",
+       "      <td>2.944951</td>\n",
+       "      <td>0.082719</td>\n",
+       "      <td>5.672197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>26</td>\n",
+       "      <td>2.711031</td>\n",
+       "      <td>0.071522</td>\n",
+       "      <td>5.380509</td>\n",
+       "      <td>2.734204</td>\n",
+       "      <td>0.077759</td>\n",
+       "      <td>5.285149</td>\n",
+       "      <td>2.918622</td>\n",
+       "      <td>0.082023</td>\n",
+       "      <td>5.660174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>27</td>\n",
+       "      <td>2.706926</td>\n",
+       "      <td>0.071461</td>\n",
+       "      <td>5.371559</td>\n",
+       "      <td>2.714002</td>\n",
+       "      <td>0.074089</td>\n",
+       "      <td>5.269619</td>\n",
+       "      <td>2.892962</td>\n",
+       "      <td>0.078009</td>\n",
+       "      <td>5.613719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>28</td>\n",
+       "      <td>2.700536</td>\n",
+       "      <td>0.071178</td>\n",
+       "      <td>5.356307</td>\n",
+       "      <td>2.718301</td>\n",
+       "      <td>0.076518</td>\n",
+       "      <td>5.268320</td>\n",
+       "      <td>2.926138</td>\n",
+       "      <td>0.080713</td>\n",
+       "      <td>5.684271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>29</td>\n",
+       "      <td>2.698387</td>\n",
+       "      <td>0.071131</td>\n",
+       "      <td>5.352316</td>\n",
+       "      <td>2.690880</td>\n",
+       "      <td>0.074577</td>\n",
+       "      <td>5.201421</td>\n",
+       "      <td>2.877115</td>\n",
+       "      <td>0.078454</td>\n",
+       "      <td>5.582287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>30</td>\n",
+       "      <td>2.693100</td>\n",
+       "      <td>0.070937</td>\n",
+       "      <td>5.339080</td>\n",
+       "      <td>2.717581</td>\n",
+       "      <td>0.075414</td>\n",
+       "      <td>5.258919</td>\n",
+       "      <td>2.921202</td>\n",
+       "      <td>0.079984</td>\n",
+       "      <td>5.688081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>31</td>\n",
+       "      <td>2.691529</td>\n",
+       "      <td>0.070828</td>\n",
+       "      <td>5.333288</td>\n",
+       "      <td>2.706162</td>\n",
+       "      <td>0.073003</td>\n",
+       "      <td>5.255993</td>\n",
+       "      <td>2.902936</td>\n",
+       "      <td>0.077126</td>\n",
+       "      <td>5.651939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>32</td>\n",
+       "      <td>2.687615</td>\n",
+       "      <td>0.070709</td>\n",
+       "      <td>5.326705</td>\n",
+       "      <td>2.718692</td>\n",
+       "      <td>0.077074</td>\n",
+       "      <td>5.320095</td>\n",
+       "      <td>2.918123</td>\n",
+       "      <td>0.081288</td>\n",
+       "      <td>5.730975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>33</td>\n",
+       "      <td>2.686692</td>\n",
+       "      <td>0.070675</td>\n",
+       "      <td>5.322541</td>\n",
+       "      <td>2.711421</td>\n",
+       "      <td>0.074545</td>\n",
+       "      <td>5.289933</td>\n",
+       "      <td>2.903701</td>\n",
+       "      <td>0.078350</td>\n",
+       "      <td>5.674293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>34</td>\n",
+       "      <td>2.684048</td>\n",
+       "      <td>0.070564</td>\n",
+       "      <td>5.313589</td>\n",
+       "      <td>2.734631</td>\n",
+       "      <td>0.075678</td>\n",
+       "      <td>5.340473</td>\n",
+       "      <td>2.934421</td>\n",
+       "      <td>0.079883</td>\n",
+       "      <td>5.725385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>35</td>\n",
+       "      <td>2.682448</td>\n",
+       "      <td>0.070517</td>\n",
+       "      <td>5.310117</td>\n",
+       "      <td>2.708929</td>\n",
+       "      <td>0.075115</td>\n",
+       "      <td>5.282584</td>\n",
+       "      <td>2.894936</td>\n",
+       "      <td>0.079077</td>\n",
+       "      <td>5.659821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>36</td>\n",
+       "      <td>2.676796</td>\n",
+       "      <td>0.070346</td>\n",
+       "      <td>5.297045</td>\n",
+       "      <td>2.724053</td>\n",
+       "      <td>0.076313</td>\n",
+       "      <td>5.290583</td>\n",
+       "      <td>2.918993</td>\n",
+       "      <td>0.080696</td>\n",
+       "      <td>5.686016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>37</td>\n",
+       "      <td>2.674427</td>\n",
+       "      <td>0.070235</td>\n",
+       "      <td>5.293426</td>\n",
+       "      <td>2.721129</td>\n",
+       "      <td>0.075326</td>\n",
+       "      <td>5.307876</td>\n",
+       "      <td>2.903804</td>\n",
+       "      <td>0.079157</td>\n",
+       "      <td>5.674864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>38</td>\n",
+       "      <td>2.671436</td>\n",
+       "      <td>0.070133</td>\n",
+       "      <td>5.286114</td>\n",
+       "      <td>2.716339</td>\n",
+       "      <td>0.076414</td>\n",
+       "      <td>5.268670</td>\n",
+       "      <td>2.906175</td>\n",
+       "      <td>0.080882</td>\n",
+       "      <td>5.663535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>39</td>\n",
+       "      <td>2.671594</td>\n",
+       "      <td>0.070164</td>\n",
+       "      <td>5.284625</td>\n",
+       "      <td>2.716701</td>\n",
+       "      <td>0.073541</td>\n",
+       "      <td>5.319814</td>\n",
+       "      <td>2.904806</td>\n",
+       "      <td>0.077266</td>\n",
+       "      <td>5.681988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>40</td>\n",
+       "      <td>2.667896</td>\n",
+       "      <td>0.070013</td>\n",
+       "      <td>5.275750</td>\n",
+       "      <td>2.706809</td>\n",
+       "      <td>0.073834</td>\n",
+       "      <td>5.257073</td>\n",
+       "      <td>2.903478</td>\n",
+       "      <td>0.077709</td>\n",
+       "      <td>5.644004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>41</td>\n",
+       "      <td>2.667861</td>\n",
+       "      <td>0.069989</td>\n",
+       "      <td>5.274635</td>\n",
+       "      <td>2.717091</td>\n",
+       "      <td>0.074872</td>\n",
+       "      <td>5.320876</td>\n",
+       "      <td>2.905294</td>\n",
+       "      <td>0.078803</td>\n",
+       "      <td>5.678414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>42</td>\n",
+       "      <td>2.663520</td>\n",
+       "      <td>0.069911</td>\n",
+       "      <td>5.264440</td>\n",
+       "      <td>2.714897</td>\n",
+       "      <td>0.074344</td>\n",
+       "      <td>5.293347</td>\n",
+       "      <td>2.908684</td>\n",
+       "      <td>0.078499</td>\n",
+       "      <td>5.688362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>43</td>\n",
+       "      <td>2.660564</td>\n",
+       "      <td>0.069713</td>\n",
+       "      <td>5.255615</td>\n",
+       "      <td>2.712268</td>\n",
+       "      <td>0.073859</td>\n",
+       "      <td>5.299463</td>\n",
+       "      <td>2.921474</td>\n",
+       "      <td>0.077969</td>\n",
+       "      <td>5.705796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>44</td>\n",
+       "      <td>2.657909</td>\n",
+       "      <td>0.069665</td>\n",
+       "      <td>5.249836</td>\n",
+       "      <td>2.721322</td>\n",
+       "      <td>0.076187</td>\n",
+       "      <td>5.331461</td>\n",
+       "      <td>2.910690</td>\n",
+       "      <td>0.080208</td>\n",
+       "      <td>5.705160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>45</td>\n",
+       "      <td>2.656384</td>\n",
+       "      <td>0.069595</td>\n",
+       "      <td>5.246388</td>\n",
+       "      <td>2.708752</td>\n",
+       "      <td>0.074398</td>\n",
+       "      <td>5.270038</td>\n",
+       "      <td>2.898063</td>\n",
+       "      <td>0.078282</td>\n",
+       "      <td>5.629544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>46</td>\n",
+       "      <td>2.654222</td>\n",
+       "      <td>0.069545</td>\n",
+       "      <td>5.241196</td>\n",
+       "      <td>2.718320</td>\n",
+       "      <td>0.075297</td>\n",
+       "      <td>5.253670</td>\n",
+       "      <td>2.919800</td>\n",
+       "      <td>0.079597</td>\n",
+       "      <td>5.649931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>47</td>\n",
+       "      <td>2.652755</td>\n",
+       "      <td>0.069477</td>\n",
+       "      <td>5.237012</td>\n",
+       "      <td>2.714803</td>\n",
+       "      <td>0.074820</td>\n",
+       "      <td>5.258219</td>\n",
+       "      <td>2.918576</td>\n",
+       "      <td>0.079422</td>\n",
+       "      <td>5.656625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>48</td>\n",
+       "      <td>2.651266</td>\n",
+       "      <td>0.069426</td>\n",
+       "      <td>5.232809</td>\n",
+       "      <td>2.706733</td>\n",
+       "      <td>0.073762</td>\n",
+       "      <td>5.272619</td>\n",
+       "      <td>2.904989</td>\n",
+       "      <td>0.078147</td>\n",
+       "      <td>5.657381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>49</td>\n",
+       "      <td>2.648636</td>\n",
+       "      <td>0.069324</td>\n",
+       "      <td>5.227820</td>\n",
+       "      <td>2.742976</td>\n",
+       "      <td>0.076184</td>\n",
+       "      <td>5.345828</td>\n",
+       "      <td>2.942097</td>\n",
+       "      <td>0.080472</td>\n",
+       "      <td>5.716380</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    train_loss  train_mape  train_rmse  valid_loss  valid_mape  valid_rmse  \\\n",
+       "0     3.015238    0.081299    5.905416    2.763850    0.075851    5.301300   \n",
+       "1     2.878663    0.077395    5.736143    2.759017    0.077593    5.319852   \n",
+       "2     2.855020    0.076603    5.700277    2.748445    0.078143    5.301726   \n",
+       "3     2.845773    0.076285    5.686201    2.756769    0.080107    5.304424   \n",
+       "4     2.833188    0.075857    5.659127    2.793560    0.079724    5.347400   \n",
+       "5     2.822660    0.075456    5.635772    2.733875    0.075189    5.243294   \n",
+       "6     2.812965    0.075122    5.613942    2.744125    0.078774    5.265176   \n",
+       "7     2.806497    0.074926    5.601945    2.721878    0.075483    5.199852   \n",
+       "8     2.797231    0.074550    5.580240    2.769696    0.079436    5.343644   \n",
+       "9     2.793452    0.074429    5.572321    2.739749    0.078516    5.277908   \n",
+       "10    2.786675    0.074253    5.558450    2.784132    0.080520    5.359370   \n",
+       "11    2.777624    0.073927    5.540169    2.776885    0.074610    5.410938   \n",
+       "12    2.775612    0.073829    5.532881    2.769046    0.079336    5.413217   \n",
+       "13    2.766773    0.073440    5.513285    2.746747    0.075394    5.353852   \n",
+       "14    2.763540    0.073338    5.506307    2.723890    0.074809    5.290591   \n",
+       "15    2.760132    0.073325    5.496188    2.741411    0.075785    5.317901   \n",
+       "16    2.751319    0.072971    5.475735    2.774490    0.080613    5.368562   \n",
+       "17    2.747433    0.072877    5.470366    2.715501    0.075824    5.275898   \n",
+       "18    2.742455    0.072636    5.457284    2.716192    0.074233    5.269914   \n",
+       "19    2.737459    0.072504    5.445713    2.728917    0.073995    5.259087   \n",
+       "20    2.732890    0.072320    5.434697    2.711882    0.076398    5.261203   \n",
+       "21    2.728321    0.072149    5.418555    2.724890    0.073479    5.271303   \n",
+       "22    2.722630    0.071923    5.408564    2.724854    0.073840    5.254061   \n",
+       "23    2.718412    0.071771    5.398759    2.718729    0.075438    5.271355   \n",
+       "24    2.714758    0.071658    5.389404    2.720055    0.074273    5.236692   \n",
+       "25    2.713580    0.071650    5.385610    2.774897    0.078653    5.291463   \n",
+       "26    2.711031    0.071522    5.380509    2.734204    0.077759    5.285149   \n",
+       "27    2.706926    0.071461    5.371559    2.714002    0.074089    5.269619   \n",
+       "28    2.700536    0.071178    5.356307    2.718301    0.076518    5.268320   \n",
+       "29    2.698387    0.071131    5.352316    2.690880    0.074577    5.201421   \n",
+       "30    2.693100    0.070937    5.339080    2.717581    0.075414    5.258919   \n",
+       "31    2.691529    0.070828    5.333288    2.706162    0.073003    5.255993   \n",
+       "32    2.687615    0.070709    5.326705    2.718692    0.077074    5.320095   \n",
+       "33    2.686692    0.070675    5.322541    2.711421    0.074545    5.289933   \n",
+       "34    2.684048    0.070564    5.313589    2.734631    0.075678    5.340473   \n",
+       "35    2.682448    0.070517    5.310117    2.708929    0.075115    5.282584   \n",
+       "36    2.676796    0.070346    5.297045    2.724053    0.076313    5.290583   \n",
+       "37    2.674427    0.070235    5.293426    2.721129    0.075326    5.307876   \n",
+       "38    2.671436    0.070133    5.286114    2.716339    0.076414    5.268670   \n",
+       "39    2.671594    0.070164    5.284625    2.716701    0.073541    5.319814   \n",
+       "40    2.667896    0.070013    5.275750    2.706809    0.073834    5.257073   \n",
+       "41    2.667861    0.069989    5.274635    2.717091    0.074872    5.320876   \n",
+       "42    2.663520    0.069911    5.264440    2.714897    0.074344    5.293347   \n",
+       "43    2.660564    0.069713    5.255615    2.712268    0.073859    5.299463   \n",
+       "44    2.657909    0.069665    5.249836    2.721322    0.076187    5.331461   \n",
+       "45    2.656384    0.069595    5.246388    2.708752    0.074398    5.270038   \n",
+       "46    2.654222    0.069545    5.241196    2.718320    0.075297    5.253670   \n",
+       "47    2.652755    0.069477    5.237012    2.714803    0.074820    5.258219   \n",
+       "48    2.651266    0.069426    5.232809    2.706733    0.073762    5.272619   \n",
+       "49    2.648636    0.069324    5.227820    2.742976    0.076184    5.345828   \n",
+       "\n",
+       "    test_loss  test_mape  test_rmse  \n",
+       "0    2.964000   0.079803   5.681182  \n",
+       "1    2.939792   0.081759   5.695280  \n",
+       "2    2.925142   0.081511   5.633650  \n",
+       "3    2.925313   0.083246   5.630223  \n",
+       "4    2.973964   0.082975   5.694450  \n",
+       "5    2.912289   0.078403   5.588139  \n",
+       "6    2.933120   0.082986   5.643149  \n",
+       "7    2.905680   0.078748   5.550199  \n",
+       "8    2.923900   0.081994   5.638175  \n",
+       "9    2.911309   0.082028   5.628092  \n",
+       "10   2.961336   0.084558   5.723536  \n",
+       "11   2.958202   0.077901   5.747124  \n",
+       "12   2.927833   0.083003   5.752684  \n",
+       "13   2.912329   0.079060   5.669528  \n",
+       "14   2.893322   0.078049   5.630763  \n",
+       "15   2.908101   0.079337   5.654812  \n",
+       "16   2.955025   0.084583   5.754783  \n",
+       "17   2.886395   0.079102   5.625157  \n",
+       "18   2.898089   0.078067   5.626609  \n",
+       "19   2.898954   0.077453   5.611806  \n",
+       "20   2.891528   0.080203   5.636918  \n",
+       "21   2.911381   0.077315   5.642815  \n",
+       "22   2.910983   0.077527   5.603779  \n",
+       "23   2.903010   0.079159   5.643558  \n",
+       "24   2.908485   0.078486   5.624601  \n",
+       "25   2.944951   0.082719   5.672197  \n",
+       "26   2.918622   0.082023   5.660174  \n",
+       "27   2.892962   0.078009   5.613719  \n",
+       "28   2.926138   0.080713   5.684271  \n",
+       "29   2.877115   0.078454   5.582287  \n",
+       "30   2.921202   0.079984   5.688081  \n",
+       "31   2.902936   0.077126   5.651939  \n",
+       "32   2.918123   0.081288   5.730975  \n",
+       "33   2.903701   0.078350   5.674293  \n",
+       "34   2.934421   0.079883   5.725385  \n",
+       "35   2.894936   0.079077   5.659821  \n",
+       "36   2.918993   0.080696   5.686016  \n",
+       "37   2.903804   0.079157   5.674864  \n",
+       "38   2.906175   0.080882   5.663535  \n",
+       "39   2.904806   0.077266   5.681988  \n",
+       "40   2.903478   0.077709   5.644004  \n",
+       "41   2.905294   0.078803   5.678414  \n",
+       "42   2.908684   0.078499   5.688362  \n",
+       "43   2.921474   0.077969   5.705796  \n",
+       "44   2.910690   0.080208   5.705160  \n",
+       "45   2.898063   0.078282   5.629544  \n",
+       "46   2.919800   0.079597   5.649931  \n",
+       "47   2.918576   0.079422   5.656625  \n",
+       "48   2.904989   0.078147   5.657381  \n",
+       "49   2.942097   0.080472   5.716380  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T21:58:34.037518Z",
+     "start_time": "2019-12-04T21:58:33.992287Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'date': '2019-12-03-22:27:27',\n",
+       " 'branch': 'cyclical-lr',\n",
+       " 'runtime': 90.92834924062093}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pickle_load('logs/catgc_nhid40_lrd_0.99_v2/metadata.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T21:58:53.525924Z",
+     "start_time": "2019-12-04T21:58:53.480635Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'date': '2019-12-03-19:46:37',\n",
+       " 'branch': 'master',\n",
+       " 'runtime': 84.69123902320862}"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pickle_load('logs/catgc_clip_4_lrd_97_nhid40/metadata.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T21:59:03.676413Z",
+     "start_time": "2019-12-04T21:59:03.635519Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'date': '2019-12-03-18:17:15',\n",
+       " 'branch': 'master',\n",
+       " 'runtime': 84.40258767604828}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pickle_load('logs/catgc_clip_3_lrd_97_nhid40/metadata.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-04T22:00:05.051671Z",
+     "start_time": "2019-12-04T22:00:05.028752Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'date': '2019-12-04-13:12:43',\n",
+       " 'branch': 'master',\n",
+       " 'runtime': 78.89185889164607}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pickle_load('logs/catgc_nhid40_seq_len_6/metadata.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:28:57.472268Z",
+     "start_time": "2019-11-29T17:28:57.332205Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mv logs/no_graph_convv2   logs/no_graph_conv2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:27:17.869974Z",
+     "start_time": "2019-11-29T17:27:17.731337Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "best_model.pth  metrics.csv  preds.csv  test_metrics.csv\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls logs/apt_only_20_fixed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:12:04.292583Z",
+     "start_time": "2019-11-29T17:12:04.156523Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!mv logs/apt_only/ logs/no_graph_conv3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:29:22.934584Z",
+     "start_time": "2019-11-29T17:29:22.914003Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = pickle_load('logs/apt_only_aptsize256/args.pkl').__dict__\n",
+    "b = pickle_load('logs/no_graph_conv2/args.pkl').__dict__\n",
+    "#c = pickle_load('logs/apt_only_20_fixed/args.pkl').__dict__\n",
+    "baseline = pickle_load('logs/cheaper-metric-fixed/args.pkl').__dict__\n",
+    "#aptinit = pickle_load('logs/aptinit/args.pkl').__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-02T06:34:18.797197Z",
+     "start_time": "2019-12-02T06:34:18.769052Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def r_args(strang):\n",
+    "    return pickle_load(f'logs/{strang}/args.pkl').__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-02T06:35:04.702115Z",
+     "start_time": "2019-12-02T06:35:04.674561Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct_compare(r_args('logs_0.001/'), r_args('baseline_lr_decay/'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:30:02.692830Z",
+     "start_time": "2019-11-29T17:30:02.556167Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mv logs/no_graph_conv2/ logs/no_add_apt_adj"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:26:40.605397Z",
+     "start_time": "2019-11-29T17:26:40.468208Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!mv logs/apt_only_aptsize20 logs/no_graph_conv4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-30T03:00:43.439775Z",
+     "start_time": "2019-11-30T03:00:43.419781Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'apt_size': (256, 10)}"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct_compare(r_args('cat_feat_early_gc_256_rand'), r_args('cat_feat_early'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:29:36.710122Z",
+     "start_time": "2019-11-29T17:29:36.689180Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'apt_size': (None, 10), 'addaptadj': (True, False)}"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct_compare(baseline, b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:30:47.316524Z",
+     "start_time": "2019-11-29T17:30:47.294513Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'apt_size': (None, 256), 'aptonly': (False, True)}"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct_compare(baseline, a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:10:03.353644Z",
+     "start_time": "2019-11-29T17:10:03.333076Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'learning_rate': (0.004, 0.001), 'batch_size': (512, 64)}"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct_compare(pickle_load('logs/bs_512_fixed_lr/args.pkl').__dict__, baseline)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:12:25.583710Z",
+     "start_time": "2019-11-29T17:12:25.563893Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'apt_size': (None, '20'),\n",
+       " 'addaptadj': (True, False),\n",
+       " 'aptonly': (False, True)}"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct_compare(baseline, c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T17:25:43.521013Z",
+     "start_time": "2019-11-29T17:25:43.498990Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'apt_size': (None, 256), 'aptonly': (False, True)}"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct_compare(baseline, a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-02T06:34:13.176097Z",
+     "start_time": "2019-12-02T06:34:13.145161Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def dct_compare(a,b, ignore_keys=('save')):\n",
+    "    allk = set(a.keys()).union(b)\n",
+    "    res = {}\n",
+    "    for k in allk:\n",
+    "        if k in ignore_keys: continue\n",
+    "        av,bv = a.get(k, None), b.get(k, None)\n",
+    "        if av != bv: res[k] = (av, bv)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T05:35:20.771250Z",
+     "start_time": "2019-11-29T05:35:20.745261Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'apt_size': (256, None), 'addaptadj': (True, False)}"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct_compare(a,b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-11-29T05:35:20.961493Z",
+     "start_time": "2019-11-29T05:35:20.936540Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'apt_size': (256, '20'), 'addaptadj': (True, False)}"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct_compare(a,c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cfe = pd.read_csv('logs/cat_feat_early/metrics.csv', index_col=0)\n",
+    "no_apt = pd.read_csv('logs/no_apt_adj/metrics.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "t       5.500000\n",
+       "mae     3.078333\n",
+       "mape    0.086167\n",
+       "rmse    6.108000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('logs/cat_feat_early/test_metrics.csv').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "t       5.500000\n",
+       "mae     3.078333\n",
+       "mape    0.086167\n",
+       "rmse    6.108000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "pd.read_csv('logs/no_apt_adj/test_metrics.csv').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train_loss    2.7557\n",
+       "train_mape    0.0730\n",
+       "train_rmse    5.4684\n",
+       "valid_loss    2.7437\n",
+       "valid_mape    0.0742\n",
+       "valid_rmse    5.2934\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfe.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train_loss    2.8266\n",
+       "train_mape    0.0752\n",
+       "train_rmse    5.6161\n",
+       "valid_loss    2.7791\n",
+       "valid_mape    0.0756\n",
+       "valid_rmse    5.3568\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_apt.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train_loss    2.853006\n",
+       "train_mape    0.076463\n",
+       "train_rmse    5.673001\n",
+       "valid_loss    2.790653\n",
+       "valid_mape    0.073916\n",
+       "valid_rmse    5.586741\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('bs_512_fixed_lr/metrics.csv', index_col=0).min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train_loss    2.806\n",
+       "train_mape    0.075\n",
+       "train_rmse    5.567\n",
+       "valid_loss    2.744\n",
+       "valid_mape    0.075\n",
+       "valid_rmse    5.286\n",
+       "test_mae      3.064\n",
+       "test_mape     0.082\n",
+       "test_rmse     6.090\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "train_loss    2.861\n",
+       "train_mape    0.077\n",
+       "train_rmse    5.682\n",
+       "valid_loss    2.791\n",
+       "valid_mape    0.077\n",
+       "valid_rmse    5.619\n",
+       "test_mae      3.082\n",
+       "test_mape     0.085\n",
+       "test_rmse     6.111\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary('bs_512_fixed_lr/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mae</th>\n",
+       "      <th>mape</th>\n",
+       "      <th>rmse</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>tensor(9.0320, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(20.9467, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>tensor(9.3068, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(21.1640, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>tensor(9.4969, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(21.3153, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>tensor(9.6529, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(21.4574, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>tensor(9.7829, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(21.5728, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>tensor(9.8915, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(21.6725, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>tensor(9.9942, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(21.7760, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>tensor(10.0769, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(21.8443, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>tensor(10.1518, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(21.9093, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>tensor(10.2255, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(21.9941, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>tensor(10.2843, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(22.0433, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>tensor(10.3477, device='cuda:0')</td>\n",
+       "      <td>tensor(inf, device='cuda:0')</td>\n",
+       "      <td>tensor(22.1056, device='cuda:0')</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 mae                          mape  \\\n",
+       "t                                                                    \n",
+       "0    tensor(9.0320, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "1    tensor(9.3068, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "2    tensor(9.4969, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "3    tensor(9.6529, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "4    tensor(9.7829, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "5    tensor(9.8915, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "6    tensor(9.9942, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "7   tensor(10.0769, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "8   tensor(10.1518, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "9   tensor(10.2255, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "10  tensor(10.2843, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "11  tensor(10.3477, device='cuda:0')  tensor(inf, device='cuda:0')   \n",
+       "\n",
+       "                                rmse  \n",
+       "t                                     \n",
+       "0   tensor(20.9467, device='cuda:0')  \n",
+       "1   tensor(21.1640, device='cuda:0')  \n",
+       "2   tensor(21.3153, device='cuda:0')  \n",
+       "3   tensor(21.4574, device='cuda:0')  \n",
+       "4   tensor(21.5728, device='cuda:0')  \n",
+       "5   tensor(21.6725, device='cuda:0')  \n",
+       "6   tensor(21.7760, device='cuda:0')  \n",
+       "7   tensor(21.8443, device='cuda:0')  \n",
+       "8   tensor(21.9093, device='cuda:0')  \n",
+       "9   tensor(21.9941, device='cuda:0')  \n",
+       "10  tensor(22.0433, device='cuda:0')  \n",
+       "11  tensor(22.1056, device='cuda:0')  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('smart-trunc/test_metrics.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mae</th>\n",
+       "      <th>mape</th>\n",
+       "      <th>rmse</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>2.266</td>\n",
+       "      <td>0.056</td>\n",
+       "      <td>3.912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2.542</td>\n",
+       "      <td>0.066</td>\n",
+       "      <td>4.692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.730</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>5.208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2.877</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>5.610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>3.000</td>\n",
+       "      <td>0.083</td>\n",
+       "      <td>5.935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>3.106</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>6.213</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>3.205</td>\n",
+       "      <td>0.090</td>\n",
+       "      <td>6.464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>3.299</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>6.696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>3.383</td>\n",
+       "      <td>0.095</td>\n",
+       "      <td>6.903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>3.458</td>\n",
+       "      <td>0.097</td>\n",
+       "      <td>7.082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>3.527</td>\n",
+       "      <td>0.100</td>\n",
+       "      <td>7.237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>3.594</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>7.386</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mae   mape   rmse\n",
+       "t                      \n",
+       "0   2.266  0.056  3.912\n",
+       "1   2.542  0.066  4.692\n",
+       "2   2.730  0.073  5.208\n",
+       "3   2.877  0.078  5.610\n",
+       "4   3.000  0.083  5.935\n",
+       "5   3.106  0.086  6.213\n",
+       "6   3.205  0.090  6.464\n",
+       "7   3.299  0.092  6.696\n",
+       "8   3.383  0.095  6.903\n",
+       "9   3.458  0.097  7.082\n",
+       "10  3.527  0.100  7.237\n",
+       "11  3.594  0.102  7.386"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('bs_512_fixed_lr/test_metrics.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1cca013d50>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 249,
+       "width": 385
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cheap_met = pd.read_csv('smart-trunc/metrics.csv', index_col=0)\n",
+    "cheap_met.pipe(subset, ['mape']).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1cc9156150>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 248,
+       "width": 372
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 399a2702ae6d8da6297da8c4af58a48e4717ef11 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 9 Dec 2019 00:34:36 -0800
Subject: [PATCH 121/130] some surgery docs

---
 train.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/train.py b/train.py
index 7531d38..4829213 100644
--- a/train.py
+++ b/train.py
@@ -12,11 +12,13 @@
 from util import calc_tstep_metrics
 from exp_results import summary
 
-bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']
 
 
-def surgery(model, surg_checkpoint):
-    state_dict = torch.load(surg_checkpoint)
+
+def load_checkpoint(model, state_dict):
+    """If ckpt_path was trained for a different sequence length.
+     It is assumed that ckpt was trained to predict a subset of timesteps."""
+    bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']  # only weights that vary based on seq_length.
     b,w = state_dict.pop(bk), state_dict.pop(wk)
     model.load_state_dict(state_dict, strict=False)
     cur_state_dict = model.state_dict()
@@ -25,6 +27,12 @@ def surgery(model, surg_checkpoint):
     model.load_state_dict(cur_state_dict)
     return model
 
+def load_checkpoint_from_different_graph(model, state_dict):
+    state_dict.pop('nodevec1', None)
+    state_dict.pop('nodevec2', None)
+    model.load_state_dict(state_dict, strict=False)
+    return model
+
 
 
 def main(args, **model_kwargs):
@@ -36,7 +44,7 @@ def main(args, **model_kwargs):
 
     model = GWNet.from_args(args, device, supports, aptinit, **model_kwargs)
     if args.checkpoint:
-        model = surgery(model, args.checkpoint)
+        model = load_checkpoint(model, torch.load(args.checkpoint))
     model.to(device)
     engine = Trainer.from_args(model, scaler, args)
     metrics = []

From 4f488332907aae9f27ef4b236938a2e689bd78b2 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 9 Dec 2019 10:46:50 -0800
Subject: [PATCH 122/130] better baseline_args

---
 baseline_args.pkl | Bin 722 -> 680 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/baseline_args.pkl b/baseline_args.pkl
index 064e0db863873041c660eb2c8f78231a74f964a5..b68ca90f137dd52bc6835e2aa8a33af90cd481ba 100644
GIT binary patch
delta 86
zcmcb_x`K6sFe9VFWD!PB4*P&4-%B4?{+XQ0xR3n<0~k!!V$##@jNk@xQj6mY5=$~u
j^O92w6}&|vxPW5G8L7$H1^JnIC54I+APtk(GN}RpZFLz2

delta 128
zcmZ3%dWm&|Fe9VJWD!PB4hATgoXWV5{XLg$!Q$&&leL)iLOUY3fl{f(@db$`nW=fn
zsf7yODG^*ivE+=@<m`g{%)F99#Rx5+Ku&&ov3_!5NqTa8UPfk$i9vjEYGHg%YF@mV
Xeo|_2Nqla8N@|W?K}p8sW+qht?W!jU


From 215640aa2dabc94aea382cea110477724eccf5d6 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Mon, 9 Dec 2019 13:26:12 -0800
Subject: [PATCH 123/130] del unused

---
 train.py | 13 ++-----------
 1 file changed, 2 insertions(+), 11 deletions(-)

diff --git a/train.py b/train.py
index 4829213..460f2e5 100644
--- a/train.py
+++ b/train.py
@@ -13,13 +13,11 @@
 from exp_results import summary
 
 
-
-
 def load_checkpoint(model, state_dict):
     """If ckpt_path was trained for a different sequence length.
      It is assumed that ckpt was trained to predict a subset of timesteps."""
-    bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']  # only weights that vary based on seq_length.
-    b,w = state_dict.pop(bk), state_dict.pop(wk)
+    bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']  # only weights that depend on seq_length
+    b, w = state_dict.pop(bk), state_dict.pop(wk)
     model.load_state_dict(state_dict, strict=False)
     cur_state_dict = model.state_dict()
     cur_state_dict[bk][:b.shape[0]] = b
@@ -27,13 +25,6 @@ def load_checkpoint(model, state_dict):
     model.load_state_dict(cur_state_dict)
     return model
 
-def load_checkpoint_from_different_graph(model, state_dict):
-    state_dict.pop('nodevec1', None)
-    state_dict.pop('nodevec2', None)
-    model.load_state_dict(state_dict, strict=False)
-    return model
-
-
 
 def main(args, **model_kwargs):
     log_test = getattr(args, 'log_test', False)

From 38c2f781d2e6a651c3f3e2d93fee2cd2fdf573e0 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 10 Dec 2019 00:40:48 -0800
Subject: [PATCH 124/130] ADJ_CHOICES

---
 util.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/util.py b/util.py
index a4f49ca..f504346 100644
--- a/util.py
+++ b/util.py
@@ -125,6 +125,7 @@ def load_pickle(pickle_file):
         raise
     return pickle_data
 
+ADJ_CHOICES = ['scalap', 'normlap', 'symnadj', 'transition', 'identity']
 def load_adj(pkl_filename, adjtype):
     sensor_ids, sensor_id_to_ind, adj_mx = load_pickle(pkl_filename)
     if adjtype == "scalap":
@@ -238,7 +239,7 @@ def get_shared_arg_parser():
     parser.add_argument('--data', type=str, default='data/METR-LA', help='data path')
     parser.add_argument('--adjdata', type=str, default='data/sensor_graph/adj_mx.pkl',
                         help='adj data path')
-    parser.add_argument('--adjtype', type=str, default='doubletransition', help='adj type')
+    parser.add_argument('--adjtype', type=str, default='doubletransition', help='adj type', choices=ADJ_CHOICES)
     parser.add_argument('--do_graph_conv', action='store_true',
                         help='whether to add graph convolution layer')
     parser.add_argument('--aptonly', action='store_true', help='whether only adaptive adj')

From 2d5830a3bb9247dba4506101a10deb40773df365 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 11 Dec 2019 14:40:11 -0800
Subject: [PATCH 125/130] fill_zeroes arg

---
 test.py       |  8 ++++----
 test_gwnet.py |  2 +-
 train.py      |  8 +-------
 util.py       | 31 ++++++++-----------------------
 4 files changed, 14 insertions(+), 35 deletions(-)

diff --git a/test.py b/test.py
index 9d3d0a5..9ad0378 100644
--- a/test.py
+++ b/test.py
@@ -14,11 +14,11 @@ def main(args, save_pred_path='preds.csv', save_metrics_path='last_test_metrics.
     model.load_state_dict(torch.load(args.checkpoint))
     model.eval()
     print('model loaded successfully')
-    dataloader = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
-    scaler = dataloader['scaler']
-    realy = torch.Tensor(dataloader[f'y_{loader}']).to(device)
+    data = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs, fill_zeroes=args.fill_zeroes)
+    scaler = data['scaler']
+    realy = torch.Tensor(data[f'y_{loader}']).to(device)
     realy = realy.transpose(1,3)[:,0,:,:]
-    met_df, yhat = util.calc_tstep_metrics(model, device, dataloader[f'{loader}_loader'], scaler, realy, args.seq_length)
+    met_df, yhat = util.calc_tstep_metrics(model, device, data[f'{loader}_loader'], scaler, realy, args.seq_length)
     df2 = util.make_pred_df(realy, yhat, scaler, args.seq_length)
     met_df.to_csv(save_metrics_path)
     df2.to_csv(save_pred_path, index=False)
diff --git a/test_gwnet.py b/test_gwnet.py
index 784a4e2..99e7065 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -15,7 +15,7 @@
                'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
                'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.,
                'in_dim': 1, 'cat_feat_gc': True, 'clip': 1, 'es_patience': 10,
-               'checkpoint': '', 'end_conv_lr': None}
+               'checkpoint': '', 'end_conv_lr': None, 'fill_zeroes': False}
 
 MODEL_KWARGS = {'end_channels': 4, 'skip_channels': 2}
 def modify_args(args, updates):
diff --git a/train.py b/train.py
index 460f2e5..85bc72c 100644
--- a/train.py
+++ b/train.py
@@ -27,9 +27,8 @@ def load_checkpoint(model, state_dict):
 
 
 def main(args, **model_kwargs):
-    log_test = getattr(args, 'log_test', False)
     device = torch.device(args.device)
-    data = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs)
+    data = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs, fill_zeroes=args.fill_zeroes)
     scaler = data['scaler']
     aptinit, supports = util.make_graph_inputs(args, device)
 
@@ -62,11 +61,6 @@ def main(args, **model_kwargs):
         m = dict(train_loss=np.mean(train_loss), train_mape=np.mean(train_mape),
                  train_rmse=np.mean(train_rmse), valid_loss=np.mean(valid_loss),
                  valid_mape=np.mean(valid_mape), valid_rmse=np.mean(valid_rmse))
-        if log_test:
-            _, test_loss, test_mape, test_rmse = eval_(data['test_loader'], device, engine)
-            mn = np.mean
-            test_m = dict(test_loss=mn(test_loss), test_mape=mn(test_mape), test_rmse=mn(test_rmse))
-            m.update(test_m)
 
         m = pd.Series(m)
         metrics.append(m)
diff --git a/util.py b/util.py
index f504346..0e4d0fb 100644
--- a/util.py
+++ b/util.py
@@ -54,13 +54,15 @@ def _wrapper():
 
 class StandardScaler():
 
-    def __init__(self, mean, std):
+    def __init__(self, mean, std, fill_zeroes=False):
         self.mean = mean
         self.std = std
+        self.fill_zeroes = fill_zeroes
 
     def transform(self, data):
-        mask = (data == 0)
-        data[mask] = self.mean
+        if self.fill_zeroes:
+            mask = (data == 0)
+            data[mask] = self.mean
         return (data - self.mean) / self.std
 
     def inverse_transform(self, data):
@@ -146,7 +148,7 @@ def load_adj(pkl_filename, adjtype):
     return sensor_ids, sensor_id_to_ind, adj
 
 
-def load_dataset(dataset_dir, batch_size, valid_batch_size= None, test_batch_size=None, n_obs=None):
+def load_dataset(dataset_dir, batch_size, valid_batch_size=None, test_batch_size=None, n_obs=None, fill_zeroes=True):
     data = {}
     for category in ['train', 'val', 'test']:
         cat_data = np.load(os.path.join(dataset_dir, category + '.npz'))
@@ -155,7 +157,7 @@ def load_dataset(dataset_dir, batch_size, valid_batch_size= None, test_batch_siz
         if n_obs is not None:
             data['x_' + category] = data['x_' + category][:n_obs]
             data['y_' + category] = data['y_' + category][:n_obs]
-    scaler = StandardScaler(mean=data['x_train'][..., 0].mean(), std=data['x_train'][..., 0].std())
+    scaler = StandardScaler(mean=data['x_train'][..., 0].mean(), std=data['x_train'][..., 0].std(), fill_zeroes=fill_zeroes)
     # Data format
     for category in ['train', 'val', 'test']:
         data['x_' + category][..., 0] = scaler.transform(data['x_' + category][..., 0])
@@ -256,23 +258,6 @@ def get_shared_arg_parser():
     parser.add_argument('--apt_size', default=10, type=int)
     parser.add_argument('--softmax_temp', default=1., type=float)
     parser.add_argument('--cat_feat_gc', action='store_true')
+    parser.add_argument('--fill_zeroes', action='store_true')
     parser.add_argument('--checkpoint', type=str, help='')
     return parser
-
-
-def plot_grad_flow(named_parameters):
-    import matplotlib.pyplot as plt
-    ave_grads = []
-    layers = []
-    for n, p in named_parameters:
-        if (p.requires_grad) and ("bias" not in n):
-            layers.append(n)
-            ave_grads.append(p.grad.abs().mean())
-    plt.plot(ave_grads, alpha=0.3, color="b")
-    plt.hlines(0, 0, len(ave_grads) + 1, linewidth=1, color="k")
-    plt.xticks(range(0, len(ave_grads), 1), layers, rotation="vertical")
-    plt.xlim(xmin=0, xmax=len(ave_grads))
-    plt.xlabel("Layers")
-    plt.ylabel("Average Gradient")
-    plt.title("Gradient flow")
-    plt.grid(True)

From dae86d540c9c1c8182b55cf1b18f82a126f65ef6 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 11 Dec 2019 14:46:27 -0800
Subject: [PATCH 126/130] move surgery func

---
 model.py | 24 +++++++++++++++++-------
 test.py  |  2 +-
 train.py | 23 +++++------------------
 3 files changed, 23 insertions(+), 26 deletions(-)

diff --git a/model.py b/model.py
index 28642eb..0d71ab3 100644
--- a/model.py
+++ b/model.py
@@ -107,17 +107,27 @@ def svd_init(apt_size, aptinit):
 
     @classmethod
     def from_args(cls, args, device, supports, aptinit, **kwargs):
-        defaults =  dict(dropout=args.dropout, supports=supports,
-                    do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=aptinit,
-                    in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
-                    softmax_temp=args.softmax_temp,
-                    residual_channels=args.nhid, dilation_channels=args.nhid,
-                    skip_channels=args.nhid * 8, end_channels=args.nhid * 16,
-                    cat_feat_gc=args.cat_feat_gc)
+        defaults = dict(dropout=args.dropout, supports=supports,
+                        do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=aptinit,
+                        in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
+                        softmax_temp=args.softmax_temp,
+                        residual_channels=args.nhid, dilation_channels=args.nhid,
+                        skip_channels=args.nhid * 8, end_channels=args.nhid * 16,
+                        cat_feat_gc=args.cat_feat_gc)
         defaults.update(**kwargs)
         model = cls(device, args.num_nodes, **defaults)
         return model
 
+    def load_checkpoint(self, state_dict):
+        """It is assumed that ckpt was trained to predict a subset of timesteps."""
+        bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']  # only weights that depend on seq_length
+        b, w = state_dict.pop(bk), state_dict.pop(wk)
+        self.load_state_dict(state_dict, strict=False)
+        cur_state_dict = self.state_dict()
+        cur_state_dict[bk][:b.shape[0]] = b
+        cur_state_dict[wk][:w.shape[0]] = w
+        self.load_state_dict(cur_state_dict)
+
     @property
     def conv_group(self):
         bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']
diff --git a/test.py b/test.py
index 9ad0378..f874512 100644
--- a/test.py
+++ b/test.py
@@ -10,8 +10,8 @@ def main(args, save_pred_path='preds.csv', save_metrics_path='last_test_metrics.
     device = torch.device(args.device)
     adjinit, supports = util.make_graph_inputs(args, device)
     model = GWNet.from_args(args, device, supports, adjinit, **model_kwargs)
-    model.to(device)
     model.load_state_dict(torch.load(args.checkpoint))
+    model.to(device)
     model.eval()
     print('model loaded successfully')
     data = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs, fill_zeroes=args.fill_zeroes)
diff --git a/train.py b/train.py
index 85bc72c..677378d 100644
--- a/train.py
+++ b/train.py
@@ -13,19 +13,6 @@
 from exp_results import summary
 
 
-def load_checkpoint(model, state_dict):
-    """If ckpt_path was trained for a different sequence length.
-     It is assumed that ckpt was trained to predict a subset of timesteps."""
-    bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']  # only weights that depend on seq_length
-    b, w = state_dict.pop(bk), state_dict.pop(wk)
-    model.load_state_dict(state_dict, strict=False)
-    cur_state_dict = model.state_dict()
-    cur_state_dict[bk][:b.shape[0]] = b
-    cur_state_dict[wk][:w.shape[0]] = w
-    model.load_state_dict(cur_state_dict)
-    return model
-
-
 def main(args, **model_kwargs):
     device = torch.device(args.device)
     data = util.load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size, n_obs=args.n_obs, fill_zeroes=args.fill_zeroes)
@@ -34,14 +21,14 @@ def main(args, **model_kwargs):
 
     model = GWNet.from_args(args, device, supports, aptinit, **model_kwargs)
     if args.checkpoint:
-        model = load_checkpoint(model, torch.load(args.checkpoint))
+        model.load_checkpoint(torch.load(args.checkpoint))
     model.to(device)
     engine = Trainer.from_args(model, scaler, args)
     metrics = []
     best_model_save_path = os.path.join(args.save, 'best_model.pth')
     lowest_mae_yet = 100  # high value, will get overwritten
     mb = progress_bar(list(range(1, args.epochs + 1)))
-    since_best = 0
+    epochs_since_best_mae = 0
     for _ in mb:
         train_loss, train_mape, train_rmse = [], [], []
         data['train_loader'].shuffle()
@@ -67,13 +54,13 @@ def main(args, **model_kwargs):
         if m.valid_loss < lowest_mae_yet:
             torch.save(engine.model.state_dict(), best_model_save_path)
             lowest_mae_yet = m.valid_loss
-            since_best = 0
+            epochs_since_best_mae = 0
         else:
-            since_best += 1
+            epochs_since_best_mae += 1
         met_df = pd.DataFrame(metrics)
         mb.comment = f'best val_loss: {met_df.valid_loss.min(): .3f}, current val_loss: {m.valid_loss:.3f}, current train loss: {m.train_loss: .3f}'
         met_df.round(6).to_csv(f'{args.save}/metrics.csv')
-        if since_best >= args.es_patience: break  #
+        if epochs_since_best_mae >= args.es_patience: break
     # Metrics on test data
     engine.model.load_state_dict(torch.load(best_model_save_path))
     realy = torch.Tensor(data['y_test']).transpose(1, 3)[:, 0, :, :].to(device)

From 7a2a7c2a9e6bd3c34488fc064b18dc004cce31e4 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Wed, 11 Dec 2019 14:55:59 -0800
Subject: [PATCH 127/130] README updates

---
 README.md     | 11 ++++-------
 engine.py     | 41 +++++++++--------------------------------
 model.py      | 17 ++---------------
 test_gwnet.py |  4 ++--
 train.py      |  5 +----
 util.py       |  3 +--
 6 files changed, 19 insertions(+), 62 deletions(-)

diff --git a/README.md b/README.md
index 8e65403..5f8ff6a 100644
--- a/README.md
+++ b/README.md
@@ -10,27 +10,24 @@ This is the original pytorch implementation of Graph WaveNet in the following pa
 ## Requirements
 - python 3
 - pytorch
-- scipy
-- numpy
-- pandas
-- pyaml
+- rest are in requirements.txt
 
 
 ## Data Preparation
 
 ### Step1: Download METR-LA and PEMS-BAY data from [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](https://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g) links provided by [DCRNN](https://github.com/liyaguang/DCRNN).
 
-### Step2: Follow [DCRNN](https://github.com/liyaguang/DCRNN)'s scripts to preprocess data.
+### Step2:
 
 ```
 # Create data directories
 mkdir -p data/{METR-LA,PEMS-BAY}
 
 # METR-LA
-python -m scripts.generate_training_data --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5
+python generate_training_data.py --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5
 
 # PEMS-BAY
-python -m scripts.generate_training_data --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
+python generate_training_data.py --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
 
 ```
 ## New Experiment Command
diff --git a/engine.py b/engine.py
index 2bb1d6d..f2651df 100644
--- a/engine.py
+++ b/engine.py
@@ -3,35 +3,19 @@
 import util
 
 class Trainer():
-    def __init__(self, model: GWNet, scaler, lrate, wdecay, clip=5, lr_decay_rate=.97, fp16='', end_conv_lr=None):
+    def __init__(self, model: GWNet, scaler, lrate, wdecay, clip=3, lr_decay_rate=.97):
         self.model = model
-        if end_conv_lr:
-            end_conv2, other_params = model.conv_group
-            groups = [{'params': end_conv2, 'lr': end_conv_lr}]
-            if lrate > 0:
-                groups.append({'params': other_params})
-            self.model.freeze_group_b()
-            self.optimizer = optim.Adam(groups, lr=lrate, weight_decay=wdecay)
-        else:
-            self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
+
+        self.optimizer = optim.Adam(self.model.parameters(), lr=lrate, weight_decay=wdecay)
         self.scaler = scaler
         self.clip = clip
-        self.fp16 = fp16
-        l1 = lambda epoch: lr_decay_rate ** epoch
-        self.scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=l1)
-        if self.fp16:
-            try:
-                from apex import amp  # Apex is only required if we use fp16 training
-            except ImportError:
-                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-            amp.register_half_function(torch, 'einsum')
-            self.model, self.optimizer = amp.initialize(self.model, self.optimizer, opt_level=self.fp16)
+        self.scheduler = optim.lr_scheduler.LambdaLR(
+            self.optimizer, lr_lambda=lambda epoch: lr_decay_rate ** epoch)
 
     @classmethod
     def from_args(cls, model, scaler, args):
-        end_conv_lr = getattr(args, 'end_conv_lr', None)
         return cls(model, scaler, args.learning_rate, args.weight_decay, clip=args.clip,
-                   lr_decay_rate=args.lr_decay_rate, fp16=args.fp16, end_conv_lr=end_conv_lr)
+                   lr_decay_rate=args.lr_decay_rate)
 
     def train(self, input, real_val):
         self.model.train()
@@ -41,16 +25,9 @@ def train(self, input, real_val):
         predict = self.scaler.inverse_transform(output)
         assert predict.shape[1] == 1
         mae, mape, rmse = util.calc_metrics(predict.squeeze(1), real_val, null_val=0.0)
-
-        if self.fp16:
-            from apex import amp
-            with amp.scale_loss(mae, self.optimizer) as scaled_loss:
-                scaled_loss.backward()
-            torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.clip)
-        else:
-            mae.backward()
-            if self.clip is not None:
-                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
+        mae.backward()
+        if self.clip is not None:
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
         self.optimizer.step()
         return mae.item(),mape.item(),rmse.item()
 
diff --git a/model.py b/model.py
index 0d71ab3..a28abc1 100644
--- a/model.py
+++ b/model.py
@@ -34,13 +34,12 @@ def forward(self, x, support: list):
 
 class GWNet(nn.Module):
     def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=True,
-                 softmax_temp=1., addaptadj=True, aptinit=None, in_dim=2, out_dim=12,
+                 addaptadj=True, aptinit=None, in_dim=2, out_dim=12,
                  residual_channels=32, dilation_channels=32, cat_feat_gc=False,
                  skip_channels=256, end_channels=512, kernel_size=2, blocks=4, layers=2,
                  apt_size=10):
         super().__init__()
         self.dropout = dropout
-        self.softmax_temp = softmax_temp
         self.blocks = blocks
         self.layers = layers
         self.do_graph_conv = do_graph_conv
@@ -110,7 +109,6 @@ def from_args(cls, args, device, supports, aptinit, **kwargs):
         defaults = dict(dropout=args.dropout, supports=supports,
                         do_graph_conv=args.do_graph_conv, addaptadj=args.addaptadj, aptinit=aptinit,
                         in_dim=args.in_dim, apt_size=args.apt_size, out_dim=args.seq_length,
-                        softmax_temp=args.softmax_temp,
                         residual_channels=args.nhid, dilation_channels=args.nhid,
                         skip_channels=args.nhid * 8, end_channels=args.nhid * 16,
                         cat_feat_gc=args.cat_feat_gc)
@@ -128,17 +126,6 @@ def load_checkpoint(self, state_dict):
         cur_state_dict[wk][:w.shape[0]] = w
         self.load_state_dict(cur_state_dict)
 
-    @property
-    def conv_group(self):
-        bk, wk = ['end_conv_2.bias', 'end_conv_2.weight']
-        grpa = [v for k, v in self.named_parameters() if k not in [bk, wk]]
-        grpb = [v for k, v in self.named_parameters() if k in [bk, wk]]
-        return grpa, grpb
-
-    def freeze_group_b(self):
-        for param in self.conv_group[1]:
-            param.requires_grad = False
-
     def forward(self, x):
         # Input shape is (bs, features, n_nodes, n_timesteps)
         in_len = x.size(3)
@@ -155,7 +142,7 @@ def forward(self, x):
         adjacency_matrices = self.fixed_supports
         # calculate the current adaptive adj matrix once per iteration
         if self.addaptadj:
-            adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)) / self.softmax_temp, dim=1)
+            adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)), dim=1)
             adjacency_matrices = self.fixed_supports + [adp]
 
         # WaveNet layers
diff --git a/test_gwnet.py b/test_gwnet.py
index 99e7065..4e8022f 100644
--- a/test_gwnet.py
+++ b/test_gwnet.py
@@ -13,9 +13,9 @@
 SAVE_DIR = 'utest_experiment/'
 ARG_UPDATES = {'epochs': 1, 'n_iters': 1, 'batch_size': 2, 'n_obs': 2,
                'device': util.DEFAULT_DEVICE, 'save': SAVE_DIR, 'addaptadj': True,
-               'apt_size': 2, 'nhid': 1, 'softmax_temp': 1., 'lr_decay_rate': 1.,
+               'apt_size': 2, 'nhid': 1, 'lr_decay_rate': 1.,
                'in_dim': 1, 'cat_feat_gc': True, 'clip': 1, 'es_patience': 10,
-               'checkpoint': '', 'end_conv_lr': None, 'fill_zeroes': False}
+               'checkpoint': '', 'fill_zeroes': False}
 
 MODEL_KWARGS = {'end_channels': 4, 'skip_channels': 2}
 def modify_args(args, updates):
diff --git a/train.py b/train.py
index 677378d..eca218c 100644
--- a/train.py
+++ b/train.py
@@ -88,16 +88,13 @@ def eval_(ds, device, engine):
 if __name__ == "__main__":
     parser = util.get_shared_arg_parser()
     parser.add_argument('--epochs', type=int, default=100, help='')
-    parser.add_argument('--clip', type=int, default=5, help='Gradient Clipping')
+    parser.add_argument('--clip', type=int, default=3, help='Gradient Clipping')
     parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay rate')
     parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
     parser.add_argument('--lr_decay_rate', type=float, default=0.97, help='learning rate')
-    parser.add_argument("--fp16", type=str, default="",
-                        help="Set to O0, O1, O2 or O3 for fp16 training (see apex documentation)")
     parser.add_argument('--save', type=str, default='experiment', help='save path')
     parser.add_argument('--n_iters', default=None, help='quit after this many iterations')
     parser.add_argument('--es_patience', type=int, default=20, help='quit if no improvement after this many iterations')
-    parser.add_argument('--end_conv_lr', type=float, default=None)
 
     args = parser.parse_args()
     t1 = time.time()
diff --git a/util.py b/util.py
index 0e4d0fb..b93b546 100644
--- a/util.py
+++ b/util.py
@@ -249,14 +249,13 @@ def get_shared_arg_parser():
     parser.add_argument('--randomadj', action='store_true',
                         help='whether random initialize adaptive adj')
     parser.add_argument('--seq_length', type=int, default=12, help='')
-    parser.add_argument('--nhid', type=int, default=32, help='')
+    parser.add_argument('--nhid', type=int, default=40, help='Number of channels for internal conv')
     parser.add_argument('--in_dim', type=int, default=2, help='inputs dimension')
     parser.add_argument('--num_nodes', type=int, default=207, help='number of nodes')
     parser.add_argument('--batch_size', type=int, default=64, help='batch size')
     parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
     parser.add_argument('--n_obs', default=None, help='Only use this many observations')
     parser.add_argument('--apt_size', default=10, type=int)
-    parser.add_argument('--softmax_temp', default=1., type=float)
     parser.add_argument('--cat_feat_gc', action='store_true')
     parser.add_argument('--fill_zeroes', action='store_true')
     parser.add_argument('--checkpoint', type=str, help='')

From 03684b8f968f3714cde28a4aeec63713b085bc51 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 17 Dec 2019 10:12:48 -0800
Subject: [PATCH 128/130] update README for new cli

---
 README.md        | 70 +++++++++++++++++++++---------------------------
 requirements.txt |  3 ++-
 util.py          |  2 +-
 3 files changed, 34 insertions(+), 41 deletions(-)

diff --git a/README.md b/README.md
index bafb450..fcd3fd1 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,9 @@
 # Graph WaveNet for Deep Spatial-Temporal Graph Modeling
 
 This is the original pytorch implementation of Graph WaveNet in the following paper: 
-[Graph WaveNet for Deep Spatial-Temporal Graph Modeling, IJCAI 2019] (https://arxiv.org/abs/1906.00121).
+[Graph WaveNet for Deep Spatial-Temporal Graph Modeling, IJCAI 2019] (https://arxiv.org/abs/1906.00121),
+with modifications presented in [Incrementally Improving Graph WaveNet Performance on Traffic Prediction] (https://arxiv.org/abs/1912.07390):
+
 
 <p align="center">
   <img width="350" height="400" src=./fig/model.png>
@@ -12,12 +14,11 @@ This is the original pytorch implementation of Graph WaveNet in the following pa
 - see `requirements.txt`
 
 
-
 ## Data Preparation
 
-### Step1: Download METR-LA and PEMS-BAY data from [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](https://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g) links provided by [DCRNN](https://github.com/liyaguang/DCRNN).
+1) Download METR-LA and PEMS-BAY data from [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](https://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g) links provided by [DCRNN](https://github.com/liyaguang/DCRNN).
 
-### Step2:
+2)
 
 ```
 # Create data directories
@@ -30,47 +31,38 @@ python generate_training_data.py --output_dir=data/METR-LA --traffic_df_filename
 python generate_training_data.py --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
 
 ```
-## New Experiment Command
+
+## Train Commands
+Model that gets (3.00 - 3.02 MAE)
 ```
-expid=bs_64
-mkdir $expid
-python train.py --batch_size 64 --learning_rate .004 --do_graph_conv --addaptadj  --randomadj   --save $expid | tee -a $expid.log
+python train.py --cat_feat_gc --fill_zeroes --do_graph_conv --addaptadj  --randomadj --es_patience 20 --save logs/baseline_v2
 ```
 
-## Old Experiment Commands
-Train models configured in Table 3 of the paper.
+Finetuning (2.99 - 3.00 MAE)
+```
+python generate_training_data.py --seq_length_y 6 --output_dir data/METR-LA_12_6
+python train.py --data  data/METR-LA_12_6 --cat_feat_gc --fill_zeroes --do_graph_conv --addaptadj  --randomadj --es_patience 20 --save logs/front_6
+python train.py --checkpoint  logs/front_6/best_model.pth --cat_feat_gc --fill_zeroes --do_graph_conv --addaptadj  --randomadj --es_patience 20 --save logs/finetuned
 
 ```
-ep=100
-dv=cuda:0
-mkdir experiment
-mkdir experiment/metr
-
-#identity
-expid=1
-python train.py --device $dv --gcn_bool --adjtype identity  --epoch $ep --expid $expid  --save ./experiment/metr/metr > ./experiment/metr/train-$expid.log
-rm ./experiment/metr/metr_epoch*
-
-#forward-only
-expid=2
-python train.py --device $dv --gcn_bool --adjtype transition --epoch $ep --expid $expid  --save ./experiment/metr/metr > ./experiment/metr/train-$expid.log
-rm ./experiment/metr/metr_epoch*
-
-#adaptive-only
-expid=3
-python train.py --device $dv --gcn_bool --adjtype transition --aptonly  --addaptadj --randomadj --epoch $ep --expid $expid  --save ./experiment/metr/metr > ./experiment/metr/train-$expid.log
-rm ./experiment/metr/metr_epoch*
-
-#forward-backward
-expid=4
-python train.py --device $dv --gcn_bool --adjtype doubletransition  --epoch $ep --expid $expid  --save ./experiment/metr/metr > ./experiment/metr/train-$expid.log
-rm ./experiment/metr/metr_epoch*
-
-#forward-backward-adaptive
-expid=5
-python train.py --device $dv --gcn_bool --adjtype doubletransition --addaptadj  --randomadj  --epoch $ep --expid $expid  --save ./experiment/metr/metr > ./experiment/metr/train-$expid.log
-rm ./experiment/metr/metr_epoch*
+Original Graph Wavenet Model (3.04-3.07 MAE)
+```
+python train.py --clip 5 --lr_decay_rate 1. --nhid 32 --do_graph_conv --addaptadj  --randomadj --save logs/baseline
+```
 
+You can also train from a jupyter notebook with
+```{python}
+from train import main
+from durbango import pickle_load
+args = pickle_load('baseline_args.pkl') # manipulate these in python
+args.lr_decay_rate = .97
+args.clip = 3
+args.save = 'logs/from_jupyter'
+main(args) # takes roughly an hour depending on nhid, and early_stopping
 ```
 
+Train models configured in Table 3 of the original GraphWavenet paper by using the `--adjtype, --addaptadj, --aptonly` command line argument.
+These flags are (somewhat) documented in util.py.
+
+Run unitests with `pytest`
 
diff --git a/requirements.txt b/requirements.txt
index 845cd3f..af87cb5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,9 @@
+torch>=1.3.1
 matplotlib
 numpy
 scipy
 pandas
-torch
 argparse
 durbango
 fastprogress
+pytest
diff --git a/util.py b/util.py
index b93b546..65bbb35 100644
--- a/util.py
+++ b/util.py
@@ -254,7 +254,7 @@ def get_shared_arg_parser():
     parser.add_argument('--num_nodes', type=int, default=207, help='number of nodes')
     parser.add_argument('--batch_size', type=int, default=64, help='batch size')
     parser.add_argument('--dropout', type=float, default=0.3, help='dropout rate')
-    parser.add_argument('--n_obs', default=None, help='Only use this many observations')
+    parser.add_argument('--n_obs', default=None, help='Only use this many observations. For unit testing.')
     parser.add_argument('--apt_size', default=10, type=int)
     parser.add_argument('--cat_feat_gc', action='store_true')
     parser.add_argument('--fill_zeroes', action='store_true')

From efbc44bea02cb618ee14f0b79f8a84eb7011d2c7 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 17 Dec 2019 10:21:04 -0800
Subject: [PATCH 129/130] check in less data

---
 adj_mx_stats.py                         |     37 -
 baseline_ckpt.pth                       |    Bin 1245270 -> 0 bytes
 data/sensor_graph/adj_mat.pkl           |    Bin 177424 -> 0 bytes
 data/sensor_graph/adj_mat_0pt001.pkl    |    Bin 182340 -> 0 bytes
 data/sensor_graph/adj_mat_0pt01.pkl     |    Bin 179762 -> 0 bytes
 data/sensor_graph/adj_mat_no_thresh.pkl |    Bin 192458 -> 0 bytes
 data/sensor_graph/distances_la_2012.csv | 295375 ---------------------
 data/sensor_graph/graph_sensor_ids.txt  |      1 -
 lr_finder.py                            |    319 -
 util.py                                 |      2 -
 10 files changed, 295734 deletions(-)
 delete mode 100644 adj_mx_stats.py
 delete mode 100644 baseline_ckpt.pth
 delete mode 100644 data/sensor_graph/adj_mat.pkl
 delete mode 100644 data/sensor_graph/adj_mat_0pt001.pkl
 delete mode 100644 data/sensor_graph/adj_mat_0pt01.pkl
 delete mode 100644 data/sensor_graph/adj_mat_no_thresh.pkl
 delete mode 100644 data/sensor_graph/distances_la_2012.csv
 delete mode 100644 data/sensor_graph/graph_sensor_ids.txt
 delete mode 100644 lr_finder.py

diff --git a/adj_mx_stats.py b/adj_mx_stats.py
deleted file mode 100644
index c5c8308..0000000
--- a/adj_mx_stats.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import argparse
-import pickle
-import numpy as np
-from util import *
-import matplotlib.pyplot as plt
-
-def print_stats(args):
-  if '/' not in args.adj_mx:
-    full_path = 'data/sensor_graph/' + args.adj_mx
-  else:
-    full_path = args.adj_mx
-  
-  A = load_pickle(full_path)
-  adj_mx = A[2]
-
-  degrees = np.sum(adj_mx > 0, 1)
-  print(f"Minimum Degree: {np.min(degrees)}")
-  print(f"Maximum Degree: {np.max(degrees)}")
-  print(f"Average Degree: {np.mean(degrees)}")
-
-  plt.imshow(adj_mx, cmap='hot', interpolation='nearest')
-  plt.title('Adjacency Matrix Heatmap')
-  plt.show()
-
-  plt.hist(degrees) 
-  plt.xticks(range(0,max(degrees) + 5,5))
-  plt.title('LA Degree Distribution')
-  plt.xlabel('Node Degree')
-  plt.ylabel('Number of Nodes')
-  plt.show()
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument('--adj_mx', type=str, default='adj_mat.pkl',
-    help='Pickle file in data/sensor_graph containing adjacency matrix')
-  args = parser.parse_args()
-  print_stats(args)
diff --git a/baseline_ckpt.pth b/baseline_ckpt.pth
deleted file mode 100644
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diff --git a/data/sensor_graph/distances_la_2012.csv b/data/sensor_graph/distances_la_2012.csv
deleted file mode 100644
index 759d2bc..0000000
--- a/data/sensor_graph/distances_la_2012.csv
+++ /dev/null
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-825515,823636,10275.5
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-825515,823663,9894.1
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-825515,825513,6934.2
-825515,825515,0.0
\ No newline at end of file
diff --git a/data/sensor_graph/graph_sensor_ids.txt b/data/sensor_graph/graph_sensor_ids.txt
deleted file mode 100644
index e2df022..0000000
--- a/data/sensor_graph/graph_sensor_ids.txt
+++ /dev/null
@@ -1 +0,0 @@
-773869,767541,767542,717447,717446,717445,773062,767620,737529,717816,765604,767471,716339,773906,765273,716331,771667,716337,769953,769402,769403,769819,769405,716941,717578,716960,717804,767572,767573,773012,773013,764424,769388,716328,717819,769941,760987,718204,718045,769418,768066,772140,773927,760024,774012,774011,767609,769359,760650,716956,769831,761604,717495,716554,773953,767470,716955,764949,773954,767366,769444,773939,774067,769443,767750,767751,767610,773880,764766,717497,717490,717491,717492,717493,765176,717498,717499,765171,718064,718066,765164,769431,769430,717610,767053,767621,772596,772597,767350,767351,716571,773023,767585,773024,717483,718379,717481,717480,717486,764120,772151,718371,717489,717488,717818,718076,718072,767455,767454,761599,717099,773916,716968,769467,717576,717573,717572,717571,717570,764760,718089,769847,717608,767523,716942,718090,769867,717472,717473,759591,764781,765099,762329,716953,716951,767509,765182,769358,772513,716958,718496,769346,773904,718499,764853,761003,717502,759602,717504,763995,717508,765265,773996,773995,717469,717468,764106,717465,764794,717466,717461,717460,717463,717462,769345,716943,772669,717582,717583,717580,716949,717587,772178,717585,716939,768469,764101,767554,773975,773974,717510,717513,717825,767495,767494,717821,717823,717458,717459,769926,764858,717450,717452,717453,759772,717456,771673,772167,769372,774204,769806,717590,717592,717595,772168,718141,769373
\ No newline at end of file
diff --git a/lr_finder.py b/lr_finder.py
deleted file mode 100644
index 73816bb..0000000
--- a/lr_finder.py
+++ /dev/null
@@ -1,319 +0,0 @@
-from __future__ import print_function, with_statement, division
-import copy
-import os
-import torch
-from tqdm.autonotebook import tqdm
-from torch.optim.lr_scheduler import _LRScheduler
-import matplotlib.pyplot as plt
-
-
-class LRFinder(object):
-    """Learning rate range test.
-    The learning rate range test increases the learning rate in a pre-training run
-    between two boundaries in a linear or exponential manner. It provides valuable
-    information on how well the network can be trained over a range of learning rates
-    and what is the optimal learning rate.
-    Arguments:
-        model (torch.nn.Module): wrapped model.
-        optimizer (torch.optim.Optimizer): wrapped optimizer where the defined learning
-            is assumed to be the lower boundary of the range test.
-        criterion (torch.nn.Module): wrapped loss function.
-        device (str or torch.device, optional): a string ("cpu" or "cuda") with an
-            optional ordinal for the device type (e.g. "cuda:X", where is the ordinal).
-            Alternatively, can be an object representing the device on which the
-            computation will take place. Default: None, uses the same device as `model`.
-        memory_cache (boolean): if this flag is set to True, `state_dict` of model and
-            optimizer will be cached in memory. Otherwise, they will be saved to files
-            under the `cache_dir`.
-        cache_dir (string): path for storing temporary files. If no path is specified,
-            system-wide temporary directory is used.
-            Notice that this parameter will be ignored if `memory_cache` is True.
-    Example:
-        >>> lr_finder = LRFinder(net, optimizer, criterion, device="cuda")
-        >>> lr_finder.range_test(dataloader, end_lr=100, num_iter=100)
-    Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
-    fastai/lr_find: https://github.com/fastai/fastai
-    """
-
-    def __init__(self, engine, device=None, memory_cache=True, cache_dir=None):
-        self.engine = engine
-        self.model = engine.model
-        self.optimizer = engine.optimizer
-        self.history = {"lr": [], "loss": []}
-        self.best_loss = None
-        self.memory_cache = memory_cache
-        self.cache_dir = cache_dir
-
-        # Save the original state of the model and optimizer so they can be restored if
-        # needed
-        self.model_device = next(self.model.parameters()).device
-        self.state_cacher = StateCacher(memory_cache, cache_dir=cache_dir)
-        self.state_cacher.store('model', self.model.state_dict())
-        self.state_cacher.store('optimizer', self.optimizer.state_dict())
-
-        # If device is None, use the same as the model
-        if device:
-            self.device = device
-        else:
-            self.device = self.model_device
-
-    def reset(self):
-        """Restores the model and optimizer to their initial states."""
-        self.model.load_state_dict(self.state_cacher.retrieve('model'))
-        self.optimizer.load_state_dict(self.state_cacher.retrieve('optimizer'))
-        self.model.to(self.model_device)
-
-    def range_test(
-            self,
-            train_loader,
-            val_loader=None,
-            end_lr=10,
-            num_iter=100,
-            step_mode="exp",
-            smooth_f=0.05,
-            diverge_th=5,
-    ):
-        """Performs the learning rate range test.
-        Arguments:
-            train_loader (torch.utils.data.DataLoader): the training set data laoder.
-            val_loader (torch.utils.data.DataLoader, optional): if `None` the range test
-                will only use the training loss. When given a data loader, the model is
-                evaluated after each iteration on that dataset and the evaluation loss
-                is used. Note that in this mode the test takes significantly longer but
-                generally produces more precise results. Default: None.
-            end_lr (float, optional): the maximum learning rate to test. Default: 10.
-            num_iter (int, optional): the number of iterations over which the test
-                occurs. Default: 100.
-            step_mode (str, optional): one of the available learning rate policies,
-                linear or exponential ("linear", "exp"). Default: "exp".
-            smooth_f (float, optional): the loss smoothing factor within the [0, 1[
-                interval. Disabled if set to 0, otherwise the loss is smoothed using
-                exponential smoothing. Default: 0.05.
-            diverge_th (int, optional): the test is stopped when the loss surpasses the
-                threshold:  diverge_th * best_loss. Default: 5.
-        """
-        # Reset test results
-        self.history = {"lr": [], "loss": []}
-        self.best_loss = None
-
-        # Move the model to the proper device
-        self.model.to(self.device)
-
-        # Initialize the proper learning rate policy
-        if step_mode.lower() == "exp":
-            lr_schedule = ExponentialLR(self.optimizer, end_lr, num_iter)
-        elif step_mode.lower() == "linear":
-            lr_schedule = LinearLR(self.optimizer, end_lr, num_iter)
-        else:
-            raise ValueError("expected one of (exp, linear), got {}".format(step_mode))
-
-        if smooth_f < 0 or smooth_f >= 1:
-            raise ValueError("smooth_f is outside the range [0, 1[")
-
-        # Create an iterator to get data batch by batch
-        iterator = iter(train_loader)
-        for iteration in tqdm(range(num_iter)):
-            # Get a new set of inputs and labels
-            try:
-                inputs, labels = next(iterator)
-            except StopIteration:
-                iterator = iter(train_loader)
-                inputs, labels = next(iterator)
-
-            # Train on batch and retrieve loss
-            loss = self._train_batch(inputs, labels)
-            if val_loader:
-                loss = self._validate(val_loader)
-
-            # Update the learning rate
-            lr_schedule.step()
-            self.history["lr"].append(lr_schedule.get_lr()[0])
-
-            # Track the best loss and smooth it if smooth_f is specified
-            if iteration == 0:
-                self.best_loss = loss
-            else:
-                if smooth_f > 0:
-                    loss = smooth_f * loss + (1 - smooth_f) * self.history["loss"][-1]
-                if loss < self.best_loss:
-                    self.best_loss = loss
-
-            # Check if the loss has diverged; if it has, stop the test
-            self.history["loss"].append(loss)
-            if loss > diverge_th * self.best_loss:
-                print("Stopping early, the loss has diverged")
-                break
-
-        print("Learning rate search finished. See the graph with {finder_name}.plot()")
-
-    def _train_batch(self, x, y):
-        # Set model to training mode
-        self.model.train()
-        trainx = torch.Tensor(x).to(self.device).transpose(1, 3)
-        trainy = torch.Tensor(y).to(self.device).transpose(1, 3)
-        mae, mape, rmse = self.engine.train(trainx, trainy[:, 0, :, :])
-        return mae
-
-    def _move_to_device(self, inputs, labels):
-        def move(obj, device):
-            if isinstance(obj, tuple):
-                return tuple(move(o, device) for o in obj)
-            elif torch.is_tensor(obj):
-                return obj.to(device)
-            else:
-                return obj
-
-        inputs = move(inputs, self.device)
-        labels = move(labels, self.device)
-        return inputs, labels
-
-    def _validate(self, dataloader):
-        # Set model to evaluation mode and disable gradient computation
-        running_loss = 0
-        self.model.eval()
-        with torch.no_grad():
-            for inputs, labels in dataloader:
-                # Move data to the correct device
-                inputs, labels = self._move_to_device(inputs, labels)
-
-                # Forward pass and loss computation
-                outputs = self.model(inputs)
-                loss = self.criterion(outputs, labels)
-                running_loss += loss.item() * inputs.size(0)
-
-        return running_loss / len(dataloader.dataset)
-
-    def plot(self, skip_start=10, skip_end=5, log_lr=True, show_lr=None):
-        """Plots the learning rate range test.
-        Arguments:
-            skip_start (int, optional): number of batches to trim from the start.
-                Default: 10.
-            skip_end (int, optional): number of batches to trim from the start.
-                Default: 5.
-            log_lr (bool, optional): True to plot the learning rate in a logarithmic
-                scale; otherwise, plotted in a linear scale. Default: True.
-            show_lr (float, optional): is set, will add vertical line to visualize
-                specified learning rate; Default: None
-        """
-
-        if skip_start < 0:
-            raise ValueError("skip_start cannot be negative")
-        if skip_end < 0:
-            raise ValueError("skip_end cannot be negative")
-        if show_lr is not None and not isinstance(show_lr, float):
-            raise ValueError("show_lr must be float")
-
-        # Get the data to plot from the history dictionary. Also, handle skip_end=0
-        # properly so the behaviour is the expected
-        lrs = self.history["lr"]
-        losses = self.history["loss"]
-        if skip_end == 0:
-            lrs = lrs[skip_start:]
-            losses = losses[skip_start:]
-        else:
-            lrs = lrs[skip_start:-skip_end]
-            losses = losses[skip_start:-skip_end]
-
-        # Plot loss as a function of the learning rate
-        plt.plot(lrs, losses)
-        if log_lr:
-            plt.xscale("log")
-        plt.xlabel("Learning rate")
-        plt.ylabel("Loss")
-
-        if show_lr is not None:
-            plt.axvline(x=show_lr, color="red")
-        plt.show()
-
-
-class LinearLR(_LRScheduler):
-    """Linearly increases the learning rate between two boundaries over a number of
-    iterations.
-    Arguments:
-        optimizer (torch.optim.Optimizer): wrapped optimizer.
-        end_lr (float, optional): the initial learning rate which is the lower
-            boundary of the test. Default: 10.
-        num_iter (int, optional): the number of iterations over which the test
-            occurs. Default: 100.
-        last_epoch (int): the index of last epoch. Default: -1.
-    """
-
-    def __init__(self, optimizer, end_lr, num_iter, last_epoch=-1):
-        self.end_lr = end_lr
-        self.num_iter = num_iter
-        super(LinearLR, self).__init__(optimizer, last_epoch)
-
-    def get_lr(self):
-        curr_iter = self.last_epoch + 1
-        r = curr_iter / self.num_iter
-        return [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]
-
-
-class ExponentialLR(_LRScheduler):
-    """Exponentially increases the learning rate between two boundaries over a number of
-    iterations.
-    Arguments:
-        optimizer (torch.optim.Optimizer): wrapped optimizer.
-        end_lr (float, optional): the initial learning rate which is the lower
-            boundary of the test. Default: 10.
-        num_iter (int, optional): the number of iterations over which the test
-            occurs. Default: 100.
-        last_epoch (int): the index of last epoch. Default: -1.
-    """
-
-    def __init__(self, optimizer, end_lr, num_iter, last_epoch=-1):
-        self.end_lr = end_lr
-        self.num_iter = num_iter
-        super(ExponentialLR, self).__init__(optimizer, last_epoch)
-
-    def get_lr(self):
-        curr_iter = self.last_epoch + 1
-        r = curr_iter / self.num_iter
-        return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]
-
-
-class StateCacher(object):
-    def __init__(self, in_memory, cache_dir=None):
-        self.in_memory = in_memory
-        self.cache_dir = cache_dir
-
-        if self.cache_dir is None:
-            import tempfile
-            self.cache_dir = tempfile.gettempdir()
-        else:
-            if not os.path.isdir(self.cache_dir):
-                raise ValueError('Given `cache_dir` is not a valid directory.')
-
-        self.cached = {}
-
-    def store(self, key, state_dict):
-        if self.in_memory:
-            self.cached.update({key: copy.deepcopy(state_dict)})
-        else:
-            fn = os.path.join(self.cache_dir, 'state_{}_{}.pt'.format(key, id(self)))
-            self.cached.update({key: fn})
-            torch.save(state_dict, fn)
-
-    def retrieve(self, key):
-        if key not in self.cached:
-            raise KeyError('Target {} was not cached.'.format(key))
-
-        if self.in_memory:
-            return self.cached.get(key)
-        else:
-            fn = self.cached.get(key)
-            if not os.path.exists(fn):
-                raise RuntimeError(
-                    'Failed to load state in {}. File does not exist anymore.'.format(fn))
-            state_dict = torch.load(fn, map_location=lambda storage, location: storage)
-            return state_dict
-
-    def __del__(self):
-        """Check whether there are unused cached files existing in `cache_dir` before
-        this instance being destroyed."""
-        if self.in_memory:
-            return
-
-        for k in self.cached:
-            if os.path.exists(self.cached[k]):
-                os.remove(self.cached[k])
diff --git a/util.py b/util.py
index 65bbb35..ef61743 100644
--- a/util.py
+++ b/util.py
@@ -161,8 +161,6 @@ def load_dataset(dataset_dir, batch_size, valid_batch_size=None, test_batch_size
     # Data format
     for category in ['train', 'val', 'test']:
         data['x_' + category][..., 0] = scaler.transform(data['x_' + category][..., 0])
-
-
     data['train_loader'] = DataLoader(data['x_train'], data['y_train'], batch_size)
     data['val_loader'] = DataLoader(data['x_val'], data['y_val'], valid_batch_size)
     data['test_loader'] = DataLoader(data['x_test'], data['y_test'], test_batch_size)

From 2237839858d5365e7a2b6311195b1e499c943114 Mon Sep 17 00:00:00 2001
From: sshleifer <sshleifer@gmail.com>
Date: Tue, 17 Dec 2019 10:26:50 -0800
Subject: [PATCH 130/130] support cat_feat_gc and dow

---
 .gitignore     |  2 --
 README.md      |  6 +++++-
 bug_example.py | 14 --------------
 exp_results.py |  3 ++-
 model.py       |  4 ++--
 5 files changed, 9 insertions(+), 20 deletions(-)
 delete mode 100644 bug_example.py

diff --git a/.gitignore b/.gitignore
index 57db9b8..98fa2d5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,4 @@ preds.csv
 .ipynb_checkpoints/
 data/
 .DS_Store
-*.xlsx
-*.mp
 *.pkl
diff --git a/README.md b/README.md
index fcd3fd1..324c4c2 100644
--- a/README.md
+++ b/README.md
@@ -33,7 +33,9 @@ python generate_training_data.py --output_dir=data/PEMS-BAY --traffic_df_filenam
 ```
 
 ## Train Commands
-Model that gets (3.00 - 3.02 MAE)
+Note: train.py saves metrics to a directory specified by the `--save` arg in metrics.csv and test_metrics.csv
+
+Model that gets (3.00 - 3.02 Test MAE, ~2.73 Validation MAE)
 ```
 python train.py --cat_feat_gc --fill_zeroes --do_graph_conv --addaptadj  --randomadj --es_patience 20 --save logs/baseline_v2
 ```
@@ -66,3 +68,5 @@ These flags are (somewhat) documented in util.py.
 
 Run unitests with `pytest`
 
+### Possible Improvements
+* move redundant `.transpose(1,3)` to dataloader or `load_dataset`
diff --git a/bug_example.py b/bug_example.py
deleted file mode 100644
index e392a89..0000000
--- a/bug_example.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import torch
-from torch import nn
-
-class Model(nn.Module):
-
-    def __init__(self, device):
-        super().__init__()
-        self.vector = nn.Parameter(torch.randn(5, 5).to(device), requires_grad=True)
-
-model_cpu = Model('cpu')
-assert 'vector' in model_cpu.state_dict()
-model_gpu = Model('cuda:0')
-assert 'vector' in model_gpu.state_dict() # Raise assertionerror
-
diff --git a/exp_results.py b/exp_results.py
index 1034aa4..c86dce1 100644
--- a/exp_results.py
+++ b/exp_results.py
@@ -1,3 +1,4 @@
+"""Utilities for comparing metrics saved by train.py"""
 import pandas as pd
 import os
 from glob import glob
@@ -33,5 +34,5 @@ def plot_loss_curve(log_dir):
     plt.axhline(d.valid_loss.min())
     print(d.valid_loss.idxmin())
 
-def make_table():
+def make_results_table():
     return pd.DataFrame({os.path.basename(c): summary(c) for c in glob('logs/*')}).T.sort_values('valid_loss')
diff --git a/model.py b/model.py
index a28abc1..c01ef04 100644
--- a/model.py
+++ b/model.py
@@ -51,7 +51,7 @@ def __init__(self, device, num_nodes, dropout=0.3, supports=None, do_graph_conv=
             self.start_conv = nn.Conv2d(in_channels=1,  # hard code to avoid errors
                                         out_channels=residual_channels,
                                         kernel_size=(1, 1))
-            self.cat_feature_conv = nn.Conv2d(in_channels=1,
+            self.cat_feature_conv = nn.Conv2d(in_channels=in_dim - 1,
                                               out_channels=residual_channels,
                                               kernel_size=(1, 1))
         else:
@@ -132,7 +132,7 @@ def forward(self, x):
         if in_len < self.receptive_field:
             x = nn.functional.pad(x, (self.receptive_field - in_len, 0, 0, 0))
         if self.cat_feat_gc:
-            f1, f2 = x[:,[0]], x[:,[1]]
+            f1, f2 = x[:, [0]], x[:, 1:]
             x1 = self.start_conv(f1)
             x2 = F.leaky_relu(self.cat_feature_conv(f2))
             x = x1 + x2