Feature/rename

.github/ISSUE_TEMPLATE/bug_report.md

@@ -22,7 +22,7 @@ If applicable, add screenshots to help explain your problem.
 **Desktop (please complete the following information):**
  - OS: [e.g. Ubuntu]
  - Python version [e.g. 3.8]
- - Mambular Version [e.g. 0.1.2]
+ - DeepTabular Version [e.g. 1.6.0]
 
 **Additional context**
 Add any other context about the problem here.

.github/workflows/pr-tests.yml

@@ -36,7 +36,13 @@ jobs:
       - name: Install Dependencies
         run: |
           python -m pip install --upgrade pip
-          poetry install --with dev
+          poetry install
+          pip install pytest
+
+      - name: Install Package Locally
+        run: |
+          poetry build
+          pip install dist/*.whl  # Install the built package to fix "No module named 'deeptabular'"
 
       - name: Run Unit Tests
         env:

README.md

@@ -2,24 +2,24 @@
   <img src="./docs/images/logo/mamba_tabular.jpg" width="400"/>
 
 
-[![PyPI](https://img.shields.io/pypi/v/mambular)](https://pypi.org/project/mambular)
-![PyPI - Downloads](https://img.shields.io/pypi/dm/mambular)
-[![docs build](https://readthedocs.org/projects/mambular/badge/?version=latest)](https://mambular.readthedocs.io/en/latest/?badge=latest)
-[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mambular.readthedocs.io/en/latest/)
-[![open issues](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/basf/mamba-tabular/issues)
+[![PyPI](https://img.shields.io/pypi/v/deeptabular)](https://pypi.org/project/deeptabular)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/deeptabular)
+[![docs build](https://readthedocs.org/projects/deeptabular/badge/?version=latest)](https://deeptabular.readthedocs.io/en/latest/?badge=latest)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://deeptabular.readthedocs.io/en/latest/)
+[![open issues](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/OpenTabular/DeepTabular/issues)
 
 
-[📘Documentation](https://mambular.readthedocs.io/en/latest/index.html) |
-[🛠️Installation](https://mambular.readthedocs.io/en/latest/installation.html) |
-[Models](https://mambular.readthedocs.io/en/latest/api/models/index.html) |
-[🤔Report Issues](https://github.com/basf/mamba-tabular/issues)
+[📘Documentation](https://deeptabular.readthedocs.io/en/latest/index.html) |
+[🛠️Installation](https://deeptabular.readthedocs.io/en/latest/installation.html) |
+[Models](https://deeptabular.readthedocs.io/en/latest/api/models/index.html) |
+[🤔Report Issues](https://github.com/OpenTabular/DeepTabular/issues)
 </div>
 
 <div style="text-align: center;">
-    <h1>Mambular: Tabular Deep Learning Made Simple</h1>
+    <h1>DeepTabular: Tabular Deep Learning Made Simple</h1>
 </div>
 
-Mambular is a Python library for tabular deep learning. It includes models that leverage the Mamba (State Space Model) architecture, as well as other popular models like TabTransformer, FTTransformer, TabM and tabular ResNets. Check out our paper `Mambular: A Sequential Model for Tabular Deep Learning`, available [here](https://arxiv.org/abs/2408.06291). Also check out our paper introducing [TabulaRNN](https://arxiv.org/pdf/2411.17207) and analyzing the efficiency of NLP inspired tabular models.
+DeepTabular is a Python library for tabular deep learning. It includes models that leverage the Mamba (State Space Model) architecture, as well as other popular models like TabTransformer, FTTransformer, TabM and tabular ResNets. Check out our paper `Mambular: A Sequential Model for Tabular Deep Learning`, available [here](https://arxiv.org/abs/2408.06291). Also check out our paper introducing [TabulaRNN](https://arxiv.org/pdf/2411.17207) and analyzing the efficiency of NLP inspired tabular models.
 
 <h3>⚡ What's New ⚡</h3>
 <ul>
@@ -48,10 +48,10 @@ Mambular is a Python library for tabular deep learning. It includes models that
 
 
 # 🏃 Quickstart
-Similar to any sklearn model, Mambular models can be fit as easy as this:
+Similar to any sklearn model, DeepTabular models can be fit as easy as this:
 
 ```python
-from mambular.models import MambularClassifier
+from deeptabular.models import MambularClassifier
 # Initialize and fit your model
 model = MambularClassifier()
 
@@ -60,7 +60,7 @@ model.fit(X, y, max_epochs=150, lr=1e-04)
 ```
 
 # 📖 Introduction
-Mambular is a Python package that brings the power of advanced deep learning architectures to tabular data, offering a suite of models for regression, classification, and distributional regression tasks. Designed with ease of use in mind, Mambular models adhere to scikit-learn's `BaseEstimator` interface, making them highly compatible with the familiar scikit-learn ecosystem. This means you can fit, predict, and evaluate using Mambular models just as you would with any traditional scikit-learn model, but with the added performance and flexibility of deep learning.
+DeepTabular is a Python package that brings the power of advanced deep learning architectures to tabular data, offering a suite of models for regression, classification, and distributional regression tasks. Designed with ease of use in mind, DeepTabular models adhere to scikit-learn's `BaseEstimator` interface, making them highly compatible with the familiar scikit-learn ecosystem. This means you can fit, predict, and evaluate using DeepTabular models just as you would with any traditional scikit-learn model, but with the added performance and flexibility of deep learning.
 
 
 # 🤖 Models
@@ -94,13 +94,13 @@ Hence, they are available as e.g. `MambularRegressor`, `MambularClassifier` or `
 
 # 📚 Documentation
 
-You can find the Mamba-Tabular API documentation [here](https://mambular.readthedocs.io/en/latest/).
+You can find the DeepTabular API documentation [here](https://deeptabular.readthedocs.io/en/latest/).
 
 # 🛠️ Installation
 
-Install Mambular using pip:
+Install DeepTabular using pip:
 ```sh
-pip install mambular
+pip install deeptabular
 ```
 
 If you want to use the original mamba and mamba2 implementations, additionally install mamba-ssm via:
@@ -120,7 +120,7 @@ pip install mamba-ssm
 
 <h2> Preprocessing </h2>
 
-Mambular uses pretab preprocessing: https://github.com/OpenTabular/PreTab
+DeepTabular uses pretab preprocessing: https://github.com/OpenTabular/PreTab
 
 Hence, datatypes etc. are detected automatically and all preprocessing methods from pretab as well as from Sklearn.preprocessing are available.
 Additionally, you can specify that each feature is preprocessed differently, according to your requirements, by setting the `feature_preprocessing={}`argument during model initialization.
@@ -144,10 +144,10 @@ For an overview over all available methods: [pretab](https://github.com/OpenTabu
 
 
 <h2> Fit a Model </h2>
-Fitting a model in mambular is as simple as it gets. All models in mambular are sklearn BaseEstimators. Thus the `.fit` method is implemented for all of them. Additionally, this allows for using all other sklearn inherent methods such as their built in hyperparameter optimization tools.
+Fitting a model in deeptabular is as simple as it gets. All models in deeptabular are sklearn BaseEstimators. Thus the `.fit` method is implemented for all of them. Additionally, this allows for using all other sklearn inherent methods such as their built in hyperparameter optimization tools.
 
 ```python
-from mambular.models import MambularClassifier
+from deeptabular.models import MambularClassifier
 # Initialize and fit your model
 model = MambularClassifier(
     d_model=64,
@@ -243,12 +243,12 @@ Or use the built-in bayesian hpo simply by running:
 best_params = model.optimize_hparams(X, y)
 ```
 
-This automatically sets the search space based on the default config from ``mambular.configs``. See the documentation for all params with regard to ``optimize_hparams()``. However, the preprocessor arguments are fixed and cannot be optimized here.
+This automatically sets the search space based on the default config from ``deeptabular.configs``. See the documentation for all params with regard to ``optimize_hparams()``. However, the preprocessor arguments are fixed and cannot be optimized here.
 
 
 <h2> ⚖️ Distributional Regression with MambularLSS </h2>
 
-MambularLSS allows you to model the full distribution of a response variable, not just its mean. This is crucial when understanding variability, skewness, or kurtosis is important. All Mambular models are available as distributional models.
+MambularLSS allows you to model the full distribution of a response variable, not just its mean. This is crucial when understanding variability, skewness, or kurtosis is important. All DeepTabular models are available as distributional models.
 
 <h3> Key Features of MambularLSS: </h3>
 
@@ -277,10 +277,10 @@ These distribution classes make MambularLSS versatile in modeling various data t
 
 <h3> Getting Started with MambularLSS: </h3>
 
-To integrate distributional regression into your workflow with `MambularLSS`, start by initializing the model with your desired configuration, similar to other Mambular models:
+To integrate distributional regression into your workflow with `MambularLSS`, start by initializing the model with your desired configuration, similar to other DeepTabular models:
 
 ```python
-from mambular.models import MambularLSS
+from deeptabular.models import MambularLSS
 
 # Initialize the MambularLSS model
 model = MambularLSS(
@@ -305,18 +305,18 @@ model.fit(
 
 # 💻 Implement Your Own Model
 
-Mambular allows users to easily integrate their custom models into the existing logic. This process is designed to be straightforward, making it simple to create a PyTorch model and define its forward pass. Instead of inheriting from `nn.Module`, you inherit from Mambular's `BaseModel`. Each Mambular model takes three main arguments: the number of classes (e.g., 1 for regression or 2 for binary classification), `cat_feature_info`, and `num_feature_info` for categorical and numerical feature information, respectively. Additionally, you can provide a config argument, which can either be a custom configuration or one of the provided default configs.
+DeepTabular allows users to easily integrate their custom models into the existing logic. This process is designed to be straightforward, making it simple to create a PyTorch model and define its forward pass. Instead of inheriting from `nn.Module`, you inherit from DeepTabular's `BaseModel`. Each DeepTabular model takes three main arguments: the number of classes (e.g., 1 for regression or 2 for binary classification), `cat_feature_info`, and `num_feature_info` for categorical and numerical feature information, respectively. Additionally, you can provide a config argument, which can either be a custom configuration or one of the provided default configs.
 
-One of the key advantages of using Mambular is that the inputs to the forward passes are lists of tensors. While this might be unconventional, it is highly beneficial for models that treat different data types differently. For example, the TabTransformer model leverages this feature to handle categorical and numerical data separately, applying different transformations and processing steps to each type of data.
+One of the key advantages of using DeepTabular is that the inputs to the forward passes are lists of tensors. While this might be unconventional, it is highly beneficial for models that treat different data types differently. For example, the TabTransformer model leverages this feature to handle categorical and numerical data separately, applying different transformations and processing steps to each type of data.
 
-Here's how you can implement a custom model with Mambular:
+Here's how you can implement a custom model with DeepTabular:
 
 1. **First, define your config:**  
    The configuration class allows you to specify hyperparameters and other settings for your model. This can be done using a simple dataclass.
 
    ```python
    from dataclasses import dataclass
-   from mambular.configs import BaseConfig
+   from deeptabular.configs import BaseConfig
 
    @dataclass
    class MyConfig(BaseConfig):
@@ -332,8 +332,8 @@ Here's how you can implement a custom model with Mambular:
    Define your custom model just as you would for an `nn.Module`. The main difference is that you will inherit from `BaseModel` and use the provided feature information to construct your layers. To integrate your model into the existing API, you only need to define the architecture and the forward pass.
 
    ```python
-   from mambular.base_models.utils import BaseModel
-   from mambular.utils.get_feature_dimensions import get_feature_dimensions
+   from deeptabular.base_models.utils import BaseModel
+   from deeptabular.utils.get_feature_dimensions import get_feature_dimensions
    import torch
    import torch.nn
 
@@ -372,19 +372,19 @@ Here's how you can implement a custom model with Mambular:
            return output
    ```
 
-3. **Leverage the Mambular API:**  
-   You can build a regression, classification, or distributional regression model that can leverage all of Mambular's built-in methods by using the following:
+3. **Leverage the DeepTabular API:**  
+   You can build a regression, classification, or distributional regression model that can leverage all of DeepTabular's built-in methods by using the following:
 
    ```python
-   from mambular.models.utils import SklearnBaseRegressor
+   from deeptabular.models.utils import SklearnBaseRegressor
 
    class MyRegressor(SklearnBaseRegressor):
        def __init__(self, **kwargs):
            super().__init__(model=MyCustomModel, config=MyConfig, **kwargs)
    ```
 
 4. **Train and evaluate your model:**  
-   You can now fit, evaluate, and predict with your custom model just like with any other Mambular model. For classification or distributional regression, inherit from `SklearnBaseClassifier` or `SklearnBaseLSS` respectively.
+   You can now fit, evaluate, and predict with your custom model just like with any other DeepTabular model. For classification or distributional regression, inherit from `SklearnBaseClassifier` or `SklearnBaseLSS` respectively.
 
    ```python
    regressor = MyRegressor(numerical_preprocessing="ple")

mambular/__version__.py → deeptabular/__version__.py

@@ -17,5 +17,5 @@
 
 # The following line *must* be the last in the module, exactly as formatted:
 
-__version__ = "1.5.0"
+__version__ = "1.6.0"
 

mambular/arch_utils/enode_utils.py → deeptabular/arch_utils/enode_utils.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from mambular.arch_utils.layer_utils.sparsemax import sparsemax, sparsemoid
+from deeptabular.arch_utils.layer_utils.sparsemax import sparsemax, sparsemoid
 from .data_aware_initialization import ModuleWithInit
 from .numpy_utils import check_numpy
 import numpy as np

mambular/data_utils/dataset.py → deeptabular/data_utils/dataset.py

@@ -24,6 +24,8 @@ def __init__(
         labels=None,
         regression=True,
     ):
+        assert cat_features_list or num_features_list
+
         self.cat_features_list = cat_features_list  # Categorical features tensors
         self.num_features_list = num_features_list  # Numerical features tensors
         self.embeddings_list = embeddings_list  # Embeddings tensors (optional)
@@ -44,7 +46,8 @@ def __init__(
             self.labels = None  # No labels in prediction mode
 
     def __len__(self):
-        return len(self.num_features_list[0])  # Use numerical features length
+        _feats = self.num_features_list if self.num_features_list else self.cat_features_list
+        return len(_feats[0])
 
     def __getitem__(self, idx):
         """Retrieves the features and label for a given index.

mambular/models/autoint.py → deeptabular/models/autoint.py

@@ -15,7 +15,7 @@ class and uses the AutoInt model with the default AutoInt
         configuration.
         """,
         examples="""
-        >>> from mambular.models import AutoIntRegressor
+        >>> from deeptabular.models import AutoIntRegressor
         >>> model = AutoIntRegressor(d_model=64, n_layers=8)
         >>> model.fit(X_train, y_train)
         >>> preds = model.predict(X_test)
@@ -33,7 +33,7 @@ class AutoIntClassifier(SklearnBaseClassifier):
         """AutoInt Classifier. This class extends the SklearnBaseClassifier class
         and uses the AutoInt model with the default AutoInt configuration.""",
         examples="""
-        >>> from mambular.models import AutoIntClassifier
+        >>> from deeptabular.models import AutoIntClassifier
         >>> model = AutoIntClassifier(d_model=64, n_layers=8)
         >>> model.fit(X_train, y_train)
         >>> preds = model.predict(X_test)
@@ -52,7 +52,7 @@ class AutoIntLSS(SklearnBaseLSS):
         This class extends the SklearnBaseLSS class and uses the
         AutoInt model with the default AutoInt configuration.""",
         examples="""
-        >>> from mambular.models import AutoIntLSS
+        >>> from deeptabular.models import AutoIntLSS
         >>> model = AutoIntLSS(d_model=64, n_layers=8)
         >>> model.fit(X_train, y_train, family="normal")
         >>> preds = model.predict(X_test)

mambular/models/enode.py → deeptabular/models/enode.py

@@ -14,7 +14,7 @@ class ENODERegressor(SklearnBaseRegressor):
         with the default ENODE configuration.
         """,
         examples="""
-        >>> from mambular.models import ENODERegressor
+        >>> from deeptabular.models import ENODERegressor
         >>> model = ENODERegressor()
         >>> model.fit(X_train, y_train)
         >>> preds = model.predict(X_test)
@@ -35,7 +35,7 @@ class ENODEClassifier(SklearnBaseClassifier):
         with the default ENODE configuration.
         """,
         examples="""
-        >>> from mambular.models import ENODEClassifier
+        >>> from deeptabular.models import ENODEClassifier
         >>> model = ENODEClassifier()
         >>> model.fit(X_train, y_train)
         >>> preds = model.predict(X_test)
@@ -56,7 +56,7 @@ class ENODELSS(SklearnBaseLSS):
         with the default ENODE configuration.
         """,
         examples="""
-        >>> from mambular.models import ENODELSS
+        >>> from deeptabular.models import ENODELSS
         >>> model = ENODELSS()
         >>> model.fit(X_train, y_train, family='normal')
         >>> preds = model.predict(X_test)

mambular/models/fttransformer.py → deeptabular/models/fttransformer.py

@@ -15,7 +15,7 @@ class and uses the FTTransformer model with the default FTTransformer
         configuration.
         """,
         examples="""
-        >>> from mambular.models import FTTransformerRegressor
+        >>> from deeptabular.models import FTTransformerRegressor
         >>> model = FTTransformerRegressor(d_model=64, n_layers=8)
         >>> model.fit(X_train, y_train)
         >>> preds = model.predict(X_test)
@@ -35,7 +35,7 @@ class FTTransformerClassifier(SklearnBaseClassifier):
         """FTTransformer Classifier. This class extends the SklearnBaseClassifier class
         and uses the FTTransformer model with the default FTTransformer configuration.""",
         examples="""
-        >>> from mambular.models import FTTransformerClassifier
+        >>> from deeptabular.models import FTTransformerClassifier
         >>> model = FTTransformerClassifier(d_model=64, n_layers=8)
         >>> model.fit(X_train, y_train)
         >>> preds = model.predict(X_test)
@@ -56,7 +56,7 @@ class FTTransformerLSS(SklearnBaseLSS):
         This class extends the SklearnBaseLSS class and uses the
         FTTransformer model with the default FTTransformer configuration.""",
         examples="""
-        >>> from mambular.models import FTTransformerLSS
+        >>> from deeptabular.models import FTTransformerLSS
         >>> model = FTTransformerLSS(d_model=64, n_layers=8)
         >>> model.fit(X_train, y_train, family="normal")
         >>> preds = model.predict(X_test)

mambular/models/mambattention.py → deeptabular/models/mambattention.py

@@ -14,7 +14,7 @@ class MambAttentionRegressor(SklearnBaseRegressor):
         with the default MambAttention configuration.
         """,
         examples="""
-        >>> from mambular.models import MambAttentionRegressor
+        >>> from deeptabular.models import MambAttentionRegressor
         >>> model = MambAttentionRegressor(d_model=64, n_layers=8)
         >>> model.fit(X_train, y_train)
         >>> preds = model.predict(X_test)