diff --git a/.gitignore b/.gitignore index 0cbe470..10e6c3b 100644 --- a/.gitignore +++ b/.gitignore @@ -1,11 +1,8 @@ *.ipynb_checkpoints *.pyc +*.DS_Store __pycache__/ -1337/epoch=11-step=623.ckpt -1337/* -4017/* outputs/* -2676/* -job_* -checkpoints* -data/ \ No newline at end of file +model_checkpoints/ +data/ +experiments_outputs/ \ No newline at end of file diff --git a/README.md b/README.md index 65484b9..8dda68f 100644 --- a/README.md +++ b/README.md @@ -1,50 +1,181 @@ -# SDM with Partial Labels +# CISO-SDM πŸ¦πŸ¦‹πŸŒΏ -### Evaluation: -We use the parameter `predict_family_of_species` to control which family subset of species we are evaluating -### Species within a single taxonomy setup: -SatBird: -- `predict_family_of_species = 0` : evaluate non-songbirds -- `predict_family_of_species = 1` : evaluate songbirds +This repository contains the code to reproduce the results from the paper: -splot: -- `predict_family_of_species = 0` : evaluate non-trees -- `predict_family_of_species = 1` : evaluate trees +### CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations -### Species in Multi-taxa setup: -SatBird & SatButterfly: -- To evaluate with **no partial labels** given (everything is unknown), set `eval_known_rate == 0 ` -- `predict_family_of_species = 0` : evaluate birds -- `predict_family_of_species = 1` : evaluate butterflies - -- To evaluate with **partial labels** given (some labels known), set `eval_known_rate == 1 ` -- `predict_family_of_species = 0` : evaluate birds -- `predict_family_of_species = 1` : evaluate butterflies +
+
+Figure +
-### Running code: +
-#### Installation -Code runs on Python 3.10. You can create conda env using `requirements/environment.yaml` or install pip packages from `requirements/requirements.txt` +## πŸ› οΈ Installation -We recommend following these steps for installing the required packages: +### βš™οΈ Requirements -```conda env create -f requirements/environment.yaml``` +This project requires **Python 3.11**. All dependencies are listed in `requirements/requirements.txt`. -```conda activate satbird``` +We recommend using **Conda** to create and manage a clean environment: -```conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia``` +```bash +conda create -n ciso_env python=3.11 +conda activate ciso_env +pip install -r requirements/requirements.txt +``` -#### Training and testing +#### πŸ’‘Not using Conda? -* To train the model (check `run_files/job.sh`) : `python train.py args.config=configs/base.yaml`. Examples of all config files for different baselines -are available in `configs`. -* To train a model: `python train.py args.config=$CONFIG_FILE_NAME ` -* To test a model: `python test.py args.config=$CONFIG_FILE_NAME ` +You can also use a standard Python virtual environment: -To log experiments on comet-ml, make sure you have exported your COMET_API_KEY and COMET_WORKSPACE in your environmental variables. -You can do so with `export COMET_API_KEY=your_comet_api_key` in your terminal. +```bash +python3.11 -m venv ciso_env +source ciso_env/bin/activate # On Windows use `ciso_env\Scripts\activate` +pip install -r requirements/requirements.txt +``` +### πŸ–₯️ Hardware Support +The results can be reproduced on any device (GPU, eGPU, or CPU), though the computational time will vary depending on the hardware's parallel processing capabilities. If you encounter memory issues, especially on lower-end devices, consider reducing the batch size in the training configuration to mitigate them. You may also need to install or update the appropriate NVIDIA drivers to work with PyTorch, depending on your specific setup. + +## πŸ“‚ Datasets + +All datasets used in the paper are publicly available [here](https://huggingface.co/cisosdm/datasets) on Hugging Face. + +#### Data preparation scripts + +Data preparation code is located in the `data_preprocessing/` folder: + +* SatButterfly: `ebutterfly_data_preparation.ipynb` +* sPlotOpen: `prepare_sPlotOpen_data.ipynb` +* SatBird Γ— sPlotOpen (co-located data): `prepare_satbirdxsplots.ipynb` + +## πŸ”¬ Experiment configurations: + +The `configs` directory contains subfolders for each dataset setup: + +* `configs/satbird` +* `configs/satbirdxsatbutterfly` +* `configs/satbirdxsplot` +* `configs/splot` + +Each subfolder includes YAML config files for models reported in the paper. These configs are used for both training and evaluation. + +| File | Model Description | +|----------------------------|-----------------------------------| +| `config_ciso.yaml` | CISO model | +| `config_linear.yaml` | Linear model | +| `config_maxent.yaml` | Linear model with MaxEnt features | +| `config_mlp.yaml` | MLP | +| `config_mlp_plusplus.yaml` | MLP++ | + +## πŸ€– Trained model checkpoints: + +All trained model checkpoints are available [here](https://huggingface.co/cisosdm/model_checkpoints). +For each dataset, each folder includes 3 sub-folders corresponding to 3 different runs (seeds). + +| Folder | Description | +|--------------------------|----------------------------------------------------------| +| `1_sPlotOpen` | Within-dataset experiments for sPlotOpen | +| `2_SatBird` | Within-dataset experiments for SatBird | +| `3_SatBirdxSatButterfly` | Across-datasets experiments for SatBird and SatButterfly | +| `4_SatBirdxsPlotOpen` | Across-datasets experiments for SatBird and sPlotOpen | + +## πŸš€ Running code + +### πŸ”Ή Training +[Optional] You can log experiments using [Comet ML](https://www.comet.com/site/), a platform for visualizing metrics as your models are training. To enable logging, make sure to export your `COMET_API_KEY` and `COMET_WORKSPACE` environment variables: + +```bash +export COMET_API_KEY=your_comet_api_key +export COMET_WORKSPACE=your_workspace +``` + +To train a model, set `config.mode = "train"` ,and run: + +```bash +python main.py --config=configs//.yaml +``` + +Examples of configuration files for different datasets and models can be found in the `configs/` directory. + +### πŸ”Ή Evaluation + +Use the `predict_family_of_species` parameter to control the subset of species evaluated. This parameter defaults to`-1` during training (i.e., not used). + +#### Evaluating species groups *within* a dataset + +🐦 **SatBird**: +- `predict_family_of_species = 0` β†’ evaluate **non-songbirds** +- `predict_family_of_species = 1` β†’ evaluate **songbirds** + +🌿 **sPlotOpen**: +- `predict_family_of_species = 0` β†’ evaluate **non-trees** +- `predict_family_of_species = 1` β†’ evaluate **trees** + + +#### Evaluating species groups *across* datasets +πŸ¦πŸ¦‹ **SatBird & SatButterfly**: +- `predict_family_of_species = 0` β†’ evaluate **birds** +- `predict_family_of_species = 1` β†’ evaluate **butterflies** + +🐦🌿**SatBird & sPlotOpen**: +- `predict_family_of_species = 0` β†’ evaluate **plants** +- `predict_family_of_species = 1` β†’ evaluate **birds** + + +#### Conditioning on other species groups + +For models that support partial labels (i.e., CISO and MLP++), evaluation can be conditioned on the observations of species from another group: + +- `partial_labels.eval_known_rate == 0 ` β†’ evaluate with no partial labels (all other species groups are unknown) +- `partial_labels.eval_known_rate == 1 ` β†’ evaluate with partial labels (labels from the other species group are + provided) + + +## πŸ“Š Reproducing Results and Figures + +You can reproduce the key results and figures from the paper using the scripts and notebooks provided below: + +### πŸ“ˆ Results + +To reproduce the results on Tables 1 & 2, as well as additional metrics in Table 5, you first download +the `model_checkpoints` folder from [here](https://huggingface.co/cisosdm/model_checkpoints). +For a certain dataset and model, you run the following command given the corresponding config file, and specify the +desired file name to save results. +For the config file specified, you need to control the following parameters: + +| Parameter | Description | +|-----------------------------------|-----------------------------------------------------------------------------| +| `load_ckpt_path` | Path to the checkpoints folder or exact checkpoint path. | +| `predict_family_of_species` | Controls which family of species to evaluate as shown above in Evaluation. | +| `partial_labels/eval_known_ratio` | Set to 1, to condition on known labels for the other group of species. | + +Set `config.mode = "test"` +```bash +python main.py --config=configs//.yaml --results_file_name= +``` + +##### Example: + +To reproduce results for CISO on SatBird, use `configs/satbird/config_ciso.yaml`. Set `load_ckpt_path` +to `model_checkpoints/2_SatBird/satbird_ciso` to evaluate the 3 different runs. + +* Evaluate **songbirds Unconditioned**: `predict_family_of_species = 1`. +* Evaluate **non-songbirds Unconditioned**: `predict_family_of_species = 0`. +* Evaluate **songbirds** **Conditioned** on non-songbirds: `predict_family_of_species = 1` and `eval_known_ratio = 1`. +* Evaluate **non-songbirds** **Conditioned** on songbirds: `predict_family_of_species = 0` and `eval_known_ratio = 1`. + +### πŸ–ΌοΈ Figures +* Figure 3: `figures/generate_figure_3.ipynb` +* Figure 4: `figures/generate_figure_4.ipynb` +* Figure 5: `figures/generate_figure_5.ipynb` + +### Revision 1: +Code for additional baselines such as Random Forest and sjSDM is available under `src/models`. + +## πŸ“œ License This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License](https://creativecommons.org/licenses/by-nc/4.0/). diff --git a/configs/defaults.yaml b/configs/defaults.yaml index 5016f37..09e63f4 100644 --- a/configs/defaults.yaml +++ b/configs/defaults.yaml @@ -75,39 +75,15 @@ losses: criterion: "CE" #or MAE or MSE (loss to choosefor optim ) metrics: - - name: ce - ignore: True - #weights on the cross entropy - lambd_pres: 1 - lambd_abs: 1 - scale : 1 - name: mae ignore: False scale: 10 - - name: nonzero_mae - ignore: True - scale: 10 - name: mse ignore: False scale: 10 - - name: nonzero_mse - ignore: True - scale: 10 - name: topk ignore: False scale: 1 - - name: topk2 - ignore: True - scale: 1 - - name: r2 - ignore: True #False - scale: 1 - - name: kl - ignore: True - scale : 1 - - name: accuracy - ignore: True - scale: 1 - name: top10 ignore: False scale: 1 diff --git a/configs/satbird/config_ctran.yaml b/configs/satbird/config_ciso.yaml similarity index 78% rename from configs/satbird/config_ctran.yaml rename to configs/satbird/config_ciso.yaml index 7309a2b..8c90662 100644 --- a/configs/satbird/config_ctran.yaml +++ b/configs/satbird/config_ciso.yaml @@ -1,21 +1,21 @@ #where to save checkpoints -save_path: "multirun_experiments/satbird_ctran" +save_path: "model_checkpoints/2_SatBird/satbird_ciso" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/satbird_ctran" -save_preds_path: "" #"/network/scratch/h/hager.radi/ecosystem-embedding/baseline_resnet18_RGBNIR_ENV/preds_path" +load_ckpt_path: "model_checkpoints/2_SatBird/satbird_ciso" +save_preds_path: "" dataloader_to_use: "SDMEnvMaskedDataset" comet: project_name: "SDMPartialLabels" tags: ["Ctran", "corrected_targets", "satbird"] - experiment_name: "satbird_ctran" # specify for training, or use to report test results, TODO: also use to resume training + experiment_name: "satbird_ciso" # specify for training, or use to report test results, experiment_key: "" # use to report test results, model: - name: "CTranModel" + name: "CISOModel" input_dim: 27 hidden_dim: 256 backbone: "SimpleMLPBackbone" @@ -35,8 +35,6 @@ losses: partial_labels: use: true - # mask known labels out of the loss (true or false) - masked_loss: False # quantized mask (1 if all positives to 1, > 1 to indicate bins) quantized_mask_bins: 4 # max ratio of unknown labels during training @@ -64,7 +62,7 @@ data: 'phihox', 'sltppt', 'sndppt'] files: - base: "/network/projects/ecosystem-embeddings/SatBird_data_v2/USA_summer" + base: "data/SatBird_data_v2/USA_summer" train: ["train_split.csv"] val: ["valid_split.csv"] test: ["test_split.csv"] diff --git a/configs/satbird/config_glm.yaml b/configs/satbird/config_linear.yaml similarity index 57% rename from configs/satbird/config_glm.yaml rename to configs/satbird/config_linear.yaml index 3b53603..0ffa6ba 100644 --- a/configs/satbird/config_glm.yaml +++ b/configs/satbird/config_linear.yaml @@ -1,14 +1,14 @@ #where to save checkpoints -save_path: "multirun_experiments/satbird_linear_v1" +save_path: "model_checkpoints/2_SatBird/satbird_linear" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/satbird_linear_v1" +load_ckpt_path: "model_checkpoints/2_SatBird/satbird_linear" save_preds_path: "" comet: project_name: "SDMPartialLabels" tags: [ "MLP", "corrected_targets", "EnvNormalization", "satbird" ] - experiment_name: "satbird_linear_v1" # specify for training, or use to report test results, TODO: also use to resume training + experiment_name: "satbird_linear" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvDataset" @@ -28,19 +28,8 @@ training: max_epochs: 50 accelerator: "cpu" -partial_labels: - use: false - # mask known labels out of the loss (true or false) - masked_loss: False - # quantized mask (1 if all positives to 1, > 1 to indicate bins) - quantized_mask_bins: 4 - # max ratio of unknown labels during training - train_known_ratio: 0.75 - # what known ratios do we consider when testing - eval_known_ratio: 0.0 # [1.0, 0.9, 0.8, 0.5] - # During testing, eval family of non-songbirds (0), or family of songbirds (1) -predict_family_of_species: 1 +predict_family_of_species: -1 data: loaders: @@ -55,13 +44,14 @@ data: 'phihox', 'sltppt', 'sndppt' ] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: [ "SatBird/train_split.csv" ] - val: [ "SatBird/valid_split.csv" ] - test: [ "SatBird/test_split.csv" ] + base: "/data/SatBird_data_v2/USA_summer" + train: [ "train_split.csv" ] + val: [ "valid_split.csv" ] + test: [ "test_split.csv" ] + + targets_file: [ "satbird_usa_summer_targets.pkl" ] - targets_file: [ "satbird/satbird_usa_summer_targets.pkl" ] - satbird_species_indices_path: "satbird/stats" + satbird_species_indices_path: "stats" multi_taxa: False per_taxa_species_count: [ 670 ] diff --git a/configs/satbird/config_glm_maxent.yaml b/configs/satbird/config_maxent.yaml similarity index 57% rename from configs/satbird/config_glm_maxent.yaml rename to configs/satbird/config_maxent.yaml index 2b110af..1277e02 100644 --- a/configs/satbird/config_glm_maxent.yaml +++ b/configs/satbird/config_maxent.yaml @@ -1,14 +1,14 @@ #where to save checkpoints -save_path: "multirun_experiments/satbird_linear_maxent_v1" +save_path: "model_checkpoints/2_SatBird/satbird_maxent" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/satbird_linear_maxent_v1" +load_ckpt_path: "model_checkpoints/2_SatBird/satbird_maxent" save_preds_path: "" comet: project_name: "SDMPartialLabels" tags: [ "MLP", "corrected_targets", "EnvNormalization", "satbird" ] - experiment_name: "satbird_linear_maxent_v1" # specify for training, or use to report test results, TODO: also use to resume training + experiment_name: "satbird_maxent" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvDataset" @@ -28,19 +28,8 @@ training: max_epochs: 50 accelerator: "cpu" -partial_labels: - use: false - # mask known labels out of the loss (true or false) - masked_loss: False - # quantized mask (1 if all positives to 1, > 1 to indicate bins) - quantized_mask_bins: 4 - # max ratio of unknown labels during training - train_known_ratio: 0.75 - # what known ratios do we consider when testing - eval_known_ratio: 0.0 # [1.0, 0.9, 0.8, 0.5] - # During testing, eval family of non-songbirds (0), or family of songbirds (1) -predict_family_of_species: 0 +predict_family_of_species: -1 data: @@ -57,14 +46,14 @@ data: 'phihox', 'sltppt', 'sndppt' ] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: [ "SatBird/train_split.csv" ] - val: [ "SatBird/valid_split.csv" ] - test: [ "SatBird/test_split.csv" ] + base: "/data/SatBird_data_v2/USA_summer" + train: [ "train_split.csv" ] + val: [ "valid_split.csv" ] + test: [ "test_split.csv" ] - targets_file: [ "satbird/satbird_usa_summer_targets.pkl" ] - satbird_species_indices_path: "satbird/stats" + targets_file: [ "satbird_usa_summer_targets.pkl" ] + satbird_species_indices_path: "stats" multi_taxa: False per_taxa_species_count: [ 670 ] total_species: 670 \ No newline at end of file diff --git a/configs/satbird/config_mlp.yaml b/configs/satbird/config_mlp.yaml index b413e7f..3f29934 100644 --- a/configs/satbird/config_mlp.yaml +++ b/configs/satbird/config_mlp.yaml @@ -1,14 +1,14 @@ #where to save checkpoints -save_path: "multirun_experiments/satbird_MLP" +save_path: "model_checkpoints/2_SatBird/satbird_mlp" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/satbird_MLP" +load_ckpt_path: "model_checkpoints/2_SatBird/satbird_mlp" save_preds_path: "" comet: project_name: "SDMPartialLabels" tags: ["MLP", "corrected_targets", "EnvNormalization", "satbird"] - experiment_name: "satbird_MLP" # specify for training, or use to report test results, TODO: also use to resume training + experiment_name: "satbird_mlp" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvDataset" @@ -28,17 +28,6 @@ training: max_epochs: 50 accelerator: "cpu" -partial_labels: - use: false - # mask known labels out of the loss (true or false) - masked_loss: False - # quantized mask (1 if all positives to 1, > 1 to indicate bins) - quantized_mask_bins: 4 - # max ratio of unknown labels during training - train_known_ratio: 0.75 - # what known ratios do we consider when testing - eval_known_ratio: 0.0 # [1.0, 0.9, 0.8, 0.5] - # During testing, eval family of non-songbirds (0), or family of songbirds (1) predict_family_of_species: -1 @@ -55,13 +44,14 @@ data: 'phihox', 'sltppt', 'sndppt'] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: ["SatBird/train_split.csv"] - val: ["SatBird/valid_split.csv"] - test: ["SatBird/test_split.csv"] + base: "/data/SatBird_data_v2/USA_summer" + train: [ "train_split.csv" ] + val: [ "valid_split.csv" ] + test: [ "test_split.csv" ] + + targets_file: [ "satbird_usa_summer_targets.pkl" ] - targets_file: ["satbird/satbird_usa_summer_targets.pkl"] - satbird_species_indices_path: "satbird/stats" + satbird_species_indices_path: "stats" multi_taxa: False per_taxa_species_count: [670] diff --git a/configs/satbird/config_maskedmlp.yaml b/configs/satbird/config_mlp_plusplus.yaml similarity index 77% rename from configs/satbird/config_maskedmlp.yaml rename to configs/satbird/config_mlp_plusplus.yaml index 4fcf815..59b3324 100644 --- a/configs/satbird/config_maskedmlp.yaml +++ b/configs/satbird/config_mlp_plusplus.yaml @@ -1,20 +1,20 @@ #where to save checkpoints -save_path: "multirun_experiments/satbird_maskedmlp" +save_path: "model_checkpoints/2_SatBird/satbird_mlp_plusplus" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/satbird_maskedmlp" +load_ckpt_path: "model_checkpoints/2_SatBird/satbird_mlp_plusplus" save_preds_path: "" dataloader_to_use: "SDMEnvMaskedDataset" comet: project_name: "SDMPartialLabels" - tags: ["MaskedMLP_v1", "direct_encounter_rates", "EnvNormalization", "satbird"] - experiment_name: "satbird_maskedmlp" # specify for training, or use to report test results, TODO: also use to resume training + tags: [ "direct_encounter_rates", "EnvNormalization", "satbird" ] + experiment_name: "satbird_mlp_plusplus" # specify for training, or use to report test results experiment_key: "" # use to report test results, model: - name: "SimpleMLPMasked_v1" + name: "SimpleMLP_PlusPlus" input_dim: 27 hidden_dim: 256 backbone: @@ -30,8 +30,6 @@ losses: partial_labels: use: true - # mask known labels out of the loss (true or false) - masked_loss: False # quantized mask (1 if all positives to 1, > 1 to indicate bins) quantized_mask_bins: 4 # max ratio of unknown labels during training @@ -55,7 +53,7 @@ data: 'phihox', 'sltppt', 'sndppt'] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data/SatBird" + base: "/data/SatBird_data_v2/USA_summer" train: ["train_split.csv"] val: ["valid_split.csv"] test: ["test_split.csv"] diff --git a/configs/satbirdxsatbutterfly/config_ctran.yaml b/configs/satbirdxsatbutterfly/config_ciso.yaml similarity index 61% rename from configs/satbirdxsatbutterfly/config_ctran.yaml rename to configs/satbirdxsatbutterfly/config_ciso.yaml index a520287..be59629 100644 --- a/configs/satbirdxsatbutterfly/config_ctran.yaml +++ b/configs/satbirdxsatbutterfly/config_ciso.yaml @@ -1,21 +1,21 @@ #where to save checkpoints -save_path: "multirun_experiments/final_satbirdxsatbutterfly/satbirdxsatbutterfly_ctran_v4" +save_path: "model_checkpoints/3_SatBirdxSatButterfly/satbirdxsatbutterfly_ciso" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/final_satbirdxsatbutterfly/satbirdxsatbutterfly_ctran_v4" -save_preds_path: "" #"/network/scratch/h/hager.radi/ecosystem-embedding/baseline_resnet18_RGBNIR_ENV/preds_path" +load_ckpt_path: "model_checkpoints/3_SatBirdxSatButterfly/satbirdxsatbutterfly_ciso" +save_preds_path: "" comet: project_name: "SDMPartialLabels" - tags: [ "ctran", "172butterfly_species", "50epochs", "satbird+ satbutterflyv1 + satbutterflyv2" ] - experiment_name: "satbirdxsatbutterfly_ctran_v4" # specify for training, or use to report test results, TODO: also use to resume training + tags: [ "172butterfly_species", "50epochs", "satbird+ satbutterflyv1 + satbutterflyv2" ] + experiment_name: "satbirdxsatbutterfly_ciso" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvMaskedDataset" model: - name: "CTranModel" + name: "CISOModel" input_dim: 27 hidden_dim: 256 backbone: "SimpleMLPBackbone" @@ -35,8 +35,6 @@ losses: partial_labels: use: true - # mask known labels out of the loss (true or false) - masked_loss: False # quantized mask (1 if all positives to 1, > 1 to indicate bins) quantized_mask_bins: 4 # max ratio of unknown labels during training @@ -62,15 +60,15 @@ data: 'phihox', 'sltppt', 'sndppt'] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: ["SatBirdButterfly/train_split_fillna.csv"] - val: [ "SatBirdButterfly/valid_split_fillna_colocated_only.csv" ] - test: ["SatBirdButterfly/test_split_fillna.csv"] + base: "/data" + train: [ "SatBirdxSatButterfly/train_split.csv" ] + val: [ "SatBirdxSatButterfly/valid_split.csv" ] + test: [ "SatBirdxSatButterfly/test_split.csv" ] - targets_file: [ "SatBird/satbird_usa_summer_targets.pkl", - "SatButterfly_dataset/combined_SatButterfly_v1Andv2_targets.pkl" ] + targets_file: [ "SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", + "SatButterfly/combined_SatButterfly_v1Andv2_targets.pkl" ] - satbird_species_indices_path: "SatBird/stats" + satbird_species_indices_path: "SatBird_data_v2/USA_summer/stats" multi_taxa: True per_taxa_species_count: {"bird": 670, "butterfly": 172} diff --git a/configs/satbirdxsatbutterfly/config_glm.yaml b/configs/satbirdxsatbutterfly/config_linear.yaml similarity index 54% rename from configs/satbirdxsatbutterfly/config_glm.yaml rename to configs/satbirdxsatbutterfly/config_linear.yaml index 1bf9017..758f53d 100644 --- a/configs/satbirdxsatbutterfly/config_glm.yaml +++ b/configs/satbirdxsatbutterfly/config_linear.yaml @@ -1,15 +1,15 @@ #where to save checkpoints -save_path: "multirun_experiments/final_satbirdxsatbutterfly/satbirdxsatbutterfly_glm_v1" +save_path: "model_checkpoints/3_SatBirdxSatButterfly/satbirdxsatbutterfly_linear" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/final_satbirdxsatbutterfly/satbirdxsatbutterfly_glm_v1" +load_ckpt_path: "model_checkpoints/3_SatBirdxSatButterfly/satbirdxsatbutterfly_linear" save_preds_path: "" comet: project_name: "SDMPartialLabels" tags: [ "172butterfly_species", "satbird + satbutterflyv1 + satbutterflyv2" ] - experiment_name: "satbirdxsatbutterfly_glm_v1" # specify for training, or use to report test results, TODO: also use to resume training + experiment_name: "satbirdxsatbutterfly_linear" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvDataset" @@ -29,17 +29,6 @@ training: losses: criterion: "BCE" -partial_labels: - use: false - # mask known labels out of the loss (true or false) - masked_loss: False - # quantized mask (1 if all positives to 1, > 1 to indicate bins) - quantized_mask_bins: 4 - # max ratio of unknown labels during training - train_known_ratio: 0.75 - # what known ratios do we consider when testing - eval_known_ratio: 0.0 # [1.0, 0.9, 0.8, 0.5] - # During testing, eval family of birds (0), or family of butterflies (1) predict_family_of_species: -1 @@ -57,16 +46,16 @@ data: 'phihox', 'sltppt', 'sndppt' ] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: [ "SatBirdButterfly/train_split_fillna.csv" ] - val: [ "SatBirdButterfly/valid_split_fillna_colocated_only.csv" ] - test: [ "SatBirdButterfly/test_split_fillna.csv" ] + base: "/data" + train: [ "SatBirdxSatButterfly/train_split.csv" ] + val: [ "SatBirdxSatButterfly/valid_split.csv" ] + test: [ "SatBirdxSatButterfly/test_split.csv" ] + + targets_file: [ "SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", + "SatButterfly/combined_SatButterfly_v1Andv2_targets.pkl" ] - # it has to be in this order [bird, butterfly, colocated] - targets_file: [ "SatBird/satbird_usa_summer_targets.pkl", - "SatButterfly_dataset/combined_SatButterfly_v1Andv2_targets.pkl" ] + satbird_species_indices_path: "SatBird_data_v2/USA_summer/stats" - satbird_species_indices_path: "SatBird/stats" # when combining two different taxa multi_taxa: True diff --git a/configs/satbirdxsatbutterfly/config_glm_maxent.yaml b/configs/satbirdxsatbutterfly/config_maxent.yaml similarity index 55% rename from configs/satbirdxsatbutterfly/config_glm_maxent.yaml rename to configs/satbirdxsatbutterfly/config_maxent.yaml index 10c0418..ceaa95a 100644 --- a/configs/satbirdxsatbutterfly/config_glm_maxent.yaml +++ b/configs/satbirdxsatbutterfly/config_maxent.yaml @@ -1,15 +1,15 @@ #where to save checkpoints -save_path: "multirun_experiments/final_satbirdxsatbutterfly/satbirdxsatbutterfly_glm_maxenet_v1" +save_path: "model_checkpoints/3_SatBirdxSatButterfly/satbirdxsatbutterfly_maxenet" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/final_satbirdxsatbutterfly/satbirdxsatbutterfly_glm_maxenet_v1" +load_ckpt_path: "model_checkpoints/3_SatBirdxSatButterfly/satbirdxsatbutterfly_maxenet" save_preds_path: "" comet: project_name: "SDMPartialLabels" tags: [ "172butterfly_species", "50epochs", "satbird + satbutterflyv1 + satbutterflyv2" ] - experiment_name: "satbirdxsatbutterfly_glm_maxenet_v1" # specify for training, or use to report test results, TODO: also use to resume training + experiment_name: "satbirdxsatbutterfly_maxenet" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvDataset" @@ -29,17 +29,6 @@ training: losses: criterion: "BCE" -partial_labels: - use: false - # mask known labels out of the loss (true or false) - masked_loss: False - # quantized mask (1 if all positives to 1, > 1 to indicate bins) - quantized_mask_bins: 4 - # max ratio of unknown labels during training - train_known_ratio: 0.75 - # what known ratios do we consider when testing - eval_known_ratio: 0.0 # [1.0, 0.9, 0.8, 0.5] - # During testing, eval family of birds (0), or family of butterflies (1) predict_family_of_species: -1 @@ -58,16 +47,15 @@ data: 'phihox', 'sltppt', 'sndppt' ] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: [ "SatBirdButterfly/train_split_fillna.csv" ] - val: [ "SatBirdButterfly/valid_split_fillna_colocated_only.csv" ] - test: [ "SatBirdButterfly/test_split_fillna.csv" ] + base: "/data" + train: [ "SatBirdxSatButterfly/train_split.csv" ] + val: [ "SatBirdxSatButterfly/valid_split.csv" ] + test: [ "SatBirdxSatButterfly/test_split.csv" ] - # it has to be in this order [bird, butterfly, colocated] - targets_file: [ "SatBird/satbird_usa_summer_targets.pkl", - "SatButterfly_dataset/combined_SatButterfly_v1Andv2_targets.pkl" ] + targets_file: [ "SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", + "SatButterfly/combined_SatButterfly_v1Andv2_targets.pkl" ] - satbird_species_indices_path: "SatBird/stats" + satbird_species_indices_path: "SatBird_data_v2/USA_summer/stats" # when combining two different taxa multi_taxa: True diff --git a/configs/satbirdxsatbutterfly/config_mlp.yaml b/configs/satbirdxsatbutterfly/config_mlp.yaml index f5848c9..0173515 100644 --- a/configs/satbirdxsatbutterfly/config_mlp.yaml +++ b/configs/satbirdxsatbutterfly/config_mlp.yaml @@ -1,15 +1,15 @@ #where to save checkpoints -save_path: "multirun_experiments/final_satbirdxsatbutterfly/satbirdxsatbutterfly_mlp_v2" +save_path: "model_checkpoints/3_SatBirdxSatButterfly/satbirdxsatbutterfly_mlp" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/final_satbirdxsatbutterfly/satbirdxsatbutterfly_mlp_v2" +load_ckpt_path: "model_checkpoints/3_SatBirdxSatButterfly/satbirdxsatbutterfly_mlp" save_preds_path: "" comet: project_name: "SDMPartialLabels" tags: [ "172butterfly_species", "50epochs", "satbird + satbutterflyv1 + satbutterflyv2" ] - experiment_name: "satbirdxsatbutterfly_mlp_v2" # specify for training, or use to report test results, TODO: also use to resume training + experiment_name: "satbirdxsatbutterfly_mlp" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvDataset" @@ -29,17 +29,6 @@ training: losses: criterion: "BCE" -partial_labels: - use: false - # mask known labels out of the loss (true or false) - masked_loss: False - # quantized mask (1 if all positives to 1, > 1 to indicate bins) - quantized_mask_bins: 4 - # max ratio of unknown labels during training - train_known_ratio: 0.75 - # what known ratios do we consider when testing - eval_known_ratio: 0.0 # [1.0, 0.9, 0.8, 0.5] - # During testing, eval family of birds (0), or family of butterflies (1) predict_family_of_species: -1 @@ -57,16 +46,15 @@ data: 'phihox', 'sltppt', 'sndppt'] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: ["SatBirdButterfly/train_split_fillna.csv"] - val: [ "SatBirdButterfly/valid_split_fillna_colocated_only.csv" ] - test: ["SatBirdButterfly/test_split_fillna.csv"] + base: "/data" + train: [ "SatBirdxSatButterfly/train_split.csv" ] + val: [ "SatBirdxSatButterfly/valid_split.csv" ] + test: [ "SatBirdxSatButterfly/test_split.csv" ] - # it has to be in this order [bird, butterfly, colocated] - targets_file: ["SatBird/satbird_usa_summer_targets.pkl", - "SatButterfly_dataset/combined_SatButterfly_v1Andv2_targets.pkl" ] + targets_file: [ "SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", + "SatButterfly/combined_SatButterfly_v1Andv2_targets.pkl" ] - satbird_species_indices_path: "SatBird/stats" + satbird_species_indices_path: "SatBird_data_v2/USA_summer/stats" # when combining two different taxa multi_taxa: True diff --git a/configs/satbirdxsatbutterfly/config_maskedmlp.yaml b/configs/satbirdxsatbutterfly/config_mlp_plusplus.yaml similarity index 63% rename from configs/satbirdxsatbutterfly/config_maskedmlp.yaml rename to configs/satbirdxsatbutterfly/config_mlp_plusplus.yaml index 8fed1b2..f761ad0 100644 --- a/configs/satbirdxsatbutterfly/config_maskedmlp.yaml +++ b/configs/satbirdxsatbutterfly/config_mlp_plusplus.yaml @@ -1,8 +1,8 @@ #where to save checkpoints -save_path: "multirun_experiments/final_satbirdxsatbutterfly/satbirdxsatbutterfly_maskedmlp_multi_ckpt_callback_v5" +save_path: "model_checkpoints/3_SatBirdxSatButterfly/satbirdxsatbutterfly_mlp_plusplus" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. -load_ckpt_path: "multirun_experiments/final_satbirdxsatbutterfly/satbirdxsatbutterfly_maskedmlp_multi_ckpt_callback_v5/" +load_ckpt_path: "model_checkpoints/3_SatBirdxSatButterfly/satbirdxsatbutterfly_mlp_plusplus/" save_preds_path: "" dataloader_to_use: "SDMEnvMaskedDataset" @@ -10,11 +10,11 @@ dataloader_to_use: "SDMEnvMaskedDataset" comet: project_name: "SDMPartialLabels" tags: [ "172butterfly_species", "bins=4", "50_epochs", "trainratio=0.75", "two_encoder_maskedmlp", "satbird + satbutterflyv1 + satbutterflyv2" ] - experiment_name: "satbirdxsatbutterfly_maskedmlp_multi_ckpt_callback_v5" # specify for training, or use to report test results, TODO: also use to resume training + experiment_name: "satbirdxsatbutterfly_mlp_plusplus" # specify for training, or use to report test results experiment_key: "" # use to report test results, model: - name: "SimpleMLPMasked_v1" + name: "SimpleMLP_PlusPlus" input_dim: 27 hidden_dim: 256 backbone: @@ -30,8 +30,6 @@ losses: partial_labels: use: true - # mask known labels out of the loss (true or false) - masked_loss: False # quantized mask (1 if all positives to 1, > 1 to indicate bins) quantized_mask_bins: 4 # max ratio of unknown labels during training @@ -56,18 +54,15 @@ data: 'phihox', 'sltppt', 'sndppt'] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: ["SatBirdButterfly/train_split_fillna.csv"] - val: [ "SatBirdButterfly/valid_split_fillna_colocated_only.csv" ] - test: ["SatBirdButterfly/test_split_fillna.csv"] + base: "/data" + train: [ "SatBirdxSatButterfly/train_split.csv" ] + val: [ "SatBirdxSatButterfly/valid_split.csv" ] + test: [ "SatBirdxSatButterfly/test_split.csv" ] - # it has to be in this order [bird, butterfly, colocated] - targets_file: ["SatBird/satbird_usa_summer_targets.pkl", - "SatButterfly_dataset/combined_SatButterfly_v1Andv2_targets.pkl" ] - - - satbird_species_indices_path: "SatBird/stats" + targets_file: [ "SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", + "SatButterfly/combined_SatButterfly_v1Andv2_targets.pkl" ] + satbird_species_indices_path: "SatBird_data_v2/USA_summer/stats" # when combining two different taxa multi_taxa: True per_taxa_species_count: {"bird": 670, "butterfly": 172} diff --git a/configs/satbirdxsplot/config_ctran.yaml b/configs/satbirdxsplot/config_ciso.yaml similarity index 67% rename from configs/satbirdxsplot/config_ctran.yaml rename to configs/satbirdxsplot/config_ciso.yaml index ff423ea..f80156c 100644 --- a/configs/satbirdxsplot/config_ctran.yaml +++ b/configs/satbirdxsplot/config_ciso.yaml @@ -1,21 +1,21 @@ #where to save checkpoints -save_path: "multirun_experiments/satbirdxsplots_ctran_v7" +save_path: "model_checkpoints/4_SatBirdxsPlotOpen/satbirdxsplots_ciso" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/satbirdxsplots_ctran_v7" +load_ckpt_path: "model_checkpoints/4_SatBirdxsPlotOpen/satbirdxsplots_ciso" save_preds_path: "" comet: project_name: "SDMPartialLabels" - tags: [ "satbird + splots", "same_as_v2", "US_species_only", "bins=1", "train_ratio=0.75" ] - experiment_name: "satbirdxsplots_ctran_v7" # specify for training, or use to report test results + tags: [ "satbird + splots", "US_species_only", "bins=1", "train_ratio=0.75" ] + experiment_name: "satbirdxsplots_ciso" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvMaskedDataset" model: - name: "CTranModel" + name: "CISOModel" input_dim: 27 hidden_dim: 256 backbone: "SimpleMLPBackbone" @@ -35,8 +35,6 @@ losses: partial_labels: use: true - # mask known labels out of the loss (true or false) - masked_loss: False # quantized mask (1 if all positives to 1, > 1 to indicate bins) quantized_mask_bins: 1 # max ratio of unknown labels during training @@ -61,19 +59,19 @@ data: 'phihox', 'sltppt', 'sndppt'] files: - base: "/network/projects/ecosystem-embeddings" - train: ["SatBirdxsPlots/satbird_splots_train_split.csv"] - val: ["SatBirdxsPlots/satbird_splots_valid_split_coloc.csv"] - test: ["SatBirdxsPlots/satbird_splots_test_split.csv"] + base: "/data" + train: [ "SatBirdxsPlotOpen/satbird_splots_train_split.csv" ] + val: [ "SatBirdxsPlotOpen/satbird_splots_valid_split.csv" ] + test: [ "SatBirdxsPlotOpen/satbird_splots_test_split.csv" ] # it has to be in this order as the keys in per_taxa_species_count targets_file: ["SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", - "sPlotOpen/sPlotOpen_targets_US_only.pkl"] # target files follow the same format as SatBird (hotspotID: target_array) + "SatBirdxsPlotOpen/sPlotOpen_targets_US_only.pkl" ] # target files follow the same format as SatBird (hotspotID: target_array) satbird_species_indices_path: "SatBird_data_v2/USA_summer/stats" #TODO: remove this and compute dynamically - plant_test_species_indices_file: "SatBirdxsPlots/plant_test_species_indices_US_only.npy" - plant_val_species_indices_file: "SatBirdxsPlots/plant_validation_species_indices_US_only.npy" + plant_test_species_indices_file: "SatBirdxsPlotOpen/plant_test_species_indices_US_only.npy" + plant_val_species_indices_file: "SatBirdxsPlotOpen/plant_validation_species_indices_US_only.npy" # when combining two different taxa multi_taxa: True diff --git a/configs/satbirdxsplot/config_glm.yaml b/configs/satbirdxsplot/config_linear.yaml similarity index 57% rename from configs/satbirdxsplot/config_glm.yaml rename to configs/satbirdxsplot/config_linear.yaml index bbe8911..38654d3 100644 --- a/configs/satbirdxsplot/config_glm.yaml +++ b/configs/satbirdxsplot/config_linear.yaml @@ -1,15 +1,15 @@ #where to save checkpoints -save_path: "multirun_experiments/final_satbirdxsplot/satbirdxsplot_glm_v1" +save_path: "model_checkpoints/4_SatBirdxsPlotOpen/satbirdxsplot_linear" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/final_satbirdxsplot/satbirdxsplot_glm_v1" +load_ckpt_path: "model_checkpoints/4_SatBirdxsPlotOpen/satbirdxsplot_linear" save_preds_path: "" comet: project_name: "SDMPartialLabels" tags: [ "satbird + splots", "baseline", "multimetric" ] - experiment_name: "satbirdxsplot_glm_v1" # specify for training, or use to report test results + experiment_name: "satbirdxsplot_linear" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvDataset" @@ -29,17 +29,6 @@ training: losses: criterion: "BCE" -partial_labels: - use: false - # mask known labels out of the loss (true or false) - masked_loss: False - # quantized mask (1 if all positives to 1, > 1 to indicate bins) - quantized_mask_bins: 4 - # max ratio of unknown labels during training - train_known_ratio: 0.75 - # what known ratios do we consider when testing - eval_known_ratio: 0 # [1.0, 0.9, 0.8, 0.5] - # During testing, eval family of birds (0), or family of plants (1) predict_family_of_species: -1 @@ -57,19 +46,19 @@ data: 'phihox', 'sltppt', 'sndppt' ] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: [ "satbirdxsplotopen/satbird_splots_train_split.csv" ] - val: [ "satbirdxsplotopen/satbird_splots_valid_split_coloc.csv" ] - test: [ "satbirdxsplotopen/satbird_splots_test_split.csv" ] + base: "/data" + train: [ "SatBirdxsPlotOpen/satbird_splots_train_split.csv" ] + val: [ "SatBirdxsPlotOpen/satbird_splots_valid_split.csv" ] + test: [ "SatBirdxsPlotOpen/satbird_splots_test_split.csv" ] # it has to be in this order as the keys in per_taxa_species_count - targets_file: [ "SatBird/satbird_usa_summer_targets.pkl", - "sPlotOpen/sPlotOpen_targets_US_only.pkl" ] # target files follow the same format as SatBird (hotspotID: target_array) + targets_file: [ "SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", + "SatBirdxsPlotOpen/sPlotOpen_targets_US_only.pkl" ] # target files follow the same format as SatBird (hotspotID: target_array) - satbird_species_indices_path: "SatBird/stats" + satbird_species_indices_path: "SatBird_data_v2/USA_summer/stats" #TODO: remove this and compute dynamically - plant_test_species_indices_file: "satbirdxsplotopen/plant_test_species_indices_US_only.npy" - plant_val_species_indices_file: "satbirdxsplotopen/plant_validation_species_indices_US_only.npy" + plant_test_species_indices_file: "SatBirdxsPlotOpen/plant_test_species_indices_US_only.npy" + plant_val_species_indices_file: "SatBirdxsPlotOpen/plant_validation_species_indices_US_only.npy" # when combining two different taxa multi_taxa: True diff --git a/configs/satbirdxsplot/config_glm_maxent.yaml b/configs/satbirdxsplot/config_maxent.yaml similarity index 56% rename from configs/satbirdxsplot/config_glm_maxent.yaml rename to configs/satbirdxsplot/config_maxent.yaml index 7ed9915..529e983 100644 --- a/configs/satbirdxsplot/config_glm_maxent.yaml +++ b/configs/satbirdxsplot/config_maxent.yaml @@ -1,15 +1,15 @@ #where to save checkpoints -save_path: "multirun_experiments/final_satbirdxsplot/satbirdxsplot_glm_maxent_v1" +save_path: "model_checkpoints/4_SatBirdxsPlotOpen/satbirdxsplot_maxent" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/final_satbirdxsplot/satbirdxsplot_glm_maxent_v1" +load_ckpt_path: "model_checkpoints/4_SatBirdxsPlotOpen/satbirdxsplot_maxent" save_preds_path: "" comet: project_name: "SDMPartialLabels" tags: [ "satbird + splots", "baseline", "multimetric" ] - experiment_name: "satbirdxsplot_glm_maxent_v1" # specify for training, or use to report test results + experiment_name: "satbirdxsplot_maxent" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvDataset" @@ -29,17 +29,6 @@ training: losses: criterion: "BCE" -partial_labels: - use: false - # mask known labels out of the loss (true or false) - masked_loss: False - # quantized mask (1 if all positives to 1, > 1 to indicate bins) - quantized_mask_bins: 4 - # max ratio of unknown labels during training - train_known_ratio: 0.75 - # what known ratios do we consider when testing - eval_known_ratio: 0 # [1.0, 0.9, 0.8, 0.5] - # During testing, eval family of birds (0), or family of plants (1) predict_family_of_species: -1 @@ -58,19 +47,19 @@ data: 'phihox', 'sltppt', 'sndppt' ] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: [ "satbirdxsplotopen/satbird_splots_train_split.csv" ] - val: [ "satbirdxsplotopen/satbird_splots_valid_split_coloc.csv" ] - test: [ "satbirdxsplotopen/satbird_splots_test_split.csv" ] + base: "/data" + train: [ "SatBirdxsPlotOpen/satbird_splots_train_split.csv" ] + val: [ "SatBirdxsPlotOpen/satbird_splots_valid_split.csv" ] + test: [ "SatBirdxsPlotOpen/satbird_splots_test_split.csv" ] # it has to be in this order as the keys in per_taxa_species_count - targets_file: [ "SatBird/satbird_usa_summer_targets.pkl", - "sPlotOpen/sPlotOpen_targets_US_only.pkl" ] # target files follow the same format as SatBird (hotspotID: target_array) + targets_file: [ "SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", + "SatBirdxsPlotOpen/sPlotOpen_targets_US_only.pkl" ] # target files follow the same format as SatBird (hotspotID: target_array) - satbird_species_indices_path: "SatBird/stats" + satbird_species_indices_path: "SatBird_data_v2/USA_summer/stats" #TODO: remove this and compute dynamically - plant_test_species_indices_file: "satbirdxsplotopen/plant_test_species_indices_US_only.npy" - plant_val_species_indices_file: "satbirdxsplotopen/plant_validation_species_indices_US_only.npy" + plant_test_species_indices_file: "SatBirdxsPlotOpen/plant_test_species_indices_US_only.npy" + plant_val_species_indices_file: "SatBirdxsPlotOpen/plant_validation_species_indices_US_only.npy" # when combining two different taxa multi_taxa: True diff --git a/configs/satbirdxsplot/config_mlp.yaml b/configs/satbirdxsplot/config_mlp.yaml index b65a405..47fdc93 100644 --- a/configs/satbirdxsplot/config_mlp.yaml +++ b/configs/satbirdxsplot/config_mlp.yaml @@ -1,15 +1,15 @@ #where to save checkpoints -save_path: "multirun_experiments/final_satbirdxsplot/satbirdxsplot_MLP_v4" +save_path: "model_checkpoints/4_SatBirdxsPlotOpen/satbirdxsplot_mlp" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/final_satbirdxsplot/satbirdxsplot_MLP_v4" +load_ckpt_path: "model_checkpoints/4_SatBirdxsPlotOpen/satbirdxsplot_mlp" save_preds_path: "" comet: project_name: "SDMPartialLabels" tags: [ "satbird + splots", "baseline", "multimetric" ] - experiment_name: "satbirdxsplot_MLP_v4" # specify for training, or use to report test results + experiment_name: "satbirdxsplot_mlp" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvDataset" @@ -29,17 +29,6 @@ training: losses: criterion: "BCE" -partial_labels: - use: false - # mask known labels out of the loss (true or false) - masked_loss: False - # quantized mask (1 if all positives to 1, > 1 to indicate bins) - quantized_mask_bins: 4 - # max ratio of unknown labels during training - train_known_ratio: 0.75 - # what known ratios do we consider when testing - eval_known_ratio: 0 # [1.0, 0.9, 0.8, 0.5] - # During testing, eval family of birds (0), or family of plants (1) predict_family_of_species: -1 @@ -57,19 +46,19 @@ data: 'phihox', 'sltppt', 'sndppt'] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: ["satbirdxsplotopen/satbird_splots_train_split.csv"] - val: ["satbirdxsplotopen/satbird_splots_valid_split_coloc.csv"] - test: ["satbirdxsplotopen/satbird_splots_test_split.csv"] + base: "/data" + train: [ "SatBirdxsPlotOpen/satbird_splots_train_split.csv" ] + val: [ "SatBirdxsPlotOpen/satbird_splots_valid_split.csv" ] + test: [ "SatBirdxsPlotOpen/satbird_splots_test_split.csv" ] # it has to be in this order as the keys in per_taxa_species_count - targets_file: ["SatBird/satbird_usa_summer_targets.pkl", - "sPlotOpen/sPlotOpen_targets_US_only.pkl" ] # target files follow the same format as SatBird (hotspotID: target_array) + targets_file: [ "SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", + "SatBirdxsPlotOpen/sPlotOpen_targets_US_only.pkl" ] # target files follow the same format as SatBird (hotspotID: target_array) - satbird_species_indices_path: "SatBird/stats" + satbird_species_indices_path: "SatBird_data_v2/USA_summer/stats" #TODO: remove this and compute dynamically - plant_test_species_indices_file: "satbirdxsplotopen/plant_test_species_indices_US_only.npy" - plant_val_species_indices_file: "satbirdxsplotopen/plant_validation_species_indices_US_only.npy" + plant_test_species_indices_file: "SatBirdxsPlotOpen/plant_test_species_indices_US_only.npy" + plant_val_species_indices_file: "SatBirdxsPlotOpen/plant_validation_species_indices_US_only.npy" # when combining two different taxa multi_taxa: True diff --git a/configs/satbirdxsplot/config_maskedmlp.yaml b/configs/satbirdxsplot/config_mlp_plusplus.yaml similarity index 62% rename from configs/satbirdxsplot/config_maskedmlp.yaml rename to configs/satbirdxsplot/config_mlp_plusplus.yaml index 00fd5d8..93df736 100644 --- a/configs/satbirdxsplot/config_maskedmlp.yaml +++ b/configs/satbirdxsplot/config_mlp_plusplus.yaml @@ -1,21 +1,21 @@ #where to save checkpoints -save_path: "multirun_experiments/final_satbirdxsplot/satbirdxsplots_MaskedMLP_v1" +save_path: "model_checkpoints/4_SatBirdxsPlotOpen/satbirdxsplots_mlp_plusplus" # load existing checkpoint for inference. If passing experiment folder instead (for multiple seeds), it will evaluate all of them. # always use the best checkpoint -load_ckpt_path: "multirun_experiments/final_satbirdxsplot/satbirdxsplots_MaskedMLP_v1" +load_ckpt_path: "model_checkpoints/4_SatBirdxsPlotOpen/satbirdxsplots_mlp_plusplus" save_preds_path: "" comet: project_name: "SDMPartialLabels" - tags: [ "satbird + splots", "saving_multimetrixc", "US_species_only", "bins=1", "ratio=0.75" , "weightedsamplingTrue" ] - experiment_name: "satbirdxsplots_MaskedMLP_v1" # specify for training, or use to report test results + tags: [ "satbird + splots", "US_species_only", "bins=1", "ratio=0.75" , "weightedsamplingTrue" ] + experiment_name: "satbirdxsplots_mlp_plusplus" # specify for training, or use to report test results experiment_key: "" # use to report test results, dataloader_to_use: "SDMEnvMaskedDataset" model: - name: "SimpleMLPMasked_v1" + name: "SimpleMLP_PlusPlus" input_dim: 27 hidden_dim: 256 backbone: @@ -31,8 +31,6 @@ losses: partial_labels: use: true - # mask known labels out of the loss (true or false) - masked_loss: False # quantized mask (1 if all positives to 1, > 1 to indicate bins) quantized_mask_bins: 1 # max ratio of unknown labels during training @@ -56,19 +54,19 @@ data: 'phihox', 'sltppt', 'sndppt'] files: - base: "/Users/hagerradi/Projects/SDMPartialLabels/data" - train: ["satbirdxsplotopen/satbird_splots_train_split.csv"] - val: ["satbirdxsplotopen/satbird_splots_valid_split_coloc.csv"] - test: ["satbirdxsplotopen/satbird_splots_test_split.csv"] + base: "/data" + train: [ "SatBirdxsPlotOpen/satbird_splots_train_split.csv" ] + val: [ "SatBirdxsPlotOpen/satbird_splots_valid_split.csv" ] + test: [ "SatBirdxsPlotOpen/satbird_splots_test_split.csv" ] # it has to be in this order as the keys in per_taxa_species_count - targets_file: ["SatBird/satbird_usa_summer_targets.pkl", - "sPlotOpen/sPlotOpen_targets_US_only.pkl"] # target files follow the same format as SatBird (hotspotID: target_array) + targets_file: [ "SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", + "SatBirdxsPlotOpen/sPlotOpen_targets_US_only.pkl" ] # target files follow the same format as SatBird (hotspotID: target_array) - satbird_species_indices_path: "SatBird/stats" + satbird_species_indices_path: "SatBird_data_v2/USA_summer/stats" #TODO: remove this and compute dynamically - plant_test_species_indices_file: "satbirdxsplotopen/plant_test_species_indices_US_only.npy" - plant_val_species_indices_file: "satbirdxsplotopen/plant_validation_species_indices_US_only.npy" + plant_test_species_indices_file: "SatBirdxsPlotOpen/plant_test_species_indices_US_only.npy" + plant_val_species_indices_file: "SatBirdxsPlotOpen/plant_validation_species_indices_US_only.npy" # when combining two different taxa multi_taxa: True diff --git a/configs/splot/config_ctran.yaml b/configs/splot/config_ciso.yaml similarity index 81% rename from configs/splot/config_ctran.yaml rename to configs/splot/config_ciso.yaml index 9855cc4..f9e3e98 100644 --- a/configs/splot/config_ctran.yaml +++ b/configs/splot/config_ciso.yaml @@ -1,7 +1,7 @@ -mode: "train" # "test" +mode: "test" # "test" dataset_name: "sPlot" model: - name: "CTranModel" + name: "CISOModel" input_dim: 27 hidden_dim: 256 num_classes: 3951 @@ -16,15 +16,15 @@ training: logger: project_name: "sPlotOpen" - experiment_name: "splot_ctran" # specify for training, or use to report test results + experiment_name: "splot_ciso" # specify for training, or use to report test results experiment_key: "" # use to report test results, - checkpoint_path: "./multirun_experiments" - checkpoint_name: "last.ckpt" + checkpoint_path: "model_checkpoints/1_sPlotOpen" + checkpoint_name: "" save_preds_path: "" data: dataloader_to_use: "sPlotMaskedDataset" - base: "/network/projects/ecosystem-embeddings/sPlotOpen" + base: "data/sPlotOpen" train: "train_indices.npy" validation: "validation_indices.npy" test: "test_indices.npy" diff --git a/configs/splot/config_glm.yaml b/configs/splot/config_linear.yaml similarity index 80% rename from configs/splot/config_glm.yaml rename to configs/splot/config_linear.yaml index 1ba8e5d..e375e1f 100644 --- a/configs/splot/config_glm.yaml +++ b/configs/splot/config_linear.yaml @@ -1,4 +1,4 @@ -mode: "test" # "test" +mode: "train" # "test" dataset_name: "sPlot" model: name: "Linear" @@ -16,14 +16,14 @@ training: logger: project_name: "sPlotOpen" - experiment_name: "splot_glm" # specify for training, or use to report test results + experiment_name: "splot_linear" # specify for training, or use to report test results experiment_key: "" # use to report test results, - checkpoint_path: "./multirun_experiments" + checkpoint_path: "./model_checkpoints/1_sPlotOpen" checkpoint_name: "" data: dataloader_to_use: "sPlotDataset" - base: "/Users/hagerradi/Projects/SDMPartialLabels/data/sPlotOpen" + base: "/data/sPlotOpen" train: "train_indices.npy" validation: "validation_indices.npy" test: "test_indices.npy" @@ -41,4 +41,4 @@ data: partial_labels: use: False - predict_family_of_species: 1 \ No newline at end of file + predict_family_of_species: -1 \ No newline at end of file diff --git a/configs/splot/config_glm_maxent.yaml b/configs/splot/config_maxent.yaml similarity index 80% rename from configs/splot/config_glm_maxent.yaml rename to configs/splot/config_maxent.yaml index cb515b9..128f8c5 100644 --- a/configs/splot/config_glm_maxent.yaml +++ b/configs/splot/config_maxent.yaml @@ -1,4 +1,4 @@ -mode: "test" # "test" +mode: "train" # "test" dataset_name: "sPlot" model: name: "Linear" @@ -16,15 +16,15 @@ training: logger: project_name: "sPlotOpen" - experiment_name: "splot_glm_maxent" # specify for training, or use to report test results + experiment_name: "splot_maxent" # specify for training, or use to report test results experiment_key: "" # use to report test results, - checkpoint_path: "./multirun_experiments" + checkpoint_path: "./model_checkpoints/1_sPlotOpen" checkpoint_name: "" data: maxent_transform: True dataloader_to_use: "sPlotDataset" - base: "/Users/hagerradi/Projects/SDMPartialLabels/data/sPlotOpen" + base: "/data/sPlotOpen" train: "train_indices.npy" validation: "validation_indices.npy" test: "test_indices.npy" @@ -42,4 +42,4 @@ data: partial_labels: use: False - predict_family_of_species: 1 \ No newline at end of file + predict_family_of_species: -1 \ No newline at end of file diff --git a/configs/splot/config_mlp.yaml b/configs/splot/config_mlp.yaml index 227a641..9eb2a02 100644 --- a/configs/splot/config_mlp.yaml +++ b/configs/splot/config_mlp.yaml @@ -18,12 +18,12 @@ logger: project_name: "sPlotOpen" experiment_name: "splot_mlp" # specify for training, or use to report test results experiment_key: "" # use to report test results, - checkpoint_path: "./multirun_experiments" + checkpoint_path: "./model_checkpoints/1_sPlotOpen" checkpoint_name: "" data: dataloader_to_use: "sPlotDataset" - base: "/Users/hagerradi/Projects/SDMPartialLabels/data/sPlotOpen" + base: "/data/sPlotOpen" train: "train_indices.npy" validation: "validation_indices.npy" test: "test_indices.npy" diff --git a/configs/splot/config_maskedmlp.yaml b/configs/splot/config_mlp_plusplus.yaml similarity index 79% rename from configs/splot/config_maskedmlp.yaml rename to configs/splot/config_mlp_plusplus.yaml index 8dbc686..c769393 100644 --- a/configs/splot/config_maskedmlp.yaml +++ b/configs/splot/config_mlp_plusplus.yaml @@ -1,7 +1,7 @@ -mode: "test" # "test" +mode: "train" # "test" dataset_name: "sPlot" model: - name: "SimpleMLPMasked_v1" + name: "SimpleMLP_PlusPlus" input_dim: 27 hidden_dim: 256 num_classes: 3951 @@ -16,14 +16,14 @@ training: logger: project_name: "sPlotOpen" - experiment_name: "splot_maskedmlp" # specify for training, or use to report test results + experiment_name: "splot_mlp_plusplus" # specify for training, or use to report test results experiment_key: "" # use to report test results - checkpoint_path: "./multirun_experiments" + checkpoint_path: "./model_checkpoints/1_sPlotOpen" checkpoint_name: "" data: dataloader_to_use: "sPlotMaskedDataset" - base: "/Users/hagerradi/Projects/SDMPartialLabels/data/sPlotOpen" + base: "/data/sPlotOpen" train: "train_indices.npy" validation: "validation_indices.npy" test: "test_indices.npy" @@ -45,4 +45,4 @@ data: train_known_ratio: 0.75 eval_known_ratio: 1 # 0: non-trees, 1: trees - predict_family_of_species: 0 \ No newline at end of file + predict_family_of_species: -1 \ No newline at end of file diff --git a/data_processing/ebutterfly_data_preparation/prepare_ebutterfly_independent_from_ebird.ipynb b/data_processing/ebutterfly_data_preparation/prepare_ebutterfly_independent_from_ebird.ipynb index 163b5ea..06a9353 100644 --- a/data_processing/ebutterfly_data_preparation/prepare_ebutterfly_independent_from_ebird.ipynb +++ b/data_processing/ebutterfly_data_preparation/prepare_ebutterfly_independent_from_ebird.ipynb @@ -12,8 +12,6 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], "source": [ "import pandas as pd \n", "import geopandas as gpd\n", @@ -38,22 +36,32 @@ "from pathlib import Path " ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T16:51:12.903966Z", + "start_time": "2025-06-26T16:51:12.894763Z" + } }, - "id": "c350abb8f8422d5d" + "id": "c350abb8f8422d5d", + "outputs": [], + "execution_count": 6 }, { "cell_type": "code", - "execution_count": null, - "outputs": [], "source": [ - "root_dir = \"SatButterfly_dataset\"\n", + "root_dir = \"/data/SatButterfly\"\n", "dataset_tag = \"SatButterfly_v1\"" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T16:51:13.396258Z", + "start_time": "2025-06-26T16:51:13.394293Z" + } }, - "id": "eafed9059d44cbce" + "id": "eafed9059d44cbce", + "outputs": [], + "execution_count": 7 }, { "cell_type": "code", @@ -714,8 +722,6 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], "source": [ "# plot final splits\n", "import os\n", @@ -735,32 +741,54 @@ "from shapely.geometry import Point\n", "\n", "\n", - "def plot_final_splits(df):\n", - " geoDatav = gpd.read_file('https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/US-counties.geojson')\n", - "\n", - " geometry = [Point(xy) for xy in zip(df['lon'], df['lat'])]\n", - " gdf = gpd.GeoDataFrame(df, geometry=geometry) \n", - "\n", - " ig, ax = plt.subplots(figsize =(15,10))\n", - " #train_gdf.drop_duplicates([\"geometry\"]).boundary.plot(ax = ax, alpha = 0.4, edgecolor = \"gray\")\n", - " geoDatav[~geoDatav[\"STATE\"].isin([\"02\", \"15\"])].boundary.plot(ax=ax, alpha = 0.1, edgecolor = \"gray\" )\n", - " gdf[gdf[\"split\"]==\"train\"].plot(ax=ax,marker='o', color='mediumslateblue', markersize=1, label = \"train\")\n", - " gdf[gdf[\"split\"]==\"valid\"].plot(ax=ax, marker='o', color='lightseagreen', markersize=1, label = \"valid\")\n", - " gdf[gdf[\"split\"]==\"test\"].plot(ax=ax, marker='o', color='lightsalmon', markersize=1, label = \"test\")\n", - "\n", - " plt.legend(fontsize=16, markerscale=5,loc='lower right', bbox_to_anchor=(0.92, 0.25))\n", - " plt.title(\"butterfly Hotspots\")\n", + "def plot_final_splits(train_df, valid_df, test_df, save_path):\n", + " geoDatav = gpd.read_file(\n", + " 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/US-counties.geojson'\n", + " )\n", + " \n", + " # Add geometry columns\n", + " train_gdf = gpd.GeoDataFrame(train_df, geometry=[Point(xy) for xy in zip(train_df['lon'], train_df['lat'])])\n", + " valid_gdf = gpd.GeoDataFrame(valid_df, geometry=[Point(xy) for xy in zip(valid_df['lon'], valid_df['lat'])])\n", + " test_gdf = gpd.GeoDataFrame(test_df, geometry=[Point(xy) for xy in zip(test_df['lon'], test_df['lat'])])\n", + "\n", + " fig, ax = plt.subplots(figsize=(15, 10))\n", + " geoDatav[~geoDatav[\"STATE\"].isin([\"02\", \"15\"])].boundary.plot(ax=ax, alpha=0.1, edgecolor=\"gray\")\n", + " train_gdf.plot(ax=ax, marker='o', color='mediumslateblue', markersize=1, label=\"train\")\n", + " valid_gdf.plot(ax=ax, marker='o', color='lightseagreen', markersize=1, label=\"valid\")\n", + " test_gdf.plot(ax=ax, marker='o', color='lightsalmon', markersize=1, label=\"test\")\n", + "\n", + " plt.legend(fontsize=16, markerscale=5, loc='lower right', bbox_to_anchor=(0.92, 0.25))\n", + " # plt.title(\"Butterfly Hotspots\")\n", + " ax.axis('off')\n", " plt.show()\n", - " ig.savefig(os.path.join(root_dir, dataset_tag, \"satbutterfly_v1_data_dist.pdf\"), bbox_inches='tight')\n", + " # Save figure\n", + " fig.savefig(os.path.join(save_path, \"satbutterfly_v1_data_dist.jpeg\"), bbox_inches='tight')\n", "\n", - " \n", - "path = os.path.join(root_dir, dataset_tag, \"butterfly_hotspots_with_splits.csv\")\n", - "plot_final_splits(df=pd.read_csv(path))" + "\n", + "path = os.path.join(root_dir, dataset_tag)\n", + "plot_final_splits(train_df=pd.read_csv(path + \"/train_split.csv\"), valid_df=pd.read_csv(path + \"/valid_split.csv\"), test_df=pd.read_csv(path + \"/test_split.csv\"), save_path=path)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T16:56:06.157040Z", + "start_time": "2025-06-26T16:56:05.308684Z" + } }, - "id": "1078ec29fbfb3a12" + "id": "1078ec29fbfb3a12", + "outputs": [ + { + "data": { + "text/plain": [ + "
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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 15 }, { "cell_type": "markdown", diff --git a/data_processing/ebutterfly_data_preparation/prepare_ebutterfly_with_ebird.ipynb b/data_processing/ebutterfly_data_preparation/prepare_ebutterfly_with_ebird.ipynb index 2096ad1..7a0823e 100644 --- a/data_processing/ebutterfly_data_preparation/prepare_ebutterfly_with_ebird.ipynb +++ b/data_processing/ebutterfly_data_preparation/prepare_ebutterfly_with_ebird.ipynb @@ -30,16 +30,20 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], "source": [ - "root_dir = \"SatButterfly_dataset\"\n", + "root_dir = \"/data/SatButterfly\"\n", "dataset_tag = \"SatButterfly_v2\"" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T17:01:03.193176Z", + "start_time": "2025-06-26T17:01:03.190612Z" + } }, - "id": "37088bcb70036394" + "id": "37088bcb70036394", + "outputs": [], + "execution_count": 3 }, { "cell_type": "code", @@ -431,8 +435,6 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], "source": [ "import os\n", "import sys\n", @@ -450,32 +452,53 @@ "\n", "from shapely.geometry import Point\n", "\n", - "path = os.path.join(root_dir, dataset_tag, \"butterfly_hotspots_ebird_splits.csv\")\n", - "\n", - "df = pd.read_csv(path)\n", - "df = df.drop_duplicates(\"hotspot_id\")\n", - "\n", - "geoDatav = gpd.read_file('https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/US-counties.geojson')\n", + "def plot_final_splits(train_df, valid_df, test_df, save_path):\n", + " geoDatav = gpd.read_file(\n", + " 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/US-counties.geojson'\n", + " )\n", + " \n", + " # Add geometry columns\n", + " train_gdf = gpd.GeoDataFrame(train_df, geometry=[Point(xy) for xy in zip(train_df['lon'], train_df['lat'])])\n", + " valid_gdf = gpd.GeoDataFrame(valid_df, geometry=[Point(xy) for xy in zip(valid_df['lon'], valid_df['lat'])])\n", + " test_gdf = gpd.GeoDataFrame(test_df, geometry=[Point(xy) for xy in zip(test_df['lon'], test_df['lat'])])\n", "\n", - "geometry = [Point(xy) for xy in zip(df['lon'], df['lat'])]\n", - "gdf = gpd.GeoDataFrame(df, geometry=geometry) \n", + " fig, ax = plt.subplots(figsize=(15, 10))\n", + " geoDatav[~geoDatav[\"STATE\"].isin([\"02\", \"15\"])].boundary.plot(ax=ax, alpha=0.1, edgecolor=\"gray\")\n", + " train_gdf.plot(ax=ax, marker='o', color='mediumslateblue', markersize=1, label=\"train\")\n", + " valid_gdf.plot(ax=ax, marker='o', color='lightseagreen', markersize=1, label=\"valid\")\n", + " test_gdf.plot(ax=ax, marker='o', color='lightsalmon', markersize=1, label=\"test\")\n", "\n", - "ig, ax = plt.subplots(figsize =(15,10))\n", - "#train_gdf.drop_duplicates([\"geometry\"]).boundary.plot(ax = ax, alpha = 0.4, edgecolor = \"gray\")\n", - "geoDatav[~geoDatav[\"STATE\"].isin([\"02\", \"15\"])].boundary.plot(ax=ax, alpha = 0.1, edgecolor = \"gray\" )\n", - "gdf[gdf[\"split\"]==\"train\"].plot(ax=ax,marker='o', color='mediumslateblue', markersize=1, label = \"train\")\n", - "gdf[gdf[\"split\"]==\"valid\"].plot(ax=ax, marker='o', color='lightseagreen', markersize=1, label = \"valid\")\n", - "gdf[gdf[\"split\"]==\"test\"].plot(ax=ax, marker='o', color='lightsalmon', markersize=1, label = \"test\")\n", + " plt.legend(fontsize=16, markerscale=5, loc='lower right', bbox_to_anchor=(0.92, 0.25))\n", + " # plt.title(\"Butterfly Hotspots\")\n", + " ax.axis('off')\n", + " plt.show()\n", + " # Save figure\n", + " fig.savefig(os.path.join(save_path, \"satbutterfly_v2_data_dist.jpeg\"), bbox_inches='tight')\n", "\n", - "plt.legend(fontsize=16, markerscale=5,loc='lower right', bbox_to_anchor=(0.92, 0.25))\n", - "plt.title(\"butterfly Hotspots\")\n", - "ig.savefig(os.path.join(root_dir, dataset_tag, \"satbutterfly_v2_data_dist.pdf\"), bbox_inches='tight')\n", - "plt.show()" + "path = os.path.join(root_dir, dataset_tag)\n", + "plot_final_splits(train_df=pd.read_csv(path + \"/train_split.csv\"), valid_df=pd.read_csv(path + \"/valid_split.csv\"), test_df=pd.read_csv(path + \"/test_split.csv\"), save_path=path)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-06-26T17:01:46.326792Z", + "start_time": "2025-06-26T17:01:44.431344Z" + } }, - "id": "38c8feea72a7e3c" + "id": "38c8feea72a7e3c", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 5 }, { "cell_type": "markdown", diff --git a/data_processing/join_target_files.py b/data_processing/join_target_files.py deleted file mode 100644 index 69dde7a..0000000 --- a/data_processing/join_target_files.py +++ /dev/null @@ -1,42 +0,0 @@ -import json -import os -import pickle - -import numpy as np - - -def create_dataframe_from_jsons(folder_path): - all_data = {} - - for json_file in os.listdir(folder_path): - file_path = os.path.join(folder_path, json_file) - - # Ensure we are processing only JSON files - if json_file.endswith(".json") and os.path.isfile(file_path): - with open(file_path, "r") as f: - data = json.load(f) # Load JSON data as a dictionary - k = data["hotspot_id"] - v = np.array(data["probs"]) - all_data[k] = v - - return all_data - - -index = 0 - -folder_path = [ - "/network/projects/ecosystem-embeddings/SatBird_data_v2/USA_summer/targets", - "/network/projects/ecosystem-embeddings/SatButterfly_dataset/SatButterfly_v1/USA/butterfly_targets_v1.2", - "/network/projects/ecosystem-embeddings/SatButterfly_dataset/SatButterfly_v2/USA/butterfly_targets_v1.2", -] - -out_file = [ - "/network/projects/ecosystem-embeddings/SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl", - "/network/projects/ecosystem-embeddings/SatButterfly_dataset/SatButterfly_v1/USA/butterfly_all_targets_v1.2.pkl", - "/network/projects/ecosystem-embeddings/SatButterfly_dataset/SatButterfly_v2/USA/butterfly_all_targets_v1.2.pkl", -] - -data_dict = create_dataframe_from_jsons(folder_path[index]) - -with open(out_file[index], "wb") as pickle_file: - pickle.dump(data_dict, pickle_file) diff --git a/data_processing/prepare_satbirdxsplots.ipynb b/data_processing/prepare_satbirdxsplots.ipynb index a8fd5a9..21dc045 100644 --- a/data_processing/prepare_satbirdxsplots.ipynb +++ b/data_processing/prepare_satbirdxsplots.ipynb @@ -219,18 +219,14 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "colocations = pd.read_csv(\"/network/projects/ecosystem-embeddings/SatBirdxsPlots/colocations_satbird_splot.csv\")" - ] + "source": "colocations = pd.read_csv(\"/data/SatBirdxsPlots/colocations_satbird_splot.csv\")" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "satbird = pd.read_csv(\"/network/projects/ecosystem-embeddings/SatBird_data_v2/USA_summer/all_summer_hotspots_final.csv\")" - ] + "source": "satbird = pd.read_csv(\"/data/SatBird_data_v2/USA_summer/all_summer_hotspots_final.csv\")" }, { "cell_type": "code", @@ -291,9 +287,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "splots = pd.read_csv(\"/network/projects/ecosystem-embeddings/sPlotOpen/location_data.csv\")" - ] + "source": "splots = pd.read_csv(\"/data/sPlotOpen/location_data.csv\")" }, { "cell_type": "markdown", @@ -324,7 +318,7 @@ "\n", "# Load the continental U.S. boundary shapefile\n", "# You can get this shapefile from Natural Earth or similar resources\n", - "gdf = gpd.read_file(\"/network/projects/ecosystem-embeddings/US_boundaries/cb_2018_us_nation_5m.shp\")\n", + "gdf = gpd.read_file(\"/data/US_boundaries/cb_2018_us_nation_5m.shp\")\n", "\n", "multi_poly = gdf[\"geometry\"][0]\n", "\n", @@ -454,18 +448,14 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "bioclim = pd.read_csv(\"/network/projects/ecosystem-embeddings/sPlotOpen/worldclim_data.csv\")" - ] + "source": "bioclim = pd.read_csv(\"/data/sPlotOpen/worldclim_data.csv\")" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "soilgrids = pd.read_csv(\"/network/projects/ecosystem-embeddings/sPlotOpen/soilgrid_data.csv\")" - ] + "source": "soilgrids = pd.read_csv(\"/data/sPlotOpen/soilgrid_data.csv\")" }, { "cell_type": "code", @@ -529,9 +519,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "merged.to_csv(\"/network/projects/ecosystem-embeddings/SatBirdxsPlots/satbird_splots.csv\")" - ] + "source": "merged.to_csv(\"/data/SatBirdxsPlots/satbird_splots.csv\")" }, { "cell_type": "markdown", @@ -566,9 +554,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "coloc_test.to_csv(\"/network/projects/ecosystem-embeddings/SatBirdxsPlots/satbird_splots_test_split.csv\")" - ] + "source": "coloc_test.to_csv(\"/data/SatBirdxsPlots/satbird_splots_test_split.csv\")" }, { "cell_type": "code", @@ -587,7 +573,7 @@ "outputs": [], "source": [ "#validation split that only has colocations\n", - "coloc_valid.to_csv(\"/network/projects/ecosystem-embeddings/SatBirdxsPlots/satbird_splots_valid_split_coloc.csv\")" + "coloc_valid.to_csv(\"/data/SatBirdxsPlots/satbird_splots_valid_split_coloc.csv\")" ] }, { @@ -597,7 +583,7 @@ "outputs": [], "source": [ "#validation split that also has bird observations locations that are not co-located with sPlotOpen\n", - "valid.to_csv(\"/network/projects/ecosystem-embeddings/SatBirdxsPlots/satbird_splots_valid_split.csv\")" + "valid.to_csv(\"/data/SatBirdxsPlots/satbird_splots_valid_split.csv\")" ] }, { @@ -614,16 +600,141 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": "train.to_csv(\"/data/SatBirdxsPlots/satbird_splots_train_split.csv\")" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# SatBirdxsPlot utils: targets for splot in US only" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ - "train.to_csv(\"/network/projects/ecosystem-embeddings/SatBirdxsPlots/satbird_splots_train_split.csv\")" + "import pickle\n", + "\n", + "def write_splot_targets_as_pickle(species_indices,\n", + " output_file_path,\n", + " targets_file=\"merged_species_occurrences_v2.npy\",\n", + " splot_location_data_file=\"location_data.csv\"):\n", + " \"\"\"\n", + " save splots targets as pkl to co-train with SatBird\n", + " given splot species indices to be considered\n", + " \"\"\"\n", + " targets = np.load(targets_file).astype(int)\n", + "\n", + " targets = targets[:, species_indices]\n", + "\n", + " loc = pd.read_csv(splot_location_data_file)\n", + " plot_observations = loc[\"PlotObservationID\"].astype(int).to_list()\n", + " print(len(plot_observations))\n", + " print(targets.shape)\n", + " d = {}\n", + " for i, obs_id in enumerate(plot_observations):\n", + " d[obs_id] = targets[i]\n", + "\n", + " with open(output_file_path, 'wb') as output_file:\n", + " pickle.dump(d, output_file)" ] }, { + "metadata": {}, "cell_type": "code", + "outputs": [], "execution_count": null, + "source": [ + "def filter_species_based_on_occurrences(species_occ_file,\n", + " species_indices = None,\n", + " data_indices = None,\n", + " threshold = 1):\n", + " \"\"\"\n", + " filter species occurrences based on occurrences threshold\n", + " \"\"\"\n", + " \n", + " species_occ = np.load(species_occ_file).astype(int)\n", + " if species_indices:\n", + " species_occ = species_occ[:, species_indices]\n", + " if data_indices:\n", + " species_occ = species_occ[data_indices, :]\n", + " result_species_indices = np.where(\n", + " species_occ.sum(axis=0) >= threshold\n", + " )[0]\n", + " return result_species_indices\n", + "\n", + "def filter_out_species(split_file_name : str, species_occ_file: str, all_splot_data_file_name: str,threshold = 1):\n", + " \"\"\"\n", + " filters out splot species based on csv data\n", + " \"\"\"\n", + " all_data_hotspots = pd.read_csv(split_file_name)\n", + " all_data_hotspots = all_data_hotspots[all_data_hotspots[\"plant\"] == 1]\n", + " ids = all_data_hotspots[\"PlotObservationID\"].astype(int).to_list()\n", + " \n", + " main_splot_data = pd.read_csv(all_splot_data_file_name)\n", + " custom_indices = main_splot_data.index[main_splot_data[\"PlotObservationID\"].astype(int).isin(ids)].tolist()\n", + " species_indices = filter_species_based_on_occurrences(species_occ_file = species_occ_file,\n", + " data_indices = custom_indices,\n", + " threshold = threshold)\n", + "\n", + " return species_indices" + ] + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "splot_US_only_present_species = filter_out_species(split_file_name=\"../data/satbirdxsplotopen/all_splits.csv\",\n", + " species_occ_file = \"../data/sPlotOpen/merged_species_occurrences_v2.npy\",\n", + " all_splot_data_file_name = \"../data/sPlotOpen/location_data.csv\",\n", + " threshold = 100)\n", + "\n", + "np.save(\"/data/satbirdxsplotopen/plant_US_species_indices.npy\", splot_US_only_present_species)\n", + "\n", + "write_splot_targets_as_pickle(targets_file=\"../data/sPlotOpen/merged_species_occurrences_v2.npy\",\n", + " species_indices=splot_US_only_present_species,\n", + " output_file_path=\"../data/sPlotOpen/sPlotOpen_targets_US_only.pkl\",\n", + " splot_location_data_file=\"../data/sPlotOpen/location_data.csv\")" + ] + }, + { "metadata": {}, + "cell_type": "code", "outputs": [], - "source": [] + "execution_count": null, + "source": [ + "from numpy import sort\n", + "import pickle\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "def get_non_zero_indices(data_split, targets):\n", + " \"\"\"\n", + " This is for validation and test in the setup: satbirdxsplots\n", + " :param data_split: \n", + " :return: \n", + " \"\"\"\n", + " observations = []\n", + " for plot_id in data_split['PlotObservationID'].tolist():\n", + " target = targets.get(int(plot_id))\n", + " observations.append(target)\n", + " observations = np.array(observations)\n", + " print(observations.shape)\n", + " non_zero_indices = observations.sum(0) != 0\n", + " num_classes = int(non_zero_indices.sum(0))\n", + " print(num_classes)\n", + " non_zero_indices = np.nonzero(observations.sum(axis=0))[0]\n", + " return non_zero_indices\n", + "\n", + "data_split = pd.read_csv(\"/data/satbirdxsplotopen/valid_split.csv\")\n", + "with open(\"/data/sPlotOpen/sPlotOpen_targets_US_only.pkl\", \"rb\") as pickle_file:\n", + " targets = pickle.load(pickle_file)\n", + "\n", + "non_zero_indices = get_non_zero_indices(data_split=data_split, targets = targets)\n", + "np.save('/data/satbirdxsplotopen/plant_validation_species_indices_US_only.npy', non_zero_indices)" + ] } ], "metadata": { diff --git a/data_processing/utils.py b/data_processing/utils.py new file mode 100644 index 0000000..2e1f58a --- /dev/null +++ b/data_processing/utils.py @@ -0,0 +1,72 @@ +# create a single pickle target file for SatBird and Satbutterfly +import json +import os +import pickle + +import numpy as np + + +def merge_satbutterfly_targets(src_file_1: str, src_file_2: str, dst_file: str): + """ + merge two pickle files into one + :return: + """ + # Open and load the pickle files + with open(src_file_1, 'rb') as file1: + d1 = pickle.load(file1) + + with open(src_file_2, 'rb') as file2: + d2 = pickle.load(file2) + + # Merge the dictionaries (d1 will be updated with the contents of d2) + combined_dict = {**d1, **d2} + + # Save the combined dictionary to a new pickle file + output_file_path = dst_file + with open(output_file_path, 'wb') as output_file: + pickle.dump(combined_dict, output_file) + + print(f"Combined dictionary saved to {output_file_path}") + + +def create_dataframe_from_jsons(folder_path: str): + """ + create targets dataframe from single json files for satbird/satbutterfly + """ + all_data = {} + + for json_file in os.listdir(folder_path): + file_path = os.path.join(folder_path, json_file) + + # Ensure we are processing only JSON files + if json_file.endswith(".json") and os.path.isfile(file_path): + with open(file_path, "r") as f: + data = json.load(f) # Load JSON data as a dictionary + k = data["hotspot_id"] + v = np.array(data["probs"]) + all_data[k] = v + + return all_data + + +folder_path = [ + "/data/SatBird/USA_summer/targets", + "/data/SatButterfly/SatButterfly_v1/USA/butterfly_targets_v1.2", + "/data/SatButterfly/SatButterfly_v2/USA/butterfly_targets_v1.2", +] + +out_file = [ + "/data/SatBird/USA_summer/satbird_usa_summer_targets.pkl", + "/data/SatButterfly/SatButterfly_v1/USA/butterfly_all_targets_v1.2.pkl", + "/data/network/projects/ecosystem-embeddings/SatButterfly/SatButterfly_v2/USA/butterfly_all_targets_v1.2.pkl", +] + +index = 0 +data_dict = create_dataframe_from_jsons(folder_path=folder_path[index]) +with open(out_file[index], "wb") as pickle_file: + pickle.dump(data_dict, pickle_file) + +# save butterfly v1 and v2 targets in a single pickle file +merge_satbutterfly_targets(src_file_1='/data/SatButterfly/SatButterfly_v1/USA/butterfly_all_targets_v1.2.pkl', + src_file_2='/data/SatButterfly/SatButterfly_v2/USA/butterfly_all_targets_v1.2.pkl', + dst_file='/data/SatButterfly/combined_SatButterfly_v1Andv2_targets.pkl') diff --git a/figures/architecture.pdf b/figures/architecture.pdf new file mode 100644 index 0000000..2133a6b Binary files /dev/null and b/figures/architecture.pdf differ diff --git a/figures/architecture.png b/figures/architecture.png new file mode 100644 index 0000000..6c00b42 Binary files /dev/null and b/figures/architecture.png differ diff --git a/figures/generate_figure_10.ipynb b/figures/generate_figure_10.ipynb new file mode 100644 index 0000000..417c9c6 --- /dev/null +++ b/figures/generate_figure_10.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "90c30d5f", + "metadata": {}, + "source": [ + "# πŸ–ΌοΈ Generating Figure 10" + ] + }, + { + "cell_type": "markdown", + "id": "a5dc2e25", + "metadata": {}, + "source": [ + "The following code generates Figure 10 of the paper plotting the results of Table 1." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fbc9ea1a", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "143c4cf5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "models_uncond = [\"Logistic\", \"Maxent\", \"MLP\", \"MLP++\", \"CISO\"]\n", + "models_cond = [\"MLP++ (Cond.)\", \"CISO (Cond.)\"]\n", + "\n", + "means = {\n", + " \"AUC sPlotOpen: tree\": np.array([96.69, 98.24, 98.39, 98.15, 98.49, 98.82, 99.12]),\n", + " \"AUC sPlotOpen: non-tree\": np.array([93.74, 96.17, 96.58, 96.11, 96.49, 96.92, 97.39]),\n", + " \"Top-k Satbird: songbird\": np.array([61.75, 67.08, 67.99, 65.81, 69.30, 69.98, 72.86]),\n", + " \"Top-k Satbird: non-songbird\": np.array([52.26, 57.52, 58.15, 56.76, 59.41, 61.76, 63.56]),\n", + " \"MAE Satbird: songbird\": np.array([8.99, 3.43, 3.34, 3.49, 3.19, 2.75, 2.57]),\n", + " \"MAE Satbird: non-songbird\": np.array([7.66, 1.39, 1.37, 1.37, 1.28, 1.06, 1.04]),\n", + "}\n", + "\n", + "stds = {\n", + " \"AUC sPlotOpen: tree\": np.array([0.01, 0.00, 0.03, 0.04, 0.05, 0.03, 0.04]),\n", + " \"AUC sPlotOpen: non-tree\": np.array([0.00, 0.00, 0.01, 0.03, 0.07, 0.02, 0.14]),\n", + " \"Top-k Satbird: songbird\": np.array([0.04, 0.02, 0.04, 0.19, 0.21, 0.30, 0.37]),\n", + " \"Top-k Satbird: non-songbird\": np.array([0.08, 0.04, 0.09, 0.14, 0.25, 0.27, 0.49]),\n", + " \"MAE Satbird: songbird\": np.array([0.02, 0.01, 0.02, 0.06, 0.09, 0.02, 0.08]),\n", + " \"MAE Satbird: non-songbird\": np.array([0.01, 0.01, 0.01, 0.03, 0.04, 0.02, 0.03]),\n", + "}\n", + "\n", + "\n", + "n_uncond = len(models_uncond)\n", + "n_cond = len(models_cond)\n", + "\n", + "gap = 1.0\n", + "x_uncond = np.arange(n_uncond)\n", + "x_cond = np.arange(n_cond) + n_uncond + gap\n", + "x_all = np.concatenate([x_uncond, x_cond])\n", + "\n", + "model_labels = models_uncond + models_cond\n", + "\n", + "colors_indices = [0, 7, 4, 6, 1]\n", + "base_colors = plt.cm.Set1(colors_indices[:n_uncond])\n", + "\n", + "def lighten(c, factor=0.5):\n", + " return c + (1 - c) * factor\n", + "\n", + "colors_uncond = np.array([lighten(c, 0.2) for c in base_colors])\n", + "colors_cond = base_colors[-n_cond:]\n", + "\n", + "ordered_tasks = [\n", + " \"AUC sPlotOpen: tree\", \"Top-k Satbird: songbird\", \"MAE Satbird: songbird\", \n", + " \"AUC sPlotOpen: non-tree\", \"Top-k Satbird: non-songbird\", \"MAE Satbird: non-songbird\", \n", + "]\n", + "\n", + "fig, axes = plt.subplots(2, 3, figsize=(11, 6))\n", + "axes = axes.flatten()\n", + "\n", + "for ax, task in zip(axes, ordered_tasks):\n", + " mean = means[task]\n", + " std = stds[task]\n", + "\n", + " # Unconditioned bars\n", + " for i in range(n_uncond):\n", + " ax.bar(x_uncond[i], mean[i],\n", + " yerr=std[i], color=colors_uncond[i], capsize=3)\n", + "\n", + " # Conditional bars\n", + " for j in range(n_cond):\n", + " ax.bar(x_cond[j], mean[n_uncond + j],\n", + " yerr=std[n_uncond + j], color=colors_cond[j], capsize=3)\n", + "\n", + " # Formatting\n", + " ax.set_title(' '.join(task.split(' ')[1:]))\n", + " ax.set_xticks(x_all)\n", + " ax.set_xticklabels(model_labels, rotation=25, ha=\"right\")\n", + " ax.grid(axis=\"y\", linestyle=\":\", alpha=0.5)\n", + "\n", + " # y-limits / labels\n", + " if \"MAE\" in task:\n", + " ax.set_ylabel(r\"MAE [$\\times 10^{2}$]\")\n", + " ax.set_ylim(0, 10)\n", + " elif \"AUC\" in task:\n", + " ax.set_ylabel(\"AUC (%)\")\n", + " ax.set_ylim(92, 100)\n", + " elif \"Top-k\" in task:\n", + " ax.set_ylabel(\"Top-k (%)\")\n", + " ax.set_ylim(50, 80)\n", + "\n", + "# Legend\n", + "legend_handles = []\n", + "legend_labels = []\n", + "\n", + "for name, color in zip(models_uncond, colors_uncond):\n", + " legend_handles.append(plt.Rectangle((0,0),1,1,color=color))\n", + " legend_labels.append(name)\n", + "\n", + "for name, color in zip(models_cond, colors_cond):\n", + " legend_handles.append(plt.Rectangle((0,0),1,1,color=color))\n", + " legend_labels.append(name)\n", + "\n", + "fig.legend(legend_handles, legend_labels,\n", + " loc=\"upper center\", ncol=7, fontsize=11)\n", + "\n", + "plt.tight_layout(rect=[0, 0, 1, 0.93])\n", + "plt.savefig(\"figure_results.pdf\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5913911", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "robin_env", + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/figures/generate_figure_3.ipynb b/figures/generate_figure_3.ipynb index 092b391..54cae1b 100644 --- a/figures/generate_figure_3.ipynb +++ b/figures/generate_figure_3.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "d8db17cb", "metadata": {}, "outputs": [], @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "15759033", "metadata": {}, "outputs": [], @@ -186,7 +186,16 @@ "execution_count": null, "id": "38ae724e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.9662)\n", + "tensor(0.9749)\n" + ] + } + ], "source": [ "non_zero_indices = tree_labels.sum(0) != 0\n", "\n", @@ -257,7 +266,16 @@ "execution_count": null, "id": "eff4d62f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.9860)\n", + "tensor(0.9910)\n" + ] + } + ], "source": [ "non_zero_indices = tree_labels.sum(0) != 0\n", "\n", @@ -287,10 +305,35 @@ { "cell_type": "code", "execution_count": null, - "id": "8583372f", + "id": "ac6fafe6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1039492/3002756892.py:219: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "bins = 45\n", + "\n", "s = 4\n", "font_size = 13\n", "plt.rcParams.update({'font.size': font_size})\n", @@ -298,100 +341,228 @@ "bbox_props = dict(facecolor=\"white\", edgecolor=\"gray\", boxstyle=\"round,pad=0.3\", alpha=0.5)\n", "\n", "fontsize_txt = 12\n", - "cmap = plt.cm.viridis\n", "\n", - "# Create subplots\n", - "fig, axes = plt.subplots(1, 4, figsize=(16, 5))\n", + "# --- 2 x 4 layout: top row scatter, bottom row histograms ---\n", + "fig, axes = plt.subplots(\n", + " 2, 4,\n", + " figsize=(19.5, 6.3),\n", + " gridspec_kw={\"height_ratios\": [3, 1], \"hspace\": 0.4, \"wspace\": 0.3},\n", + ")\n", "\n", + "scatter_axes = axes[0, :]\n", + "hist_axes = axes[1, :]\n", + "\n", + "# ============================================================\n", + "# 1) SONG BIRDS (MAE)\n", + "# ============================================================\n", "xlim_min = 0\n", "xlim_max = 0.25\n", "\n", - "x_txt_up = 0.01\n", - "y_txt_up = 0.225\n", - "x_txt_low = 0.09\n", - "y_txt_low = 0.02\n", - "\n", - "axes[0].scatter(cond_mae_list, uncond_mae_list, s=s, color=sns.color_palette(\"Paired\")[9])\n", - "axes[0].set_xlabel(\"conditioned CISO (MAE$\\\\!\\\\downarrow\\!\\!$)\")\n", - "axes[0].set_ylabel(\"unconditioned CISO (MAE$\\\\!\\\\downarrow\\!\\!$)\")\n", - "axes[0].set_xlim(xlim_min, xlim_max)\n", - "axes[0].set_ylim(xlim_min, xlim_max)\n", + "x_txt_up = 0.015\n", + "y_txt_up = 0.225\n", + "x_txt_low = 0.092\n", + "y_txt_low = 0.0185\n", + "\n", + "# scatter\n", + "scatter_axes[0].scatter(\n", + " cond_mae_list, uncond_mae_list, s=s, color=sns.color_palette(\"Paired\")[9]\n", + ")\n", + "scatter_axes[0].set_xlabel(\"conditioned CISO (MAE$\\\\!\\\\downarrow\\!\\!$)\")\n", + "scatter_axes[0].set_ylabel(\"unconditioned CISO (MAE$\\\\!\\\\downarrow\\!\\!$)\")\n", + "scatter_axes[0].set_xlim(xlim_min, xlim_max)\n", + "scatter_axes[0].set_ylim(xlim_min, xlim_max)\n", "x = np.linspace(xlim_min, xlim_max, 100)\n", - "axes[0].plot(x, x, linestyle='--', color='black')\n", - "axes[0].grid()\n", - "axes[0].set_aspect('equal', adjustable='box')\n", - "axes[0].set_title(\"Songbirds\")\n", - "\n", - "axes[0].text(x_txt_low , y_txt_low, \"Unconditioned better\", fontsize=fontsize_txt, rotation=0, bbox=bbox_props)\n", - "axes[0].text(x_txt_up, y_txt_up, \"Conditioned better\", fontsize=fontsize_txt, rotation=0, bbox=bbox_props)\n", - "\n", - "axes[1].scatter(non_cond_mae_list, non_uncond_mae_list, s=s, color=sns.color_palette(\"Paired\")[8])\n", - "axes[1].set_xlabel(\"conditioned CISO (MAE$\\\\!\\\\downarrow\\!\\!$)\")\n", - "axes[1].set_ylabel(\"unconditioned CISO (MAE$\\\\!\\\\downarrow\\!\\!$)\")\n", - "axes[1].set_xlim(xlim_min, xlim_max)\n", - "axes[1].set_ylim(xlim_min, xlim_max)\n", - "axes[1].plot(x, x, linestyle='--', color='black')\n", - "axes[1].grid()\n", - "axes[1].set_aspect('equal', adjustable='box')\n", - "axes[1].set_title(\"Non-Songbirds\")\n", - "\n", - "axes[1].text(x_txt_low , y_txt_low, \"Unconditioned better\", fontsize=fontsize_txt, rotation=0, bbox=bbox_props)\n", - "axes[1].text(x_txt_up, y_txt_up, \"Conditioned better\", fontsize=fontsize_txt, rotation=0, bbox=bbox_props)\n", - "\n", + "scatter_axes[0].plot(x, x, linestyle=\"--\", color=\"black\")\n", + "scatter_axes[0].grid()\n", + "#scatter_axes[0].set_aspect(\"equal\", adjustable=\"box\")\n", + "scatter_axes[0].set_title(\"Songbirds\")\n", + "\n", + "scatter_axes[0].text(\n", + " x_txt_low,\n", + " y_txt_low,\n", + " \"Unconditioned better\",\n", + " fontsize=fontsize_txt,\n", + " bbox=bbox_props,\n", + ")\n", + "scatter_axes[0].text(\n", + " x_txt_up,\n", + " y_txt_up,\n", + " \"Conditioned better\",\n", + " fontsize=fontsize_txt,\n", + " bbox=bbox_props,\n", + ")\n", + "\n", + "# histogram of differences: Ξ” = cond - uncond\n", + "diff_songbirds = np.array(uncond_mae_list) - np.array(cond_mae_list)\n", + "hist_axes[0].hist(\n", + " diff_songbirds,\n", + " bins=bins,\n", + " color=sns.color_palette(\"Paired\")[9],\n", + " edgecolor=\"black\",\n", + ")\n", + "hist_axes[0].axvline(0, linestyle=\"--\", color=\"black\", linewidth=1)\n", + "hist_axes[0].set_xlabel(r\"$\\Delta$ MAE (uncond. - cond.)\")\n", + "hist_axes[0].set_ylabel(\"# species\")\n", + "hist_axes[0].set_yscale(\"log\")\n", + "\n", + "# ============================================================\n", + "# 2) NON-SONG BIRDS (MAE)\n", + "# ============================================================\n", + "scatter_axes[1].scatter(\n", + " non_cond_mae_list, non_uncond_mae_list, s=s, color=sns.color_palette(\"Paired\")[8]\n", + ")\n", + "scatter_axes[1].set_xlabel(\"conditioned CISO (MAE$\\\\!\\\\downarrow\\!\\!$)\")\n", + "scatter_axes[1].set_ylabel(\"unconditioned CISO (MAE$\\\\!\\\\downarrow\\!\\!$)\")\n", + "scatter_axes[1].set_xlim(xlim_min, xlim_max)\n", + "scatter_axes[1].set_ylim(xlim_min, xlim_max)\n", + "scatter_axes[1].plot(x, x, linestyle=\"--\", color=\"black\")\n", + "scatter_axes[1].grid()\n", + "#scatter_axes[1].set_aspect(\"equal\", adjustable=\"box\")\n", + "scatter_axes[1].set_title(\"Non-Songbirds\")\n", + "\n", + "scatter_axes[1].text(\n", + " x_txt_low,\n", + " y_txt_low,\n", + " \"Unconditioned better\",\n", + " fontsize=fontsize_txt,\n", + " bbox=bbox_props,\n", + ")\n", + "scatter_axes[1].text(\n", + " x_txt_up,\n", + " y_txt_up,\n", + " \"Conditioned better\",\n", + " fontsize=fontsize_txt,\n", + " bbox=bbox_props,\n", + ")\n", + "\n", + "diff_nonsong = np.array(non_uncond_mae_list) - np.array(non_cond_mae_list)\n", + "hist_axes[1].hist(\n", + " diff_nonsong,\n", + " bins=bins,\n", + " color=sns.color_palette(\"Paired\")[8],\n", + " edgecolor=\"black\",\n", + ")\n", + "hist_axes[1].axvline(0, linestyle=\"--\", color=\"black\", linewidth=1)\n", + "hist_axes[1].set_xlabel(r\"$\\Delta$ MAE (uncond. - cond.)\")\n", + "hist_axes[1].set_yscale(\"log\")\n", + "\n", + "# ============================================================\n", + "# 3) TREES (AUC)\n", + "# ============================================================\n", "xlim_min = 0.6\n", - "xlim_max = 1\n", + "xlim_max = 1.0\n", "\n", - "x_txt_up = 0.62\n", - "y_txt_up = 0.96\n", + "x_txt_up = 0.62\n", + "y_txt_up = 0.96\n", "x_txt_low = 0.77\n", "y_txt_low = 0.63\n", "\n", - "axes[2].scatter(tree_cond_aucs, tree_uncond_aucs, s=s, color=sns.color_palette(\"Paired\")[3])\n", - "axes[2].set_xlabel(\"conditioned CISO (AUC$\\\\!\\\\uparrow\\!\\!$)\")\n", - "axes[2].set_ylabel(\"unconditioned CISO (AUC$\\\\!\\\\uparrow\\!\\!$)\")\n", - "axes[2].set_xlim(xlim_min, xlim_max)\n", - "axes[2].set_ylim(xlim_min, xlim_max)\n", + "scatter_axes[2].scatter(\n", + " tree_cond_aucs, tree_uncond_aucs, s=s, color=sns.color_palette(\"Paired\")[3]\n", + ")\n", + "scatter_axes[2].set_xlabel(\"conditioned CISO (AUC$\\\\!\\\\uparrow\\!\\!$)\")\n", + "scatter_axes[2].set_ylabel(\"unconditioned CISO (AUC$\\\\!\\\\uparrow\\!\\!$)\")\n", + "scatter_axes[2].set_xlim(xlim_min, xlim_max)\n", + "scatter_axes[2].set_ylim(xlim_min, xlim_max)\n", "x = np.linspace(xlim_min, xlim_max, 100)\n", - "axes[2].plot(x, x, linestyle='--', color='black')\n", - "axes[2].grid()\n", - "axes[2].set_xticks([0.6, 0.7, 0.8, 0.9, 1])\n", - "axes[2].set_yticks([0.6, 0.7, 0.8, 0.9, 1])\n", - "axes[2].set_aspect('equal', adjustable='box')\n", - "axes[2].set_title(\"Trees\")\n", - "\n", - "axes[2].text(x_txt_low , y_txt_low, \"Conditioned better\", fontsize=fontsize_txt, rotation=0, bbox=bbox_props)\n", - "axes[2].text(x_txt_up, y_txt_up, \"Unconditioned better\", fontsize=fontsize_txt, rotation=0, bbox=bbox_props)\n", - "\n", - "# axes[3].scatter(nontree_cond_aucs, nontree_uncond_aucs, s=s, color=sns.color_palette(\"Paired\")[2])\n", - "occurrences = tree_labels[:, non_zero_indices].sum(0)/max(tree_labels[:, non_zero_indices].sum(0))\n", - "sc = axes[3].scatter(nontree_cond_aucs, nontree_uncond_aucs, s=s, c=occurrences, cmap=cmap)\n", - "axes[3].set_xlabel(\"conditioned CISO (AUC$\\\\!\\\\uparrow\\!\\!$)\")\n", - "axes[3].set_ylabel(\"unconditioned CISO (AUC$\\\\!\\\\uparrow\\!\\!$)\")\n", - "axes[3].set_xlim(xlim_min, xlim_max)\n", - "axes[3].set_ylim(xlim_min, xlim_max)\n", - "axes[3].plot(x, x, linestyle='--', color='black')\n", - "axes[3].grid()\n", - "axes[3].set_xticks([0.6, 0.7, 0.8, 0.9, 1])\n", - "axes[3].set_yticks([0.6, 0.7, 0.8, 0.9, 1])\n", - "axes[3].set_aspect('equal', adjustable='box')\n", - "axes[3].set_title(\"Non-Trees\")\n", - "\n", - "axes[3].text(x_txt_low , y_txt_low, \"Conditioned better\", fontsize=fontsize_txt, rotation=0, bbox=bbox_props)\n", - "axes[3].text(x_txt_up, y_txt_up, \"Unconditioned better\", fontsize=fontsize_txt, rotation=0, bbox=bbox_props)\n", - "\n", - "# Add colorbars to indicate occurrences scale\n", - "cbar = fig.colorbar(sc, ax=axes, orientation=\"horizontal\", fraction=0.5, pad=0.1)\n", - "cbar.set_label(\"Occurrences\")\n", + "scatter_axes[2].plot(x, x, linestyle=\"--\", color=\"black\")\n", + "scatter_axes[2].grid()\n", + "scatter_axes[2].set_xticks([0.6, 0.7, 0.8, 0.9, 1.0])\n", + "scatter_axes[2].set_yticks([0.6, 0.7, 0.8, 0.9, 1.0])\n", + "#scatter_axes[2].set_aspect(\"equal\", adjustable=\"box\")\n", + "scatter_axes[2].set_title(\"Trees\")\n", + "\n", + "scatter_axes[2].text(\n", + " x_txt_low,\n", + " y_txt_low,\n", + " \"Conditioned better\",\n", + " fontsize=fontsize_txt,\n", + " bbox=bbox_props,\n", + ")\n", + "scatter_axes[2].text(\n", + " x_txt_up,\n", + " y_txt_up,\n", + " \"Unconditioned better\",\n", + " fontsize=fontsize_txt,\n", + " bbox=bbox_props,\n", + ")\n", + "\n", + "# AUC: higher is better\n", + "diff_trees = np.array(tree_cond_aucs) - np.array(tree_uncond_aucs)\n", + "hist_axes[2].hist(\n", + " diff_trees,\n", + " bins=bins,\n", + " color=sns.color_palette(\"Paired\")[3],\n", + " edgecolor=\"black\",\n", + ")\n", + "hist_axes[2].axvline(0, linestyle=\"--\", color=\"black\", linewidth=1)\n", + "hist_axes[2].set_xlabel(r\"$\\Delta$ AUC (cond. - uncond.)\")\n", + "hist_axes[2].set_xticks([-0.2, -0.1, 0, 0.1])\n", + "#hist_axes[2].set_xlim(-0.36, 0.12)\n", + "hist_axes[2].set_yscale(\"log\")\n", + "\n", + "# ============================================================\n", + "# 4) NON-TREES (AUC)\n", + "# ============================================================\n", + "scatter_axes[3].scatter(\n", + " nontree_cond_aucs, nontree_uncond_aucs, s=s, color=sns.color_palette(\"Paired\")[2]\n", + ")\n", + "scatter_axes[3].set_xlabel(\"conditioned CISO (AUC$\\\\!\\\\uparrow\\!\\!$)\")\n", + "scatter_axes[3].set_ylabel(\"unconditioned CISO (AUC$\\\\!\\\\uparrow\\!\\!$)\")\n", + "scatter_axes[3].set_xlim(xlim_min, xlim_max)\n", + "scatter_axes[3].set_ylim(xlim_min, xlim_max)\n", + "scatter_axes[3].plot(x, x, linestyle=\"--\", color=\"black\")\n", + "scatter_axes[3].grid()\n", + "scatter_axes[3].set_xticks([0.6, 0.7, 0.8, 0.9, 1.0])\n", + "scatter_axes[3].set_yticks([0.6, 0.7, 0.8, 0.9, 1.0])\n", + "#scatter_axes[3].set_aspect(\"equal\", adjustable=\"box\")\n", + "scatter_axes[3].set_title(\"Non-Trees\")\n", + "\n", + "scatter_axes[3].text(\n", + " x_txt_low,\n", + " y_txt_low,\n", + " \"Conditioned better\",\n", + " fontsize=fontsize_txt,\n", + " bbox=bbox_props,\n", + ")\n", + "scatter_axes[3].text(\n", + " x_txt_up,\n", + " y_txt_up,\n", + " \"Unconditioned better\",\n", + " fontsize=fontsize_txt,\n", + " bbox=bbox_props,\n", + ")\n", + "\n", + "diff_nontrees = np.array(nontree_cond_aucs) - np.array(nontree_uncond_aucs)\n", + "hist_axes[3].hist(\n", + " diff_nontrees,\n", + " bins=bins,\n", + " color=sns.color_palette(\"Paired\")[2],\n", + " edgecolor=\"black\",\n", + ")\n", + "hist_axes[3].axvline(0, linestyle=\"--\", color=\"black\", linewidth=1)\n", + "hist_axes[3].set_xlabel(r\"$\\Delta$ AUC (cond. - uncond.)\")\n", + "hist_axes[3].set_xticks([-0.3, -0.2, -0.1, 0, 0.1])\n", + "#hist_axes[3].set_xlim(-0.36, 0.12)\n", + "hist_axes[3].set_yscale(\"log\")\n", "\n", "plt.tight_layout()\n", - "plt.savefig(\"cond_vs_uncond_distribution.pdf\", bbox_inches='tight')\n", + "plt.savefig(\"cond_vs_uncond_distribution_with_hist.pdf\", bbox_inches=\"tight\")\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b644bed1", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "deepHSM", + "display_name": "pa_env", "language": "python", "name": "python3" }, @@ -405,7 +576,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/figures/generate_figure_5.ipynb b/figures/generate_figure_5.ipynb new file mode 100644 index 0000000..d12e861 --- /dev/null +++ b/figures/generate_figure_5.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# πŸ–ΌοΈ Generating Figure 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code generates one row of Figure 5 of the paper by mapping the ground truth, and predictions of unconditioned, conditioned, and difference between unconditiond and conditioned encounter rates of a given species. " + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import matplotlib.cm as cm\n", + "from matplotlib.colors import LinearSegmentedColormap\n", + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Path to predictions: should be folders with .npy files for each hotspot\n", + "\n", + "#Unconditioned predictions path\n", + "unc_path = \"/network/scratch/t/tengmeli/baseline_satbird_ctran_v0/1337/preds_unconditioned/\"\n", + "#e.g. Predictions of Callipepla californica conditioned on everything else\n", + "pred_path = \"/network/scratch/t/tengmeli/baseline_satbird_ctran_v0/1337/preds_callipepla/\" " + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"/network/projects/ecosystem-embeddings/SatBird_data_v2/species_list_USA.txt\", \"r\") as f:\n", + " list_species = [u.strip(\"\\n\") for u in f.readlines()]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([137]),)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(np.array(list_species)==\"Callipepla californica\")" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "test_split = pd.read_csv(\"/network/projects/ecosystem-embeddings/SatBird_data_v2/USA_summer/test_split.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "test_split[\"unc\"] = 0.\n", + "test_split[\"cond\"] = 0.\n", + "index =137" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "#create df with prediction values for the species of interest in the unconditioned and conditioned cases\n", + "for i, row in test_split.iterrows():\n", + " pred = np.load(os.path.join(unc_path, row[\"hotspot_id\"]+\".npy\"))[index]\n", + " test_split.loc[i, \"unc\"] = pred\n", + " pred_cond = np.load(os.path.join(pred_path, row[\"hotspot_id\"]+\".npy\"))[index]\n", + " test_split.loc[i, \"cond\"] = pred_cond" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "#get ground truth\n", + "birds_targets = \"/network/projects/ecosystem-embeddings/SatBird_data_v2/USA_summer/satbird_usa_summer_targets.pkl\"\n", + "with open(birds_targets, \"rb\") as f:\n", + " birddata = pickle.load(f)\n", + " \n", + "hotspots = test_split[\"hotspot_id\"].to_list()\n", + "birddata_test = {i:birddata[i] for i in birddata.keys() if i in hotspots}\n", + "list_keys = list(birddata_test.keys())\n", + "len(list_keys)\n", + "gt = np.zeros((len(list_keys)))\n", + "for i in range(len(list_keys)):\n", + " gt[i] = birddata_test[list_keys[i]][index]" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "test_split[\"ground_truth\"] = 0.\n", + "for i, row in test_split.iterrows(): \n", + " pred_gt =birddata_test[row[\"hotspot_id\"]][index]\n", + " test_split.loc[i, \"ground_truth\"] = pred_gt" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "test_split_ = test_split[[\"lon\", \"lat\", \"unc\", \"cond\", \"ground_truth\"]]\n", + "\n", + "# Create a GeoDataFrame\n", + "gdf = gpd.GeoDataFrame(test_split_ , geometry=gpd.points_from_xy(test_split_ ['lon'], test_split_ ['lat']))\n", + "gdf= gdf.sort_values(by='unc', ascending=True)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "# Using highly saturated standard colors\n", + "color_list_vibrant = [\n", + " (0.0, 'blue'), # Start: Saturated Blue\n", + " (0.5, 'gainsboro'), # Center: Light Gray (e.g., #D3D3D3)\n", + " (1.0, 'magenta') # End: Saturated Red\n", + "]\n", + "\n", + "new_cmap_vibrant = LinearSegmentedColormap.from_list('SeismicLG_Vibrant', \n", + " [c[1] for c in color_list_vibrant])" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = gdf.sort_values(by=['ground_truth'])" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "norm = mcolors.PowerNorm(gamma=1, vmin=0, vmax=1) #colorscale for encounter rates\n", + "newnorm = mcolors.PowerNorm(gamma=1, vmin=-1, vmax=1) #colorscale for encounter rates difference\n", + "\n", + "# Set up the figure and axes\n", + "fig, axarr = plt.subplots(1, 4, figsize=(15, 6), constrained_layout=True)\n", + "#geoDatav = gpd.read_file('https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/US-counties.geojson')\n", + "geoDatav = gpd.read_file('https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/us.geojson')\n", + "\n", + "# Define a colormap and normalization\n", + "cmap = plt.cm.Reds\n", + "\n", + "Z_ORDER_POINTS = 5\n", + "gdf.plot(column='ground_truth', cmap=cmap, marker='s', markersize=1, ax=axarr[0], legend=False, norm=norm, zorder=Z_ORDER_POINTS)\n", + "gdf = gdf.sort_values(by=['unc'])\n", + "gdf.plot(column='unc', cmap=cmap, marker='s', markersize=1, ax=axarr[1], legend=False, norm=norm, zorder=Z_ORDER_POINTS )\n", + "gdf = gdf.sort_values(by=['cond'])\n", + "gdf.plot(column='cond', cmap=cmap, marker='s', markersize=1, ax=axarr[2], legend=False, norm=norm, zorder=Z_ORDER_POINTS)\n", + "\n", + "gdf[\"diff_cond_uncabs\"] = (gdf[\"cond\"]-gdf[\"unc\"]).abs()\n", + "gdf[\"diff_cond_unc\"] = (gdf[\"cond\"]-gdf[\"unc\"])#.abs()\n", + "gdf = gdf.sort_values(by=['diff_cond_uncabs'])\n", + "gdf.plot(column='diff_cond_unc', cmap=new_cmap_vibrant, marker='s', markersize=1, ax=axarr[3], legend=False, norm=newnorm, zorder=Z_ORDER_POINTS)\n", + "\n", + "# Remove axis labels for clarity\n", + "for i, ax in enumerate(axarr):\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " ax.set_frame_on(False) # Removes the square border\n", + " ax.axis(\"off\")\n", + " geoDatav[~geoDatav[\"state\"].isin([\"Hawaii\", \"Alaska\"])].boundary.plot(ax=ax, alpha = 0.4, edgecolor = \"lightgray\" )\n", + " \n", + " if i ==1:\n", + " ax.set_title(\"Unconditioned\", fontsize = 16) # Set different titles if needed\n", + " if i ==2:\n", + " ax.set_title(\"Conditioned\", fontsize = 16)\n", + " if i==0: \n", + " ax.set_title(\"Ground truth\", fontsize = 16)\n", + " if i==3: \n", + " ax.set_title(\"Conditioned-Unconditioned\", fontsize = 16)\n", + " \n", + "# Create a single colorbar below the maps\n", + "sm = cm.ScalarMappable(cmap=cmap, norm=norm)\n", + "sm.set_array([]) # Required for colorbar creation\n", + "cbar_ax = fig.add_axes([-0.01, 0.3, 0.01, 0.35]) # [left, bottom, width, height] --> Right side\n", + "cbar = plt.colorbar(sm, cax=cbar_ax, orientation='vertical')\n", + "cbar.set_label('Encounter rate', fontsize=12)\n", + "\n", + "sm = cm.ScalarMappable(cmap=new_cmap_vibrant, norm=newnorm)\n", + "sm.set_array([]) # Required for colorbar creation\n", + "cbar_ax = fig.add_axes([1, 0.3, 0.01, 0.35]) # [left, bottom, width, height] --> Right side\n", + "# 1. Move the tick marks themselves to the left side\n", + "cbar.ax.yaxis.set_ticks_position('left')\n", + "\n", + "# 2. Move the tick labels (the numbers/text legend) to the left side\n", + "cbar.ax.yaxis.set_label_position('left')\n", + "cbar = plt.colorbar(sm, cax=cbar_ax, orientation='vertical')\n", + "cbar.set_label('Encounter rate difference', fontsize=12)\n", + "plt.savefig(\"cali_differences.png\", bbox_inches='tight', dpi=300)\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (trees)", + "language": "python", + "name": "trees" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/figures/generate_figure_6.ipynb b/figures/generate_figure_6.ipynb new file mode 100644 index 0000000..0322131 --- /dev/null +++ b/figures/generate_figure_6.ipynb @@ -0,0 +1,163 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "46d923c3", + "metadata": {}, + "source": [ + "# πŸ–ΌοΈ Generating Figure 6" + ] + }, + { + "cell_type": "markdown", + "id": "0742847a", + "metadata": {}, + "source": [ + "The following code generates Figure 6 of the paper by plotting the performance of the unconditioned model against the conditioned model for SatBird and sPlotOpen when varying the size of the training data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c03807ff", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59ab3f5e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams.update({'font.size': 10.3})\n", + "\n", + "ciso = np.array([1, 5, 10, 20, 50, 100])\n", + "\n", + "non_song_uncond = np.array([48.69, 55.18, 55.98, 56.98, 58.84, 59.41])\n", + "non_song_cond = np.array([48.31, 56.60, 58.37, 60.46, 62.61, 63.56])\n", + "\n", + "song_uncond = np.array([57.02, 64.41, 65.79, 67.01, 68.49, 69.30])\n", + "song_cond = np.array([57.20, 66.19, 68.30, 70.33, 71.94, 72.86])\n", + "\n", + "non_tree_uncond = np.array([74.10, 92.63, 94.21, 95.28, 96.07, 96.49])\n", + "non_tree_cond = np.array([73.46, 92.97, 94.93, 96.26, 96.98, 97.39])\n", + "\n", + "tree_uncond = np.array([82.57, 95.89, 96.75, 97.73, 98.24, 98.49])\n", + "tree_cond = np.array([81.64, 96.26, 97.62, 98.46, 98.93, 99.12])\n", + "\n", + "fig, axes = plt.subplots(1, 4, figsize=(12, 3), sharey=False)\n", + "\n", + "# -------------------------\n", + "# Plot 1: Songbirds\n", + "# -------------------------\n", + "axes[0].plot(ciso, song_uncond, marker='s', label=\"Unconditioned\", linestyle='--', color=sns.color_palette(\"Paired\")[9])\n", + "axes[0].plot(ciso, song_cond, marker='o', label=\"Conditioned\", color=sns.color_palette(\"Paired\")[9])\n", + "\n", + "axes[0].set_title(\"Songbirds\")\n", + "axes[0].set_xlabel(\"Fraction of training data (%)\")\n", + "\n", + "axes[0].set_ylabel(\"Top-$k$ (%)\")\n", + "#axes[1].set_xlim(0, 100)\n", + "\n", + "axes[0].legend()\n", + "axes[0].grid(True)\n", + "\n", + "# -------------------------\n", + "# Plot 2: Non-songbirds\n", + "# -------------------------\n", + "# Make uncond dashed\n", + "axes[1].plot(ciso, non_song_uncond, marker='s', label=\"Unconditioned\", linestyle='--', color=sns.color_palette(\"Paired\")[8])\n", + "axes[1].plot(ciso, non_song_cond, marker='o', label=\"Conditioned\", color=sns.color_palette(\"Paired\")[8])\n", + "\n", + "axes[1].set_title(\"Non-Songbirds\")\n", + "axes[1].set_xlabel(\"Fraction of training data (%)\")\n", + "\n", + "axes[1].set_ylabel(\"Top-$k$ (%)\")\n", + "#axes[1].set_xlim(0, 100)\n", + "axes[1].legend()\n", + "axes[1].grid(True)\n", + "\n", + "# -------------------------\n", + "# Plot 3: Trees\n", + "# -------------------------\n", + "axes[2].plot(ciso, tree_uncond, marker='s', label=\"Unconditioned\", linestyle='--', color=sns.color_palette(\"Paired\")[2])\n", + "axes[2].plot(ciso, tree_cond, marker='o', label=\"Conditioned\", color=sns.color_palette(\"Paired\")[2])\n", + "\n", + "axes[2].set_title(\"Trees\")\n", + "axes[2].set_xlabel(\"Fraction of training data (%)\")\n", + "axes[2].set_ylabel(\"AUC (%)\")\n", + "#axes[1].set_xlim(0, 100)\n", + "axes[2].legend()\n", + "axes[2].grid(True)\n", + "\n", + "# -------------------------\n", + "# Plot 4: Non-trees\n", + "# -------------------------\n", + "axes[3].plot(ciso, non_tree_uncond, marker='s', label=\"Unconditioned\", linestyle='--', color=sns.color_palette(\"Paired\")[3])\n", + "axes[3].plot(ciso, non_tree_cond, marker='o', label=\"Conditioned\", color=sns.color_palette(\"Paired\")[3])\n", + "\n", + "axes[3].set_title(\"Non-Trees\")\n", + "axes[3].set_xlabel(\"Fraction of training data (%)\")\n", + "#axes[3].set_xticks([1, 5, 10, 20, 50, 100])\n", + "#axes[1].set_xlim(0, 100)\n", + "axes[3].legend()\n", + "axes[3].grid(True)\n", + "\n", + "axes[3].set_ylabel(\"AUC (%)\")\n", + "\n", + "plt.tight_layout()\n", + "# reduce space between subplots\n", + "plt.subplots_adjust(wspace=0.34)\n", + "\n", + "# reduce space on the left and right\n", + "plt.subplots_adjust(left=0.053, right=0.996, top=0.92, bottom=0.145)\n", + "plt.savefig(\"training_data_fraction.pdf\", dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1cf83c37", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "robin_env", + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/figures/overview_dataset.pdf b/figures/overview_dataset.pdf new file mode 100644 index 0000000..d050589 Binary files /dev/null and b/figures/overview_dataset.pdf differ diff --git a/figures/overview_dataset.png b/figures/overview_dataset.png new file mode 100644 index 0000000..7b61d93 Binary files /dev/null and b/figures/overview_dataset.png differ diff --git a/getting_started.ipynb b/getting_started.ipynb new file mode 100644 index 0000000..2f58e6f --- /dev/null +++ b/getting_started.ipynb @@ -0,0 +1,1067 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "672c89ba", + "metadata": {}, + "source": [ + "# πŸš€ Getting Started" + ] + }, + { + "cell_type": "markdown", + "id": "32ea73da", + "metadata": {}, + "source": "We provide this detailed Python notebook to assist users who may be less familiar with Python and PyTorch in understanding how the code of the CISO works. CISO is described in the paper: ****CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations****" + }, + { + "cell_type": "markdown", + "id": "15cfbaa8", + "metadata": {}, + "source": [ + "## βš™οΈ Requirements" + ] + }, + { + "cell_type": "markdown", + "id": "bebd0e01", + "metadata": {}, + "source": [ + "The code works with Python version 3.11, but it should also be compatible with more recent Python versions. All Python dependencies should be installed within a virtual environment. All dependencies are listed in `requirements/requirements.txt`. \n", + "\n", + "We recommend using **Conda** to create and manage a clean environment:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91f98105", + "metadata": {}, + "outputs": [], + "source": [ + "!conda create -n ciso_env python=3.11\n", + "!conda activate ciso_env\n", + "!pip install -r requirements/requirements.txt" + ] + }, + { + "cell_type": "markdown", + "id": "1b42aef3", + "metadata": {}, + "source": [ + "Now that the virtual environment is set up, we need to activate it. \n", + "\n", + "In **Jupyter Notebook**, you can select the environment's kernel from the top menu: **Kernel** β†’ **Change Kernel** β†’ choose `ciso_env`. If the environment does not appear in the list, run the following command to register it with Jupyter and reload the page to make it visible: `python3 -m ipykernel install --user --name=ciso_env`\n", + "\n", + "In **VS code**, you can select the environment's kernel from the top menu: **Kernel** β†’ **Select Another Kernel** β†’ **Python Environments** β†’ choose `ciso_env`." + ] + }, + { + "cell_type": "markdown", + "id": "4ea6ef22", + "metadata": {}, + "source": [ + "Now we need to install all the required dependencies using the following command. You may also need to install the `ipykernel` package if it isn’t already available in your environment. Make sure that the dependencies are installed in the `my_venv` environment. If they are not, you can copy and paste the commands (removing the `!`) directly into a terminal in the current folder with the `my_venv` environment activated. Restarting the notebook afterward may also help." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd359a99", + "metadata": {}, + "outputs": [], + "source": [ + "!python3 -m pip install --upgrade pip\n", + "!pip3 install -r requirements.txt" + ] + }, + { + "cell_type": "markdown", + "id": "79c2f44b", + "metadata": {}, + "source": [ + "#### Installation difficulties\n", + "\n", + "If you encounter issues installing the requirements directly from within a notebook, we recommend installing them from a terminal in this folder using the following commands (with Python 3.10.18 installed and activated):\n", + "\n", + "```sh\n", + "python3 -m venv ciso_env\n", + "source ciso_env/bin/activate # On Linux/MacOS\n", + "ciso_env\\Scripts\\activate # On Windows\n", + "python3 -m pip install --upgrade pip\n", + "pip3 install -r requirements.txt\n", + "```\n", + "\n", + "After completing these steps, you can select and use the `ciso_env` environment as the kernel within your notebook." + ] + }, + { + "cell_type": "markdown", + "id": "0be6a792", + "metadata": {}, + "source": [ + "#### Imports\n", + "\n", + "**Your environment should now be ready to run** any code from this repository! We’ll now import the required packages used throughout this notebook:" + ] + }, + { + "cell_type": "code", + "id": "1d39e11d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:05:51.625222Z", + "start_time": "2025-12-02T17:05:48.358982Z" + } + }, + "source": [ + "import torch\n", + "import numpy as np\n", + "import pandas as pd\n", + "import yaml\n", + "import os\n", + "from pathlib import Path\n", + "from sklearn.metrics import roc_auc_score\n", + "from tqdm import tqdm\n", + "\n", + "from src.config import Config\n", + "from src.dataloaders.splot_dataloader import sPlotDataModule\n", + "from src.models import CISOModel" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "a97096f0", + "metadata": {}, + "source": [ + "## πŸ–₯️ Hardware Support" + ] + }, + { + "cell_type": "markdown", + "id": "a35c80bd", + "metadata": {}, + "source": "The results can be reproduced on any device (GPU, eGPU, or CPU), though the computational time will vary depending on the hardware's parallel processing capabilities. The code automatically detects and uses a GPU if available; otherwise, it defaults to running on the CPU. Note that you may need to adjust the NVIDIA-related packages to match your hardware setup. Training for CISO models was performed on GPUs, but inference was performed on CPUs." + }, + { + "cell_type": "code", + "id": "70ec9608", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:12:07.984939Z", + "start_time": "2025-12-02T17:12:07.982722Z" + } + }, + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "torch.set_default_device(device)\n", + "print(\"You are using device:\", device)" + ], + "outputs": [], + "execution_count": 18 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We first need to load the configuration file for data and model:", + "id": "a1b548a8ed575159" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:06:00.471735Z", + "start_time": "2025-12-02T17:06:00.464566Z" + } + }, + "cell_type": "code", + "source": [ + "config_path = os.path.join(Path.cwd(), \"configs/splot/config_ciso.yaml\")\n", + "\n", + "with open(config_path, \"r\") as f:\n", + " config_dict = yaml.safe_load(f)\n", + "\n", + "config = Config(**config_dict)" + ], + "id": "d919bde4d31b3b62", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## πŸ“Š Data Acquisition\n", + "id": "b2e238da" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "With the environment configured and the device identified, we can now move on to the data.", + "id": "0f01e987" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### πŸ’Ύ Processed Data\n", + "\n", + "To avoid downloading and preprocessing the complete original raw datasets, you can directly access the processed versions [here on HuggingFace](https://huggingface.co/cisosdm/datasets). All the data can be downloaded and added to `data` folder in this repository. \n" + ], + "id": "0be0bf0f" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## πŸ“₯ Data Loading\n", + "In our work, we experiment with 3 different datasets; throughout this tutorial, we will focus on 🌿 **sPlotOpen** dataset only." + ], + "id": "e6c412c0" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "The data is now ready to be loaded. The following cell will load the dataset.", + "id": "15bdcfeb" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We can read the dataset from the original files (without the masks) for all baseline models", + "id": "99cb75d2acf0277d" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:06:20.090549Z", + "start_time": "2025-12-02T17:06:04.825445Z" + } + }, + "cell_type": "code", + "source": [ + "worldclim_df = pd.read_csv(\"data/sPlotOpen/worldclim_data.csv\")\n", + "soilgrid_df = pd.read_csv(\"data/sPlotOpen/soilgrid_data.csv\")\n", + "species_df = pd.read_csv(\"data/sPlotOpen/species_merge_duplicates_v2.csv\")\n", + "\n", + "data = pd.concat([worldclim_df, soilgrid_df], axis=1)\n", + "# remove any duplicate columns after concatenation, example: PlotObservationID\n", + "data = data.loc[:, ~data.T.duplicated()]\n", + "hotspots = data[\"PlotObservationID\"].astype(str).to_list()\n", + "\n", + "data = data[config.data.env_columns].to_numpy()\n", + "\n", + "train_split = np.load(\"data/sPlotOpen/train_indices.npy\")\n", + "val_split = np.load(\"data/sPlotOpen/validation_indices.npy\")\n", + "test_split = np.load(\"data/sPlotOpen/test_indices.npy\")\n", + "\n", + "targets = np.load(\"data/sPlotOpen/merged_species_occurrences_v2.npy\")\n", + "\n", + "# only consider species with >= 100 occurrences\n", + "species_indices = np.where(\n", + " targets.sum(axis=0) >= 100\n", + ")[0]\n", + "\n", + "species_df = species_df.loc[species_indices].reset_index(drop=True)\n", + "\n", + "# Precompute normalization\n", + "normalization_means = np.mean(data[train_split, :], axis=0)\n", + "normalization_stds = np.std(data[train_split, :], axis=0)\n", + "\n", + "data = (data - normalization_means) / (normalization_stds + 1e-8)\n", + "\n", + "train_data = data[train_split]\n", + "val_data = data[val_split]\n", + "test_data = data[test_split]\n", + "\n", + "print(\"Training Data: \", train_data.shape)\n", + "print(\"Validation Data: \", val_data.shape)\n", + "print(\"Test data: \", test_data.shape)" + ], + "id": "67d7d1b4e7cb3580", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Data: (66848, 27)\n", + "Validation Data: (14067, 27)\n", + "Test data: (14189, 27)\n" + ] + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Let's visualize the geographic distribution of the dataset:", + "id": "0e2296a8" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:06:30.082126Z", + "start_time": "2025-12-02T17:06:27.528115Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import geopandas as gpd\n", + "import seaborn as sns\n", + "from shapely.geometry import Point\n", + "\n", + "# Read coordinates\n", + "df_locations = pd.read_csv(\"data/sPlotOpen/location_data.csv\")\n", + "coordinates = (\n", + " df_locations[[\"Longitude\", \"Latitude\"]]\n", + " .rename(columns={\"Longitude\": \"lon\", \"Latitude\": \"lat\"})\n", + " .reset_index(drop=True) \n", + ")\n", + "\n", + "# Read world shapefile\n", + "url = \"https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip\"\n", + "world = gpd.read_file(url)\n", + "world = world[world.geometry.centroid.y > -60] # clip Antarctica-ish\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 12))\n", + "world.plot(ax=ax, color=\"lightgray\")\n", + "\n", + "markersize = 0.5 # 0.01 is basically invisible\n", + "palette = sns.color_palette(\"Accent\")\n", + "\n", + "#helper to turn a subset of coordinates into a GeoDataFrame\n", + "def coords_to_gdf(coords_subset):\n", + " return gpd.GeoDataFrame(\n", + " geometry=[Point(lon, lat) for lon, lat in zip(coords_subset[\"lon\"], coords_subset[\"lat\"])],\n", + " crs=\"EPSG:4326\",\n", + " )\n", + "\n", + "gdf_train = coords_to_gdf(coordinates.iloc[train_split])\n", + "gdf_train.plot(ax=ax, color=palette[0], markersize=markersize, label=\"Training\")\n", + "\n", + "gdf_val = coords_to_gdf(coordinates.iloc[val_split])\n", + "gdf_val.plot(ax=ax, color=palette[4], markersize=markersize, label=\"Validation\")\n", + "\n", + "gdf_test = coords_to_gdf(coordinates.iloc[test_split])\n", + "gdf_test.plot(ax=ax, color=palette[5], markersize=markersize, label=\"Testing\")\n", + "\n", + "ax.grid(False)\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])\n", + "\n", + "legend = ax.legend(loc=\"lower left\", title=\"Data Splits\")\n", + "for handle in legend.legend_handles:\n", + " handle.set_sizes([25]) # increase the size of the markers in the legend\n", + "\n", + "ax.margins(0)\n", + "ax.set_ylim((-63, 90))\n", + "\n", + "plt.show()" + ], + "id": "e8fbfca9", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/hg/b17zwx8s39j69mjn_njzb9gm0000gq/T/ipykernel_29375/3632573664.py:18: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", + "\n", + " world = world[world.geometry.centroid.y > -60] # clip Antarctica-ish\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Next, we can re-use the already implemented dataloader to load the training, validation and test splits directly. It also implements the masked dataset, that will be used by CISO.", + "id": "40728d8c5cbd9604" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:09:01.279870Z", + "start_time": "2025-12-02T17:08:23.543949Z" + } + }, + "cell_type": "code", + "source": [ + "# setup the dataloader\n", + "splot_data_loader = sPlotDataModule(config.data)\n", + "splot_data_loader.setup()\n", + "\n", + "# get train, val and test loaders, with the data normalized for x, y, and mask (essential for CISO)\n", + "# x (N, 27), Y: (N, 3951), mask: (N, 3951)\n", + "# if you are using macOS, pass the following parameters\n", + "splot_train_data = splot_data_loader.train_dataloader()\n", + "splot_val_data = splot_data_loader.val_dataloader()\n", + "splot_test_data = splot_data_loader.test_dataloader()" + ], + "id": "11e334460585bd5", + "outputs": [], + "execution_count": 10 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:09:03.190828Z", + "start_time": "2025-12-02T17:09:03.185784Z" + } + }, + "cell_type": "code", + "source": [ + "print(\"Number of training samples: \", len(splot_data_loader.train_dataset))\n", + "print(\"Number of validation samples: \", len(splot_data_loader.val_dataset))\n", + "print(\"Number of testing samples: \", len(splot_data_loader.test_dataset))" + ], + "id": "3a4769d750783320", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of training samples: 66848\n", + "Number of validation samples: 14067\n", + "Number of testing samples: 14189\n" + ] + } + ], + "execution_count": 11 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Let's inspect one batch of the data", + "id": "2cc1c0c0aea5d2dd" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:09:46.165608Z", + "start_time": "2025-12-02T17:09:05.513020Z" + } + }, + "cell_type": "code", + "source": [ + "first_batch = next(iter(splot_train_data))\n", + "\n", + "data = first_batch[\"data\"]\n", + "targets = first_batch[\"targets\"]\n", + "mask = first_batch[\"mask\"]\n", + "\n", + "print(\"Input features shape:\", data.shape)\n", + "print(\"Target shape:\", targets.shape)\n", + "print(\"Mask shape:\", mask.shape)" + ], + "id": "a4c9c7097a13d7a", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input features shape: torch.Size([64, 27])\n", + "Target shape: torch.Size([64, 3951])\n", + "Mask shape: torch.Size([64, 3951])\n" + ] + } + ], + "execution_count": 12 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Let's inspect one mask to see how we condition on species. A mask would consist of 3 unique values: 0 to indicate the absence of a species, 1 to indicate the presence of species, -1 to indicate the species to predict, which will be conditioned on both absent and present species. ", + "id": "e1f93d3c953eef94" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:10:00.252588Z", + "start_time": "2025-12-02T17:10:00.249151Z" + } + }, + "cell_type": "code", + "source": "np.unique(mask[-1], return_counts=True)", + "id": "fa2e74fdc04dcc54", + "outputs": [ + { + "data": { + "text/plain": [ + "(array([-1, 0]), array([3736, 215]))" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 14 + }, + { + "cell_type": "markdown", + "id": "03cd33ad", + "metadata": {}, + "source": "## πŸ€– CISO" + }, + { + "cell_type": "markdown", + "id": "40d64f52", + "metadata": {}, + "source": [ + "Now that the data is ready, we can start playing with the **CISO** model. To avoid retraining the model (which can be time-consuming), **we can use the weights of already trained models**, known as *checkpoints*. Trained checkpoints of the model are available [here on HuggingFace](https://huggingface.co/cisosdm/model_checkpoints/tree/main), allowing you to skip the training step and use the model directly. The associated files are in the `model_checkpoints` zipped folder and should be copied and unzipped into the corresponding `models` folder of this repository while maintaining the same folder structure.\n", + "\n", + "That said, **if you'd prefer to train the model yourself**, you can change the config mode variable to `train` instead of `test` and run the next cell to begin training, which executes the `main.py` script. During training, the model’s AUC performance on the validation set will be displayed, and checkpoints will be automatically saved to the `models` folder. \n", + "\n", + "All key model and training parameters are defined in `config` dictionary and config file: `configs/splot/config_ciso.yaml` and can be modified as needed. One important set of parameters for CISO are under `partial_labels`, the parameters that control the conditional predictions in CISO.\n", + "\n", + "* `train_known_ratio` : During training, we set this value to `0.75`. This means that we randomly sample known number of labels between `0` and `0.75L`, where `L` is the total number of labels. These would be the labels to condition on.\n", + "\n", + "* `eval_known_ratio` : During evaluation, this parameter controls whether to condition on known species or not. \n", + "\n", + " - `eval_known_rate == 0 ` β†’ evaluate with no partial labels (all other species groups are unknown)\n", + " - `eval_known_rate == 1 ` β†’ evaluate with partial labels (labels from the other species group are provided)\n", + "\n", + "* `predict_family_of_species`: This parameter defaults to`-1` during training. During evaluation, this controls which group of species to condition on. \n", + "\n", + " - `predict_family_of_species = 0` β†’ evaluate **non-trees**\n", + " - `predict_family_of_species = 1` β†’ evaluate **trees**\n", + "\n" + ] + }, + { + "cell_type": "code", + "id": "72511921", + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-28T12:32:14.527160Z", + "start_time": "2025-11-28T12:32:14.525036Z" + } + }, + "source": [ + "config.mode = \"train\"\n", + "\n", + "if config.mode == \"train\":\n", + " !python main.py" + ], + "outputs": [], + "execution_count": 9 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "In this tutorial, we will focus on evaluation, let's set config.mode back to to `test`", + "id": "7b669dea78cef5b5" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T04:52:25.205464Z", + "start_time": "2025-12-02T04:52:25.202651Z" + } + }, + "cell_type": "code", + "source": "config.mode = \"test\"", + "id": "9e8036882139b557", + "outputs": [], + "execution_count": 27 + }, + { + "cell_type": "markdown", + "id": "93ed86e5", + "metadata": {}, + "source": "The CISO model checkpoints for sPlotOpen should now be located in the `model_checkpoints/1_sPlotOpen` folder. We can now define the model, load the saved weights, and inspect its architecture." + }, + { + "cell_type": "code", + "id": "dbfea28b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:12:23.124842Z", + "start_time": "2025-12-02T17:12:23.020266Z" + } + }, + "source": [ + "# Define model parameters (these are already defined in `configs/splot/config_ciso.yaml`)\n", + "num_classes = 3951 \n", + "\n", + "input_dim = 27\n", + "hidden_dim = 256\n", + "n_attention_layers = 3\n", + "n_heads = 4 \n", + "dropout = 0.2\n", + "n_backbone_layers = 2\n", + "\n", + "# Load the model\n", + "model = CISOModel(num_classes,\n", + " input_dim=input_dim,\n", + " hidden_dim=hidden_dim,\n", + " n_attention_layers=n_attention_layers,\n", + " n_heads=n_heads,\n", + " dropout=dropout,\n", + " n_backbone_layers=n_backbone_layers).to(device)\n" + ], + "outputs": [], + "execution_count": 19 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:12:25.152829Z", + "start_time": "2025-12-02T17:12:25.051381Z" + } + }, + "cell_type": "code", + "source": [ + "# change path for the desired checkpoint\n", + "ckpt_path = \"model_checkpoints/1_sPlotOpen/splot_ciso/1337/epoch=17-step=18810.ckpt\"\n", + "ckpt = torch.load(ckpt_path, map_location=device, weights_only=True)\n", + "state_dict = ckpt[\"state_dict\"] # Lightning always stores weights here\n", + "\n", + "# weights are saved with PyTorch lightning module, so we need to modify them to strip model. from keys\n", + "cleaned_state_dict = {}\n", + "for k, v in state_dict.items():\n", + " # if keys look like \"model.layer1.weight\", drop the \"model.\" part\n", + " if k.startswith(\"model.\"):\n", + " new_key = k[len(\"model.\"):]\n", + " else:\n", + " new_key = k\n", + " cleaned_state_dict[new_key] = v\n", + "\n", + "model.load_state_dict(cleaned_state_dict, strict=True)\n", + "model.eval()" + ], + "id": "9bcbeb879e01feac", + "outputs": [ + { + "data": { + "text/plain": [ + "CISOModel(\n", + " (backbone): SimpleMLPBackbone(\n", + " (layer_1): Linear(in_features=27, out_features=256, bias=True)\n", + " (layer_2): Linear(in_features=256, out_features=256, bias=True)\n", + " )\n", + " (label_embeddings): Embedding(3951, 256)\n", + " (state_embeddings): Embedding(3, 256, padding_idx=0)\n", + " (self_attn_layers): ModuleList(\n", + " (0-2): 3 x SelfAttnLayer(\n", + " (transformer_layer): TransformerEncoderLayer(\n", + " (self_attn): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=256, out_features=2048, bias=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " (linear2): Linear(in_features=2048, out_features=256, bias=True)\n", + " (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.2, inplace=False)\n", + " (dropout2): Dropout(p=0.2, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (dense_layer): Linear(in_features=3951, out_features=256, bias=True)\n", + " (output_linear): Linear(in_features=256, out_features=3951, bias=True)\n", + " (LayerNorm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + ")" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 20 + }, + { + "cell_type": "markdown", + "id": "17924dc3", + "metadata": {}, + "source": "The model is now ready to generate predictions for all 3951 species. Let's try it on the first sample from the test set:" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:13:09.255253Z", + "start_time": "2025-12-02T17:12:30.986788Z" + } + }, + "cell_type": "code", + "source": [ + "# Load data for the first sample of the test set\n", + "first_batch = next(iter(splot_test_data))" + ], + "id": "e0611507a49042a", + "outputs": [], + "execution_count": 21 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:14:57.332825Z", + "start_time": "2025-12-02T17:14:57.203841Z" + } + }, + "cell_type": "code", + "source": [ + "# first sample only\n", + "data = first_batch[\"data\"][0:1]\n", + "targets = first_batch[\"targets\"][0:1]\n", + "mask = first_batch[\"mask\"][0:1]\n", + "id = first_batch[\"hotspot_id\"][0:1]\n", + "\n", + "row = df_locations[df_locations[\"PlotObservationID\"] == int(id[0])].iloc[0].to_dict()\n", + "coordinates = row[\"Longitude\"], row[\"Latitude\"]\n", + "\n", + "# Plot the location of the test sample\n", + "print(\"Coordinates of the test sample:\", coordinates)\n", + "fig, ax = plt.subplots(figsize=(9, 8))\n", + "world.plot(ax=ax, color='lightgray')\n", + "gdf = gpd.GeoDataFrame(geometry=[Point(coordinates[0], coordinates[1])])\n", + "gdf.plot(ax=ax, color=\"red\", markersize=50, label=\"Test Sample\", marker=\"x\")\n", + "ax.grid(False)\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])\n", + "legend = ax.legend(loc='lower left')\n", + "plt.plot()" + ], + "id": "fa18dccf1c2f92ac", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coordinates of the test sample: (-148.716222, 69.674083)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 32 + }, + { + "cell_type": "code", + "id": "3efd8d51", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:13:40.865075Z", + "start_time": "2025-12-02T17:13:38.770082Z" + } + }, + "source": [ + "# Get the model predictions in this 1 location\n", + "with torch.no_grad():\n", + " y_pred = torch.sigmoid(model(data.to(device), mask.to(device)))" + ], + "outputs": [], + "execution_count": 24 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "So far, the mask is all -1, meaning that it covers the unconditioned case of CISO. In order to condition on species, let's mark it as present or absent in the mask.", + "id": "f326e84e3d261875" + }, + { + "cell_type": "markdown", + "id": "615b7728", + "metadata": {}, + "source": "Based on the predictions obtained, let's identify present species at this location and inspect predictions and ground-truth values of those species:" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:15:03.431756Z", + "start_time": "2025-12-02T17:15:03.424267Z" + } + }, + "cell_type": "code", + "source": [ + "y_pred_np = y_pred.detach().cpu().numpy().reshape(-1)\n", + "targets_np = targets.detach().cpu().numpy().reshape(-1)\n", + "mask_ = targets_np != 0\n", + "\n", + "species = species_df.loc[mask_, \"Species Name\"].to_numpy()\n", + "preds_ = y_pred_np[mask_]\n", + "targets_ = targets_np[mask_]\n", + "\n", + "print(f\"{'Species':<30} | {'Prediction'} | {'Target'}\")\n", + "print(\"-\" * 45)\n", + "for sp, p, t in zip(species, preds_, targets_):\n", + " print(f\"{sp:<30} | {p:10.4f} | {t:10.4f}\")" + ], + "id": "9cc76e40", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Species | Prediction | Target\n", + "---------------------------------------------\n", + "Carex capillaris | 0.0722 | 1.0000\n", + "Dryas integrifolia | 0.7239 | 1.0000\n", + "Equisetum arvense | 0.1140 | 1.0000\n", + "Eriophorum angustifolium | 0.2447 | 1.0000\n", + "Eriophorum vaginatum | 0.0044 | 1.0000\n", + "Juncus biglumis | 0.0516 | 1.0000\n", + "Juncus triglumis | 0.0023 | 1.0000\n", + "Persicaria vivipara | 0.4435 | 1.0000\n", + "Salix arctica | 0.0571 | 1.0000\n", + "Saxifraga oppositifolia | 0.4159 | 1.0000\n" + ] + } + ], + "execution_count": 33 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Now, let's condition on a specific species, where we pass its presence or absence to the mask:", + "id": "a3cd30a62874b839" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:15:13.433980Z", + "start_time": "2025-12-02T17:15:13.430346Z" + } + }, + "cell_type": "code", + "source": "print(np.unique(mask[0], return_counts=True))\n", + "id": "8dfa6d7e2802620d", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([-1, 0]), array([3950, 1]))\n" + ] + } + ], + "execution_count": 34 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:15:16.043202Z", + "start_time": "2025-12-02T17:15:16.019207Z" + } + }, + "cell_type": "code", + "source": [ + "species_name = \"Quercus alba\"\n", + "\n", + "idx = species_df.index[species_df[\"Species Name\"] == species_name].tolist()[0]\n", + "mask[0, idx] = targets[0, idx]\n", + "print(np.unique(mask[0], return_counts=True))\n", + "# re-run inference\n", + "with torch.no_grad():\n", + " y_pred = torch.sigmoid(model(data.to(device), mask.to(device)))" + ], + "id": "fc38471659cad9c6", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([-1, 0]), array([3950, 1]))\n" + ] + } + ], + "execution_count": 35 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Now, let's see how the predictions change:", + "id": "a1544e67922b5a21" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T17:15:31.766352Z", + "start_time": "2025-12-02T17:15:31.761058Z" + } + }, + "cell_type": "code", + "source": [ + "y_pred_np = y_pred.detach().cpu().numpy().reshape(-1)\n", + "targets_np = targets.detach().cpu().numpy().reshape(-1)\n", + "mask_ = targets_np != 0\n", + "\n", + "species = species_df.loc[mask_, \"Species Name\"].to_numpy()\n", + "preds_ = y_pred_np[mask_]\n", + "targets_ = targets_np[mask_]\n", + "\n", + "print(f\"{'Species':<30} | {'Prediction'} | {'Target'}\")\n", + "print(\"-\" * 45)\n", + "for sp, p, t in zip(species, preds_, targets_):\n", + " print(f\"{sp:<30} | {p:10.4f} | {t:10.4f}\")" + ], + "id": "9041d8ded93e1865", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Species | Prediction | Target\n", + "---------------------------------------------\n", + "Carex capillaris | 0.0722 | 1.0000\n", + "Dryas integrifolia | 0.7239 | 1.0000\n", + "Equisetum arvense | 0.1140 | 1.0000\n", + "Eriophorum angustifolium | 0.2447 | 1.0000\n", + "Eriophorum vaginatum | 0.0044 | 1.0000\n", + "Juncus biglumis | 0.0516 | 1.0000\n", + "Juncus triglumis | 0.0023 | 1.0000\n", + "Persicaria vivipara | 0.4435 | 1.0000\n", + "Salix arctica | 0.0571 | 1.0000\n", + "Saxifraga oppositifolia | 0.4159 | 1.0000\n" + ] + } + ], + "execution_count": 37 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Let's now compute predictions for all test set locations across all species, without conditioning:", + "id": "e75b8cbf" + }, + { + "cell_type": "code", + "id": "5a5265a0", + "metadata": {}, + "source": [ + "with torch.no_grad():\n", + " preds = []\n", + " ys = []\n", + "\n", + " for batch in tqdm(splot_test_data):\n", + " data = batch[\"data\"].to(device)\n", + " mask = batch[\"mask\"].to(device)\n", + " targets = batch[\"targets\"] # keep on CPU for now\n", + "\n", + " y_pred = model(data, mask)\n", + "\n", + " preds.append(y_pred.detach().cpu())\n", + " ys.append(targets.detach().cpu())\n", + "\n", + " preds = torch.concatenate(preds, axis=0).float()\n", + " ys = torch.concatenate(ys, axis=0).int()\n", + "\n", + "print(\"Predictions shape:\", preds.numpy().shape)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "id": "f8b412c0", + "metadata": {}, + "source": "Using these predictions, we can now compute the AUC score over all species:" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "y_true = np.asarray(targets)\n", + "y_pred = np.asarray(preds)\n", + "\n", + "auc = roc_auc_score(y_true, y_pred, average=\"macro\")\n", + "\n", + "print(f\"Mean AUC over species: {auc}\")" + ], + "id": "2f461045ad72ca54" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "# Species with at least one presence in the training, validation, and test sets\n", + "indices_non_zeros_samples = np.intersect1d(np.intersect1d(np.sum(train_data, axis=0).nonzero()[0], np.sum(val_data, axis=0).nonzero()[0]),\n", + " np.sum(test_data, axis=0).nonzero()[0]).tolist()\n", + "\n", + "# Compute AUC for each species\n", + "aucs = roc_auc_score(preds[:, indices_non_zeros_samples].T, \n", + " targets[:, indices_non_zeros_samples].T, num_tasks=len(indices_non_zeros_samples)).cpu().numpy()\n", + "\n", + "# Create a DataFrame with species names and AUC values\n", + "species_auc_df = pd.DataFrame({\n", + " 'Species': species_df.iloc[indices_non_zeros_samples]['Species Name'].values,\n", + " 'AUC': aucs * 100\n", + "})\n", + "\n", + "print(\"Sample of 10 species with their AUC values:\")\n", + "display(species_auc_df.sample(10))\n", + "print(\"Average AUC over the species:\", round(species_auc_df[\"AUC\"].mean(), 1))" + ], + "id": "209ae80f", + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "id": "5fdfe182", + "metadata": {}, + "source": [ + "We hope this notebook helped you understand the basics of how to use CISO and how to reproduce the results presented in the paper.\n", + " \n", + "This notebook was inspired by a similar tutorial for [MaskSDM](https://github.com/zbirobin/MaskSDM-MEE/blob/main/getting_started.ipynb )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "deepHSM", + "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": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main.py b/main.py index f3561c1..c0357bb 100644 --- a/main.py +++ b/main.py @@ -40,7 +40,7 @@ def main(): parser = argparse.ArgumentParser( description="PyTorch Lightning Tabular Data MLP Training" ) - parser.add_argument("--config", type=str, default="config.yaml", required=True) + parser.add_argument("--config", type=str, default="configs/splot/config_ciso.yaml", required=True) parser.add_argument("--run_id", type=int, default=1) parser.add_argument("--results_file_name", type=str, default="test_results.csv") args = parser.parse_args() @@ -97,7 +97,8 @@ def main(): save_last=True, auto_insert_metric_name=True, ) - + # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # torch.set_default_device(device) # Train the model trainer = Trainer( max_epochs=config.training.max_epochs, diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..c1e882e --- /dev/null +++ b/requirements.txt @@ -0,0 +1,21 @@ +elapid==1.0.2 +geopandas==1.0.1 +hydra-core==1.3.2 +numpy==1.26.4 +omegaconf==2.3.0 +pandas==2.3.0 +planetary_computer==1.0.0 +pydantic==2.11.7 +pystac_client==0.8.6 +pytest==8.4.1 +pytorch_lightning==2.4.0 +PyYAML==6.0.2 +rasterio==1.3.9 +retrying==1.4.0 +Shapely==2.1.1 +tifffile==2023.12.9 +torch==2.4.1 +torchmetrics==1.4.0.post0 +tqdm==4.66.1 +seaborn==0.13.2 +comet-ml==3.54.2 \ No newline at end of file diff --git a/run_files/hyperparam.sh b/run_files/hyperparam.sh deleted file mode 100755 index bfc7a37..0000000 --- a/run_files/hyperparam.sh +++ /dev/null @@ -1,11 +0,0 @@ -#!/bin/bash - -init=('no' 'means') -bands=(['r','g','b'] ['r','g','b','nir']) - -for b in ${bands[@]}; do - for i in ${init[@]}; do - sbatch ./train_.sh ++comet.project_name="resnet18_base" ++data.bands=$b ++experiment.module.init_bias=$i ++trainer.log_every_n_steps=5 args.config="/home/mila/t/tengmeli/ecosystem-embedding/configs/weight_loss/base_means_rgb.yaml" - done -done - diff --git a/run_files/job.sh b/run_files/job_satbird.sh similarity index 73% rename from run_files/job.sh rename to run_files/job_satbird.sh index dcce13d..517bdff 100644 --- a/run_files/job.sh +++ b/run_files/job_satbird.sh @@ -1,5 +1,5 @@ #!/bin/bash -#SBATCH --job-name=satbird_ctran +#SBATCH --job-name=satbird_ciso #SBATCH --output=job_output_test.txt #SBATCH --error=job_error_test.txt #SBATCH --ntasks=1 @@ -11,4 +11,4 @@ module load miniconda/3 conda activate new_env export COMET_API_KEY=$COMET_API_KEY -python train.py args.config=configs/satbird/satbird_ctran_base.yaml +python train.py args.config=configs/satbird/satbird_ciso.yaml diff --git a/run_files/job_splots.sh b/run_files/job_splots.sh index 333be88..1f72f2b 100644 --- a/run_files/job_splots.sh +++ b/run_files/job_splots.sh @@ -12,4 +12,4 @@ module load miniconda/3 conda activate new_env export COMET_API_KEY=$COMET_API_KEY -python main.py --config="configs/splot/config_ctran.yaml" +python main.py --config="configs/splot/config_ciso.yaml" diff --git a/run_files/multiple_runs.sh b/run_files/multiple_runs.sh index 207ad96..ddd1391 100644 --- a/run_files/multiple_runs.sh +++ b/run_files/multiple_runs.sh @@ -13,4 +13,4 @@ module load miniconda/3 conda activate new_env export COMET_API_KEY=$COMET_API_KEY -python train.py args.config="configs/satbirdxsatbutterfly/config_ctran.yaml" args.run_id=$SLURM_ARRAY_TASK_ID +python train.py args.config="configs/satbirdxsatbutterfly/config_ciso.yaml" args.run_id=$SLURM_ARRAY_TASK_ID diff --git a/run_files/run_test.sh b/run_files/run_test.sh deleted file mode 100755 index 5b074f8..0000000 --- a/run_files/run_test.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=testing -#SBATCH --output=job_output_test_baselines.txt -#SBATCH --error=job_error_test_baselines.txt -#SBATCH --ntasks=1 -#SBATCH --time=5:59:00 -#SBATCH --mem-per-cpu=20Gb -#SBATCH --cpus-per-task=1 -#SBATCH --gres=gpu:1 -#SBATCH --partition=long - -module load anaconda/3 -conda activate eco - -python test.py args.config=configs/SatBird-USA-summer/resnet18_RGB.yaml -python test.py args.config=configs/SatBird-USA-summer/resnet18_RGB_ENV.yaml -python test.py args.config=configs/SatBird-USA-summer/resnet18_RGBNIR.yaml -python test.py args.config=configs/SatBird-USA-summer/resnet18_RGBNIR_ENV.yaml -python test.py args.config=configs/SatBird-USA-summer/resnet18_RGBNIR_ENV_RM.yaml diff --git a/run_files/unittest.sh b/run_files/unittest.sh deleted file mode 100644 index cdd351d..0000000 --- a/run_files/unittest.sh +++ /dev/null @@ -1,11 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=test -#SBATCH --output=job_output.txt -#SBATCH --error=job_error.txt -#SBATCH --ntasks=1 -#SBATCH --time=5:00:00 -#SBATCH --mem=10Gb - -module load anaconda/3 -conda activate eco -pytest tests/data/test_data_files.py -k "test_nan_refl_image_values" -s \ No newline at end of file diff --git a/src/config.py b/src/config.py index f074eb1..ee1dcf3 100644 --- a/src/config.py +++ b/src/config.py @@ -18,15 +18,15 @@ class TrainingConfig(BaseModel): ) max_epochs: Optional[int] = Field(..., description="Number of epochs to train") accelerator: Optional[str] = Field( - ..., description="Accelerator for training: gpu, cpu, or auto" + ..., description="Accelerator for training: gpu, cpu, mps (for mac) or auto" ) devices: Optional[int] = Field( - ..., description="Accelerator for training: gpu, cpu, or auto" + ..., description="Number of devices for training: gpu, cpu, or auto" ) class PartialLabels(BaseModel): - use: bool = Field(..., description="Training with Partial labels or not") + use: bool = Field(False, description="Training with Partial labels or not") predict_family_of_species: int = Field( ..., description="what family of species to predict during testing" ) diff --git a/src/dataloaders/dataloader.py b/src/dataloaders/dataloader.py index 12d01b8..f6fd166 100644 --- a/src/dataloaders/dataloader.py +++ b/src/dataloaders/dataloader.py @@ -195,7 +195,7 @@ def __init__(self, opts) -> None: self.seed = self.config.training.seed self.batch_size = self.config.data.loaders.batch_size self.num_workers = self.config.data.loaders.num_workers - self.data_base_dir = self.config.data.files.base + self.data_base_dir = os.path.join(self.config.base_dir, self.config.data.files.base) self.targets_file = self.config.data.files.targets_file # combining multiple train files diff --git a/src/dataloaders/splot_dataloader.py b/src/dataloaders/splot_dataloader.py index d5006d6..d0d8c5e 100644 --- a/src/dataloaders/splot_dataloader.py +++ b/src/dataloaders/splot_dataloader.py @@ -182,29 +182,29 @@ def setup(self, stage: Optional[str] = None) -> None: maximum_known_labels_ratio=self.config.partial_labels.eval_known_ratio, ) - def train_dataloader(self): + def train_dataloader(self, num_workers = 8, pin_memory = True, persistent_workers = True) -> DataLoader: return DataLoader( self.train_dataset, batch_size=self.batch_size, shuffle=True, - persistent_workers=True, - pin_memory=True, - num_workers=16, + persistent_workers=persistent_workers, + pin_memory=pin_memory, + num_workers=num_workers, ) - def val_dataloader(self): + def val_dataloader(self, num_workers = 8, pin_memory = True, persistent_workers = True): return DataLoader( self.val_dataset, batch_size=self.batch_size, - persistent_workers=True, - pin_memory=True, - num_workers=16, + persistent_workers=persistent_workers, + pin_memory=pin_memory, + num_workers=num_workers, ) - def test_dataloader(self): + def test_dataloader(self, num_workers = 8, pin_memory = True, persistent_workers = True): return DataLoader( self.test_dataset, batch_size=self.batch_size, - pin_memory=True, - num_workers=16, + pin_memory=pin_memory, + num_workers=num_workers, ) diff --git a/src/metrics.py b/src/metrics.py index f2a0b35..c8e00c4 100644 --- a/src/metrics.py +++ b/src/metrics.py @@ -1,11 +1,8 @@ # Src code for all metrics used import torch import torch.nn as nn -import torchmetrics from torchmetrics import Metric -from src.losses import CustomCrossEntropy - class CustomKL(Metric): def __init__(self, dist_sync_on_step=False): @@ -297,34 +294,20 @@ def get_metric(metric): an addict.Dict """ - if metric.name == "mae" and not metric.ignore is True: + if metric.name == "mae" and not metric.ignore: return MaskedMAE() - elif metric.name == "mse" and not metric.ignore is True: + elif metric.name == "mse" and not metric.ignore: return MaskedMSE() - elif metric.name == "nonzero_mae" and not metric.ignore is True: - return NonZeroMAE() - elif metric.name == "nonzero_mse" and not metric.ignore is True: - return NonZeroMSE() - elif metric.name == "topk" and not metric.ignore is True: + elif metric.name == "topk" and not metric.ignore: return CustomTopK() - elif metric.name == "topk2" and not metric.ignore is True: - return CustomTopK_bounded() - elif metric.name == "top10" and not metric.ignore is True: + elif metric.name == "top10" and not metric.ignore: return CustomTop10() - elif metric.name == "top30" and not metric.ignore is True: + elif metric.name == "top30" and not metric.ignore: return CustomTop30() - elif metric.name == "ce" and not metric.ignore is True: - return CustomCrossEntropy(metric.lambd_pres, metric.lambd_abs) - elif metric.name == "r2" and not metric.ignore is True: - return torchmetrics.ExplainedVariance(multioutput="variance_weighted") - elif metric.name == "kl" and not metric.ignore is True: - return CustomKL() - elif metric.name == "accuracy" and not metric.ignore is True: - return CustomMultiLabelAcc() elif metric.ignore is True: return None else: - return None # raise ValueError("Unknown metric_item {}".format(metric)) + return None def get_metrics(config): diff --git a/src/models/__init__.py b/src/models/__init__.py index bdee893..78c749e 100644 --- a/src/models/__init__.py +++ b/src/models/__init__.py @@ -1,4 +1,4 @@ -from src.models.ctran import CTranModel -from src.models.baselines import SimpleMLP, SimpleMLPBackbone, SimpleMLPMasked_v0, SimpleMLPMasked_v1 +from src.models.ciso import CISOModel +from src.models.baselines import SimpleMLP, SimpleMLPBackbone, SimpleMLP_PlusPlus -__all__ = ["CTranModel", "SimpleMLP", "SimpleMLPBackbone", "SimpleMLPMasked_v0", "SimpleMLPMasked_v1"] +__all__ = ["CISOModel", "SimpleMLP", "SimpleMLPBackbone", "SimpleMLP_PlusPlus"] diff --git a/src/models/baselines.py b/src/models/baselines.py index d9ff0d0..4d5d2d6 100644 --- a/src/models/baselines.py +++ b/src/models/baselines.py @@ -1,6 +1,5 @@ """ NN models -Code is based on the C-tran paper: https://github.com/QData/C-Tran """ import torch @@ -44,7 +43,7 @@ def forward(self, x): return x -class SimpleMLPMasked_v1(nn.Module): +class SimpleMLP_PlusPlus(nn.Module): """ Simple MLP Masked where env features and mask use separate encoders """ @@ -57,7 +56,7 @@ def __init__( quantized_mask_bins=1, quantize_encounter_rates=True, ): - super(SimpleMLPMasked_v1, self).__init__() + super(SimpleMLP_PlusPlus, self).__init__() self.quantize_encounter_rates = quantize_encounter_rates self.num_unique_mask_values = quantized_mask_bins @@ -100,41 +99,6 @@ def forward(self, x, mask_q): return x -class SimpleMLPMasked_v0(nn.Module): - """ - Simple MLP Masked where env features and mask share the same encoder. For splot only - """ - - def __init__( - self, - input_dim, - hidden_dim, - num_classes, - backbone=None, - attention_layers=2, - heads=2, - num_unique_mask_values=3, - ): - super(SimpleMLPMasked_v0, self).__init__() - - self.num_unique_mask_values = num_unique_mask_values - - self.layer_1 = nn.Linear(input_dim + (self.num_unique_mask_values * num_classes), hidden_dim) - self.layer_2 = nn.Linear(hidden_dim, hidden_dim) - self.out_layer = nn.Linear(hidden_dim, num_classes) - - def forward(self, x, mask): - mask = mask.long() - one_hot_mask = F.one_hot(mask, num_classes=self.num_unique_mask_values).float() # One-hot encoding - one_hot_mask_flattened = one_hot_mask.view(mask.size(0), -1) # Flatten to (batch_size, num_classes * 3) - - x_combined = torch.cat((x, one_hot_mask_flattened), dim=1) - x = F.relu(self.layer_1(x_combined)) - x = F.relu(self.layer_2(x)) - x = self.out_layer(x) - return x - - class SimpleMLPBackbone(nn.Module): def __init__(self, input_dim, hidden_dim=64, num_layers=2): super(SimpleMLPBackbone, self).__init__() diff --git a/src/models/ctran.py b/src/models/ciso.py similarity index 96% rename from src/models/ctran.py rename to src/models/ciso.py index 99229ef..4bd38fe 100644 --- a/src/models/ctran.py +++ b/src/models/ciso.py @@ -14,14 +14,14 @@ from src.models.utils import custom_replace_n, weights_init -class CTranModel(nn.Module): +class CISOModel(nn.Module): def __init__( self, num_classes, - backbone="SimpleMLPBackbone", - quantized_mask_bins=4, + backbone="SimpleMLPBackbone", + quantized_mask_bins=1, input_dim=27, - hidden_dim=256, + hidden_dim=256, n_attention_layers=3, n_heads=4, dropout=0.2, @@ -30,7 +30,6 @@ def __init__( use_unknown_token=False, ): """ - pos_emb is false by default num_classes: total number of species species_list: list of species backbone: backbone to process input (MLP) @@ -43,7 +42,7 @@ def __init__( dropout: dropout ratio use_unknown_token: add special parameter to encode unknown state when state is linearly tokenized """ - super(CTranModel, self).__init__() + super(CISOModel, self).__init__() self.hidden_dim = hidden_dim # this should match the backbone output feature size (512 for Resnet18, 2048 for Resnet50) self.quantized_mask_bins = quantized_mask_bins self.n_embedding_state = self.quantized_mask_bins + 2 diff --git a/src/models/random_forest/__init__.py b/src/models/random_forest/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/models/random_forest/random_forest_satbird.py b/src/models/random_forest/random_forest_satbird.py new file mode 100644 index 0000000..68f9cd5 --- /dev/null +++ b/src/models/random_forest/random_forest_satbird.py @@ -0,0 +1,249 @@ +""" +random_forest_satbird.py + +End-to-end Random Forest baseline using a PyTorch Dataset for SatBird +- Uses scikit-learn RandomForestRegressor +- Works with any PyTorch Dataset that returns (x, y) +""" +import csv +import os +import random + +import numpy as np +import torch +from torch.utils.data import DataLoader +from sklearn.ensemble import RandomForestRegressor + +from src.dataloaders.dataloader import SDMDataModule +from src.metrics import CustomTopK, MaskedMAE +from src.utils import load_opts, eval_species_split + + +# ============================================================ +# 1. Reproducibility +# ============================================================ + +def set_seed(seed: int = 1337): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +# ============================================================ +# 3. Helper: DataLoader -> NumPy arrays +# ============================================================ + +def dataloader_to_numpy(dataloader: DataLoader, device: str = "cpu"): + """ + Collects all batches from a DataLoader into NumPy arrays. + + Assumes each batch is (x, y) or {"data": x, "targets": y}. + """ + xs = [] + ys = [] + + for batch in dataloader: + # Support (x, y) or dict-style batches + if isinstance(batch, (list, tuple)) and len(batch) == 2: + x, y = batch + elif isinstance(batch, dict): + x = batch["data"] + y = batch["targets"] + else: + raise ValueError("Unsupported batch format, expected (x, y) or dict with 'data' and 'targets'.") + + x = x.to(device) + y = y.to(device) + + xs.append(x.detach().cpu()) + ys.append(y.detach().cpu()) + + X = torch.cat(xs, dim=0).numpy() + y = torch.cat(ys, dim=0).numpy() + return X, y + + +def compute_masked_mae_from_numpy(y_true_np, y_pred_np, mask_np=None): + """ + y_true_np: (N, ...) ground truth + y_pred_np: (N, ...) predictions + mask_np: (N, ...) optional boolean or {0,1} mask; if None, all elements used + """ + y_true = torch.from_numpy(y_true_np).float() + preds = torch.from_numpy(y_pred_np).float() + + if mask_np is not None: + mask = torch.from_numpy(mask_np.astype(bool)) + else: + mask = None + + metric = MaskedMAE() + metric.update(target=y_true, preds=preds, mask=mask) + return metric.compute().item() + + +def compute_custom_topk_from_numpy(y_true_np, y_pred_scores_np): + """ + y_true_np: (N,) with class indices OR (N, C) with multi-hot targets. + y_pred_scores_np: (N, C) with per-class scores/probabilities. + """ + y_true = torch.from_numpy(y_true_np) + preds = torch.from_numpy(y_pred_scores_np).float() + + # If y is 1D (class indices), convert to one-hot so it matches CustomTopK expectation. + if y_true.ndim == 1: + num_classes = preds.shape[1] + y_onehot = torch.zeros((y_true.shape[0], num_classes), dtype=torch.float32) + y_onehot[torch.arange(y_true.shape[0]), y_true.long()] = 1.0 + else: + # assume already multi-hot, just cast + y_onehot = y_true.float() + + metric = CustomTopK() + metric.update(target=y_onehot, preds=preds) + return metric.compute().item() + +# ============================================================ +# 4. Train + Evaluate Random Forest +# ============================================================ +# NUM_CLASSES = 1000 + + +def train_random_forest( + train_loader: DataLoader, + val_loader: DataLoader, + test_loader: DataLoader, + config: dict, + n_estimators: int = 2, + max_depth=None, + n_jobs: int = -1, + random_state: int = 42, + device: str = "cpu", +): + """ + Trains a RandomForestClassifier on data from PyTorch DataLoaders. + Works for standard single-label classification (y shape: (N,)) + """ + + # ----- 4.1 Collect train data ----- + print("Collecting training data from DataLoader...") + X_train, y_train = dataloader_to_numpy(train_loader, device=device) + print(f"X_train shape: {X_train.shape}, y_train shape: {y_train.shape}") + + # ----- 4.2 Initialize RF ----- + rf = RandomForestRegressor( + n_estimators=n_estimators, + max_depth=max_depth, + n_jobs=n_jobs, + random_state=random_state, + verbose=1 + ) + + # ----- 4.3 Fit ----- + print("Fitting RandomForestClassifier...") + rf.fit(X_train, y_train) + + # ----- 4.4 Validation evaluation ----- + if val_loader is not None: + print("\n=== Validation Evaluation ===") + X_val, y_val = dataloader_to_numpy(val_loader, device=device) + + # For RandomForest, use predict_proba to get per-class scores + # predict_proba returns (N, C) for single-output multi-class + y_val_proba = rf.predict(X_val) + # In binary classification, predict_proba returns (N, 2) + # that's still fine for CustomTopK + + val_custom_k = compute_custom_topk_from_numpy(y_val, y_val_proba) + print(f"Validation TopK: {val_custom_k:.4f}") + # Masked MAE (no mask here – use mask_np if you have one) + val_mae = compute_masked_mae_from_numpy(y_val, y_val_proba) + + print(f"Validation MAE: {val_mae:.4f}") + + test_topk = [] + test_mae = [] + # ----- 4.5 Test evaluation ----- + print("\n=== Test Evaluation ===") + X_test, y_test = dataloader_to_numpy(test_loader, device=device) + y_test_proba = rf.predict(X_test) + + test_topk_ = compute_custom_topk_from_numpy(y_test, y_test_proba) + test_mae_ = compute_masked_mae_from_numpy(y_test, y_test_proba) + + print(f"Test TopK: {test_topk_:.4f}") + print(f"Test MAE: {test_mae_:.4f}") + test_topk.append(test_topk_) + test_mae.append(test_mae_) + # next test on sub-species we have such as + indices = [0, 1] + for ind in indices: + print("=====species index===:: ", ind) + base_data_folder = os.path.join( + config.data.files.base, + config.data.files.satbird_species_indices_path, + ) + species_indices_to_eval = eval_species_split(index=ind, base_data_folder=base_data_folder) + predictions = y_test_proba[:, species_indices_to_eval] + targets = y_test[:, species_indices_to_eval] + + test_topk_ = compute_custom_topk_from_numpy(targets, predictions) + test_mae_ = compute_masked_mae_from_numpy(targets, predictions) + + print(f"Test TopK: {test_topk_:.4f}") + print(f"Test MAE: {test_mae_:.4f}") + test_topk.append(test_topk_) + test_mae.append(test_mae_) + + return rf, test_topk, test_mae + + +def main(): + seed = 1337 + run_id = 1 + global_seed = (run_id * (seed + (run_id - 1))) % (2**31 - 1) + + set_seed(global_seed) + device = "cuda" if torch.cuda.is_available() else "cpu" + print("Using device:", device) + + # ---- 5.1 Create datasets (REPLACE with your real datasets) ---- + default_config = os.path.join(os.getcwd(), "configs/defaults.yaml") + + config = load_opts(os.path.join(os.getcwd(), "configs/satbird/config_mlp.yaml"), default=default_config) + data_module_class = SDMDataModule(config) + data_module_class.setup() + + # ---- 5.2 DataLoaders ---- + train_loader = data_module_class.train_dataloader() + val_loader = data_module_class.val_dataloader() + test_loader = data_module_class.test_dataloader() + + # ---- 5.3 Train RF baseline ---- + rf_model, test_topk, test_mae = train_random_forest( + train_loader=train_loader, + val_loader=val_loader, + test_loader=test_loader, + config = config, + n_estimators=200, + max_depth=None, + n_jobs=-1, + random_state=global_seed, + device=device, + ) + with open(f"RF_satbird_topk_results.csv", "a", newline="") as f: + writer = csv.writer(f) + # writer.writerow(["All_species", "non-songbird", "songbird"]) + writer.writerow(test_topk) + + with open(f"RF_satbird_mae_results.csv", "a", newline="") as f: + writer = csv.writer(f) + # writer.writerow(["All_species", "non-songbird", "songbird"]) + writer.writerow(test_mae) + + print("\nDone. Random Forest baseline trained.") + + +if __name__ == "__main__": + main() diff --git a/src/models/random_forest/random_forest_splot.py b/src/models/random_forest/random_forest_splot.py new file mode 100644 index 0000000..6fad36a --- /dev/null +++ b/src/models/random_forest/random_forest_splot.py @@ -0,0 +1,222 @@ +""" +random_forest_splots.py + +End-to-end Random Forest baseline using a PyTorch Dataset for sPlotOpen +- Uses scikit-learn RandomForestClassifier +- Works with any PyTorch Dataset that returns (x, y) +""" +import csv +import os +import random +import numpy as np +import pandas as pd +import torch +from torch.utils.data import DataLoader +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +from torchmetrics.classification import MultilabelAUROC + +from main import load_config +from src.dataloaders.splot_dataloader import sPlotDataModule + + +# ============================================================ +# Reproducibility +# ============================================================ + +def set_seed(seed: int = 1337): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + +def trees_masking(index, config): + targets = np.load(os.path.join(config.base, config.targets)) + species_df = pd.read_csv(os.path.join(config.base, config.species_list)) + + species_indices = np.where( + targets.sum(axis=0) >= config.species_occurrences_threshold + )[0] + + species_df = species_df.loc[species_indices] + species_df = species_df.reset_index(drop=True) + + # 0: not trees, 1 : trees + indices_to_predict = np.where( + species_df["isTree"] == index + )[0] + + return indices_to_predict + +# ============================================================ +# DataLoader -> NumPy arrays +# ============================================================ + +def dataloader_to_numpy(dataloader: DataLoader, device: str = "cpu"): + """ + Collects all batches from a DataLoader into NumPy arrays. + + Assumes each batch is (x, y) or {"data": x, "targets": y}. + """ + xs = [] + ys = [] + + for batch in dataloader: + # Support (x, y) or dict-style batches + if isinstance(batch, (list, tuple)) and len(batch) == 2: + x, y = batch + elif isinstance(batch, dict): + x = batch["data"] + y = batch["targets"] + else: + raise ValueError("Unsupported batch format, expected (x, y) or dict with 'data' and 'targets'.") + + x = x.to(device) + y = y.to(device) + + xs.append(x.detach().cpu()) + ys.append(y.detach().cpu()) + + X = torch.cat(xs, dim=0).numpy() + y = torch.cat(ys, dim=0).numpy() + return X, y + + +# ============================================================ +# 4. Train + Evaluate Random Forest +# ============================================================ + +def train_random_forest( + train_loader: DataLoader, + val_loader: DataLoader, + test_loader: DataLoader, + config: dict, + n_estimators: int = 2, + max_depth=None, + n_jobs: int = -1, + random_state: int = 42, + device: str = "cpu", +): + """ + Trains a RandomForestClassifier on data from PyTorch DataLoaders. + Works for standard single-label classification (y shape: (N,)) + """ + + # ----- 4.1 Collect train data ----- + print("Collecting training data from DataLoader...") + X_train, y_train = dataloader_to_numpy(train_loader, device=device) + # y_train = y_train[:, :NUM_CLASSES] + print(f"X_train shape: {X_train.shape}, y_train shape: {y_train.shape}") + + # ----- 4.2 Initialize RF ----- + rf = RandomForestRegressor( + n_estimators=n_estimators, # don’t go crazy with 500+ here + max_depth=20, # limit depth + min_samples_leaf=5, + max_features="sqrt", + n_jobs=-1, + random_state=random_state, + ) + + # ----- 4.3 Fit ----- + print("Fitting RandomForestClassifier...") + rf.fit(X_train, y_train) + + # ----- 4.4 Validation evaluation ----- + print("\n=== Validation Evaluation ===") + X_val, y_val = dataloader_to_numpy(val_loader, device=device) + probs_val = rf.predict(X_val) + print("Example probs shape:", np.unique(probs_val[0], return_counts=True)) + y_val_valid = y_val + # probs_val, y_val_valid = rf_predict_proba_matrix_and_valid_outputs( + # rf, X_val, y_val + # ) + + if probs_val is None: + print("Validation AUROC: no valid outputs (all single-class). Setting to NaN.") + val_auc = float("nan") + else: + tm = MultilabelAUROC(num_labels=probs_val.shape[1], average="macro") + val_auc = tm( + torch.tensor(probs_val, dtype=torch.float32), + torch.tensor(y_val_valid, dtype=torch.int64), + ).item() + print(f"Validation AUROC (macro over {probs_val.shape[1]} valid outputs): {val_auc:.4f}") + + print(f"Validation accuracy: {val_auc:.4f}") + + test_AUC = [] + # ----- 4.5 Test evaluation ----- + print("\n=== Test Evaluation ===") + X_test, y_test = dataloader_to_numpy(test_loader, device=device) + probs_test = rf.predict(X_test) + y_test_valid = y_test + + tm = MultilabelAUROC(num_labels=probs_test.shape[1], average="macro") + test_auc = tm( + torch.tensor(probs_test, dtype=torch.float32), + torch.tensor(y_test_valid, dtype=torch.int64), + ).item() + print(f"Test AUROC (macro over {probs_test.shape[1]} valid outputs): {val_auc:.4f}") + print(f"Test AUC: {test_auc:.4f}") + test_AUC.append(test_auc) + + # next test on sub-species we have such as + indices = [0, 1] + for ind in indices: + print("=====species index===:: ", ind) + species_indices_to_eval = trees_masking(index=ind, config=config) + predictions = probs_test[:, species_indices_to_eval] + targets = y_test_valid[:, species_indices_to_eval] + + tm = MultilabelAUROC(num_labels=predictions.shape[1], average="macro") + test_auc_ = tm( + torch.tensor(predictions, dtype=torch.float32), + torch.tensor(targets, dtype=torch.int64), + ).item() + print(f"Test AUC: {test_auc_:.4f}") + test_AUC.append(test_auc_) + + return rf, test_AUC + +def main(): + seed = 1337 + run_id = 1 + global_seed = (run_id * (seed + (run_id - 1))) % (2 ** 31 - 1) + + set_seed(global_seed) + + device = "cuda" if torch.cuda.is_available() else "cpu" + print("Using device:", device) + + # ---- 5.1 Create datasets (REPLACE with your real datasets) ---- + config = load_config(os.path.join(os.getcwd(), "configs/splot/config_mlp.yaml")) + + data_module_class = sPlotDataModule(config.data) + data_module_class.setup() + # ---- 5.2 DataLoaders ---- + train_loader = data_module_class.train_dataloader() + val_loader = data_module_class.val_dataloader() + test_loader = data_module_class.test_dataloader() + + # ---- 5.3 Train RF baseline ---- + rf_model, test_auc = train_random_forest( + train_loader=train_loader, + val_loader=val_loader, + test_loader=test_loader, + config=config.data, + n_estimators=60, + max_depth=None, + n_jobs=-1, + random_state=1337, + device="mps", + ) + + with open(f"RF_splot_auc_results.csv", "a", newline="") as f: + writer = csv.writer(f) + # writer.writerow(["All_species", "non-tree", "tree"]) + writer.writerow(test_auc) + + print("\nDone. Random Forest baseline trained.") + +if __name__ == "__main__": + main() diff --git a/src/models/sjsdm/README.md b/src/models/sjsdm/README.md new file mode 100644 index 0000000..0847184 --- /dev/null +++ b/src/models/sjsdm/README.md @@ -0,0 +1,32 @@ +## Getthing started with sjSDM + +We use the implementation of sjSDM at [https://github.com/TheoreticalEcology/s-jSDM](https://github.com/TheoreticalEcology/s-jSDM) using the Python implementation. + +We install the following requirements: +```filelock 3.20.0 +fsspec 2025.10.0 +Jinja2 3.1.6 +madgrad 1.3 +MarkupSafe 3.0.3 +mpmath 1.3.0 +networkx 3.6 +numpy 2.3.5 +opt_einsum 3.4.0 +pillow 12.0.0 +pip 24.2 +pyro-api 0.1.2 +pyro-ppl 1.9.1 +pytorch-ranger 0.1.1 +setuptools 80.9.0 +sympy 1.14.0 +torch 2.9.1 +torch-optimizer 0.3.0 +torchvision 0.24.1 +tqdm 4.67.1 +typing_extensions 4.15.0 +``` +and run the script inside the `s-jSDM/sjSDM/inst/python/` folder. + +In order to run sjSDM, we prepare the environmental data in a num_sites x num_covariates matrix (`env.npy` in the script) which was created following the script in `utils.py`. + +The code the train an sjSDM on sPlotOpen and run inference in the conditioned and unconditioned case is in `sjsdm-splot.py`. diff --git a/src/models/sjsdm/sjsdm-splot.py b/src/models/sjsdm/sjsdm-splot.py new file mode 100644 index 0000000..be1b449 --- /dev/null +++ b/src/models/sjsdm/sjsdm-splot.py @@ -0,0 +1,169 @@ +import sjSDM_py as fa +import numpy as np +import torch +import argparse +import json +import os +from tqdm import tqdm +import time + +def iterate_in_batches(Env, Occ, batch_size): + """ + Yields batches (slices) of a NumPy array. + + :param arr: The input NumPy array (must be 2D). + :param batch_size: The number of rows in each batch. + :yield: A NumPy array slice for the current batch. + """ + num_rows = Env.shape[0] + + # Iterate through the array in steps of batch_size + for i in tqdm(range(0, num_rows, batch_size)): + # Slice the array from index i up to i + batch_size + if i + batch_size < num_rows: + yield (Env[i: i + batch_size], Occ[i: i + batch_size]) + +def merge_arrays(array_list, save_path): + """ + :param array_list: list of paths to arrays to be merged + :param save_path: .npy path + """ + arrs = [] + for elem in array_lost: + arr = np.load(elem) + arrs.append(arr) + array =np.concatenate(arrs) + np.save(save_path, array) + + +def main(seed_value, + lr=0.001, + batch_size=24, + epochs=50, + num_env=27): + + if torch.cuda.is_available(): + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #torch.cuda.set_device(0)#torch.device("cuda") + torch.cuda.manual_seed_all(seed_value) + print("GPU is available and will be used.") + else: + device = torch.device("cpu") + print("GPU is not available, using CPU.") + + train_indices = np.load("data/sPlotOpen/train_indices.npy") + test_indices = np.load("data/sPlotOpen/test_indices.npy") + + Env = np.load("data/sPlotOpen/env.npy") + Occ = np.load("data/sPlotOpen/merged_species_occurrences_v2.npy") + + #only keep species with more than 100 occurrences + species_indices = np.where( + Occ.sum(axis=0) >= 100 + )[0] + + + subset = species_indices + model = fa.Model_sjSDM(device=device, dtype=torch.float32) + model.add_env(27, len(subset)) + model.build(len(subset), optimizer=fa.optimizer_adamax(lr),scheduler=False) + print("fit model") + Occ_train = Occ[train_indices, :] + model.fit(Env[train_indices, :], Occ_train[ :, subset], batch_size = batch_size, epochs = epochs) + + #subnontrees ,subtrees + Env_test = Env[test_indices, :] + Y = Occ[test_indices, :] + Y = Y[:, subset] + + #get trees and non trees indices + csv = pd.read_csv("data/sPlotOpen/species_merge_duplicates_v2.csv") + csv = csv.iloc[subset] + nontrees = np.where(~csv.isTree==True)[0] + trees = np.where(csv.isTree==True)[0] + + print(len(trees), len(nontrees)) + os.makedirs(f"/save_dir/sjsdm/predictions_splotopenall/unc_{lr}_{seed_value}/", exist_ok = True) + os.makedirs(f"/save_dir/sjsdm/predictions_splotopenall/condtrees_{lr}_{seed_value}/", exist_ok = True) + os.makedirs(f"/save_dir/sjsdm/predictions_splotopenall/condnontrees_{lr}_{seed_value}/", exist_ok = True) + uncond_time = [] + condtrees_time = [] + condnontrees_time = [] + + #model.predict() already processes predictions in batches, however, we ran into out-of-memory errors when trying to generate predictions in a single array on the whole dataset + with torch.no_grad(): + for i, (env_batch, occ_batch) in enumerate(iterate_in_batches(Env_test, Y, 24)): + print(f"\nProcessing Batch #{i + 1} (Size: {len(env_batch)}):") + start = time.time() + preds = model.predict(env_batch) + t = time.time()-start + uncond_time.append(t) + print("uncond", t) + np.save(f"/save_dir/sjsdm/predictions_splotopenall/unc_{lr}_{seed_value}/batch_{i}.npy", preds) + + print("uncond_time", len(uncond_time), np.mean(uncond_time)) + for i, (env_batch, occ_batch) in enumerate(iterate_in_batches(Env_test, Y, 24)): + print(f"\nProcessing Batch #{i + 1} (Size: {len(env_batch)}):") + + Y_masktrees = occ_batch.astype(np.float64).copy() + Y_masktrees[:,trees] = np.nan + Y_masknontrees = occ_batch.astype(np.float64).copy() + Y_masknontrees[:,nontrees] = np.nan + + start = time.time() + preds_trees= model.predict(env_batch, Y=Y_masktrees) + t = time.time()-start + print("cond", t) + condtrees_time.append(t) + + start = time.time() + preds_nontrees = model.predict(env_batch, Y=Y_masknontrees) + t = time.time()-start + condnontrees_time.append(t) + np.save(f"/save_dir/sjsdm/predictions_splotopenall/condtrees_{lr}_{seed_value}/batch_{i}.npy", preds_trees) + np.save(f"/save_dir/sjsdm/predictions_splotopenall/condnontrees_{lr}_{seed_value}/batch_{i}.npy", preds_nontrees) + print("condtree_time", len(condtrees_time), np.mean(condtrees_time)) + print("condnontree_time", len(condnontrees_time), np.mean(condnontrees_time)) + + + +if __name__=="__main__": + parser = argparse.ArgumentParser( + description="A script to demonstrate argparse for common ML parameters.", + formatter_class=argparse.RawTextHelpFormatter + ) + + # 2. Add the --lr (learning rate) argument + parser.add_argument( + '--lr', + type=float, + default=0.001, + help='The learning rate for the optimization algorithm. (Default: 0.001)' + ) + + # 3. Add the --batchsize argument + # Note the use of 'dest' to map to a standard variable name (batch_size) + parser.add_argument( + '--batchsize', + type=int, + default=12, + help='The size of the data batches for training. (Default: 32)' + ) + + parser.add_argument( + '--seedvalue', + type=int, + default=42 + ) + + parser.add_argument( + '--epochs', + type=int, + default=50 + ) + + + # 4. Parse the arguments + args = parser.parse_args() + + + main(args.seedvalue, args.lr, args.batchsize, args.epochs) diff --git a/src/models/sjsdm/utils.py b/src/models/sjsdm/utils.py new file mode 100644 index 0000000..f45195e --- /dev/null +++ b/src/models/sjsdm/utils.py @@ -0,0 +1,14 @@ +import pandas as pd + +def prepare_env(): + world = pd.read_csv("data/sPlotOpen/worldclim_data.csv") + soil = pd.read_csv("data/sPlotOpen/soilgrid_data.csv") + world = np.array(world[['bio_1', 'bio_2', 'bio_3', 'bio_4', 'bio_5', 'bio_6', + 'bio_7', 'bio_8', 'bio_9', 'bio_10', 'bio_11', 'bio_12', 'bio_13', + 'bio_14', 'bio_15', 'bio_16', 'bio_17', 'bio_18', 'bio_19']]) + soil = np.array(soil[['ORCDRC', 'PHIHOX', 'CECSOL', 'BDTICM', 'CLYPPT', + 'SLTPPT', 'SNDPPT', 'BLDFIE']]) + env = np.concatenate((world, soil), axis = 1) + + np.save("data/sPlotOpen/env.npy", env) + diff --git a/src/models/utils.py b/src/models/utils.py index 4d181ef..16da81a 100644 --- a/src/models/utils.py +++ b/src/models/utils.py @@ -2,84 +2,10 @@ utility functions for CISO model """ -import json import math -from collections import Counter -from datetime import time -import numpy as np -import tifffile as tiff import torch from torch import nn -from torch.optim.lr_scheduler import LambdaLR - - -# from gensim.models import KeyedVectors - - -def load_word2vec_pretrained_weights(word_to_idx, vocab_size, embedding_dim): - # Path to the downloaded model - model_path = "/home/mila/h/hager.radi/scratch/ecosystem-embedding/GoogleNews-vectors-negative300.bin.gz" - # model_path = '/home/mila/h/hager.radi/scratch/ecosystem-embedding/wiki-news-300d-1M-subword.vec.zip' - # Load the model - word2vec_model = KeyedVectors.load_word2vec_format(model_path, binary=True) - # Initialize the embedding matrix - embedding_matrix = np.zeros((vocab_size, embedding_dim)) - present_words = 0 - absent_words = 0 - absent_ids = [] - present_ids = [] - for word, idx in word_to_idx.items(): - if word in word2vec_model: - # Use the Word2Vec embedding if the word is in the model - present_words += 1 - present_ids.append(idx) - embedding_matrix[idx] = np.repeat(word2vec_model[word], 2)[:embedding_dim] - else: - # Random initialization for words not in the model - # embedding_matrix[idx] = np.random.normal(scale=0.6, size=(embedding_dim, )) - absent_words += 1 - absent_ids.append(idx) - - mean_embeddings = np.mean(embedding_matrix[present_ids], axis=0) - embedding_matrix[absent_ids] = mean_embeddings - return embedding_matrix - - -def tokenize_species(species_file_name): - with open(species_file_name) as f: - species_names = [line.rstrip() for line in f] - - # Tokenize - tokenized_data = [name.lower().split() for name in species_names] - - # Flatten the list and count word frequencies - word_freq = Counter([word for sp in tokenized_data for word in sp]) - - # Create word to index mapping - word_to_idx = { - word: i + 1 for i, (word, _) in enumerate(word_freq.items()) - } # Start indexing from 1 - word_to_idx[""] = 0 # Add a token for unknown words - - def encode_species(species_name): - return [ - word_to_idx.get(word, word_to_idx[""]) - for word in species_name.lower().split() - ] - - encoded_species = [encode_species(sp) for sp in species_names] - - max_length = max(len(sp) for sp in encoded_species) - - def pad_encoded_sp(encoded_sp): - return np.pad(encoded_sp, (0, max_length - len(encoded_sp)), mode="constant") - - padded_species = np.array([pad_encoded_sp(shop) for shop in encoded_species]) - - vocab_size = len(word_to_idx) - - return padded_species, word_to_idx, vocab_size def weights_init(module): @@ -94,14 +20,6 @@ def weights_init(module): module.weight.data.fill_(1.0) -def custom_replace(tensor, on_neg_1, on_zero, on_one): - res = tensor.clone() - res[tensor == -1] = on_neg_1 - res[tensor == 0] = on_zero - res[tensor == 1] = on_one - return res - - def custom_replace_n(data: torch.tensor, n: int): """ replacing unique values with their index @@ -116,283 +34,3 @@ def custom_replace_n(data: torch.tensor, n: int): res[mask] = new_value return res - - -def masked_loss_custom_replace(tensor, on_neg_2, on_neg_1, on_zero, on_one): - res = tensor.clone() - res[tensor == -2] = on_neg_2 - res[tensor == -1] = on_neg_1 - res[tensor == 0] = on_zero - res[tensor == 1] = on_one - return res - - -def positional_encoding_2d(height, width, d_model): - assert ( - d_model % 4 == 0 - ), "Dimension of model must be divisible by 4 for 2D positional encoding" - - pos_enc = np.zeros((height, width, d_model)) - y, x = np.meshgrid(np.arange(height), np.arange(width), indexing="ij") - - div_term = 10000 ** (np.arange(0, d_model, 4) / d_model) - - pos_enc[:, :, 0::4] = np.sin(x[:, :, None] / div_term) - pos_enc[:, :, 1::4] = np.cos(x[:, :, None] / div_term) - pos_enc[:, :, 2::4] = np.sin(y[:, :, None] / div_term) - pos_enc[:, :, 3::4] = np.cos(y[:, :, None] / div_term) - - return pos_enc - - -def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): - assert embed_dim % 2 == 0 - - # use half of dimensions to encode grid_h - emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) - emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) - - emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) - return emb - - -def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): - """ - embed_dim: output dimension for each position - pos: a list of positions to be encoded: size (M,) - out: (M, D) - """ - assert embed_dim % 2 == 0 - omega = np.arange(embed_dim // 2, dtype=np.float) - omega /= embed_dim / 2.0 - omega = 1.0 / 10000**omega # (D/2,) - - pos = pos.reshape(-1) # (M,) - out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product - - emb_sin = np.sin(out) # (M, D/2) - emb_cos = np.cos(out) # (M, D/2) - - emb = emb_sin + emb_cos # np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) - return emb - - -def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): - """ - grid_size: int of the grid height and width - return: - pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) - """ - grid_h = np.arange(grid_size, dtype=np.float32) - grid_w = np.arange(grid_size, dtype=np.float32) - grid = np.meshgrid(grid_w, grid_h) # here w goes first - grid = np.stack(grid, axis=0) - - grid = grid.reshape([2, 1, grid_size, grid_size]) - pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) - if cls_token: - pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0) - return pos_embed - - -def positionalencoding2d(d_model, height, width): - """ - :param d_model: dimension of the model - :param height: height of the positions - :param width: width of the positions - :return: d_model*height*width position matrix - """ - if d_model % 4 != 0: - raise ValueError( - "Cannot use sin/cos positional encoding with " - "odd dimension (got dim={:d})".format(d_model) - ) - pe = torch.zeros(d_model, height, width) - # Each dimension use half of d_model - d_model = int(d_model / 2) - div_term = torch.exp(torch.arange(0.0, d_model, 2) * -(math.log(10000.0) / d_model)) - pos_w = torch.arange(0.0, width).unsqueeze(1) - pos_h = torch.arange(0.0, height).unsqueeze(1) - pe[0:d_model:2, :, :] = ( - torch.sin(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1) - ) - pe[1:d_model:2, :, :] = ( - torch.cos(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1) - ) - pe[d_model::2, :, :] = ( - torch.sin(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width) - ) - pe[d_model + 1 :: 2, :, :] = ( - torch.cos(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width) - ) - - return pe - - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__( - self, num_pos_feats=64, temperature=10000, normalize=False, scale=None - ): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, mask): - # x = tensor_list.tensors - # mask = tensor_list.mask - assert mask is not None - not_mask = ~mask - # stop() - y_embed = not_mask.cumsum(1) # , dtype=torch.float32) - x_embed = not_mask.cumsum(2) # , dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats) # , dtype=torch.float32) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - # stop() - - pos_x = torch.stack( - (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos_y = torch.stack( - (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - - -class WarmupLinearSchedule(LambdaLR): - """Linear warmup and then linear decay. - Linearly increases learning rate from 0 to 1 over `warmup_steps` training steps. - Linearly decreases learning rate from 1. to 0. over remaining `t_total - warmup_steps` - steps. - """ - - def __init__(self, optimizer, warmup_steps, t_total, last_epoch=-1): - self.warmup_steps = warmup_steps - self.t_total = t_total - super(WarmupLinearSchedule, self).__init__( - optimizer, self.lr_lambda, last_epoch=last_epoch - ) - - def lr_lambda(self, step): - if step < self.warmup_steps: - return float(step) / float(max(1, self.warmup_steps)) - return max( - 0.0, - float(self.t_total - step) - / float(max(1.0, self.t_total - self.warmup_steps)), - ) - - -def init_first_layer_weights( - in_channels: int, rgb_weights, hs_weight_init: str = "random" -): - """Initializes the weights for filters in the first conv layer. - If we are using RGB-only, then just initializes var to rgb_weights. Otherwise, uses - hs_weight_init to determine how to initialize the weights for non-RGB bands. - Args - - int: in_channesl, input channels - - in_channesl is either 3 (RGB), 7 (lxv3), or 9 (Landsat7) or 2 (NL) - - rgb_weights: ndarray of np.float32, shape [64, 3, F, F] - - hs_weight_init: str, one of ['random', 'same', 'samescaled'] - Returs - -torch tensor : final_weights - """ - - out_channels, rgb_channels, H, W = rgb_weights.shape - rgb_weights = torch.tensor(rgb_weights, device=rgb_weights.device) - ms_channels = in_channels - rgb_channels - if in_channels == 3: - final_weights = rgb_weights - - elif in_channels < 3: - with torch.no_grad(): - mean = rgb_weights.mean() - std = rgb_weights.std() - final_weights = torch.empty( - (out_channels, in_channels, H, W), device=rgb_weights.device - ) - final_weights = torch.nn.init.trunc_normal_(final_weights, mean, std) - elif in_channels > 3: - # spectral images - - if hs_weight_init == "same": - - with torch.no_grad(): - mean = rgb_weights.mean( - dim=1, keepdim=True - ) # mean across the in_channel dimension - mean = torch.tile(mean, (1, ms_channels, 1, 1)) - ms_weights = mean - - elif hs_weight_init == "random": - start = time.time() - with torch.no_grad(): - mean = rgb_weights.mean() - std = rgb_weights.std() - ms_weights = torch.empty( - (out_channels, ms_channels, H, W), device=rgb_weights.device - ) - ms_weights = torch.nn.init.trunc_normal_(ms_weights, mean, std) - print(f"random: {time.time() - start}") - - elif hs_weight_init == "samescaled": - start = time.time() - with torch.no_grad(): - mean = rgb_weights.mean( - dim=1, keepdim=True - ) # mean across the in_channel dimension - mean = torch.tile(mean, (1, ms_channels, 1, 1)) - ms_weights = (mean * 3) / (3 + ms_channels) - # scale both rgb_weights and ms_weights - rgb_weights = (rgb_weights * 3) / (3 + ms_channels) - - else: - - raise ValueError(f"Unknown hs_weight_init type: {hs_weight_init}") - - final_weights = torch.cat([rgb_weights, ms_weights], dim=1) - return final_weights - - -def load_geotiff_visual(file): - img = tiff.imread(file).astype(np.float32) - - img = np.reshape(img, (img.shape[2], img.shape[0], img.shape[1])) - - return img - - -def load_geotiff(file): - img = tiff.imread(file) - new_band_order = [2, 1, 0, 3] # r, g, b, nir - img = img[:, :, new_band_order].astype(float) - img = np.reshape(img, (img.shape[2], img.shape[0], img.shape[1])) - - return img - - -def json_load(file_path): - """ - loads a json file given path - """ - with open(file_path, "r") as f: - return json.load(f) diff --git a/src/trainers/sdm_partial_trainer.py b/src/trainers/sdm_partial_trainer.py index a05c97a..2a84f95 100644 --- a/src/trainers/sdm_partial_trainer.py +++ b/src/trainers/sdm_partial_trainer.py @@ -1,5 +1,5 @@ """ -Trainer for the ctran framework +Trainer for the CISO-SDM framework """ import inspect @@ -8,7 +8,7 @@ from src.trainers.base import BaseTrainer from src.utils import eval_species_split from src.models.baselines import * -from src.models.ctran import * +from src.models.ciso import * class SDMPartialTrainer(BaseTrainer): diff --git a/src/trainers/splot_trainer.py b/src/trainers/splot_trainer.py index bb36d7f..4bf9212 100644 --- a/src/trainers/splot_trainer.py +++ b/src/trainers/splot_trainer.py @@ -8,7 +8,7 @@ from src.models.baselines import * from src.utils import multi_label_accuracy, trees_masking -from src.models.ctran import * +from src.models.ciso import * class sPlotTrainer(pl.LightningModule): diff --git a/src/utils.py b/src/utils.py index c35d049..8e2ca5a 100644 --- a/src/utils.py +++ b/src/utils.py @@ -12,7 +12,7 @@ def eval_species_split( index: int, base_data_folder, - multi_taxa: bool, + multi_taxa: bool = False, per_taxa_species_count: dict = None, ) -> np.ndarray: if not multi_taxa: @@ -100,7 +100,7 @@ def save_test_results_to_csv(results, root_dir, results_file_name="test_results. print(f"CSV file '{output_file}' has been saved.") -def load_opts(path, default, commandline_opts): +def load_opts(path, default = None, commandline_opts = None): """ Args: path (pathlib.Path): where to find the overriding configuration diff --git a/test.py b/test.py index 4ea93c3..ae43fd4 100644 --- a/test.py +++ b/test.py @@ -117,22 +117,6 @@ def test_task(task): ckpt_dir = os.path.join(config.base_dir, run_id_path) files = os.listdir(ckpt_dir) - # to choose epoch < 50, if trained for longer - # def extract_epoch(filename): - # # Adjust regex if your naming pattern is different - # match = re.search(r"epoch(\d+)", filename) - # return int(match.group(1)) if match else None - # - # best_checkpoint_file_name = max( - # ( - # f for f in files - # if "last" not in f - # and f.endswith(".ckpt") - # and (extract_epoch(f) is not None and extract_epoch(f) < 50) - # ), - # key=lambda f: os.path.getmtime(os.path.join(ckpt_dir, f)), - # default=None # In case no file matches - # ) best_checkpoint_file_name = max( (f for f in files if "last" not in f and f.endswith(".ckpt")), key=lambda f: os.path.getmtime(os.path.join(ckpt_dir, f)) diff --git a/train.py b/train.py index b3b140c..70dacd5 100644 --- a/train.py +++ b/train.py @@ -146,7 +146,6 @@ def main(opts): trainer_args["logger"] = comet_logger else: print("no COMET API Key found..continuing without logging..") - return if config.data.multi_taxa: val_monitor_1 = config.data.monitor_metric_1 @@ -167,7 +166,7 @@ def main(opts): monitor="val_topk", dirpath=config.save_path, save_top_k=1, - filename="best_val_topk_-{epoch:02d}-{val_topk:.4f}", + filename="best-{epoch:02d}-{val_topk:.4f}", mode="max", save_last=True, save_weights_only=True, @@ -184,12 +183,6 @@ def main(opts): trainer.fit(model=task, datamodule=datamodule) trainer.test(model=task, datamodule=datamodule) - # logging the best checkpoint to comet ML - # print(multi_ckpt_callback.best_model_path) - # trainer.logger.experiment.log_asset( - # multi_ckpt_callback.best_model_path, file_name="best_checkpoint.ckpt" - # ) - if __name__ == "__main__": main()