Config files refactor, Examples polish, Perturbation in GraphDefinition

examples/01_icetray/01_convert_i3_files.py

@@ -5,6 +5,7 @@
 from graphnet.constants import EXAMPLE_OUTPUT_DIR, TEST_DATA_DIR
 from graphnet.data.extractors import (
     I3FeatureExtractorIceCubeUpgrade,
+    I3FeatureExtractorIceCube86,
     I3RetroExtractor,
     I3TruthExtractor,
     I3GenericExtractor,
@@ -34,12 +35,7 @@ def main_icecube86(backend: str) -> None:
 
     converter: DataConverter = CONVERTER_CLASS[backend](
         [
-            I3GenericExtractor(
-                keys=[
-                    "SRTInIcePulses",
-                    "I3MCTree",
-                ]
-            ),
+            I3FeatureExtractorIceCube86("SRTInIcePulses"),
             I3TruthExtractor(),
         ],
         outdir,

examples/02_data/01_read_dataset.py

@@ -17,7 +17,10 @@
 from graphnet.data.dataset import ParquetDataset
 from graphnet.utilities.argparse import ArgumentParser
 from graphnet.utilities.logging import Logger
-
+from graphnet.models.graphs import KNNGraph
+from graphnet.models.detector.icecube import (
+    IceCubeDeepCore,
+)
 
 DATASET_CLASS = {
     "sqlite": SQLiteDataset,
@@ -44,6 +47,9 @@ def main(backend: str) -> None:
     num_workers = 30
     wait_time = 0.00  # sec.
 
+    # Define graph representation
+    graph_definition = KNNGraph(detector=IceCubeDeepCore())
+
     for table in [pulsemap, truth_table]:
         # Get column names from backend
         if backend == "sqlite":
@@ -62,15 +68,16 @@ def main(backend: str) -> None:
 
     # Common variables
     dataset = DATASET_CLASS[backend](
-        path,
-        pulsemap,
-        features,
-        truth,
+        path=path,
+        pulsemaps=pulsemap,
+        features=features,
+        truth=truth,
         truth_table=truth_table,
+        graph_definition=graph_definition,
     )
     assert isinstance(dataset, Dataset)
 
-    logger.info(dataset[1])
+    logger.info(str(dataset[1]))
     logger.info(dataset[1].x)
     if backend == "sqlite":
         assert isinstance(dataset, SQLiteDataset)
@@ -92,7 +99,7 @@ def main(backend: str) -> None:
         for batch in tqdm(dataloader, unit=" batches", colour="green"):
             time.sleep(wait_time)
 
-    logger.info(batch)
+    logger.info(str(batch))
     logger.info(batch.size())
     logger.info(batch.num_graphs)
 

examples/02_data/02_plot_feature_distributions.py

@@ -1,4 +1,4 @@
-"""Example of plotting feature distributions from SQLite database."""
+"""Example of visualization of input data from a configured Dataset."""
 
 import os.path
 
@@ -8,8 +8,6 @@
 
 from graphnet.constants import CONFIG_DIR
 from graphnet.data.dataset import Dataset
-from graphnet.models.detector.icecube import IceCubeDeepCore
-from graphnet.models.graph_builders import KNNGraphBuilder
 from graphnet.utilities.logging import Logger
 from graphnet.utilities.argparse import ArgumentParser
 
@@ -27,46 +25,20 @@ def main() -> None:
     assert isinstance(dataset, Dataset)
     features = dataset._features[1:]
 
-    # Building model
-    detector = IceCubeDeepCore(
-        graph_builder=KNNGraphBuilder(nb_nearest_neighbours=8),
-    )
-
     # Get feature matrix
-    x_original_list = []
     x_preprocessed_list = []
     for batch in tqdm(dataset, colour="green"):
-        x_original_list.append(batch.x.numpy())
-        x_preprocessed_list.append(detector(batch).x.numpy())
+        x_preprocessed_list.append(batch.x.numpy())
 
-    x_original = np.concatenate(x_original_list, axis=0)
     x_preprocessed = np.concatenate(x_preprocessed_list, axis=0)
-
-    logger.info(f"Number of NaNs: {np.sum(np.isnan(x_original))}")
-    logger.info(f"Number of infs: {np.sum(np.isinf(x_original))}")
+    logger.info(f"Number of NaNs: {np.sum(np.isnan(x_preprocessed))}")
+    logger.info(f"Number of infs: {np.sum(np.isinf(x_preprocessed))}")
 
     # Plot feature distributions
-    nb_features_original = x_original.shape[1]
     nb_features_preprocessed = x_preprocessed.shape[1]
     dim = int(np.ceil(np.sqrt(nb_features_preprocessed)))
     axis_size = 4
-    bins = 100
-
-    # -- Original
-    fig, axes = plt.subplots(
-        dim, dim, figsize=(dim * axis_size, dim * axis_size)
-    )
-    for ix, ax in enumerate(axes.ravel()[:nb_features_original]):
-        ax.hist(x_original[:, ix], bins=bins)
-        ax.set_xlabel(
-            f"x{ix}: {features[ix] if ix < len(features) else 'N/A'}"
-        )
-        ax.set_yscale("log")
-
-    fig.tight_layout
-    figure_name_original = "feature_distribution_original.png"
-    fig.savefig(figure_name_original)
-    logger.info(f"Figure written to {figure_name_original}")
+    bins = 50
 
     # -- Preprocessed
     fig, axes = plt.subplots(

examples/04_training/02_train_model_without_configs.py → examples/04_training/01_train_dynedge.py

@@ -79,12 +79,7 @@ def main(
         wandb_logger.experiment.config.update(config)
 
     # Define graph representation
-    graph_definition = KNNGraph(
-        detector=Prometheus(),
-        node_definition=NodesAsPulses(),
-        nb_nearest_neighbours=8,
-        node_feature_names=features,
-    )
+    graph_definition = KNNGraph(detector=Prometheus())
 
     (
         training_dataloader,
@@ -166,10 +161,19 @@ def main(
     logger.info(f"Writing results to {path}")
     os.makedirs(path, exist_ok=True)
 
+    # Save results as .csv
     results.to_csv(f"{path}/results.csv")
-    model.save_state_dict(f"{path}/state_dict.pth")
+
+    # Save full model (including weights) to .pth file - not version safe
+    # Note: Models saved as .pth files in one version of graphnet
+    #       may not be compatible with a different version of graphnet.
     model.save(f"{path}/model.pth")
 
+    # Save model config and state dict - Version safe save method.
+    # This method of saving models is the safest way.
+    model.save_state_dict(f"{path}/state_dict.pth")
+    model.save_config(f"{path}/model_config.yml")
+
 
 if __name__ == "__main__":
 

examples/04_training/04_train_tito_model_without_configs.py → examples/04_training/02_train_tito_model.py

@@ -14,7 +14,6 @@
 from graphnet.models.detector.prometheus import Prometheus
 from graphnet.models.gnn import DynEdgeTITO
 from graphnet.models.graphs import KNNGraph
-from graphnet.models.graphs.nodes import NodesAsPulses
 from graphnet.models.task.reconstruction import (
     DirectionReconstructionWithKappa,
 )
@@ -28,7 +27,6 @@
 # Constants
 features = FEATURES.PROMETHEUS
 truth = TRUTH.PROMETHEUS
-DYNTRANS_LAYER_SIZES = [(256, 256), (256, 256), (256, 256)]
 
 
 def main(
@@ -76,12 +74,7 @@ def main(
         },
     }
 
-    graph_definition = KNNGraph(
-        detector=Prometheus(),
-        node_definition=NodesAsPulses(),
-        nb_nearest_neighbours=8,
-        node_feature_names=features,
-    )
+    graph_definition = KNNGraph(detector=Prometheus())
     archive = os.path.join(EXAMPLE_OUTPUT_DIR, "train_tito_model")
     run_name = "dynedgeTITO_{}_example".format(config["target"])
     if wandb:
@@ -115,7 +108,6 @@ def main(
     gnn = DynEdgeTITO(
         nb_inputs=graph_definition.nb_outputs,
         global_pooling_schemes=["max"],
-        dyntrans_layer_sizes=DYNTRANS_LAYER_SIZES,
     )
     task = DirectionReconstructionWithKappa(
         hidden_size=gnn.nb_outputs,
@@ -182,10 +174,16 @@ def main(
     logger.info(f"Writing results to {path}")
     os.makedirs(path, exist_ok=True)
 
+    # Save results as .csv
     results.to_csv(f"{path}/results.csv")
-    model.save_state_dict(f"{path}/state_dict.pth")
+
+    # Save full model (including weights) to .pth file - Not version proof
     model.save(f"{path}/model.pth")
 
+    # Save model config and state dict - Version safe save method.
+    model.save_state_dict(f"{path}/state_dict.pth")
+    model.save_config(f"{path}/model_config.yml")
+
 
 if __name__ == "__main__":
 

examples/04_training/01_train_model.py → examples/04_training/03_train_dynedge_from_config.py

@@ -1,4 +1,4 @@
-"""Simplified example of training Model."""
+"""Simplified example of training DynEdge from pre-defined config files."""
 
 from typing import List, Optional
 import os
@@ -46,7 +46,7 @@ def main(
             log_model=True,
         )
 
-    # Build model
+    # Build model from pre-defined config file made from Model.save_config
     model_config = ModelConfig.load(model_config_path)
     model: StandardModel = StandardModel.from_config(model_config, trust=True)
 
@@ -69,7 +69,8 @@ def main(
         archive = os.path.join(EXAMPLE_OUTPUT_DIR, "train_model")
     run_name = "dynedge_{}_example".format("_".join(config.target))
 
-    # Construct dataloaders
+    # Construct dataloaders from pre-defined dataset config files.
+    # i.e. from Dataset.save_config
     dataset_config = DatasetConfig.load(dataset_config_path)
     dataloaders = DataLoader.from_dataset_config(
         dataset_config,

examples/04_training/03_train_classification_model.py → examples/04_training/04_train_multiclassifier_from_configs.py

@@ -1,4 +1,4 @@
-"""Simplified example of multi-class classification training Model."""
+"""Multi-class classification using DynEdge from pre-defined config files."""
 
 import os
 from typing import List, Optional, Dict, Any

examples/04_training/README.md

@@ -2,44 +2,44 @@
 
 This subfolder contains two main training scripts:
 
-**`01_train_model.py`** Shows how to train a GNN on neutrino telescope data **using configuration files** to construct the dataset that loads the data and the model that is trained. This is the recommended way to configure standard dataset and models, as it is easier to ready and share than doing so in pure code. This example can be run using a few different models targeting different physics use cases. For instance, you can try running:
+**`01_train_dynedge.py`** ** Shows how to train a GNN on neutrino telescope data **without configuration files,** i.e., by programatically constructing the dataset and model used. This is good for debugging and experimenting with different dataset settings and model configurations, as it is easier to build the model using the API than by writing configuration files from scratch. **This is our recommended way of getting started with the library**. For instance, try running:
 
 ```bash
 # Show the CLI
-(graphnet) $ python examples/04_training/01_train_model.py --help
+(graphnet) $ python examples/04_training/01_train_dynedge.py --help
 
 # Train energy regression model
-(graphnet) $ python examples/04_training/01_train_model.py
+(graphnet) $ python examples/04_training/01_train_dynedge.py
 
 # Same as above, as this is the default model config.
 (graphnet) $ python examples/04_training/01_train_model.py \
     --model-config configs/models/example_energy_reconstruction_model.yml
 
 # Train using a single GPU
-(graphnet) $ python examples/04_training/01_train_model.py --gpus 0
+(graphnet) $ python examples/04_training/01_train_dynedge.py --gpus 0
 
 # Train using multiple GPUs
-(graphnet) $ python examples/04_training/01_train_model.py --gpus 0 1
+(graphnet) $ python examples/04_training/01_train_dynedge.py --gpus 0 1
 
 # Train a vertex position reconstruction model
-(graphnet) $ python examples/04_training/01_train_model.py \
+(graphnet) $ python examples/04_training/01_train_dynedge.py \
     --model-config configs/models/example_vertex_position_reconstruction_model.yml
 
 # Trains a direction (zenith, azimuth) reconstruction model. Note that the
 # chosen `Task` in the model config file also returns estimated "kappa" values,
 # i.e. inverse variance, for each predicted feature, meaning that we need to
 # manually specify the names of these.
-(graphnet) $ python examples/04_training/01_train_model.py --gpus 0 \
+(graphnet) $ python examples/04_training/01_train_model_dynedge.py --gpus 0 \
     --model-config configs/models/example_direction_reconstruction_model.yml  \
     --prediction-names zenith_pred zenith_kappa_pred azimuth_pred azimuth_kappa_pred
 ```
 
-**`02_train_model_without_configs.py`** Shows how to train a GNN on neutrino telescope data **without configuration files,** i.e., by programatically constructing the dataset and model used. This is good for debugging and experimenting with different dataset settings and model configurations, as it is easier to build the model using the API than by writing configuration files from scratch. For instance, try running:
+**`03_train_model_dynedge_from_config.py** Shows how to train a GNN on neutrino telescope data **using configuration files** to construct the dataset that loads the data and the model that is trained. This is the recommended way to configure standard dataset and models, as it is easier to ready and share than doing so in pure code. This example can be run using a few different models targeting different physics use cases. For instance, you can try running:
 
 ```bash
 # Show the CLI
-(graphnet) $ python examples/04_training/02_train_model_without_configs.py --help
+(graphnet) $ python examples/04_training/02_train_dynedge_from_config.py --help
 
 # Train energy regression model
-(graphnet) $ python examples/04_training/02_train_model_without_configs.py
+(graphnet) $ python examples/04_training/02_train_dynedge_from_config.py
 ```

src/graphnet/data/dataset/__init__.py

@@ -7,7 +7,6 @@
     from .dataset import EnsembleDataset, Dataset, ColumnMissingException
     from .parquet.parquet_dataset import ParquetDataset
     from .sqlite.sqlite_dataset import SQLiteDataset
-    from .sqlite.sqlite_dataset_perturbed import SQLiteDatasetPerturbed
 
     torch.multiprocessing.set_sharing_strategy("file_system")