From cb4a1ceb9e307137221af4c608305007ab6b7f0a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreas=20S=C3=B8gaard?= Date: Wed, 14 Feb 2024 04:30:48 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20Aske-Ros?= =?UTF-8?q?ted/graphnet@90b20d4080ae56f2bd5db8b316d48d182c04654a=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- _modules/graphnet/data/datamodule.html | 828 ++++++++++++++++++ _modules/graphnet/models/standard_model.html | 3 + _modules/graphnet/training/utils.html | 18 + _modules/index.html | 1 + _sources/api/graphnet.data.datamodule.rst.txt | 8 + _sources/api/graphnet.data.rst.txt | 1 + api/graphnet.data.constants.html | 7 + api/graphnet.data.dataconverter.html | 7 + api/graphnet.data.dataloader.html | 13 +- api/graphnet.data.datamodule.html | 725 +++++++++++++++ api/graphnet.data.dataset.dataset.html | 7 + api/graphnet.data.dataset.html | 7 + api/graphnet.data.dataset.parquet.html | 7 + ....data.dataset.parquet.parquet_dataset.html | 7 + api/graphnet.data.dataset.sqlite.html | 7 + ...et.data.dataset.sqlite.sqlite_dataset.html | 7 + api/graphnet.data.extractors.html | 7 + api/graphnet.data.extractors.i3extractor.html | 7 + ...et.data.extractors.i3featureextractor.html | 7 + ...et.data.extractors.i3genericextractor.html | 7 + ...data.extractors.i3hybridrecoextractor.html | 7 + ...ta.extractors.i3ntmuonlabelsextractor.html | 7 + ...t.data.extractors.i3particleextractor.html | 7 + ...phnet.data.extractors.i3pisaextractor.html | 7 + ...hnet.data.extractors.i3quesoextractor.html | 7 + ...hnet.data.extractors.i3retroextractor.html | 7 + ....data.extractors.i3splinempeextractor.html | 7 + ...hnet.data.extractors.i3truthextractor.html | 7 + ...aphnet.data.extractors.i3tumextractor.html | 7 + ...data.extractors.utilities.collections.html | 7 + ...hnet.data.extractors.utilities.frames.html | 7 + api/graphnet.data.extractors.utilities.html | 7 + ...phnet.data.extractors.utilities.types.html | 7 + api/graphnet.data.filters.html | 13 +- api/graphnet.data.html | 11 + api/graphnet.data.parquet.html | 7 + ...et.data.parquet.parquet_dataconverter.html | 7 + api/graphnet.data.pipeline.html | 7 + api/graphnet.data.sqlite.html | 7 + ...hnet.data.sqlite.sqlite_dataconverter.html | 7 + ...graphnet.data.sqlite.sqlite_utilities.html | 7 + api/graphnet.data.utilities.html | 7 + ...hnet.data.utilities.parquet_to_sqlite.html | 7 + api/graphnet.data.utilities.random.html | 7 + ...a.utilities.string_selection_resolver.html | 7 + api/graphnet.html | 1 + api/graphnet.training.html | 1 + api/graphnet.training.utils.html | 27 + genindex.html | 37 +- objects.inv | Bin 6697 -> 6780 bytes py-modindex.html | 5 + searchindex.js | 2 +- sitemap.xml | 2 +- 53 files changed, 1928 insertions(+), 13 deletions(-) create mode 100644 _modules/graphnet/data/datamodule.html create mode 100644 _sources/api/graphnet.data.datamodule.rst.txt create mode 100644 api/graphnet.data.datamodule.html diff --git a/_modules/graphnet/data/datamodule.html b/_modules/graphnet/data/datamodule.html new file mode 100644 index 000000000..9813c0db0 --- /dev/null +++ b/_modules/graphnet/data/datamodule.html @@ -0,0 +1,828 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + graphnet.data.datamodule — graphnet documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Skip to content +
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Source code for graphnet.data.datamodule

+"""Base `Dataloader` class(es) used in `graphnet`."""
+from typing import Dict, Any, Optional, List, Tuple, Union
+import pytorch_lightning as pl
+from torch.utils.data import DataLoader
+from copy import deepcopy
+from sklearn.model_selection import train_test_split
+import pandas as pd
+
+from graphnet.data.dataset import (
+    Dataset,
+    EnsembleDataset,
+    SQLiteDataset,
+    ParquetDataset,
+)
+from graphnet.utilities.logging import Logger
+
+
+

+[docs]
+class GraphNeTDataModule(pl.LightningDataModule, Logger):
+    """General Class for DataLoader Construction."""
+
+    def __init__(
+        self,
+        dataset_reference: Union[SQLiteDataset, ParquetDataset, Dataset],
+        dataset_args: Dict[str, Any],
+        selection: Optional[Union[List[int], List[List[int]]]] = None,
+        test_selection: Optional[Union[List[int], List[List[int]]]] = None,
+        train_dataloader_kwargs: Optional[Dict[str, Any]] = None,
+        validation_dataloader_kwargs: Optional[Dict[str, Any]] = None,
+        test_dataloader_kwargs: Optional[Dict[str, Any]] = None,
+        train_val_split: Optional[List[float]] = [0.9, 0.10],
+        split_seed: int = 42,
+    ) -> None:
+        """Create dataloaders from dataset.
+
+        Args:
+            dataset_reference: A non-instantiated reference
+                                to the dataset class.
+            dataset_args: Arguments to instantiate
+                            graphnet.data.dataset.Dataset with.
+            selection: (Optional) a list of event id's used for training
+                    and validation, Default None.
+            test_selection: (Optional) a list of event id's used for testing,
+                            Default None.
+            train_dataloader_kwargs: Arguments for the training DataLoader,
+                                    Default None.
+            validation_dataloader_kwargs: Arguments for the validation
+                                        DataLoader, Default None.
+            test_dataloader_kwargs: Arguments for the test DataLoader,
+                                    Default None.
+            train_val_split (Optional): Split ratio for training and
+                                validation sets. Default is [0.9, 0.10].
+            split_seed: seed used for shuffling and splitting selections into
+                        train/validation, Default 42.
+        """
+        Logger.__init__(self)
+        self._make_sure_root_logger_is_configured()
+        self._dataset = dataset_reference
+        self._dataset_args = dataset_args
+        self._selection = selection
+        self._test_selection = test_selection
+        self._train_val_split = train_val_split or [0.0]
+        self._rng = split_seed
+
+        self._train_dataloader_kwargs = train_dataloader_kwargs or {}
+        self._validation_dataloader_kwargs = validation_dataloader_kwargs or {}
+        self._test_dataloader_kwargs = test_dataloader_kwargs or {}
+
+        # If multiple dataset paths are given, we should use EnsembleDataset
+        self._use_ensemble_dataset = isinstance(
+            self._dataset_args["path"], list
+        )
+
+        self.setup("fit")
+
+

+[docs]
+    def prepare_data(self) -> None:
+        """Prepare the dataset for training."""
+        # Download method for curated datasets. Method for download is
+        # likely dataset-specific, so we can leave it as-is
+        pass

+
+
+

+[docs]
+    def setup(self, stage: str) -> None:
+        """Prepare Datasets for DataLoaders.
+
+        Args:
+            stage: lightning stage. Either "fit, validate, test, predict"
+        """
+        # Sanity Checks
+        self._validate_dataset_class()
+        self._validate_dataset_args()
+        self._validate_dataloader_args()
+
+        # Case-handling of selection arguments
+        self._resolve_selections()
+
+        # Creation of Datasets
+        if (
+            self._test_selection is not None
+            or len(self._test_dataloader_kwargs) > 0
+        ):
+            self._test_dataset = self._create_dataset(
+                self._test_selection  # type: ignore
+            )
+        if stage == "fit" or stage == "validate":
+            if self._train_selection is not None:
+                self._train_dataset = self._create_dataset(
+                    self._train_selection
+                )
+            if self._val_selection is not None:
+                self._val_dataset = self._create_dataset(self._val_selection)
+
+        return

+
+
+    @property
+    def train_dataloader(self) -> DataLoader:  # type: ignore[override]
+        """Prepare and return the training DataLoader.
+
+        Returns:
+            DataLoader: The DataLoader configured for training.
+        """
+        return self._create_dataloader(self._train_dataset)
+
+    @property
+    def val_dataloader(self) -> DataLoader:  # type: ignore[override]
+        """Prepare and return the validation DataLoader.
+
+        Returns:
+            DataLoader: The DataLoader configured for validation.
+        """
+        return self._create_dataloader(self._val_dataset)
+
+    @property
+    def test_dataloader(self) -> DataLoader:  # type: ignore[override]
+        """Prepare and return the test DataLoader.
+
+        Returns:
+            DataLoader: The DataLoader configured for testing.
+        """
+        return self._create_dataloader(self._test_dataset)
+
+

+[docs]
+    def teardown(self) -> None:  # type: ignore[override]
+        """Perform any necessary cleanup or shutdown procedures.
+
+        This method can be used for tasks such as closing SQLite connections
+        after training. Override this method as needed.
+
+        Returns:
+            None
+        """
+        if hasattr(self, "_train_dataset") and isinstance(
+            self._train_dataset, SQLiteDataset
+        ):
+            self._train_dataset._close_connection()
+
+        if hasattr(self, "_val_dataset") and isinstance(
+            self._val_dataset, SQLiteDataset
+        ):
+            self._val_dataset._close_connection()
+
+        if hasattr(self, "_test_dataset") and isinstance(
+            self._test_dataset, SQLiteDataset
+        ):
+            self._test_dataset._close_connection()
+
+        return

+
+
+    def _create_dataloader(
+        self, dataset: Union[Dataset, EnsembleDataset]
+    ) -> DataLoader:
+        """Create a DataLoader for the given dataset.
+
+        Args:
+            dataset (Union[Dataset, EnsembleDataset]):
+                                        The dataset to create a DataLoader for.
+
+        Returns:
+            DataLoader: The DataLoader configured for the given dataset.
+        """
+        if dataset == self._train_dataset:
+            dataloader_args = self._train_dataloader_kwargs
+        elif dataset == self._val_dataset:
+            dataloader_args = self._validation_dataloader_kwargs
+        elif dataset == self._test_dataset:
+            dataloader_args = self._test_dataloader_kwargs
+        else:
+            raise ValueError(
+                "Unknown dataset encountered during dataloader creation."
+            )
+
+        if dataloader_args is None:
+            raise AttributeError("Dataloader arguments not provided.")
+
+        return DataLoader(dataset=dataset, **dataloader_args)
+
+    def _validate_dataset_class(self) -> None:
+        """Sanity checks on the dataset reference (self._dataset).
+
+        Checks whether the dataset is an instance of SQLiteDataset,
+        ParquetDataset, or Dataset. Raises a TypeError if an invalid dataset
+        type is detected, or if an EnsembleDataset is used.
+        """
+        allowed_types = (SQLiteDataset, ParquetDataset, Dataset)
+        if self._dataset not in allowed_types:
+            raise TypeError(
+                "dataset_reference must be an instance "
+                "of SQLiteDataset, ParquetDataset, or Dataset."
+            )
+        if self._dataset is EnsembleDataset:
+            raise TypeError(
+                "EnsembleDataset is not allowed as dataset_reference."
+            )
+
+    def _validate_dataset_args(self) -> None:
+        """Sanity checks on the arguments for the dataset reference."""
+        if isinstance(self._dataset_args["path"], list):
+            if self._selection is not None:
+                try:
+                    # Check that the number of dataset paths is equal to the
+                    # number of selections given as arg.
+                    assert len(self._dataset_args["path"]) == len(
+                        self._selection
+                    )
+                except AssertionError:
+                    raise ValueError(
+                        "The number of dataset paths"
+                        f" ({len(self._dataset_args['path'])})"
+                        " does not match the number of"
+                        f" selections ({len(self._selection)})."
+                    )
+
+            if self._test_selection is not None:
+                try:
+                    # Check that the number of dataset paths is equal to the
+                    # number of test selections.
+                    assert len(self._dataset_args["path"]) == len(
+                        self._test_selection
+                    )
+                except AssertionError:
+                    raise ValueError(
+                        "The number of dataset paths "
+                        f" ({len(self._dataset_args['path'])}) does not match "
+                        "the number of test selections "
+                        f"({len(self._test_selection)}).If you'd like to test "
+                        "on only a subset of the "
+                        f"{len(self._dataset_args['path'])} datasets, "
+                        "please provide empty test selections for the others."
+                    )
+
+    def _validate_dataloader_args(self) -> None:
+        """Sanity check on `dataloader_args`."""
+        if "dataset" in self._train_dataloader_kwargs:
+            raise ValueError(
+                "`train_dataloader_kwargs` must not contain `dataset`"
+            )
+        if "dataset" in self._validation_dataloader_kwargs:
+            raise ValueError(
+                "`validation_dataloader_kwargs` must not contain `dataset`"
+            )
+        if "dataset" in self._test_dataloader_kwargs:
+            raise ValueError(
+                "`test_dataloader_kwargs` must not contain `dataset`"
+            )
+
+    def _resolve_selections(self) -> None:
+        if self._test_selection is None:
+            self.warning_once(
+                f"{self.__class__.__name__} did not receive an"
+                " argument for `test_selection` and will "
+                "therefore not have a prediction dataloader available."
+            )
+        if self._selection is not None:
+            # Split the selection into train/validation
+            if self._use_ensemble_dataset:
+                # Split every selection
+                self._train_selection = []
+                self._val_selection = []
+                for selection in self._selection:
+                    train_selection, val_selection = self._split_selection(
+                        selection
+                    )
+                    self._train_selection.append(train_selection)
+                    self._val_selection.append(val_selection)
+
+            else:
+                # Split the only selection we got
+                assert isinstance(self._selection, list)
+                (
+                    self._train_selection,
+                    self._val_selection,
+                ) = self._split_selection(  # type: ignore
+                    self._selection
+                )
+
+        else:  # selection is None
+            # If not provided, we infer it by grabbing
+            # all event ids in the dataset.
+            self.info(
+                f"{self.__class__.__name__} did not receive an"
+                " for `selection`. Selection will "
+                "will automatically be created with a split of "
+                f"train: {self._train_val_split[0]} and "
+                f"validation: {self._train_val_split[1]}"
+            )
+            (
+                self._train_selection,
+                self._val_selection,
+            ) = self._infer_selections()  # type: ignore
+
+    def _split_selection(
+        self, selection: Union[int, List[int], List[List[int]]]
+    ) -> Tuple[List[int], List[int]]:
+        """Split train selection into train/validation.
+
+        Args:
+            selection: Training selection to be split
+
+        Returns:
+            Training selection, Validation selection.
+        """
+        assert isinstance(selection, (int, list))
+        if isinstance(selection, int):
+            flat_selection = [selection]
+        elif isinstance(selection[0], list):
+            flat_selection = [
+                item
+                for sublist in selection
+                for item in sublist  # type: ignore
+            ]
+        else:
+            flat_selection = selection  # type: ignore
+        assert isinstance(flat_selection, list)
+
+        train_selection, val_selection = train_test_split(
+            flat_selection,
+            train_size=self._train_val_split[0],
+            test_size=self._train_val_split[1],
+            random_state=self._rng,
+        )
+        return train_selection, val_selection
+
+    def _infer_selections(self) -> Tuple[List[int], List[int]]:
+        """Automatically infer training and validation selections.
+
+        Returns:
+            Training selection, Validation selection
+        """
+        if self._use_ensemble_dataset:
+            # We must iterate through the dataset paths and infer a train/val
+            # selection for each.
+            self._train_selection = []
+            self._val_selection = []
+            for dataset_path in self._dataset_args["path"]:
+                (
+                    train_selection,
+                    val_selection,
+                ) = self._infer_selections_on_single_dataset(dataset_path)
+                self._train_selection.append(train_selection)  # type: ignore
+                self._val_selection.append(val_selection)  # type: ignore
+        else:
+            # Infer selection on a single dataset
+            (
+                self._train_selection,
+                self._val_selection,
+            ) = self._infer_selections_on_single_dataset(  # type: ignore
+                self._dataset_args["path"]
+            )
+
+        return (self._train_selection, self._val_selection)  # type: ignore
+
+    def _infer_selections_on_single_dataset(
+        self, dataset_path: str
+    ) -> Tuple[List[int], List[int]]:
+        """Automatically infers dataset train/val selections.
+
+        Args:
+            dataset_path (str): The path to the dataset.
+
+        Returns:
+            Tuple[List[int], List[int]]: Training and validation selections.
+        """
+        tmp_args = deepcopy(self._dataset_args)
+        tmp_args["path"] = dataset_path
+        tmp_dataset = self._construct_dataset(tmp_args)
+
+        all_events = (
+            tmp_dataset._get_all_indices()
+        )  # unshuffled list, sequential index
+
+        # Multiple lines to avoid one large
+        all_events = (
+            pd.DataFrame(all_events)
+            .sample(frac=1, replace=False, random_state=self._rng)
+            .values.tolist()
+        )  # shuffled list
+
+        return self._split_selection(all_events)
+
+    def _construct_dataset(self, tmp_args: Dict[str, Any]) -> Dataset:
+        """Construct dataset.
+
+        Return:
+            Dataset object constructed from input arguments.
+        """
+        dataset = self._dataset(**tmp_args)  # type: ignore
+        return dataset
+
+    def _create_dataset(
+        self, selection: Union[List[int], List[List[int]], List[float]]
+    ) -> Union[EnsembleDataset, Dataset]:
+        """Instantiate `dataset_reference`.
+
+        Args:
+            selection: The selected event id's.
+
+        Returns:
+            A dataset, either an instance of `EnsembleDataset` or `Dataset`.
+        """
+        if self._use_ensemble_dataset:
+            # Construct multiple datasets and pass to EnsembleDataset
+            # len(selection) == len(dataset_args['path'])
+            datasets = []
+            for dataset_idx in range(len(selection)):
+                datasets.append(
+                    self._create_single_dataset(
+                        selection=selection[dataset_idx],  # type: ignore
+                        path=self._dataset_args["path"][dataset_idx],
+                    )
+                )
+
+            dataset = EnsembleDataset(datasets)
+
+        else:
+            # Construct single dataset
+            dataset = self._create_single_dataset(
+                selection=selection,
+                path=self._dataset_args["path"],  # type:ignore
+            )
+        return dataset
+
+    def _create_single_dataset(
+        self,
+        selection: Union[List[int], List[List[int]], List[float]],
+        path: str,
+    ) -> Dataset:
+        """Instantiate a single `Dataset`.
+
+        Args:
+            selection: A selection for a single dataset.
+            path: Path to a single dataset
+
+        Returns:
+            An instance of `Dataset`.
+        """
+        tmp_args = deepcopy(self._dataset_args)
+        tmp_args["path"] = path
+        tmp_args["selection"] = selection
+        return self._construct_dataset(tmp_args)

+
+
+ +
+
+
+
+
+ + + + + \ No newline at end of file diff --git a/_modules/graphnet/models/standard_model.html b/_modules/graphnet/models/standard_model.html index 8d219a1e4..6b72ed310 100644 --- a/_modules/graphnet/models/standard_model.html +++ b/_modules/graphnet/models/standard_model.html @@ -613,6 +613,9 @@

Source code for graph on_step=False, sync_dist=True, ) + + current_lr = self.trainer.optimizers[0].param_groups[0]["lr"] + self.log("lr", current_lr, prog_bar=True, on_step=True) return loss diff --git a/_modules/graphnet/training/utils.html b/_modules/graphnet/training/utils.html index 547bf5619..b2e9890bd 100644 --- a/_modules/graphnet/training/utils.html +++ b/_modules/graphnet/training/utils.html @@ -659,6 +659,24 @@

Source code for graphnet.tra model.save(path + "/" + tag + "_model.pth") Logger().info("Results saved at: \n %s" % path) + + +
+[docs] +def save_selection(selection: List[int], file_path: str) -> None: + """Save the list of event numbers to a CSV file. + + Args: + selection: List of event ids. + file_path: File path to save the selection. + """ + assert isinstance( + selection, list + ), "Selection should be a list of integers." + with open(file_path, "w") as f: + f.write(",".join(map(str, selection))) + f.write("\n")
+ diff --git a/_modules/index.html b/_modules/index.html index b5d45ae22..d74b408da 100644 --- a/_modules/index.html +++ b/_modules/index.html @@ -324,6 +324,7 @@

All modules for which code is available

+ +
  • + + + datamodule + +
  • @@ -604,12 +611,12 @@ -
    Skip to content +
    + +
    + + +
    + + + + +
    +
    + +
    +
    +
    + +
    +
    +
    +
    +
    +
    + + +
    +
    +
    + +
    +
    + +
    +

    datamodule

    +

    Base Dataloader class(es) used in graphnet.

    +
    +
    +class graphnet.data.datamodule.GraphNeTDataModule(dataset_reference, dataset_args, selection, test_selection, train_dataloader_kwargs, validation_dataloader_kwargs, test_dataloader_kwargs, train_val_split=[0.9, 0.1], split_seed)[source]
    +

    Bases: LightningDataModule, Logger

    +

    General Class for DataLoader Construction.

    +

    Create dataloaders from dataset.

    +
    +
    Parameters:
    +
      +

    • dataset_reference (Union[SQLiteDataset, ParquetDataset, Dataset]) – A non-instantiated reference +to the dataset class.

      • +

    • dataset_args (Dict[str, Any]) – Arguments to instantiate +graphnet.data.dataset.Dataset with.

      • +

    • selection (Union[List[int], List[List[int]], None], default: None) – (Optional) a list of event id’s used for training +and validation, Default None.

      • +

    • test_selection (Union[List[int], List[List[int]], None], default: None) – (Optional) a list of event id’s used for testing, +Default None.

      • +

    • train_dataloader_kwargs (Optional[Dict[str, Any]], default: None) – Arguments for the training DataLoader, +Default None.

      • +

    • validation_dataloader_kwargs (Optional[Dict[str, Any]], default: None) – Arguments for the validation +DataLoader, Default None.

      • +

    • test_dataloader_kwargs (Optional[Dict[str, Any]], default: None) – Arguments for the test DataLoader, +Default None.

      • +

    • train_val_split (Optional) – Split ratio for training and +validation sets. Default is [0.9, 0.10].

      • +

    • split_seed (int, default: 42) – seed used for shuffling and splitting selections into +train/validation, Default 42.

      • +
    +
    +
    +
    +
    +prepare_data()[source]
    +

    Prepare the dataset for training.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    +
    +
    +setup(stage)[source]
    +

    Prepare Datasets for DataLoaders.

    +
    +
    Parameters:
    +

    stage (str) – lightning stage. Either “fit, validate, test, predict”

    +
    +
    Return type:
    +

    None

    +
    +
    +
    +
    +
    +property train_dataloader: DataLoader
    +

    Prepare and return the training DataLoader.

    +
    +
    Returns:
    +

    The DataLoader configured for training.

    +
    +
    Return type:
    +

    DataLoader

    +
    +
    +
    +
    +
    +property val_dataloader: DataLoader
    +

    Prepare and return the validation DataLoader.

    +
    +
    Returns:
    +

    The DataLoader configured for validation.

    +
    +
    Return type:
    +

    DataLoader

    +
    +
    +
    +
    +
    +property test_dataloader: DataLoader
    +

    Prepare and return the test DataLoader.

    +
    +
    Returns:
    +

    The DataLoader configured for testing.

    +
    +
    Return type:
    +

    DataLoader

    +
    +
    +
    +
    +
    +teardown()[source]
    +

    Perform any necessary cleanup or shutdown procedures.

    +

    This method can be used for tasks such as closing SQLite connections +after training. Override this method as needed.

    +
    +
    Return type:
    +

    None

    +
    +
    Returns:
    +

    None

    +
    +
    +
    +
    +
    + + +
    +
    +
    +
    +
    + + + + + \ No newline at end of file diff --git a/api/graphnet.data.dataset.dataset.html b/api/graphnet.data.dataset.dataset.html index fae36ebe0..bfd116024 100644 --- a/api/graphnet.data.dataset.dataset.html +++ b/api/graphnet.data.dataset.dataset.html @@ -498,6 +498,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.dataset.html b/api/graphnet.data.dataset.html index 4dc0f578f..cefcbc843 100644 --- a/api/graphnet.data.dataset.html +++ b/api/graphnet.data.dataset.html @@ -391,6 +391,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.dataset.parquet.html b/api/graphnet.data.dataset.parquet.html index d66b67d51..25a99d006 100644 --- a/api/graphnet.data.dataset.parquet.html +++ b/api/graphnet.data.dataset.parquet.html @@ -399,6 +399,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.dataset.parquet.parquet_dataset.html b/api/graphnet.data.dataset.parquet.parquet_dataset.html index 349f42c6b..7461072d4 100644 --- a/api/graphnet.data.dataset.parquet.parquet_dataset.html +++ b/api/graphnet.data.dataset.parquet.parquet_dataset.html @@ -425,6 +425,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.dataset.sqlite.html b/api/graphnet.data.dataset.sqlite.html index 8c8466a90..7cfb3ec50 100644 --- a/api/graphnet.data.dataset.sqlite.html +++ b/api/graphnet.data.dataset.sqlite.html @@ -399,6 +399,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.dataset.sqlite.sqlite_dataset.html b/api/graphnet.data.dataset.sqlite.sqlite_dataset.html index e7c699424..cabd2fa0b 100644 --- a/api/graphnet.data.dataset.sqlite.sqlite_dataset.html +++ b/api/graphnet.data.dataset.sqlite.sqlite_dataset.html @@ -425,6 +425,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.html b/api/graphnet.data.extractors.html index 602f50a76..386e3f8af 100644 --- a/api/graphnet.data.extractors.html +++ b/api/graphnet.data.extractors.html @@ -461,6 +461,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3extractor.html b/api/graphnet.data.extractors.i3extractor.html index 088aa5ada..f13c0bef6 100644 --- a/api/graphnet.data.extractors.i3extractor.html +++ b/api/graphnet.data.extractors.i3extractor.html @@ -516,6 +516,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3featureextractor.html b/api/graphnet.data.extractors.i3featureextractor.html index ac3be3fbb..36d5ebe5e 100644 --- a/api/graphnet.data.extractors.i3featureextractor.html +++ b/api/graphnet.data.extractors.i3featureextractor.html @@ -512,6 +512,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3genericextractor.html b/api/graphnet.data.extractors.i3genericextractor.html index ace919977..ea92ae1de 100644 --- a/api/graphnet.data.extractors.i3genericextractor.html +++ b/api/graphnet.data.extractors.i3genericextractor.html @@ -476,6 +476,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3hybridrecoextractor.html b/api/graphnet.data.extractors.i3hybridrecoextractor.html index 2219ddab5..f9878446e 100644 --- a/api/graphnet.data.extractors.i3hybridrecoextractor.html +++ b/api/graphnet.data.extractors.i3hybridrecoextractor.html @@ -476,6 +476,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3ntmuonlabelsextractor.html b/api/graphnet.data.extractors.i3ntmuonlabelsextractor.html index c8a12e6b6..3a9424cce 100644 --- a/api/graphnet.data.extractors.i3ntmuonlabelsextractor.html +++ b/api/graphnet.data.extractors.i3ntmuonlabelsextractor.html @@ -476,6 +476,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3particleextractor.html b/api/graphnet.data.extractors.i3particleextractor.html index d0d8cf1bc..6835ca634 100644 --- a/api/graphnet.data.extractors.i3particleextractor.html +++ b/api/graphnet.data.extractors.i3particleextractor.html @@ -476,6 +476,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3pisaextractor.html b/api/graphnet.data.extractors.i3pisaextractor.html index 6bbf3f02b..6813730e8 100644 --- a/api/graphnet.data.extractors.i3pisaextractor.html +++ b/api/graphnet.data.extractors.i3pisaextractor.html @@ -476,6 +476,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3quesoextractor.html b/api/graphnet.data.extractors.i3quesoextractor.html index 42497984b..ac54a3905 100644 --- a/api/graphnet.data.extractors.i3quesoextractor.html +++ b/api/graphnet.data.extractors.i3quesoextractor.html @@ -476,6 +476,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3retroextractor.html b/api/graphnet.data.extractors.i3retroextractor.html index f7aee6ee2..230065dd6 100644 --- a/api/graphnet.data.extractors.i3retroextractor.html +++ b/api/graphnet.data.extractors.i3retroextractor.html @@ -476,6 +476,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3splinempeextractor.html b/api/graphnet.data.extractors.i3splinempeextractor.html index f9755a130..121cbff6c 100644 --- a/api/graphnet.data.extractors.i3splinempeextractor.html +++ b/api/graphnet.data.extractors.i3splinempeextractor.html @@ -476,6 +476,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3truthextractor.html b/api/graphnet.data.extractors.i3truthextractor.html index ded247fae..bf98709d4 100644 --- a/api/graphnet.data.extractors.i3truthextractor.html +++ b/api/graphnet.data.extractors.i3truthextractor.html @@ -476,6 +476,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.i3tumextractor.html b/api/graphnet.data.extractors.i3tumextractor.html index 2bda092dc..f32febcbb 100644 --- a/api/graphnet.data.extractors.i3tumextractor.html +++ b/api/graphnet.data.extractors.i3tumextractor.html @@ -476,6 +476,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.utilities.collections.html b/api/graphnet.data.extractors.utilities.collections.html index 2ba4e4c1a..2d75afdc0 100644 --- a/api/graphnet.data.extractors.utilities.collections.html +++ b/api/graphnet.data.extractors.utilities.collections.html @@ -516,6 +516,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.utilities.frames.html b/api/graphnet.data.extractors.utilities.frames.html index 596e21fca..615ecb4cc 100644 --- a/api/graphnet.data.extractors.utilities.frames.html +++ b/api/graphnet.data.extractors.utilities.frames.html @@ -516,6 +516,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.utilities.html b/api/graphnet.data.extractors.utilities.html index 4240ace5f..79d4aa2d9 100644 --- a/api/graphnet.data.extractors.utilities.html +++ b/api/graphnet.data.extractors.utilities.html @@ -483,6 +483,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.extractors.utilities.types.html b/api/graphnet.data.extractors.utilities.types.html index cbf5c13bb..82941ae6d 100644 --- a/api/graphnet.data.extractors.utilities.types.html +++ b/api/graphnet.data.extractors.utilities.types.html @@ -570,6 +570,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • diff --git a/api/graphnet.data.filters.html b/api/graphnet.data.filters.html index ab303e539..91e99443b 100644 --- a/api/graphnet.data.filters.html +++ b/api/graphnet.data.filters.html @@ -130,7 +130,7 @@ - + @@ -361,6 +361,13 @@ dataloader +
    • +
  • + + + datamodule + +
  • @@ -573,7 +580,7 @@
    • +
  • + + + datamodule + +
  • @@ -520,6 +527,10 @@
  • DataLoader
    • +
  • datamodule +
  • filters diff --git a/api/graphnet.training.html b/api/graphnet.training.html index d33bd01fc..43a1760a0 100644 --- a/api/graphnet.training.html +++ b/api/graphnet.training.html @@ -456,6 +456,7 @@
  • make_train_validation_dataloader()
  • get_predictions()
  • save_results()
    • +
  • save_selection()
  • weight_fitting
    • @@ -427,6 +429,13 @@ save_results() + +
  • + + + save_selection() + +
    • @@ -482,6 +491,8 @@
  • get_predictions()
  • save_results() +
    • +
  • save_selection()
    • @@ -631,6 +642,22 @@ +
    +
    +graphnet.training.utils.save_selection(selection, file_path)[source]
    +

    Save the list of event numbers to a CSV file.

    +
    +
    Parameters:
    +
      +

    • selection (List[int]) – List of event ids.

      • +

    • file_path (str) – File path to save the selection.

      • +
    +
    +
    Return type:
    +

    None

    +
    +
    +
    diff --git a/genindex.html b/genindex.html index 5138df542..cfa4f7184 100644 --- a/genindex.html +++ b/genindex.html @@ -833,6 +833,13 @@

    G

    +
  • + graphnet.data.datamodule + +
  • @@ -1474,6 +1481,8 @@

    G

  • module
    • +
  • GraphNeTDataModule (class in graphnet.data.datamodule) +
  • GraphnetEarlyStopping (class in graphnet.training.callbacks)
  • GraphNeTI3Module (class in graphnet.deployment.i3modules.graphnet_module) @@ -1765,6 +1774,8 @@

    M

  • graphnet.data.dataconverter
  • graphnet.data.dataloader +
    • +
  • graphnet.data.datamodule
  • graphnet.data.dataset
    • @@ -2079,10 +2090,10 @@

    P

  • plot_1D_contour() (in module graphnet.pisa.plotting)
    • - - + + -