deepscreen/data/dti.py.bak

from functools import partial
from numbers import Number
from pathlib import Path
from typing import Any, Dict, Optional, Sequence, Union, Literal

from lightning import LightningDataModule
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from torch.utils.data import Dataset, DataLoader

from deepscreen.data.utils import label_transform, collate_fn, SafeBatchSampler
from deepscreen.utils import get_logger

log = get_logger(__name__)


# TODO: save a list of corrupted records


class DTIDataset(Dataset):
    def __init__(
            self,
            task: Literal['regression', 'binary', 'multiclass'],
            n_class: Optional[int],
            data_path: str | Path,
            drug_featurizer: callable,
            protein_featurizer: callable,
            thresholds: Optional[Union[Number, Sequence[Number]]] = None,
            discard_intermediate: Optional[bool] = False,
    ):
        df = pd.read_csv(
            data_path,
            engine='python',
            header=0,
            usecols=lambda x: x in ['X1', 'ID1', 'X2', 'ID2', 'Y', 'U'],
            dtype={
                'X1': 'str',
                'ID1': 'str',
                'X2': 'str',
                'ID2': 'str',
                'Y': 'float32',
                'U': 'str',
            },
        )
        # Read the whole data table

        # if 'ID1' in df:
        #     self.x1_to_id1 = dict(zip(df['X1'], df['ID1']))
        # if 'ID2' in df:
        #     self.x2_to_id2 = dict(zip(df['X2'], df['ID2']))
        #     self.id2_to_indexes = dict(zip(df['ID2'], range(len(df['ID2']))))
        # self.x2_to_indexes = dict(zip(df['X2'], range(len(df['X2']))))

        # # train and eval mode data processing (fully labelled)
        # if 'Y' in df.columns and df['Y'].notnull().all():
        log.info(f"Processing data file: {data_path}")

        # Forward-fill all non-label columns
        df.loc[:, df.columns != 'Y'] = df.loc[:, df.columns != 'Y'].ffill(axis=0)

        if 'Y' in df:
            log.info(f"Performing pre-transformation target validation.")
            # TODO: check sklearn.utils.multiclass.check_classification_targets
            match task:
                case 'regression':
                    assert all(df['Y'].apply(lambda x: isinstance(x, Number))), \
                        f"""`Y` must be numeric for `regression` task,
                        but it has {set(df['Y'].apply(type))}."""

                case 'binary':
                    if all(df['Y'].isin([0, 1])):
                        assert not thresholds, \
                            f"""`Y` is already 0 or 1 for `binary` (classification) `task`,
                            but still got `thresholds` {thresholds}.
                            Double check your choices of `task` and `thresholds` and records in the `Y` column."""
                    else:
                        assert thresholds, \
                            f"""`Y` must be 0 or 1 for `binary` (classification) `task`,
                            but it has {pd.unique(df['Y'])}.
                            You must set `thresholds` to discretize continuous labels."""

                case 'multiclass':
                    assert n_class >= 3, f'`n_class` for `multiclass` (classification) `task` must be at least 3.'

                    if all(df['Y'].apply(lambda x: x.is_integer() and x >= 0)):
                        assert not thresholds, \
                            f"""`Y` is already non-negative integers for 
                            `multiclass` (classification) `task`, but still got `thresholds` {thresholds}.
                            Double check your choice of `task`, `thresholds` and records in the `Y` column."""
                    else:
                        assert thresholds, \
                            f"""`Y` must be non-negative integers for
                            `multiclass` (classification) 'task',but it has {pd.unique(df['Y'])}.
                            You must set `thresholds` to discretize continuous labels."""

            if 'U' in df.columns:
                units = df['U']
            else:
                units = None
                log.warning("Units ('U') not in the data table. "
                            "Assuming all labels to be discrete or in p-scale (-log10[M]).")

            # Transform labels
            df['Y'] = label_transform(labels=df['Y'], units=units, thresholds=thresholds,
                                      discard_intermediate=discard_intermediate)

            # Filter out rows with a NaN in Y (missing values)
            df.dropna(subset=['Y'], inplace=True)

            log.info(f"Performing post-transformation target validation.")
            match task:
                case 'regression':
                    df['Y'] = df['Y'].astype('float32')
                    assert all(df['Y'].apply(lambda x: isinstance(x, Number))), \
                        f"""`Y` must be numeric for `regression` task, 
                        but after transformation it still has {set(df['Y'].apply(type))}.
                        Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""

                case 'binary':
                    df['Y'] = df['Y'].astype('int')
                    assert all(df['Y'].isin([0, 1])), \
                        f"""`Y` must be 0 or 1 for `binary` (classification) `task`, "
                        but after transformation it still has {pd.unique(df['Y'])}.
                        Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""

                case 'multiclass':
                    df['Y'] = df['Y'].astype('int')
                    assert all(df['Y'].apply(lambda x: x.is_integer() and x >= 0)), \
                        f"""Y must be non-negative integers for task `multiclass` (classification)
                        but after transformation it still has {pd.unique(df['Y'])}.
                        Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""

                    target_n_unique = df['Y'].nunique()
                    assert target_n_unique == n_class, \
                        f"""You have set `n_class` for `multiclass` (classification) `task` to {n_class},
                        but after transformation Y still has {target_n_unique} unique labels.
                        Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""

        # Indexed protein/FASTA for retrieval metrics
        df['IDX'] = LabelEncoder().fit_transform(df['X2'])

        self.df = df
        self.drug_featurizer = drug_featurizer if drug_featurizer is not None else (lambda x: x)
        self.protein_featurizer = protein_featurizer if protein_featurizer is not None else (lambda x: x)

    def __len__(self):
        return len(self.df.index)

    def __getitem__(self, i):
        sample = self.df.loc[i]
        return {
            'N': i,
            'X1': self.drug_featurizer(sample['X1']),
            'ID1': sample.get('ID1', sample['X1']),
            'X2': self.protein_featurizer(sample['X2']),
            'ID2': sample.get('ID2', sample['X2']),
            'Y': sample.get('Y'),
            'IDX': sample['IDX'],
        }


class DTIDataModule(LightningDataModule):
    """
    DTI DataModule

    A DataModule implements 5 key methods:

        def prepare_data(self):
            # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
            # download data, pre-process, split, save to disk, etc.
        def setup(self, stage):
            # things to do on every process in DDP
            # load data, set variables, etc.
        def train_dataloader(self):
            # return train dataloader
        def val_dataloader(self):
            # return validation dataloader
        def test_dataloader(self):
            # return test dataloader
        def teardown(self):
            # called on every process in DDP
            # clean up after fit or test

    This allows you to share a full dataset without explaining how to download,
    split, transform and process the data.

    Read the docs:
        https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html
    """

    def __init__(
            self,
            task: Literal['regression', 'binary', 'multiclass'],
            n_class: Optional[int],
            batch_size: int,
            # train: bool,
            drug_featurizer: callable,
            protein_featurizer: callable,
            collator: callable = collate_fn,
            data_dir: str = "data/",
            data_file: Optional[str] = None,
            train_val_test_split: Optional[Union[Sequence[Number | str]]] = None,
            split: Optional[callable] = None,
            thresholds: Optional[Union[Number, Sequence[Number]]] = None,
            discard_intermediate: Optional[bool] = False,
            num_workers: int = 0,
            pin_memory: bool = False,
    ):
        super().__init__()

        self.train_data: Optional[Dataset] = None
        self.val_data: Optional[Dataset] = None
        self.test_data: Optional[Dataset] = None
        self.predict_data: Optional[Dataset] = None
        self.split = split
        self.collator = collator
        self.dataset = partial(
            DTIDataset,
            task=task,
            n_class=n_class,
            drug_featurizer=drug_featurizer,
            protein_featurizer=protein_featurizer,
            thresholds=thresholds,
            discard_intermediate=discard_intermediate
        )

        if train_val_test_split:
            # TODO test behavior for trainer.test and predict when this is passed
            if len(train_val_test_split) not in [2, 3]:
                raise ValueError('Length of `train_val_test_split` must be 2 (for training without testing) or 3.')
            if all([data_file, split]):
                if all(isinstance(split, Number) for split in train_val_test_split):
                    pass
                else:
                    raise ValueError('`train_val_test_split` must be a sequence numbers '
                                     '(float for percentages and int for sample numbers) '
                                     'if both `data_file` and `split` have been specified.')
            elif all(isinstance(split, str) for split in train_val_test_split) and not any([data_file, split]):
                split_paths = []
                for split in train_val_test_split:
                    split = Path(split)
                    if not split.is_absolute():
                        split = Path(data_dir, split)
                    split_paths.append(split)

                self.train_data = self.dataset(data_path=split_paths[0])
                self.val_data = self.dataset(data_path=split_paths[1])
                if len(train_val_test_split) == 3:
                    self.test_data = self.dataset(data_path=split_paths[2])
            else:
                raise ValueError('For training, you must specify either `data_file`, `split`, '
                                 'and `train_val_test_split` as a sequence of numbers or '
                                 'solely `train_val_test_split` as a sequence of data file paths.')

        elif data_file and not any([split, train_val_test_split]):
            data_file = Path(data_file)
            if not data_file.is_absolute():
                data_file = Path(data_dir, data_file)
            self.test_data = self.predict_data = self.dataset(data_path=data_file)
        else:
            raise ValueError("For training, you must specify `train_val_test_split`. "
                             "For testing/predicting, you must specify only `data_file` without "
                             "`train_val_test_split` or `split`.")

        # this line allows to access init params with 'self.hparams' attribute
        # also ensures init params will be stored in ckpt
        self.save_hyperparameters(logger=False)  # ignore=['split']

    def prepare_data(self):
        """
        Download data if needed.
        Do not use it to assign state (e.g., self.x = x).
        """

    def setup(self, stage: Optional[str] = None, encoding: str = None):
        """
        Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
        This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be
        careful not to execute data splitting twice.
        """
        # TODO test SafeBatchSampler (which skips samples with any None without introducing variable batch size)
        # load and split datasets only if not loaded in initialization
        if not any([self.train_data, self.test_data, self.val_data, self.predict_data]):
            self.train_data, self.val_data, self.test_data = self.split(
                dataset=self.dataset(data_path=Path(self.hparams.data_dir, self.hparams.data_file)),
                lengths=self.hparams.train_val_test_split
            )

    def train_dataloader(self):
        return DataLoader(
            dataset=self.train_data,
            batch_sampler=SafeBatchSampler(
                data_source=self.train_data,
                batch_size=self.hparams.batch_size,
                # Dropping the last batch prevents problems caused by variable batch sizes in training, e.g.,
                # batch_size=1 in BatchNorm, and shuffling ensures the model be trained on all samples over epochs.
                drop_last=True,
                shuffle=True,
            ),
            # batch_size=self.hparams.batch_size,
            # shuffle=True,
            num_workers=self.hparams.num_workers,
            pin_memory=self.hparams.pin_memory,
            collate_fn=self.collator,
            persistent_workers=True if self.hparams.num_workers > 0 else False
        )

    def val_dataloader(self):
        return DataLoader(
            dataset=self.val_data,
            batch_sampler=SafeBatchSampler(
                data_source=self.val_data,
                batch_size=self.hparams.batch_size,
                drop_last=False,
                shuffle=False
            ),
            # batch_size=self.hparams.batch_size,
            # shuffle=False,
            num_workers=self.hparams.num_workers,
            pin_memory=self.hparams.pin_memory,
            collate_fn=self.collator,
            persistent_workers=True if self.hparams.num_workers > 0 else False
        )

    def test_dataloader(self):
        return DataLoader(
            dataset=self.test_data,
            batch_sampler=SafeBatchSampler(
                data_source=self.test_data,
                batch_size=self.hparams.batch_size,
                drop_last=False,
                shuffle=False
            ),
            # batch_size=self.hparams.batch_size,
            # shuffle=False,
            num_workers=self.hparams.num_workers,
            pin_memory=self.hparams.pin_memory,
            collate_fn=self.collator,
            persistent_workers=True if self.hparams.num_workers > 0 else False
        )

    def predict_dataloader(self):
        return DataLoader(
            dataset=self.predict_data,
            batch_sampler=SafeBatchSampler(
                data_source=self.predict_data,
                batch_size=self.hparams.batch_size,
                drop_last=False,
                shuffle=False
            ),
            # batch_size=self.hparams.batch_size,
            # shuffle=False,
            num_workers=self.hparams.num_workers,
            pin_memory=self.hparams.pin_memory,
            collate_fn=self.collator,
            persistent_workers=True if self.hparams.num_workers > 0 else False
        )

    def teardown(self, stage: Optional[str] = None):
        """Clean up after fit or test."""
        pass

    def state_dict(self):
        """Extra things to save to checkpoint."""
        return {}

    def load_state_dict(self, state_dict: Dict[str, Any]):
        """Things to do when loading checkpoint."""
        pass