Loading deepchem/molnet/load_function/uspto_datasets.py +57 −12 Original line number Diff line number Diff line Loading @@ -28,25 +28,70 @@ USPTO_STEREO_VALID = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/U class _USPTOLoader(_MolnetLoader): def __init__(self, *args, subset: str, sep_reagent: bool, **kwargs): super(_USPTOLoader, self).__init__(*args, **kwargs) self.subset = subset self.sep_reagent = sep_reagent self.name = 'USPTO_' + subset def create_dataset(self) -> DiskDataset: dataset_file = os.path.join(self.data_dir, "USPTO_MIT_test.csv") if not os.path.exists(dataset_file): dc.utils.data_utils.download_url(url=USPTO_MIT_TEST, dest_dir=self.data_dir) loader = dc.data.CSVLoader(tasks=self.tasks, feature_field="smiles", featurizer=self.featurizer) return loader.create_dataset(dataset_file, shard_size=8192) if self.subset not in ['MIT', 'STEREO']: raise ValueError("Valid Subset names are MIT and STEREO.") if self.subset == 'MIT': train_file = os.path.join(self.data_dir, USPTO_MIT_TRAIN) test_file = os.path.join(self.data_dir, USPTO_MIT_TEST) valid_file = os.path.join(self.data_dir, USPTO_MIT_VALID) if not os.path.exists(train_file): logger.info("Downloading training file...") dc.utils.data_utils.download_url( url=USPTO_MIT_TRAIN, dest_dir=self.data_dir) logger.info("Training file download complete.") logger.info("Downloading test file...") dc.utils.data_utils.download_url( url=USPTO_MIT_TRAIN, dest_dir=self.data_dir) logger.info("Test file download complete.") logger.info("Downloading validation file...") dc.utils.data_utils.download_url( url=USPTO_MIT_TRAIN, dest_dir=self.data_dir) logger.info("Validation file download complete.") loader = dc.data.CSVLoader( tasks=self.tasks, feature_field="smiles", featurizer=self.featurizer) logger.info("Loading train dataset..") train_file = loader.create_dataset(train_file, shard_size=8192) logger.info("Loading test dataset..") test_file = loader.create_dataset(test_file, shard_size=8192) logger.info("Loading validation dataset..") valid_file = loader.create_dataset(valid_file, shard_size=8192) logger.info("Loading successful!") return train_file, test_file, valid_file def load_uspto( featurizer: Union[dc.feat.Featurizer, str] = None, splitter: Union[dc.splits.Splitter, str, None] = None, transformers: List[Union[TransformerGenerator, str]] = None, featurizer=None, splitter=None, transformers=None, reload: bool = True, data_dir: Optional[str] = None, save_dir: Optional[str] = None, subset: str = "MIT", sep_reagent: bool = True, **kwargs ) -> Tuple[List[str], Tuple[DiskDataset, ...], List[dc.trans.Transformer]]: pass loader = _USPTOLoader( featurizer, splitter, transformers, data_dir, save_dir, subset=subset, sep_reagent=sep_reagent, **kwargs) return loader.load_dataset(loader.name, reload) Loading
deepchem/molnet/load_function/uspto_datasets.py +57 −12 Original line number Diff line number Diff line Loading @@ -28,25 +28,70 @@ USPTO_STEREO_VALID = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/U class _USPTOLoader(_MolnetLoader): def __init__(self, *args, subset: str, sep_reagent: bool, **kwargs): super(_USPTOLoader, self).__init__(*args, **kwargs) self.subset = subset self.sep_reagent = sep_reagent self.name = 'USPTO_' + subset def create_dataset(self) -> DiskDataset: dataset_file = os.path.join(self.data_dir, "USPTO_MIT_test.csv") if not os.path.exists(dataset_file): dc.utils.data_utils.download_url(url=USPTO_MIT_TEST, dest_dir=self.data_dir) loader = dc.data.CSVLoader(tasks=self.tasks, feature_field="smiles", featurizer=self.featurizer) return loader.create_dataset(dataset_file, shard_size=8192) if self.subset not in ['MIT', 'STEREO']: raise ValueError("Valid Subset names are MIT and STEREO.") if self.subset == 'MIT': train_file = os.path.join(self.data_dir, USPTO_MIT_TRAIN) test_file = os.path.join(self.data_dir, USPTO_MIT_TEST) valid_file = os.path.join(self.data_dir, USPTO_MIT_VALID) if not os.path.exists(train_file): logger.info("Downloading training file...") dc.utils.data_utils.download_url( url=USPTO_MIT_TRAIN, dest_dir=self.data_dir) logger.info("Training file download complete.") logger.info("Downloading test file...") dc.utils.data_utils.download_url( url=USPTO_MIT_TRAIN, dest_dir=self.data_dir) logger.info("Test file download complete.") logger.info("Downloading validation file...") dc.utils.data_utils.download_url( url=USPTO_MIT_TRAIN, dest_dir=self.data_dir) logger.info("Validation file download complete.") loader = dc.data.CSVLoader( tasks=self.tasks, feature_field="smiles", featurizer=self.featurizer) logger.info("Loading train dataset..") train_file = loader.create_dataset(train_file, shard_size=8192) logger.info("Loading test dataset..") test_file = loader.create_dataset(test_file, shard_size=8192) logger.info("Loading validation dataset..") valid_file = loader.create_dataset(valid_file, shard_size=8192) logger.info("Loading successful!") return train_file, test_file, valid_file def load_uspto( featurizer: Union[dc.feat.Featurizer, str] = None, splitter: Union[dc.splits.Splitter, str, None] = None, transformers: List[Union[TransformerGenerator, str]] = None, featurizer=None, splitter=None, transformers=None, reload: bool = True, data_dir: Optional[str] = None, save_dir: Optional[str] = None, subset: str = "MIT", sep_reagent: bool = True, **kwargs ) -> Tuple[List[str], Tuple[DiskDataset, ...], List[dc.trans.Transformer]]: pass loader = _USPTOLoader( featurizer, splitter, transformers, data_dir, save_dir, subset=subset, sep_reagent=sep_reagent, **kwargs) return loader.load_dataset(loader.name, reload)
