🚧 wip commit

import numpy as np

from deepchem.utils.typing import OneOrMany

from deepchem.utils.save import load_image_files, load_csv_files, load_json_files, load_sdf_files

from deepchem.utils.data_utils import load_image_files, load_csv_files, load_json_files, load_sdf_files

from deepchem.utils.genomics_utils import encode_bio_sequence

from deepchem.feat import UserDefinedFeaturizer, Featurizer

from deepchem.data import Dataset, DiskDataset, NumpyDataset, ImageDataset

import deepchem as dc

from deepchem.utils.typing import OneOrMany, Shape

from deepchem.utils.save import save_to_disk, load_from_disk, load_image_files

from deepchem.utils.data_utils import save_to_disk, load_from_disk, load_image_files

Batch = Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]

def from_one_hot(y: np.ndarray, axis: int = 1) -> np.ndarray:

  """Transorms label vector from one-hot encoding.

  """Transforms label vector from one-hot encoding.

  Parameters

  ----------

from deepchem.data import Dataset, pad_features

from deepchem.metrics import Metric

from deepchem.trans import Transformer, undo_transforms

from deepchem.utils.save import load_from_disk

from deepchem.utils.save import save_to_disk

from deepchem.utils.data_utils import load_from_disk

from deepchem.utils.data_utils import save_to_disk

from deepchem.utils.evaluate import Evaluator

from typing import Any, Dict, List, Optional, Sequence

from sklearn.linear_model import ElasticNetCV

from sklearn.linear_model import LassoLarsCV

from deepchem.models import Model

from deepchem.utils.save import load_from_disk

from deepchem.utils.save import save_to_disk

from deepchem.utils.data_utils import load_from_disk

from deepchem.utils.data_utils import save_to_disk

NON_WEIGHTED_MODELS = [

    LogisticRegression, PLSRegression, GaussianProcessRegressor, ElasticNetCV,