Fixed import unused warnings

Featurizers, transformers, and splitters for MolNet.

"""

import os

import importlib

import inspect

import logging

import json

from typing import Dict, List, Any

from typing import Dict, Any

import deepchem as dc

from deepchem.feat.base_classes import Featurizer

from deepchem.trans.transformers import Transformer

from deepchem.splits.splitters import Splitter

from collections import OrderedDict

import numpy as np

from deepchem.molnet.load_function.molnet_loader import TransformerGenerator, _MolnetLoader

from deepchem.data import Dataset

from typing import List, Optional, Tuple, Union

from deepchem.molnet.load_function.bace_features import bace_user_specified_features

BACE_URL = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv"

BACE_REGRESSION_TASKS = ["pIC50"]

"""

Tests for zinc15 loader.

"""

import os

import numpy as np

from deepchem.molnet import load_zinc15

# def test_zinc15_loader():

#   current_dir = os.path.dirname(os.path.abspath(__file__))

#

#   tasks, datasets, transformers = load_zinc15(

#       reload=False,

#       data_dir=current_dir,

#       splitter_kwargs={

#           'seed': 42,

#           'frac_train': 0.6,

#           'frac_valid': 0.2,

#           'frac_test': 0.2

#       })

#

#   test_vec = np.array([

#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,

#       0.0, -1.224744871391589, 0.0, 0.0, 0.0, 0.0, 2.0, -0.5, 0.0, 0.0, 0.0,

#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0

#   ])

#

#   train, val, test = datasets

#   assert tasks == ['mwt', 'logp', 'reactive']

#   assert train.X.shape == (3, 100, 35)

#   assert np.allclose(train.X[0][0], test_vec, atol=0.01)

#

#   if os.path.exists(os.path.join(current_dir, 'zinc15_250K_2D.csv')):

#     os.remove(os.path.join(current_dir, 'zinc15_250K_2D.csv'))

"""

Tests for qm9 loader.

"""

import os

import numpy as np

from deepchem.molnet import load_qm9

# def test_qm9_loader():

#   current_dir = os.path.dirname(os.path.abspath(__file__))

#   tasks, datasets, transformers = load_qm9(

#       reload=False,

#       data_dir=current_dir,

#       featurizer='ECFP',

#       splitter_kwargs={

#           'seed': 42,

#           'frac_train': 0.6,

#           'frac_valid': 0.2,

#           'frac_test': 0.2

#       })

#

#   assert len(tasks) == 12

#   assert tasks[0] == 'mu'

#   assert datasets[0].X.shape == (8, 1024)