🚨 apply flake8 in trans

"""

Gathers all transformers in one place for convenient imports

"""

# flake8: noqa

from deepchem.trans.transformers import undo_transforms

from deepchem.trans.transformers import undo_grad_transforms

from deepchem.trans.transformers import Transformer

    self.classes = sorted(np.unique(y))

    # Remove labels with zero weights

    y = y[w != 0]

    N = len(y)

    class_weights = []

    # Note that we may have 0 elements of a given class since we remove those

    # labels with zero weight.

import os

import deepchem as dc

import numpy as np

import deepchem as dc

def test_DAG_transformer():

  """Tests the DAG transformer."""

  np.random.seed(123)

  n_tasks = 1

  # Load mini log-solubility dataset.

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

  input_file = os.path.join(current_dir,

                            "../../models/tests/example_regression.csv")

  loader = dc.data.CSVLoader(

      tasks=tasks, smiles_field="smiles", featurizer=featurizer)

      tasks=tasks, feature_field="smiles", featurizer=featurizer)

  dataset = loader.create_dataset(input_file)

  transformer = dc.trans.DAGTransformer(max_atoms=50)

  dataset = transformer.transform(dataset)

import os

import numpy as np

import deepchem as dc

import itertools

import tempfile

import numpy as np

import deepchem as dc

def test_binary_1d():

  """Test balancing transformer on single-task dataset without explicit task dimension."""

  for ind, task in enumerate(dataset.get_task_names()):

    y_task = y_t[:, ind]

    w_task = w_t[:, ind]

    w_orig_task = w[:, ind]

    # Check that sum of 0s equals sum of 1s in transformed for each task

    for i, j in itertools.product(range(n_classes), range(n_classes)):

      if i == j:

import os

import deepchem as dc

import numpy as np

import deepchem as dc

def load_gaussian_cdf_data():

  """Load example with numbers sampled from Gaussian normal distribution.

  bins = 1001

  cdf_transformer = dc.trans.CDFTransformer(

      transform_X=True, dataset=gaussian_dataset, bins=bins)

  X, y, w, ids = (gaussian_dataset.X, gaussian_dataset.y, gaussian_dataset.w,

                  gaussian_dataset.ids)

  y, w, ids = (gaussian_dataset.y, gaussian_dataset.w, gaussian_dataset.ids)

  gaussian_dataset = cdf_transformer.transform(gaussian_dataset)

  X_t, y_t, w_t, ids_t = (gaussian_dataset.X, gaussian_dataset.y,

                          gaussian_dataset.w, gaussian_dataset.ids)

  # Check that untransform does the right thing.

  y_restored = cdf_transformer.untransform(y_t)

  assert np.max(y_restored - y) < 1e-5

  #np.testing.assert_allclose(y_restored, y)