Adding in test for complete_shuffle

import deepchem as dc

import numpy as np

#def test_shuffle():

#  """Test that datasets can be merged."""

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

#  dataset_file = os.path.join(

#      current_dir, "../../models/tests/example.csv")

#  featurizer = dc.feat.CircularFingerprint(size=1024)

#  tasks = ["log-solubility"]

#  loader = dc.data.CSVLoader(

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

#  dataset = loader.featurize(dataset_file, shard_size=2)

#  X_orig, y_orig, w_orig, orig_ids = (dataset.X, dataset.y, dataset.w,

#                                      dataset.ids)

#  orig_len = len(dataset)

#  dataset.shuffle(iterations=5)

#  X_new, y_new, w_new, new_ids = (dataset.X, dataset.y, dataset.w,

#                                  dataset.ids)

#

#  assert len(dataset) == orig_len

#  # The shuffling should have switched up the ordering

#  assert not np.array_equal(orig_ids, new_ids)

#  # But all the same entries should still be present

#  assert sorted(orig_ids) == sorted(new_ids)

#  # All the data should have same shape

#  assert X_orig.shape == X_new.shape

#  assert y_orig.shape == y_new.shape

#  assert w_orig.shape == w_new.shape

def test_complete_shuffle():

  """Test that complete shuffle."""

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

  dataset_file = os.path.join(current_dir, "../../models/tests/example.csv")

  featurizer = dc.feat.CircularFingerprint(size=1024)

  tasks = ["log-solubility"]

  loader = dc.data.CSVLoader(

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

  dataset = loader.featurize(dataset_file, shard_size=2)

  X_orig, y_orig, w_orig, orig_ids = (dataset.X, dataset.y, dataset.w,

                                      dataset.ids)

  orig_len = len(dataset)

  dataset = dataset.complete_shuffle()

  X_new, y_new, w_new, new_ids = (dataset.X, dataset.y, dataset.w, dataset.ids)

  assert len(dataset) == orig_len

  # The shuffling should have switched up the ordering

  assert not np.array_equal(orig_ids, new_ids)

  # But all the same entries should still be present

  assert sorted(orig_ids) == sorted(new_ids)

  # All the data should have same shape

  assert X_orig.shape == X_new.shape

  assert y_orig.shape == y_new.shape

  assert w_orig.shape == w_new.shape

def test_sparse_shuffle():