done till test predict

    - name: PyTest

      if: ${{ (success() || failure()) && (steps.install.outcome == 'failure' || steps.install.outcome == 'success') }}

      shell: bash -l {0}

      run: pytest -v -m torch deepchem

      run: pytest -v -m tensorflow deepchem

import pytest

import numpy as np

from deepchem.models import atomic_conv

from deepchem.data import NumpyDataset

from deepchem.feat import AtomicConvFeaturizer

try:

  import tensorflow as tf

  from deepchem.models import atomic_conv

  has_tensorflow = True

except:

  has_tensorflow = False

@pytest.mark.tensorflow

def test_atomic_conv_initialize():

  """Quick test of AtomicConv."""

  acm = atomic_conv.AtomicConvModel(

@pytest.mark.slow

@pytest.mark.tensorflow

def test_atomic_conv():

  """A simple test that initializes and fits an AtomicConvModel."""

  # For simplicity, let's assume both molecules have same number of

@pytest.mark.slow

@pytest.mark.tensorflow

def test_atomic_conv_variable():

  """A simple test that initializes and fits an AtomicConvModel on variable input size."""

  frag1_num_atoms = 1000

@pytest.mark.slow

@pytest.mark.tensorflow

def test_atomic_conv_with_feat():

  """A simple test for running an atomic convolution on featurized data."""

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

import unittest

import pytest

import tempfile

import deepchem as dc

import numpy as np

import tensorflow as tf

try:

  from StringIO import StringIO

except ImportError:

  from io import StringIO

try:

  import tensorflow as tf

  has_tensorflow = True

except:

  has_tensorflow = False

class TestCallbacks(unittest.TestCase):

  @pytest.mark.tensorflow

  def test_validation(self):

    """Test ValidationCallback."""

    tasks, datasets, transformers = dc.molnet.load_clintox()

import pytest

import deepchem as dc

from deepchem.models import Smiles2Vec, ChemCeption

from deepchem.feat import create_char_to_idx, SmilesToSeq, SmilesToImage

from deepchem.molnet.load_function.chembl25_datasets import CHEMBL25_TASKS

try:

  import tensorflow as tf

  from deepchem.models import Smiles2Vec, ChemCeption

  has_tensorflow = True

except:

  has_tensorflow = False

@pytest.mark.tensorflow

def get_dataset(mode="classification",

                featurizer="smiles2seq",

                max_seq_len=20,

@pytest.mark.slow

@pytest.mark.tensorflow

def test_chemception_regression():

  n_tasks = 5

  dataset, metric = get_dataset(

@pytest.mark.slow

@pytest.mark.tensorflow

def test_chemception_classification():

  n_tasks = 5

  dataset, metric = get_dataset(

@pytest.mark.slow

@pytest.mark.tensorflow

def test_smiles_to_vec_regression():

  n_tasks = 5

  max_seq_len = 20

@pytest.mark.slow

@pytest.mark.tensorflow

def test_smiles_to_vec_classification():

  n_tasks = 5

  max_seq_len = 20

@pytest.mark.slow

@pytest.mark.tensorflow

def test_chemception_fit_with_augmentation():

  n_tasks = 5

  dataset, metric = get_dataset(

import deepchem as dc

import tensorflow as tf

import numpy as np

import pytest

try:

  import tensorflow as tf

  from tensorflow.python.framework import test_util

  has_tensorflow = True

except:

  has_tensorflow = False

class TestCNN(test_util.TensorFlowTestCase):

  @pytest.mark.tensorflow

  def test_1d_cnn_regression(self):

    """Test that a 1D CNN can overfit simple regression datasets."""

    n_samples = 10

    scores = model.evaluate(dataset, [regression_metric])

    assert scores[regression_metric.name] < 0.1

  @pytest.mark.tensorflow

  def test_2d_cnn_classification(self):

    """Test that a 2D CNN can overfit simple classification datasets."""

    n_samples = 10

    scores = model.evaluate(dataset, [classification_metric])

    assert scores[classification_metric.name] > 0.9

  @pytest.mark.tensorflow

  def test_residual_cnn_classification(self):

    """Test that a residual CNN can overfit simple classification datasets."""

    n_samples = 10

    scores = model.evaluate(dataset, [classification_metric])

    assert scores[classification_metric.name] > 0.9

  @pytest.mark.tensorflow

  def test_cnn_regression_uncertainty(self):

    """Test computing uncertainty for a CNN regression model."""

    n_samples = 10