Merge pull request #1773 from deepchem/travisbump

language: python

language: python

python:

python:

- '3.5'

- '3.6'

- '3.7'

- '3.7'

sudo: required

sudo: required

dist: xenial

dist: xenial

      self.assertAlmostEqual(rgf.angle_between(v1, -v1), np.pi)

      self.assertAlmostEqual(rgf.angle_between(v1, -v1), np.pi)

  def test_hash_ecfp(self):

  def test_hash_ecfp(self):

    from six import integer_types

    for power in (2, 16, 64):

    for power in (2, 16, 64):

      for _ in range(10):

      for _ in range(10):

        string = random_string(10)

        string = random_string(10)

        string_hash = rgf.hash_ecfp(string, power)

        string_hash = rgf.hash_ecfp(string, power)

        self.assertIsInstance(string_hash, integer_types)

        self.assertIsInstance(string_hash, int)

        self.assertLess(string_hash, 2**power)

        self.assertLess(string_hash, 2**power)

        self.assertGreaterEqual(string_hash, 0)

        self.assertGreaterEqual(string_hash, 0)

  def test_hash_ecfp_pair(self):

  def test_hash_ecfp_pair(self):

    from six import integer_types

    for power in (2, 16, 64):

    for power in (2, 16, 64):

      for _ in range(10):

      for _ in range(10):

        string1 = random_string(10)

        string1 = random_string(10)

        string2 = random_string(10)

        string2 = random_string(10)

        pair_hash = rgf.hash_ecfp_pair((string1, string2), power)

        pair_hash = rgf.hash_ecfp_pair((string1, string2), power)

        self.assertIsInstance(pair_hash, integer_types)

        self.assertIsInstance(pair_hash, int)

        self.assertLess(pair_hash, 2**power)

        self.assertLess(pair_hash, 2**power)

        self.assertGreaterEqual(pair_hash, 0)

        self.assertGreaterEqual(pair_hash, 0)

from deepchem.molnet import load_delaney

from deepchem.molnet import load_delaney

from deepchem.trans.transformers import FeaturizationTransformer

from deepchem.trans.transformers import FeaturizationTransformer

from deepchem.trans.transformers import DataTransforms

from deepchem.trans.transformers import DataTransforms

from tensorflow.examples.tutorials.mnist import input_data

__author__ = "Bharath Ramsundar"

__author__ = "Bharath Ramsundar"

__copyright__ = "Copyright 2016, Stanford University"

__copyright__ = "Copyright 2016, Stanford University"

import numpy as np

import numpy as np

import pandas as pd

import pandas as pd

import deepchem as dc

import deepchem as dc

import tensorflow as tf

import scipy.ndimage

import scipy.ndimage

from PIL import Image

from PIL import Image

    '''

    '''

       init to load the MNIST data for DataTransforms Tests

       init to load the MNIST data for DataTransforms Tests

      '''

      '''

    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

    # extracting validation set of MNIST for testing the DataTransforms

    train = dc.data.NumpyDataset(x_train, y_train)

    valid = dc.data.NumpyDataset(mnist.validation.images,

                                 mnist.validation.labels)

    # extract only the images (no need of the labels)

    # extract only the images (no need of the labels)

    data = (valid.X)[0]

    data = (train.X)[0]

    # reshaping the vector to image

    # reshaping the vector to image

    data = np.reshape(data, (28, 28))

    data = np.reshape(data, (28, 28))

    self.d = data

    self.d = data

    pdbfixer \

    pdbfixer \

    rdkit \

    rdkit \

    joblib \

    joblib \

    six \

    scikit-learn \

    scikit-learn \

    networkx \

    networkx \

    pillow \

    pillow \

    requests \

    requests \

    xgboost \

    xgboost \

    simdna \

    simdna \

    pbr \

    setuptools \

    setuptools \

    biopython \

    biopython \

    numpy

    numpy