Loading .travis.yml +1 −0 Original line number Diff line number Diff line Loading @@ -22,6 +22,7 @@ install: - conda install h5py - pip install six #- pip install keras - conda install -c conda-forge protobuf=3.1.0 - export KERAS_BACKEND=tensorflow - conda install -c omnia mdtraj - pip install tensorflow Loading deepchem/data/tests/test_support_generator.py +3 −3 Original line number Diff line number Diff line Loading @@ -80,7 +80,7 @@ class TestSupports(unittest.TestCase): y = np.random.randint(2, size=(n_samples, n_tasks)) w = np.ones((n_samples, n_tasks)) # Set last n_samples/2 weights to 0 w[n_samples/2:] = 0 w[n_samples//2:] = 0 dataset = dc.data.NumpyDataset(X, y, w, ids) n_episodes = 20 Loading Loading @@ -307,12 +307,12 @@ class TestSupports(unittest.TestCase): y = np.random.randint(2, size=(n_samples, n_tasks)) w = np.ones((n_samples, n_tasks)) # Set last n_samples/2 weights to 0 w[n_samples/2:] = 0 w[n_samples//2:] = 0 dataset = dc.data.NumpyDataset(X, y, w, ids) # Sample from first n_samples/2 elements for support support_inds = sorted(np.random.choice( np.arange(n_samples/2), (n_support,), replace=False)) np.arange(n_samples//2), (n_support,), replace=False)) support_dataset = dc.data.NumpyDataset(X[support_inds], y[support_inds], w[support_inds], ids[support_inds]) Loading Loading
.travis.yml +1 −0 Original line number Diff line number Diff line Loading @@ -22,6 +22,7 @@ install: - conda install h5py - pip install six #- pip install keras - conda install -c conda-forge protobuf=3.1.0 - export KERAS_BACKEND=tensorflow - conda install -c omnia mdtraj - pip install tensorflow Loading
deepchem/data/tests/test_support_generator.py +3 −3 Original line number Diff line number Diff line Loading @@ -80,7 +80,7 @@ class TestSupports(unittest.TestCase): y = np.random.randint(2, size=(n_samples, n_tasks)) w = np.ones((n_samples, n_tasks)) # Set last n_samples/2 weights to 0 w[n_samples/2:] = 0 w[n_samples//2:] = 0 dataset = dc.data.NumpyDataset(X, y, w, ids) n_episodes = 20 Loading Loading @@ -307,12 +307,12 @@ class TestSupports(unittest.TestCase): y = np.random.randint(2, size=(n_samples, n_tasks)) w = np.ones((n_samples, n_tasks)) # Set last n_samples/2 weights to 0 w[n_samples/2:] = 0 w[n_samples//2:] = 0 dataset = dc.data.NumpyDataset(X, y, w, ids) # Sample from first n_samples/2 elements for support support_inds = sorted(np.random.choice( np.arange(n_samples/2), (n_support,), replace=False)) np.arange(n_samples//2), (n_support,), replace=False)) support_dataset = dc.data.NumpyDataset(X[support_inds], y[support_inds], w[support_inds], ids[support_inds]) Loading
