Merge branch 'master' of https://github.com/pandegroup/deep-learning into multitask

* [sklearn](https://github.com/scikit-learn/scikit-learn.git)

* [numpy](https://store.continuum.io/cshop/anaconda/)

* [keras](keras.io)

from deep_chem.utils.preprocess import train_test_random_split

from deep_chem.utils.load import load_and_transform_dataset

from deep_chem.utils.preprocess import tensor_dataset_to_numpy

from deep_chem.datasets.shapes_3d import load_data

from deep_chem.utils.evaluate import eval_model

from deep_chem.utils.evaluate import compute_r2_scores

# TODO(rbharath): Factor this out into a separate function in utils. Duplicates

# code in deep.py

# TODO(rbharath): paths is to handle sharded input pickle files. Might be

# better to use hdf5 datasets like in MSMBuilder

def process_3D_convolutions(paths, task_transforms, seed=None, splittype="random"):

  """Loads 3D Convolution datasets.

      modeltype="keras", mode="tensor")

  local_task_types = task_types.copy()

  r2s = compute_r2_scores(results, local_task_types)

  if r2s:

  print "Mean R^2: %f" % np.mean(np.array(r2s.values()))

def train_3D_convolution(X, y, axis_length=32, batch_size=50, nb_epoch=1):

  nb_conv = [7, 5, 3]

  model = Sequential()

  # TODO(rbharath): Avoid hard coding the number of staks here

  model.add(Convolution3D(nb_filter=nb_filters[0], stack_size=3,

  model.add(Convolution3D(nb_filter=nb_filters[0], stack_size=n_channels,

     nb_row=nb_conv[0], nb_col=nb_conv[0], nb_depth=nb_conv[0],

     border_mode='valid'))

  model.add(Activation('relu'))