Merge remote-tracking branch 'remotes/origin/master'

- conda install h5py

- pip install keras

- export KERAS_BACKEND=tensorflow

- conda install seaborn

- conda install six

- conda install dill

- conda install runipy

- conda install ipyparallel

- pip install runipy

- pip install nglview

- conda install -c omnia mdtraj

- python setup.py install

#- if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp27-none-linux_x86_64.whl;

#  -O else export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp35-cp35m-linux_x86_64.whl;

#  -O fi 

#- if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then pip install --ignore-installed --upgrade $TF_BINARY_URL;

#  -O else pip3 install --ignore-installed --upgrade $TF_BINARY_URL;

#  -O fi

- conda install -c https://conda.anaconda.org/jjhelmus tensorflow=0.10.0rc0

script:

- nosetests -v deepchem --nologcapture

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

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

* [keras](http://keras.io)

* [six](https://pypi.python.org/pypi/six)

* [dill](https://pypi.python.org/pypi/dill)

* [ipyparallel](https://ipyparallel.readthedocs.io/en/latest/)

* [mdtraj](http://mdtraj.org/)

* [tensorflow](https://www.tensorflow.org/)

   ```

   See [keras docs](https://keras.io/backend/) for more details and alternate methods of setting backend.

8. `six`

   ```bash

   conda install six

   ```

9. `dill`

    ```bash

    conda install dill

    ```

10. `ipyparallel`

    ```bash

    conda install ipyparallel

    ```

11. `mdtraj`

8. `mdtraj`

   ```bash

   conda install -c omnia mdtraj

   ```

12. `scikit-learn`

9. `scikit-learn`

   ```bash

   conda install scikit-learn 

   ```

13. `tensorflow`: Installing `tensorflow` on older versions of Linux (which

10. `tensorflow`: Installing `tensorflow` on older versions of Linux (which

    have glibc < 2.17) can be very challenging. For these older Linux versions,

    contact your local sysadmin to work out a custom installation. If your

    version of Linux is recent, then the following command will work:

    conda install -c https://conda.anaconda.org/jjhelmus tensorflow

    ```

14. `h5py`:

11. `h5py`:

    ```

    conda install h5py

    ```

15. `deepchem`: Clone the `deepchem` github repo:

12. `deepchem`: Clone the `deepchem` github repo:

    ```bash

    git clone https://github.com/deepchem/deepchem.git

    ```

    python setup.py install

    ```

16. To run test suite, install `nosetests`:

13. To run test suite, install `nosetests`:

    ```bash

    pip install nose --user

    ```

    You might need to uninstall a system install of `nosetests` if

    there is a conflict.

17. If installation has been successful, all tests in test suite should pass:

14. If installation has been successful, all tests in test suite should pass:

    ```bash

    nosetests -v deepchem --nologcapture 

    ```

|Dataset    |Model               |Splitting   |Train score/R2|Valid score/R2|

|-----------|--------------------|------------|--------------|--------------|

|delaney    |MT-NN regression    |Index       |0.773         |0.574         |

|           |graphconv regression|Index       |0.964         |0.829         |

|           |MT-NN regression    |Random      |0.769         |0.591         |

|           |graphconv regression|Random      |0.959         |0.821         |

|           |MT-NN regression    |Scaffold    |0.782         |0.426         |

|           |graphconv regression|Scaffold    |0.976         |0.581         |

|kaggle     |MT-NN regression    |User-defined|0.748         |0.452         |

* General features

Once you've got a sense of how the package works, we encourage the use of Github issues to discuss more complex changes,  raise requests for new features or propose changes to the global architecture of DeepChem. Once consensus is reached on the issue, please submit a PR with proposed modifications. All contributed code to DeepChem will be reviewed by a member of the DeepChem team, so please make sure your code style and documentation style match our guidelines!

### Code Style Guidelines

DeepChem broadly follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html). In terms of practical changes, the biggest effect is that all code uses 2-space indents instead of 4-space indents. We encourage new contributors to make use of [pylint](https://www.pylint.org/). Aim for a score of at least 8/10 on contributed files.

DeepChem broadly follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html). In terms of practical changes, the biggest effect is that all code uses 2-space indents instead of 4-space indents. We encourage new contributors to make use of [pylint](https://www.pylint.org/) with the following command

```

pylint --disable=invalid-name --indent-string "  " --extension-pkg-whitelist=numpy [file.py]

```

Aim for a score of at least 8/10 on contributed files.

### Documentation Style Guidelines

DeepChem uses [NumPy style documentation](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt). Please follow these conventions when documenting code, since we use [Sphinx+Napoleon](http://www.sphinx-doc.org/en/stable/ext/napoleon.html) to automatically generate docs on [deepchem.io](deepchem.io). 

import numpy as np

import csv

import numbers

import dill

import tempfile

from rdkit import Chem

import time

    2) Complex featurization

      -) PDB files for interacting molecules.

    3) User specified featurizations.

  """

  time1 = time.time()

  df[featurizer.feature_fields] = df[featurizer.feature_fields].apply(pd.to_numeric)

import os

import numpy as np

import pandas as pd

import multiprocessing as mp

import random

from functools import partial

from deepchem.utils.save import save_to_disk

            os.path.join(dataset.data_dir, row['X'])))

        y = np.array(load_from_disk(

            os.path.join(dataset.data_dir, row['y'])))

        w = np.array(load_from_disk(

            os.path.join(dataset.data_dir, row['w'])))

        w_filename = os.path.join(dataset.data_dir, row['w'])

        if os.path.exists(w_filename):

            w = np.array(load_from_disk(w_filename))

        else:

            w = np.ones(y.shape)

        ids = np.array(load_from_disk(

            os.path.join(dataset.data_dir, row['ids'])), dtype=object)

        yield (X, y, w, ids)

        os.path.join(self.data_dir, row['X'])))

    y = np.array(load_from_disk(

        os.path.join(self.data_dir, row['y'])))

    w = np.array(load_from_disk(

        os.path.join(self.data_dir, row['w'])))

    w_filename = os.path.join(self.data_dir, row['w'])

    if os.path.exists(w_filename):

        w = np.array(load_from_disk(w_filename))

    else:

        w = np.ones(y.shape)

    ids = np.array(load_from_disk(

        os.path.join(self.data_dir, row['ids'])), dtype=object)

    return (X, y, w, ids)

    mols = list(mols)

    features = []

    for i, mol in enumerate(mols):

      if verbose and i % log_every_n == 0:

        log("Featurizing %d / %d" % (i, len(mols)))

      if mol is not None:

        features.append(self._featurize(mol))

      else: