Merge branch 'master' into xgboostModel

# DeepChem

## Contributing to DeepChem

We actively encourage community contributions to DeepChem. The first place to start getting involved is by running our examples locally. Afterwards, we encourage contributors to give a shot to improving our documentation. While we take effort to provide good docs, there's plenty of room for improvement. All docs are hosted on Github, either in `README.md` file, or in the `docs/` directory.

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!

## Contributor License Agreement

In order to get a pull request accepted you must fill out our [License Agreement](https://www.clahub.com/agreements/lilleswing/deepchem).  The purpose of this agreement is to enable DeepChem to distribute your code and its derivatives.

### The Agreement

Contributor offers to license certain software (a “Contribution” or multiple “Contributions”) to DeepChem, and DeepChem agrees to accept said Contributions, under the terms of the open source license [The MIT License](https://opensource.org/licenses/MIT)

The Contributor understands and agrees that DeepChem shall have the irrevocable and perpetual right to make and distribute copies of any Contribution, as well as to create and distribute collective works and derivative works of any Contribution, under [The MIT License](https://opensource.org/licenses/MIT).

DeepChem understands and agrees that Contributor retains copyright in its Contributions. Nothing in this Contributor Agreement shall be interpreted to prohibit Contributor from licensing its Contributions under different terms from the [The MIT License](https://opensource.org/licenses/MIT) or this Contributor Agreement.

### Code Style Guidelines

DeepChem uses [yapf](https://github.com/google/yapf) to autoformat code.

``` bash

pip install yapf==0.16.0

cd <git_root>

yapf -i <python_files changed>

```

Our integration tests will fail if code is not formatted correctly

### 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).

```bash

bash scripts/install_deepchem_conda.sh deepchem

pip install tensorflow-gpu==0.12.1                      # If you want GPU support

pip install tensorflow-gpu==1.0.1                      # If you want GPU support

git clone https://github.com/deepchem/deepchem.git      # Clone deepchem source code from GitHub

cd deepchem

python setup.py install                                 # Manual install

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

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

    ```

    pip install tensorflow-gpu==0.12.1

    pip install tensorflow-gpu==1.0.1

    ```

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

|kaggle          |MT-NN regression    |2200            |3200           |

## Contributing to DeepChem

We actively encourage community contributions to DeepChem. The first place to start getting involved is by running our examples locally. Afterwards, we encourage contributors to give a shot to improving our documentation. While we take effort to provide good docs, there's plenty of room for improvement. All docs are hosted on Github, either in this `README.md` file, or in the `docs/` directory.

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 uses [yapf](https://github.com/google/yapf) to autoformat code.  We created a git pre-commit hook to make this process easier.

``` bash

cp devtools/travis-ci/pre-commit .git/hooks

pip install yapf==0.16.0

```

### 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).

### Gitter

Join us on gitter at [https://gitter.im/deepchem/Lobby](https://gitter.im/deepchem/Lobby). Probably the easiest place to ask simple questions or float requests for new features.

## About Us

DeepChem is a package by the [Pande group](https://pande.stanford.edu/) at Stanford. DeepChem was originally created by [Bharath Ramsundar](http://rbharath.github.io/), and has grown through the contributions of a number of undergraduate, graduate, and postdoctoral researchers working with the Pande lab.

## Version

1.0.1

__author__ = "Bharath Ramsundar"

__copyright__ = "Copyright 2016, Stanford University"

__license__ = "GPL"

__license__ = "MIT"

def sparsify_features(X):

__author__ = "Bharath Ramsundar"

__copyright__ = "Copyright 2016, Stanford University"

__license__ = "GPL"

__license__ = "MIT"

import unittest

import tempfile

import numpy as np

import deepchem as dc

def load_solubility_data():

  """Loads solubility dataset"""

  current_dir = os.path.dirname(os.path.abspath(__file__))

  return loader.featurize(input_file)

def load_butina_data():

  """Loads solubility dataset"""

  current_dir = os.path.dirname(os.path.abspath(__file__))

  featurizer = dc.feat.CircularFingerprint(size=1024)

  tasks = ["task"]

  # task_type = "regression"

  input_file = os.path.join(current_dir, "../../models/tests/butina_example.csv")

  input_file = os.path.join(current_dir,

                            "../../models/tests/butina_example.csv")

  loader = dc.data.CSVLoader(

      tasks=tasks, smiles_field="smiles", featurizer=featurizer)

  return loader.featurize(input_file)

def load_multitask_data():

  """Load example multitask data."""

  current_dir = os.path.dirname(os.path.abspath(__file__))

  featurizer = dc.feat.CircularFingerprint(size=1024)

  tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6",

           "task7", "task8", "task9", "task10", "task11", "task12",

           "task13", "task14", "task15", "task16"]

  input_file = os.path.join(

      current_dir, "../../models/tests/multitask_example.csv")

  tasks = [

      "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7",

      "task8", "task9", "task10", "task11", "task12", "task13", "task14",

      "task15", "task16"

  ]

  input_file = os.path.join(current_dir,

                            "../../models/tests/multitask_example.csv")

  loader = dc.data.CSVLoader(

      tasks=tasks, smiles_field="smiles", featurizer=featurizer)

  return loader.featurize(input_file)

def load_classification_data():

  """Loads classification data from example.csv"""

  current_dir = os.path.dirname(os.path.abspath(__file__))

  featurizer = dc.feat.CircularFingerprint(size=1024)

  tasks = ["outcome"]

  task_type = "classification"

  input_file = os.path.join(

      current_dir, "../../models/tests/example_classification.csv")

  input_file = os.path.join(current_dir,

                            "../../models/tests/example_classification.csv")

  loader = dc.data.CSVLoader(

      tasks=tasks, smiles_field="smiles", featurizer=featurizer)

  return loader.featurize(input_file)

  """Load sparse tox multitask data, sample dataset."""

  current_dir = os.path.dirname(os.path.abspath(__file__))

  featurizer = dc.feat.CircularFingerprint(size=1024)

  tasks = ["task1", "task2", "task3", "task4", "task5", "task6",

           "task7", "task8", "task9"]

  input_file = os.path.join(

      current_dir, "../../models/tests/sparse_multitask_example.csv")

  tasks = [

      "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8",

      "task9"

  ]

  input_file = os.path.join(current_dir,

                            "../../models/tests/sparse_multitask_example.csv")

  loader = dc.data.CSVLoader(

      tasks=tasks, smiles_field="smiles", featurizer=featurizer)

  return loader.featurize(input_file)

def load_feat_multitask_data():

  """Load example with numerical features, tasks."""

  current_dir = os.path.dirname(os.path.abspath(__file__))

  features = ["feat0", "feat1", "feat2", "feat3", "feat4", "feat5"]

  featurizer = dc.feat.UserDefinedFeaturizer(features)

  tasks = ["task0", "task1", "task2", "task3", "task4", "task5"]

  input_file = os.path.join(

      current_dir, "../../models/tests/feat_multitask_example.csv")

  input_file = os.path.join(current_dir,

                            "../../models/tests/feat_multitask_example.csv")

  loader = dc.data.UserCSVLoader(

      tasks=tasks, featurizer=featurizer, id_field="id")

  return loader.featurize(input_file)

def load_gaussian_cdf_data():

  """Load example with numbers sampled from Gaussian normal distribution.

     Each feature and task is a column of values that is sampled

  features = ["feat0", "feat1"]

  featurizer = dc.feat.UserDefinedFeaturizer(features)

  tasks = ["task0", "task1"]

  input_file = os.path.join(

      current_dir, "../../models/tests/gaussian_cdf_example.csv")

  input_file = os.path.join(current_dir,

                            "../../models/tests/gaussian_cdf_example.csv")

  loader = dc.data.UserCSVLoader(

      tasks=tasks, featurizer=featurizer, id_field="id")

  return loader.featurize(input_file)

def load_unlabelled_data():

  current_dir = os.path.dirname(os.path.abspath(__file__))

  featurizer = dc.feat.CircularFingerprint(size=1024)