Merge branch 'master' of https://github.com/deepchem/deepchem into tf12

- 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

* [Getting Started](#getting-started)

    * [Input Formats](#input-formats)

    * [Data Featurization](#data-featurization)

    * [Performances](#performances)

* [Contributing to DeepChem](#contributing-to-deepchem)

    * [Code Style Guidelines](#code-style-guidelines)

    * [Documentation Style Guidelines](#documentation-style-guidelines)

* [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/)

   conda install -c omnia openbabel=2.4.0

   ``` 

3. `pandas`

   ```bash

   conda install pandas 

   ```

4. `rdkit`

3. `rdkit`

   ```bash

   conda install -c omnia rdkit

   ```

5. `boost`

   ```bash

   conda install -c omnia boost=1.59.0

   ```

6. `joblib`

4. `joblib`

   ```bash

   conda install joblib 

   ```

7. `keras`

   ```bash

   pip install keras --user

   ```

   `deepchem` only supports the `tensorflow` backend for keras. To set the backend to `tensorflow`,

   add the following line to your `~/.bashrc`

   ```bash

   export KERAS_BACKEND=tensorflow

   ```

   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`

5. `keras`

   ```bash

    conda install ipyparallel

   pip install keras

   ```

   `deepchem` only supports the `tensorflow` (default) backend for keras.

11. `mdtraj`

6. `mdtraj`

   ```bash

   conda install -c omnia mdtraj

   ```

12. `scikit-learn`

    ```bash

    conda install scikit-learn 

    ```

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

7. `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`:

    ```

    conda install h5py

    ```

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

8. `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`:

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

   ```bash

    pip install nose --user

   pip install nose

   ```

   Make sure that the correct version of `nosetests` is active by running

   ```bash

   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:

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

    ```bash

    nosetests -v deepchem --nologcapture 

    ```

Most machine learning algorithms require that input data form vectors.

However, input data for drug-discovery datasets routinely come in the

format of lists of molecules and associated experimental readouts. To

transform lists of molecules into vectors, we need to use the DeepChem

loader class ``dc.load.DataLoader``. Instances of this class must be

passed a ``Featurizer`` object. DeepChem provides a number of

different subclasses of ``Featurizer`` for convenience:

transform lists of molecules into vectors, we need to subclasses of DeepChem

loader class ```dc.data.DataLoader``` such as ```dc.data.CSVLoader``` or 

```dc.data.SDFLoader```. Users can subclass ```dc.data.DataLoader``` to

load arbitrary file formats. All loaders must be

passed a ```dc.feat.Featurizer``` object. DeepChem provides a number of

different subclasses of ```dc.feat.Featurizer``` for convenience.

### Performances

* Classification

|Dataset    |Model               |Train score/ROC-AUC|Valid score/ROC-AUC|

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

|tox21      |logistic regression |0.903              |0.741              |

|           |Multitask network   |0.846              |0.812              |

|           |robust MT-NN        |0.844              |0.793              |

|           |graph convolution   |0.872              |0.816              |

|muv        |logistic regression |0.961              |0.696              |

|           |Multitask network   |0.895              |0.740              |

|           |robust MT-NN        |0.914              |0.667              |

|           |graph convolution   |0.846              |0.776              |

|pcba       |logistic regression |0.807        	     |0.772              |

|           |Multitask network   |0.811        	     |0.787              |

|           |robust MT-NN        |0.809              |0.778              |

|           |graph convolution   |0.875       	     |0.844              |

|sider      |logistic regression |0.932        	     |0.628              |

|           |Multitask network   |0.779        	     |0.665              |

|           |robust MT-NN        |0.761              |0.621              |

|           |graph convolution   |0.706        	     |0.638              |

|toxcast    |logistic regression |0.737        	     |0.543              |

|           |Multitask network   |0.831        	     |0.684              |

|           |robust MT-NN        |0.814              |0.692              |

|           |graph convolution   |0.820        	     |0.692              |

|tox21      |logistic regression |0.903              |0.735              |

|           |Multitask network   |0.856              |0.783              |

|           |robust MT-NN        |0.855              |0.773              |

|           |graph convolution   |0.865              |0.827              |

|muv        |logistic regression |0.957              |0.719              |

|           |Multitask network   |0.902              |0.734              |

|           |robust MT-NN        |0.933              |0.732              |

|           |graph convolution   |0.860              |0.730              |

|pcba       |logistic regression |0.808        	     |0.776              |

|           |Multitask network   |0.811        	     |0.778              |

|           |robust MT-NN        |0.811              |0.771              |

|           |graph convolution   |0.872       	     |0.844              |

|sider      |logistic regression |0.929        	     |0.656              |

|           |Multitask network   |0.777        	     |0.655              |

|           |robust MT-NN        |0.804              |0.630              |

|           |graph convolution   |0.705        	     |0.618              |

|toxcast    |logistic regression |0.725        	     |0.586              |

|           |Multitask network   |0.836        	     |0.684              |

|           |robust MT-NN        |0.822              |0.681              |

|           |graph convolution   |0.820        	     |0.717              |

Scaffold splitting

* Regression

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

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

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

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

|                |graphconv regression|Index       |0.991         |0.825         |

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

|                |graphconv regression|Random      |0.996         |0.873         |

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

|                |graphconv regression|Scaffold    |0.994         |0.606         |

|nci             |MT-NN regression    |Index       |0.171         |0.062         |

|                |graphconv regression|Index       |0.123         |0.048         |

|                |MT-NN regression    |Random      |0.168         |0.085         |

|                |graphconv regression|Random      |0.117         |0.076         |

|                |MT-NN regression    |Scaffold    |0.180         |0.052         |

|                |graphconv regression|Scaffold    |0.131         |0.046         |

|pdbbind(core)   |MT-NN regression    |Random      |0.973         |0.494         |

|pdbbind(refined)|MT-NN regression    |Random      |0.987         |0.503         |

|pdbbind(full)   |MT-NN regression    |Random      |0.983         |0.528         |

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

* General features

Number of tasks and examples in the datasets

|Dataset         |N(tasks)	|N(samples) |

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

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

|tox21           |12         |8014       |

|muv             |17         |93127      |

|pcba            |128        |439863     |

|toxcast         |617        |8615       |

|delaney         |1          |1128       |

|kaggle          |15         |173065     |

|nci             |60         |19127      |

|pdbbind(core)   |1          |195        |

|pdbbind(refined)|1          |3706       |

|pdbbind(full)   |1          |11908      |

Time needed for benchmark test(~20h in total)

|Dataset         |Model               |Time(loading)/s |Time(running)/s|

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

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

|tox21           |logistic regression |30              |60             |

|                |Multitask network   |30              |60             |

|                |robust MT-NN        |30              |90             |

|                |robust MT-NN        |80              |4000           |

|                |graph convolution   |80              |900            |

|delaney         |MT-NN regression    |10              |40             |

|                |graphconv regression|10              |40             |

|nci             |MT-NN regression    |400             |1200           |

|                |graphconv regression|400             |2500           |

|pdbbind(core)   |MT-NN regression    |0(featurized)   |30             |

|pdbbind(refined)|MT-NN regression    |0(featurized)   |40             |

|pdbbind(full)   |MT-NN regression    |0(featurized)   |60             |

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

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 deepchem.splits

import deepchem.trans

import deepchem.utils

import deepchem.load

import deepchem.dock

from __future__ import unicode_literals

# TODO(rbharath): Get rid of * import

from deepchem.data.datasets import *

from deepchem.data.datasets import pad_features

from deepchem.data.datasets import pad_batch

from deepchem.data.datasets import Dataset

from deepchem.data.datasets import NumpyDataset

from deepchem.data.datasets import DiskDataset

from deepchem.data.datasets import sparsify_features

from deepchem.data.datasets import densify_features

from deepchem.data.supports import *

from deepchem.data.data_loader import DataLoader

from deepchem.data.data_loader import CSVLoader

from deepchem.data.data_loader import UserCSVLoader

from deepchem.data.data_loader import SDFLoader

import deepchem.data.tests