Merge pull request #2002 from nd-02110114/fix-dos

- if [ $TRAVIS_PYTHON_VERSION == '3.7' ]; then

  find ./deepchem -name "*.py" ! -name '*load_dataset_template.py' | xargs python -m doctest -v; fi

- bash devtools/travis-ci/test_format_code.sh

- if [ $TRAVIS_PYTHON_VERSION == '3.7' ]; then

  cd docs && pip install -r requirements.txt && make clean html && cd ..; fi

after_success:

- echo $TRAVIS_SECURE_ENV_VARS

- coveralls

  metric that allows a network to modify its internal notion of

  distance.

  See references [1]_ [2]_ for more details.

  References

  ----------

  .. [1] Matching Networks for One Shot Learning, https://arxiv.org/abs/1606.04080

  .. [2] Order Matters: Sequence to sequence for sets, https://arxiv.org/abs/1511.06391

  .. [1] Vinyals, Oriol, et al. "Matching networks for one shot learning." 

         Advances in neural information processing systems. 2016.

  .. [2] Vinyals, Oriol, Samy Bengio, and Manjunath Kudlur. "Order matters:

         Sequence to sequence for sets." arXiv preprint arXiv:1511.06391 (2015).

  """

  def __init__(self, n_test, n_support, n_feat, max_depth, **kwargs):

  3. Prune conformers using an RMSD threshold.

  Note that pruning is done _after_ minimization, which differs from the

  protocol described in the references.

  protocol described in the references [1]_ [2]_.

  References

  ----------

      fn, start=os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))

  if 'dev' in deepchem.__version__:

    return "https://github.com/deepchem/deepchem/blob/master/deepchem/%s%s" % (

        fn, linespec)

    return "https://github.com/deepchem/deepchem/blob/master/%s%s" % \

      (fn, linespec)

  else:

    return "https://github.com/deepchem/deepchem/blob/v%s/deepchem/%s%s" % (

        deepchem.__version__, fn, linespec)

    return "https://github.com/deepchem/deepchem/blob/%s/%s%s" % \

      (deepchem.__version__, fn, linespec)

# Document __init__ methods

MoleculeNet

===========

The DeepChem library is packaged alongside the MoleculeNet suite of datasets. One of the most important parts of machine learning applications is finding a suitable dataset. The MoleculeNet suite has curated a whole range of datasets and loaded them into DeepChem :code:`dc.data.Dataset` objects for convenience.

The DeepChem library is packaged alongside the MoleculeNet suite of datasets.

One of the most important parts of machine learning applications is finding a suitable dataset.

The MoleculeNet suite has curated a whole range of datasets and loaded them into DeepChem

:code:`dc.data.Dataset` objects for convenience.

Contributing a new dataset to MoleculeNet

-----------------------------------------

If you are proposing a new dataset to be included in the MoleculeNet benchmarking suite, 

please follow the instructions below. Please review the `datasets already available in MolNet <http://moleculenet.ai/datasets-1>`_ before contributing.

If you are proposing a new dataset to be included in the 

MoleculeNet benchmarking suite, please follow the instructions below. 

Please review the `datasets already available in MolNet`_ before contributing.

0. Read the `Contribution guidelines <https://github.com/deepchem/deepchem/blob/master/CONTRIBUTING.md>`_.

0. Read the `Contribution guidelines`_.

1. Open an `issue <https://github.com/deepchem/deepchem/issues>`_ to discuss the dataset you want to add to MolNet.

1. Open an `issue`_ to discuss the dataset you want to add to MolNet.

2. Implement a function in the `deepchem.molnet.load_function <https://github.com/deepchem/deepchem/tree/master/deepchem/molnet/load_function>`_ module following the template function `deepchem.molnet.load_function.load_mydataset <https://github.com/deepchem/deepchem/blob/master/deepchem/molnet/load_function/load_mydataset.py>`_. Specify which featurizers, transformers, and splitters (available from `deepchem.molnet.defaults <https://github.com/deepchem/deepchem/tree/master/deepchem/molnet/defaults.py>`_) are supported for your dataset. 

2. Implement a function in the `deepchem.molnet.load_function`_ 

   module following the template function `deepchem.molnet.load_function.load_dataset_template`_.

   Specify which featurizers, transformers, and splitters (available from

   `deepchem.molnet.defaults`_) are supported for your dataset. 

3. Add your load function to `deepchem.molnet.__init__.py <https://github.com/deepchem/deepchem/blob/master/deepchem/molnet/__init__.py>`_ for easy importing.

3. Add your load function to `deepchem.molnet.__init__.py`_ for easy importing.

4. Prepare your dataset as a .tar.gz or .zip file. Accepted filetypes include CSV, JSON, and SDF.

5. Ask a member of the technical steering committee to add your .tar.gz or .zip file to the DeepChem AWS bucket. Modify your load function to pull down the dataset from AWS.

5. Ask a member of the technical steering committee to add your .tar.gz or .zip file 

   to the DeepChem AWS bucket. Modify your load function to pull down the dataset from AWS.

6. Submit a [WIP] PR (Work in progress pull request) following the PR `template <https://github.com/deepchem/deepchem/blob/master/docs/molnet_pr_template.md>`_.  

6. Submit a [WIP] PR (Work in progress pull request) following the PR `template`_.

Load Dataset Template

---------------------

.. autofunction:: deepchem.molnet.load_function.load_dataset_template.load_mydataset

BACE Dataset

------------

-----------

.. autofunction:: deepchem.molnet.load_uv

.. _`datasets already available in MolNet`: http://moleculenet.ai/datasets-1

.. _`Contribution guidelines`: https://github.com/deepchem/deepchem/blob/master/CONTRIBUTING.md

.. _`issue`: https://github.com/deepchem/deepchem/issues

.. _`deepchem.molnet.load_function`: https://github.com/deepchem/deepchem/tree/master/deepchem/molnet/load_function

.. _`deepchem.molnet.load_function.load_dataset_template`: https://github.com/deepchem/deepchem/blob/master/deepchem/molnet/load_function/load_dataset_template.py

.. _`deepchem.molnet.defaults`: https://github.com/deepchem/deepchem/tree/master/deepchem/molnet/defaults.py

.. _`deepchem.molnet.__init__.py`: https://github.com/deepchem/deepchem/blob/master/deepchem/molnet/__init__.py

.. _`template`: https://github.com/deepchem/deepchem/blob/master/.github/PULL_REQUEST_TEMPLATE/molnet_pr_template.md