Merged changes from master branch

datasets/qm8.csv

.vscode/

.python-version

# Build documentation in the docs/ directory with Sphinx

sphinx:

  configuration: docs/conf.py

# Build documentation with MkDocs

# mkdocs:

#   configuration: mkdocs.yml

  configuration: docs/source/conf.py

# Optionally build your docs in additional formats such as PDF and ePub

formats: all

  - if [[ "$CHECK_ONLY_DOCS" == "true" ]]; then

      cd docs && pip install -r requirements.txt;

      make clean html;

      make doctest_tutorials;

      make doctest_examples;

      travis_terminate $?;

    fi

## Requirements

DeepChem currently supports Python 3.5 through 3.7 and requires these packages on any condition.

DeepChem currently supports Python 3.6 through 3.7 and requires these packages on any condition.

- [joblib](https://pypi.python.org/pypi/joblib)

- [NumPy](https://numpy.org/)

        edge_attr=edge_features,

        pos=node_pos_features)

  def to_dgl_graph(self):

  def to_dgl_graph(self, self_loop: bool = False):

    """Convert to DGL graph data instance

    Returns

    -------

    dgl.DGLGraph

      Graph data for DGL

    self_loop: bool

      Whether to add self loops for the nodes, i.e. edges from nodes

      to themselves. Default to False.

    Notes

    -----

    This method requires DGL to be installed.

    """

    try:

      import dgl

      import torch

      from dgl import DGLGraph

    except ModuleNotFoundError:

      raise ImportError("This function requires DGL to be installed.")

    g = DGLGraph()

    g.add_nodes(self.num_nodes)

    g.add_edges(

        torch.from_numpy(self.edge_index[0]).long(),

        torch.from_numpy(self.edge_index[1]).long())

    src = self.edge_index[0]

    dst = self.edge_index[1]

    if self_loop:

      src = np.concatenate([src, np.arange(self.num_nodes)])

      dst = np.concatenate([dst, np.arange(self.num_nodes)])

    g = dgl.graph(

        (torch.from_numpy(src).long(), torch.from_numpy(dst).long()),

        num_nodes=self.num_nodes)

    g.ndata['x'] = torch.from_numpy(self.node_features).float()

    if self.node_pos_features is not None: