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

datasets/pdbbind_v2019_other_PL.tar.gz

datasets/pdbbind_v2019_refined.tar.gz

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

jobs:

  include:

    - name: Python 3.6

    - name: Linux Python 3.6

      language: python

      python: '3.6'

      sudo: required

      dist: xenial

    - name: Python 3.7

    - name: Linux Python 3.7

      language: python

      python: '3.7'

      sudo: required

      dist: xenial

    - name: Windows

      env: NIGHTLY_PKG_PUBLISH=true

    - name: Windows Python 3.7

      language: c

      python: '3.7'

      os: windows

    - name: Documentation

      language: python

      python: '3.7'

      sudo: required

      dist: xenial

      env: CHECK_ONLY_DOCS=true

cache: pip

install:

  - if [[ "$TRAVIS_OS_NAME" != "windows" ]]; then

  - hash -r

  - conda config --set always_yes yes --set changeps1 no

  - conda update -q conda

  - bash scripts/install_deepchem_conda.sh deepchem

  - bash scripts/install_deepchem_conda.sh cpu

  - conda activate deepchem

  - python setup.py install

  - pip install -e .

script:

  - 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

  - bash devtools/run_yapf.sh

  - bash devtools/run_flake8.sh

  - mypy -p deepchem

  - pytest -m "not slow" --cov=deepchem deepchem

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

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

      make clean html && cd ..;

    fi

  - pytest -v -m "not slow" --cov=deepchem deepchem

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

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

      pytest -v --ignore-glob='deepchem/**/test*.py' --doctest-modules deepchem;

    fi

after_success:

  - echo $TRAVIS_SECURE_ENV_VARS

    secure: 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

  edge: true

  on:

    condition: $TRAVIS_OS_NAME = linux && $TRAVIS_PYTHON_VERSION = 3.7

    condition: $NIGHTLY_PKG_PUBLISH = true

## 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/)

You install the nightly build version via pip. The nightly version is built by the HEAD of DeepChem.

```bash

pip install tensorflow==2.2.0

pip install tensorflow==2.3.0

pip install --pre deepchem

```

from deepchem.models.tensorgraph.layers import Feature, Weights, Label, Layer

import numpy as np

import tensorflow as tf

import collections

try:

  from collections.abc import Sequence as SequenceCollection

except:

  from collections import Sequence as SequenceCollection

import copy

import time

    self.n_search_episodes = n_search_episodes

    self.discount_factor = discount_factor

    self.value_weight = value_weight

    self._state_is_list = isinstance(env.state_shape[0], collections.Sequence)

    self._state_is_list = isinstance(env.state_shape[0], SequenceCollection)

    if optimizer is None:

      self._optimizer = Adam(learning_rate=0.001, beta1=0.9, beta2=0.999)

    else: