Forgot to add in test_tf_api

sudo: required

dist: trusty

install:

#- sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y

#- sudo apt-get update

#- sudo apt-get install gcc-4.9

#- sudo apt-get upgrade libstdc++6

- wget http://repo.continuum.io/archive/Anaconda2-2.4.1-Linux-x86_64.sh -O anaconda.sh;

- bash anaconda.sh -b -p $HOME/anaconda

- export PATH="$HOME/anaconda/bin:$PATH"

- python setup.py install

- conda install -c https://conda.anaconda.org/jjhelmus tensorflow

script:

- nosetests -v deepchem

- nosetests -v deepchem --nologcapture

after_success:

- source devtools/travis-ci/after_sucess.sh

# AWS access_key and secret key secured through travis secure var api.

    self.samples_dir = tempfile.mkdtemp()

    self.train_dir = tempfile.mkdtemp()

    self.test_dir = tempfile.mkdtemp()

    #self.model_dir = tempfile.mkdtemp()

    self.model_dir = "/home/rbharath/model_dir" 

    self.model_dir = tempfile.mkdtemp()

    if not os.path.exists(self.model_dir):

      os.makedirs(self.model_dir)

"""

Integration tests for singletask vector feature models.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

__author__ = "Bharath Ramsundar"

__copyright__ = "Copyright 2016, Stanford University"

__license__ = "LGPL"

import os

import unittest

import tempfile

import shutil

from deepchem.featurizers.featurize import DataFeaturizer

from deepchem.featurizers.featurize import FeaturizedSamples

from deepchem.featurizers.fingerprints import CircularFingerprint

from deepchem.featurizers.basic import RDKitDescriptors

from deepchem.featurizers.nnscore import NNScoreComplexFeaturizer

from deepchem.featurizers.grid_featurizer import GridFeaturizer

from deepchem.datasets import Dataset

from deepchem.utils.evaluate import Evaluator

from deepchem.models import Model

from deepchem.models.sklearn_models import SklearnModel

from deepchem.models.tensorflow_models import TensorflowModel

from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskClassifier

from deepchem.models.tensorflow_models.model_config import ModelConfig

from deepchem.transformers import NormalizationTransformer

from deepchem.transformers import LogTransformer

from deepchem.transformers import ClippingTransformer

from sklearn.ensemble import RandomForestRegressor

from deepchem.models.test import TestAPI

class TestTensorflowAPI(TestAPI):

  """

  Test top-level API for ML models."

  """

  def test_singletask_tf_mlp_ECFP_classification_API(self):

    """Straightforward test of Tensorflow singletask deepchem classification API."""

    splittype = "scaffold"

    output_transformers = []

    input_transformers = []

    task_type = "classification"

    compound_featurizers = [CircularFingerprint(size=1024)]

    complex_featurizers = []

    task_types = {"outcome": "classification"}

    input_file = "example_classification.csv"

    input_transformers = []

    output_transformers = [NormalizationTransformer]

    train_dataset, test_dataset, _, transformers = self._featurize_train_test_split(

        splittype, compound_featurizers, 

        complex_featurizers, input_transformers,

        output_transformers, input_file, task_types.keys())

    # TODO(rbharath): Tensorflow doesn't elegantly handle partial batches.

    # What's the right fix here?

    model_params = {

      "batch_size": 2,

      "num_classification_tasks": 1,

      "num_features": 1024,

      "layer_sizes": [1024],

      "weight_init_stddevs": [1.],

      "bias_init_consts": [0.],

      "dropouts": [.5],

      "num_classes": 2,

      "penalty": 0.0,

      "optimizer": "adam",

      "learning_rate": .001,

      "data_shape": train_dataset.get_data_shape()

    }

    model = TensorflowMultiTaskClassifier(

        task_types, model_params, train=True, logdir=self.model_dir)

    def test_model_creator():

      return TensorflowMultiTaskClassifier(

          task_types, model_params, train=False, logdir=self.model_dir)

    self._create_model(train_dataset, test_dataset, model, transformers,

                       test_model_creator=test_model_creator)