Moar tests

        return np.concatenate(results, axis=0)

  def evaluate(self, dataset, metrics, transformers=[], per_task_metrics=False):

    if not self.built:

      self.build()

    return self.evaluate_generator(

        self.default_generator(dataset),

        metrics,

import numpy as np

import deepchem

from deepchem.data import NumpyDataset

from deepchem.models import GraphConvTensorGraph

from deepchem.models import TensorGraph

from deepchem.molnet.load_function.delaney_datasets import load_delaney

def get_dataset(mode='classification', featurizer='GraphConv'):

  data_points = 10

  tasks, all_dataset, transformers = load_delaney(featurizer)

  train, valid, test = all_dataset

  if mode == 'classification':

    y = np.random.randint(0, 2, size=(data_points, len(tasks)))

    metric = deepchem.metrics.Metric(

        deepchem.metrics.roc_auc_score, np.mean, mode="classification")

  if mode == 'regression':

    y = np.random.normal(size=(data_points, len(tasks)))

    metric = deepchem.metrics.Metric(

        deepchem.metrics.mean_absolute_error, mode="regression")

  ds = NumpyDataset(train.X[:10], y, train.w[:10], train.ids[:10])

  return tasks, ds, transformers, metric

def test_graph_conv_model():

  tasks, dataset, transformers, metric = get_dataset('classification',

                                                     'GraphConv')

  batch_size = 50

  model = GraphConvTensorGraph(

      len(tasks), batch_size=batch_size, mode='classification')

  model.fit(dataset, nb_epoch=1)

  scores = model.evaluate(dataset, [metric], transformers)

  model.save()

  model = TensorGraph.load_from_dir(model.model_dir)

  scores = model.evaluate(dataset, [metric], transformers)

def test_graph_conv_regression_model():

  tasks, dataset, transformers, metric = get_dataset('regression', 'GraphConv')

  batch_size = 50

  model = GraphConvTensorGraph(

      len(tasks), batch_size=batch_size, mode='regression')

  model.fit(dataset, nb_epoch=1)

  scores = model.evaluate(dataset, [metric], transformers)

  model.save()

  model = TensorGraph.load_from_dir(model.model_dir)

  scores = model.evaluate(dataset, [metric], transformers)

      tensorgraph.built = False

      return tensorgraph

  def __del__(self):

    pass

def _enqueue_batch(tg, generator, graph, sess, coord):

  """