tests now pass

from tensorflow.python.platform import gfile

from biology import metrics as biology_metrics

from biology import model_ops

from biology import utils as biology_utils

from deepchem.utils import metrics

from deepchem.models.tensorflow_models import model_ops

from deepchem.models.tensorflow_models import utils as tf_utils

class Model(object):

      self.AddOutputOps()  # add softmax heads

      saver = tf.train.Saver(tf.variables.all_variables())

      saver.restore(self._SharedSession(),

                    biology_utils.ParseCheckpoint(checkpoint))

                    tf_utils.ParseCheckpoint(checkpoint))

      self.global_step_number = int(self._SharedSession().run(self.global_step))

    self._restored_model = True

      y_true: A list of arrays containing true values for each task.

      y_pred: A list of arrays containing predicted values for each task.

      metric_str: String description of the metric to compute. Must be in

        biology_metrics.METRICS.

        metrics.METRICS.

      threshold: Float threshold to apply to probabilities for positive/negative

        class assignment.

      yt = y_true[task]

      yp = y_pred[task]

      try:

        metric_value = biology_metrics.compute_metric(yt, yp, metric_str,

        metric_value = metrics.compute_metric(yt, yp, metric_str,

                                                      threshold=threshold)

      except (AssertionError, ValueError) as e:

        warnings.warn('Error calculating metric %s for task %d: %s'

  def setUp(self):

    super(ModelConfigTest, self).setUp()

    self.root = tempfile.mkdtemp(dir=flags.FLAGS.test_tmpdir)

    self.root = tempfile.mkdtemp()

  def _assertMatchesExample(self, config):

    self.assertEqual(config.hello, 'world')