generator

from deepchem.models.models import Model

from deepchem.models.losses import Loss

from deepchem.models.optimizers import Optimizer

from deepchem.utils.evaluate import GeneratorEvaluator

from typing import Any, Callable, Iterable, List, Optional, Tuple, Union, Sequence

from deepchem.utils.typing import LossFn, OneOrMany, ArrayLike

                         metrics: List[Metric],

                         transformers: List[Transformer] = [],

                         per_task_metrics: bool = False):

    pass

    """Evaluate the performance of this model on the data produced by a generator.

    Parameters

    ----------

    generator: generator

      this should generate batches, each represented as a tuple of the form

      (inputs, labels, weights).

    metric: list of deepchem.metrics.Metric

      Evaluation metric

    transformers: list of dc.trans.Transformers

      Transformers that the input data has been transformed by.  The output

      is passed through these transformers to undo the transformations.

    per_task_metrics: bool

      If True, return per-task scores.

    Returns

    -------

    dict

      Maps tasks to scores under metric.

    """

    evaluator = GeneratorEvaluator(self, generator, transformers)

    return evaluator.compute_model_performance(metrics, per_task_metrics)

  def _get_trainable_params(self):

    """