Changed print statements to log statements

import collections

from operator import mul

from deepchem.utils.evaluate import Evaluator

from deepchem.utils.save import log

class HyperparamOpt(object):

  """

      else:

        shutil.rmtree(model_dir)

      print("Model %d/%d, Metric %s, Validation set %s: %f" %

            (ind, number_combinations, metric.name, ind, valid_score))

      print("\tbest_validation_score so  far: %f" % best_validation_score)

      log("Model %d/%d, Metric %s, Validation set %s: %f" %

          (ind, number_combinations, metric.name, ind, valid_score),

          self.verbosity)

      log("\tbest_validation_score so  far: %f" % best_validation_score,

          self.verbosity)

    train_csv_out = tempfile.NamedTemporaryFile()

    train_stats_out = tempfile.NamedTemporaryFile()

    train_df, train_score = train_evaluator.compute_model_performance(

        [metric], train_csv_out, train_stats_out)

    train_score = train_score.iloc[0][metric.name]

    print("Best hyperparameters: %s" % str(zip(hyperparams, best_hyperparams)))

    print("train_score: %f" % train_score)

    print("validation_score: %f" % best_validation_score)

    log("Best hyperparameters: %s" % str(zip(hyperparams, best_hyperparams)),

        self.verbosity)

    log("train_score: %f" % train_score, self.verbosity)

    log("validation_score: %f" % best_validation_score, self.verbosity)

    return best_model, best_hyperparams, all_scores

  def _hyperparam_opt(self, model_builder, params_dict, train_dataset,

                      valid_dataset, output_transformers, task_types, metric):

    optimizer = HyperparamOpt(model_builder, task_types)

    optimizer = HyperparamOpt(model_builder, task_types, verbosity="low")

    best_model, best_hyperparams, all_results = optimizer.hyperparam_search(

      params_dict, train_dataset, valid_dataset, output_transformers,

      metric)