class_docstr: Provides simple grid hyperparameter search capabilities.
  
  This class performs a grid hyperparameter search over the specified
  hyperparameter space. This implementation is simple and simply does
  a direct iteration over all possible hyperparameters and doesn't use
  parallelization to speed up the search.
  
  Examples
  --------
  This example shows the type of constructor function expected.
  
  >>> import sklearn
  >>> import deepchem as dc
  >>> optimizer = dc.hyper.GridHyperparamOpt(lambda **p: dc.models.GraphConvModel(**p))
  
  Here's a more sophisticated example that shows how to optimize only
  some parameters of a model. In this case, we have some parameters we
  want to optimize, and others which we don't. To handle this type of
  search, we create a `model_builder` which hard codes some arguments
  (in this case, `max_iter` is a hyperparameter which we don't want
  to search over)
  
  >>> import deepchem as dc
  >>> import numpy as np
  >>> from sklearn.linear_model import LogisticRegression as LR
  >>> # generating data
  >>> X = np.arange(1, 11, 1).reshape(-1, 1)
  >>> y = np.hstack((np.zeros(5), np.ones(5)))
  >>> dataset = dc.data.NumpyDataset(X, y)
  >>> # splitting dataset into train and test
  >>> splitter = dc.splits.RandomSplitter()
  >>> train_dataset, test_dataset = splitter.train_test_split(dataset)
  >>> # metric to evaluate result of a set of parameters
  >>> metric = dc.metrics.Metric(dc.metrics.accuracy_score)
  >>> # defining `model_builder`
  >>> def model_builder(**model_params):
  ...   penalty = model_params['penalty']
  ...   solver = model_params['solver']
  ...   lr = LR(penalty=penalty, solver=solver, max_iter=100)
  ...   return dc.models.SklearnModel(lr)
  >>> # the parameters which are to be optimized
  >>> params = {
  ...   'penalty': ['l1', 'l2'],
  ...   'solver': ['liblinear', 'saga']
  ...   }
  >>> # Creating optimizer and searching over hyperparameters
  >>> optimizer = dc.hyper.GridHyperparamOpt(model_builder)
  >>> best_model, best_hyperparams, all_results =     optimizer.hyperparam_search(params, train_dataset, test_dataset, metric)
  >>> best_hyperparams  # the best hyperparameters
  {'penalty': 'l2', 'solver': 'saga'}
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docstr: Perform hyperparams search according to params_dict.
  
  Each key to hyperparams_dict is a model_param. The values should
  be a list of potential values for that hyperparam.
  
  Parameters
  ----------
  params_dict: Dict
      Maps hyperparameter names (strings) to lists of possible
      parameter values.
  train_dataset: Dataset
      dataset used for training
  valid_dataset: Dataset
      dataset used for validation(optimization on valid scores)
  metric: Metric
      metric used for evaluation
  output_transformers: list[Transformer]
      Transformers for evaluation. This argument is needed since
      `train_dataset` and `valid_dataset` may have been transformed
      for learning and need the transform to be inverted before
      the metric can be evaluated on a model.
  nb_epoch: int, (default 10)
      Specifies the number of training epochs during each iteration of optimization.
      Not used by all model types.
  use_max: bool, optional
      If True, return the model with the highest score. Else return
      model with the minimum score.
  logdir: str, optional
      The directory in which to store created models. If not set, will
      use a temporary directory.
  logfile: str, optional (default `results.txt`)
      Name of logfile to write results to. If specified, this is must
      be a valid file name. If not specified, results of hyperparameter
      search will be written to `logdir/results.txt`.
  
  Returns
  -------
  Tuple[`best_model`, `best_hyperparams`, `all_scores`]
  `(best_model, best_hyperparams, all_scores)` where `best_model` is
  an instance of `dc.model.Model`, `best_hyperparams` is a
  dictionary of parameters, and `all_scores` is a dictionary mapping
  string representations of hyperparameter sets to validation
  scores.
  
  Notes
  -----
  From DeepChem 2.6, the return type of `best_hyperparams` is a dictionary of
  parameters rather than a tuple of parameters as it was previously. The new
  changes have been made to standardize the behaviour across different
  hyperparameter optimization techniques available in DeepChem.
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code: def hyperparam_search(
      self,
      params_dict: Dict,
      train_dataset: Dataset,
      valid_dataset: Dataset,
      metric: Metric,
      output_transformers: List[Transformer] = [],
      nb_epoch: int = 10,
      use_max: bool = True,
      logfile: str = "results.txt",
      logdir: Optional[str] = None,
      **kwargs,
  ) -> Tuple[Model, Dict, Dict]:
      """Perform hyperparams search according to params_dict.
  
      Each key to hyperparams_dict is a model_param. The values should
      be a list of potential values for that hyperparam.
  
      Parameters
      ----------
      params_dict: Dict
          Maps hyperparameter names (strings) to lists of possible
          parameter values.
      train_dataset: Dataset
          dataset used for training
      valid_dataset: Dataset
          dataset used for validation(optimization on valid scores)
      metric: Metric
          metric used for evaluation
      output_transformers: list[Transformer]
          Transformers for evaluation. This argument is needed since
          `train_dataset` and `valid_dataset` may have been transformed
          for learning and need the transform to be inverted before
          the metric can be evaluated on a model.
      nb_epoch: int, (default 10)
          Specifies the number of training epochs during each iteration of optimization.
          Not used by all model types.
      use_max: bool, optional
          If True, return the model with the highest score. Else return
          model with the minimum score.
      logdir: str, optional
          The directory in which to store created models. If not set, will
          use a temporary directory.
      logfile: str, optional (default `results.txt`)
          Name of logfile to write results to. If specified, this is must
          be a valid file name. If not specified, results of hyperparameter
          search will be written to `logdir/results.txt`.
  
      Returns
      -------
      Tuple[`best_model`, `best_hyperparams`, `all_scores`]
      `(best_model, best_hyperparams, all_scores)` where `best_model` is
      an instance of `dc.model.Model`, `best_hyperparams` is a
      dictionary of parameters, and `all_scores` is a dictionary mapping
      string representations of hyperparameter sets to validation
      scores.
  
      Notes
      -----
      From DeepChem 2.6, the return type of `best_hyperparams` is a dictionary of
      parameters rather than a tuple of parameters as it was previously. The new
      changes have been made to standardize the behaviour across different
      hyperparameter optimization techniques available in DeepChem.
      """
      hyperparams = params_dict.keys()
      hyperparam_vals = params_dict.values()
      for hyperparam_list in params_dict.values():
          assert isinstance(hyperparam_list, collections.abc.Iterable)
  
      number_combinations = reduce(
          mul, [len(vals) for vals in hyperparam_vals]
      )
  
      if use_max:
          best_validation_score = -np.inf
      else:
          best_validation_score = np.inf
  
      best_model = None
      all_scores = {}
  
      if logdir is not None:
          if not os.path.exists(logdir):
              os.makedirs(logdir, exist_ok=True)
          log_file = os.path.join(logdir, logfile)
  
      for ind, hyperparameter_tuple in enumerate(
          itertools.product(*hyperparam_vals)
      ):
          model_params = {}
          logger.info(
              "Fitting model %d/%d" % (ind + 1, number_combinations)
          )
          # Construction dictionary mapping hyperparameter names to values
          hyper_params = dict(zip(hyperparams, hyperparameter_tuple))
          for hyperparam, hyperparam_val in zip(
              hyperparams, hyperparameter_tuple
          ):
              model_params[hyperparam] = hyperparam_val
          logger.info("hyperparameters: %s" % str(model_params))
  
          hp_str = _convert_hyperparam_dict_to_filename(hyper_params)
          if logdir is not None:
              model_dir = os.path.join(logdir, hp_str)
              logger.info("model_dir is %s" % model_dir)
              try:
                  os.makedirs(model_dir)
              except OSError:
                  if not os.path.isdir(model_dir):
                      logger.info(
                          "Error creating model_dir, using tempfile directory"
                      )
                      model_dir = tempfile.mkdtemp()
          else:
              model_dir = tempfile.mkdtemp()
          model_params["model_dir"] = model_dir
          model = self.model_builder(**model_params)
          # mypy test throws error, so ignoring it in try
          try:
              model.fit(train_dataset, nb_epoch=nb_epoch)  # type: ignore
          # Not all models have nb_epoch
          except TypeError:
              model.fit(train_dataset)
          try:
              model.save()
          # Some models autosave
          except NotImplementedError:
              pass
  
          multitask_scores = model.evaluate(
              valid_dataset, [metric], output_transformers
          )
          valid_score = multitask_scores[metric.name]
          all_scores[hp_str] = valid_score
  
          if (use_max and valid_score >= best_validation_score) or (
              not use_max and valid_score <= best_validation_score
          ):
              best_validation_score = valid_score
              best_hyperparams = hyper_params
              best_model = model
  
          logger.info(
              "Model %d/%d, Metric %s, Validation set %s: %f"
              % (
                  ind + 1,
                  number_combinations,
                  metric.name,
                  ind,
                  valid_score,
              )
          )
          logger.info(
              "\tbest_validation_score so far: %f"
              % best_validation_score
          )
      if best_model is None:
          logger.info("No models trained correctly.")
          # arbitrarily return last model
          if logdir is not None:
              with open(log_file, "w+") as f:
                  f.write(
                      "No model trained correctly. Arbitary models returned"
                  )
          best_model, best_hyperparams = model, hyperparameter_tuple  # type: ignore
          return best_model, best_hyperparams, all_scores
      multitask_scores = best_model.evaluate(
          train_dataset, [metric], output_transformers
      )
      train_score = multitask_scores[metric.name]
      logger.info("Best hyperparameters: %s" % str(best_hyperparams))
      logger.info("best train score: %f" % train_score)
      logger.info("best validation score: %f" % best_validation_score)
      if logdir is not None:
          with open(log_file, "w+") as f:
              f.write(
                  "Best Hyperparameters dictionary %s\n"
                  % str(best_hyperparams)
              )
              f.write(
                  "Best validation score %f\n" % best_validation_score
              )
              f.write("Best train_score: %f\n" % train_score)
      return best_model, best_hyperparams, all_scores
  
file_path: deepchem/deepchem/hyper/grid_search.py
imports: import numpy
  import os
  import itertools
  import tempfile
  import collections
  import logging
  from functools import reduce
  from operator import mul
  from typing import Dict, List, Optional, Tuple
  from deepchem.data import Dataset
  from deepchem.trans import Transformer
  from deepchem.models import Model
  from deepchem.metrics import Metric
  from deepchem.hyper.base_classes import HyperparamOpt
  from deepchem.hyper.base_classes import _convert_hyperparam_dict_to_filename
label_desc: Use image node_id 1 for calling the GridHyperparamOpt obj's underlying member method code with example usage: obj.hyperparam_search(params_dict, train_dataset, valid_dataset, metric, output_transformers, nb_epoch, use_max, logfile, logdir) and returns: best_model,  best_hyperparams,  all_scores, best_model,  best_hyperparams,  all_scores