Merge pull request #819 from LRParser/master

from deepchem.models.sklearn_models import SklearnModel

from deepchem.utils.evaluate import Evaluator

np.random.seed(123)

# Set some global variables up top

reload = True

verbosity = "high"

is_verbose = False

base_dir = "/tmp/pcba_sklearn"

model_dir = os.path.join(base_dir, "model")

  shutil.rmtree(base_dir)

os.makedirs(base_dir)

pcba_tasks, pcba_datasets, transformers = load_pcba(

    base_dir, reload=reload)

(train_dataset, valid_dataset) = pcba_datasets

pcba_tasks, pcba_datasets, transformers = load_pcba()

(train_dataset, valid_dataset, test_dataset) = pcba_datasets

classification_metric = Metric(

    metrics.roc_auc_score, np.mean, verbose=is_verbose, mode="classification")

classification_metric = Metric(metrics.roc_auc_score, np.mean,

                               verbosity=verbosity,

                               mode="classification")

def model_builder(model_dir):

  sklearn_model = RandomForestClassifier(

      class_weight="balanced", n_estimators=500)

  return SklearnModel(sklearn_model, model_dir)

model = SingletaskToMultitask(muv_tasks, model_builder, model_dir)

model = SingletaskToMultitask(pcba_tasks, model_builder, model_dir)

# Fit trained model

model.fit(train_dataset)

model.save()

train_evaluator = Evaluator(model, train_dataset, transformers, verbosity=verbosity)

train_scores = train_evaluator.compute_model_performance([classification_metric])

train_evaluator = Evaluator(

    model, train_dataset, transformers, verbose=is_verbose)

train_scores = train_evaluator.compute_model_performance(

    [classification_metric])

print("Train scores")

print(train_scores)

valid_evaluator = Evaluator(model, valid_dataset, transformers, verbosity=verbosity)

valid_scores = valid_evaluator.compute_model_performance([classification_metric])

valid_evaluator = Evaluator(

    model, valid_dataset, transformers, verbose=is_verbose)

valid_scores = valid_evaluator.compute_model_performance(

    [classification_metric])

print("Validation scores")

print(valid_scores)