text: """
  Script that trains Sklearn multitask models on nci dataset.
  """
  from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  
  import os
  import numpy as np
  import shutil
  from deepchem.molnet import load_nci
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestRegressor
  from deepchem.data import Dataset
  from deepchem.models.multitask import SingletaskToMultitask
  from deepchem import metrics
  from deepchem.metrics import Metric
  from deepchem.models.sklearn_models import SklearnModel
  from deepchem.utils.evaluate import Evaluator
  
  np.random.seed(123)
  
  # Set some global variables up top
  verbosity = "high"
  
  nci_tasks, nci_dataset, transformers = load_nci()
  
  (train_dataset, valid_dataset, test_dataset) = nci_dataset
  
  classification_metric = Metric(
      metrics.roc_auc_score, np.mean, mode="classification")
  
  
  def model_builder(model_dir):
    sklearn_model = RandomForestRegressor(n_estimators=500)
    return SklearnModel(sklearn_model, model_dir)
  
  
  model = SingletaskToMultitask(nci_tasks, model_builder)
  
  # Fit trained model
  model.fit(train_dataset)
  
  train_evaluator = Evaluator(
      model, train_dataset, transformers, verbosity=verbosity)
  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])
  
  print("Validation scores")
  print(valid_scores)
  