Forgot to add actual tests

"""

Integration tests for hyperparam optimization.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

__author__ = "Bharath Ramsundar"

__copyright__ = "Copyright 2016, Stanford University"

__license__ = "LGPL"

import os

import unittest

import tempfile

import shutil

from deepchem.models.test import TestAPI

from deepchem.models.sklearn_models import SklearnModel

from deepchem.featurizers.fingerprints import CircularFingerprint

from deepchem.transformers import NormalizationTransformer

from deepchem import metrics

from deepchem.metrics import Metric

from sklearn.ensemble import RandomForestRegressor

def rf_model_builder(task_types, params_dict, logdir=None,

                     train=True):

    """Builds random forests given hyperparameters.

    Last two arguments only for tensorflow models and ignored.

    """

    n_estimators = params_dict["n_estimators"]

    max_features = params_dict["max_features"]

    return SklearnModel(

        task_types, params_dict,

        model_instance=RandomForestRegressor(n_estimators=n_estimators,

                                             max_features=max_features))

class TestHyperparamOptAPI(TestAPI):

  """

  Test hyperparameter optimization API.

  """

  def test_singletask_sklearn_rf_ECFP_regression_hyperparam_opt(self):

    """Test of singletask RF ECFP regression API."""

    splittype = "scaffold"

    compound_featurizers = [CircularFingerprint(size=1024)]

    complex_featurizers = []

    input_transformer_classes = []

    output_transformer_classes = [NormalizationTransformer]

    task_types = {"log-solubility": "regression"}

    input_file = "example.csv"

    train_dataset, valid_dataset, _, output_transformers, = \

        self._featurize_train_test_split(

            splittype, compound_featurizers, 

            complex_featurizers, input_transformer_classes,

            output_transformer_classes, input_file, task_types.keys())

    params_dict = {

      "n_estimators": [10, 100],

      "max_features": ["auto"],

      "data_shape": train_dataset.get_data_shape()

    }

    metric = Metric(metrics.r2_score)

    self._hyperparam_opt(rf_model_builder, params_dict, train_dataset,

                         valid_dataset, output_transformers, task_types,

                         metric)