Add new multitask order tests

"""

Integration tests for multitask datasets.

"""

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 numpy as np

import tempfile

import shutil

from deepchem.featurizers.fingerprints import CircularFingerprint

from deepchem.datasets import Dataset

from deepchem.models.test import TestAPI

class TestMultitaskData(TestAPI):

  """

  Sanity tests for multitask data.

  """

  def test_multitask_order(self):

    """Test that order of tasks in multitask datasets is preserved."""

    from deepchem.models.keras_models.fcnet import MultiTaskDNN

    splittype = "scaffold"

    output_transformers = []

    input_transformers = []

    task_type = "classification"

    # TODO(rbharath): There should be some automatic check to ensure that all

    # required model_params are specified.

    model_params = {"nb_hidden": 10, "activation": "relu",

                    "dropout": .5, "learning_rate": .01,

                    "momentum": .9, "nesterov": False,

                    "decay": 1e-4, "batch_size": 5,

                    "nb_epoch": 2, "init": "glorot_uniform",

                    "nb_layers": 1, "batchnorm": False}

    input_file = os.path.join(self.current_dir, "multitask_example.csv")

    tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6",

             "task7", "task8", "task9", "task10", "task11", "task12",

             "task13", "task14", "task15", "task16"]

    task_types = {task: task_type for task in tasks}

    compound_featurizers = [CircularFingerprint(size=1024)]

    complex_featurizers = []

    train_dataset, test_dataset, _, transformers = self._featurize_train_test_split(

        splittype, compound_featurizers, 

        complex_featurizers, input_transformers,

        output_transformers, input_file, tasks)

    assert train_dataset.get_task_names() == tasks

    assert test_dataset.get_task_names() == tasks

  def test_multitask_data(self):

    """Test that data associated with a tasks stays associated with it."""

    tasks = ["task0", "task1"]

    n_samples = 100

    n_features = 3

    n_tasks = len(tasks)

    # Generate dummy dataset

    ids = np.array(["C"] * n_samples, dtype=object)

    X = np.random.rand(n_samples, n_features)

    y = np.random.randint(2, size=(n_samples, n_tasks))

    w = np.ones((n_samples, n_tasks))

    dataset = Dataset.from_numpy(self.train_dir, tasks, X, y, w, ids)

    X_out, y_out, w_out, _ = dataset.to_numpy()

    np.testing.assert_allclose(X, X_out)

    np.testing.assert_allclose(y, y_out)

    np.testing.assert_allclose(w, w_out)