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"""

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

from deepchem.featurizers.fingerprints import CircularFingerprint

from deepchem.models.test import TestAPI

from deepchem.metrics import Metric

from deepchem import metrics

import numpy as np

from deepchem.models.tensorflow_models import TensorflowModel

from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskClassifier

class TestTFHyperparamOptAPI(TestAPI):

  """

  Test hyperparameter optimization API.

  """

  def test_multitask_keras_mlp_ECFP_classification_hyperparam_opt(self):

    """Straightforward test of Tensorflow multitask deepchem classification API."""

    splittype = "scaffold"

    output_transformers = []

    input_transformers = []

    task_type = "classification"

    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, valid_dataset, _, transformers = self._featurize_train_test_split(

        splittype, compound_featurizers, 

        complex_featurizers, input_transformers,

        output_transformers, input_file, task_types.keys())

    metric = Metric(metrics.matthews_corrcoef, np.mean)

    params_dict = {"activation": ["relu"],

                    "momentum": [.9],

                    "batch_size": [50],

                    "init": ["glorot_uniform"],

                    "data_shape": [train_dataset.get_data_shape()],

                    "learning_rate": [1e-3],

                    "decay": [1e-6],

                    "nb_hidden": [1000], 

                    "nb_epoch": [1],

                    "nesterov": [False],

                    "dropouts": [(.5,)],

                    "nb_layers": [1],

                    "batchnorm": [False],

                    "layer_sizes": [(1000,)],

                    "weight_init_stddevs": [(.1,)],

                    "bias_init_consts": [(1.,)],

                    "num_classes": [2],

                    "penalty": [0.], 

                    "optimizer": ["sgd"],

                    "num_classification_tasks": [len(task_types)]

                  }

    def model_builder(task_types, params_dict, logdir, verbosity=None):

        return TensorflowModel(

            task_types, params_dict, logdir, 

            tf_class=TensorflowMultiTaskClassifier,

            verbosity=verbosity)

    self._hyperparam_opt(model_builder, params_dict, train_dataset,

                         valid_dataset, output_transformers, task_types,

                         metric, logdir=self.model_dir)