# Copyright 2017 Abien Fred Agarap
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Implementation of K-fold Cross Validation for ML Algorithms"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

__author__ = "Abien Fred Agarap"
__version__ = "0.1.0"

from sklearn import datasets
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC


def validate_model(model, parameter_set, train_data, test_data):
    clf = GridSearchCV(estimator=model, param_grid=parameter_set, n_jobs=3, cv=10)

    clf.fit(train_data[0], train_data[1])

    grid_scores = clf.grid_scores_
    best_score = clf.best_score_
    best_params = clf.best_params_
    test_score = clf.score(test_data[0], test_data[1])

    return grid_scores, best_score, best_params, test_score


def main():
    dataset = datasets.load_breast_cancer()
    features = dataset.data
    labels = dataset.target

    train_features, test_features, train_labels, test_labels = train_test_split(
        features, labels, test_size=0.3, stratify=labels
    )

    parameter_set = {
        "loss": ("hinge", "squared_hinge"),
        "C": [1, 10, 100, 1000, 5, 50, 500, 5000],
    }

    model = LinearSVC()
    grid_scores, best_score, best_params, test_score = validate_model(
        model=model,
        parameter_set=parameter_set,
        train_data=[train_features, train_labels],
        test_data=[test_features, test_labels],
    )

    print(grid_scores)
    print("SVM best score: {}".format(best_score))
    print("SVM best params : {}".format(best_params))
    print("SVM test score : {}".format(test_score))

    parameter_set = {
        "activation": ["identity", "logistic", "tanh", "relu"],
        "solver": ["sgd", "adam"],
        "batch_size": [16, 32, 64, 128],
        "hidden_layer_sizes": [(16,), (32,), (64,), (128,)],
        "learning_rate_init": [1e-1, 1e-2],
    }

    model = MLPClassifier()

    grid_scores, best_score, best_params, test_score = validate_model(
        model=model,
        parameter_set=parameter_set,
        train_data=[train_features, train_labels],
        test_data=[test_features, test_labels],
    )

    print(grid_scores)
    print("MLP best score: {}".format(best_score))
    print("MLP best params : {}".format(best_params))
    print("MLP test score : {}".format(test_score))


if __name__ == "__main__":
    main()
