# 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.

"""Main program using the SVM class"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

__version__ = "0.1.5"
__author__ = "Abien Fred Agarap"

import argparse
from models.svm import SVM
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import utils

BATCH_SIZE = 128
LEARNING_RATE = 1e-3
NUM_CLASSES = 2


def parse_args():
    parser = argparse.ArgumentParser(
        description="SVM built using TensorFlow, for Wisconsin Breast Cancer Diagnostic Dataset"
    )
    group = parser.add_argument_group("Arguments")
    group.add_argument(
        "-c", "--svm_c", required=True, type=int, help="Penalty parameter C of the SVM"
    )
    group.add_argument(
        "-n", "--num_epochs", required=True, type=int, help="number of epochs"
    )
    group.add_argument(
        "-l",
        "--log_path",
        required=True,
        type=str,
        help="path where to save the TensorBoard logs",
    )
    group.add_argument(
        "-r",
        "--result_path",
        required=True,
        type=str,
        help="path where to save the NumPy array consisting of the actual and predicted labels",
    )
    arguments = parser.parse_args()
    return arguments


def main(arguments):
    # load the features of the dataset
    features = datasets.load_breast_cancer().data

    # standardize the features
    features = StandardScaler().fit_transform(features)

    # get the number of features
    num_features = features.shape[1]

    # load the corresponding labels for the features
    labels = datasets.load_breast_cancer().target

    # transform the labels to {-1, +1}
    labels[labels == 0] = -1

    # split the dataset to 70/30 partition: 70% train, 30% test
    train_features, test_features, train_labels, test_labels = train_test_split(
        features, labels, test_size=0.3, stratify=labels
    )

    train_size = train_features.shape[0]
    test_size = test_features.shape[0]

    # slice the dataset as per the batch size
    train_features = train_features[: train_size - (train_size % BATCH_SIZE)]
    train_labels = train_labels[: train_size - (train_size % BATCH_SIZE)]
    test_features = test_features[: test_size - (test_size % BATCH_SIZE)]
    test_labels = test_labels[: test_size - (test_size % BATCH_SIZE)]

    # instantiate the SVM class
    model = SVM(
        alpha=LEARNING_RATE,
        batch_size=BATCH_SIZE,
        svm_c=arguments.svm_c,
        num_classes=NUM_CLASSES,
        num_features=num_features,
    )

    # train the instantiated model
    model.train(
        epochs=arguments.num_epochs,
        log_path=arguments.log_path,
        train_data=[train_features, train_labels],
        train_size=train_features.shape[0],
        validation_data=[test_features, test_labels],
        validation_size=test_features.shape[0],
        result_path=arguments.result_path,
    )

    test_conf, test_accuracy = utils.plot_confusion_matrix(
        phase="testing", path=arguments.result_path, class_names=["benign", "malignant"]
    )

    print("True negatives : {}".format(test_conf[0][0]))
    print("False negatives : {}".format(test_conf[1][0]))
    print("True positives : {}".format(test_conf[1][1]))
    print("False positives : {}".format(test_conf[0][1]))
    print("Testing accuracy : {}".format(test_accuracy))


if __name__ == "__main__":
    args = parse_args()

    main(args)
