# 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 Linear Regression"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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

import numpy as np
import os
import time
import tensorflow as tf
import sys


class LinearRegression:
    """Implementation of the Linear Regression algorithm using TensorFlow"""

    def __init__(self, alpha, batch_size, num_classes, sequence_length):
        """Initialize the Linear Regression class

        Parameter
        ---------
        alpha : float
          The learning rate for the Linear Regression model.
        batch_size : int
          The number of batches to use for training/validation/testing.
        num_classes : int
          The number of classes in a dataset.
        sequence_length : int
          The number of features in a dataset.
        """
        self.alpha = alpha
        self.batch_size = batch_size
        self.num_classes = num_classes
        self.sequence_length = sequence_length

        def __graph__():

            with tf.name_scope("input"):
                # [BATCH_SIZE, SEQUENCE_LENGTH]
                x_input = tf.placeholder(
                    dtype=tf.float32, shape=[None, self.sequence_length], name="x_input"
                )

                # [BATCH_SIZE]
                y_input = tf.placeholder(dtype=tf.uint8, shape=[None], name="y_input")

                # [BATCH_SIZE, NUM_CLASSES]
                y_onehot = tf.one_hot(
                    indices=y_input,
                    depth=self.num_classes,
                    on_value=1.0,
                    off_value=0.0,
                    name="y_onehot",
                )

            with tf.name_scope("training_ops"):
                with tf.name_scope("weights"):
                    weight = tf.Variable(
                        tf.zeros([self.sequence_length, self.num_classes]),
                        name="weight",
                    )
                    self.variable_summaries(weight)
                with tf.name_scope("biases"):
                    bias = tf.Variable(tf.zeros([self.num_classes]), name="bias")
                    self.variable_summaries(bias)
                with tf.name_scope("decision_function"):
                    output = tf.matmul(x_input, weight) + bias
                    output = tf.identity(output, name="output")
                    tf.summary.histogram("output", output)

            with tf.name_scope("mean_squared_loss"):
                loss = tf.reduce_mean(tf.square(output - y_onehot))
            tf.summary.scalar("loss", loss)

            train_op = tf.train.GradientDescentOptimizer(learning_rate=alpha).minimize(
                loss
            )

            with tf.name_scope("accuracy"):
                predicted_class = self.discretize(output)
                predicted_class = tf.identity(predicted_class, name="prediction")
                with tf.name_scope("correct_prediction"):
                    correct = tf.equal(
                        tf.argmax(predicted_class, 1), tf.argmax(y_onehot, 1)
                    )
                with tf.name_scope("accuracy"):
                    accuracy = tf.reduce_mean(tf.cast(correct, "float"))
            tf.summary.scalar("accuracy", accuracy)

            merged = tf.summary.merge_all()

            self.x_input = x_input
            self.y_input = y_input
            self.y_onehot = y_onehot
            self.output = output
            self.loss = loss
            self.train_op = train_op
            self.predicted_class = predicted_class
            self.accuracy = accuracy
            self.merged = merged

        sys.stdout.write("\n<log> Building graph...")
        __graph__()
        sys.stdout.write("</log>\n")

    def train(
        self,
        epochs,
        log_path,
        train_data,
        train_size,
        validation_data,
        validation_size,
        result_path,
    ):
        """Trains the Linear Regression model

        Parameter
        ---------
        epochs : int
          The number of passes through the entire dataset.
        log_path : str
          The directory where to save the TensorBoard logs.
        train_data : numpy.ndarray
          The numpy.ndarray to be used as the training dataset.
        train_size : int
          The number of data in `train_data`.
        validation_data : numpy.ndarray
          The numpy.ndarray to be used as the validation dataset.
        validation_size : int
          The number of data in `validation_data`.
        result_path : str
          The path where to save the NPY files consisting of the actual and predicted labels.
        """

        init_op = tf.group(
            tf.global_variables_initializer(), tf.local_variables_initializer()
        )

        timestamp = str(time.asctime())

        train_writer = tf.summary.FileWriter(
            os.path.join(log_path, timestamp + "-training"),
            graph=tf.get_default_graph(),
        )
        test_writer = tf.summary.FileWriter(
            os.path.join(log_path, timestamp + "-testing"), graph=tf.get_default_graph()
        )

        with tf.Session() as sess:
            sess.run(init_op)

            try:
                for step in range(epochs * train_size // self.batch_size):
                    offset = (step * self.batch_size) % train_size
                    batch_train_data = train_data[0][
                        offset : (offset + self.batch_size)
                    ]
                    batch_train_labels = train_data[1][
                        offset : (offset + self.batch_size)
                    ]

                    feed_dict = {
                        self.x_input: batch_train_data,
                        self.y_input: batch_train_labels,
                    }

                    summary, _, step_loss, predicted, actual = sess.run(
                        [
                            self.merged,
                            self.train_op,
                            self.loss,
                            self.predicted_class,
                            self.y_onehot,
                        ],
                        feed_dict=feed_dict,
                    )

                    if step % 100 == 0:
                        train_accuracy = sess.run(self.accuracy, feed_dict=feed_dict)
                        print(
                            "step [{}] train -- loss : {}, accuracy : {}".format(
                                step, step_loss, train_accuracy
                            )
                        )
                        train_writer.add_summary(summary=summary, global_step=step)

                    self.save_labels(
                        predictions=predicted,
                        actual=actual,
                        result_path=result_path,
                        step=step,
                        phase="training",
                    )

            except KeyboardInterrupt:
                print("Training interrupted at step {}".format(step))
                os._exit(1)
            finally:
                print("EOF -- training done at step {}".format(step))

                for step in range(epochs * validation_size // self.batch_size):
                    offset = (step * self.batch_size) % validation_size
                    test_example_batch = validation_data[0][
                        offset : (offset + self.batch_size)
                    ]
                    test_label_batch = validation_data[1][
                        offset : (offset + self.batch_size)
                    ]

                    feed_dict = {
                        self.x_input: test_example_batch,
                        self.y_input: test_label_batch,
                    }

                    test_summary, predicted, actual = sess.run(
                        [self.merged, self.predicted_class, self.y_onehot],
                        feed_dict=feed_dict,
                    )

                    if step % 100 == 0 and step > 0:
                        test_accuracy, test_loss = sess.run(
                            [self.accuracy, self.loss], feed_dict=feed_dict
                        )

                        print(
                            "step [{}] testing --- loss : {}, accuracy : {}".format(
                                step, test_loss, test_accuracy
                            )
                        )

                        test_writer.add_summary(summary=test_summary, global_step=step)

                    self.save_labels(
                        predictions=predicted,
                        actual=actual,
                        result_path=result_path,
                        step=step,
                        phase="testing",
                    )

                print("EOF -- testing done at step {}".format(step))

    @staticmethod
    def variable_summaries(var):
        with tf.name_scope("summaries"):
            mean = tf.reduce_mean(var)
            tf.summary.scalar("mean", mean)
            with tf.name_scope("stddev"):
                stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
            tf.summary.scalar("stddev", stddev)
            tf.summary.scalar("max", tf.reduce_max(var))
            tf.summary.scalar("min", tf.reduce_min(var))
            tf.summary.histogram("histogram", var)

    @staticmethod
    def save_labels(predictions, actual, result_path, step, phase):
        """Saves the actual and predicted labels to a NPY file

        Parameter
        ---------
        predictions : numpy.ndarray
          The NumPy array containing the predicted labels.
        actual : numpy.ndarray
          The NumPy array containing the actual labels.
        result_path : str
          The path where to save the concatenated actual and predicted labels.
        step : int
          The time step for the NumPy arrays.
        phase : str
          The phase for which the predictions is, i.e. training/validation/testing.
        """

        if not os.path.exists(path=result_path):
            os.mkdir(result_path)

        # Concatenate the predicted and actual labels
        labels = np.concatenate((predictions, actual), axis=1)

        # save the labels array to NPY file
        np.save(
            file=os.path.join(
                result_path, "{}-linear_regression-{}.npy".format(phase, step)
            ),
            arr=labels,
        )

    @staticmethod
    def discretize(output):
        """Discretizes the predicted classes to [0, 1]

        Parameter
        ---------
        output : numpy.ndarray
          The NumPy array containing the predicted classes

        Returns
        -------
        output : numpy.ndarray
          The discretized predicted classes.
        """

        output = output >= 0.5
        output = tf.cast(output, "float")
        return output
