ACDC_Ablation_B.py

# Marcus Vinicius Sousa Leite de Carvalho
# marcus.decarvalho@ntu.edu.sg
# ivsucram@gmail.com
#
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from ACDCDataManipulator import DataManipulator
from NeuralNetwork import NeuralNetwork
from AutoEncoder import DenoisingAutoEncoder
from MySingletons import MyDevice
from colorama import Fore, Back, Style
from itertools import cycle
import numpy as np
import matplotlib.pylab as plt
import math
import torch
import time


def __copy_weights(source: NeuralNetwork, targets: list, layer_numbers=None, copy_moment: bool = True):
    if layer_numbers is None:
        layer_numbers = [1]
    if type(targets) is not list:
        targets = [targets]
    for layer_number in layer_numbers:
        layer_number -= 1
        for target in targets:
            if layer_number >= source.number_hidden_layers:
                target.output_weight = source.output_weight.detach()
                target.output_bias = source.output_bias.detach()
                if copy_moment:
                    target.output_momentum = source.output_momentum.detach()
                    target.output_bias_momentum = source.output_bias_momentum.detach()
            else:
                target.weight[layer_number] = source.weight[layer_number].detach()
                target.bias[layer_number] = source.bias[layer_number].detach()
                if copy_moment:
                    target.momentum[layer_number] = source.momentum[layer_number].detach()
                    target.bias_momentum[layer_number] = source.bias_momentum[layer_number].detach()


def __grow_nodes(*networks):
    origin = networks[0]
    if origin.growable[origin.number_hidden_layers]:
        nodes = 1
        for i in range(nodes):
            for network in networks:
                network.grow_node(origin.number_hidden_layers)
        return True
    else:
        return False


def __prune_nodes(*networks):
    origin = networks[0]
    if origin.prunable[origin.number_hidden_layers][0] >= 0:
        nodes_to_prune = origin.prunable[origin.number_hidden_layers].tolist()
        for network in networks:
            for node_to_prune in nodes_to_prune[::-1]:
                network.prune_node(origin.number_hidden_layers, node_to_prune)
        return True
    return False


def __width_evolution(network: NeuralNetwork, x: torch.tensor, y: torch.tensor = None):
    if y is None:
        y = x

    network.feedforward(x, y)
    network.width_adaptation_stepwise(y)


def __discriminative(network: NeuralNetwork, x: torch.tensor, y: torch.tensor = None, is_neg_grad: bool = False):
    y = x.detach() if y is None else y
    network.train(x=x, y=y, is_neg_grad=is_neg_grad)


def __generative(network: DenoisingAutoEncoder, x: torch.tensor, y: torch.tensor = None,
                 is_tied_weight=True, noise_ratio=0.1, glw_epochs: int = 1):
    y = x.detach() if y is None else y
    network.greedy_layer_wise_pretrain(x=x, number_epochs=glw_epochs, noise_ratio=noise_ratio)
    network.train(x=x, y=y, noise_ratio=noise_ratio, is_tied_weight=is_tied_weight)


def __test(network: NeuralNetwork, x: torch.tensor, y: torch.tensor = None,
           is_source: bool = False, is_discriminative: bool = False, metrics=None):
    with torch.no_grad():
        y = x.detach() if y is None else y
        network.test(x=x, y=y)

        if is_source:
            if is_discriminative:
                metrics['classification_rate_source'].append(network.classification_rate)
                metrics['classification_source_loss'].append(float(network.loss_value))
                metrics['classification_source_misclassified'].append(float(network.misclassified))
            else:
                metrics['reconstruction_source_loss'].append(float(network.loss_value))
        else:
            if is_discriminative:
                metrics['classification_rate_target'].append(network.classification_rate)
                metrics['classification_target_loss'].append(float(network.loss_value))
                metrics['classification_target_misclassified'].append(float(network.misclassified))
            else:
                metrics['reconstruction_target_loss'].append(float(network.loss_value))


def __force_same_size(a_tensor, b_tensor, shuffle=True, strategy='max'):
    common = np.min([a_tensor.shape[0], b_tensor.shape[0]])

    if shuffle:
        a_tensor = a_tensor[torch.randperm(a_tensor.shape[0])]
        b_tensor = b_tensor[torch.randperm(b_tensor.shape[0])]

    if strategy == 'max':
        if math.ceil(a_tensor.shape[0] / common) <= math.ceil(b_tensor.shape[0] / common):
            b_tensor = torch.stack(list(target for target, source
                                        in zip(b_tensor[torch.randperm(b_tensor.shape[0])],
                                               cycle(a_tensor[torch.randperm(a_tensor.shape[0])]))))
            a_tensor = torch.stack(list(source for target, source
                                        in zip(b_tensor[torch.randperm(b_tensor.shape[0])],
                                               cycle(a_tensor[torch.randperm(a_tensor.shape[0])]))))
        else:
            b_tensor = torch.stack(list(target for target, source
                                        in zip(cycle(b_tensor[torch.randperm(b_tensor.shape[0])]),
                                               a_tensor[torch.randperm(a_tensor.shape[0])])))
            a_tensor = torch.stack(list(source for target, source
                                        in zip(cycle(b_tensor[torch.randperm(b_tensor.shape[0])]),
                                               a_tensor[torch.randperm(a_tensor.shape[0])])))

    elif strategy == 'min':
        a_tensor = a_tensor[:common]
        b_tensor = b_tensor[:common]

    if shuffle:
        a_tensor = a_tensor[torch.randperm(a_tensor.shape[0])]
        b_tensor = b_tensor[torch.randperm(b_tensor.shape[0])]

    return a_tensor, b_tensor


def __print_annotation(lst):
    def custom_range(xx):
        step = int(len(xx) * 0.25) - 1
        return range(0, len(xx), 1 if step == 0 else step)

    for idx in custom_range(lst):
        pos = lst[idx] if isinstance(lst[idx], (int, float, np.int32)) else lst[idx][0]
        plt.annotate(format(pos, '.2f'), (idx, pos))
    pos = lst[-1] if isinstance(lst[-1], (int, float, np.int32)) else lst[-1][0]
    plt.annotate(format(pos, '.2f'), (len(lst), pos))


def __plot_time(train_time: np.ndarray,
                test_time: np.ndarray,
                annotation=True):
    plt.title('Processing time')
    plt.ylabel('Seconds')
    plt.xlabel('Minibatches')

    plt.plot(train_time, linewidth=1,
             label=('Train time: %f (Mean) %f (Accumulated)' %
                    (np.nanmean(train_time), np.sum(train_time))))
    plt.plot(test_time, linewidth=1,
             label=('Test time: %f (Mean) %f (Accumulated)' %
                    (np.nanmean(test_time), np.sum(test_time))))
    plt.legend()

    if annotation:
        __print_annotation(train_time)
        __print_annotation(test_time)

    plt.tight_layout()
    plt.show()


def __plot_node_evolution(nodes_discriminator: np.ndarray,
                          nodes_domain_classifier: np.ndarray,
                          nodes_feature_extraction: np.ndarray,
                          annotation=True):
    plt.title('Node evolution')
    plt.ylabel('Nodes')
    plt.xlabel('Minibatches')

    plt.plot(nodes_discriminator, linewidth=1,
             label=('Discriminator HL nodes: %f (Mean) %d (Final)' %
                    (np.nanmean(nodes_discriminator), nodes_discriminator[-1])))
    plt.plot(nodes_domain_classifier, linewidth=1,
             label=('Domain Classifier HL nodes: %f (Mean) %d (Final)' %
                    (np.nanmean(nodes_domain_classifier), nodes_domain_classifier[-1])))
    plt.plot(nodes_feature_extraction, linewidth=1,
             label=('Feature Extraction HL nodes: %f (Mean) %d (Final)' %
                    (np.nanmean(nodes_feature_extraction), nodes_feature_extraction[-1])))
    plt.legend()

    if annotation:
        __print_annotation(nodes_discriminator)
        __print_annotation(nodes_domain_classifier)
        __print_annotation(nodes_feature_extraction)

    plt.tight_layout()
    plt.show()


def __plot_losses(classification_source_loss: np.ndarray,
                  classification_target_loss: np.ndarray,
                  reconstruction_source_loss: np.ndarray,
                  reconstruction_target_loss: np.ndarray,
                  domain_classifier_loss: np.ndarray,
                  annotation=True):
    plt.title('Losses evolution')
    plt.ylabel('Loss value')
    plt.xlabel('Minibatches')

    plt.plot(classification_source_loss, linewidth=1,
             label=('Classification Source Loss mean: %f' %
                    (np.nanmean(classification_source_loss))))
    plt.plot(classification_target_loss, linewidth=1,
             label=('Classification Target Loss mean: %f' %
                    (np.nanmean(classification_target_loss))))
    plt.plot(reconstruction_source_loss, linewidth=1,
             label=('Reconstruction Source Loss mean: %f' %
                    (np.nanmean(reconstruction_source_loss))))
    plt.plot(reconstruction_target_loss, linewidth=1,
             label=('Reconstruction Target Loss mean: %f' %
                    (np.nanmean(reconstruction_target_loss))))
    plt.plot(domain_classifier_loss, linewidth=1,
             label=('Domain Classifier Loss mean: %f' %
                    (np.nanmean(domain_classifier_loss))))
    plt.legend()

    if annotation:
        __print_annotation(classification_source_loss)
        __print_annotation(classification_target_loss)
        __print_annotation(reconstruction_source_loss)
        __print_annotation(reconstruction_target_loss)
        __print_annotation(domain_classifier_loss)

    plt.tight_layout()
    plt.show()


def __plot_classification_rates(source_rate: np.ndarray,
                                target_rate: np.ndarray,
                                domain_rate: np.ndarray,
                                total_source_rate: float,
                                total_target_rate: float,
                                total_domain_classification_rate: float,
                                annotation=True,
                                class_number=None):
    plt.title('Source and Target Classification Rates')
    plt.ylabel('Classification Rate')
    plt.xlabel('Minibatches')

    plt.plot(source_rate, linewidth=1, label=('Source CR: %f (batch) | %f (dataset)' %
                                              (np.nanmean(source_rate), total_source_rate)))
    plt.plot(target_rate, linewidth=1, label=('Target CR: %f (batch) | %f (dataset)' %
                                              (np.nanmean(target_rate), total_target_rate)))
    plt.plot(domain_rate, linewidth=1, label=('Domain CR: %f (batch) | %f (dataset)' %
                                              (np.nanmean(domain_rate), total_domain_classification_rate)))

    if annotation:
        __print_annotation(source_rate)
        __print_annotation(target_rate)
        __print_annotation(domain_rate)

    if class_number is not None:
        plt.plot(np.ones(len(source_rate)) * 1 / class_number,
                 linewidth=1, label='Random Classification Threshold: %f' % (1 / class_number))

    plt.plot(np.ones(len(source_rate)) * 1 / 2,
             linewidth=1, label='Random Domain Classification Threshold: %f' % (1 / 2))

    plt.legend()

    plt.tight_layout()
    plt.show()


def __plot_ns(bias, var, ns, annotation=True):
    plt.plot(bias, linewidth=1, label=('Bias mean: %f' % (np.nanmean(bias))))
    plt.plot(var, linewidth=1, label=('Variance mean: %f' % (np.nanmean(var))))
    plt.plot(ns, linewidth=1, label=('NS (Bias + Variance) mean: %f' % (np.nanmean(ns))))
    plt.legend()

    if annotation:
        __print_annotation(bias)
        __print_annotation(var)
        __print_annotation(ns)

    plt.tight_layout()
    plt.show()


def __plot_discriminative_network_significance(bias, var, annotation=True):
    plt.title('Discriminative Network Significance')
    plt.ylabel('Value')
    plt.xlabel('Sample')

    __plot_ns(bias, var, (np.array(bias) + np.array(var)).tolist(), annotation)


def __plot_domain_classifier_network_significance(bias, var, annotation=True):
    plt.title('Domain Classifier Network Significance')
    plt.ylabel('Value')
    plt.xlabel('Sample')

    __plot_ns(bias, var, (np.array(bias) + np.array(var)).tolist(), annotation)


def __plot_feature_extractor_network_significance(bias, var, annotation=True):
    plt.title('Feature Extractor Network Significance')
    plt.ylabel('Value')
    plt.xlabel('Sample')

    __plot_ns(bias, var, (np.array(bias) + np.array(var)).tolist(), annotation)


def __load_source_target(source: str, target: str, n_source_concept_drift: int = 1, n_target_concept_drift: int = 1):
    dm_s = DataManipulator()
    dm_t = DataManipulator()

    source = source.replace('_', '-').replace(' ', '-').lower()
    target = target.replace('_', '-').replace(' ', '-').lower()

    if source == 'mnist-28':
        dm_s.load_mnist(resize=28, n_concept_drifts=n_source_concept_drift)
    elif source == 'mnist-26':
        dm_s.load_mnist(resize=26, n_concept_drifts=n_source_concept_drift)
    elif source == 'mnist-24':
        dm_s.load_mnist(resize=24, n_concept_drifts=n_source_concept_drift)
    elif source == 'mnist-22':
        dm_s.load_mnist(resize=22, n_concept_drifts=n_source_concept_drift)
    elif source == 'mnist-20':
        dm_s.load_mnist(resize=20, n_concept_drifts=n_source_concept_drift)
    elif source == 'mnist-18':
        dm_s.load_mnist(resize=18, n_concept_drifts=n_source_concept_drift)
    elif source == 'mnist-16':
        dm_s.load_mnist(resize=16, n_concept_drifts=n_source_concept_drift)
    elif source == 'usps-28':
        dm_s.load_usps(resize=28, n_concept_drifts=n_source_concept_drift)
    elif source == 'usps-26':
        dm_s.load_usps(resize=26, n_concept_drifts=n_source_concept_drift)
    elif source == 'usps-24':
        dm_s.load_usps(resize=24, n_concept_drifts=n_source_concept_drift)
    elif source == 'usps-22':
        dm_s.load_usps(resize=22, n_concept_drifts=n_source_concept_drift)
    elif source == 'usps-20':
        dm_s.load_usps(resize=20, n_concept_drifts=n_source_concept_drift)
    elif source == 'usps-18':
        dm_s.load_usps(resize=18, n_concept_drifts=n_source_concept_drift)
    elif source == 'usps-16':
        dm_s.load_usps(resize=16, n_concept_drifts=n_source_concept_drift)
    elif source == 'cifar10':
        dm_s.load_cifar10(n_concept_drifts=n_source_concept_drift)
    elif source == 'stl10':
        dm_s.load_stl10(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-fashion':
        dm_s.load_amazon_review_fashion(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-all-beauty':
        dm_s.load_amazon_review_all_beauty(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-appliances':
        dm_s.load_amazon_review_appliances(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-arts-crafts-sewing':
        dm_s.load_amazon_review_arts_crafts_sewing(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-automotive':
        dm_s.load_amazon_review_automotive(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-books':
        dm_s.load_amazon_review_books(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-cds-vinyl':
        dm_s.load_amazon_review_cds_vinyl(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-cellphones_accessories':
        dm_s.load_amazon_review_cellphones_accessories(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-clothing-shoes-jewelry':
        dm_s.load_amazon_review_clothing_shoes_jewelry(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-digital-music':
        dm_s.load_amazon_review_digital_music(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-electronics':
        dm_s.load_amazon_review_electronics(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-gift-card':
        dm_s.load_amazon_review_gift_card(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-grocery-gourmet-food':
        dm_s.load_amazon_review_grocery_gourmet_food(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-home-kitchen':
        dm_s.load_amazon_review_home_kitchen(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-industrial-scientific':
        dm_s.load_amazon_review_industrial_scientific(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-kindle-store':
        dm_s.load_amazon_review_kindle_store(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-luxury-beauty':
        dm_s.load_amazon_review_luxury_beauty(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-magazine-subscription':
        dm_s.load_amazon_review_magazine_subscription(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-movies-tv':
        dm_s.load_amazon_review_movies_tv(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-musical-instruments':
        dm_s.load_amazon_review_musical_instruments(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-office-products':
        dm_s.load_amazon_review_office_products(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-patio-lawn-garden':
        dm_s.load_amazon_review_patio_lawn_garden(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-pet-supplies':
        dm_s.load_amazon_review_pet_supplies(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-prime-pantry':
        dm_s.load_amazon_review_prime_pantry(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-software':
        dm_s.load_amazon_review_software(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-sports-outdoors':
        dm_s.load_amazon_review_sports_outdoors(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-tools-home-improvements':
        dm_s.load_amazon_review_tools_home_improvements(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-toys-games':
        dm_s.load_amazon_review_toys_games(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-video-games':
        dm_s.load_amazon_review_video_games(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-nips-books':
        dm_s.load_amazon_review_nips_books(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-nips-dvd':
        dm_s.load_amazon_review_nips_dvd(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-nips-electronics':
        dm_s.load_amazon_review_nips_electronics(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-nips-kitchen':
        dm_s.load_amazon_review_nips_kitchen(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-apparel':
        dm_s.load_amazon_review_acl_apparel(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-automotive':
        dm_s.load_amazon_review_acl_automotive(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-baby':
        dm_s.load_amazon_review_acl_baby(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-beauty':
        dm_s.load_amazon_review_acl_beauty(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-books':
        dm_s.load_amazon_review_acl_books(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-camera_photo':
        dm_s.load_amazon_review_acl_camera_photo(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-cell_phones_service':
        dm_s.load_amazon_review_acl_cell_phones_service(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-computer_video_games':
        dm_s.load_amazon_review_acl_computer_video_games(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-dvd':
        dm_s.load_amazon_review_acl_dvd(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-electronics':
        dm_s.load_amazon_review_acl_electronics(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-gourmet_food':
        dm_s.load_amazon_review_acl_gourmet_food(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-grocery':
        dm_s.load_amazon_review_acl_grocery(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-health_personal_care':
        dm_s.load_amazon_review_acl_health_personal_care(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-jewelry_watches':
        dm_s.load_amazon_review_acl_jewelry_watches(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-kitchen_housewares':
        dm_s.load_amazon_review_acl_kitchen_housewares(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-magazines':
        dm_s.load_amazon_review_acl_magazines(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-music':
        dm_s.load_amazon_review_acl_music(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-musical_instruments':
        dm_s.load_amazon_review_acl_musical_instruments(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-office_products':
        dm_s.load_amazon_review_acl_office_products(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-outdoor_living':
        dm_s.load_amazon_review_acl_outdoor_living(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-software':
        dm_s.load_amazon_review_acl_software(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-sports_outdoors':
        dm_s.load_amazon_review_acl_sports_outdoors(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-tools_hardware':
        dm_s.load_amazon_review_acl_tools_hardware(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-toys_games':
        dm_s.load_amazon_review_acl_toys_games(n_concept_drifts=n_source_concept_drift)
    elif source == 'amazon-review-acl-video':
        dm_s.load_amazon_review_acl_video(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-obama-all':
        dm_s.load_news_popularity_obama_all(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-economy-all':
        dm_s.load_news_popularity_economy_all(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-microsoft-all':
        dm_s.load_news_popularity_microsoft_all(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-palestine-all':
        dm_s.load_news_popularity_palestine_all(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-obama-facebook':
        dm_s.load_news_popularity_obama_facebook(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-economy-facebook':
        dm_s.load_news_popularity_economy_facebook(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-microsoft-facebook':
        dm_s.load_news_popularity_microsoft_facebook(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-palestine-facebook':
        dm_s.load_news_popularity_palestine_facebook(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-obama-googleplus':
        dm_s.load_news_popularity_obama_googleplus(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-economy-googleplus':
        dm_s.load_news_popularity_economy_googleplus(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-microsoft-googleplus':
        dm_s.load_news_popularity_microsoft_googleplus(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-palestine-googleplus':
        dm_s.load_news_popularity_palestine_googleplus(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-obama-linkedin':
        dm_s.load_news_popularity_obama_linkedin(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-economy-linkedin':
        dm_s.load_news_popularity_economy_linkedin(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-microsoft-linkedin':
        dm_s.load_news_popularity_microsoft_linkedin(n_concept_drifts=n_source_concept_drift)
    elif source == 'news-palestine-linkedin':
        dm_s.load_news_popularity_palestine_linkedin(n_concept_drifts=n_source_concept_drift)

    if target == 'mnist-28':
        dm_t.load_mnist(resize=28, n_concept_drifts=n_target_concept_drift)
    elif target == 'mnist-26':
        dm_t.load_mnist(resize=26, n_concept_drifts=n_target_concept_drift)
    elif target == 'mnist-24':
        dm_t.load_mnist(resize=24, n_concept_drifts=n_target_concept_drift)
    elif target == 'mnist-22':
        dm_t.load_mnist(resize=22, n_concept_drifts=n_target_concept_drift)
    elif target == 'mnist-20':
        dm_t.load_mnist(resize=20, n_concept_drifts=n_target_concept_drift)
    elif target == 'mnist-18':
        dm_t.load_mnist(resize=18, n_concept_drifts=n_target_concept_drift)
    elif target == 'mnist-16':
        dm_t.load_mnist(resize=16, n_concept_drifts=n_target_concept_drift)
    elif target == 'usps-28':
        dm_t.load_usps(resize=28, n_concept_drifts=n_target_concept_drift)
    elif target == 'usps-26':
        dm_t.load_usps(resize=26, n_concept_drifts=n_target_concept_drift)
    elif target == 'usps-24':
        dm_t.load_usps(resize=24, n_concept_drifts=n_target_concept_drift)
    elif target == 'usps-22':
        dm_t.load_usps(resize=22, n_concept_drifts=n_target_concept_drift)
    elif target == 'usps-20':
        dm_t.load_usps(resize=20, n_concept_drifts=n_target_concept_drift)
    elif target == 'usps-18':
        dm_t.load_usps(resize=18, n_concept_drifts=n_target_concept_drift)
    elif target == 'usps-16':
        dm_t.load_usps(resize=16, n_concept_drifts=n_target_concept_drift)
    elif target == 'cifar10':
        dm_t.load_cifar10(n_concept_drifts=n_target_concept_drift)
    elif target == 'stl10':
        dm_t.load_stl10(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-fashion':
        dm_t.load_amazon_review_fashion(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-all-beauty':
        dm_t.load_amazon_review_all_beauty(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-appliances':
        dm_t.load_amazon_review_appliances(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-arts-crafts-sewing':
        dm_t.load_amazon_review_arts_crafts_sewing(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-automotive':
        dm_t.load_amazon_review_automotive(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-books':
        dm_t.load_amazon_review_books(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-cds-vinyl':
        dm_t.load_amazon_review_cds_vinyl(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-cellphones_accessories':
        dm_t.load_amazon_review_cellphones_accessories(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-clothing-shoes-jewelry':
        dm_t.load_amazon_review_clothing_shoes_jewelry(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-digital-music':
        dm_t.load_amazon_review_digital_music(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-electronics':
        dm_t.load_amazon_review_electronics(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-gift-card':
        dm_t.load_amazon_review_gift_card(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-grocery-gourmet-food':
        dm_t.load_amazon_review_grocery_gourmet_food(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-home-kitchen':
        dm_t.load_amazon_review_home_kitchen(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-industrial-scientific':
        dm_t.load_amazon_review_industrial_scientific(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-kindle-store':
        dm_t.load_amazon_review_kindle_store(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-luxury-beauty':
        dm_t.load_amazon_review_luxury_beauty(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-magazine-subscription':
        dm_t.load_amazon_review_magazine_subscription(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-movies-tv':
        dm_t.load_amazon_review_movies_tv(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-musical-instruments':
        dm_t.load_amazon_review_musical_instruments(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-office-products':
        dm_t.load_amazon_review_office_products(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-patio-lawn-garden':
        dm_t.load_amazon_review_patio_lawn_garden(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-pet-supplies':
        dm_t.load_amazon_review_pet_supplies(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-prime-pantry':
        dm_t.load_amazon_review_prime_pantry(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-software':
        dm_t.load_amazon_review_software(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-sports-outdoors':
        dm_t.load_amazon_review_sports_outdoors(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-tools-home-improvements':
        dm_t.load_amazon_review_tools_home_improvements(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-toys-games':
        dm_t.load_amazon_review_toys_games(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-video-games':
        dm_t.load_amazon_review_video_games(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-nips-books':
        dm_t.load_amazon_review_nips_books(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-nips-dvd':
        dm_t.load_amazon_review_nips_dvd(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-nips-electronics':
        dm_t.load_amazon_review_nips_electronics(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-nips-kitchen':
        dm_t.load_amazon_review_nips_kitchen(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-apparel':
        dm_t.load_amazon_review_acl_apparel(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-automotive':
        dm_t.load_amazon_review_acl_automotive(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-baby':
        dm_t.load_amazon_review_acl_baby(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-beauty':
        dm_t.load_amazon_review_acl_beauty(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-books':
        dm_t.load_amazon_review_acl_books(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-camera_photo':
        dm_t.load_amazon_review_acl_camera_photo(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-cell_phones_service':
        dm_t.load_amazon_review_acl_cell_phones_service(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-computer_video_games':
        dm_t.load_amazon_review_acl_computer_video_games(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-dvd':
        dm_t.load_amazon_review_acl_dvd(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-electronics':
        dm_t.load_amazon_review_acl_electronics(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-gourmet_food':
        dm_t.load_amazon_review_acl_gourmet_food(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-grocery':
        dm_t.load_amazon_review_acl_grocery(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-health_personal_care':
        dm_t.load_amazon_review_acl_health_personal_care(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-jewelry_watches':
        dm_t.load_amazon_review_acl_jewelry_watches(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-kitchen_housewares':
        dm_t.load_amazon_review_acl_kitchen_housewares(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-magazines':
        dm_t.load_amazon_review_acl_magazines(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-music':
        dm_t.load_amazon_review_acl_music(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-musical_instruments':
        dm_t.load_amazon_review_acl_musical_instruments(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-office_products':
        dm_t.load_amazon_review_acl_office_products(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-outdoor_living':
        dm_t.load_amazon_review_acl_outdoor_living(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-software':
        dm_t.load_amazon_review_acl_software(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-sports_outdoors':
        dm_t.load_amazon_review_acl_sports_outdoors(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-tools_hardware':
        dm_t.load_amazon_review_acl_tools_hardware(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-toys_games':
        dm_t.load_amazon_review_acl_toys_games(n_concept_drifts=n_target_concept_drift)
    elif target == 'amazon-review-acl-video':
        dm_t.load_amazon_review_acl_video(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-obama-all':
        dm_t.load_news_popularity_obama_all(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-economy-all':
        dm_t.load_news_popularity_economy_all(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-microsoft-all':
        dm_t.load_news_popularity_microsoft_all(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-palestine-all':
        dm_t.load_news_popularity_palestine_all(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-obama-facebook':
        dm_t.load_news_popularity_obama_facebook(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-economy-facebook':
        dm_t.load_news_popularity_economy_facebook(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-microsoft-facebook':
        dm_t.load_news_popularity_microsoft_facebook(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-palestine-facebook':
        dm_t.load_news_popularity_palestine_facebook(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-obama-googleplus':
        dm_t.load_news_popularity_obama_googleplus(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-economy-googleplus':
        dm_t.load_news_popularity_economy_googleplus(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-microsoft-googleplus':
        dm_t.load_news_popularity_microsoft_googleplus(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-palestine-googleplus':
        dm_t.load_news_popularity_palestine_googleplus(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-obama-linkedin':
        dm_t.load_news_popularity_obama_linkedin(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-economy-linkedin':
        dm_t.load_news_popularity_economy_linkedin(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-microsoft-linkedin':
        dm_t.load_news_popularity_microsoft_linkedin(n_concept_drifts=n_target_concept_drift)
    elif target == 'news-palestine-linkedin':
        dm_t.load_news_popularity_palestine_linkedin(n_concept_drifts=n_target_concept_drift)

    return dm_s, dm_t


def acdc(source, target,
         n_source_concept_drift: int = 5,
         n_target_concept_drift: int = 7,
         internal_epochs: int = 1, is_gpu=False):
    def print_metrics(minibatch, metrics, DMs, DMt, NN, DAEt, DA):
        print('Minibatch: %d | Execution time (dataset load/pre-processing + model run): %f' % (
            minibatch, time.time() - metrics['start_execution_time']))
        if minibatch > 1:
            print((
                          'Total of samples:' + Fore.BLUE + ' %d + %d = %d/%d (%.2f%%) Source' + Style.RESET_ALL + ' |' + Fore.RED + ' %d + %d = %d/%d (%.2f%%) Target' + Style.RESET_ALL + ' | %d/%d (%.2f%%) Samples in total') % (
                      metrics['number_evaluated_samples_source'][-2],
                      metrics['number_evaluated_samples_source'][-1] - metrics['number_evaluated_samples_source'][-2],
                      metrics['number_evaluated_samples_source'][-1],
                      DMs.number_samples(),
                      float(metrics['number_evaluated_samples_source'][-1] / DMs.number_samples()) * 100,
                      metrics['number_evaluated_samples_target'][-2],
                      metrics['number_evaluated_samples_target'][-1] - metrics['number_evaluated_samples_target'][-2],
                      metrics['number_evaluated_samples_target'][-1],
                      DMt.number_samples(),
                      float(metrics['number_evaluated_samples_target'][-1] / DMt.number_samples()) * 100,
                      metrics['number_evaluated_samples_source'][-1] + metrics['number_evaluated_samples_target'][-1],
                      DMs.number_samples() + DMt.number_samples(),
                      float((metrics['number_evaluated_samples_source'][-1] +
                             metrics['number_evaluated_samples_target'][-1]) / (
                                    DMs.number_samples() + DMt.number_samples())) * 100))
        else:
            print((
                          'Total of samples:' + Fore.BLUE + ' %d/%d (%.2f%%) Source' + Style.RESET_ALL + ' |' + Fore.RED + ' %d/%d (%.2f%%) Target' + Style.RESET_ALL + ' | %d/%d (%.2f%%) Samples in total') % (
                      metrics['number_evaluated_samples_source'][-1],
                      DMs.number_samples(),
                      float(metrics['number_evaluated_samples_source'][-1] / DMs.number_samples()) * 100,
                      metrics['number_evaluated_samples_target'][-1],
                      DMt.number_samples(),
                      float(metrics['number_evaluated_samples_target'][-1] / DMt.number_samples()) * 100,
                      metrics['number_evaluated_samples_source'][-1] + metrics['number_evaluated_samples_target'][-1],
                      DMs.number_samples() + DMt.number_samples(),
                      float((metrics['number_evaluated_samples_source'][-1] +
                             metrics['number_evaluated_samples_target'][-1]) / (
                                    DMs.number_samples() + DMt.number_samples())) * 100))

        if minibatch > 1:
            string_max = '' + Fore.GREEN + 'Max' + Style.RESET_ALL
            string_mean = '' + Fore.YELLOW + 'Mean' + Style.RESET_ALL
            string_min = '' + Fore.RED + 'Min' + Style.RESET_ALL
            string_now = '' + Fore.BLUE + 'Now' + Style.RESET_ALL
            string_accu = '' + Fore.MAGENTA + 'Accu' + Style.RESET_ALL

            print((
                          '%s %s %s %s %s Training time:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Fore.MAGENTA + ' %f' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now, string_accu,
                      np.max(metrics['train_time']),
                      np.nanmean(metrics['train_time']),
                      np.min(metrics['train_time']),
                      metrics['train_time'][-1],
                      np.sum(metrics['train_time'])))
            print((
                          '%s %s %s %s %s Testing time:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Fore.MAGENTA + ' %f' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now, string_accu,
                      np.max(metrics['test_time']),
                      np.nanmean(metrics['test_time']),
                      np.min(metrics['test_time']),
                      metrics['test_time'][-1],
                      np.sum(metrics['test_time'])))
            print((
                          '%s %s %s %s CR Source:' + Fore.GREEN + ' %f%% ' + Back.BLUE + Fore.YELLOW + Style.BRIGHT + '%f%%' + Style.RESET_ALL + Fore.RED + ' %f%%' + Fore.BLUE + ' %f%%' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['classification_rate_source']) * 100,
                      np.nanmean(metrics['classification_rate_source']) * 100,
                      np.min(metrics['classification_rate_source']) * 100,
                      metrics['classification_rate_source'][-1] * 100))
            print((
                          '%s %s %s %s CR Target:' + Fore.GREEN + ' %f%% ' + Back.RED + Fore.YELLOW + Style.BRIGHT + '%f%%' + Style.RESET_ALL + Fore.RED + ' %f%%' + Fore.BLUE + ' %f%%' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['classification_rate_target']) * 100,
                      np.nanmean(metrics['classification_rate_target']) * 100,
                      np.min(metrics['classification_rate_target']) * 100,
                      metrics['classification_rate_target'][-1] * 100))
            print((
                          '%s %s %s %s CR Domain Discriminator:' + Fore.GREEN + ' %f%% ' + Fore.YELLOW + '%f%%' + Style.RESET_ALL + Fore.RED + ' %f%%' + Fore.BLUE + ' %f%%' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['classification_rate_domain']) * 100,
                      np.nanmean(metrics['classification_rate_domain']) * 100,
                      np.min(metrics['classification_rate_domain']) * 100,
                      metrics['classification_rate_domain'][-1] * 100))
            print((
                          '%s %s %s %s Classification Source Loss:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['classification_source_loss']),
                      np.nanmean(metrics['classification_source_loss']),
                      np.min(metrics['classification_source_loss']),
                      metrics['classification_source_loss'][-1]))
            print((
                          '%s %s %s %s Classification Target Loss:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['classification_target_loss']),
                      np.nanmean(metrics['classification_target_loss']),
                      np.min(metrics['classification_target_loss']),
                      metrics['classification_target_loss'][-1]))
            print((
                          '%s %s %s %s Domain Discriminator Loss:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['domain_regression_loss']),
                      np.nanmean(metrics['domain_regression_loss']),
                      np.min(metrics['domain_regression_loss']),
                      metrics['domain_regression_loss'][-1]))
            print((
                          '%s %s %s %s Reconstruction Source Loss:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['reconstruction_source_loss']),
                      np.nanmean(metrics['reconstruction_source_loss']),
                      np.min(metrics['reconstruction_source_loss']),
                      metrics['reconstruction_source_loss'][-1]))
            print((
                          '%s %s %s %s Reconstruction Target Loss:' + Fore.GREEN + ' %f' + Fore.YELLOW + ' %f' + Fore.RED + ' %f' + Fore.BLUE + ' %f' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['reconstruction_target_loss']),
                      np.nanmean(metrics['reconstruction_target_loss']),
                      np.min(metrics['reconstruction_target_loss']),
                      metrics['reconstruction_target_loss'][-1]))
            print((
                          '%s %s %s %s Discriminator Nodes:' + Fore.GREEN + ' %d' + Fore.YELLOW + ' %f' + Fore.RED + ' %d' + Fore.BLUE + ' %d' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['node_evolution_discriminator']),
                      np.nanmean(metrics['node_evolution_discriminator']),
                      np.min(metrics['node_evolution_discriminator']),
                      metrics['node_evolution_discriminator'][-1]))
            print((
                          '%s %s %s %s Denoising Autoencoder Nodes:' + Fore.GREEN + ' %d' + Fore.YELLOW + ' %f' + Fore.RED + ' %d' + Fore.BLUE + ' %d' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['node_evolution_feature_extraction']),
                      np.nanmean(metrics['node_evolution_feature_extraction']),
                      np.min(metrics['node_evolution_feature_extraction']),
                      metrics['node_evolution_feature_extraction'][-1]))
            print((
                          '%s %s %s %s Domain Classifier Nodes:' + Fore.GREEN + ' %d' + Fore.YELLOW + ' %f' + Fore.RED + ' %d' + Fore.BLUE + ' %d' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['node_evolution_domain_classifier']),
                      np.nanmean(metrics['node_evolution_domain_classifier']),
                      np.min(metrics['node_evolution_domain_classifier']),
                      metrics['node_evolution_domain_classifier'][-1]))
            print(('Network structure:' + Fore.BLUE + ' %s' + Style.RESET_ALL) % (
                " ".join(map(str, NN.layers))))
            print(('Domain Discriminator structure:' + Fore.GREEN + ' %s' + Style.RESET_ALL) % (
                " ".join(map(str, DA.layers))))
            print(('Denoising Auto Encoder:' + Fore.RED + ' %s' + Style.RESET_ALL) % (
                " ".join(map(str, DAEt.layers))))
        print(Style.RESET_ALL)

    metrics = {'classification_rate_source': [],
               'classification_rate_target': [],
               'classification_rate_domain': [],
               'number_evaluated_samples_source': [],
               'number_evaluated_samples_target': [],
               'train_time': [],
               'test_time': [],
               'node_evolution_discriminator': [],
               'node_evolution_domain_classifier': [],
               'node_evolution_feature_extraction': [],
               'classification_target_loss': [],
               'classification_source_loss': [],
               'reconstruction_source_loss': [],
               'reconstruction_target_loss': [],
               'domain_regression_loss': [],
               'classification_source_misclassified': [],
               'classification_target_misclassified': [],
               'domain_classification_misclassified': [],
               'start_execution_time': time.time()}
    MyDevice().set(is_gpu=is_gpu)
    internal_epochs = internal_epochs if internal_epochs >= 1 else 1

    SOURCE_DOMAIN_LABEL = torch.tensor([[1, 0]], dtype=torch.float, device=MyDevice().get())
    TARGET_DOMAIN_LABEL = torch.tensor([[0, 1]], dtype=torch.float, device=MyDevice().get())

    dm_s, dm_t = __load_source_target(source, target, n_source_concept_drift, n_target_concept_drift)

    dae = DenoisingAutoEncoder([dm_s.number_features(),
                                100,
                                dm_s.number_features()])
    nn = NeuralNetwork([dm_s.number_features(),
                        dae.layers[1],
                        100,
                        dm_s.number_classes()])
    da = NeuralNetwork([dm_s.number_features(),
                        dae.layers[1],
                        100,
                        2])

    count_source = 0
    count_target = 0
    count_window = 0
    window_size = 1000
    batch_counter = 0

    x_source = []
    y_source = []
    x_target = []
    y_target = []

    while count_source < dm_s.number_samples() \
            or count_target < dm_t.number_samples():
        if count_window < window_size \
                and (count_source < dm_s.number_samples()
                     or count_target < dm_t.number_samples()):

            source_prob = (dm_s.number_samples() - count_source) / (
                    dm_s.number_samples() - count_source + dm_t.number_samples() - count_target + 0.)

            if (np.random.rand() <= source_prob and count_source < dm_s.number_samples()) or (
                    count_target >= dm_t.number_samples() and count_source < dm_s.number_samples()):
                x, y = dm_s.get_x_y(count_source)
                x_source.append(x)
                y_source.append(y)
                count_source += 1
                count_window += 1
            elif count_target < dm_t.number_samples():
                x, y = dm_t.get_x_y(count_target)
                x_target.append(x)
                y_target.append(y)
                count_target += 1
                count_window += 1
        else:
            batch_counter += 1
            metrics['number_evaluated_samples_source'].append(count_source)
            metrics['number_evaluated_samples_target'].append(count_target)

            # Workaround to avoid empty stream
            if batch_counter > 1:
                if (count_source - metrics['number_evaluated_samples_source'][-2] == 0):
                    x, y = dm_s.get_x_y(np.random.randint(0, count_source))
                    x_source.append(x)
                    y_source.append(y)
                if (count_target - metrics['number_evaluated_samples_target'][-2] == 0):
                    x, y = dm_t.get_x_y(np.random.randint(0, count_target))
                    x_target.append(x)
                    y_target.append(y)
            # Workaround to avoid empty stream

            x_source = torch.tensor(x_source, dtype=torch.float, device=MyDevice().get())
            y_source = torch.tensor(y_source, dtype=torch.float, device=MyDevice().get())
            x_target = torch.tensor(x_target, dtype=torch.float, device=MyDevice().get())
            y_target = torch.tensor(y_target, dtype=torch.float, device=MyDevice().get())

            # TEST
            if batch_counter > 1:
                metrics['test_time'].append(time.time())
                __test(network=nn, x=x_source, y=y_source,
                       is_source=True, is_discriminative=True, metrics=metrics)
                __test(network=nn, x=x_target, y=y_target,
                       is_source=False, is_discriminative=True, metrics=metrics)
                __test(network=dae, x=x_source,
                       is_source=True, is_discriminative=False, metrics=metrics)
                __test(network=dae, x=x_target,
                       is_source=False, is_discriminative=False, metrics=metrics)

                da.test(x=torch.cat([x_source, x_target]),
                        y=torch.cat([SOURCE_DOMAIN_LABEL.repeat(x_source.shape[0], 1),
                                     TARGET_DOMAIN_LABEL.repeat(x_target.shape[0], 1)]))
                metrics['domain_regression_loss'].append(float(da.loss_value))
                metrics['classification_rate_domain'].append(da.classification_rate)
                metrics['domain_classification_misclassified'].append(da.misclassified)
                metrics['test_time'][-1] = time.time() - metrics['test_time'][-1]

            # TRAIN
            metrics['train_time'].append(time.time())

            common_source, x_target = __force_same_size(torch.cat((x_source.T, y_source.T)).T, x_target)
            x_source, y_source = common_source.T.split(x_source.shape[1])
            x_source, y_source = x_source.T, y_source.T

            epoch = 1
            while epoch <= internal_epochs:
                for xs, xt, ys in [(xs.view(1, xs.shape[0]), xt.view(1, xt.shape[0]), ys.view(1, ys.shape[0]))
                                   for xs, xt, ys in zip(x_source, x_target, cycle(y_source))]:
                    # Evolving
                    # if epoch == 1:
                    #     # Evolving Feature Extraction
                    #     for j in range(0, 2):
                    #         if j == 0:
                    #             __width_evolution(network=dae, x=xs, y=xt)
                    #         elif i == 1:
                    #             __width_evolution(network=dae, x=xt, y=xs)
                    #         if __grow_nodes(dae, da, nn):
                    #             __copy_weights(source=dae, targets=[da, nn], layer_numbers=[1], copy_moment=False)
                    #         elif __prune_nodes(dae, da, nn):
                    #             __copy_weights(source=dae, targets=[da, nn], layer_numbers=[1], copy_moment=False)

                    #     # Evolving Source
                    #     __width_evolution(network=nn, x=xs, y=ys)
                    #     __width_evolution(network=da, x=xs, y=SOURCE_DOMAIN_LABEL)
                    #     if not __grow_nodes(da, nn):
                    #         if __prune_nodes(da):
                    #             __prune_nodes(nn)
                    #         elif not __grow_nodes(nn):
                    #             __prune_nodes(nn)

                    #     # Evolving Target
                    #     __width_evolution(network=da, x=xt, y=TARGET_DOMAIN_LABEL)
                    #     if not __grow_nodes(da, nn):
                    #         __prune_nodes(da)

                    # Denoising AutoEncoder
                    __generative(network=dae, x=xs, y=xt)
                    __copy_weights(source=dae, targets=[da, nn], layer_numbers=[1], copy_moment=False)

                    __generative(network=dae, x=xt, y=xs)
                    __copy_weights(source=dae, targets=[da, nn], layer_numbers=[1], copy_moment=False)

                    # Domain Discriminator
                    da.feedforward(x=xs, y=SOURCE_DOMAIN_LABEL, train=True).backpropagate()
                    dae.weight[0] = dae.weight[0] - da.learning_rate * da.weight[0].grad.neg()
                    dae.bias[0] = dae.bias[0] - da.learning_rate * da.bias[0].grad.neg()
                    for weight_no in range(da.number_hidden_layers, 0, -1):
                        da.update_weight(weight_no=weight_no)

                    da.feedforward(x=xt, y=TARGET_DOMAIN_LABEL, train=True).backpropagate()
                    dae.weight[0] = dae.weight[0] - da.learning_rate * da.weight[0].grad.neg()
                    dae.bias[0] = dae.bias[0] - da.learning_rate * da.bias[0].grad.neg()
                    for weight_no in range(da.number_hidden_layers, 0, -1):
                        da.update_weight(weight_no=weight_no)
                    __copy_weights(source=dae, targets=[da, nn], layer_numbers=[1], copy_moment=False)

                    # Discriminator
                    __discriminative(network=nn, x=xs, y=ys)
                    __copy_weights(source=nn, targets=[da, dae], layer_numbers=[1], copy_moment=True)

                epoch += 1
                da.test(x=torch.cat([x_source, x_target]),
                        y=torch.cat([SOURCE_DOMAIN_LABEL.repeat(x_source.shape[0], 1),
                                     TARGET_DOMAIN_LABEL.repeat(x_target.shape[0], 1)]))

            # Metrics
            metrics['train_time'][-1] = time.time() - metrics['train_time'][-1]
            metrics['node_evolution_discriminator'].append(nn.layers[-2])
            metrics['node_evolution_domain_classifier'].append(da.layers[-2])
            metrics['node_evolution_feature_extraction'].append(dae.layers[-2])
            print_metrics(batch_counter, metrics, dm_s, dm_t, nn, dae, da)

            # Reset variables for the next batch
            x_source = []
            y_source = []
            x_target = []
            y_target = []
            count_window = 0

    result_string = '%f (T) | %f (S) \t ' \
                    '%f | %d \t ' \
                    '%f | %d \t ' \
                    '%f | %d \t ' \
                    '%f | %f' % (
                        np.mean(metrics['classification_rate_target']),
                        np.mean(metrics['classification_rate_source']),

                        np.mean(metrics['node_evolution_feature_extraction']),
                        metrics['node_evolution_feature_extraction'][-1],

                        np.mean(metrics['node_evolution_discriminator']),
                        metrics['node_evolution_discriminator'][-1],

                        np.mean(metrics['node_evolution_domain_classifier']),
                        metrics['node_evolution_domain_classifier'][-1],

                        np.mean(metrics['train_time']),
                        np.sum(metrics['train_time']))

    print('CR Rate (Target) | CR Rate (Source) | \t ' \
          'Feature Extractor Node Evolution (mean | final) \t ' \
          'Discriminator Node Evolution (mean | final) \t ' \
          'Domain Classifier Node Evolution (mean | final) \t ' \
          'Train Time (mean | total)')
    print(result_string)

    result = {}
    result['string'] = result_string
    result['classification_rate_source_batch'] = np.nanmean(metrics['classification_rate_source'])
    result['classification_rate_target_batch'] = np.nanmean(metrics['classification_rate_target'])
    result['classification_rate_domain_batch'] = np.nanmean(metrics['classification_rate_domain'])
    result['classification_rate_source_total'] = 1 - np.sum(
        metrics['classification_source_misclassified']) / dm_s.number_samples()
    result['classification_rate_target_total'] = 1 - np.sum(
        metrics['classification_target_misclassified']) / dm_t.number_samples()
    result['classification_rate_domain_total'] = 1 - np.sum(metrics['domain_classification_misclassified']) / (
            dm_s.number_samples() + dm_t.number_samples())
    result['source_node_mean'] = np.nanmean(metrics['node_evolution_discriminator'])
    result['target_node_mean'] = np.nanmean(metrics['node_evolution_feature_extraction'])
    result['domain_node_mean'] = np.nanmean(metrics['node_evolution_domain_classifier'])
    result['source_node_final'] = metrics['node_evolution_discriminator'][-1]
    result['target_node_final'] = metrics['node_evolution_feature_extraction'][-1]
    result['domain_node_final'] = metrics['node_evolution_domain_classifier'][-1]
    result['train_time_mean'] = np.nanmean(metrics['train_time'])
    result['train_time_final'] = np.nansum(metrics['train_time'])
    result['test_time_mean'] = np.nanmean(metrics['test_time'])
    result['test_time_final'] = np.nansum(metrics['test_time'])
    result['classification_source_loss_mean'] = np.nanmean(metrics['classification_source_loss'])
    result['classification_target_loss_mean'] = np.nanmean(metrics['classification_target_loss'])
    result['reconstruction_source_loss_mean'] = np.nanmean(metrics['reconstruction_source_loss'])
    result['reconstruction_target_loss_mean'] = np.nanmean(metrics['reconstruction_target_loss'])
    result['domain_adaptation_loss_mean'] = np.nanmean(metrics['domain_regression_loss'])

    print()
    print(result)

    __plot_time(metrics['train_time'],
                metrics['test_time'],
                annotation=False)
    __plot_classification_rates(metrics['classification_rate_source'],
                                metrics['classification_rate_target'],
                                metrics['classification_rate_domain'],
                                1 - np.sum(metrics['classification_source_misclassified']) / dm_s.number_samples(),
                                1 - np.sum(metrics['classification_target_misclassified']) / dm_t.number_samples(),
                                1 - np.sum(metrics['domain_classification_misclassified']) / (
                                        dm_s.number_samples() + dm_t.number_samples()),
                                class_number=dm_s.number_classes(),
                                annotation=False)
    __plot_node_evolution(metrics['node_evolution_discriminator'],
                          metrics['node_evolution_domain_classifier'],
                          metrics['node_evolution_feature_extraction'],
                          annotation=False)
    __plot_losses(metrics['classification_source_loss'],
                  metrics['classification_target_loss'],
                  metrics['reconstruction_source_loss'],
                  metrics['reconstruction_target_loss'],
                  metrics['domain_regression_loss'],
                  annotation=False)
    __plot_discriminative_network_significance(nn.BIAS, nn.VAR, annotation=False)
    __plot_domain_classifier_network_significance(da.BIAS, da.VAR, annotation=False)
    __plot_feature_extractor_network_significance(dae.BIAS, dae.VAR, annotation=False)

    return result


def generate_csv_from_dataset(dataset_name: str,
                              n_concept_drift: int = 1,
                              is_source: bool = True,
                              is_one_hot_encoding: bool = True,
                              label_starts_at: int = 0):
    import csv, os
    from tqdm import tqdm
    filename = 'source.csv' if is_source else 'target.csv'

    dm, _ = __load_source_target(source=dataset_name,
                                 target='',
                                 n_source_concept_drift=n_concept_drift)

    try:
        os.remove(filename)
    except:
        pass
    f = open(filename, 'x')
    f.close()

    print('Exporting dataset "%s" as file "%s"' % (dataset_name, filename))
    with open(filename, 'w', newline='') as csv_file:
        writer = csv.writer(csv_file, delimiter=',')
        pbar = tqdm(total=dm.number_samples())
        for i in range(dm.number_samples()):
            x, y = dm.get_x_y(i)
            temp_y = np.zeros(dm.number_classes() + label_starts_at)
            temp_y[y.argmax() + label_starts_at] = 1
            y = temp_y
            if not is_one_hot_encoding:
                y = np.asarray([y.argmax()])

            writer.writerow(np.concatenate((x, y)).tolist())
            pbar.update(1)
        pbar.close()
    print('Done!')


def pre_download_benchmarks():
    def print_info(dm):
        print('Number of samples: %d' % dm.number_samples())
        print('Number of features: %d' % dm.number_features())
        print('Number of classes: %d' % dm.number_classes())
        return DataManipulator()

    dm = DataManipulator()
    dm.load_mnist()
    dm = print_info(dm)
    dm.load_usps()
    dm = print_info(dm)
    dm.load_cifar10()
    dm = print_info(dm)
    dm.load_stl10()
    dm = print_info(dm)
    # dm.load_news_popularity_obama_all()
    # dm = print_info(dm)
    # dm.load_news_popularity_economy_all()
    # dm = print_info(dm)
    # dm.load_news_popularity_microsoft_all()
    # dm = print_info(dm)
    # dm.load_news_popularity_palestine_all()
    # dm = print_info(dm)
    # dm.load_amazon_review_fashion()
    # dm = print_info(dm)
    dm.load_amazon_review_all_beauty()
    dm = print_info(dm)
    # dm.load_amazon_review_appliances()
    # dm = print_info(dm)
    # dm.load_amazon_review_arts_crafts_sewing()
    # dm = print_info(dm)
    # dm.load_amazon_review_automotive()
    # dm = print_info(dm)
    # dm.load_amazon_review_cds_vinyl()
    # dm = print_info(dm)
    # dm.load_amazon_review_cellphones_accessories()
    # dm = print_info(dm)
    # dm.load_amazon_review_clothing_shoes_jewelry()
    # dm = print_info(dm)
    # dm.load_amazon_review_digital_music()
    # dm = print_info(dm)
    # dm.load_amazon_review_electronics()
    # dm = print_info(dm)
    # dm.load_amazon_review_gift_card()
    # dm = print_info(dm)
    # dm.load_amazon_review_grocery_gourmet_food()
    # dm = print_info(dm)
    # dm.load_amazon_review_home_kitchen()
    # dm = print_info(dm)
    dm.load_amazon_review_industrial_scientific()
    dm = print_info(dm)
    # dm.load_amazon_review_kindle_store()
    # dm = print_info(dm)
    dm.load_amazon_review_luxury_beauty()
    dm = print_info(dm)
    dm.load_amazon_review_magazine_subscription()
    dm = print_info(dm)
    # dm.load_amazon_review_movies_tv()
    # dm = print_info(dm)
    # dm.load_amazon_review_musical_instruments()
    # dm = print_info(dm)
    # dm.load_amazon_review_office_products()
    # dm = print_info(dm)
    # dm.load_amazon_review_patio_lawn_garden()
    # dm = print_info(dm)
    # dm.load_amazon_review_pet_supplies()
    # dm = print_info(dm)
    # dm.load_amazon_review_prime_pantry()
    # dm = print_info(dm)
    # dm.load_amazon_review_software()
    # dm = print_info(dm)
    # dm.load_amazon_review_sports_outdoors()
    # dm = print_info(dm)
    # dm.load_amazon_review_tools_home_improvements()
    # dm = print_info(dm)
    # dm.load_amazon_review_toys_games()
    # dm = print_info(dm)
    # dm.load_amazon_review_video_games()
    # dm = print_info(dm)
    dm.load_amazon_review_books()
    print_info(dm)


print('ACDC: Autonomous Cross Domain Conversion')
print('')
print('Available methods:')
print('************************************************************')
print('def acdc(%s,%s,%s,%s,%s,%s\n\t)' % (
    '\n\tsource: str',
    '\n\ttarget: str',
    '\n\tn_source_concept_drift: int = 5',
    '\n\tn_target_concept_drift: int = 7',
    '\n\tinternal_epochs: int = 1',
    '\n\tis_gpu: bool = False'))
print(' ')
print('source: String representing the source benchmark')
print('target: String representing the target benchmark')
print('n_source_concept_drift: Number of concept drifts at the source stream')
print('n_target_concept_drift: Number of concept drifts at the target stream')
print('internal_epochs: Number of internal epochs per minibatch')
print('is_gpu: False to run on CPU. True to run on GPU. The paper were generated on CPU. The code is not optimized for GPU. Only runs if you have a huge ammount of GRAM. Also, the adaptation procedure is slower on GPU.')
print(' ')
print('Returns a dictionary with all results for the run')
print('************************************************************')
print(' ')
print('************************************************************')
print('pre_download_benchmarks()')
print('************************************************************')
print(' ')
print('************************************************************')
print('generate_csv_from_dataset(%s,%s,%s,%s,%s\n\t)' % (
    '\n\tdataset_name: str',
    '\n\tn_concept_drift: int = 1',
    '\n\tis_source: bool = True',
    '\n\tis_one_hot_enconding: bool = True',
    '\n\tlabel_starts_at: int = 0'))
print(' ')
print('dataset_name: String representing which benchmark should be converted to CSV')
print('n_concept_drift: Number of concept drifts applied into the CSV dataset')
print('is_source: True to generate a file "source.csv", False  to generate a file "target.csv"')
print('is_one_hot_enconding: If True, label will be the n last columns in an one-hot-encoding format, if False, label will be the last column as a number')
print('label_starts_at: The smallest label. Usually it is 0, but some source_code, specially made in Matlab, can start from 1')
print('************************************************************')
print(' ')
print('List of possible strings for datasets:')
print(' ')
print('mnist-28: MNIST resized to 28x28, which is original size ~ 784 features')
print('mnist-16: MNIST resized to 16x16 ~ 256 features')
print('usps-28: USPS resized to 28x28 ~ 784 features')
print('usps-16: USPS resized to 16x16, which is original size ~ 256 features')
print('cifar10: CIFAR10 extracted from Resnet ~ 512 features')
print('stl10: STL10 extracted from Resnet ~512 features')
print('amazon-review-all-beauty: Amazon Review | All Beauty | Word2Vec applied ~ 300 features')
print('amazon-review-books: Amazon Review | Books | Word2Vec applied ~ 300 features')
print('amazon-review-industrial-scientific: Amazon Review | Industrial and Scientific | Word2Vec applied ~ 300 features')
print('amazon-review-luxury-beauty: Amazon Review | Luxury Beauty | Word2Vec applied ~ 300 features')
print('amazon-review-magazine-subscription: Amazon Review | Magazine Subscription | Word2Vec applied ~ 300 features')