ACDC.py

# Marcus Vinicius Sousa Leite de Carvalho
# marcus.decarvalho@ntu.edu.sg
# ivsucram@gmail.com
#
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#
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# Copyright (c) NTUITIVE. All rights reserved.

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, cycle(a_tensor))))
            a_tensor = torch.stack(list(source for target, source in zip(b_tensor, cycle(a_tensor))))
        else:
            b_tensor = torch.stack(list(target for target, source in zip(cycle(b_tensor), a_tensor)))
            a_tensor = torch.stack(list(source for target, source in zip(cycle(b_tensor), a_tensor)))

    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 == 'london-bike':
        dm_s.load_london_bike_sharing(n_concept_drifts=n_source_concept_drift)
    elif source == 'washington-bike':
        dm_s.load_washington_bike_sharing(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 == 'london-bike':
        dm_t.load_london_bike_sharing(n_concept_drifts=n_source_concept_drift)
    elif target == 'washington-bike':
        dm_t.load_washington_bike_sharing(n_concept_drifts=n_source_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(),
                                int(dm_s.number_features() * 0.5),
                                dm_s.number_features()])
    nn = NeuralNetwork([dm_s.number_features(),
                        dae.layers[1],
                        1,
                        dm_s.number_classes()])
    da = NeuralNetwork([dm_s.number_features(),
                        dae.layers[1],
                        1,
                        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, shuffle=False)
            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, y_source)]:
                    # Evolving
                    if epoch == 1:
                        # Evolving Feature Extraction
                        for i in range(0, 2):
                            if i == 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=torch.cat(xs.shape[0]*[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=torch.cat(xt.shape[0]*[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=torch.cat(xs.shape[0]*[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=torch.cat(xt.shape[0]*[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 generate_arff_from_dataset(source_dataset_name: str,
                               target_dataset_name: str,
                               n_source_concept_drifts: int = 1,
                               n_target_concept_drifts: int = 1,
                               output_filename : str = None):
    import os
    import arff
    if output_filename is not None:
        filename = output_filename
    else:
        filename = 'source_target_melanie.arff'

    dm_s, dm_t = __load_source_target(source=source_dataset_name,
                                      target=target_dataset_name,
                                      n_source_concept_drift=n_source_concept_drifts,
                                      n_target_concept_drift=n_target_concept_drifts)

    try:
        os.remove(filename)
    except:
        pass

    print(f'Exporting datasets {source_dataset_name} and {target_dataset_name} as file {filename}')
    data = []

    count_source = 0
    count_target = 0

    while 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.)

        sample = []
        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)
            count_source += 1
            sample.append(1)
        elif count_target < dm_t.number_samples():
            x, y = dm_t.get_x_y(count_target)
            count_target += 1
            sample.append(0)

        for i in x.tolist():
            sample.append(i)
        sample.append(y.argmax())
        data.append(sample)

    # data = {'data': data,
    #         'relation': f'{source_dataset_name}_{target_dataset_name}',
    #         'attributes': 'something'}
    # with open(filename, 'x') as f:
    #     arff.dump(data, f)
    arff.dump(filename, data, relation=f'{source_dataset_name}_{target_dataset_name}')
    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_london_bike_sharing()
    dm = print_info(dm)
    dm.load_washington_bike_sharing()
    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')
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')
print('london-bike: London bike sharing dataset ~ 8 features')
print('washington-bike: Washington D.C. bike sharing dataset ~ 8 features')

# pre_download_benchmarks()
# acdc('mnist-28', 'usps-28', 5, 7, 1, False)
# acdc('london-bike', 'washington-bike', 5, 7, 1, False)
# acdc('washington-bike', 'london-bike', 5, 7, 1, False)
# generate_arff_from_dataset('mnist-28', 'usps-28', 5, 7, 'mnist-28_usps-28_1.arff')
# generate_arff_from_dataset('mnist-28', 'usps-28', 5, 7, 'mnist-28_usps-28_2.arff')
# generate_arff_from_dataset('mnist-28', 'usps-28', 5, 7, 'mnist-28_usps-28_3.arff')
# generate_arff_from_dataset('mnist-28', 'usps-28', 5, 7, 'mnist-28_usps-28_4.arff')
# generate_arff_from_dataset('mnist-28', 'usps-28', 5, 7, 'mnist-28_usps-28_5.arff')
#
# generate_arff_from_dataset('usps-16', 'mnist-16', 5, 7, 'usps-16_mnist-16_1.arff')
# generate_arff_from_dataset('usps-16', 'mnist-16', 5, 7, 'usps-16_mnist-16_2.arff')
# generate_arff_from_dataset('usps-16', 'mnist-16', 5, 7, 'usps-16_mnist-16_3.arff')
# generate_arff_from_dataset('usps-16', 'mnist-16', 5, 7, 'usps-16_mnist-16_4.arff')
# generate_arff_from_dataset('usps-16', 'mnist-16', 5, 7, 'usps-16_mnist-16_5.arff')
#
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-books', 5, 7, 'beauty_books_1.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-books', 5, 7, 'beauty_books_2.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-books', 5, 7, 'beauty_books_3.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-books', 5, 7, 'beauty_books_4.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-books', 5, 7, 'beauty_books_5.arff')
#
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-industrial-scientific', 5, 7, 'beauty_industrial_1.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-industrial-scientific', 5, 7, 'beauty_industrial_2.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-industrial-scientific', 5, 7, 'beauty_industrial_3.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-industrial-scientific', 5, 7, 'beauty_industrial_4.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-industrial-scientific', 5, 7, 'beauty_industrial_5.arff')
#
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-luxury-beauty', 5, 7, 'beauty_luxury_1.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-luxury-beauty', 5, 7, 'beauty_luxury_2.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-luxury-beauty', 5, 7, 'beauty_luxury_3.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-luxury-beauty', 5, 7, 'beauty_luxury_4.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-luxury-beauty', 5, 7, 'beauty_luxury_5.arff')
#
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-magazine-subscription', 5, 7, 'beauty_magazine_1.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-magazine-subscription', 5, 7, 'beauty_magazine_2.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-magazine-subscription', 5, 7, 'beauty_magazine_3.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-magazine-subscription', 5, 7, 'beauty_magazine_4.arff')
# generate_arff_from_dataset('amazon-review-all-beauty', 'amazon-review-magazine-subscription', 5, 7, 'beauty_magazine_5.arff')
#
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-all-beauty', 5, 7, 'books_beauty_1.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-all-beauty', 5, 7, 'books_beauty_2.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-all-beauty', 5, 7, 'books_beauty_3.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-all-beauty', 5, 7, 'books_beauty_4.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-all-beauty', 5, 7, 'books_beauty_5.arff')
#
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-industrial-scientific', 5, 7, 'books_industrial_1.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-industrial-scientific', 5, 7, 'books_industrial_2.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-industrial-scientific', 5, 7, 'books_industrial_3.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-industrial-scientific', 5, 7, 'books_industrial_4.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-industrial-scientific', 5, 7, 'books_industrial_5.arff')
#
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-luxury-beauty', 5, 7, 'books_luxury_1.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-luxury-beauty', 5, 7, 'books_luxury_2.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-luxury-beauty', 5, 7, 'books_luxury_3.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-luxury-beauty', 5, 7, 'books_luxury_4.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-luxury-beauty', 5, 7, 'books_luxury_5.arff')
#
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-magazine-subscription', 5, 7, 'books_magazine_1.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-magazine-subscription', 5, 7, 'books_magazine_2.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-magazine-subscription', 5, 7, 'books_magazine_3.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-magazine-subscription', 5, 7, 'books_magazine_4.arff')
# generate_arff_from_dataset('amazon-review-books', 'amazon-review-magazine-subscription', 5, 7, 'books_magazine_5.arff')
#
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-all-beauty', 5, 7, 'industrial_beauty_1.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-all-beauty', 5, 7, 'industrial_beauty_2.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-all-beauty', 5, 7, 'industrial_beauty_3.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-all-beauty', 5, 7, 'industrial_beauty_4.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-all-beauty', 5, 7, 'industrial_beauty_5.arff')
#
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-books', 5, 7, 'industrial_books_1.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-books', 5, 7, 'industrial_books_2.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-books', 5, 7, 'industrial_books_3.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-books', 5, 7, 'industrial_books_4.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-books', 5, 7, 'industrial_books_5.arff')
#
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-luxury-beauty', 5, 7, 'industrial_luxury_1.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-luxury-beauty', 5, 7, 'industrial_luxury_2.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-luxury-beauty', 5, 7, 'industrial_luxury_3.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-luxury-beauty', 5, 7, 'industrial_luxury_4.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-luxury-beauty', 5, 7, 'industrial_luxury_5.arff')
#
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-magazine-subscription', 5, 7, 'industrial_magazine_1.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-magazine-subscription', 5, 7, 'industrial_magazine_2.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-magazine-subscription', 5, 7, 'industrial_magazine_3.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-magazine-subscription', 5, 7, 'industrial_magazine_4.arff')
# generate_arff_from_dataset('amazon-review-industrial-scientific', 'amazon-review-magazine-subscription', 5, 7, 'industrial_magazine_5.arff')
#
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-all-beauty', 5, 7, 'luxury_beauty_1.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-all-beauty', 5, 7, 'luxury_beauty_2.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-all-beauty', 5, 7, 'luxury_beauty_3.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-all-beauty', 5, 7, 'luxury_beauty_4.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-all-beauty', 5, 7, 'luxury_beauty_5.arff')
#
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-books', 5, 7, 'luxury_books_1.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-books', 5, 7, 'luxury_books_2.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-books', 5, 7, 'luxury_books_3.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-books', 5, 7, 'luxury_books_4.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-books', 5, 7, 'luxury_books_5.arff')
#
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-industrial-scientific', 5, 7, 'luxury_industrial_1.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-industrial-scientific', 5, 7, 'luxury_industrial_2.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-industrial-scientific', 5, 7, 'luxury_industrial_3.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-industrial-scientific', 5, 7, 'luxury_industrial_4.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-industrial-scientific', 5, 7, 'luxury_industrial_5.arff')
#
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-magazine-subscription', 5, 7, 'luxury_magazine_1.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-magazine-subscription', 5, 7, 'luxury_magazine_2.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-magazine-subscription', 5, 7, 'luxury_magazine_3.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-magazine-subscription', 5, 7, 'luxury_magazine_4.arff')
# generate_arff_from_dataset('amazon-review-luxury-beauty', 'amazon-review-magazine-subscription', 5, 7, 'luxury_magazine_5.arff')
#
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-all-beauty', 5, 7, 'magazine_beauty_1.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-all-beauty', 5, 7, 'magazine_beauty_2.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-all-beauty', 5, 7, 'magazine_beauty_3.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-all-beauty', 5, 7, 'magazine_beauty_4.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-all-beauty', 5, 7, 'magazine_beauty_5.arff')
#
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-books', 5, 7, 'magazine_books_1.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-books', 5, 7, 'magazine_books_2.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-books', 5, 7, 'magazine_books_3.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-books', 5, 7, 'magazine_books_4.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-books', 5, 7, 'magazine_books_5.arff')
#
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-industrial-scientific', 5, 7, 'magazine_industrial_1.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-industrial-scientific', 5, 7, 'magazine_industrial_2.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-industrial-scientific', 5, 7, 'magazine_industrial_3.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-industrial-scientific', 5, 7, 'magazine_industrial_4.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-industrial-scientific', 5, 7, 'magazine_industrial_5.arff')
#
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-luxury-beauty', 5, 7, 'magazine_luxury_1.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-luxury-beauty', 5, 7, 'magazine_luxury_2.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-luxury-beauty', 5, 7, 'magazine_luxury_3.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-luxury-beauty', 5, 7, 'magazine_luxury_4.arff')
# generate_arff_from_dataset('amazon-review-magazine-subscription', 'amazon-review-luxury-beauty', 5, 7, 'magazine_luxury_5.arff')
#
# generate_arff_from_dataset('cifar10', 'stl10', 5, 7, 'cifar_stl_1.arff')
# generate_arff_from_dataset('cifar10', 'stl10', 5, 7, 'cifar_stl_2.arff')
# generate_arff_from_dataset('cifar10', 'stl10', 5, 7, 'cifar_stl_3.arff')
# generate_arff_from_dataset('cifar10', 'stl10', 5, 7, 'cifar_stl_4.arff')
# generate_arff_from_dataset('cifar10', 'stl10', 5, 7, 'cifar_stl_5.arff')
#
# generate_arff_from_dataset('stl10', 'cifar10', 5, 7, 'stl_cifar_1.arff')
# generate_arff_from_dataset('stl10', 'cifar10', 5, 7, 'stl_cifar_2.arff')
# generate_arff_from_dataset('stl10', 'cifar10', 5, 7, 'stl_cifar_3.arff')
# generate_arff_from_dataset('stl10', 'cifar10', 5, 7, 'stl_cifar_4.arff')
# generate_arff_from_dataset('stl10', 'cifar10', 5, 7, 'stl_cifar_5.arff')
#
# generate_arff_from_dataset('london-bike', 'washington-bike', 5, 7, 'london_washington_1.arff')
# generate_arff_from_dataset('london-bike', 'washington-bike', 5, 7, 'london_washington_2.arff')
# generate_arff_from_dataset('london-bike', 'washington-bike', 5, 7, 'london_washington_3.arff')
# generate_arff_from_dataset('london-bike', 'washington-bike', 5, 7, 'london_washington_4.arff')
# generate_arff_from_dataset('london-bike', 'washington-bike', 5, 7, 'london_washington_5.arff')
#
# generate_arff_from_dataset('washington-bike', 'london-bike', 5, 7, 'washington_london_1.arff')
# generate_arff_from_dataset('washington-bike', 'london-bike', 5, 7, 'washington_london_2.arff')
# generate_arff_from_dataset('washington-bike', 'london-bike', 5, 7, 'washington_london_3.arff')
# generate_arff_from_dataset('washington-bike', 'london-bike', 5, 7, 'washington_london_4.arff')
# generate_arff_from_dataset('washington-bike', 'london-bike', 5, 7, 'washington_london_5.arff')