ATL.py

# Marcus Vinicius Sousa Leite de Carvalho
# marcus.decarvalho@ntu.edu.sg
# ivsucram@gmail.com
#
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from DataManipulator import DataManipulator
from NeuralNetwork import NeuralNetwork
from AutoEncoder import DenoisingAutoEncoder
from AGMM import AGMM
from MySingletons import MyDevice, TorchDevice
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, target: NeuralNetwork, layer_numbers=[1], copy_moment: bool = True):
    for layer_number in layer_numbers:
        layer_number -= 1
        if layer_number >= source.number_hidden_layers:
            target.output_weight = source.output_weight
            target.output_bias = source.output_bias
            if copy_moment:
                target.output_momentum = source.output_momentum
                target.output_bias_momentum = source.output_bias_momentum
        else:
            target.weight[layer_number] = source.weight[layer_number]
            target.bias[layer_number] = source.bias[layer_number]
            if copy_moment:
                target.momentum[layer_number] = source.momentum[layer_number]
                target.bias_momentum[layer_number] = source.bias_momentum[layer_number]


def grow_nodes(*networks):
    origin = networks[0]
    if origin.growable[origin.number_hidden_layers]:
        if origin.get_agmm() is None:
            nodes = 1
        else:
            nodes = origin.get_agmm().M()
        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)


def width_evolution(network: NeuralNetwork, x: torch.tensor, y: torch.tensor = None, agmm: AGMM = None, train_agmm: bool = False):
    if y is None:
        y = x

    if agmm is not None:
        network.set_agmm(agmm)
        if train_agmm:
            network.forward_pass(x)
            network.run_agmm(x, y)

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


def discriminative(network: NeuralNetwork, x: torch.tensor, y: torch.tensor = None, agmm: AGMM = None):
    if agmm is not None:
        network.set_agmm(agmm)
    if y is None:
        y = x

    network.train(x, y)


def generative(network: NeuralNetwork, x: torch.tensor, y: torch.tensor = None, agmm: AGMM = None, is_tied_weight=False, noise_ratio=0.1, glw_epochs: int = 1):
    if agmm is not None:
        network.set_agmm(agmm)
    if y is None:
        y = x

    network.greedy_layer_wise_pretrain(x=x, number_epochs=glw_epochs, noise_ratio=0.0)
    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():
        if y is None:
            y = x
        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))
            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))
            else:
                metrics['reconstruction_target_loss'].append(float(network.loss_value))


def force_same_size(a_tensor, b_tensor, shuffle=True, strategy='min'):
    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 kl(ae: NeuralNetwork, x_source: torch.tensor, x_target: torch.tensor):
    x_source, x_target = force_same_size(x_source, x_target)

    ae.reset_grad()
    kl_loss = torch.nn.functional.kl_div(ae.forward_pass(x_target).layer_value[1],
                                         ae.forward_pass(x_source).layer_value[1])

    kl_loss.backward()
    ae.weight[0] = ae.weight[0] - ae.learning_rate * ae.weight[0].grad
    ae.bias[0] = ae.bias[0] - ae.learning_rate * ae.bias[0].grad

    return kl_loss.detach().cpu().numpy()


def print_annotation(lst):
    def custom_range(xx):
        return range(0, len(xx), int(len(xx) * 0.25) - 1)

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


def plot_time(train, test, annotation=True):
    plt.title('Processing time')
    plt.ylabel('Seconds')
    plt.xlabel('Minibatches')

    plt.plot(train, linewidth=1, label=('Train time Mean | Accumulative %f | %f' % (np.mean(train), np.sum(train))))
    plt.plot(test, linewidth=1, label=('Test time Mean | Accumulative %f | %f' % (np.mean(test), np.sum(test))))
    plt.legend()

    if annotation:
        print_annotation(train)
        print_annotation(test)

    plt.tight_layout()
    plt.show()


def plot_agmm(agmm_source, agmm_target, annotation=True):
    plt.title('AGMM evolution')
    plt.ylabel('GMMs')
    plt.xlabel('Samples')

    plt.plot(agmm_source, linewidth=1, label=('AGMM Source Discriminative Mean: %f' % (np.mean(agmm_source))))
    plt.plot(agmm_target, linewidth=1, label=('AGMM Target Generative Mean: %f' % (np.mean(agmm_target))))
    plt.legend()

    if annotation:
        print_annotation(agmm_source)
        print_annotation(agmm_target)

    plt.tight_layout()
    plt.show()


def plot_node_evolution(nodes, annotation=True):
    plt.title('Node evolution')
    plt.ylabel('Nodes')
    plt.xlabel('Minibatches')

    plt.plot(nodes, linewidth=1,
             label=('Hidden Layer Mean | Final: %f | %d' % (np.mean(nodes), nodes[-1])))
    plt.legend()

    if annotation:
        print_annotation(nodes)

    plt.tight_layout()
    plt.show()


def plot_losses(classification_source_loss, classification_target_loss, reconstruction_source_loss,
                reconstruction_target_loss, 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.mean(classification_source_loss))))
    plt.plot(classification_target_loss, linewidth=1,
             label=('Classification Target Loss mean: %f' % (np.mean(classification_target_loss))))
    plt.plot(reconstruction_source_loss, linewidth=1,
             label=('Reconstruction Source Loss mean: %f' % (np.mean(reconstruction_source_loss))))
    plt.plot(reconstruction_target_loss, linewidth=1,
             label=('Reconstruction Target Loss mean: %f' % (np.mean(reconstruction_target_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)

    plt.tight_layout()
    plt.show()


def plot_classification_rates(source_rate, target_rate, annotation=True):
    plt.title('Source and Target Classification Rates')
    plt.ylabel('Classification Rate')
    plt.xlabel('Minibatches')

    plt.plot(source_rate, linewidth=1, label=('Source CR mean: %f' % (np.mean(source_rate))))
    plt.plot(target_rate, linewidth=1, label=('Target CR mean: %f' % (np.mean(target_rate))))

    if annotation:
        print_annotation(source_rate)
        print_annotation(target_rate)

    plt.legend()

    plt.tight_layout()
    plt.show()


def plot_ns(bias, var, ns, annotation=True):
    plt.plot(bias, linewidth=1, label=('Bias2 mean: %f' % (np.mean(bias))))
    plt.plot(var, linewidth=1, label=('Variance mean: %f' % (np.mean(var))))
    plt.plot(ns, linewidth=1, label=('Network Significance mean: %f' % (np.mean(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 Source BIAS2, VAR, NS')
    plt.ylabel('Value')
    plt.xlabel('Sample')

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


def plot_generative_network_significance(bias, var, annotation=True, is_source=True):
    if is_source:
        plt.title('Generative Source BIAS2, VAR, NS')
    else:
        plt.title('Generative Target BIAS2, VAR, NS')
    plt.ylabel('Value')
    plt.xlabel('Sample')

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


def ATL(epochs: int = 1, n_batch: int = 1000, device='cpu'):
    def print_metrics(minibatch, metrics, nn, ae, Xs, Xt):
        print('Minibatch: %d | Execution time (dataset load/pre-processing + model run): %f' % (minibatch, time.time() - metrics['start_execution_time']))
        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(('Total of samples:' + Fore.BLUE + ' %d Source' + Style.RESET_ALL +' |' + Fore.RED +' %d Target' + Style.RESET_ALL) % (Xs.shape[0], Xt.shape[0]))
            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.mean(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.mean(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.mean(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.mean(metrics['classification_rate_target']) * 100,
                      np.min(metrics['classification_rate_target']) * 100,
                      metrics['classification_rate_target'][-1] * 100))
            print(('%s %s %s %s AGMM Source:' + Fore.GREEN + ' %d ' + Fore.YELLOW + '%f' + Style.RESET_ALL + Fore.RED + ' %d' + Fore.BLUE + ' %d' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['agmm_source_size_by_batch']),
                      np.mean(metrics['agmm_source_size_by_batch']),
                      np.min(metrics['agmm_source_size_by_batch']),
                      metrics['agmm_source_size_by_batch'][-1]))
            print(('%s %s %s %s AGMM Target:' + Fore.GREEN + ' %d ' + Fore.YELLOW + '%f' + Style.RESET_ALL + Fore.RED + ' %d' + Fore.BLUE + ' %d' + Style.RESET_ALL) % (
                      string_max, string_mean, string_min, string_now,
                      np.max(metrics['agmm_target_size_by_batch']),
                      np.mean(metrics['agmm_target_size_by_batch']),
                      np.min(metrics['agmm_target_size_by_batch']),
                      metrics['agmm_target_size_by_batch'][-1]))
            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.mean(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.mean(metrics['classification_target_loss']),
                      np.min(metrics['classification_target_loss']),
                      metrics['classification_target_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.mean(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.mean(metrics['reconstruction_target_loss']),
                      np.min(metrics['reconstruction_target_loss']),
                      metrics['reconstruction_target_loss'][-1]))
            print(('%s %s %s %s Kullback-Leibler 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['kl_loss']),
                      np.mean(metrics['kl_loss']),
                      np.min(metrics['kl_loss']),
                      metrics['kl_loss'][-1]))
            print(('%s %s %s %s 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']),
                      np.mean(metrics['node_evolution']),
                      np.min(metrics['node_evolution']),
                      metrics['node_evolution'][-1]))
            print(('Network structure:' + Fore.BLUE + ' %s (Discriminative) %s (Generative)' + Style.RESET_ALL) % (
                      " ".join(map(str, nn.layers)),
                      " ".join(map(str, ae.layers))))
        print(Style.RESET_ALL)

    metrics = {'classification_rate_source': [],
               'classification_rate_target': [],
               'train_time': [],
               'test_time': [],
               'node_evolution': [],
               'classification_target_loss': [],
               'classification_source_loss': [],
               'reconstruction_source_loss': [],
               'reconstruction_target_loss': [],
               'kl_loss': [],
               'agmm_target_size_by_batch': [],
               'agmm_source_size_by_batch': [],
               'start_execution_time': time.time()}

    TorchDevice.instance().device = device

    dm = DataManipulator('')
    dm.load_custom_csv()
    dm.normalize()

    dm.split_as_source_target_streams(n_batch, 0.5)

    nn = NeuralNetwork([dm.number_features, 1, dm.number_classes])
    ae = DenoisingAutoEncoder([nn.layers[0], nn.layers[1], nn.layers[0]])

    # I am building the greedy_layer_bias
    x = dm.get_Xs(0)
    x = torch.tensor(np.atleast_2d(x), dtype=torch.float, device=MyDevice().get())
    ae.greedy_layer_wise_pretrain(x=x, number_epochs=0)
    # I am building the greedy_layer_bias

    agmm_source_discriminative = AGMM()
    agmm_target_generative = AGMM()

    for i in range(dm.number_minibatches):
        Xs = torch.tensor(dm.get_Xs(i), dtype=torch.float, device=MyDevice().get())
        ys = torch.tensor(dm.get_ys(i), dtype=torch.float, device=MyDevice().get())
        Xt = torch.tensor(dm.get_Xt(i), dtype=torch.float, device=MyDevice().get())
        yt = torch.tensor(dm.get_yt(i), dtype=torch.float, device=MyDevice().get())

        if i > 0:
            metrics['test_time'].append(time.time())
            test(nn, Xt, yt, is_source=False, is_discriminative=True, metrics=metrics)
            metrics['test_time'][-1] = time.time() - metrics['test_time'][-1]

            test(nn, Xs, ys, is_source=True, is_discriminative=True, metrics=metrics)
            test(ae, Xt, is_source=False, is_discriminative=False, metrics=metrics)
            test(ae, Xs, is_source=True, is_discriminative=False, metrics=metrics)

        metrics['train_time'].append(time.time())
        for epoch in range(epochs):
            for x, y in [(x.view(1, x.shape[0]), y.view(1, y.shape[0])) for x, y in zip(Xs, ys)]:
                width_evolution(network=nn, x=x, y=y, agmm=agmm_source_discriminative, train_agmm=True if epoch == 0 else False)
                if not grow_nodes(nn, ae): prune_nodes(nn, ae)
                discriminative(network=nn, x=x, y=y, agmm=agmm_source_discriminative)

            copy_weights(source=nn, target=ae, layer_numbers=[1])

            for x in [x.view(1, x.shape[0]) for x in Xt]:
                width_evolution(network=ae, x=x, agmm=agmm_target_generative, train_agmm=True if epoch == 0 else False)
                if not grow_nodes(ae, nn): prune_nodes(ae, nn)
                generative(network=ae, x=x, agmm=agmm_target_generative)

            metrics['kl_loss'].append(kl(ae=ae, x_source=Xs, x_target=Xt))
            copy_weights(source=ae, target=nn, layer_numbers=[1])

        if agmm_target_generative.M() > 1: agmm_target_generative.delete_cluster()
        if agmm_source_discriminative.M() > 1: agmm_source_discriminative.delete_cluster()

        metrics['agmm_target_size_by_batch'].append(agmm_target_generative.M())
        metrics['agmm_source_size_by_batch'].append(agmm_source_discriminative.M())
        metrics['train_time'][-1] = time.time() - metrics['train_time'][-1]
        metrics['node_evolution'].append(nn.layers[1])
        print_metrics(i + 1, metrics, nn, ae, Xs, Xt)

    result = '%f (T) ''| %f (S) \t %f | %d \t %f | %f' % (
        np.mean(metrics['classification_rate_target']),
        np.mean(metrics['classification_rate_source']),
        np.mean(metrics['node_evolution']),
        metrics['node_evolution'][-1],
        np.mean(metrics['train_time']),
        np.sum(metrics['train_time']))

    print(result)

    plot_time(metrics['train_time'], metrics['test_time'])
    plot_node_evolution(metrics['node_evolution'])
    plot_classification_rates(metrics['classification_rate_source'], metrics['classification_rate_target'])
    plot_agmm(metrics['agmm_source_size_by_batch'], metrics['agmm_source_size_by_batch'])
    plot_losses(metrics['classification_source_loss'], metrics['classification_target_loss'], metrics['reconstruction_source_loss'], metrics['reconstruction_target_loss'])
    plot_generative_network_significance(nn.BIAS, nn.VAR)
    plot_discriminative_network_significance(ae.BIAS, ae.VAR)

    return result

atl = ATL(epochs = 1, device='cpu')
print(atl)