data_utils.py

import numpy as np
import pandas as pd
import pdb
import re
from time import time
import json
import random

import model

from scipy.spatial.distance import pdist, squareform
from scipy.stats import multivariate_normal, invgamma, mode
from scipy.special import gamma
from scipy.misc.pilutil import imresize
from functools import partial
from math import ceil

from sklearn.metrics.pairwise import rbf_kernel
from sklearn.preprocessing import MinMaxScaler
from sklearn import preprocessing


# --- deal with the SWaT data --- #
def swat(seq_length, seq_step, num_signals, randomize=False):
    """ Load and serialise """
    # train = np.load('./data/swat.npy')
    # print('Loaded swat from .npy')
    train = np.loadtxt(open('./data/swat.csv'), delimiter=',')
    print('Loaded swat from .csv')
    m, n = train.shape # m=496800, n=52
    for i in range(n - 1):
        A = max(train[:, i])
        if A != 0:
            train[:, i] /= max(train[:, i])
            # scale from -1 to 1
            train[:, i] = 2 * train[:, i] - 1
        else:
            train[:, i] = train[:, i]

    samples = train[21600:, 0:n-1]
    labels = train[21600:, n-1]    # the last colummn is label
    #############################
    # -- choose variable for uni-variate GAN-AD -- #
    # samples = samples[:, [1, 8, 18, 28]]
    ############################
    # -- apply PCA dimension reduction for multi-variate GAN-AD -- #
    from sklearn.decomposition import PCA
    # ALL SENSORS IDX
    # XS = [0, 1, 5, 6, 7, 8, 16, 17, 18, 25, 26, 27, 28, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47]
    # X_n = samples[:, XS]
    # X_a = samples_a[:, XS]
    # All VARIABLES
    X_n = samples
    ####################################
    ###################################
    # -- the best PC dimension is chosen pc=5 -- #
    n_components = num_signals
    pca = PCA(n_components, svd_solver='full')
    pca.fit(X_n)
    ex_var = pca.explained_variance_ratio_
    pc = pca.components_

    # projected values on the principal component
    T_n = np.matmul(X_n, pc.transpose(1, 0))
    samples = T_n

    # # only for one-dimensional
    # samples = T_n.reshape([samples.shape[0], ])
    ###########################################
    ###########################################
    # seq_length = 7200
    num_samples = (samples.shape[0]-seq_length)//seq_step
    print("num_samples:", num_samples)
    print("num_signals:", num_signals)
    aa = np.empty([num_samples, seq_length, num_signals])
    bb = np.empty([num_samples, seq_length, 1])

    for j in range(num_samples):
       bb[j, :, :] = np.reshape(labels[(j * seq_step):(j * seq_step + seq_length)], [-1,1])
       for i in range(num_signals):
           aa[j, :, i] = samples[(j * seq_step):(j*seq_step + seq_length), i]

    # samples = aa[:, 0:7200:200, :]
    # labels = bb[:, 0:7200:200, :]
    samples = aa
    labels = bb

    return samples, labels

def swat_birgan(seq_length, seq_step, num_signals, randomize=False):
    """ Load and serialise """
    # train = np.load('./data/swat.npy')
    # print('Loaded swat from .npy')
    train = np.loadtxt(open('./data/swat.csv'), delimiter=',')
    print('Loaded swat from .csv')
    m, n = train.shape # m=496800, n=52
    for i in range(n - 1):
        A = max(train[:, i])
        if A != 0:
            train[:, i] /= max(train[:, i])
            # scale from -1 to 1
            train[:, i] = 2 * train[:, i] - 1
        else:
            train[:, i] = train[:, i]

    samples = train[21600:, 0:n-1]
    labels = train[21600:, n-1]    # the last colummn is label
    #############################
    # # -- choose variable for uni-variate GAN-AD -- #
    # # samples = samples[:, [1, 8, 18, 28]]
    ###########################################
    ###########################################
    nn = samples.shape[1]
    num_samples = (samples.shape[0]-seq_length)//seq_step
    aa = np.empty([num_samples, nn, nn])
    AA = np.empty([seq_length, nn])
    bb = np.empty([num_samples, seq_length, 1])

    print('Pre-process training data...')
    for j in range(num_samples):
       # display batch progress
       model_bigan.display_batch_progression(j, num_samples)
       bb[j, :, :] = np.reshape(labels[(j * seq_step):(j * seq_step + seq_length)], [-1,1])
       for i in range(nn):
           AA[:, i] = samples[(j * seq_step):(j * seq_step + seq_length), i]
       aa[j, :, :] = np.cov(AA.T)

    samples = aa
    labels = bb

    return samples, labels

def swat_test(seq_length, seq_step, num_signals, randomize=False):
    """ Load and serialise """
    # test = np.load('./data/swat_a.npy')
    # print('Loaded swat_a from .npy')
    test = np.loadtxt(open('./data/swat_a.csv'), delimiter=',')
    print('Loaded swat_a from .csv')
    m, n = test.shape  # m1=449919, n1=52
    for i in range(n - 1):
        B = max(test[:, i])
        if B != 0:
            test[:, i] /= max(test[:, i])
            # scale from -1 to 1
            test[:, i] = 2 * test[:, i] - 1
        else:
            test[:, i] = test[:, i]

    samples = test[:, 0:n - 1]
    labels = test[:, n - 1]
    idx = np.asarray(list(range(0, m)))  # record the idx of each point
    #############################
    # -- choose variable for uni-variate GAN-AD -- #
    # samples = samples[:, [1,2,3,4]]
    # samples_a = samples_a[:, [1,2,3,4]]
    ############################
    ############################
    # -- apply PCA dimension reduction for multi-variate GAN-AD -- #
    from sklearn.decomposition import PCA
    import DR_discriminator as dr
    # ALL SENSORS IDX
    # XS = [0, 1, 5, 6, 7, 8, 16, 17, 18, 25, 26, 27, 28, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47]
    # X_n = samples[:, XS]
    # X_a = samples_a[:, XS]
    # All VARIABLES
    X_a = samples
    ####################################
    ###################################
    # -- the best PC dimension is chosen pc=5 -- #
    n_components = num_signals
    pca_a = PCA(n_components, svd_solver='full')
    pca_a.fit(X_a)
    pc_a = pca_a.components_
    # projected values on the principal component
    T_a = np.matmul(X_a, pc_a.transpose(1, 0))

    samples = T_a
    # # only for one-dimensional
    # samples = T_a.reshape([samples.shape[0], ])
    ###########################################
    ###########################################
    num_samples_t = (samples.shape[0] - seq_length) // seq_step
    aa = np.empty([num_samples_t, seq_length, num_signals])
    bb = np.empty([num_samples_t, seq_length, 1])
    bbb = np.empty([num_samples_t, seq_length, 1])

    for j in range(num_samples_t):
        bb[j, :, :] = np.reshape(labels[(j * seq_step):(j * seq_step + seq_length)], [-1, 1])
        bbb[j, :, :] = np.reshape(idx[(j * seq_step):(j * seq_step + seq_length)], [-1, 1])
        for i in range(num_signals):
            aa[j, :, i] = samples[(j * seq_step):(j * seq_step + seq_length), i]

    samples = aa
    labels = bb
    index = bbb

    return samples, labels, index


def swat_birgan_test(seq_length, seq_step, num_signals, randomize=False):
    """ Load and serialise """
    # train = np.load('./data/swat.npy')
    # print('Loaded swat from .npy')
    test = np.loadtxt(open('./data/swat_a.csv'), delimiter=',')
    print('Loaded swat_a from .csv')
    m, n = test.shape  # m1=449919, n1=52
    for i in range(n - 1):
        B = max(test[:, i])
        if B != 0:
            test[:, i] /= max(test[:, i])
            # scale from -1 to 1
            test[:, i] = 2 * test[:, i] - 1
        else:
            test[:, i] = test[:, i]

    samples = test[:, 0:n - 1]
    labels = test[:, n - 1]
    # idx = np.asarray(list(range(0, m)))  # record the idx of each point
    #############################
    # # -- choose variable for uni-variate GAN-AD -- #
    # # samples = samples[:, [1, 8, 18, 28]]
    ###########################################
    ###########################################
    nn = samples.shape[1]
    num_samples = (samples.shape[0]-seq_length)//seq_step
    aa = np.empty([num_samples, nn, nn])
    AA = np.empty([seq_length, nn])
    bb = np.empty([num_samples, seq_length, 1])

    print('Pre-process testing data...')
    for j in range(num_samples):
       # display batch progress
       model_bigan.display_batch_progression(j, num_samples)
       bb[j, :, :] = np.reshape(labels[(j * seq_step):(j * seq_step + seq_length)], [-1,1])
       for i in range(nn):
           AA[:, i] = samples[(j * seq_step):(j * seq_step + seq_length), i]
       aa[j, :, :] = np.cov(AA.T)

    samples = aa
    labels = bb

    return samples, labels


def wadi(seq_length, seq_step, num_signals, randomize=False):
    train = np.load('./data/wadi.npy')
    print('Loaded wadi from .npy')
    m, n = train.shape  # m=1048571, n=119
    for i in range(n-1):
        A = max(train[:, i])
        if A != 0:
            train[:, i] /= max(train[:, i])
            # scale from -1 to 1
            train[:, i] = 2 * train[:, i] - 1
        else:
            train[:, i] = train[:, i]

    samples = train[259200:, 0:n-1]  # normal
    labels = train[259200:, n-1]
    #############################
    samples = samples[:, [0, 3, 6, 17]]
    # samples = samples[:, 0]
    ############################
    # # -- apply PCA dimension reduction for multi-variate GAN-AD -- #
    # from sklearn.decomposition import PCA
    # import DR_discriminator as dr
    # X_n = samples
    # ####################################
    # ###################################
    # # -- the best PC dimension is chosen pc=8 -- #
    # n_components = num_signals
    # pca = PCA(n_components, svd_solver='full')
    # pca.fit(X_n)
    # pc = pca.components_
    # # projected values on the principal component
    # T_n = np.matmul(X_n, pc.transpose(1, 0))
    #
    # samples = T_n
    # # # only for one-dimensional
    # # samples = T_n.reshape([samples.shape[0], ])
    ###########################################
    ###########################################
    seq_length = 10800
    num_samples = (samples.shape[0] - seq_length) // seq_step
    print("num_samples:", num_samples)
    print("num_signals:", num_signals)
    aa = np.empty([num_samples, seq_length, num_signals])
    bb = np.empty([num_samples, seq_length, 1])

    for j in range(num_samples):
        bb[j, :, :] = np.reshape(labels[(j * seq_step):(j * seq_step + seq_length)], [-1, 1])
        # aa[j, :, :] = np.reshape(samples[(j * seq_step):(j * seq_step + seq_length)], [-1, 1])
        for i in range(num_signals):
            aa[j, :, i] = samples[(j * seq_step):(j * seq_step + seq_length), i]

    samples = aa[:, 0:10800:300, :]
    labels = bb[:, 0:10800:300, :]

    return samples, labels


def wadi_test(seq_length, seq_step, num_signals, randomize=False):
    test = np.load('./data/wadi_a.npy')
    print('Loaded wadi_a from .npy')
    m, n = test.shape  # m1=172801, n1=119

    for i in range(n - 1):
        B = max(test[:, i])
        if B != 0:
            test[:, i] /= max(test[:, i])
            # scale from -1 to 1
            test[:, i] = 2 * test[:, i] - 1
        else:
            test[:, i] = test[:, i]

    samples = test[:, 0:n - 1]
    labels = test[:, n - 1]
    idx = np.asarray(list(range(0, m)))  # record the idx of each point
    #############################
    ############################
    # -- apply PCA dimension reduction for multi-variate GAN-AD -- #
    from sklearn.decomposition import PCA
    import DR_discriminator as dr
    X_a = samples
    ####################################
    ###################################
    # -- the best PC dimension is chosen pc=8 -- #
    n_components = num_signals
    pca_a = PCA(n_components, svd_solver='full')
    pca_a.fit(X_a)
    pc_a = pca_a.components_
    # projected values on the principal component
    T_a = np.matmul(X_a, pc_a.transpose(1, 0))

    samples = T_a
    # # only for one-dimensional
    # samples = T_a.reshape([samples.shape[0], ])
    ###########################################
    ###########################################
    num_samples_t = (samples.shape[0] - seq_length) // seq_step
    aa = np.empty([num_samples_t, seq_length, num_signals])
    bb = np.empty([num_samples_t, seq_length, 1])
    bbb = np.empty([num_samples_t, seq_length, 1])

    for j in range(num_samples_t):
        bb[j, :, :] = np.reshape(labels[(j * 10):(j * seq_step + seq_length)], [-1, 1])
        bbb[j, :, :] = np.reshape(idx[(j * seq_step):(j * seq_step + seq_length)], [-1, 1])
        for i in range(num_signals):
            aa[j, :, i] = samples[(j * seq_step):(j * seq_step + seq_length), i]

    samples = aa
    labels = bb
    index = bbb

    return samples, labels, index

def kdd99(seq_length, seq_step, num_signals):
    train = np.load('./data/kdd99_train.npy')
    print('load kdd99_train from .npy')
    m, n = train.shape  # m=562387, n=35
    # normalization
    for i in range(n - 1):
        # print('i=', i)
        A = max(train[:, i])
        # print('A=', A)
        if A != 0:
            train[:, i] /= max(train[:, i])
            # scale from -1 to 1
            train[:, i] = 2 * train[:, i] - 1
        else:
            train[:, i] = train[:, i]

    samples = train[:, 0:n - 1]
    labels = train[:, n - 1]  # the last colummn is label
    #############################
    ############################
    # -- apply PCA dimension reduction for multi-variate GAN-AD -- #
    from sklearn.decomposition import PCA
    X_n = samples
    ####################################
    ###################################
    # -- the best PC dimension is chosen pc=6 -- #
    n_components = num_signals
    pca = PCA(n_components, svd_solver='full')
    pca.fit(X_n)
    ex_var = pca.explained_variance_ratio_
    pc = pca.components_
    # projected values on the principal component
    T_n = np.matmul(X_n, pc.transpose(1, 0))
    samples = T_n
    # # only for one-dimensional
    # samples = T_n.reshape([samples.shape[0], ])
    ###########################################
    ###########################################
    num_samples = (samples.shape[0] - seq_length) // seq_step
    aa = np.empty([num_samples, seq_length, num_signals])
    bb = np.empty([num_samples, seq_length, 1])

    for j in range(num_samples):
        bb[j, :, :] = np.reshape(labels[(j * seq_step):(j * seq_step + seq_length)], [-1, 1])
        for i in range(num_signals):
            aa[j, :, i] = samples[(j * seq_step):(j * seq_step + seq_length), i]

    samples = aa
    labels = bb

    return samples, labels

def kdd99_test(seq_length, seq_step, num_signals):
    test = np.load('./data/kdd99_test.npy')
    print('load kdd99_test from .npy')

    m, n = test.shape  # m1=494021, n1=35

    for i in range(n - 1):
        B = max(test[:, i])
        if B != 0:
            test[:, i] /= max(test[:, i])
            # scale from -1 to 1
            test[:, i] = 2 * test[:, i] - 1
        else:
            test[:, i] = test[:, i]

    samples = test[:, 0:n - 1]
    labels = test[:, n - 1]
    idx = np.asarray(list(range(0, m)))  # record the idx of each point
    #############################
    ############################
    # -- apply PCA dimension reduction for multi-variate GAN-AD -- #
    from sklearn.decomposition import PCA
    import DR_discriminator as dr
    X_a = samples
    ####################################
    ###################################
    # -- the best PC dimension is chosen pc=6 -- #
    n_components = num_signals
    pca_a = PCA(n_components, svd_solver='full')
    pca_a.fit(X_a)
    pc_a = pca_a.components_
    # projected values on the principal component
    T_a = np.matmul(X_a, pc_a.transpose(1, 0))
    samples = T_a
    # # only for one-dimensional
    # samples = T_a.reshape([samples.shape[0], ])
    ###########################################
    ###########################################
    num_samples_t = (samples.shape[0] - seq_length) // seq_step
    aa = np.empty([num_samples_t, seq_length, num_signals])
    bb = np.empty([num_samples_t, seq_length, 1])
    bbb = np.empty([num_samples_t, seq_length, 1])

    for j in range(num_samples_t):
        bb[j, :, :] = np.reshape(labels[(j * seq_step):(j * seq_step + seq_length)], [-1, 1])
        bbb[j, :, :] = np.reshape(idx[(j * seq_step):(j * seq_step + seq_length)], [-1, 1])
        for i in range(num_signals):
            aa[j, :, i] = samples[(j * seq_step):(j * seq_step + seq_length), i]

    samples = aa
    labels = bb
    index = bbb

    return samples, labels, index


# ############################ data pre-processing #################################
# --- to do with loading --- #
# --- to do with loading --- #
def get_samples_and_labels(settings):
    """
    Parse settings options to load or generate correct type of data,
    perform test/train split as necessary, and reform into 'samples' and 'labels'
    dictionaries.
    """
    if settings['data_load_from']:
        data_path = './experiments/data/' + settings['data_load_from'] + '.data.npy'
        print('Loading data from', data_path)
        samples, pdf, labels = get_data('load', data_path)
        train, vali, test = samples['train'], samples['vali'], samples['test']
        train_labels, vali_labels, test_labels = labels['train'], labels['vali'], labels['test']
        del samples, labels
    else:
        # generate the data
        data_vars = ['num_samples', 'num_samples_t','seq_length', 'seq_step', 'num_signals', 'freq_low',
                'freq_high', 'amplitude_low', 'amplitude_high', 'scale', 'full_mnist']
        data_settings = dict((k, settings[k]) for k in data_vars if k in settings.keys())
        samples, pdf, labels = get_data(settings['data'], settings['seq_length'], settings['seq_step'], settings['num_signals'], settings['sub_id'])
        if 'multivariate_mnist' in settings and settings['multivariate_mnist']:
            seq_length = samples.shape[1]
            samples = samples.reshape(-1, int(np.sqrt(seq_length)), int(np.sqrt(seq_length)))
        if 'normalise' in settings and settings['normalise']: # TODO this is a mess, fix
            print(settings['normalise'])
            norm = True
        else:
            norm = False
        if labels is None:
            train, vali, test = split(samples, [0.6, 0.2, 0.2], normalise=norm)
            train_labels, vali_labels, test_labels = None, None, None
        else:
            train, vali, test, labels_list = split(samples, [0.6, 0.2, 0.2], normalise=norm, labels=labels)
            train_labels, vali_labels, test_labels = labels_list

    labels = dict()
    labels['train'], labels['vali'], labels['test'] = train_labels, vali_labels, test_labels

    samples = dict()
    samples['train'], samples['vali'], samples['test'] = train, vali, test

    # futz around with labels
    # TODO refactor cause this is messy
    if 'one_hot' in settings and settings['one_hot'] and not settings['data_load_from']:
        if len(labels['train'].shape) == 1:
            # ASSUME labels go from 0 to max_val inclusive, find max-val
            max_val = int(np.max([labels['train'].max(), labels['test'].max(), labels['vali'].max()]))
            # now we have max_val + 1 dimensions
            print('Setting cond_dim to', max_val + 1, 'from', settings['cond_dim'])
            settings['cond_dim'] = max_val + 1
            print('Setting max_val to 1 from', settings['max_val'])
            settings['max_val'] = 1

            labels_oh = dict()
            for (k, v) in labels.items():
                A = np.zeros(shape=(len(v), settings['cond_dim']))
                A[np.arange(len(v)), (v).astype(int)] = 1
                labels_oh[k] = A
            labels = labels_oh
        else:
            assert settings['max_val'] == 1
            # this is already one-hot!

    if 'predict_labels' in settings and settings['predict_labels']:
        samples, labels = data_utils.make_predict_labels(samples, labels)
        print('Setting cond_dim to 0 from', settings['cond_dim'])
        settings['cond_dim'] = 0

    # update the settings dictionary to update erroneous settings
    # (mostly about the sequence length etc. - it gets set by the data!)
    settings['seq_length'] = samples['train'].shape[1]
    settings['num_samples'] = samples['train'].shape[0] + samples['vali'].shape[0] + samples['test'].shape[0]
    settings['num_signals'] = samples['train'].shape[2]

    return samples, pdf, labels


def get_data(data_type, seq_length, seq_step, num_signals, sub_id, eval_single, eval_an, data_options=None):
    """
    Helper/wrapper function to get the requested data.
    """
    print('data_type')
    labels = None
    index = None
    if data_type == 'load':
        data_dict = np.load(data_options).item()
        samples = data_dict['samples']
        pdf = data_dict['pdf']
        labels = data_dict['labels']
    elif data_type == 'swat':
        samples, labels = swat(seq_length, seq_step, num_signals)
    elif data_type == 'swat_test':
        samples, labels, index = swat_test(seq_length, seq_step, num_signals)
    elif data_type == 'kdd99':
        samples, labels = kdd99(seq_length, seq_step, num_signals)
    elif data_type == 'kdd99_test':
        samples, labels, index = kdd99_test(seq_length, seq_step, num_signals)
    elif data_type == 'wadi':
        samples, labels = wadi(seq_length, seq_step, num_signals)
    elif data_type == 'wadi_test':
        samples, labels, index = wadi_test(seq_length, seq_step, num_signals)
    else:
        raise ValueError(data_type)
    print('Generated/loaded', len(samples), 'samples from data-type', data_type)
    return samples, labels, index


def get_batch(samples, batch_size, batch_idx, labels=None):
    start_pos = batch_idx * batch_size
    end_pos = start_pos + batch_size
    if labels is None:
        return samples[start_pos:end_pos], None
    else:
        if type(labels) == tuple: # two sets of labels
            assert len(labels) == 2
            return samples[start_pos:end_pos], labels[0][start_pos:end_pos], labels[1][start_pos:end_pos]
        else:
            assert type(labels) == np.ndarray
            return samples[start_pos:end_pos], labels[start_pos:end_pos]



def split(samples, proportions, normalise=False, scale=False, labels=None, random_seed=None):
    """
    Return train/validation/test split.
    """
    if random_seed != None:
        random.seed(random_seed)
        np.random.seed(random_seed)
    assert np.sum(proportions) == 1
    n_total = samples.shape[0]
    n_train = ceil(n_total * proportions[0])
    n_test = ceil(n_total * proportions[2])
    n_vali = n_total - (n_train + n_test)
    # permutation to shuffle the samples
    shuff = np.random.permutation(n_total)
    train_indices = shuff[:n_train]
    vali_indices = shuff[n_train:(n_train + n_vali)]
    test_indices = shuff[(n_train + n_vali):]
    # TODO when we want to scale we can just return the indices
    assert len(set(train_indices).intersection(vali_indices)) == 0
    assert len(set(train_indices).intersection(test_indices)) == 0
    assert len(set(vali_indices).intersection(test_indices)) == 0
    # split up the samples
    train = samples[train_indices]
    vali = samples[vali_indices]
    test = samples[test_indices]
    # apply the same normalisation scheme to all parts of the split
    if normalise:
        if scale: raise ValueError(normalise, scale)  # mutually exclusive
        train, vali, test = normalise_data(train, vali, test)
    elif scale:
        train, vali, test = scale_data(train, vali, test)
    if labels is None:
        return train, vali, test
    else:
        print('Splitting labels...')
        if type(labels) == np.ndarray:
            train_labels = labels[train_indices]
            vali_labels = labels[vali_indices]
            test_labels = labels[test_indices]
            labels_split = [train_labels, vali_labels, test_labels]
        elif type(labels) == dict:
            # more than one set of labels!  (weird case)
            labels_split = dict()
            for (label_name, label_set) in labels.items():
                train_labels = label_set[train_indices]
                vali_labels = label_set[vali_indices]
                test_labels = label_set[test_indices]
                labels_split[label_name] = [train_labels, vali_labels, test_labels]
        else:
            raise ValueError(type(labels))
        return train, vali, test, labels_split