ATL_CIKM-2019/DataManipulator.py

# Marcus Vinicius Sousa Leite de Carvalho
# marcus.decarvalho@ntu.edu.sg
# ivsucram@gmail.com
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import numpy as np
import pandas as pd

class DataManipulator:
    data = None
    number_features = None
    number_classes = None

    number_fold_elements = None
    number_minibatches = None

    source_data = None
    target_data = None

    __X = None
    __y = None
    __Xs = None
    __ys = None
    __Xt = None
    __yt = None

    __permutedX = None
    __permutedy = None

    __index_permutation = None

    __data_folder_path = None

    def __init__(self, data_folder_path):
        self.__data_folder_path = data_folder_path

    def load_mnist(self):
        raise TypeError('Not implemented')

    def load_custom_csv(self):
        print('Loading data.csv')
        self.data = pd.read_csv(filepath_or_buffer='data.csv', header=None)
        self.check_dataset_is_even()
        self.number_features = self.data.shape[1] - 1
        self.X = self.data.iloc[:, 0:self.number_features].add_prefix('feature_').astype(dtype=np.float64)
        self.y = pd.get_dummies(self.data.iloc[:, self.number_features], prefix='class', dtype=np.float64)
        self.number_classes = self.y.shape[1]
        self.data = self.X.join(self.y)

    def normalize(self):
        print('Normalizing data')
        self.X = (self.X - self.X.min())/(self.X.max() - self.X.min())
        self.data = self.X.join(self.y)

    def normalize_image(self):
        raise TypeError('Not implemented')

    def split_as_source_target_streams(self, number_fold_elements=0, sampling_ratio=0.5):
        self.number_fold_elements = number_fold_elements if number_fold_elements is not 0 else self.data.shape[0]
        self.__split_as_source_target_streams_dallas_2(self.number_fold_elements, sampling_ratio)
        self.__create_Xs_ys_Xt_yt()

    def get_Xs(self, number_minibatch):
        return self.Xs[number_minibatch].values

    def get_ys(self, number_minibatch):
        return self.ys[number_minibatch].values

    def get_Xt(self, number_minibatch):
        return self.Xt[number_minibatch].values

    def get_yt(self, number_minibatch):
        return self.yt[number_minibatch].values

    def __split_as_source_target_streams_dallas_2(self, elements_per_fold=1000, sampling_ratio=0.5):
        rows_number = self.data.shape[0]

        number_of_folds = round(rows_number / elements_per_fold)
        chunk_size = round(rows_number / number_of_folds)
        number_of_folds_rounded = round(rows_number / chunk_size)
        if (rows_number / number_of_folds_rounded) % 2:
            self.number_fold_elements = min(elements_per_fold, np.floor(rows_number / number_of_folds_rounded) - 1)
        else:
            self.number_fold_elements = min(elements_per_fold, np.floor(rows_number / number_of_folds_rounded))

        if rows_number / number_of_folds_rounded > elements_per_fold:
            number_of_folds = number_of_folds + 1

        self.number_minibatches = number_of_folds
        ck = self.number_fold_elements

        self.source = []
        self.target = []

        def chunkify(pnds):
            nfe = self.number_fold_elements  # readability
            nof = self.number_minibatches  # readability
            return [pnds[i * nfe: (i + 1) * nfe] for i in range(nof)]

        for x, data in zip(chunkify(self.X), chunkify(self.data)):
            x_mean = np.mean(x, axis=0)
            norm_1 = np.linalg.norm(x - x_mean, axis=0)
            norm_2 = np.linalg.norm(x - x_mean, axis=1)
            numerator = norm_2
            denominator = 2. * (norm_1.std() ** 2)
            probability = np.exp(-numerator / denominator)
            idx = np.argsort(probability)

            m = data.shape[0]
            self.source.append(data.iloc[idx[: round(m * sampling_ratio)]].sort_index())
            self.target.append(data.iloc[idx[round(m * sampling_ratio):]].sort_index())

    def __create_Xs_ys_Xt_yt(self):
        self.X, self.y = [], []
        self.Xs, self.ys = [], []
        self.Xt, self.yt = [], []
        self.__permutedX, self.__permutedy = [], []
        self.__index_permutation = []

        for i in range(0, self.number_minibatches):
            self.Xs.append(self.source[i].iloc[:, : -self.number_classes])
            self.ys.append(self.source[i].iloc[:, self.number_features:])
            self.Xt.append(self.target[i].iloc[:, : -self.number_classes])
            self.yt.append(self.target[i].iloc[:, self.number_features:])
            self.X.append(pd.concat([self.Xs[i], self.Xt[i]]))
            self.y.append(pd.concat([self.ys[i], self.yt[i]]))

            x = self.X[i]
            y = self.y[i]

            p = np.random.permutation(x.shape[0])
            self.__permutedX.append(x.iloc[p])
            self.__permutedy.append(y.iloc[p])
            self.__index_permutation.append(p)

    def check_dataset_is_even(self):
        if self.data.shape[0] % 2:
            self.data.drop(axis='index', index=np.random.randint(1, self.data.shape[0]), inplace=True)