multi-class-text-classification-cnn/data_helper.py at main · jiegzhan/multi-class-text-classification-cnn · GitHub

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import re
import logging
import numpy as np
import pandas as pd


def clean_str(s):
    s = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", s)
    s = re.sub(r"\'s", " \'s", s)
    s = re.sub(r"\'ve", " \'ve", s)
    s = re.sub(r"n\'t", " n\'t", s)
    s = re.sub(r"\'re", " \'re", s)
    s = re.sub(r"\'d", " \'d", s)
    s = re.sub(r"\'ll", " \'ll", s)
    s = re.sub(r",", " , ", s)
    s = re.sub(r"!", " ! ", s)
    s = re.sub(r"\(", r" \( ", s)
    s = re.sub(r"\)", r" \) ", s)
    s = re.sub(r"\?", r" \? ", s)
    s = re.sub(r"\s{2,}", " ", s)
    s = re.sub(r'\S*(x{2,}|X{2,})\S*', "xxx", s)
    s = re.sub(r'[^\x00-\x7F]+', "", s)
    return s.strip().lower()


def load_data_and_labels(filename):
    df = pd.read_csv(filename, compression='zip', dtype={'consumer_complaint_narrative': object})
    selected = ['product', 'consumer_complaint_narrative']
    non_selected = list(set(df.columns) - set(selected))

    df = df.drop(columns=non_selected)
    df = df.dropna(axis=0, how='any', subset=selected)

    labels = sorted(list(set(df[selected[0]].tolist())))
    one_hot = np.zeros((len(labels), len(labels)), int)
    np.fill_diagonal(one_hot, 1)
    label_dict = dict(zip(labels, one_hot))

    x_raw = df[selected[1]].apply(lambda x: clean_str(x)).tolist()
    y_raw = df[selected[0]].apply(lambda y: label_dict[y]).tolist()
    return x_raw, y_raw, df, labels


if __name__ == '__main__':
    input_file = './data/consumer_complaints.csv.zip'
    load_data_and_labels(input_file)