RNN.py

import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf
from keras.callbacks import Callback
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, Reshape
from tensorflow.keras.optimizers import Adam
import os


def study_rnn(epos, socketio):
    # Загрузка датасета
    current_path = os.getcwd()
    file_path = os.path.join(current_path, 'spambase.data')

    columns = [
        "word_freq_make", "word_freq_address", "word_freq_all", "word_freq_3d", "word_freq_our", "word_freq_over",
        "word_freq_remove", "word_freq_internet", "word_freq_order", "word_freq_mail", "word_freq_receive",
        "word_freq_will", "word_freq_people", "word_freq_report", "word_freq_addresses", "word_freq_free",
        "word_freq_business", "word_freq_email", "word_freq_you", "word_freq_credit", "word_freq_your",
        "word_freq_font", "word_freq_000", "word_freq_money", "word_freq_hp", "word_freq_hpl", "word_freq_george",
        "word_freq_650", "word_freq_lab", "word_freq_labs", "word_freq_telnet", "word_freq_857", "word_freq_data",
        "word_freq_415", "word_freq_85", "word_freq_technology", "word_freq_1999", "word_freq_parts", "word_freq_pm",
        "word_freq_direct", "word_freq_cs", "word_freq_meeting", "word_freq_original", "word_freq_project",
        "word_freq_re", "word_freq_edu", "word_freq_table", "word_freq_conference", "char_freq_;", "char_freq_(",
        "char_freq_[", "char_freq_!", "char_freq_$", "char_freq_#", "capital_run_length_average",
        "capital_run_length_longest", "capital_run_length_total", "label"
    ]
    data = pd.read_csv(file_path, header=None, names=columns)

    # Разделение данных на признаки и метки
    features = data.drop('label', axis=1)
    labels = data['label']

    # Разделение данных на обучающую и тестовую выборки
    X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

    # Построение модели
    model = Sequential([
        Reshape((1, -1), input_shape=(57,)),
        LSTM(64, activation='tanh', return_sequences=False),
        Dropout(0.5),
        Dense(64, activation='relu'),
        Dense(1, activation='sigmoid')
    ])

    model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])

    class SocketCallback(Callback):
        def on_epoch_end(self, epoch, logs=None):
            progress = {'epoch': epoch, 'loss': logs['loss'], 'val_loss': logs['val_loss']}
            socketio.emit('progress_update', progress)

    # Обучение модели
    history = model.fit(X_train, y_train, epochs=epos, validation_data=(X_test, y_test), verbose=0, callbacks=[SocketCallback()])

    # Оценка модели
    loss, accuracy = model.evaluate(X_test, y_test, verbose=0)

    model.save("model_rnn1.keras")

    return f'Точность модели на тестовой выборке: {accuracy:.4f}'