import numpy as np import os import tensorflow as tf def modeling(standard_df): n = len(standard_df) test_size = int(0.3 * n) train = standard_df[:-test_size] test = standard_df[-test_size:] def make_dataset(data, label, window_size=24): feature_list = [] label_list = [] for i in range(len(data) - window_size): feature_list.append(np.array(data.iloc[i:i+window_size])) label_list.append(np.array(label.iloc[i + window_size])) return np.array(feature_list), np.array(label_list) feature_cols = ['temp_out', 'humi_out', 'press', 'wind_speed', 'Day sin', 'Day cos', 'Year sin', 'Year cos'] label_cols = ['temp_out'] train_feature = train[feature_cols] train_label = train[label_cols] test_feature = test[feature_cols] test_label = test[label_cols] train_feature, train_label = make_dataset( train_feature, train_label, window_size=6) test_feature, test_label = make_dataset( test_feature, test_label, window_size=6) model = tf.keras.Sequential([ tf.keras.layers.LSTM(16, return_sequences=False, input_shape=(6, 8)), tf.keras.layers.Dense(1) ]) model.compile(loss='mse', optimizer='adam') # model.fit(train_feature, train_label, epochs=50, batch_size=1000) model.save(os.getcwd() + '/src/data_processing/model.h5')