from preprocessing import standard_df
import numpy as np
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 = (6,7)),
        tf.keras.layers.Dense(1)
                              
    ])
    
    model.compile(loss = 'mse',optimizer = 'adam')
    model.fit(train_feature, train_label, 
                        epochs =50, batch_size = 1000)
    
    model.save('/src/dataprocessing/model.h5')


#사용할때
# new = tf.keras.models.load_model('/src/dataprocessing/model.h5')