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4_train2013_test2014.py
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4_train2013_test2014.py
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# coding: utf-8
# In[ ]:
import pandas as pd
import numpy as np
import keras
from build_model import build_model
data_x = pd.read_csv('./data/weather/v3/2013/merged/merged_2013_h0.csv', index_col = 'Time')
data_x.columns
x = data_x[['temperature-460', 'cloudiness-460',
'temperature-481', 'cloudiness-481',
'temperature-645', 'cloudiness-645',]]
data_y = pd.read_csv('./data/energy/v5/2013/Consumption_2013.csv', index_col = 'Time')
data = x.copy()
data['cons'] = data_y['Auvergne-Rhone-Alpes']
data.drop(data.index[0], inplace = True)
print(data.isnull().values.any())
print(data.isnull().sum())
# In[ ]:
data_train = data.values
x_train = data_train[:, 0:6]
y_train = data_train[:, 6]
# In[ ]:
x_train[:5, :]
# In[ ]:
y_train[:5]
# In[ ]:
#data.isnull().values.any()
#data.isnull().sum()
# In[ ]:
# Before
x_min = np.min(x_train)
print(x_min)
x_max = np.max(x_train)
print(x_max)
#x_train = (x_train - np.min(x_train)) / (np.max(x_train) - np.min(x_train))
# After
x_min = np.min(x_train)
print(x_min)
x_max = np.max(x_train)
print(x_max)
# In[ ]:
'''y_train = (y_train - np.min(y_train)) / (np.max(y_train) - np.min(y_train))'''
# # Test data
# In[ ]:
data_x = pd.read_csv('./data/weather/v3/2014/merged/merged_2014_h0.csv', index_col = 'Time')
data_x.columns
x = data_x[['temperature-460', 'cloudiness-460',
'temperature-481', 'cloudiness-481',
'temperature-645', 'cloudiness-645',]]
data_y = pd.read_csv('./data/energy/v5/2014/Consumption_2014.csv', index_col = 'Time')
data = x.copy()
data['cons'] = data_y['Auvergne-Rhone-Alpes']
#data.drop(data.index[0], inplace = True)
print(data.isnull().values.any())
print(data.isnull().sum())
data_test = data.values
x_test = data_test[:, 0:6]
y_test = data_test[:, 6]
#x_test = (x_test - np.min(x_test)) / (np.max(x_test) - np.min(x_test))
#x_test = (x_test / (np.max(x_train))
# In[ ]:
seq_len = 100
def data_to_sequence(data, n_prev = seq_len):
docX, docY = [], []
for i in range(len(data)-n_prev):
docX.append(data[i:i+n_prev, 0:6])
docY.append(data[i+n_prev, 6])
alsX = np.array(docX)
alsY = np.array(docY)
return alsX, alsY
# In[ ]:
x_train, y_train = data_to_sequence(data_train)
# In[ ]:
x_test, y_test = data_to_sequence(data_test)
# In[ ]:
model = build_model(features = 6, seq_len = seq_len, out = 1)
print(model.summary())
from keras.callbacks import TensorBoard , ModelCheckpoint, ReduceLROnPlateau
tbCallBack = TensorBoard(log_dir ='./logs/',
histogram_freq = 0,
write_graph = True)
filepath = "best_model.hdf5"
best_model = ModelCheckpoint(filepath = filepath,
monitor = 'val_loss',
verbose = 1,
save_best_only = True,
save_weights_only = False,
mode = 'auto', period = 1)
reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.5, patience = 2)
# In[ ]:
model.fit(x_train,
y_train,
batch_size = 100,
epochs = 100,
validation_data = [x_test, y_test],
callbacks = [best_model, tbCallBack, reduce_lr])
# In[ ]: