def main():
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
data = DataProcessor(os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
model = Model()
model.build_model(configs)
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=".")
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
predictions_pointbypoint = model.predict_point_by_point(x_test)
plot_results(predictions_pointbypoint, y_test)
predictions_fullseq = model.predict_sequence_full(
x_test, configs['data']['sequence_length'])
plot_results(predictions_fullseq, y_test)
def main(train_after=False):
config_file = 'web_flask/LSTM/config.json'
configs = json.load(open(config_file, 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
data = DataLoader(configs['data']['filename'],
configs['data']['train_test_split'],
configs['data']['columns'],
normalise_meth=configs['data']['normalise'])
model = Model()
model.build_model(configs) if not train_after else \
model.load_model(os.path.join( configs['model']['save_dir'],configs['model']['model_name']))
history = LossHistory()
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'],
history=history,
x_test=x_test,
y_test=y_test)
'''
# out-of memory generative training
steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
model.train_generator(
data_gen=data.generate_train_batch(
seq_len=configs['data']['sequence_length'],
batch_size=configs['training']['batch_size'],
normalise=configs['data']['normalise']
),
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
steps_per_epoch=steps_per_epoch,
save_dir=configs['model']['save_dir']
)
'''
history.loss_plot('epoch')
#loss, accuracy = model.model.evaluate(x_test, y_test)
#print(loss,accuracy)
#predictions = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'], configs['data']['sequence_length'])
#predictions = model.predict_sequence_full(x_test, configs['data']['sequence_length'])
predictions = model.predict_point_by_point(x[0]) #_test)
#plot_results_multiple(predictions, y, configs['data']['sequence_length'])
plot_results(predictions, y)
def main():
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']): os.makedirs(configs['model']['save_dir'])
data = DataLoader(
os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns']
)
model = Model()
model.build_model(configs)
x, y = data.get_train_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise']
)
if not configs['training']['train']:
model.load_model(filepath='saved_models/02102019-164727-e2.h5')
else:
model.train(
x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir']
)
# out-of memory generative training
# steps_per_epoch = math.ceil(
# (data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
# model.train_generator(
# data_gen=data.generate_train_batch(
# seq_len=configs['data']['sequence_length'],
# batch_size=configs['training']['batch_size'],
# normalise=configs['data']['normalise']
# ),
# epochs=configs['training']['epochs'],
# batch_size=configs['training']['batch_size'],
# steps_per_epoch=steps_per_epoch,
# save_dir=configs['model']['save_dir']
# )
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise']
)
# predictions = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'],
# configs['data']['sequence_length'])
# predictions = model.predict_sequence_full(x_test, configs['data']['sequence_length'])
# plot_results_multiple(predictions, y_test, configs['data']['sequence_length'])
predictions = model.predict_point_by_point(x_test)
plot_results(predictions, y_test)
def main_sin():
config = json.load(open("config_sin.json", 'r'))
data = DataLoader(os.path.join('data', config['data']['filename']),
config['data']['train_test_split'],
config['data']['columns'])
x_train, y_train = data.get_train_data(config['data']['sequence_length'],
config['data']['normalise'])
x_test, y_test = data.get_test_data()
model = Model()
model.build_model(config)
model.train(x_train, y_train, config['training']['epochs'],
config['training']['batch_size'])
def main():
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
data = DataLoader(os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
model = Model()
model.build_model(configs)
# 从已经保存的模型中加载模型，此时不需要再进行模型训练：即不需要再执行model.train()部分
# model.load_model(r'saved_models/15102019-155115-e2.h5')
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# print('x的shape是：{0}'.format(x.shape)) # (3942, 49, 2)
# print('y的shape是：{0}'.format(y.shape)) # (3942, 1)
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'])
'''
# out-of memory generative training
steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
model.train_generator(
data_gen=data.generate_train_batch(
seq_len=configs['data']['sequence_length'],
batch_size=configs['training']['batch_size'],
normalise=configs['data']['normalise']
),
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
steps_per_epoch=steps_per_epoch,
save_dir=configs['model']['save_dir']
)
'''
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# predictions = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'], configs['data']['sequence_length'])
# predictions = model.predict_sequence_full(x_test, configs['data']['sequence_length'])
predictions = model.predict_point_by_point(x_test)
# plot_results_multiple(predictions, y_test, configs['data']['sequence_length'])
plot_results(predictions, y_test)
def train_network(configs, dataloader):
# build model
model = Model(configs['data']['input_mode'],
configs['data']['output_mode'])
model.build_model(configs['model'])
# in-memory training
out_seq_len = configs['data']['input_sequence_length'] if configs['data'][
'output_mode'] == "many_to_many" else 1
x_train, y_train = dataloader.get_train_data(
in_seq_len=configs['data']['input_sequence_length'],
out_seq_len=out_seq_len)
x_test, y_test = dataloader.get_test_data(
in_seq_len=configs['data']['input_sequence_length'],
out_seq_len=out_seq_len)
history = model.train(
x_train,
y_train,
x_test,
y_test,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
shuffle=configs['model']['shuffle_training_data'],
allow_early_stop=configs['training']['allow_early_stop'],
)
return model, history
def main(choice):
data = DataLoader(os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
model = Model()
model.build_model(configs)
if (choice != 'info'):
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'])
# out-of memory generative training
# steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
# model.train_generator(
# data_gen = data.generate_train_batch(
# seq_len = configs['data']['sequence_length'],
# batch_size = configs['training']['batch_size'],
# normalise = configs['data']['normalise']
# ),
# epochs = configs['training']['epochs'],
# batch_size = configs['training']['batch_size'],
# steps_per_epoch = steps_per_epoch
# )
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
if (choice == "multi"):
predictions = model.predict_sequences_multiple(
x_test, configs['data']['sequence_length'],
configs['data']['sequence_length'])
plot_results_multiple(predictions, y_test,
configs['data']['sequence_length'])
elif (choice == "seq"):
predictions = model.predict_sequence_full(
x_test, configs['data']['sequence_length'])
plot_results(predictions, y_test)
else:
predictions = model.predict_point_by_point(x_test)
plot_results(predictions, y_test)
def main():
configs = json.load(open('config.json', 'r'))
data = DataLoader(configs['data']['filename'],
os.path.join('data', configs['data']['filepath']),
configs['data']['train_test_split'],
configs['data']['columns'])
model = Model()
model.build_model(configs)
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'])
# out-of memory generative train
'''
steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
model.train_generator(
data_gen = data.generate_train_batch(
seq_len = configs['data']['sequence_length'],
batch_size = configs['training']['batch_size'],
normalise = configs['data']['normalise']
),
epochs = configs['training']['epochs'],
batch_size = configs['training']['batch_size'],
steps_per_epoch = steps_per_epoch
)
'''
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
predictions = model.predict_sequences_multiple(
x_test, configs['data']['sequence_length'],
configs['data']['sequence_length'])
#predictions = model.predict_sequence_full(x_test, configs['data']['sequence_length'])
#predictions = model.predict_point_by_point(x_test)
plot_results_multiple(predictions, y_test,
configs['data']['sequence_length'])
def main():
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):os.makedirs(configs['model']['save_dir'])
model = Model()
my_model = model.build_model(configs)
plot_model(my_model, to_file='output\model.png', show_shapes=True)
data = DataLoader(
os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns']
)
x, y = data.get_train_data(
configs['data']['sequence_length'],
configs['data']['normalise']
)
print(x.shape)
print(y.shape)
print(configs['training']['batch_size'])
print(configs['model']['save_dir'])
model.train(x,
y,
configs['training']['epochs'],
configs['training']['batch_size'],
configs['model']['save_dir']
)
x_test, y_test = data.get_test_data(
configs['data']['sequence_length'],
configs['data']['normalise']
)
# predictions = model.predict_sequences_multiplt(x_test, configs['data']['sequence_length'], configs['data']['sequence_length'])
# predictions = model.predict_sequences_full(x_test, configs['data']['sequence_length'])
prediction_point = model.predict_point_by_point(x_test)
# print(prediction_point)
# print(np.array(predictions).shape)
# plot_results_multiple(predictions, y_test, configs['data']['sequence_length'])
plot_results(prediction_point, y_test)
def main():
#load parameters
configs = json.load(open('./data/config.json','r'))
if not os.path.exists(configs['model']['save_dir']):os.makedirs(configs['model']['save_dir'])
data = DataLoader(
os.path.join('data',configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'],
)
#create RNN model
model=Model()
model.build_model(configs)
#loading trainning data
x,y = data.get_train_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise']
)
print(x.shape)
print(y.shape)
#training model
model.train(
x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir']
)
#test results
x_test, y_test = data.get_test_data(
seq_len= configs['data']['sequence_length'],
normalise=configs['data']['normalise'],
)
#results visualization
predictions_multiseq = model.predict_sequences_multiple(x_test,configs['data']['sequence_length'],configs['data']['sequence_length'])
predictions_pointbypoint=model.predict_point_by_point(x_test)
plot_results_multiple(predictions_multiseq,y_test,configs['data']['sequence_length'])
plot_results(predictions_pointbypoint,y_test)
def main():
#读取所需参数
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
#读取数据
data = DataLoader(os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
#创建RNN模型
model = Model()
mymodel = model.build_model(configs)
plot_model(mymodel, to_file='model.png', show_shapes=True)
#加载训练数据
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
print(x.shape)
print(y.shape)
#训练模型
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'])
#测试结果
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
#展示测试效果
predictions = model.predict_sequences_multiple(
x_test,
configs['data']['sequence_length'],
configs['data']['sequence_length'],
debug=False)
print(np.array(predictions).shape)
plot_results_multiple(predictions, y_test,
configs['data']['sequence_length'])
def main():
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
data = DataLoader(
os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
)
model = Model()
model.build_model(configs)
# get train data
x, y = data.get_train_data()
#x=x.squeeze()
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'])
# # out-of memory generative training
# steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
# model.train_generator(
# data_gen=data.generate_train_batch(
# batch_size=configs['training']['batch_size'],
# ),
# epochs=configs['training']['epochs'],
# batch_size=configs['training']['batch_size'],
# steps_per_epoch=steps_per_epoch,
# save_dir=configs['model']['save_dir']
# )
# testing model
x_test, y_test = data.get_test_data()
#x_test=x_test.squeeze()
predictions = model.predict_point_by_point(x_test)
# plot_results_multiple(predictions, y_test, configs['data']['sequence_length'])
plot_results(predictions, y_test)
def main():
model_config = json.load(open("config.json", "r"))["model"]
dataset_path = 'data/'
# Requirement: Check the presence of the dataset
if check_dataset(dataset_path):
configs = json.load(open('config.json', 'r'))
# 1) Build the model
model = Model(model_config=model_config)
model.build_model(configs)
batch_size, epochs, = 4, 30
cols = configs['training']['cols']
sequence_length = configs['data']['sequence_length']
save_dir = "model"
l = 0
dataset_path = glob.glob("{}/*.txt".format(dataset_path))
# 2 ) Loop over the files in the dataset folder
for filename in dataset_path:
print("Training {}/{} - {}".format(l, len(dataset_path), filename))
l += 1
# 3) Divide the dataset in parts and loop over them
chunksize = 10**4
for chunk in pd.read_csv(filename, chunksize=chunksize):
# 4) Get and prepare data
data = DataModel()
x = data.get_train_data(
data=[x for x in chunk.get(cols).values.tolist()],
seq_len=sequence_length)
X_train, X_test, y_train, y_test = train_test_split(
data.dataX, data.dataY, test_size=0.33)
print(y_train.shape)
# 5) Train the model
model.train(X_train,
X_test,
y_train,
y_test,
epochs=epochs,
batch_size=batch_size,
save_dir=save_dir)
def main():
configs = json.load(open('configcrops.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
data = DataLoader(os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
model = Model()
model.build_model(configs)
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'])
# Yogyakarta: Kulon progo, bantul, gunung kidul, sleman, DIY
# Jawa Barat: Bandung, Tasikmalaya, Majalengka, Cirebon, Kuningan, Garut, Sumedang, Cianjut, Subang, Purwakarta, Indramayu
# Ciamis, Sukabumi, Bogor, Bekasi, Karawang
# # out-of memory generative training
# steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
# model.train_generator(
# data_gen=data.generate_train_batch(
# seq_len=configs['data']['sequence_length'],
# batch_size=configs['training']['batch_size'],
# normalise=configs['data']['normalise']
# ),
# epochs=configs['training']['epochs'],
# batch_size=configs['training']['batch_size'],
# steps_per_epoch=steps_per_epoch,
# save_dir=configs['model']['save_dir']
# )
# # save_dir = configs['model']['save_dir']
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# print(x_test)
# print(y_test)
# predictions = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'], configs['data']['sequence_length'])
predictions_point = model.predict_point_by_point(x_test)
print(len(predictions_point))
plot_results(predictions_point, y_test)
# plot_results_multiple(predictions, y_test, configs['data']['sequence_length'])
# predictions_full = model.predict_sequence_full(x_test, configs['data']['sequence_length'])
# plot_results(predictions_full, y_test)
groundtrue = data._groundtruths(1)
groundtrue = (groundtrue.ravel())
print(len(groundtrue))
RMSElist = []
for i in range(len(groundtrue)):
errorrate = groundtrue[i] - predictions_point[i]
hasilkuadrat = errorrate * errorrate
RMSElist.append(hasilkuadrat)
RMSE = sum(RMSElist) / (len(predictions_point) - 2)
RMSE = RMSE**(1 / 2)
print(RMSE)
getdataforecast = data._forecasting(5, 1)
total_prediksi = 5
takefrom = 5
forecast_result = model.forecast(total_prediksi, getdataforecast, takefrom)
# print(forecast_result[0])
# forecast_result=np.append(forecast_result,[0.0])
# print(forecast_result)
n_steps = 8
# split into samples
X, y = split_sequence(forecast_result, n_steps)
# reshape from [samples, timesteps] into [samples, timesteps, features]
n_features = 1
# print(X)
X = X.reshape((X.shape[0], X.shape[1], n_features))
# define model
model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=200, verbose=0)
# demonstrate prediction
for j in range(total_prediksi):
getxlastnumber = array(forecast_result[(-n_steps - 1):-1])
x_input = getxlastnumber
# print(x_input)
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
# print(yhat[0][0])
forecast_result = np.append(forecast_result, yhat[0])
# prediction_point=np.append(prediction_point,yhat[0])
plot_results_onlypredicted(forecast_result)
def main():
do = 4
if do == 0:
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
data = DataLoader(os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
model = Model()
model.build_model(configs)
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'],
X_test=x_test,
Y_test=y_test,
saveName=str(configs['training']['epochs']))
'''
# out-of memory generative training
steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
model.train_generator(
data_gen=data.generate_train_batch(
seq_len=configs['data']['sequence_length'],
batch_size=configs['training']['batch_size'],
normalise=configs['data']['normalise']
),
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
steps_per_epoch=steps_per_epoch,
save_dir=configs['model']['save_dir']
)
'''
predictions = model.predict_sequences_multiple(
x_test, configs['data']['sequence_length'],
configs['data']['sequence_length'])
# predictions = model.predict_sequence_full(x_test, configs['data']['sequence_length'])
# predictions = model.predict_point_by_point(x_test)
plot_results_multiple(predictions, y_test,
configs['data']['sequence_length'])
# plot_results(predictions, y_test)
elif do == -1:
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
data = DataLoader(os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
model = Model()
model.build_model(configs)
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'],
X_test=x_test,
Y_test=y_test,
saveName=str(configs['training']['epochs']))
elif do == 1:
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
model = Model()
model.build_model(configs)
data = DataLoader(os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
model.load_model(
r"C:\Users\chris\Documents\Bitcoin_Prediction\saved_models\10092020-152418-e31.h5"
)
#configs['data']['sequence_length'] = 12
predictions = model.predict_sequences_multiple(
x_test, configs['data']['sequence_length'],
configs['data']['sequence_length'])
# predictions = model.predict_sequence_full(x_test, configs['data']['sequence_length'])
#predictions = model.predict_point_by_point(x_test)
plot_results_multiple(
predictions, y_test, configs['data']
['sequence_length']) #configs['data']['sequence_length']
#plot_results(predictions, y_test)
elif do == 2:
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
model = Model()
model.build_model(configs)
model.load_model(
r"C:\Users\chris\Documents\Bitcoin_Prediction\saved_models\23092020-232350-e31.h5"
)
normed_test_data = pd.read_csv(
r"C:\Users\chris\Documents\Bitcoin_Prediction\data\2hdatarecent.csv"
)
normed_test_data = normed_test_data.get(configs['data']['columns'])
normed_test_data = normed_test_data[
-configs['data']['sequence_length'] + 1:]
norm_train_data = pd.read_csv(
r"C:\Users\chris\Documents\Bitcoin_Prediction\data\2hdatarecent.csv"
)
norm_train_data = norm_train_data.get(configs['data']['columns'])
norm_train_data = norm_train_data[-configs['data']['sequence_length'] +
1:]
normed_test_data = DataLoader.normalise_windows2(
model, window_data=normed_test_data, single_window=True)
norm_train_data = DataLoader.normalise_windows2(
model, window_data=norm_train_data, single_window=True)
print(normed_test_data)
test = data.currentData
model.predict_sequences_multipleSecondAttempt(test, 30)
else:
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
model = Model()
model.build_model(configs)
data = DataLoader(os.path.join('data', configs['data']["recentName"]),
configs['data']['train_test_split'],
configs['data']['columns'])
model.load_model(
r"C:\Users\chris\Documents\Bitcoin_Prediction\saved_models_Test\27122020-171426-e101testv3.h5"
)
testdata, printData = data.normData(seq_len=30, normalise=True)
predictions = model.predict_sequences_multipleSecondAttempt(
testdata, 29)
plot_results_multiple(predictions, printData, 29)
def main():
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
model = Model()
model.build_model(configs)
#get live sensor data from Arduino and predict next 10 sensor data
sensor_port = serial.Serial('COM7', 9600)
sensor_port.close()
sensor_port.open()
seq_len = configs['data']['sequence_length'],
sensor_data = []
predictions_data = []
live_data = np.arange(seq_len[0] - 1)
plt.ion() #real time graph
while True:
i = 0
while i < seq_len[0] - 1: # store incoming data to testing data array
b = sensor_port.readline() # read a byte string
live_data[i] = float(b.decode())
sensor_data.append(live_data[i])
i += 1
sensor_struct_data = live_data[
np.newaxis, :, np.newaxis] #contruct live data for LSTM
predictions = model.predict_sequence_live(
sensor_struct_data, configs['data']['sequence_length']
) #Shift the window by 1 new prediction each time, re-run predictions on new window
predictions_data.append(predictions)
plot_results(predictions_data[-120:], sensor_data[-100:])
plt.show()
plt.pause(0.1) #critical to display continous img
#predict every 10 seq_len
#if len(sensor_data) > 1 * seq_len[0]:
#train every 100 seq_len
if len(sensor_data) > 10 * seq_len[0]:
np.savetxt('data\sensor.csv',
sensor_data,
delimiter=',',
header='sensor_value')
#load data for training
data = DataLoader(
os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
x, y = data.get_train_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'])
sensor_data = sensor_data[-100:]
def main():
configs = json.load(open('config.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
data = DataLoader(os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
lossesMINE = []
lossesKERAS = []
# for day_prediction in [1, 2, 3, 4, 5, 10, 50]:
day_prediction = 10
print("Predicting %i days..." % day_prediction)
model = Model()
model.build_model(configs)
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'],
day_pred=day_prediction)
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'])
# out-of memory generative training
# steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
# model.train_generator(
# data_gen=data.generate_train_batch(
# seq_len=configs['data']['sequence_length'],
# batch_size=configs['training']['batch_size'],
# normalise=configs['data']['normalise'],
# day_pred=day_prediction
# ),
# epochs=configs['training']['epochs'],
# batch_size=configs['training']['batch_size'],
# steps_per_epoch=steps_per_epoch,
# save_dir=configs['model']['save_dir']
# )
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'],
day_pred=day_prediction)
# print(x_test.shape)
# print(len(data.denormalization_vals))
# print(y_test.shape)
#predictions = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'], configs['data']['sequence_length'])
#predictions = model.predict_sequence_full(x_test, configs['data']['sequence_length'])
predictions = model.predict_point_by_point(x_test)
#
# y_test_unormalized = np.zeros((y_test.shape[0], ))
# prediction_unormalized = []
#
# for i in range(4):
# for j in range(int(configs['data']['sequence_length']) - 10):
# y_test_unormalized[j*(i+1)] = (y_test[j] + 1)*data.data_test[i*int(configs['data']['sequence_length']), 0]
# prediction_unormalized.append((predictions[j*(i+1)] + 1)*data.data_test[i*int(configs['data']['sequence_length']), 0])
npPredictions = np.asarray(predictions)
# print(type(npPredictions))
# print(type(y_test))
# print(npPredictions.shape)
# print(y_test.shape)
loss = 0
for i in range(len(npPredictions)):
loss += (npPredictions[i] - y_test[i])**2
print(loss)
keras_loss = model.model.evaluate(x_test, y_test)
print(keras_loss)
lossesMINE.append(loss)
lossesKERAS.append(keras_loss)
#plot_results_multiple(predictions, y_test, configs['data']['sequence_length'])
real_y = np.reshape(y_test, (y_test.shape[0], )) * np.asarray(
data.denormalization_vals) + np.asarray(data.denormalization_vals)
real_pred = predictions * np.asarray(
data.denormalization_vals) + np.asarray(data.denormalization_vals)
# print(real_y.shape)
# print(real_pred.shape)
data.denormalization_vals = []
#plot_results(predictions, y_test)
plot_results(real_pred, real_y)
print(lossesMINE)
print(lossesKERAS)
class Classifier(object):
def __init__(self, dataset):
# self.imagegen = ImageDataGenerator(shear_range=0.2,zoom_range=0.2,horizontal_flip=True)
self.dataset = dataset
self.train_x,self.train_y = dataset[0]
self.test_x,self.test_y = dataset[1]
self.learning_rate = 0.16
self.eps = 2e-9
self.params = {}
self.model = Model()
def error(self, Y_actual, Y_output):
Y = Y_actual - Y_output
print(Y)
error = (1/Y.shape[0])*np.sum(Y*Y.T)
return error
def compute_gradients(self, cost, parameters):
grads = {}
grads["dW1"] = T.grad(cost,parameters["W1"])
grads["db1"] = T.grad(cost,parameters["b1"])
grads["dW2"] = T.grad(cost,parameters["W2"])
grads["db2"] = T.grad(cost,parameters["b2"])
grads["dW3"] = T.grad(cost,parameters["W3"])
grads["db3"] = T.grad(cost,parameters["b3"])
grads["dW5"] = T.grad(cost,parameters["W5"])
grads["dW6"] = T.grad(cost,parameters["W6"])
return grads
def train(self):
parameters = None
num_epoch = 1
while num_epoch > 0:
# mini_batch_iter = self.imagegen.flow(self.train_x,self.train_y,batch_size=16)
train_batch_inputs = np.array_split(self.train_x, 16)
train_batch_labels = np.array_split(self.train_y, 16)
train_batch_list = zip(train_batch_inputs,train_batch_labels)
for train_batch in train_batch_list:
# sh = self.model.shape_dim(train_batch[0],train_batch[1])
cost = self.model.train(train_batch[0],train_batch[1])
if current_data%100 == 0:
print(str(current_data*16)+" datas trained")
if current_data == len(self.train_x):
break
current_data += 1
# error = self.model.test(self.test_x.astype(theano.config.floatX),self.test_y)
# print("Epoch "+str(2-num_epoch)+" done")
# print("Error: "+str(error))
num_epoch -= 1
self.params = self.model.parameters()
saveModel(self.params)
def test(self, parameters = None):
error = self.model.test(self.test_x.astype(theano.config.floatX), self.test_y)
return error
# error = self.error(self.test_y,Y_predict)
# return error
def cost_function(self, y_predict, y_label):
return y_label*np.log(y_predict) + (1-y_label)*np.log(1-y_predict)
def main(args):
config_params = read_params(args.config_fpath)
if args.gpu < 0:
cuda = False
else:
cuda = True
torch.cuda.set_device(args.gpu)
print('*** Create data loader ***')
dataloader, val_dataloader, test_dataloader = make_data_loader(
args.batch_size, dataset_name='Letter-low', cuda=cuda)
print('*** Create model ***')
model = Model(config=config_params, verbose=True, cuda=cuda)
if cuda:
model.cuda()
# optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
# loss function
loss_fcn = torch.nn.CrossEntropyLoss()
# Start training
print('*** Start training ***')
step = 0
model.train()
losses = []
for epoch in range(args.n_epochs):
for iter, (graphs, labels) in enumerate(dataloader):
# forward pass
logits = model(graphs)
# compute loss
loss = loss_fcn(logits, labels)
losses.append(loss.item())
# backpropagate
optimizer.zero_grad()
loss.backward()
optimizer.step()
# testing
step += 1
if step % args.eval_every == 0:
val_loss, val_acc = test(val_dataloader, model, loss_fcn)
print(
"Step {:05d} | Train loss {:.4f} | Over {} | Val loss {:.4f} |"
"Val acc {:.4f}".format(
step,
np.mean(losses),
len(losses),
val_loss,
val_acc,
))
model.train()
print('*** Start Testing ***')
test_loss, test_acc = test(test_dataloader, model, loss_fcn)
print("Test loss {:.4f} | Test acc {:.4f}".format(test_loss, test_acc))
from core.model import Model
configs = json.load(open('config.json', 'r'))
data = DataLoader(os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns'])
model = Model()
model.build_model(configs)
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'])
x_test, y_test = data.get_test_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
def predict_point_by_point(self, data):
#Predict each timestep given the last sequence of true data, in effect only predicting 1 step ahead each time
predicted = self.model.predict(data)
predicted = np.reshape(predicted, (predicted.size, ))
return predicted
def predict_sequence_full(self, data, window_size):
def main():
configs = json.load(open('point_to_point_similar_sinewave.json', 'r'))
if not os.path.exists(configs['model']['save_dir']): os.makedirs(configs['model']['save_dir'])
data = DataLoader(
os.path.join('data', configs['data']['filename']),
configs['data']['train_test_split'],
configs['data']['columns']
)
model = Model()
model.build_model(configs)
x, y = data.get_train_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise']
)
# in-memory training
model.train(
x,
y,
epochs = configs['training']['epochs'],
batch_size = configs['training']['batch_size'],
save_dir = configs['model']['save_dir']
)
# out-of memory generative training
# steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
# model.train_generator(
# data_gen=data.generate_train_batch(
# seq_len=configs['data']['sequence_length'],
# batch_size=configs['training']['batch_size'],
# normalise=configs['data']['normalise']
# ),
# epochs=configs['training']['epochs'],
# batch_size=configs['training']['batch_size'],
# steps_per_epoch=steps_per_epoch,
# save_dir=configs['model']['save_dir']
# )
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise']
)
# predictions = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'], configs['data']['sequence_length'])
# predictions = model.predict_sequence_full(x_test, configs['data']['sequence_length'])
predictions = model.predict_point_by_point(x_test)
predictions = predictions.reshape(-1, 1)
normalized_test = data.get_normalized_test()
normalized_test = np.delete(normalized_test, [j for j in range(len(predictions), len(normalized_test))], axis=0)
my_normalized_test = np.delete(normalized_test, [0], axis=1)
# my_normalized_test = np.delete(my_normalized_test, [j for j in range(len(predictions), len(my_normalized_test))], axis=0)
final_data = np.hstack((predictions, my_normalized_test))
actual_predictions = data.inverse_data(final_data)
predictions = actual_predictions[:, 0]
actual_test = data.inverse_data(normalized_test)
y_test = actual_test[:, 0]
# plot_results_multiple(predictions, y_test, configs['data']['sequence_length'])
plot_results(predictions, y_test)
mse = mean_squared_error(y_test, predictions)
print("Mean Squared Error: " + str(mse))
print("Root Mean Squared Error: " + str(math.sqrt(mse)))
mae = mean_absolute_error(y_test, predictions)
print("Mean Absolute Error: " + str(mae))
r2 = r2_score(y_test, predictions)
print("R Squared Error: " + str(r2))
from matplotlib import pyplot as plt
from core.model import Model
from core.data_processor import DataProcessor
# 数据预处理
dp = DataProcessor('./data/AAPL_080112_200112.csv', (85, 0, 15),
['Adj Close', 'Volume'])
data_x, data_y = dp.get_data(50, 'train')
test_x, test_y = dp.get_data(50, 'test')
# 训练模型
model = Model()
model.build()
model.train(data_x, data_y, epochs=10, batch_size=32, save_dir='./saved_model')
# 预测
predicted = model.predict_sequence_multiple(test_x,
window_size=50,
prediction_len=50)
plt.plot(test_y, label='True Data')
for i, data in enumerate(predicted):
padding = [None for p in range(i * 50)]
plt.plot(padding + data, label='Prediction')
plt.legend()
plt.show()
import numpy as np
from core.model import Model
from layers.input import Input
from layers.dense import Dense
from util.cost_functions import L2
if __name__ == '__main__':
# demo MLP
data_x = np.array([1, 2])
data_y = np.array([0.2, 0.4])
train_x = np.reshape(data_x, (len(data_x), 1, 1))
train_y = np.reshape(data_y, (len(data_y), 1, 1))
model = Model()
model.add(Input(1))
model.add(Dense(3))
model.add(Dense(1))
model.compile(cost=L2(),
optimizer='sgd',
num_epochs=30000,
batch_size=1,
lr=0.1)
model.train(train_x, train_y)
test_data_x = np.array([1])
test_x = np.reshape(test_data_x, (len(test_data_x), 1))
print model.predict(test_x)
def main():
configs = json.load(open('configcrops.json', 'r'))
if not os.path.exists(configs['model']['save_dir']):
os.makedirs(configs['model']['save_dir'])
#filename1 can be changed into filename (see the configcrops.json)
namaefile = configs['data']['filename1']
with open(namaefile, 'r') as dataframe:
hasil = json.load(dataframe)
# print(hasil)
temp = []
listhasil = []
for key, value in hasil.items():
temp = [key, value]
listhasil.append(temp)
listkota = [
'Kulon Progo', 'Bantul', 'Gunung Kidul', 'Sleman', 'DIY', 'Bandung',
'Tasikmalaya', 'Majalengka', 'Cirebon', 'Kuningan', 'Garut',
'Sumedang', 'Cianjur', 'Subang', 'Purwakarta', 'Indramayu', 'Ciamis',
'Sukabumi', 'Bogor', 'Bekasi', 'Karawang'
]
kodekota = [
'KLP', 'BTL', 'GKD', 'SLM', 'DIY', 'BD', 'TKM', 'MJK', 'CRB', 'KNG',
'GRT', 'SMD', 'CJR', 'SBG', 'PWK', 'IDY', 'CMS', 'SKB', 'BGR', 'BKS',
'KRW'
]
listtahun = []
for i in range(1961, 2015):
listtahun.append(str(i))
data = []
#listhasil
#=[["Kulon Progo",{data tahun dan crops},"DIY",{data tahun dan crops}]
datacrops = []
datalengkapcrops = []
datalengkaptahun = []
datatahun_semuadaerah = []
#Variabel untuk tampung data csv
kota_untuk_csv = [] #pembuatan kolom kota pada csv
kode_untuk_csv = [] #pembuatan kolom kode untuk csv
tahun_untuk_csv = [] #pembuatan kolom tahun untuk csv
crops_untuk_csv = [] #pembuatan kolom crops untuk csv
RMSE_untuk_csv = []
semua_data_csv = [
] #untuk menampung data kota,kode,tahun, dan crops beserta rmse pada sebaris (tidak digunakan #cadangan)
#Pengulangan compiling per kota
for j in range(len(listkota)):
if (len(listhasil[j][1]) != len(listtahun)):
jlhprediksi = len(listtahun) - len(
hasil[listkota[j]]) + (6) - 1 #prediksi sampai 2020
else:
jlhprediksi = (6) - 1 #prediksi sampai 2020
datatahun_crops = listhasil[j][1] #dapat data json crops dan tahun
datatahun = list(
datatahun_crops.keys()) #dapat data tahun pada satu daerah
datatahunint = [int(x) for x in datatahun] #konversi ke integer
arraytahun = np.array(datatahunint) #dibuat jadi array
sorttahun = np.sort(arraytahun) #sort dalam bentuk array
datatahun_daerah = list(sorttahun) #buat lagi jadi list
datalengkaptahun.append(datatahun_daerah)
for n in range(len(
listhasil[j][1])): #listhasil[j][1] = data tahun dan crops
datacrops.append(float(listhasil[j][1][str(datatahun_daerah[n])]))
datalengkapcrops.append(datacrops)
datacrops = []
# print(datalengkapcrops)
# print(datalengkaptahun)
listcrops_daerah = []
hasillistcrops_daerah = []
for i in range(len(listkota)):
for j in range(len(datalengkapcrops[i])):
listcrops_daerah.append([datalengkapcrops[i][j]
]) #pecah per tahun crops dalam satu list
hasillistcrops_daerah.append(listcrops_daerah)
listcrops_daerah = []
# for i in range(len(listkota)):
# arraycrops_semua=np.array(hasillistcrops_daerah[i])
# print(arraycrops_semua[0:20])
arraycrops_semua = np.array(hasillistcrops_daerah)
for i in range(len(listkota)):
data = DataLoader(np.array(arraycrops_semua[i]),
configs['data']['train_test_split'])
model = Model()
model.build_model(configs)
x, y = data.get_train_data(seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# in-memory training
model.train(x,
y,
epochs=configs['training']['epochs'],
batch_size=configs['training']['batch_size'],
save_dir=configs['model']['save_dir'])
# Yogyakarta: Kulon progo, bantul, gunung kidul, sleman, DIY
# Jawa Barat: Bandung, Tasikmalaya, Majalengka, Cirebon, Kuningan, Garut, Sumedang, Cianjut, Subang, Purwakarta, Indramayu
# Ciamis, Sukabumi, Bogor, Bekasi, Karawang
# # out-of memory generative training
# steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
# model.train_generator(
# data_gen=data.generate_train_batch(
# seq_len=configs['data']['sequence_length'],
# batch_size=configs['training']['batch_size'],
# normalise=configs['data']['normalise']
# ),
# epochs=configs['training']['epochs'],
# batch_size=configs['training']['batch_size'],
# steps_per_epoch=steps_per_epoch,
# save_dir=configs['model']['save_dir']
# )
# # save_dir = configs['model']['save_dir']
x_test, y_test = data.get_test_data(
seq_len=configs['data']['sequence_length'],
normalise=configs['data']['normalise'])
# print(x_test)
# print(y_test)
# predictions = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'], configs['data']['sequence_length'])
predictions_point = model.predict_point_by_point(x_test)
print(len(predictions_point))
for ulang in range(len(datalengkaptahun[i]) - len(predictions_point)):
datalengkaptahun[i].remove(
datalengkaptahun[i]
[ulang]) #for equality number of ground truth and prediction
# Use the plot when you want to see the data graphically
# plot_results(predictions_point, y_test,datalengkaptahun[i],listkota[i])
groundtrue = data._groundtruths(1)
groundtrue = (groundtrue.ravel())
# print(len(groundtrue))
#Measure the RMSE
RMSElist = []
for k in range(len(predictions_point)):
errorrate = groundtrue[k + ulang] - predictions_point[k]
hasilkuadrat = errorrate * errorrate
RMSElist.append(hasilkuadrat)
RMSE = sum(RMSElist) / (len(predictions_point))
RMSE = RMSE**(1 / 2)
# print(RMSE)
getdataforecast = data._forecasting(jlhprediksi, jlhprediksi)
# print(len(getdataforecast))
total_prediksi = jlhprediksi
takefrom = jlhprediksi
forecast_result = model.forecast(total_prediksi, getdataforecast,
takefrom)
# print(len(forecast_result))
# print(forecast_result[0])
# forecast_result=np.append(forecast_result,[0.0])
# print(forecast_result)
n_steps = 8
# split into samples
X, y = split_sequence(forecast_result, n_steps)
# reshape from [samples, timesteps] into [samples, timesteps, features]
n_features = 1
# print(X)
X = X.reshape((X.shape[0], X.shape[1], n_features))
# define model
model = Sequential()
model.add(
LSTM(50, activation='relu', input_shape=(n_steps, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=200, verbose=0)
#make the number of predictions is equal to the number of the ground truth
hasilprediksi = []
hasilprediksi.append(groundtrue[-(ulang + 1):-ulang])
hasilprediksi.append(groundtrue[-ulang:])
for j in range(total_prediksi):
getxlastnumber = array(forecast_result[(-n_steps - 1):-1])
x_input = getxlastnumber
# print(x_input)
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
# print(yhat[0][0])
hasilprediksi.append(yhat[0]) #untuk dikirimkan ke json
forecast_result = np.append(forecast_result,
yhat[0]) #untuk training forecast
groundtrue = np.append(groundtrue,
yhat[0]) #untuk plotting ke grafik
# print(len(groundtrue))
# prediction_point=np.append(prediction_point,yhat[0])
# print(hasilprediksi) #hasilprediksi dalam bentuk array, hasilprediksi[0] dalam bntk list, hasilprediksi[0][0] dalam bentuk skalar
semuatahun = datalengkaptahun[i]
tahunbaru = []
terakhirtahun = datalengkaptahun[i][len(datalengkaptahun[i]) - 1]
rangetahun_input = len(groundtrue) - len(datalengkaptahun[i])
# print(rangetahun_input)
if (len(datalengkaptahun[i]) < len(groundtrue)):
for z in range(rangetahun_input):
semuatahun.append(terakhirtahun) #untuk grafik
tahunbaru.append(terakhirtahun) #untuk dikirimkan ke json
terakhirtahun = terakhirtahun + 1
# print(tahunbaru)
# Use the plot when you want to see the data graphically
# plot_results_onlypredicted(semuatahun,groundtrue,listkota[i])
#To check the length of ground true is equal to the datalengkaptahun[i] or the number of years record at specific Entity
# print(len(groundtrue))
# print(len(datalengkaptahun[i]))
# semuahasil_csv=[]
# csv_data_kota=duplikathasil.get(column).values[:]
#To record all data into LIST to make CSV
for jlh in range(len(semuatahun)):
kota_untuk_csv.append(listkota[i])
kode_untuk_csv.append(kodekota[i])
RMSE_untuk_csv.append(RMSE[0])
tahun_untuk_csv.append(semuatahun[jlh])
crops_untuk_csv.append(groundtrue[jlh])
#Alternative solution for csv
# for jlh in range(len(datalengkapcrops[i])):
# kota_untuk_csv.append(listkota[i])
# kode_untuk_csv.append(kodekota[i])
# # tahun_untuk_csv.append(datalengkaptahun[i][jlh])
# # crops_untuk_csv.append(datalengkapcrops[i][jlh])
# RMSE_untuk_csv.append(RMSE[0])
# for jlh in range (rangetahun_input):
# kota_untuk_csv.append(listkota[i])
# kode_untuk_csv.append(kodekota[i])
# # tahun_untuk_csv.append(tahunbaru[jlh])
# # crops_untuk_csv.append(hasilprediksi[jlh][0])
# RMSE_untuk_csv.append(RMSE[0])
HasilCSV = {
'Entity': kota_untuk_csv,
'Code': kode_untuk_csv,
'Year': tahun_untuk_csv,
' crop(tonnes per hectare)': crops_untuk_csv,
'RMSE': RMSE_untuk_csv
}
df = DataFrame(HasilCSV,
columns=[
'Entity', 'Code', 'Year',
' crop(tonnes per hectare)', 'RMSE'
])
print(df)
filebaca_csv = configs["data"]["newcsv"]
filebaca_csv1 = configs["data"]["newcsv1"]
#name of data export can be changed through configcrops.json (variable filebaca_csv and filebacacsv1 only for explanation)
export_csv = df.to_csv(
r'/home/biovick/Downloads/tkte/sudiro/Forecasting-and-Predicting Crops into Visualization/data/newTomatov2.csv',
index=False)
