def __init__(self, configs):
self.method = configs['model']['method']
self.model_path = configs['model']['filename_model']
self.raw_path = configs['data']['filename']
self.clean_path = configs['data']['filename_clean']
self.batch_size = configs['data']['batch_size']
self.x_window_size = configs['data']['x_window_size']
self.y_window_size = configs['data']['y_window_size']
self.y_lag = configs['data']['y_lag']
self.filter_cols = configs['data']['filter_columns']
self.train_test_split = configs['data']['train_test_split']
self.model = None
self.dl = etl.ETL(self.method)
import h5py
import json
import numpy as np
import tensorflow as tf
import etl, lstm, plot, stats
tf.set_random_seed(777) # for reproducibility
configs = json.loads(open('configs.json').read())
dl = etl.ETL(
filename_in=configs['data']['filename'],
filename_out=configs['data']['filename_clean'],
batch_size=configs['data']['batch_size'],
x_window_size=configs['data']['x_window_size'],
y_window_size=configs['data']['y_window_size'],
x_col=configs['data']['x_base_column'],
y_col=configs['data']['y_predict_column'],
filter_cols=configs['data']['filter_columns'],
target_percent_change=configs['rule']['target_percent_change'],
train_test_split=configs['data']['train_test_split']
)
# dl.create_clean_datafile()
with h5py.File(configs['data']['filename_clean'], 'r') as hf:
nrows = hf['x'].shape[0]
ncols = hf['x'].shape[2]
print(hf['x'].shape)
print(hf['y'].shape)
import gru
import etl
import h5py
import json
configs = json.loads(open('configs.json').read())
model = gru.load_network("model/model_saved_2016_20180614.h5")
dl = etl.ETL("Integer")
dl.usage = "test"
true_values = []
def generator_strip_xy(data_gen_test, true_values):
for x, y in data_gen_test:
true_values += list(y)
yield x
with h5py.File(configs['data']['filename_clean'], 'r') as hf:
nrows = hf['x'].shape[0]
ncols = hf['x'].shape[2]
ntest = nrows
steps_test = int(ntest / configs['data']['batch_size'])
tesize = steps_test * configs['data']['batch_size']
data_gen_test = dl.generate_clean_data(
configs['data']['filename_clean'],
size=tesize,
batch_size=configs['data']['batch_size'])
print('> Testing model on', ntest, 'data rows with', steps_test, 'steps')
predictions = model.predict_generator(generator_strip_xy(
data_gen_test, true_values),
for i, data in enumerate(predicted_data):
padding = [None for p in range(i * prediction_len)]
plt.plot(padding + data, label='Predicción')
plt.legend()
plt.show()
with h5py.File(configs['data']['filename_clean'], 'r') as hf:
nrows = hf['x'].shape[0]
ncols = hf['x'].shape[2]
ntrain = int(configs['data']['train_test_split'] * nrows)
ntest = nrows - ntrain
steps_test = int(ntest / configs['data']['batch_size'])
dl = etl.ETL()
model = lstm.load_network(configs['model']['filename_model'])
data_gen_test = dl.generate_clean_data(
configs['data']['filename_clean'],
batch_size=configs['data']['batch_size'],
start_index=ntrain)
predictions = model.predict_generator(generator_strip_xy(
data_gen_test, true_values),
steps=steps_test)
plot_results(predictions[:500], true_values[:500])
#Reload the data-generator
