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test.py
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test.py
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import numpy as np
import tensorflow as tf
from config import Config
import model as _model
from data_loader import get_datasets
import matplotlib.pyplot as plt
plt.style.use("seaborn-darkgrid")
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string(
"config",
"conf/SML2010.json",
"Path to json file with the configuration to be run",
)
def get_np_array(session, model, next_element):
all_true = []
all_predicted = []
while True:
try:
x, y = session.run(next_element)
predictions = session.run(
model.predictions,
{model.driving_series: x, model.past_history: y},
)
true = np.reshape(y[:, -1], [-1]).tolist()
predicted = np.reshape(predictions, [-1]).tolist()
all_true += true
all_predicted += predicted
except tf.errors.OutOfRangeError:
break
return np.array(all_true), np.array(all_predicted)
def plot(
session, model, train_next_element, val_next_element, test_next_element, name="tmp", show=True
):
train_true, train_predicted = get_np_array(
session, model, train_next_element
)
val_true, val_predicted = get_np_array(session, model, val_next_element)
test_true, test_predicted = get_np_array(session, model, test_next_element)
train_size, val_size, test_size = (
len(train_true),
len(val_true),
len(test_true),
)
plt.figure(figsize=(20, 5))
plt.plot(range(train_size), train_true, label="train true")
plt.plot(range(train_size), train_predicted, label="train predicted")
plt.plot(
range(train_size, train_size + val_size), val_true, label="val true"
)
plt.plot(
range(train_size, train_size + val_size),
val_predicted,
label="val predicted",
)
plt.plot(
range(train_size + val_size, train_size + val_size + test_size),
test_true,
label="test true",
)
plt.plot(
range(train_size + val_size, train_size + val_size + test_size),
test_predicted,
label="test predicted",
)
plt.ylabel("target serie")
plt.xlabel("time steps")
plt.legend(loc="upper left")
if show:
plt.show()
else:
plt.savefig(name, dpi=400)
plt.close()
def evaluate(config):
train_set, val_set, test_set = get_datasets(config, shuffled=False)
train_set = train_set.batch(config.batch_size, drop_remainder=True)
val_set = val_set.batch(config.batch_size, drop_remainder=True)
test_set = test_set.batch(config.batch_size, drop_remainder=True)
model = _model.TimeAttnModel(config)
saver = tf.train.Saver(max_to_keep=1)
with tf.Session() as session:
session.run(tf.global_variables_initializer())
train_iterator = train_set.make_initializable_iterator()
val_iterator = val_set.make_initializable_iterator()
test_iterator = test_set.make_initializable_iterator()
train_next_element = train_iterator.get_next()
val_next_element = val_iterator.get_next()
test_next_element = test_iterator.get_next()
# Restore from last evaluated epoch
print("Restoring from: {}".format(config.log_path / "model-max-ckpt"))
saver.restore(session, str(config.log_path / "model-max-ckpt"))
session.run(train_iterator.initializer)
train_scores = model.evaluate(session, train_next_element)
print("============Train=============")
print("RMSE: {:.5f}".format(train_scores["RMSE"]))
print("MAE: {:.5f}".format(train_scores["MAE"]))
print("MAPE: {:.5f}".format(train_scores["MAPE"]))
session.run(val_iterator.initializer)
val_scores = model.evaluate(session, val_next_element)
print("============Validation=============")
print("RMSE: {:.5f}".format(val_scores["RMSE"]))
print("MAE: {:.5f}".format(val_scores["MAE"]))
print("MAPE: {:.5f}".format(val_scores["MAPE"]))
session.run(test_iterator.initializer)
test_scores = model.evaluate(session, test_next_element)
print("============Test=============")
print("RMSE: {:.5f}".format(test_scores["RMSE"]))
print("MAE: {:.5f}".format(test_scores["MAE"]))
print("MAPE: {:.5f}".format(test_scores["MAPE"]))
session.run(train_iterator.initializer)
session.run(val_iterator.initializer)
session.run(test_iterator.initializer)
plot(
session,
model,
train_next_element,
val_next_element,
test_next_element,
)
def main(argv):
# load hyper-parameters from configuration file
config = Config.from_file(FLAGS.config)
evaluate(config)
if __name__ == "__main__":
tf.app.run(main=main)