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evaluate.py
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from __future__ import print_function
from collections import OrderedDict
from datetime import datetime, date
import numpy as np
import pluck as pluck
import tabulate
from keras.models import Sequential, load_model
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_absolute_error
from metrics import MASE, mean_absolute_percentage_error, median_percentage_error, rmse, geh, mape
from utils import load_data, train_test_split, BestWeight
EPS = 1e-6
def step_data(FPATH, end_date=None):
all_data = load_data(FPATH, EPS, end_date=end_date, use_sensors=[5])
return all_data
def do_model(all_data, steps, run_model=True):
_steps = steps
print("steps:", _steps)
scaler = MinMaxScaler()
all_data = scaler.fit_transform(all_data)
if not run_model:
return None, None, scaler
features = all_data[:-_steps]
labels = all_data[_steps:, -1:]
tts = train_test_split(features, labels, test_size=0.4)
X_train = tts[0]
X_test = tts[1]
Y_train = tts[2].astype(np.float64)
Y_test = tts[3].astype(np.float64)
optimiser = 'adam'
hidden_neurons = 200
loss_function = 'mse'
batch_size = 105
dropout = 0.056
inner_hidden_neurons = 269
dropout_inner = 0.22
X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))
X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])
print("X train shape:\t", X_train.shape)
print("X test shape:\t", X_test.shape)
# print("Y train shape:\t", Y_train.shape)
# print("Y test shape:\t", Y_test.shape)
# print("Steps:\t", _steps)
in_neurons = X_train.shape[2]
out_neurons = 1
model = Sequential()
gpu_cpu = 'cpu'
best_weight = BestWeight()
model.add(LSTM(output_dim=hidden_neurons, input_dim=in_neurons, return_sequences=True, init='uniform',
consume_less=gpu_cpu))
model.add(Dropout(dropout))
dense_input = inner_hidden_neurons
model.add(LSTM(output_dim=dense_input, input_dim=hidden_neurons, return_sequences=False, consume_less=gpu_cpu))
model.add(Dropout(dropout_inner))
model.add(Activation('relu'))
model.add(Dense(output_dim=out_neurons, input_dim=dense_input))
model.add(Activation('relu'))
model.compile(loss=loss_function, optimizer=optimiser)
history = model.fit(
X_train, Y_train,
verbose=0,
batch_size=batch_size,
nb_epoch=30,
validation_split=0.3,
shuffle=False,
callbacks=[best_weight]
)
model.set_weights(best_weight.get_best())
predicted = model.predict(X_test) + EPS
rmse_val = rmse(Y_test, predicted)
metrics = OrderedDict([
# ('hidden', hidden_neurons),
('steps', _steps),
('geh', geh(Y_test, predicted)),
('rmse', rmse_val),
('mape', mean_absolute_percentage_error(Y_test, predicted)),
# ('smape', smape(predicted, _Y_test)),
# ('median_pe', median_percentage_error(predicted, Y_test)),
# ('mase', MASE(_Y_train, _Y_test, predicted)),
# ('mae', mean_absolute_error(y_true=Y_test, y_pred=predicted)),
# ('batch_size', batch_size),
# ('optimiser', optimiser),
# ('dropout', dropout),
# ('extra_layer_dropout', dropout_inner),
# ('extra_layer_neurons', inner_hidden_neurons),
# ('loss function', loss_function)
# 'history': history.history
])
return metrics, model, scaler
if __name__ == "__main__":
import sys, os
pass
try:
file_path = sys.argv[1]
except IndexError:
quit("Usage is: evaluate.py <file_path_1> <file_path_2> ...")
start = datetime.now()
for file_path in sys.argv[1:]:
print ("Examining", file_path)
data = step_data(file_path, datetime(2013,4,23))
# metrics = []
# fname = file_path.split('/')[-1]
# for i in [1]:#, 3, 6, 9, 12]:
metric_out, model, scaler = do_model(data, 1, run_model=False)
model = load_model('models/keras_1_step_3002_scaled.h5')
# metrics.append(metric_out)
# model.save('models/keras_{}_step_{}_sensor5.h5'.format(i, fname))
# # model has: 1 1.45893 14.3746 34.0476
# # print("Loading model")
# # model = load_model('best_sensor_5_with_calendar.h5')
# #
# print("Finished in "+str(datetime.now() - start))
# print(tabulate.tabulate(metrics, headers='keys', tablefmt="latex"))
#
# model = load_model('models/keras_1_step_lane_data_3002_3001.csv_sensor5.h5')
print("Loading impute data")
predict_data = load_data(file_path, EPS, use_datetime=True, load_from=datetime(2013, 4, 23), use_sensors=[5], end_date=datetime(2013, 6, 15))
true_x = predict_data[:, 0]
true_y = predict_data[:, 1].astype(np.float32)
# replace 2046/2047 values with 50
true_y[true_y > 2045] = -1
pred_y = []
# flow_val = 8
for idx, dt in enumerate(true_x):
in_row = [[
dt.weekday(),
# is weekend
int(dt.weekday() in [5, 6]),
# hour of day
dt.isocalendar()[1],
dt.hour,
dt.minute,
max(1, true_y[idx])
]]
in_row = scaler.fit_transform(scaler.fit_transform(in_row))
pred = model.predict(np.array([in_row]))
# flow_val = pred[0][0]
pred_y.append(scaler.inverse_transform([0,0,0,0,0,pred[0][0]]))
true_x = true_x[1:]
true_y = true_y[1:]
pred_y = pred_y[:-1]
pred_y = np.array(pred_y, dtype=np.float32)
true_y_max = np.copy(true_y)
true_y_max[true_y_max == 0] = 1
print("GEH: ", np.sqrt(2*np.power(pred_y - true_y_max, 2)/(pred_y + true_y_max)).mean(axis=0))
print("MAPE: ", mape(true_y_max, pred_y))
print("RMSE: ", np.sqrt(((pred_y - true_y_max) ** 2).mean(axis=0)))
import matplotlib.pyplot as plt
plt.plot(true_x, true_y, 'b-', label='Readings')
plt.plot(true_x, pred_y, 'r-', label='Predictions')
df = "%A %d %B, %Y"
plt.title("3002: Traffic Flow from {} to {}".format(true_x[0].strftime(df), true_x[-1].strftime(df)))
plt.legend()
plt.ylabel("Vehicles/ 5 min")
plt.xlabel("Time")
plt.show()