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eval-online-3002-5.py
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eval-online-3002-5.py
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from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pyprind
import theano
from keras.models import Sequential, load_model
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from metrics import MASE, mean_absolute_percentage_error, median_percentage_error, rmse, geh, mape
from utils import load_data, train_test_split, BestWeight, ResetStatesCallback, show_plot
EPS = 1e-6
sequence_length = 50
def step_data(FPATH, end_date=None, use_sensors=None, use_datetime=False):
all_data = load_data(FPATH, EPS, use_sensors=use_sensors, use_datetime=use_datetime)
return all_data
def chunks(x, y, n):
"""Yield successive n-sized chunks from l."""
for i in xrange(0, len(x), n):
yield x[i:i + n], y[i:i + n]
def do_model(all_data, steps, dates):
_steps = steps
# trim = 100
# all_data = all_data[:trim]
# dates = dates[:trim]
print("steps:", _steps)
features = all_data[:-_steps]
labels = all_data[_steps:, -1:]
X_train = features
Y_train = labels
optimiser = 'adam'
hidden_neurons = 332
loss_function = 'mse'
dropout = 0.0923
inner_hidden_neurons = 269
dropout_inner = 0.2269
batch_size = 1
X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))
print("X train shape:\t", X_train.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()
if 'gpu' in theano.config.device:
gpu_cpu = 'gpu'
else:
gpu_cpu = 'cpu'
model.add(LSTM(output_dim=hidden_neurons, input_dim=in_neurons, batch_input_shape=(1,1, in_neurons), return_sequences=True, init='uniform',
consume_less=gpu_cpu, stateful=True))
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, stateful=True))
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)
# run through all data up to 23 April, 2013
progress = pyprind.ProgBar(len(X_train), width=50, stream=1)
mean_tr_loss = []
seq = 0
inputs = zip(X_train, Y_train)
for idx, tup in enumerate(inputs):
x_chunk, y_chunk = tup
tr_loss = model.train_on_batch(np.array([x_chunk]), y_chunk)
mean_tr_loss.append(tr_loss)
seq += 1
if seq % sequence_length == 0:
model.reset_states()
progress.update()
# for x_chunk, y_chunk in chunks(X_train, Y_train, batch_size):
# # for
# # we need to reset states when we have an error value
# if last_x is None:
#
# last_x = x_chunk
#
# tr_loss = model.train_on_batch(x_chunk, y_chunk)
#
# mean_tr_loss.append(tr_loss)
# seq += 1
# if seq % sequence_length == 0:
# model.reset_states()
# progress.update()
print("\nTraining Loss: {}".format(np.mean(mean_tr_loss)))
return model
sensors = [5,6,7]
if __name__ == "__main__":
start = datetime.now()
import sys
if len(sys.argv) >= 2:
file_path = sys.argv[1]
else:
file_path = '/scratch/Dropbox/PhD/htm_models_adelaide/engine/lane_data_3002_3001.csv'
print ("Examining", file_path)
date_split = datetime(2013, 4, 23)
data, dates = step_data(file_path, end_date=date_split, use_sensors=sensors, use_datetime=True)
fname = file_path.split('/')[-1]
print (fname)
steps = 1
model_name = 'models/keras_1_step_3002_online_no_state_reset.h5'
model = do_model(data, steps, dates)
# model.save(model_name)
# model = load_model(model_name)
true_x_row, true_x = load_data(file_path, EPS, use_datetime=True, load_from=date_split, use_sensors=sensors, end_date=datetime(2013, 6, 15), skip_error=False)
# replace 2046/2047 values with 50
pred_xy = []
true_xy = []
progress = pyprind.ProgBar(len(true_x[:-1]), width=50, stream=1)
# flow_val = 8
seq = 0
inputs = zip(true_x_row, true_x)
for idx, xy in enumerate(inputs[:-1]):
row, dt = xy
dt_next = dt+timedelta(minutes=5*steps)
npa = np.array([[row]])
if row[-1] > 300: # error value,
true_xy.append((dt, np.nan))
pred_xy.append((dt_next, np.nan))
else:
pred = model.predict(npa)
seq += 1
if seq % sequence_length == 0:
model.reset_states()
true_xy.append((dt, np.float32(row[-1])))
model.train_on_batch(npa, np.array([true_x_row[idx+1][0]]).reshape((1, 1)))
pred_xy.append((dt_next, pred[0]))
progress.update()
pred_xy = np.array(pred_xy)
true_xy = np.array(true_xy)
true_x = true_xy[:, 0]
pred_x = np.reshape(pred_xy[:, 0], (-1, 1))
pred_y = np.reshape(pred_xy[:, 1].astype(dtype=np.float32), (-1, 1))
true_y = true_xy[:,1].astype(np.float32)
true_y_max = np.copy(true_y)[:-1]
true_y_max[true_y_max == 0] = 1
np.savez('pred_data/3002-no-reset-on-error-all-sensor', true_x=true_x, true_y=true_y, pred_x=pred_x, pred_y=pred_y)
true_y_max = true_y_max.reshape((true_y_max.shape[0],1))
# print ("true_y_max", true_y_max.shape)
# print("pred_y", pred_y.shape)
print("GEH: ", geh(true_y_max, pred_y[:-1]))
print("MAPE: ", mape(true_y_max, pred_y[:-1]))
print("RMSE: ", rmse(true_y_max, pred_y[:-1]))
font = {'size': 30}
import matplotlib
matplotlib.rc('font', **font)
import matplotlib.pyplot as plt
plt.plot(true_x, true_y, 'b-', label='Readings')
plt.plot(pred_x, pred_y, 'r-', label='LSTM-Online 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")
import os
if os.getenv('DISPLAY', None):
plt.show()
else:
out_img_name = model_name.split('/')[1][:-4]+'.png'
plt.savefig(out_img_name)