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main.py
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main.py
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"""
Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).
"""
import sys
import warnings
import argparse
import math
import warnings
import numpy as np
import pandas as pd
from process_data import load_data
#from keras.models import load_model
import _pickle as cPickle
import tensorflow as tf
from keras.utils.vis_utils import plot_model
import sklearn.metrics as metrics
import matplotlib as mpl
import matplotlib.pyplot as plt
warnings.filterwarnings("ignore")
def MAPE(y_true, y_pred):
"""Mean Absolute Percentage Error
Calculate the mape.
# Arguments
y_true: List/ndarray, ture data.
y_pred: List/ndarray, predicted data.
# Returns
mape: Double, result data for train.
"""
y = [x for x in y_true if x > 0]
y_pred = [y_pred[i] for i in range(len(y_true)) if y_true[i] > 0]
num = len(y_pred)
sums = 0
for i in range(num):
tmp = abs(y[i] - y_pred[i]) / y[i]
sums += tmp
mape = sums * (100 / num)
return mape
def eva_regress(y_true, y_pred):
"""Evaluation
evaluate the predicted resul.
# Arguments
y_true: List/ndarray, ture data.
y_pred: List/ndarray, predicted data.
"""
mape = MAPE(y_true, y_pred)
vs = metrics.explained_variance_score(y_true, y_pred)
mae = metrics.mean_absolute_error(y_true, y_pred)
mse = metrics.mean_squared_error(y_true, y_pred)
r2 = metrics.r2_score(y_true, y_pred)
print('explained_variance_score:%f' % vs)
print('mape:%f%%' % mape)
print('mae:%f' % mae)
print('mse:%f' % mse)
print('rmse:%f' % math.sqrt(mse))
print('r2:%f' % r2)
def plot_results(y_true, y_preds, names):
"""Plot
Plot the true data and predicted data.
# Arguments
y_true: List/ndarray, ture data.
y_pred: List/ndarray, predicted data.
names: List, Method names.
"""
d = '2015-10-04 00:00'
x = pd.date_range(d, periods=288, freq='5min')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, y_true, label='True Data')
for name, y_pred in zip(names, y_preds):
ax.plot(x, y_pred, label=name)
plt.legend()
plt.grid(True)
plt.xlabel('Time of Day')
plt.ylabel('Flow')
date_format = mpl.dates.DateFormatter("%H:%M")
ax.xaxis.set_major_formatter(date_format)
fig.autofmt_xdate()
plt.show()
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument(
"--data",
default="pems",
help="data to use")
args = parser.parse_args()
if args.data == "pems":
lstm = tf.keras.models.load_model('model_pems/lstm.h5')
gru = tf.keras.models.load_model('model_pems/gru.h5')
saes = tf.keras.models.load_model('model_pems/saes.h5')
cnn_lstm = tf.keras.models.load_model('model_pems/cnn_lstm.h5')
with open('model_pems/rf.h5', 'rb') as f:
rf = cPickle.load(f)
en_1 = tf.keras.models.load_model('model_pems/en_1.h5')
en_2 = tf.keras.models.load_model('model_pems/en_2.h5')
en_3 = tf.keras.models.load_model('model_pems/en_3.h5')
elif args.data == "nyc":
lstm = tf.keras.models.load_model('model_nyc/lstm.h5')
gru = tf.keras.models.load_model('model_nyc/gru.h5')
saes = tf.keras.models.load_model('model_nyc/saes.h5')
cnn_lstm = tf.keras.models.load_model('model_nyc/cnn_lstm.h5')
with open('model_nyc/rf.h5', 'rb') as f:
rf = cPickle.load(f)
en_1 = tf.keras.models.load_model('model_nyc/en_1.h5')
en_2 = tf.keras.models.load_model('model_nyc/en_2.h5')
en_3 = tf.keras.models.load_model('model_nyc/en_3.h5')
models = [lstm, gru, saes, cnn_lstm, rf, en_1, en_2, en_3]
names = ['LSTM', 'GRU', 'SAEs', 'CNN_LSTM', 'rf', 'EN_1', 'EN_2', 'EN_3']
if args.data == "pems":
X_train, X_test, y_train, y_test, scaler = load_data(data = "PEMS traffic prediction", force_download = False)
elif args.data == "nyc":
X_train, X_test, y_train, y_test, scaler = load_data(data = "nyc_bike_dataset", force_download = False)
rf_bk = X_test
y_test = scaler.inverse_transform(y_test.reshape(-1, 1)).reshape(1, -1)[0]
y_preds = []
for name, model in zip(names, models):
if name == 'SAEs':
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1]))
elif name == 'LSTM' or name == 'GRU' or name == 'CNN_LSTM' or name =="EN_1" or name =="EN_2" or name =="EN_3":
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
else:
X_test = rf_bk
file = 'images/' + name + '.png'
predicted = model.predict(X_test)
predicted = scaler.inverse_transform(predicted.reshape(-1, 1)).reshape(1, -1)[0]
y_preds.append(predicted[:288])
print(name)
eva_regress(y_test, predicted)
plot_results(y_test[: 288], y_preds, names)
if __name__ == '__main__':
main(sys.argv)