mean_y_train = np.mean(Y_train)
std_y_train = np.std(Y_train)
if not std_y_train:
input("Denominator can't be 0.")
Y_train = [(y - mean_y_train) / std_y_train for y in Y_train]
print('mean_y_train: %f std_y_train: %f' % (mean_y_train, std_y_train))
fw = open(model_folder + "/%s_parameter.pickle" % target_site, 'wb')
cPickle.dump(str(mean_y_train) + ',' +
str(std_y_train), fw)
fw.close()
# --
print('Constructing time series data set ..', end='')
X_train = construct_time_steps(X_train[:-1], time_steps)
Y_train = Y_train[time_steps:]
X_test = construct_time_steps(X_test[:-1], time_steps)
Y_test = Y_test[time_steps:]
print('ok')
def ave(X, Y, interval_hours):
reserve_hours = interval_hours - 1
deadline = 0
for i in range(len(Y)):
# check the reserve data is enough or not
if (len(Y) - i - 1) < reserve_hours:
deadline = i
break # not enough
freq_feature_matrix = []
for f_j in range(len(pollution_kind)*len(site_list)):
freq_feature_vector = np.array([])
for f_k in range(((total_time_steps-fourier_time_range)/fourier_time_shift)+1):
freq_feature_vector = np.concatenate((
freq_feature_vector,
np.real(
fft(time_tensor[f_i, f_k*fourier_time_shift:f_k*fourier_time_shift+fourier_time_range, f_j])
)
))
freq_feature_matrix.append(freq_feature_vector)
freq_tensor.append(np.array(freq_feature_matrix).T)
return freq_tensor
# for frequence
X_train_freq = construct_time_steps(X_train[:-1], layer1_time_steps*layer2_time_steps)
X_test_freq = construct_time_steps(X_test[:-1], layer1_time_steps*layer2_time_steps)
# for layer 1
X_train = construct_time_steps(X_train[:-1], layer1_time_steps)
X_test = construct_time_steps(X_test[:-1], layer1_time_steps)
Y_train = Y_train[layer1_time_steps:]
Y_test = Y_test[layer1_time_steps:]
# for layer 2
X_train = construct_second_time_steps(X_train, layer1_time_steps, layer2_time_steps)
X_test = construct_second_time_steps(X_test, layer1_time_steps, layer2_time_steps)
def load_data(self,
data_file,
site_list,
target_site,
target_kind,
training_year,
training_duration,
pollution_kind,
SEQ_LENGTH_1,
SEQ_LENGTH_2,
data_update=False):
print('Reading data .. ')
X = read_data_sets(sites=site_list + [target_site],
date_range=np.atleast_1d(training_year),
beginning=training_duration[0],
finish=training_duration[-1],
feature_selection=pollution_kind,
update=data_update)
X = missing_check(X)
Y = np.array(X)[:, -len(pollution_kind):]
Y = Y[:, pollution_kind.index(target_kind)]
SeqY = []
for y in range(len(Y)):
if (y + (SEQ_LENGTH_2 - 1)) < len(Y):
Seqy = []
for time_step in range(SEQ_LENGTH_2):
Seqy.append(Y[y + time_step])
SeqY.append(Seqy)
del Seqy
else:
break
X = np.array(X)[:, :-len(pollution_kind)]
# feature process
if 'WIND_DIREC' in pollution_kind:
index_of_kind = pollution_kind.index('WIND_DIREC')
length_of_kind_list = len(pollution_kind)
len_of_sites_list = len(site_list)
X = X.tolist()
for i in range(len(X)):
for j in range(len_of_sites_list):
specific_index = index_of_kind + j * length_of_kind_list
coordin = data_coordinate_angle(X[i].pop(specific_index +
j))
X[i].insert(specific_index + j, coordin[1])
X[i].insert(specific_index + j, coordin[0])
X = np.array(X)
X = construct_time_steps(X[:-1], SEQ_LENGTH_1)
if SEQ_LENGTH_1 < SEQ_LENGTH_2:
self.X = X[0:len(SeqY)]
elif SEQ_LENGTH_1 > SEQ_LENGTH_2:
SeqY = SeqY[:len(X)]
with open(data_file, 'w') as f:
for line in SeqY:
for elem_no in range(len(line)):
f.write(str(line[elem_no]))
if elem_no < (len(line) - 1):
f.write(' ')
f.write('\n')
def ensemble_model(target_kind, local, city, target_site, training_year,
testing_year, training_duration, testing_duration,
interval_hours, data, is_training):
print('is_training(%s) = %s' % (target_site, is_training))
site_list = pollution_site_map[local][
city] # ['中山', '古亭', '士林', '松山', '萬華']
# change format from 2014-2015 to ['2014', '2015']
training_year = [
training_year[:training_year.index('-')],
training_year[training_year.index('-') + 1:]
]
testing_year = [
testing_year[:testing_year.index('-')],
testing_year[testing_year.index('-') + 1:]
]
training_duration = [
training_duration[:training_duration.index('-')],
training_duration[training_duration.index('-') + 1:]
]
testing_duration = [
testing_duration[:testing_duration.index('-')],
testing_duration[testing_duration.index('-') + 1:]
]
interval_hours = int(interval_hours)
# is_training = False
# clear redundancy work
if training_year[0] == training_year[1]:
training_year.pop(1)
if testing_year[0] == testing_year[1]:
testing_year.pop(1)
else:
input(
'The range of testing year should not more than one year or crossing the bound of years.'
)
# checking years
rangeofYear = int(training_year[-1]) - int(training_year[0])
for i in range(rangeofYear):
if not (str(i + int(training_year[0])) in training_year):
training_year.insert(i, str(i + int(training_year[0])))
# Training Parameters
# WIND_DIREC is a specific feature, that need to be processed, and it can only be element of input vector now.
if target_kind == 'PM2.5':
pollution_kind = [
'PM2.5', 'O3', 'AMB_TEMP', 'RH', 'WIND_SPEED', 'WIND_DIREC'
]
# target_kind = 'PM2.5'
data_update = False
# batch_size = 24 * 7
seed = 0
# Network Parameters
input_size = (len(site_list) * len(pollution_kind) +
len(site_list)) if 'WIND_DIREC' in pollution_kind else (
len(site_list) * len(pollution_kind))
time_steps = 12
# hidden_size = 20
output_size = 1
testing_month = testing_duration[0][:testing_duration[0].index('/')]
folder = root_path + "model/%s/%s/%sh/%s/" % (local, city, interval_hours,
target_kind)
training_begining = training_duration[0][:training_duration[0].index('/')]
training_deadline = training_duration[-1][:training_duration[-1].index('/'
)]
print('site: %s' % target_site)
print('Training for %s/%s to %s/%s' %
(training_year[0], training_duration[0], training_year[-1],
training_duration[-1]))
print('Testing for %s/%s to %s/%s' %
(testing_year[0], testing_duration[0], testing_year[-1],
testing_duration[-1]))
# for interval
def ave(X, Y, interval_hours):
reserve_hours = interval_hours - 1
deadline = 0
for i in range(len(Y)):
# check the reserve data is enough or not
if (len(Y) - i - 1) < reserve_hours:
deadline = i
break # not enough
for j in range(reserve_hours):
Y[i] += Y[i + j + 1]
Y[i] /= interval_hours
if deadline:
X = X[:deadline]
Y = Y[:deadline]
return X, Y
# for interval
def higher(X, Y, interval_hours):
reserve_hours = 1 # choose the first n number of biggest
if interval_hours > reserve_hours:
deadline = 0
for i in range(len(Y)):
# check the reserve data is enough or not
if (len(Y) - i) < interval_hours:
deadline = i
break # not enough
higher_list = []
for j in range(interval_hours):
if len(higher_list) < reserve_hours:
higher_list.append(Y[i + j])
elif Y[i + j] > higher_list[0]:
higher_list[0] = Y[i + j]
higher_list = sorted(higher_list)
Y[i] = np.array(higher_list).sum() / reserve_hours
if deadline:
X = X[:deadline]
Y = Y[:deadline]
return X, Y
if is_training:
# reading data
print('Reading data .. ')
start_time = time.time()
print('preparing training set ..')
X_train = read_data_sets(sites=site_list + [target_site],
date_range=np.atleast_1d(training_year),
beginning=training_duration[0],
finish=training_duration[-1],
feature_selection=pollution_kind,
update=data_update)
X_train = missing_check(X_train)
Y_train = np.array(X_train)[:, -len(pollution_kind):]
Y_train = Y_train[:, pollution_kind.index(target_kind)]
X_train = np.array(X_train)[:, :-len(pollution_kind)]
print('preparing testing set ..')
X_test = read_data_sets(sites=site_list + [target_site],
date_range=np.atleast_1d(testing_year),
beginning=testing_duration[0],
finish=testing_duration[-1],
feature_selection=pollution_kind,
update=data_update)
Y_test = np.array(X_test)[:, -len(pollution_kind):]
Y_test = Y_test[:, pollution_kind.index(target_kind)]
X_test = missing_check(np.array(X_test)[:, :-len(pollution_kind)])
final_time = time.time()
print('Reading data .. ok, ', end='')
time_spent_printer(start_time, final_time)
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')
if (len(X_train) < time_steps) or (len(X_test) < time_steps):
input('time_steps(%d) too long.' % time_steps)
# normalize
print('Normalize ..')
mean_X_train = np.mean(X_train, axis=0)
std_X_train = np.std(X_train, axis=0)
if 0 in std_X_train:
input("Denominator can't be 0.")
X_train = np.array([(x_train - mean_X_train) / std_X_train
for x_train in X_train])
X_test = np.array([(x_test - mean_X_train) / std_X_train
for x_test in X_test])
mean_y_train = np.mean(Y_train)
std_y_train = np.std(Y_train)
if not std_y_train:
input("Denominator can't be 0.")
Y_train = [(y - mean_y_train) / std_y_train for y in Y_train]
print('mean_y_train: %f std_y_train: %f' %
(mean_y_train, std_y_train))
fw = open(folder + "%s_parameter.pickle" % target_site, 'wb')
cPickle.dump(
str(mean_X_train) + ',' + str(std_X_train) + ',' +
str(mean_y_train) + ',' + str(std_y_train), fw)
fw.close()
# feature process
if 'WIND_DIREC' in pollution_kind:
index_of_kind = pollution_kind.index('WIND_DIREC')
length_of_kind_list = len(pollution_kind)
len_of_sites_list = len(site_list)
X_train = X_train.tolist()
X_test = X_test.tolist()
for i in range(len(X_train)):
for j in range(len_of_sites_list):
specific_index = index_of_kind + j * length_of_kind_list
coordin = data_coordinate_angle(
(X_train[i].pop(specific_index + j)) *
std_X_train[specific_index] +
mean_X_train[specific_index])
X_train[i].insert(specific_index, coordin[1])
X_train[i].insert(specific_index, coordin[0])
if i < len(X_test):
coordin = data_coordinate_angle(
(X_test[i].pop(specific_index + j)) *
std_X_train[specific_index] +
mean_X_train[specific_index])
X_test[i].insert(specific_index, coordin[1])
X_test[i].insert(specific_index, coordin[0])
X_train = np.array(X_train)
X_test = np.array(X_test)
Y_test = np.array(Y_test, dtype=np.float)
# --
print('Constructing time series data set ..')
# for rnn
X_rnn_train = construct_time_steps(X_train[:-1], time_steps)
X_rnn_test = construct_time_steps(X_test[:-1], time_steps)
X_train = concatenate_time_steps(X_train[:-1], time_steps)
Y_train = Y_train[time_steps:]
X_test = concatenate_time_steps(X_test[:-1], time_steps)
Y_test = Y_test[time_steps:]
[X_train, Y_train] = higher(X_train, Y_train, interval_hours)
[X_test, Y_test] = higher(X_test, Y_test, interval_hours)
X_rnn_train = X_rnn_train[:len(X_train)]
X_rnn_test = X_rnn_test[:len(X_test)]
# delete data which have missing values
i = 0
while i < len(Y_test):
if not (
Y_test[i] > -10000
): # check missing or not, if Y_test[i] is missing, then this command will return True
Y_test = np.delete(Y_test, i, 0)
X_test = np.delete(X_test, i, 0)
X_rnn_test = np.delete(X_rnn_test, i, 0)
i = -1
i += 1
Y_test = np.array(Y_test, dtype=np.float)
# --
X_rnn_train = np.array(X_rnn_train)
X_rnn_test = np.array(X_rnn_test)
X_train = np.array(X_train)
Y_train = np.array(Y_train)
X_test = np.array(X_test)
np.random.seed(seed)
np.random.shuffle(X_train)
np.random.seed(seed)
np.random.shuffle(Y_train)
np.random.seed(seed)
np.random.shuffle(X_rnn_train)
else: # is_training = false
# mean and std
fr = open(folder + "%s_parameter.pickle" % target_site, 'rb')
[mean_X_train, std_X_train, mean_y_train,
std_y_train] = (cPickle.load(fr)).split(',')
mean_X_train = mean_X_train.replace('[', '').replace(']', '').replace(
'\n', '').split(' ')
while '' in mean_X_train:
mean_X_train.pop(mean_X_train.index(''))
mean_X_train = np.array(mean_X_train, dtype=np.float)
std_X_train = std_X_train.replace('[', '').replace(']', '').replace(
'\n', '').split(' ')
while '' in std_X_train:
std_X_train.pop(std_X_train.index(''))
std_X_train = np.array(std_X_train, dtype=np.float)
mean_y_train = float(mean_y_train)
std_y_train = float(std_y_train)
fr.close()
# reading data
print('preparing testing set ..')
X_test = data
X_test = missing_check(np.array(X_test))
# normalize
print('Normalize ..')
if 0 in std_X_train:
input("Denominator can't be 0.")
X_test = np.array([(x_test - mean_X_train) / std_X_train
for x_test in X_test])
# feature process
if 'WIND_DIREC' in pollution_kind:
index_of_kind = pollution_kind.index('WIND_DIREC')
length_of_kind_list = len(pollution_kind)
len_of_sites_list = len(site_list)
X_test = X_test.tolist()
for i in range(len(X_test)):
for j in range(len_of_sites_list):
specific_index = index_of_kind + j * length_of_kind_list
coordin = data_coordinate_angle(
(X_test[i].pop(specific_index + j)) *
std_X_train[specific_index] +
mean_X_train[specific_index])
X_test[i].insert(specific_index, coordin[1])
X_test[i].insert(specific_index, coordin[0])
X_test = np.array(X_test)
# --
print('Constructing time series data set ..')
X_rnn_test = construct_time_steps(X_test, time_steps)
X_test = concatenate_time_steps(X_test, time_steps)
# --
X_rnn_test = np.array(X_rnn_test)
X_test = np.array(X_test)
# -- xgboost --
print('- xgboost -')
filename = ("xgboost_%s_training_%s_m%s_to_%s_m%s_interval_%s" %
(target_site, training_year[0], training_begining,
training_year[-1], training_deadline, interval_hours))
print(filename)
if is_training:
xgb_model = xgb.XGBRegressor().fit(X_train, Y_train)
fw = open(folder + filename, 'wb')
cPickle.dump(xgb_model, fw)
fw.close()
else:
fr = open(folder + filename, 'rb')
xgb_model = cPickle.load(fr)
fr.close()
xgb_pred = xgb_model.predict(X_test)
# print('rmse(xgboost): %.5f' % (np.mean((Y_test - (mean_y_train + std_y_train * xgb_pred))**2, 0)**0.5))
# -- rnn --
print('- rnn -')
filename = ("sa_DropoutLSTM_%s_training_%s_m%s_to_%s_m%s_interval_%s" %
(target_site, training_year[0], training_begining,
training_year[-1], training_deadline, interval_hours))
print(filename)
# Network Parameters
time_steps = 12
hidden_size = 20
print("Expected args: p_W, p_U, p_dense, p_emb, weight_decay, batch_size")
print("Using default args:")
param = ["", "0.5", "0.5", "0.5", "0.5", "1e-6", "128"]
args = [float(a) for a in param[1:]]
print(args)
p_W, p_U, p_dense, p_emb, weight_decay, batch_size = args
batch_size = int(batch_size)
# --
print('Build rnn model...')
start_time = time.time()
rnn_model = Sequential()
# layer 1
rnn_model.add(
BatchNormalization(epsilon=0.001,
mode=0,
axis=-1,
momentum=0.99,
weights=None,
beta_init='zero',
gamma_init='one',
gamma_regularizer=None,
beta_regularizer=None,
input_shape=(time_steps, input_size)))
rnn_model.add(
LSTM(hidden_size,
W_regularizer=l2(weight_decay),
U_regularizer=l2(weight_decay),
b_regularizer=l2(weight_decay),
dropout_W=p_W,
dropout_U=p_U)) # return_sequences=True
rnn_model.add(Dropout(p_dense))
# output layer
rnn_model.add(
BatchNormalization(epsilon=0.001,
mode=0,
axis=-1,
momentum=0.99,
weights=None,
beta_init='zero',
gamma_init='one',
gamma_regularizer=None,
beta_regularizer=None))
rnn_model.add(
Dense(output_size,
W_regularizer=l2(weight_decay),
b_regularizer=l2(weight_decay)))
# optimiser = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=False)
optimiser = 'adam'
rnn_model.compile(loss='mean_squared_error', optimizer=optimiser)
final_time = time.time()
time_spent_printer(start_time, final_time)
if is_training:
print("Train...")
start_time = time.time()
rnn_model.fit(X_rnn_train, Y_train, batch_size=batch_size, epochs=50)
# Potentially save weights
rnn_model.save_weights(folder + filename, overwrite=True)
final_time = time.time()
time_spent_printer(start_time, final_time)
else:
print('loading model ..')
# print('loading model from %s' % (folder + filename + ".hdf5"))
rnn_model.load_weights(folder + filename)
rnn_pred = rnn_model.predict(X_rnn_test, batch_size=500, verbose=1)
final_time = time.time()
time_spent_printer(start_time, final_time)
# print('rmse(rnn): %.5f' % (np.mean((np.atleast_2d(Y_test).T - (mean_y_train + std_y_train * rnn_pred))**2, 0)**0.5))
# -- ensemble --
print('stacking ..')
if is_training:
xgb_output = xgb_model.predict(X_train).reshape(len(X_train), 1)
# rf_output = rf_model.predict(X_train).reshape(len(X_train), 1)
rnn_output = rnn_model.predict(X_rnn_train, batch_size=500, verbose=1)
# ensemble_X_train = np.hstack((X_train, xgb_output, rf_output, rnn_output))
ensemble_X_train = np.hstack((X_train, xgb_output, rnn_output))
Y_alert_train = [y * std_y_train + mean_y_train for y in Y_train]
for element in range(len(Y_train)):
if Y_alert_train[element] > high_alert:
Y_alert_train[element] = 1 # [1, 0] = [high, low]
else:
Y_alert_train[element] = 0
xgb_pred = xgb_pred.reshape(len(X_test), 1)
# rf_pred = rf_pred.reshape(len(X_test), 1)
rnn_pred = rnn_pred.reshape(len(X_test), 1)
# ensemble_X_test = np.hstack((X_test, xgb_pred, rf_pred, rnn_pred))
ensemble_X_test = np.hstack((X_test, xgb_pred, rnn_pred))
# Y_alert_test = np.zeros(len(Y_test))
# for element in range(len(Y_test)):
# if Y_test[element] > high_alert:
# Y_alert_test[element] = 1 # [1, 0] = [high, low]
print('- ensemble -')
filename = ("ensemble_%s_training_%s_m%s_to_%s_m%s_interval_%s" %
(target_site, training_year[0], training_begining,
training_year[-1], training_deadline, interval_hours))
filename2 = ("classification_%s_training_%s_m%s_to_%s_m%s_interval_%s" %
(target_site, training_year[0], training_begining,
training_year[-1], training_deadline, interval_hours))
if is_training:
ensemble_model = xgb.XGBRegressor().fit(ensemble_X_train, Y_train)
classification_model = xgb.XGBClassifier().fit(ensemble_X_train,
Y_alert_train)
fw = open(folder + filename, 'wb')
cPickle.dump(ensemble_model, fw)
fw.close()
fw2 = open(folder + filename2, 'wb')
cPickle.dump(classification_model, fw2)
fw2.close()
else:
fr = open(folder + filename, 'rb')
ensemble_model = cPickle.load(fr)
fr.close()
fr2 = open(folder + filename2, 'rb')
classification_model = cPickle.load(fr2)
fr2.close()
pred = ensemble_model.predict(ensemble_X_test)
alert_pred = classification_model.predict(ensemble_X_test)
# --
predictions = mean_y_train + std_y_train * pred
# print('mse: %.5f' % mean_squared_error(Y_test, predictions))
if is_training:
print('rmse: %.5f' % (np.mean((Y_test - predictions)**2, 0)**0.5))
def target_level(target, kind='PM2.5'):
# target should be a 1d-list
if kind == 'PM2.5':
if (target >= 0) and (target < 11.5): # 0-11
return 1
elif (target >= 11.5) and (target < 23.5): # 12-23
return 2
elif (target >= 23.5) and (target < 35.5): # 24-35
return 3
elif (target >= 35.5) and (target < 41.5): # 36-41
return 4
elif (target >= 41.5) and (target < 47.5): # 42-47
return 5
elif (target >= 47.5) and (target < 53.5): # 48-53
return 6
elif (target >= 53.5) and (target < 58.5): # 54-58
return 7
elif (target >= 58.5) and (target < 64.5): # 59-64
return 8
elif (target >= 64.5) and (target < 70.5): # 65-70
return 9
elif target >= 70.5: # others(71+)
return 10
else:
print('error value: %d' % target)
return 1
pred_label = np.zeros(len(predictions))
real_target = np.zeros(len(Y_test))
pred_label_true = 0.
pred_label_false = 0.
four_label_true = 0.0
four_label_false = 0.0
# calculate the accuracy of ten level
for i in range(len(predictions)):
pred_label[i] = target_level(predictions[i])
real_target[i] = target_level(Y_test[i])
if real_target[i] == pred_label[i]:
pred_label_true += 1
else:
pred_label_false += 1
# four label
if (real_target[i] >= 1
and real_target[i] <= 3) and (pred_label[i] >= 1
and pred_label[i] <= 3):
four_label_true += 1
elif (real_target[i] >= 4
and real_target[i] <= 6) and (pred_label[i] >= 4
and pred_label[i] <= 6):
four_label_true += 1
elif (real_target[i] >= 7
and real_target[i] <= 9) and (pred_label[i] >= 7
and pred_label[i] <= 9):
four_label_true += 1
elif (real_target[i] >= 10) and (pred_label[i] >= 10):
four_label_true += 1
else:
four_label_false += 1
# print('standard_prob_accuracy: %.5f' % (standard_prob_true / (standard_prob_true + standard_prob_false)))
print('Ten level accuracy: %.5f' %
(pred_label_true / (pred_label_true + pred_label_false)))
print('Four level accuracy: %.5f' %
(four_label_true / (four_label_true + four_label_false)))
print('--')
# --
ha = 0.0 # observation high, predict high
hb = 0.0 # observation low, predict high
hc = 0.0 # observation high, predict low
hd = 0.0 # observation low, predict low
la = 0.0 # observation very high, predict very high
lb = 0.0
lc = 0.0
ld = 0.0
alert_a = 0.0
alert_b = 0.0
alert_c = 0.0
alert_d = 0.0
integration_a = 0.0
integration_b = 0.0
integration_c = 0.0
integration_d = 0.0
for each_value in range(len(Y_test)):
if Y_test[each_value] >= high_alert: # observation high
# regression
if predictions[
each_value] >= high_alert: # forecast high(with tolerance)
ha += 1
else:
hc += 1
# classification
if alert_pred[each_value]: # [1, 0] = [high, low]
alert_a += 1
else:
alert_c += 1
# integration
if alert_pred[each_value] or (predictions[each_value] >=
high_alert):
integration_a += 1
else:
integration_c += 1
else: # observation low
# regression
if predictions[each_value] >= high_alert:
hb += 1
else:
hd += 1
# classification
if alert_pred[each_value]:
alert_b += 1
else:
alert_d += 1
# integration
if alert_pred[each_value] or (predictions[each_value] >=
high_alert):
integration_b += 1
else:
integration_d += 1
# --------------------------------------------------------
if Y_test[each_value] >= low_alert: # observation higher
if predictions[each_value] >= low_alert:
la += 1
else:
lc += 1
else: # observation very low
if predictions[each_value] >= low_alert:
lb += 1
else:
ld += 1
# print('Two level accuracy: %f' % (two_label_true / (two_label_true + two_label_false)))
print('high label: (%d, %d, %d, %d)' % (ha, hb, hc, hd))
print('low label: (%d, %d, %d, %d)' % (la, lb, lc, ld))
print('alert: (%d, %d, %d, %d)' % (alert_a, alert_b, alert_c, alert_d))
return predictions
data_test[i].insert(specific_index + j, coordin[1])
data_test[i].insert(specific_index + j, coordin[0])
data_train = np.array(data_train)
data_test = np.array(data_test)
Y_test = np.array(Y_test, dtype=np.float)
# --
# X_train = data_train
# X_test = data_test
X_train = dict()
X_test = dict()
# --
print('Constructing time series data set ..')
# for rnn
X_train['rnn'] = construct_time_steps(data_train[:-1], time_steps)
X_test['rnn'] = construct_time_steps(data_test[:-1], time_steps)
X_train['concatenate'] = concatenate_time_steps(data_train[:-1], time_steps)
Y_train = Y_train[time_steps:]
X_test['concatenate'] = concatenate_time_steps(data_test[:-1], time_steps)
Y_test = Y_test[time_steps:]
# --
Y_real = np.copy(Y_test)
Y_train = higher(Y_train, interval_hours)
Y_test = higher(Y_test, interval_hours)
def rnn(pollution_kind, local, city, target_site, training_year, testing_year,
training_duration, testing_duration, interval_hours, data,
is_training):
print('is_training(%s) = %s' % (target_site, is_training))
# format of training_year and testing_year should be (start year)-(end year), like 2014-2015
# format of training_duration and testing_duration should be (start date)-(end date), like 1/1-12/31
# local = os.sys.argv[1]
# city = os.sys.argv[2]
site_list = pollution_site_map[local][city]
# change format from 2014-2015 to ['2014', '2015']
training_year = [
training_year[:training_year.index('-')],
training_year[training_year.index('-') + 1:]
]
testing_year = [
testing_year[:testing_year.index('-')],
testing_year[testing_year.index('-') + 1:]
]
training_duration = [
training_duration[:training_duration.index('-')],
training_duration[training_duration.index('-') + 1:]
]
testing_duration = [
testing_duration[:testing_duration.index('-')],
testing_duration[testing_duration.index('-') + 1:]
]
interval_hours = int(
interval_hours
) # predict the label of average data of many hours later, default is 1
# is_training = os.sys.argv[9] # True False
# clear redundancy work
if training_year[0] == training_year[1]:
training_year.pop(1)
if testing_year[0] == testing_year[1]:
testing_year.pop(1)
# Training Parameters
# WIND_DIREC is a specific feature, that need to be processed, and it can only be element of input vector now.
# pollution_kind = ['PM2.5', 'O3', 'AMB_TEMP', 'RH', 'WIND_SPEED', 'WIND_DIREC']
target_kind = 'PM2.5'
data_update = False
# batch_size = 24 * 7
seed = 0
# Network Parameters
input_size = (len(site_list) * len(pollution_kind) +
len(site_list)) if 'WIND_DIREC' in pollution_kind else (
len(site_list) * len(pollution_kind))
time_steps = 12
hidden_size = 20
output_size = 1
# print("Expected args: p_W, p_U, p_dense, p_emb, weight_decay, batch_size, maxlen")
# print("Using default args:")
param = ["", "0.5", "0.5", "0.5", "0.5", "1e-6", "128", "200"]
# args = [float(a) for a in sys.argv[1:]]
args = [float(a) for a in param[1:]]
# print(args)
p_W, p_U, p_dense, p_emb, weight_decay, batch_size, maxlen = args
batch_size = int(batch_size)
maxlen = int(maxlen)
testing_month = testing_duration[0][:testing_duration[0].index('/')]
folder = root_path + "model/%s/%s/" % (local, city)
filename = (
"sa_DropoutLSTM_pW_%.2f_pU_%.2f_pDense_%.2f_pEmb_%.2f_reg_%f_batch_size_%d_cutoff_%d_epochs_%s_%sm_%sh"
% (p_W, p_U, p_dense, p_emb, weight_decay, batch_size, maxlen,
target_site, testing_month, interval_hours))
print(filename)
if is_training:
# reading data
print('Reading data for %s .. ' % target_site)
start_time = time.time()
print('preparing training set for %s ..' % target_site)
X_train = read_data_sets(sites=site_list + [target_site],
date_range=np.atleast_1d(training_year),
beginning=training_duration[0],
finish=training_duration[-1],
feature_selection=pollution_kind,
update=data_update)
X_train = missing_check(X_train)
Y_train = np.array(X_train)[:, -len(pollution_kind):]
Y_train = Y_train[:, pollution_kind.index(target_kind)]
X_train = np.array(X_train)[:, :-len(pollution_kind)]
print('preparing testing set for %s..' % target_site)
X_test = read_data_sets(sites=site_list + [target_site],
date_range=np.atleast_1d(testing_year),
beginning=testing_duration[0],
finish=testing_duration[-1],
feature_selection=pollution_kind,
update=data_update)
Y_test = np.array(X_test)[:, -len(pollution_kind):]
Y_test = Y_test[:, pollution_kind.index(target_kind)]
X_test = missing_check(np.array(X_test)[:, :-len(pollution_kind)])
final_time = time.time()
print('Reading data for %s.. ok, ' % target_site, end='')
time_spent_printer(start_time, final_time)
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')
if (len(X_train) < time_steps) or (len(X_test) < time_steps):
input('time_steps(%d) too long.' % time_steps)
# normalize
print('Normalize for %s ..' % target_site)
mean_X_train = np.mean(X_train, axis=0)
std_X_train = np.std(X_train, axis=0)
if 0 in std_X_train:
input("Denominator can't be 0.(%s)" % target_site)
X_train = np.array([(x_train - mean_X_train) / std_X_train
for x_train in X_train])
X_test = np.array([(x_test - mean_X_train) / std_X_train
for x_test in X_test])
mean_y_train = np.mean(Y_train)
std_y_train = np.std(Y_train)
if not std_y_train:
input("Denominator can't be 0.(%s)" % target_site)
Y_train = [(y - mean_y_train) / std_y_train for y in Y_train]
print('mean_y_train: %f std_y_train: %f (%s)' %
(mean_y_train, std_y_train, target_site))
fw = open(folder + filename + ".pickle", 'wb')
cPickle.dump(
str(mean_X_train) + ',' + str(std_X_train) + ',' +
str(mean_y_train) + ',' + str(std_y_train), fw)
fw.close()
# feature process
if 'WIND_DIREC' in pollution_kind:
index_of_kind = pollution_kind.index('WIND_DIREC')
length_of_kind_list = len(pollution_kind)
len_of_sites_list = len(site_list)
X_train = X_train.tolist()
X_test = X_test.tolist()
for i in range(len(X_train)):
for j in range(len_of_sites_list):
specific_index = index_of_kind + j * length_of_kind_list
coordin = data_coordinate_angle(
(X_train[i].pop(specific_index + j)) *
std_X_train[specific_index] +
mean_X_train[specific_index])
X_train[i].insert(specific_index, coordin[1])
X_train[i].insert(specific_index, coordin[0])
if i < len(X_test):
coordin = data_coordinate_angle(
(X_test[i].pop(specific_index + j)) *
std_X_train[specific_index] +
mean_X_train[specific_index])
X_test[i].insert(specific_index, coordin[1])
X_test[i].insert(specific_index, coordin[0])
X_train = np.array(X_train)
X_test = np.array(X_test)
Y_test = np.array(Y_test, dtype=np.float)
# --
print('Constructing time series data set for %s ..' % target_site)
X_train = construct_time_steps(X_train[:-1], time_steps)
Y_train = Y_train[time_steps:]
reserve_hours = interval_hours - 1
deadline = 0
for i in range(len(Y_train)):
# check the reserve data is enough or not
if (len(Y_train) - i - 1) < reserve_hours:
deadline = i
break # not enough
for j in range(reserve_hours):
Y_train[i] += Y_train[i + j + 1]
Y_train[i] /= interval_hours
if deadline:
X_train = X_train[:deadline]
Y_train = Y_train[:deadline]
X_test = construct_time_steps(X_test[:-1], time_steps)
Y_test = Y_test[time_steps:]
deadline = 0
for i in range(len(Y_test)):
# check the reserve data is enough or not
if (len(Y_test) - i - 1) < reserve_hours:
deadline = i
break # not enough
for j in range(reserve_hours):
Y_test[i] += Y_test[i + j + 1]
Y_test[i] /= interval_hours
if deadline:
X_test = X_test[:deadline]
Y_test = Y_test[:deadline]
# delete data which have missing values
i = 0
while i < len(Y_test):
if not (
Y_test[i] > -10000
): # check missing or not, if Y_test[i] is missing, then this command will return True
Y_test = np.delete(Y_test, i, 0)
X_test = np.delete(X_test, i, 0)
i = -1
i += 1
Y_test = np.array(Y_test, dtype=np.float)
# --
X_train = np.array(X_train)
Y_train = np.array(Y_train)
X_test = np.array(X_test)
np.random.seed(seed)
np.random.shuffle(X_train)
np.random.seed(seed)
np.random.shuffle(Y_train)
# ------------------------------------
else:
fr = open(folder + filename + ".pickle", 'rb')
[mean_X_train, std_X_train, mean_y_train,
std_y_train] = (cPickle.load(fr)).split(',')
mean_X_train = mean_X_train.replace('[', '').replace(']', '').replace(
'\n', '').split(' ')
while '' in mean_X_train:
mean_X_train.pop(mean_X_train.index(''))
mean_X_train = np.array(mean_X_train, dtype=np.float)
std_X_train = std_X_train.replace('[', '').replace(']', '').replace(
'\n', '').split(' ')
while '' in std_X_train:
std_X_train.pop(std_X_train.index(''))
std_X_train = np.array(std_X_train, dtype=np.float)
mean_y_train = float(mean_y_train)
std_y_train = float(std_y_train)
fr.close()
# input data
X_test = data
# normalize
print('Normalize for %s ..' % target_site)
X_test = np.array([(x_test - mean_X_train) / std_X_train
for x_test in X_test])
# feature process
if 'WIND_DIREC' in pollution_kind:
index_of_kind = pollution_kind.index('WIND_DIREC')
length_of_kind_list = len(pollution_kind)
len_of_sites_list = len(site_list)
X_test = X_test.tolist()
for i in range(len(X_test)):
for j in range(len_of_sites_list):
specific_index = index_of_kind + j * length_of_kind_list
coordin = data_coordinate_angle(
(X_test[i].pop(specific_index + j)) *
std_X_train[specific_index] +
mean_X_train[specific_index])
X_test[i].insert(specific_index, coordin[1])
X_test[i].insert(specific_index, coordin[0])
X_test = np.array([X_test])
print('Build model for %s ..' % target_site)
start_time = time.time()
model = Sequential()
model.add(
DropoutLSTM(input_size,
hidden_size,
truncate_gradient=maxlen,
W_regularizer=l2(weight_decay),
U_regularizer=l2(weight_decay),
b_regularizer=l2(weight_decay),
p_W=p_W,
p_U=p_U))
model.add(Dropout(p_dense))
model.add(
Dense(hidden_size,
output_size,
W_regularizer=l2(weight_decay),
b_regularizer=l2(weight_decay)))
# optimiser = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=False)
optimiser = 'adam'
model.compile(loss='mean_squared_error', optimizer=optimiser)
final_time = time.time()
time_spent_printer(start_time, final_time)
# --
if is_training:
print("Train for %s .." % target_site)
start_time = time.time()
checkpointer = ModelCheckpoint(filepath=folder + filename + ".hdf5",
verbose=1,
append_epoch_name=False,
save_every_X_epochs=50)
modeltest_1 = ModelTest(X_train[:100],
mean_y_train +
std_y_train * np.atleast_2d(Y_train[:100]).T,
test_every_X_epochs=1,
verbose=0,
loss='euclidean',
mean_y_train=mean_y_train,
std_y_train=std_y_train,
tau=0.1)
modeltest_2 = ModelTest(X_test,
np.atleast_2d(Y_test).T,
test_every_X_epochs=1,
verbose=0,
loss='euclidean',
mean_y_train=mean_y_train,
std_y_train=std_y_train,
tau=0.1)
model.fit(X_train,
Y_train,
batch_size=batch_size,
nb_epoch=251,
callbacks=[checkpointer, modeltest_1, modeltest_2])
# score, acc = model.evaluate(X_test, y_test, batch_size=batch_size, show_accuracy=True)
# print('Test score:', score)
# print('Test accuracy:', acc)
# model.save_weights(folder+filename+"_250.hdf5", overwrite=True)
final_time = time.time()
time_spent_printer(start_time, final_time)
# --
print("Test for %s .." % target_site)
standard_prob = model.predict(X_train, batch_size=500, verbose=1)
print(
np.mean(((mean_y_train + std_y_train * np.atleast_2d(Y_train).T) -
(mean_y_train + std_y_train * standard_prob))**2, 0)**0.5)
# --
standard_prob = model.predict(X_test, batch_size=500, verbose=1)
T = 50
prob = np.array([
model.predict_stochastic(X_test, batch_size=500, verbose=0)
for _ in xrange(T)
])
prob_mean = np.mean(prob, 0)
print(
np.mean((np.atleast_2d(Y_test).T -
(mean_y_train + std_y_train * standard_prob))**2, 0)**0.5)
print(
np.mean((np.atleast_2d(Y_test).T -
(mean_y_train + std_y_train * prob_mean))**2, 0)**0.5)
standard_prob_pred = np.zeros(len(standard_prob))
prob_mean_pred = np.zeros(len(prob_mean))
real_target = np.zeros(len(Y_test))
standard_prob_true = 0.
standard_prob_false = 0.
prob_mean_true = 0.
prob_mean_false = 0.
# calculate the accuracy of ten level
for i in range(len(prob_mean)):
standard_prob_pred[i] = target_level(mean_y_train +
std_y_train * prob_mean[i])
prob_mean_pred[i] = target_level(mean_y_train +
std_y_train * prob_mean[i])
real_target[i] = target_level(Y_test[i])
if real_target[i] == standard_prob_pred[i]:
standard_prob_true += 1
else:
standard_prob_false += 1
if real_target[i] == prob_mean_pred[i]:
prob_mean_true += 1
else:
prob_mean_false += 1
print('standard_prob_accuracy(%s): %.5f' %
(target_site, standard_prob_true /
((standard_prob_true + standard_prob_false))))
print('prob_mean_accuracy(%s): %.5f' %
(target_site,
(prob_mean_true / (prob_mean_true + prob_mean_false))))
print('--')
ha = 0.0 # observation high, predict high
hb = 0.0 # observation low, predict high
hc = 0.0 # observation high, predict low
hd = 0.0 # observation low, predict low
vha = 0.0 # observation very high, predict very high
vhb = 0.0
vhc = 0.0
vhd = 0.0
two_label_true = 0.0
two_label_false = 0.0
# statistic of status of prediction by forecast & observation
for each_label in np.arange(len(real_target)):
if real_target[each_label] >= 7: # observation high
if prob_mean_pred[each_label] >= 7:
ha += 1
two_label_true += 1
else:
hc += 1
two_label_false += 1
else: # observation low
if prob_mean_pred[each_label] >= 7:
hb += 1
two_label_false += 1
else:
hd += 1
two_label_true += 1
if real_target[each_label] >= 10: # observation very high
if prob_mean_pred[each_label] >= 10:
vha += 1
else:
vhc += 1
else: # observation low
if prob_mean_pred[each_label] >= 10:
vhb += 1
else:
vhd += 1
print('Two level accuracy of %s : %f' %
(target_site,
(two_label_true / (two_label_true + two_label_false))))
print('high label of %s: (%d, %d, %d, %d)' %
(target_site, ha, hb, hc, hd))
print('very high label of %s: (%d, %d, %d, %d)' %
(target_site, vha, vhb, vhc, vhd))
# plot the real trend and trend of prediction
prediction = mean_y_train + std_y_train * prob_mean
plt.plot(np.arange(len(prediction)),
Y_test[:len(prediction)],
c='gray')
plt.plot(np.arange(len(prediction)), prediction, color='pink')
plt.xticks(np.arange(0, len(prediction), 24))
plt.yticks(np.arange(0, max(Y_test), 10))
plt.grid(True)
plt.rc('axes', labelsize=4)
else:
print('loading model for %s ..' % target_site)
model.load_weights(folder + filename + ".hdf5")
standard_prob = model.predict(X_test, batch_size=1, verbose=1)
T = 50
prob = np.array([
model.predict_stochastic(X_test, batch_size=1, verbose=0)
for _ in xrange(T)
])
prob_mean = np.mean(prob, 0)
return mean_y_train + std_y_train * prob_mean