#dp.windowed_denormalize(predData_TM_n_step, actual_data)
dp.saveResultToFile(dataSet,
np.reshape(predData_TM_n_step,
len(predData_TM_n_step)),
np.reshape(actual_data, len(actual_data)),
'TM',
prediction_step=_options.stepsAhead)
ignore_for_error = 5500
nTest = len(actual_data) - nTrain - _options.stepsAhead
NRMSE_TM = NRMSE(actual_data[nTrain:nTrain + nTest],
predData_TM_n_step[nTrain:nTrain + nTest])
print "NRMSE on test data: ", NRMSE_TM
predData_TM_n_step[:nTrain + _options.stepsAhead] = np.nan
MAPE_TM = errors.get_mape(np.array(predData_TM_n_step),
np.array(actual_data), ignore_for_error)
print "MAPE on test data: ", MAPE_TM
MASE_TM = errors.get_mase(np.array(predData_TM_n_step),
np.array(actual_data),
np.roll(np.array(actual_data), 48),
ignore_for_error)
print "MASE on test data: ", MASE_TM
output.close()
mae = np.nanmean(
np.abs(actual_data[nTrain:nTrain + nTest] -
predData_TM_n_step[nTrain:nTrain + nTest]))
print "MAE {}".format(mae)
def run_gru(s):
x_dims = len(x_cols[s.dataSet]) if s.dataSet in x_cols else s.lookback
random.seed(6)
np.random.seed(6)
rnn = Sequential()
rnn.add(
GRU(s.nodes,
input_shape=(None, x_dims),
kernel_initializer='he_uniform',
stateful=False))
#rnn.add(Dropout(0.15))
rnn.add(Dense(1, kernel_initializer='he_uniform'))
opt = adam(lr=s.lr, decay=0.0) #1e-3)
rnn.compile(loss='mae', optimizer=opt)
# prepare dataset as pyBrain sequential dataset
sequence = readDataSet(s.dataSet, s.dataSetDetailed, s)
if s.limit_to:
sequence = sequence[:s.limit_to]
dp = DataProcessor()
# standardize data by subtracting mean and dividing by std
#(meanSeq, stdSeq) = dp.normalize('data', sequence)
dp.windowed_normalize(sequence)
for key in sequence.keys():
if key != "data":
dp.normalize(key, sequence)
predictedInput = np.zeros((len(sequence), ))
targetInput = np.zeros((len(sequence), ))
trueData = np.zeros((len(sequence), ))
if s.dataSet in differenceSets:
predictedInputNodiff = np.zeros((len(sequence), ))
targetInputNodiff = np.zeros((len(sequence), ))
if s.dataSet in differenceSets:
backup_sequence = sequence
sequence = dp.difference(sequence, s.lookback)
allX = getX(sequence, s)
allY = np.array(sequence['data'])
allX = allX[48:]
allY = allY[48:]
#if s.dataSet not in x_cols:
# allY = allY[s.lookback:]
trainX = allX[0:s.nTrain]
trainY = allY[s.predictionStep:s.nTrain + s.predictionStep]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
curBatch = 1.0
callback = LossCallback()
temp_set = np.array(sequence['data'])[:48 + s.nTrain + 5]
configure_batches(48, s.batch_size,
np.reshape(temp_set, (temp_set.shape[0], 1, 1)))
rnn.fit(trainX,
trainY,
epochs=s.epochs,
batch_size=s.batch_size,
verbose=min(s.max_verbosity, 2),
callbacks=[callback])
for i in xrange(0, s.nTrain):
targetInput[i] = allY[i + s.predictionStep]
for i in tqdm(xrange(s.nTrain + s.predictionStep, len(allX)),
disable=s.max_verbosity == 0):
if i % s.retrain_interval == 0 and i > s.numLags + s.nTrain and s.online:
trainX = allX[i - s.nTrain - s.predictionStep:i - s.predictionStep]
trainY = allY[i - s.nTrain:i]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
temp_set = np.array(sequence['data'])[i - s.nTrain -
s.predictionStep - 48:i]
configure_batches(48, s.batch_size,
np.reshape(temp_set, (temp_set.shape[0], 1, 1)))
rnn.fit(trainX,
trainY,
epochs=s.epochs,
batch_size=s.batch_size,
verbose=2,
callbacks=[callback])
targetInput[i] = allY[i + s.predictionStep]
predictedInput[i] = rnn.predict(np.reshape(allX[i], (1, 1, x_dims)))
if s.dataSet in differenceSets:
predictedInputNodiff[i] = predictedInput[i]
targetInputNodiff[i] = targetInput[i]
predictedInput[i] = dp.inverse_difference(backup_sequence['data'],
predictedInput[i], i - 1)
targetInput[i] = dp.inverse_difference(backup_sequence['data'],
targetInput[i], i - 1)
predictedInput[0] = 0
trueData[i] = sequence['data'][i]
#predictedInput = dp.denormalize(predictedInput, meanSeq, stdSeq)
#targetInput = dp.denormalize(targetInput, meanSeq, stdSeq)
dp.windowed_denormalize(predictedInput, targetInput)
if s.dataSet in differenceSets:
# predictedInputNodiff = dp.denormalize(predictedInputNodiff)
# targetInputNodiff = dp.denormalize(targetInputNodiff)
pass
#trueData = (trueData * stdSeq) + meanSeq
dp.saveResultToFile(s.dataSet, predictedInput, targetInput, 'gru',
s.predictionStep, s.max_verbosity)
skipTrain = error_ignore_first[s.dataSet]
from plot import computeSquareDeviation
squareDeviation = computeSquareDeviation(predictedInput, targetInput)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(np.nanmean(squareDeviation)) / np.nanstd(targetInput)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInput - predictedInput))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
if s.dataSet in differenceSets:
dp.saveResultToFile(s.dataSet, predictedInputNodiff, targetInputNodiff,
'gru_nodiff', s.predictionStep, s.max_verbosity)
squareDeviation = computeSquareDeviation(predictedInputNodiff,
targetInputNodiff)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(
np.nanmean(squareDeviation)) / np.nanstd(targetInputNodiff)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInputNodiff - predictedInputNodiff))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
mase = errors.get_mase(predictedInput, targetInput,
np.roll(targetInput, 24))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
return nrmse
def run_gru(s):
global global_step
global increment_global_step_op
global reset_global_step_op
global batches
global images_placeholder
global batches_op
global_step = tf.Variable(0,
name='global_step',
trainable=False,
dtype=tf.int32)
increment_global_step_op = tf.assign(global_step, global_step + 1)
reset_global_step_op = tf.assign(global_step, 0)
batches = tf.get_variable(
"batches", [s.nTrain / int(s.batch_size), s.batch_size, 1, 1],
dtype=tf.float32,
initializer=tf.zeros_initializer)
images_placeholder = tf.placeholder(tf.float32,
shape=(s.nTrain / int(s.batch_size),
s.batch_size, 1, 1))
batches_op = tf.assign(batches, images_placeholder)
x_dims = len(x_cols[s.dataSet]) if s.dataSet in x_cols else s.lookback
random.seed(6)
np.random.seed(6)
rnn = Sequential()
rnn.add(
GRU(s.nodes,
input_shape=(None, x_dims),
kernel_initializer='he_uniform',
stateful=False))
#rnn.add(Dropout(0.15))
rnn.add(Dense(1, kernel_initializer='he_uniform'))
opt = adam(lr=s.lr, decay=0.0) #1e-3)
rnn.compile(loss='mae', optimizer=opt)
# prepare dataset as pyBrain sequential dataset
sequence = readDataSet(s.dataSet, s.dataSetDetailed, s)
if s.limit_to:
sequence = sequence[:s.limit_to]
dp = DataProcessor()
# standardize data by subtracting mean and dividing by std
(meanSeq, stdSeq) = dp.normalize('data', sequence, s.nTrain)
#dp.windowed_normalize(sequence)
for key in sequence.keys():
if key != "data":
dp.normalize(key, sequence, s.nTrain)
if s.dataSet in differenceSets:
predictedInputNodiff = np.zeros((len(sequence), ))
targetInputNodiff = np.zeros((len(sequence), ))
if s.dataSet in differenceSets:
backup_sequence = sequence
sequence = dp.difference(sequence, s.lookback)
seq_full = sequence['data'].values
seq_actual = seq_full[s.front_buffer:]
allX = getX(seq_full, s)
allY = seq_actual[s.predictionStep - 1:]
predictedInput = np.full((len(allY), ), np.nan)
#if s.dataSet not in x_cols:
# allY = allY[s.lookback:]
trainX = allX[:s.nTrain]
trainY = allY[:s.nTrain]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
rnn.fit(trainX,
trainY,
epochs=s.epochs,
batch_size=s.batch_size,
verbose=min(s.max_verbosity, 2))
#for i in xrange(0,s.nTrain):
# targetInput[i] = allY[i+s.predictionStep]
targetInput = allY
pred_diffs = []
pred_closer_to_actual = []
isFirst = True
for i in tqdm(xrange(s.nTrain + s.predictionStep, len(allX)),
disable=s.max_verbosity == 0):
#for i in tqdm(xrange(0, len(allX)), disable=s.max_verbosity == 0):
if i % s.retrain_interval == 0 and i > s.numLags + s.nTrain and s.online:
trainX = allX[i - s.nTrain - s.predictionStep:i - s.predictionStep]
trainY = allY[i - s.nTrain - s.predictionStep:i - s.predictionStep]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
rnn.fit(trainX,
trainY,
epochs=s.epochs,
batch_size=s.batch_size,
verbose=0)
#targetInput[i] = allY[i]
predictedInput[i] = rnn.predict(np.reshape(allX[i], (1, 1, x_dims)))
if isFirst:
print predictedInput[i]
isFirst = False
#predictedInput[i] = targetInput[i-1440]
pred_diffs.append(abs(predictedInput[i] - allX[i][-1]))
pred_closer_to_actual.append(
abs(predictedInput[i] - targetInput[i]) < abs(predictedInput[i] -
allX[i][-1]))
if s.dataSet in differenceSets:
predictedInputNodiff[i] = predictedInput[i]
targetInputNodiff[i] = targetInput[i]
predictedInput[i] = dp.inverse_difference(backup_sequence['data'],
predictedInput[i], i - 1)
targetInput[i] = dp.inverse_difference(backup_sequence['data'],
targetInput[i], i - 1)
for i in range(s.nTrain + s.predictionStep):
predictedInput[i] = np.nan
predictedInput = dp.denormalize(predictedInput, meanSeq, stdSeq)
targetInput = dp.denormalize(targetInput, meanSeq, stdSeq)
#dp.windowed_denormalize(predictedInput, targetInput)
print "FINAL", predictedInput[-1], targetInput[-1], len(
predictedInput), len(targetInput)
if s.dataSet in differenceSets:
# predictedInputNodiff = dp.denormalize(predictedInputNodiff)
# targetInputNodiff = dp.denormalize(targetInputNodiff)
pass
dp.saveResultToFile(s.dataSet, predictedInput, targetInput, 'gru',
s.predictionStep, s.max_verbosity)
skipTrain = error_ignore_first[s.dataSet]
from plot import computeSquareDeviation
squareDeviation = computeSquareDeviation(predictedInput, targetInput)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(np.nanmean(squareDeviation)) / np.nanstd(targetInput)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInput - predictedInput))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
mase = errors.get_mase(predictedInput, targetInput,
np.roll(targetInput, s.season))
if s.max_verbosity > 0:
print "MASE {}".format(mase)
if s.dataSet in differenceSets:
dp.saveResultToFile(s.dataSet, predictedInputNodiff, targetInputNodiff,
'gru_nodiff', s.predictionStep, s.max_verbosity)
squareDeviation = computeSquareDeviation(predictedInputNodiff,
targetInputNodiff)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(
np.nanmean(squareDeviation)) / np.nanstd(targetInputNodiff)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInputNodiff - predictedInputNodiff))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
closer_rate = pred_closer_to_actual.count(True) / float(
len(pred_closer_to_actual))
if s.max_verbosity > 0:
pred_diffs.sort()
print pred_diffs[0], pred_diffs[-1], pred_diffs[int(0.9 *
len(pred_diffs))]
print "Good results:", closer_rate
return mase, closer_rate
def run_gru(s):
prob = tf.placeholder_with_default(1.0, shape=())
global global_step
global increment_global_step_op
global reset_global_step_op
global batches
global images_placeholder
global batches_op
global_step = tf.Variable(0, name='global_step', trainable=False, dtype=tf.int32)
increment_global_step_op = tf.assign(global_step, global_step + 1)
reset_global_step_op = tf.assign(global_step, 0)
batches = tf.get_variable("batches", [s.nTrain / int(s.batch_size), s.batch_size, 1, 1], dtype=tf.float32,
initializer=tf.zeros_initializer)
images_placeholder = tf.placeholder(tf.float32, shape=(s.nTrain / int(s.batch_size), s.batch_size, 1, 1))
batches_op = tf.assign(batches, images_placeholder)
x_dims = s.lookback
random.seed(6)
np.random.seed(6)
tf.set_random_seed(6)
if s.implementation == "keras":
if s.use_binary:
raise Exception("Binary Keras not implemented")
rnn = Sequential()
if s.rnn_type == "lstm":
rnn.add(LSTM(s.nodes, input_shape=(None,x_dims), kernel_initializer='he_uniform'))
elif s.rnn_type == "gru":
rnn.add(GRU(s.nodes, input_shape=(None, x_dims), kernel_initializer='he_uniform'))
rnn.add(Dropout(0.5))
rnn.add(Dense(1, kernel_initializer='he_uniform'))
opt = rmsprop(lr=s.lr)#1e-3)
rnn.compile(loss='mae', optimizer=opt)
input = Input(shape=(1, x_dims))
dense1 = Dense(s.nodes, activation='sigmoid')(input)
dense2 = Dense(s.nodes, activation='sigmoid')(input)
dense3 = Dense(s.nodes, activation='tanh')(input)
mult1 = Multiply()([dense2, dense3])
act1 = Activation('tanh')(mult1)
mult2 = Multiply()([dense1, act1])
reshape = Reshape((s.nodes,))(mult2)
dropout = Dropout(0.5)(reshape)
dense_out = Dense(1)(dropout)
rnn = Model(inputs=[input], outputs=[dense_out])
opt = adam(lr=s.lr) # 1e-3)
rnn.compile(loss='mae', optimizer=opt)
print rnn.summary()
elif s.implementation == "tf":
data = tf.placeholder(tf.float32, [None, s.lookback, 1]) # Number of examples, number of input, dimension of each input
target = tf.placeholder(tf.float32, [None, 1])
if s.rnn_type == "lstm" and s.use_binary:
cell = rnn_tf.LSTMCell(s.nodes)
elif s.rnn_type == "lstm" and not s.use_binary:
cell = tf.nn.rnn_cell.LSTMCell(s.nodes)
elif s.rnn_type == "gru" and s.use_binary:
cell = rnn_tf.GRUCell(s.nodes)
elif s.rnn_type == "gru" and not s.use_binary:
cell = tf.nn.rnn_cell.GRUCell(s.nodes)
val, _ = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)
with tf.name_scope('rnn_summaries'):
var = val
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
val = tf.nn.dropout(val, prob)
if not s.use_binary:
dense = tf.layers.dense(val, 1)
else:
dense = core_discretize.dense(val, 1)
with tf.name_scope('dense_summaries'):
var = dense
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
pred = tf.reshape(dense, (tf.shape(dense)[0], 1))
summary = tf.summary.merge_all()
optimizer = tf.train.AdamOptimizer(learning_rate=s.lr)
#cost = tf.losses.mean_squared_error(target, pred)
cost = tf.reduce_mean(tf.abs(target - pred))
minimize = optimizer.minimize(cost)
else:
raise Exception("Unknown implementation " + s.implementation)
sequence = readDataSet(s.dataSet, s.dataSetDetailed, s)
if s.limit_to:
sequence = sequence[:s.limit_to]
#TEMP SANITY CHECK
# sequence['data'][7001] = 0
# sequence['data'][7002] = 0
# sequence['data'][7003] = 0
# sequence['data'][7004] = 0
# sequence['data'][7005] = 0
seq_full = sequence['data'].values #use .values to copy
targetInput = seq_full[s.front_buffer + s.predictionStep - 1:].copy() #grab this now to avoid having to denormalize
dp = DataProcessor()
if s.normalization_type == 'default':
(meanSeq, stdSeq) = dp.normalize('data', sequence, s.nTrain)
elif s.normalization_type == 'windowed':
dp.windowed_normalize(sequence)
elif s.normalization_type == 'AN':
an = AdaptiveNormalizer(s.lookback, s.lookback + s.predictionStep)
an.set_pruning(False)
an.set_source_data(seq_full, s.nTrain)
an.do_ma('s')
an.do_stationary()
an.remove_outliers()
seq_norm = an.do_adaptive_normalize()
else:
raise Exception("Unsupported normalization type: " + s.normalization_type)
seq_actual = seq_full[s.front_buffer:] #Leave enough headroom for MASE calculation and lookback
seq_full_norm = sequence['data'].values
seq_actual_norm = seq_full_norm[s.front_buffer:]
if s.normalization_type != "AN":
#Default and windowed change the seq itself but still require creating lookback frames
allX = getX(seq_full_norm, s)
allY = seq_actual_norm[s.predictionStep-1:]
else:
#AN creates a new array but takes care of lookback internally
allX= seq_norm[:,0:-s.predictionStep]
allY = np.reshape(seq_norm[:,-1], (-1,))
# TODO FIX PROPERLY (now rolled too far)
too_long = len(allX) - (len(seq_full) - s.front_buffer - s.predictionStep + 1)
if too_long > 0:
allX = allX[too_long:]
allY = allY[too_long:]
print len(allX), len(allY), s.front_buffer
predictedInput = np.full((len(allY),), np.nan) #Initialize all predictions to NaN
trainX = allX[:s.nTrain]
trainY = allY[:s.nTrain]
trainX = np.reshape(trainX, (trainX.shape[0],1, trainX.shape[1]))
trainY = np.reshape(trainY, ( trainY.shape[0],))
if s.implementation == "keras":
rnn.fit(trainX, trainY, epochs=s.epochs, batch_size=s.batch_size, verbose=min(s.max_verbosity, 2))
elif s.implementation == "tf":
sess = tf.Session()
writer = tf.summary.FileWriter("results/", graph=sess.graph)
init = tf.global_variables_initializer()
sess.run(init)
for v in tf.trainable_variables():
print v.name
for epoch in tqdm(range(s.epochs)):
the_cost, _, summ = sess.run([cost, minimize, summary], feed_dict={data: trainX, target: trainY, prob: 0.5})
writer.add_summary(summ, epoch)
if epoch % 10 == 0:
print the_cost
#print(psutil.Process(os.getpid()).memory_percent())
var = [v for v in tf.trainable_variables() if v.name == "rnn/gru_cell/gates/kernel:0"][0]
print sess.run(tf.reduce_min(var))
print sess.run(tf.reduce_max(var))
# var = [v for v in tf.trainable_variables() if v.name == "rnn/gru_cell/gates/bias:0"][0]
# print sess.run(tf.reduce_min(var))
# print sess.run(tf.reduce_max(var))
# var = [v for v in tf.trainable_variables() if v.name == "rnn/gru_cell/candidate/kernel:0"][0]
# print sess.run(tf.reduce_min(var))
# print sess.run(tf.reduce_max(var))
# var = [v for v in tf.trainable_variables() if v.name == "rnn/gru_cell/candidate/bias:0"][0]
# print sess.run(tf.reduce_min(var))
# print sess.run(tf.reduce_max(var))
# print "loop"
var = [v for v in tf.trainable_variables() if v.name == "dense/bias:0"]
print sess.run(var)
minval = 10
latestStart = None
for i in tqdm(xrange(s.nTrain + s.predictionStep, len(allX)), disable=s.max_verbosity == 0):
#for i in tqdm(xrange(0, len(allX)), disable=s.max_verbosity == 0):
#for i in tqdm(xrange(10475, len(allX)), disable=s.max_verbosity == 0):
if i % s.retrain_interval == 0 and i > s.numLags+s.nTrain and s.online:
if s.normalization_type == 'AN':
predictedInput = np.array(an.do_adaptive_denormalize(predictedInput, therange=(i-s.retrain_interval, i)))
latestStart = i
an.set_ignore_first_n(i-s.nTrain-s.predictionStep)
an.do_ma('s')
an.do_stationary()
an.remove_outliers()
seq_norm = an.do_adaptive_normalize()
allX = seq_norm[:, 0:-s.predictionStep]
allY = np.reshape(seq_norm[:, -1], (-1,))
trainX = allX[i-s.nTrain-s.predictionStep:i-s.predictionStep]
trainY = allY[i-s.nTrain-s.predictionStep:i-s.predictionStep]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
trainY = np.reshape(trainY, (trainY.shape[0], 1))
if s.implementation == "keras":
rnn.fit(trainX, trainY, epochs=s.epochs, batch_size=s.batch_size, verbose=0)
elif s.implementation == "tf":
for epoch in range(s.epochs):
sess.run(minimize, feed_dict={data: trainX, target: trainY, prob: 0.5})
if s.implementation == "keras":
predictedInput[i] = rnn.predict(np.reshape(allX[i], (1,1,x_dims)))
elif s.implementation == "tf":
predictedInput[i] = sess.run(dense, feed_dict={data: np.reshape(allX[i], (1, x_dims, 1))})
#if len(allX) > i+5:
# predictedInput[i] = allY[i-3000]
# if i == 10000:
# print allX[i]
# print "should be ", (targetInput[i] - meanSeq) / stdSeq
# print "predicted as ", predictedInput[i]
# for i in range(s.nTrain + s.predictionStep):
# predictedInput[i] = np.nan
print "SMALLEST", minval
# np.set_printoptions(threshold=np.nan, suppress=True)
# print "ALLY START"
# for val in allY:
# print val
# print "ALLY STOP"
if s.normalization_type == 'default':
predictedInput = dp.denormalize(predictedInput, meanSeq, stdSeq)
#targetInput = dp.denormalize(targetInput, meanSeq, stdSeq)
elif s.normalization_type == 'windowed':
dp.windowed_denormalize(predictedInput, targetInput, pred_step=s.predictionStep)
elif s.normalization_type == 'AN':
if latestStart:
predictedInput = np.array(an.do_adaptive_denormalize(predictedInput, therange=(latestStart, len(predictedInput))))
else:
predictedInput = np.array(an.do_adaptive_denormalize(predictedInput))
if an.pruning:
targetInput = np.delete(targetInput, an.deletes)
print len(predictedInput), len(targetInput), "LENS"
#TEMP SANITY CHECK
#print predictedInput[7005 - s.front_buffer - s.predictionStep +1]
#print predictedInput[7006 - s.front_buffer - s.predictionStep + 1]
dp.saveResultToFile(s.dataSet, predictedInput, targetInput, 'gru', s.predictionStep, s.max_verbosity)
skipTrain = s.ignore_for_error
from plot import computeSquareDeviation
squareDeviation = computeSquareDeviation(predictedInput, targetInput)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(np.nanmean(squareDeviation)) / np.nanstd(targetInput)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInput-predictedInput))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
mape = errors.get_mape(predictedInput,targetInput, s.ignore_for_error)
if s.max_verbosity > 0:
print "MAPE {}".format(mape)
mase = errors.get_mase(predictedInput, targetInput, np.roll(targetInput, s.season), s.ignore_for_error)
if s.max_verbosity > 0:
print "MASE {}".format(mase)
if s.implementation == "tf":
sess.close()
return mase
def run_gru(s):
s.print_settings()
prob = tf.placeholder_with_default(
1.0, shape=()) #Retain probability for TF dropout
start_time = timeit.default_timer()
if s.implementation == "keras":
if s.use_binary:
raise Exception("Binary Keras not implemented")
input = Input(shape=(1, s.x_dims))
dense1 = Dense(s.nodes, activation='sigmoid')(input)
dense2 = Dense(s.nodes, activation='sigmoid')(input)
dense3 = Dense(s.nodes, activation='tanh')(input)
mult1 = Multiply()([dense2, dense3])
act1 = Activation('tanh')(mult1)
mult2 = Multiply()([dense1, act1])
reshape = Reshape((s.nodes, ))(mult2)
dropout = Dropout(0.5)(reshape)
dense_out = Dense(1)(dropout)
rnn = Model(inputs=[input], outputs=[dense_out])
opt = adam(lr=s.lr, decay=0.0,
epsilon=s.adam_eps) #, clipvalue=1.)#1e-3)
#opt = rmsprop(lr=s.lr)
rnn.compile(loss=s.loss, optimizer=opt)
if s.max_verbosity > 0:
print(rnn.summary())
else:
raise Exception("Unknown implementation " + s.implementation)
sequence = readDataSet(s.dataSet, s.dataSetDetailed, s).values
if s.limit_to:
sequence = sequence[:s.limit_to]
#Get rid of unneeded columns
sequence = sequence[:, 0:s.feature_count]
#sequence[-1000,0] = 666
#print "Changed -1000 to 666"
"""
We need to leave some values unpredicted in front so that
- We can fill the lookback window for each prediction
- We can get the value from 1 season earlier for MASE
--> Don't use the first `front_buffer` values as prediction
--> Independent from `prediction_step`, so the first actual value predicted is `front_buffer`\
plus however many steps the `prediction_step` is higher than 1
In other words, the most recent X-value for the first prediction will be the final value in the `front_buffer`
"""
first_prediction_index = s.front_buffer + s.predictionStep - 1
targetInput = sequence[
first_prediction_index:,
0].copy() #grab this now to avoid having to denormalize
dp = DataProcessor()
if s.normalization_type == 'default':
(meanSeq, stdSeq) = dp.normalize(
sequence, s.nTrain if s.cutoff_normalize else len(sequence))
elif s.normalization_type == 'windowed':
dp.windowed_normalize(sequence, columns=[0])
if s.feature_count > 1:
dp.normalize(sequence, s.nTrain, columns=range(1, s.feature_count))
elif s.normalization_type == 'AN':
an = AdaptiveNormalizer(s.lookback, s.lookback + s.predictionStep)
an.set_pruning(False)
an.set_source_data(sequence, s.nTrain)
an.do_ma('s')
an.do_stationary()
an.remove_outliers()
seq_norm = an.do_adaptive_normalize()
print seq_norm.shape
if s.feature_count > 1:
dp.normalize(sequence, s.nTrain, columns=range(1, s.feature_count))
start = sequence.shape[0] - seq_norm.shape[
0] - s.lookback - s.predictionStep + 1
for i in range(seq_norm.shape[0]):
seq_norm[i, :,
1:s.feature_count] = sequence[start + i:start + i +
seq_norm.shape[1],
1:s.feature_count]
#an.do_ma('s')
#an.do_stationary()
#an.remove_outliers()
#seq_norm = an.do_adaptive_normalize()
#print seq_norm[15000,0,0]
#exit(1)
else:
raise Exception("Unsupported normalization type: " +
s.normalization_type)
#seq_actual = sequence[s.front_buffer:] #Leave enough headroom for MASE calculation and lookback
#seq_full_norm = np.reshape(sequence[:,0], (sequence.shape[0],))
#seq_actual_norm = seq_full_norm[s.front_buffer:]
if s.normalization_type != "AN":
#Default and windowed change the seq itself but still require creating lookback frames
allX = getX(sequence, s)
allY = sequence[first_prediction_index:, 0]
else:
#AN creates a new array but takes care of lookback internally
allX = seq_norm[:, 0:-s.predictionStep]
allY = np.reshape(seq_norm[:, -1, 0], (-1, ))
predictedInput = np.full((len(allY), ),
np.nan) #Initialize all predictions to NaN
#print "TESTT", allX[15000,0,1:]
print "FIRST", allX[875]
trainX = allX[:s.nTrain]
trainY = allY[:s.nTrain]
#print "FIRST", trainX[0], trainY[0]
trainX = np.reshape(trainX, s.actual_input_shape_train)
trainY = np.reshape(trainY, s.actual_output_shape_train)
#print "FIRST", trainX[0], trainY[0]
if s.implementation == "keras":
#for _ in tqdm(range(s.epochs)):
for _ in range(1):
rnn.fit(
trainX,
trainY,
epochs=s.epochs,
batch_size=s.batch_size,
verbose=min(s.max_verbosity, 2),
shuffle=not s.stateful
) #, validation_data=(trainX, trainY), callbacks=[TensorBoard(log_dir='./logs', histogram_freq=1, write_grads=True)])
if s.stateful:
rnn_layer.reset_states()
# for layer in rnn.layers:
# print layer.get_weights()
#for i in xrange(0, s.nTrain + s.predictionStep):
# rnn.predict(np.reshape(allX[i], (1, 1, x_dims)))
#predictedInput[s.nTrain + s.predictionStep : len(allX)] = rnn.predict(np.reshape(allX[s.nTrain + s.predictionStep : len(allX)], (1, 12510, x_dims)))
latestStart = None
do_non_lookback = True
latest_onego = 0
#buffer = s.retrain_interval / 2
buffer = 0
for i in tqdm(xrange(s.nTrain + s.predictionStep, len(allX)),
disable=s.max_verbosity == 0):
if i % s.retrain_interval == 0 and s.online and i > s.nTrain + s.predictionStep + buffer:
do_non_lookback = True
if s.normalization_type == 'AN':
#print "TEST", seq_norm[15000,0,1]
predictedInput = np.array(
an.do_adaptive_denormalize(
predictedInput, therange=(i - s.retrain_interval, i)))
latestStart = i
an.set_ignore_first_n(i - s.nTrain - s.predictionStep)
an.do_ma('s')
an.do_stationary()
an.remove_outliers()
seq_norm = an.do_adaptive_normalize()
print seq_norm[15000, 0, 0]
print seq_norm.shape
#exit(1)
#print "FIRST", seq_norm[i-s.nTrain -s.predictionStep,0]#, trainY[0]
#print sequence[start+i-s.nTrain-s.predictionStep:start+
if s.feature_count > 1:
#dp.normalize(sequence, s.nTrain, columns=range(1,s.feature_count))
start = sequence.shape[0] - seq_norm.shape[
0] - s.lookback - s.predictionStep + 1
for j in range(seq_norm.shape[0]):
seq_norm[j, :, 1:s.feature_count] = sequence[
start + j:start + j + seq_norm.shape[1],
1:s.feature_count]
#print "FIRST", seq_norm[i-s.nTrain -s.predictionStep,0]#, trainY[0]
allX = seq_norm[:, 0:-s.predictionStep]
allY = np.reshape(seq_norm[:, -1, 0], (-1, ))
if s.lookback:
trainX = allX[i - s.nTrain - s.predictionStep:i -
s.predictionStep]
trainY = allY[i - s.nTrain - s.predictionStep:i -
s.predictionStep]
else:
trainX = allX[i - s.nTrain - s.predictionStep:i -
s.predictionStep]
trainY = allY[i - s.nTrain - s.predictionStep:i -
s.predictionStep]
#print "TESTT", allX[15000,0,:]
print "at", i - s.nTrain - s.predictionStep
print "FIRST", allX[875] #, trainY[0]
#exit(1)
trainX = np.reshape(trainX, s.actual_input_shape_train)
trainY = np.reshape(trainY, s.actual_output_shape_train)
#print "FIRST", trainX[0], trainY[0]
#exit(1)
if s.implementation == "keras":
if s.reset_on_retrain:
input = Input(shape=(1, s.x_dims))
dense1 = Dense(s.nodes, activation='sigmoid')(input)
dense2 = Dense(s.nodes, activation='sigmoid')(input)
dense3 = Dense(s.nodes, activation='tanh')(input)
mult1 = Multiply()([dense2, dense3])
act1 = Activation('tanh')(mult1)
mult2 = Multiply()([dense1, act1])
reshape = Reshape((s.nodes, ))(mult2)
dropout = Dropout(0.5)(reshape)
dense_out = Dense(1)(dropout)
rnn = Model(inputs=[input], outputs=[dense_out])
opt = adam(lr=s.lr, decay=0.0,
epsilon=s.adam_eps) # , clipvalue=1.)#1e-3)
#opt = rmsprop(lr=s.lr)
rnn.compile(loss=s.loss, optimizer=opt)
for _ in range(1):
rnn.fit(trainX,
trainY,
epochs=s.epochs_retrain
if s.epochs_retrain else s.epochs,
batch_size=s.batch_size,
verbose=2,
shuffle=not s.stateful)
if s.stateful:
rnn_layer.reset_states()
if s.lookback:
if s.implementation == "keras":
predictedInput[i] = rnn.predict(
np.reshape(allX[i], s.predict_input_shape))
elif do_non_lookback:
do_non_lookback = False
up_to = min(allX.shape[0], i - (i % s.retrain_interval) +
s.retrain_interval) if s.online else allX.shape[0]
start_time = time.time()
#print allX[0]
start = 0 if s.refeed_on_retrain else latest_onego
new_predicts = rnn.predict(
np.reshape(allX[start:up_to], (1, -1, s.x_dims)))
new_predicts = np.reshape(new_predicts, (new_predicts.shape[1], ))
predictedInput[i:up_to] = new_predicts[-(up_to - i):]
latest_onego = up_to
for i in range(s.nTrain + s.predictionStep):
predictedInput[i] = np.nan
if s.normalization_type == 'default':
predictedInput = dp.denormalize(predictedInput, meanSeq[0], stdSeq[0])
elif s.normalization_type == 'windowed':
dp.windowed_denormalize(predictedInput, pred_step=s.predictionStep)
elif s.normalization_type == 'AN':
if latestStart:
predictedInput = np.array(
an.do_adaptive_denormalize(predictedInput,
therange=(latestStart,
len(predictedInput))))
else:
predictedInput = np.array(
an.do_adaptive_denormalize(predictedInput))
if an.pruning:
targetInput = np.delete(targetInput, an.deletes)
print "Final time", (timeit.default_timer() - start_time)
#print "Last not to change:", predictedInput[-996], targetInput[-996]
#print "First to change:", predictedInput[-995], targetInput[-995]
dp.saveResultToFile(s.dataSet, predictedInput, targetInput, 'gru',
s.predictionStep, s.max_verbosity)
for ignore in s.ignore_for_error:
skipTrain = ignore
from plot import computeSquareDeviation
squareDeviation = computeSquareDeviation(predictedInput, targetInput)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(np.nanmean(squareDeviation)) / np.nanstd(targetInput)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInput - predictedInput))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
mape = errors.get_mape(predictedInput, targetInput, skipTrain)
if s.max_verbosity > 0:
print "MAPE {}".format(mape)
mase = errors.get_mase(predictedInput, targetInput,
np.roll(targetInput, s.season), skipTrain)
if s.max_verbosity > 0:
print "MASE {}".format(mase)
return mase