def test_predict_save_restore(self):
config = {"batch_size": 128}
self.model.fit_eval((self.train_data[0], self.train_data[1]),
self.val_data, **config)
pred = self.model.predict(self.test_data[0])
assert pred.shape == self.test_data[1].shape
with tempfile.TemporaryDirectory() as tmp_dir_name:
ckpt_name = os.path.join(tmp_dir_name, "ckpt")
self.model.save(ckpt_name)
model_1 = TCNPytorch()
model_1.restore(ckpt_name)
pred_1 = model_1.predict(self.test_data[0])
assert np.allclose(pred, pred_1)
class TestTcn(TestCase):
train_data, val_data, test_data = create_data()
model = TCNPytorch()
def test_fit_evaluate(self):
config = {"batch_size": 128}
self.model.fit_eval((self.train_data[0], self.train_data[1]),
self.val_data, **config)
mse, smape = self.model.evaluate(self.val_data[0],
self.val_data[1],
metrics=["mse", "smape"])
assert len(mse) == self.val_data[1].shape[-2]
assert len(mse[0]) == self.val_data[1].shape[-1]
assert len(smape) == self.val_data[1].shape[-2]
assert len(smape[0]) == self.val_data[1].shape[-1]
def test_predict_save_restore(self):
config = {"batch_size": 128}
self.model.fit_eval((self.train_data[0], self.train_data[1]),
self.val_data, **config)
pred = self.model.predict(self.test_data[0])
assert pred.shape == self.test_data[1].shape
with tempfile.TemporaryDirectory() as tmp_dir_name:
ckpt_name = os.path.join(tmp_dir_name, "ckpt")
self.model.save(ckpt_name)
model_1 = TCNPytorch()
model_1.restore(ckpt_name)
pred_1 = model_1.predict(self.test_data[0])
assert np.allclose(pred, pred_1)
def __init__(self,
past_seq_len,
future_seq_len,
input_feature_num,
output_feature_num,
num_channels=[30]*8,
kernel_size=7,
dropout=0.2,
optimizer="Adam",
loss="mse",
lr=0.001):
"""
Build a TCN Forecast Model.
:param past_seq_len: Specify the history time steps (i.e. lookback).
:param future_seq_len: Specify the output time steps (i.e. horizon).
:param input_feature_num: Specify the feature dimension.
:param output_feature_num: Specify the output dimension.
:param num_channels: Specify the convolutional layer filter number in
TCN's encoder. This value defaults to [30]*8.
:param kernel_size: Specify convolutional layer filter height in TCN's
encoder. This value defaults to 7.
:param dropout: Specify the dropout close possibility (i.e. the close
possibility to a neuron). This value defaults to 0.2.
:param optimizer: Specify the optimizer used for training. This value
defaults to "Adam".
:param loss: Specify the loss function used for training. This value
defaults to "mse". You can choose from "mse", "mae" and
"huber_loss".
:param lr: Specify the learning rate. This value defaults to 0.001.
"""
self.internal = TCNPytorch(check_optional_config=False)
self.data_config = {
"past_seq_len": past_seq_len,
"future_seq_len": future_seq_len,
"input_feature_num": input_feature_num,
"output_feature_num": output_feature_num
}
self.config = {
"lr": lr,
"loss": loss,
"num_channels": num_channels,
"kernel_size": kernel_size,
"optim": optimizer,
"dropout": dropout
}
class TCNForecaster(Forecaster):
def __init__(self,
past_seq_len,
future_seq_len,
input_feature_num,
output_feature_num,
num_channels=[30]*8,
kernel_size=7,
dropout=0.2,
optimizer="Adam",
loss="mse",
lr=0.001):
"""
Build a TCN Forecast Model.
:param past_seq_len: Specify the history time steps (i.e. lookback).
:param future_seq_len: Specify the output time steps (i.e. horizon).
:param input_feature_num: Specify the feature dimension.
:param output_feature_num: Specify the output dimension.
:param num_channels: Specify the convolutional layer filter number in
TCN's encoder. This value defaults to [30]*8.
:param kernel_size: Specify convolutional layer filter height in TCN's
encoder. This value defaults to 7.
:param dropout: Specify the dropout close possibility (i.e. the close
possibility to a neuron). This value defaults to 0.2.
:param optimizer: Specify the optimizer used for training. This value
defaults to "Adam".
:param loss: Specify the loss function used for training. This value
defaults to "mse". You can choose from "mse", "mae" and
"huber_loss".
:param lr: Specify the learning rate. This value defaults to 0.001.
"""
self.internal = TCNPytorch(check_optional_config=False)
self.data_config = {
"past_seq_len": past_seq_len,
"future_seq_len": future_seq_len,
"input_feature_num": input_feature_num,
"output_feature_num": output_feature_num
}
self.config = {
"lr": lr,
"loss": loss,
"num_channels": num_channels,
"kernel_size": kernel_size,
"optim": optimizer,
"dropout": dropout
}
def fit(self, x, y, validation_data=None, epochs=1, metric="mse", batch_size=32):
"""
Fit(Train) the forecaster.
:param x: A numpy array with shape (num_samples, lookback, feature_dim).
lookback and feature_dim should be the same as past_seq_len and input_feature_num.
:param y: A numpy array with shape (num_samples, horizon, target_dim).
horizon and target_dim should be the same as future_seq_len and output_feature_num.
:param validation_data: A tuple (x_valid, y_valid) as validation data. Default to None.
:param epochs: Number of epochs you want to train.
:param metric: The metric for training data.
:param batch_size: Number of batch size you want to train.
"""
if validation_data is None:
validation_data = (x, y)
self.config["batch_size"] = batch_size
self._check_data(x, y)
return self.internal.fit_eval(x,
y,
validation_data=validation_data,
epochs=epochs,
metric=metric,
**self.config)
def _check_data(self, x, y):
assert self.data_config["past_seq_len"] == x.shape[-2], \
"The x shape should be (batch_size, past_seq_len, input_feature_num), \
Got past_seq_len of {} in config while x input shape of {}."\
.format(self.data_config["past_seq_len"], x.shape[-2])
assert self.data_config["future_seq_len"] == y.shape[-2], \
"The y shape should be (batch_size, future_seq_len, output_feature_num), \
Got future_seq_len of {} in config while y input shape of {}."\
.format(self.data_config["future_seq_len"], y.shape[-2])
assert self.data_config["input_feature_num"] == x.shape[-1],\
"The x shape should be (batch_size, past_seq_len, input_feature_num), \
Got input_feature_num of {} in config while x input shape of {}."\
.format(self.data_config["input_feature_num"], x.shape[-1])
assert self.data_config["output_feature_num"] == y.shape[-1], \
"The y shape should be (batch_size, future_seq_len, output_feature_num), \
Got output_feature_num of {} in config while y input shape of {}."\
.format(self.data_config["output_feature_num"], y.shape[-1])
def predict(self, x):
"""
Predict using a trained forecaster.
:param x: A numpy array with shape (num_samples, lookback, feature_dim).
"""
if not self.internal.model_built:
raise RuntimeError("You must call fit or restore first before calling predict!")
return self.internal.predict(x)
def predict_with_onnx(self, x, dirname=None):
"""
Predict using a trained forecaster with onnxruntime.
:param x: A numpy array with shape (num_samples, lookback, feature_dim).
:param dirname: The directory to save onnx model file. This value defaults
to None for no saving file.
"""
if not self.internal.model_built:
raise RuntimeError("You must call fit or restore first before calling predict!")
return self.internal.predict_with_onnx(x, dirname=dirname)
def evaluate(self, x, y, metrics=['mse'], multioutput="raw_values"):
"""
Evaluate using a trained forecaster.
:param x: A numpy array with shape (num_samples, lookback, feature_dim).
:param y: A numpy array with shape (num_samples, horizon, target_dim).
:param metrics: A list contains metrics for test/valid data.
:param multioutput: Defines aggregating of multiple output values.
String in ['raw_values', 'uniform_average']. The value defaults to
'raw_values'.
"""
if not self.internal.model_built:
raise RuntimeError("You must call fit or restore first before calling evaluate!")
return self.internal.evaluate(x, y, metrics=metrics, multioutput=multioutput)
def evaluate_with_onnx(self, x, y,
metrics=['mse'],
dirname=None,
multioutput="raw_values"):
"""
Evaluate using a trained forecaster with onnxruntime.
:param x: A numpy array with shape (num_samples, lookback, feature_dim).
:param y: A numpy array with shape (num_samples, horizon, target_dim).
:param metrics: A list contains metrics for test/valid data.
:param dirname: The directory to save onnx model file. This value defaults
to None for no saving file.
:param multioutput: Defines aggregating of multiple output values.
String in ['raw_values', 'uniform_average']. The value defaults to
'raw_values'.
"""
if not self.internal.model_built:
raise RuntimeError("You must call fit or restore first before calling evaluate!")
return self.internal.evaluate_with_onnx(x, y,
metrics=metrics,
dirname=dirname,
multioutput=multioutput)
def save(self, checkpoint_file):
"""
Save the forecaster.
:param checkpoint_file: The location you want to save the forecaster.
"""
if not self.internal.model_built:
raise RuntimeError("You must call fit or restore first before calling save!")
self.internal.save(checkpoint_file)
def restore(self, checkpoint_file):
"""
restore the forecaster.
:param checkpoint_file: The checkpoint file location you want to load the forecaster.
"""
self.internal.restore(checkpoint_file)