def __init__(self,
input_feature_num,
output_target_num,
past_seq_len,
optimizer,
loss,
metric,
hidden_dim=32,
layer_num=1,
lr=0.001,
dropout=0.2,
backend="torch",
logs_dir="/tmp/auto_lstm",
cpus_per_trial=1,
name="auto_lstm"):
"""
Create an AutoLSTM.
:param input_feature_num: Int. The number of features in the input
:param output_target_num: Int. The number of targets in the output
:param past_seq_len: Int or hp sampling function The number of historical
steps used for forecasting.
:param optimizer: String or pyTorch optimizer creator function or
tf.keras optimizer instance.
:param loss: String or pytorch/tf.keras loss instance or pytorch loss creator function.
:param metric: String. The evaluation metric name to optimize. e.g. "mse"
:param hidden_dim: Int or hp sampling function from an integer space. The number of features
in the hidden state `h`. For hp sampling, see zoo.chronos.orca.automl.hp for more
details. e.g. hp.grid_search([32, 64]).
:param layer_num: Int or hp sampling function from an integer space. Number of recurrent
layers. e.g. hp.randint(1, 3)
:param lr: float or hp sampling function from a float space. Learning rate.
e.g. hp.choice([0.001, 0.003, 0.01])
:param dropout: float or hp sampling function from a float space. Learning rate. Dropout
rate. e.g. hp.uniform(0.1, 0.3)
:param backend: The backend of the lstm model. We only support backend as "torch" for now.
:param logs_dir: Local directory to save logs and results. It defaults to "/tmp/auto_lstm"
:param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.
:param name: name of the AutoLSTM. It defaults to "auto_lstm"
"""
# todo: support backend = 'keras'
if backend != "torch":
raise ValueError(f"We only support backend as torch. Got {backend}")
self.search_space = dict(
hidden_dim=hidden_dim,
layer_num=layer_num,
lr=lr,
dropout=dropout,
input_feature_num=input_feature_num,
output_feature_num=output_target_num,
past_seq_len=past_seq_len,
future_seq_len=1
)
self.metric = metric
model_builder = PytorchModelBuilder(model_creator=model_creator,
optimizer_creator=optimizer,
loss_creator=loss,
)
self.auto_est = AutoEstimator(model_builder=model_builder,
logs_dir=logs_dir,
resources_per_trial={"cpu": cpus_per_trial},
name=name)
def __init__(self,
input_feature_num,
output_target_num,
past_seq_len,
future_seq_len,
optimizer,
loss,
metric,
hidden_units=None,
levels=None,
num_channels=None,
kernel_size=7,
lr=0.001,
dropout=0.2,
backend="torch",
logs_dir="/tmp/auto_tcn",
cpus_per_trial=1,
name="auto_tcn"):
"""
Create an AutoTCN.
:param input_feature_num: Int. The number of features in the input
:param output_target_num: Int. The number of targets in the output
:param past_seq_len: Int. The number of historical steps used for forecasting.
:param future_seq_len: Int. The number of future steps to forecast.
:param optimizer: String or pyTorch optimizer creator function or
tf.keras optimizer instance.
:param loss: String or pytorch/tf.keras loss instance or pytorch loss creator function.
:param metric: String. The evaluation metric name to optimize. e.g. "mse"
:param hidden_units: Int or hp sampling function from an integer space. The number of hidden
units or filters for each convolutional layer. It is similar to `units` for LSTM.
It defaults to 30. We will omit the hidden_units value if num_channels is specified.
For hp sampling, see zoo.orca.automl.hp for more details.
e.g. hp.grid_search([32, 64]).
:param levels: Int or hp sampling function from an integer space. The number of levels of
TemporalBlocks to use. It defaults to 8. We will omit the levels value if
num_channels is specified.
:param num_channels: List of integers. A list of hidden_units for each level. You could
specify num_channels if you want different hidden_units for different levels.
By default, num_channels equals to
[hidden_units] * (levels - 1) + [output_target_num].
:param kernel_size: Int or hp sampling function from an integer space.
The size of the kernel to use in each convolutional layer.
:param lr: float or hp sampling function from a float space. Learning rate.
e.g. hp.choice([0.001, 0.003, 0.01])
:param dropout: float or hp sampling function from a float space. Learning rate. Dropout
rate. e.g. hp.uniform(0.1, 0.3)
:param backend: The backend of the TCN model. We only support backend as "torch" for now.
:param logs_dir: Local directory to save logs and results. It defaults to "/tmp/auto_tcn"
:param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.
:param name: name of the AutoTCN. It defaults to "auto_tcn"
"""
# todo: support search for past_seq_len.
# todo: add input check.
if backend != "torch":
raise ValueError(
f"We only support backend as torch. Got {backend}")
self.search_space = dict(
input_feature_num=input_feature_num,
output_feature_num=output_target_num,
past_seq_len=past_seq_len,
future_seq_len=future_seq_len,
nhid=hidden_units,
levels=levels,
num_channels=num_channels,
kernel_size=kernel_size,
lr=lr,
dropout=dropout,
)
self.metric = metric
model_builder = PytorchModelBuilder(
model_creator=model_creator,
optimizer_creator=optimizer,
loss_creator=loss,
)
self.auto_est = AutoEstimator(
model_builder=model_builder,
logs_dir=logs_dir,
resources_per_trial={"cpu": cpus_per_trial},
name=name)
def __init__(self,
input_feature_num,
output_target_num,
past_seq_len,
future_seq_len,
optimizer,
loss,
metric,
lr=0.001,
lstm_hidden_dim=128,
lstm_layer_num=2,
dropout=0.25,
teacher_forcing=False,
backend="torch",
logs_dir="/tmp/auto_seq2seq",
cpus_per_trial=1,
name="auto_seq2seq"):
"""
Create an AutoSeq2Seq.
:param input_feature_num: Int. The number of features in the input
:param output_target_num: Int. The number of targets in the output
:param past_seq_len: Int. The number of historical steps used for forecasting.
:param future_seq_len: Int. The number of future steps to forecast.
:param optimizer: String or pyTorch optimizer creator function or
tf.keras optimizer instance.
:param loss: String or pytorch/tf.keras loss instance or pytorch loss creator function.
:param metric: String. The evaluation metric name to optimize. e.g. "mse"
:param lr: float or hp sampling function from a float space. Learning rate.
e.g. hp.choice([0.001, 0.003, 0.01])
:param lstm_hidden_dim: LSTM hidden channel for decoder and encoder.
hp.grid_search([32, 64, 128])
:param lstm_layer_num: LSTM layer number for decoder and encoder.
e.g. hp.randint(1, 4)
:param dropout: float or hp sampling function from a float space. Learning rate. Dropout
rate. e.g. hp.uniform(0.1, 0.3)
:param teacher_forcing: If use teacher forcing in training. e.g. hp.choice([True, False])
:param backend: The backend of the Seq2Seq model. We only support backend as "torch"
for now.
:param logs_dir: Local directory to save logs and results. It defaults to
"/tmp/auto_seq2seq"
:param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.
:param name: name of the AutoSeq2Seq. It defaults to "auto_seq2seq"
"""
# todo: support search for past_seq_len.
# todo: add input check.
if backend != "torch":
raise ValueError(
f"We only support backend as torch. Got {backend}")
self.search_space = dict(input_feature_num=input_feature_num,
output_feature_num=output_target_num,
past_seq_len=past_seq_len,
future_seq_len=future_seq_len,
lstm_hidden_dim=lstm_hidden_dim,
lstm_layer_num=lstm_layer_num,
lr=lr,
dropout=dropout,
teacher_forcing=teacher_forcing)
self.metric = metric
model_builder = PytorchModelBuilder(
model_creator=model_creator,
optimizer_creator=optimizer,
loss_creator=loss,
)
self.auto_est = AutoEstimator(
model_builder=model_builder,
logs_dir=logs_dir,
resources_per_trial={"cpu": cpus_per_trial},
name=name)