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# Copyright 2018 Analytics Zoo Authors.
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from zoo.automl.model.base_pytorch_model import PytorchModelBuilder
from zoo.orca.automl.auto_estimator import AutoEstimator
from zoo.chronos.model.VanillaLSTM_pytorch import model_creator
[docs]class AutoLSTM:
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)
[docs] def fit(self,
data,
epochs=1,
batch_size=32,
validation_data=None,
metric_threshold=None,
n_sampling=1,
search_alg=None,
search_alg_params=None,
scheduler=None,
scheduler_params=None,
):
"""
Automatically fit the model and search for the best hyper parameters.
:param data: train data.
For backend of "torch", data can be a tuple of ndarrays or a function that takes a
config dictionary as parameter and returns a PyTorch DataLoader.
For backend of "keras", data can be a tuple of ndarrays.
If data is a tuple of ndarrays, it should be in the form of (x, y),
where x is training input data and y is training target data.
:param epochs: Max number of epochs to train in each trial. Defaults to 1.
If you have also set metric_threshold, a trial will stop if either it has been
optimized to the metric_threshold or it has been trained for {epochs} epochs.
:param batch_size: Int or hp sampling function from an integer space. Training batch size.
It defaults to 32.
:param validation_data: Validation data. Validation data type should be the same as data.
:param metric_threshold: a trial will be terminated when metric threshold is met
:param n_sampling: Number of times to sample from the search_space. Defaults to 1.
If hp.grid_search is in search_space, the grid will be repeated n_sampling of times.
If this is -1, (virtually) infinite samples are generated
until a stopping condition is met.
:param search_alg: str, all supported searcher provided by ray tune
(i.e."variant_generator", "random", "ax", "dragonfly", "skopt",
"hyperopt", "bayesopt", "bohb", "nevergrad", "optuna", "zoopt" and
"sigopt")
:param search_alg_params: extra parameters for searcher algorithm besides search_space,
metric and searcher mode
:param scheduler: str, all supported scheduler provided by ray tune
:param scheduler_params: parameters for scheduler
:return:
"""
self.search_space["batch_size"] = batch_size
self.auto_est.fit(
data=data,
epochs=epochs,
validation_data=validation_data,
metric=self.metric,
metric_threshold=metric_threshold,
n_sampling=n_sampling,
search_space=self.search_space,
search_alg=search_alg,
search_alg_params=search_alg_params,
scheduler=scheduler,
scheduler_params=scheduler_params,
)
[docs] def get_best_model(self):
"""
Get the best lstm model.
"""
return self.auto_est.get_best_model()
[docs] def get_best_config(self):
"""
Get the best configuration
:return: A dictionary of best hyper parameters
"""
return self.auto_est.get_best_config()
def _get_best_automl_model(self):
return self.auto_est._get_best_automl_model()