def test_create_not_torch_model(self):
def model_creator(config):
return torch.Tensor(3, 5)
modelBuilder = PytorchModelBuilder(model_creator=model_creator,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator)
with pytest.raises(ValueError):
model = modelBuilder.build(config={
"lr": 1e-2,
"batch_size": 32,
})
def test_fit_evaluate(self):
metric_name = "rmse"
modelBuilder = PytorchModelBuilder(model_creator=model_creator_pytorch,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator)
model = modelBuilder.build(config={
"lr": 1e-2,
"batch_size": 32,
})
val_result = model.fit_eval(data=(self.data["x"], self.data["y"]),
validation_data=(self.data["val_x"],
self.data["val_y"]),
metric=metric_name,
epochs=20)
assert val_result.get(metric_name)
def prepare_searcher(data,
search_space,
stop,
validation_data=None,
model_creator=linear_model_creator,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator,
metric="mse",
metric_mode="min",
name="demo"):
modelBuilder = PytorchModelBuilder(model_creator=model_creator,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator)
searcher = SearchEngineFactory.create_engine(
backend="ray",
logs_dir="~/zoo_automl_logs",
resources_per_trial={"cpu": 2},
name=name)
searcher.compile(data=data,
validation_data=validation_data,
model_builder=modelBuilder,
search_space=search_space,
n_sampling=2,
epochs=stop["training_iteration"],
metric_threshold=stop["reward_metric"],
metric_mode=metric_mode,
metric=metric)
return searcher
def test_dataloader_fit_evaluate(self):
metric_name = "rmse"
modelBuilder = PytorchModelBuilder(model_creator=model_creator_pytorch,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator)
model = modelBuilder.build(
config={
"lr": 1e-2,
"batch_size": 32,
"train_size": 500,
"valid_size": 100,
"shuffle": True
})
val_result = model.fit_eval(data=train_dataloader_creator,
validation_data=valid_dataloader_creator,
metric=metric_name,
epochs=20)
assert model.config["train_size"] == 500
assert model.config["valid_size"] == 100
assert model.config["shuffle"] is True
assert val_result.get(metric_name)
def test_evaluate(self):
modelBuilder = PytorchModelBuilder(model_creator=model_creator_pytorch,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator)
model = modelBuilder.build(config={
"lr": 1e-2,
"batch_size": 32,
})
model.fit_eval(data=(self.data["x"], self.data["y"]),
validation_data=(self.data["val_x"],
self.data["val_y"]),
metric="rmse",
epochs=20)
mse_eval = model.evaluate(x=self.data["val_x"], y=self.data["val_y"])
try:
import onnx
import onnxruntime
mse_eval_onnx = model.evaluate_with_onnx(x=self.data["val_x"],
y=self.data["val_y"])
np.testing.assert_almost_equal(mse_eval, mse_eval_onnx)
except ImportError:
pass
# incremental training test
model.fit_eval(data=(self.data["x"], self.data["y"]),
validation_data=(self.data["val_x"],
self.data["val_y"]),
metric="rmse",
epochs=20)
mse_eval = model.evaluate(x=self.data["val_x"], y=self.data["val_y"])
try:
import onnx
import onnxruntime
mse_eval_onnx = model.evaluate_with_onnx(x=self.data["val_x"],
y=self.data["val_y"])
np.testing.assert_almost_equal(mse_eval, mse_eval_onnx)
except ImportError:
pass
def test_predict(self):
modelBuilder = PytorchModelBuilder(model_creator=model_creator_pytorch,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator)
model = modelBuilder.build(config={
"lr": 1e-2,
"batch_size": 32,
})
model.fit_eval(data=(self.data["x"], self.data["y"]),
validation_data=(self.data["val_x"],
self.data["val_y"]),
metric="rmse",
epochs=20)
pred = model.predict(x=self.data["val_x"])
pred_full_batch = model.predict(x=self.data["val_x"],
batch_size=len(self.data["val_x"]))
np.testing.assert_almost_equal(pred, pred_full_batch)
try:
import onnx
import onnxruntime
pred_onnx = model.predict_with_onnx(x=self.data["val_x"])
np.testing.assert_almost_equal(pred, pred_onnx)
except ImportError:
pass
def from_torch(
*,
model_creator,
optimizer,
loss,
logs_dir="/tmp/auto_estimator_logs",
resources_per_trial=None,
name="auto_pytorch_estimator",
remote_dir=None,
):
"""
Create an AutoEstimator for torch.
:param model_creator: PyTorch model creator function.
:param optimizer: PyTorch optimizer creator function or pytorch optimizer name (string).
Note that you should specify learning rate search space with key as "lr" or LR_NAME
(from zoo.orca.automl.pytorch_utils import LR_NAME) if input optimizer name.
Without learning rate search space specified, the default learning rate value of 1e-3
will be used for all estimators.
:param loss: PyTorch loss instance or PyTorch loss creator function
or pytorch loss name (string).
:param logs_dir: Local directory to save logs and results. It defaults to
"/tmp/auto_estimator_logs"
:param resources_per_trial: Dict. resources for each trial. e.g. {"cpu": 2}.
:param name: Name of the auto estimator. It defaults to "auto_pytorch_estimator"
:param remote_dir: String. Remote directory to sync training results and checkpoints. It
defaults to None and doesn't take effects while running in local. While running in
cluster, it defaults to "hdfs:///tmp/{name}".
:return: an AutoEstimator object.
"""
from zoo.orca.automl.model.base_pytorch_model import PytorchModelBuilder
model_builder = PytorchModelBuilder(model_creator=model_creator,
optimizer_creator=optimizer,
loss_creator=loss)
return AutoEstimator(model_builder=model_builder,
logs_dir=logs_dir,
resources_per_trial=resources_per_trial,
remote_dir=remote_dir,
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",
remote_dir=None,
):
"""
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"
:param remote_dir: String. Remote directory to sync training results and checkpoints. It
defaults to None and doesn't take effects while running in local. While running in
cluster, it defaults to "hdfs:///tmp/{name}".
"""
super().__init__()
# 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},
remote_dir=remote_dir,
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",
remote_dir=None,
):
"""
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"
:param remote_dir: String. Remote directory to sync training results and checkpoints. It
defaults to None and doesn't take effects while running in local. While running in
cluster, it defaults to "hdfs:///tmp/{name}".
"""
super().__init__()
# 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},
remote_dir=remote_dir,
name=name)
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",
remote_dir=None,
):
"""
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"
:param remote_dir: String. Remote directory to sync training results and checkpoints. It
defaults to None and doesn't take effects while running in local. While running in
cluster, it defaults to "hdfs:///tmp/{name}".
"""
super().__init__()
# 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},
remote_dir=remote_dir,
name=name)