def __init__(self,
output_dim: Optional[int] = None,
loss: types.LossType = "mean_squared_error",
metrics: Optional[types.MetricsType] = None,
project_name: str = "text_regressor",
max_trials: int = 100,
directory: Union[str, Path, None] = None,
objective: str = "val_loss",
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite: bool = False,
seed: Optional[int] = None,
max_model_size: Optional[int] = None,
**kwargs):
if tuner is None:
tuner = greedy.Greedy
super().__init__(outputs=blocks.RegressionHead(output_dim=output_dim,
loss=loss,
metrics=metrics),
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=tuner,
overwrite=overwrite,
seed=seed,
max_model_size=max_model_size,
**kwargs)
def __init__(
self,
column_names: Optional[List[str]] = None,
column_types: Optional[Dict[str, str]] = None,
output_dim: Optional[int] = None,
loss: types.LossType = "mean_squared_error",
metrics: Optional[types.MetricsType] = None,
project_name: str = "structured_data_regressor",
max_trials: int = 100,
directory: Union[str, pathlib.Path, None] = None,
objective: str = "val_loss",
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite: bool = False,
seed: Optional[int] = None,
max_model_size: Optional[int] = None,
**kwargs
):
if tuner is None:
tuner = task_specific.StructuredDataRegressorTuner
super().__init__(
outputs=blocks.RegressionHead(
output_dim=output_dim, loss=loss, metrics=metrics
),
column_names=column_names,
column_types=column_types,
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=tuner,
overwrite=overwrite,
seed=seed,
max_model_size=max_model_size,
**kwargs
)
def __init__(self,
output_dim=None,
loss='mean_squared_error',
metrics=None,
project_name='text_regressor',
max_trials=100,
directory=None,
objective='val_loss',
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite=False,
seed=None,
**kwargs):
if tuner is None:
tuner = greedy.Greedy
super().__init__(outputs=blocks.RegressionHead(output_dim=output_dim,
loss=loss,
metrics=metrics),
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=tuner,
overwrite=overwrite,
seed=seed,
**kwargs)
def __init__(self,
output_dim=None,
column_names=None,
column_types=None,
lookback=None,
predict_from=1,
predict_until=None,
loss='mean_squared_error',
metrics=None,
project_name='time_series_forecaster',
max_trials=100,
directory=None,
objective='val_loss',
overwrite=True,
seed=None,
**kwargs):
super().__init__(outputs=blocks.RegressionHead(output_dim=output_dim,
loss=loss,
metrics=metrics),
column_names=column_names,
column_types=column_types,
lookback=lookback,
predict_from=predict_from,
predict_until=predict_until,
project_name=project_name,
max_trials=max_trials,
directory=directory,
objective=objective,
tuner=greedy.Greedy,
overwrite=overwrite,
seed=seed,
**kwargs)
self.lookback = lookback
self.predict_from = predict_from
self.predict_until = predict_until
def __init__(self,
column_names: Optional[List[str]] = None,
column_types: Optional[Dict[str, str]] = None,
output_dim: Optional[int] = None,
loss: types.LossType = 'mean_squared_error',
metrics: Optional[types.MetricsType] = None,
project_name: str = 'structured_data_regressor',
max_trials: int = 100,
directory: Union[str, pathlib.Path, None] = None,
objective: str = 'val_loss',
overwrite: bool = True,
seed: Optional[int] = None,
**kwargs):
super().__init__(outputs=blocks.RegressionHead(output_dim=output_dim,
loss=loss,
metrics=metrics),
column_names=column_names,
column_types=column_types,
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=greedy.Greedy,
overwrite=overwrite,
seed=seed,
**kwargs)
def __init__(
self,
output_dim=None,
column_names: Optional[List[str]] = None,
column_types: Optional[Dict[str, str]] = None,
lookback: Optional[int] = None,
predict_from: int = 1,
predict_until: Optional[int] = None,
loss: types.LossType = "mean_squared_error",
metrics: Optional[types.MetricsType] = None,
project_name: str = "time_series_forecaster",
max_trials: int = 100,
directory: Union[str, Path, None] = None,
objective: str = "val_loss",
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite: bool = False,
seed: Optional[int] = None,
**kwargs
):
if tuner is None:
tuner = greedy.Greedy
super().__init__(
outputs=blocks.RegressionHead(
output_dim=output_dim, loss=loss, metrics=metrics
),
column_names=column_names,
column_types=column_types,
lookback=lookback,
predict_from=predict_from,
predict_until=predict_until,
project_name=project_name,
max_trials=max_trials,
directory=directory,
objective=objective,
tuner=tuner,
overwrite=overwrite,
seed=seed,
**kwargs
)
self.lookback = lookback
self.predict_from = predict_from
self.predict_until = predict_until
def __init__(self,
output_dim=None,
loss='mean_squared_error',
metrics=None,
project_name='text_regressor',
max_trials=100,
directory=None,
objective='val_loss',
overwrite=True,
seed=None,
**kwargs):
super().__init__(outputs=blocks.RegressionHead(output_dim=output_dim,
loss=loss,
metrics=metrics),
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=greedy.Greedy,
overwrite=overwrite,
seed=seed,
**kwargs)
