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Pipeline(steps: typing.List[typing.Tuple[str, bigframes.ml.base.BaseEstimator]])
Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator.
Intermediate steps of the pipeline must be transforms
. That is, they
must implement fit
and transform
methods.
The final estimator only needs to implement fit
.
The purpose of the pipeline is to assemble several steps that can be
cross-validated together while setting different parameters. This
simplifies code and allows for deploying an estimator and peprocessing
together, e.g. with Pipeline.to_gbq(...).
Methods
__repr__
__repr__()
Print the estimator's constructor with all non-default parameter values.
fit
fit(
X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
y: typing.Optional[
typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
] = None,
) -> bigframes.ml.pipeline.Pipeline
Fit the model.
Fit all the transformers one after the other and transform the data. Finally, fit the transformed data using the final estimator.
Parameters | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series
A DataFrame or Series representing training data. Must match the input requirements of the first step of the pipeline. |
y |
bigframes.dataframe.DataFrame or bigframes.series.Series
A DataFrame or Series representing training targets, if applicable. |
Returns | |
---|---|
Type | Description |
Pipeline | Pipeline with fitted steps. |
get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]
Get parameters for this estimator.
Parameter | |
---|---|
Name | Description |
deep |
bool, default True
Default |
Returns | |
---|---|
Type | Description |
Dictionary | A dictionary of parameter names mapped to their values. |
predict
predict(
X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame
API documentation for predict
method.
score
score(
X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
y: typing.Optional[
typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
] = None,
) -> bigframes.dataframe.DataFrame
API documentation for score
method.
to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.pipeline.Pipeline
Save the pipeline to BigQuery.
Parameters | |
---|---|
Name | Description |
model_name |
str
the name of the model(pipeline). |
replace |
bool, default False
whether to replace if the model(pipeline) already exists. Default to False. |
Returns | |
---|---|
Type | Description |
Pipeline | saved model(pipeline). |