- 1.26.0 (latest)
- 1.25.0
- 1.24.0
- 1.22.0
- 1.21.0
- 1.20.0
- 1.19.0
- 1.18.0
- 1.17.0
- 1.16.0
- 1.15.0
- 1.14.0
- 1.13.0
- 1.12.0
- 1.11.1
- 1.10.0
- 1.9.0
- 1.8.0
- 1.7.0
- 1.6.0
- 1.5.0
- 1.4.0
- 1.3.0
- 1.2.0
- 1.1.0
- 1.0.0
- 0.26.0
- 0.25.0
- 0.24.0
- 0.23.0
- 0.22.0
- 0.21.0
- 0.20.1
- 0.19.2
- 0.18.0
- 0.17.0
- 0.16.0
- 0.15.0
- 0.14.1
- 0.13.0
- 0.12.0
- 0.11.0
- 0.10.0
- 0.9.0
- 0.8.0
- 0.7.0
- 0.6.0
- 0.5.0
- 0.4.0
- 0.3.0
- 0.2.0
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 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). |