Class Pipeline (2.17.0)

  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 for deploying an estimator and preprocessing 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 True . If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

Type

Description

Dictionary

A dictionary of parameter names mapped to their values.

predict

  predict 
 ( 
 X 
 : 
 typing 
 . 
 Union 
 [ 
 bigframes 
 . 
 dataframe 
 . 
 DataFrame 
 , 
 bigframes 
 . 
 series 
 . 
 Series 
 , 
 pandas 
 . 
 core 
 . 
 frame 
 . 
 DataFrame 
 , 
 pandas 
 . 
 core 
 . 
 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).

Class Pipeline (2.17.0) Stay organized with collections Save and categorize content based on your preferences.

Methods

__repr__

fit

get_params

predict

score

to_gbq

Class Pipeline (2.17.0)

repr