snowflake.ml.modeling.pipeline.Pipeline¶

class snowflake.ml.modeling.pipeline.Pipeline(steps: List[Tuple[str, Any]])¶

Bases: BaseTransformer

Methods

fit(dataset)	Fit the entire pipeline using the dataset.
fit_predict(dataset)	Fits all the transformer objs one after another and transforms the data.
fit_transform(dataset)	Fits all the transformer objs one after another and transforms the data.
get_input_cols()	Input columns getter.
get_label_cols()	Label column getter.
get_output_cols()	Output columns getter.
get_params([deep])	Get parameters for this transformer.
get_passthrough_cols()	Passthrough columns getter.
get_sample_weight_col()	Sample weight column getter.
get_sklearn_args([default_sklearn_obj, ...])	Get sklearn keyword arguments.
predict(dataset)	Transform the dataset by applying all the transformers in order and predict using the estimator.
predict_log_proba(dataset)	Transform the dataset by applying all the transformers in order and apply predict_log_proba using the estimator.
predict_proba(dataset)	Transform the dataset by applying all the transformers in order and apply predict_proba using the estimator.
score(dataset)	Transform the dataset by applying all the transformers in order and apply score using the estimator.
set_drop_input_cols([drop_input_cols])
set_input_cols(input_cols)	Input columns setter.
set_label_cols(label_cols)	Label column setter.
set_output_cols(output_cols)	Output columns setter.
set_params(**params)	Set the parameters of this transformer.
set_passthrough_cols(passthrough_cols)	Passthrough columns setter.
set_sample_weight_col(sample_weight_col)	Sample weight column setter.
to_lightgbm()
to_sklearn()
to_xgboost()
transform(dataset)	Call transform of each transformer in the pipeline.

Attributes

model_signatures