gators.pipeline package

Module contents

class gators.pipeline.Pipeline

Bases: gators.transformer._base_transformer._BaseTransformer

Pipeline of transformers for Polars DataFrames.

Sequentially applies a list of transforms. This is a lightweight alternative to sklearn.pipeline.Pipeline specifically designed for Gators transformers that work with Polars DataFrames.

Parameters:

• steps (list[tuple[ str, Any]]) – List of (name, transform) tuples that are chained in the order they are specified. Each transform must implement fit and transform methods.

• verbose (bool, default=False) – If True, emits a one-line summary per step to stdout showing the step name, row count, column count, total null count, and wall-clock time. When False there is zero measurement overhead.

Examples

>>> from gators.pipeline import Pipeline
>>> from gators.imputers import NumericImputer, StringImputer
>>> from gators.encoders import WOEEncoder
>>>
>>> steps = [
...     ('numeric_imputer', NumericImputer(strategy='median')),
...     ('string_imputer', StringImputer(strategy='constant', value='MISSING')),
...     ('woe_encoder', WOEEncoder(subset=['cat_col']))
... ]
>>> pipe = Pipeline(steps=steps)
>>> pipe.fit(X_train, y=y_train)
>>> X_transformed = pipe.transform(X_train)

fit(X: polars.DataFrame, y: polars.Series | None = None) → gators.pipeline.pipeline.Pipeline

Fit all transformers in the pipeline.

Fits each transformer sequentially, transforming the data before fitting the next transformer. This ensures each transformer sees the output of the previous transformer.

Parameters:

• X (pl.DataFrame) – Input DataFrame to fit.

• y (pl.Series, default=None) – Target series for supervised transformers (e.g., WOEEncoder).

Returns:

The fitted pipeline instance.

Return type:

Pipeline

transform(X: polars.DataFrame) → polars.DataFrame

Transform data by applying all transformers in sequence.

Parameters:

X (pl.DataFrame) – Input DataFrame to transform.

Returns:

Transformed DataFrame.

Return type:

pl.DataFrame

fit_transform(X: polars.DataFrame, y: polars.Series | None = None) → polars.DataFrame

Fit all transformers and transform the data.

Fits and transforms each transformer sequentially. This is more efficient than calling fit() followed by transform() separately.

Parameters:

• X (pl.DataFrame) – Input DataFrame to fit and transform.

• y (pl.Series, default=None) – Target series for supervised transformers.

Returns:

Transformed DataFrame.

Return type:

pl.DataFrame

get_params(deep: bool = True) → dict

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, returns parameters of all sub-estimators. If False, only returns pipeline-level parameters.

Returns:

Parameter names mapped to their values. Nested parameters use double underscore notation (e.g., ‘step_name__param’).

Return type:

dict

clone() → gators.pipeline.pipeline.Pipeline

Return a new unfitted pipeline with the same hyperparameters.

Each transformer is re-instantiated using only its public constructor parameters (obtained via get_params()). Private attributes that hold fitted state (e.g. _statistics, mapping_) are not copied, so the returned pipeline is guaranteed to be unfitted.

This is the recommended alternative to copy.deepcopy for cross-validation workflows where you need multiple independent copies of the same pipeline configuration.

Returns:

A new, unfitted Pipeline instance with identical hyperparameters.

Return type:

Pipeline

Examples

>>> from gators.pipeline import Pipeline
>>> from gators.imputers import NumericImputer
>>> from gators.scalers import StandardScaler

>>> pipe = Pipeline(steps=[
...     ('impute', NumericImputer(strategy='median')),
...     ('scale', StandardScaler()),
... ])
>>> pipe_clone = pipe.clone()
>>> pipe_clone is pipe
False
>>> pipe_clone.named_steps['impute'] is pipe.named_steps['impute']
False

set_params(**params)

Set parameters for this estimator.

Parameters:

**params (dict) – Estimator parameters. Use double underscore notation for nested parameters (e.g., step_name__param_name=value).

Returns:

The pipeline instance.

Return type:

Pipeline

Raises:

ValueError – If an invalid parameter name is provided.