gators.pipeline.Pipeline¶

class gators.pipeline.Pipeline(steps: List[Tuple[gators.transformers.transformer.Transformer]], memory=None, verbose=False)[source]¶

SciKitPileline Chain the gators transformers together.

Parameters

stepsList[Tuple[Transformer]]: List of transformers ending, or not, by an estimator.
memorystr, default None.: Scilit-learn pipeline memory parameter.
verbosebool, default False.: Verbosity.

Examples

Imports and initialization:

>>> from gators.imputers import NumericsImputer
>>> from gators.imputers import ObjectImputer
>>> from gators.pipeline import Pipeline
>>> steps = [
... ('ObjectImputer', ObjectImputer(strategy='constant', value='MISSING')),
... ('NumericsImputerMedian', NumericsImputer(strategy='median', columns=['A'])),
... ('NumericsImputerFrequent', NumericsImputer(strategy='most_frequent', columns=['B']))]
>>> obj = Pipeline(steps=steps)

The fit, transform, and fit_transform methods accept:

dask dataframes:

>>> import dask.dataframe as dd
>>> import pandas as pd
>>> X = dd.from_pandas(pd.DataFrame({
... 'A': [0.1, 0.2, 0.3, np.nan],
... 'B': [1, 2, 2, np.nan],
... 'C': ['a', 'b', 'c', None]}), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame({
... 'A': [0.1, 0.2, 0.3, np.nan],
... 'B': [1, 2, 2, np.nan],
... 'C': ['a', 'b', 'c', None]})

and pandas dataframes:

>>> import pandas as pd
>>> X = pd.DataFrame({
... 'A': [0.1, 0.2, 0.3, np.nan],
... 'B': [1, 2, 2, np.nan],
... 'C': ['a', 'b', 'c', None]})

The result is a transformed dataframe belonging to the same dataframe library.

>>> obj.fit_transform(X)
     A    B        C
0  0.1  1.0        a
1  0.2  2.0        b
2  0.3  2.0        c
3  0.2  2.0  MISSING

>>> X = pd.DataFrame({
... 'A': [0.1, 0.2, 0.3, np.nan],
... 'B': [1, 2, 2, np.nan],
... 'C': ['a', 'b', 'c', None]})
>>> _ = obj.fit(X)
>>> obj.transform_numpy(X.to_numpy())
array([[0.1, 1.0, 'a'],
       [0.2, 2.0, 'b'],
       [0.3, 2.0, 'c'],
       [0.2, 2.0, 'MISSING']], dtype=object)

transform_numpy(X: numpy.ndarray) → numpy.ndarray[source]¶

Transform the array X.

Parameters

Xnp.ndarray: Input array.

Returns

Xnp.ndarray: Transformed array.

static check_array(X: numpy.ndarray)¶

Validate array.

Parameters

Xnp.ndarray: Array.

check_array_is_numerics(X: numpy.ndarray)¶

Check if array is only numerics.

Parameters

Xnp.ndarray: Array.

static check_binary_target(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])¶

Raise an error if the target is not binary.

Parameters

ySeries: Target values.

static check_dataframe(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶

Validate dataframe.

Parameters

XDataFrame: Dataframe.

static check_dataframe_contains_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶

Check if dataframe is only numerics.

Parameters

XDataFrame: Dataframe.

static check_dataframe_is_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶

Check if dataframe is only numerics.

Parameters

XDataFrame: Dataframe.

check_dataframe_with_objects(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶

Check if dataframe contains object columns.

Parameters

XDataFrame: Dataframe.

check_datatype(dtype, accepted_dtypes)¶

Check if dataframe is only numerics.

Parameters

XDataFrame: Dataframe.

static check_multiclass_target(y: Union[pd.Series, ks.Series, dd.Series])¶

Raise an error if the target is not discrete.

Parameters

ySeries: Target values.

check_nans(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], columns: List[str])¶

Raise an error if X contains NaN values.

Parameters

XDataFrame: Dataframe.
theta_vecList[float]: List of columns.

static check_regression_target(y: Union[pd.Series, ks.Series, dd.Series])¶

Raise an error if the target is not discrete.

Parameters

ySeries: Target values.

static check_target(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])¶

Validate target.

Parameters

XDataFrame: Dataframe.
ySeries: Target values.

property classes_¶: The classes labels. Only exist if the last step is a classifier.

decision_function(X)[source]¶

Transform the data, and apply decision_function with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls decision_function method. Only valid if the final estimator implements decision_function.

Parameters

Xiterable: Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_scorendarray of shape (n_samples, n_classes): Result of calling decision_function on the final estimator.

property feature_names_in_¶: Names of features seen during first step fit method.

fit(X, y=None, **fit_params)[source]¶

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

Xiterable: Training data. Must fulfill input requirements of first step of the pipeline.
yiterable, default=None: Training targets. Must fulfill label requirements for all steps of the pipeline.
**fit_paramsdict of string -> object: Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

selfobject: Pipeline with fitted steps.

fit_predict(X, y=None, **fit_params)[source]¶

Transform the data, and apply fit_predict with the final estimator.

Call fit_transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls fit_predict method. Only valid if the final estimator implements fit_predict.

Parameters

Xiterable: Training data. Must fulfill input requirements of first step of the pipeline.
yiterable, default=None: Training targets. Must fulfill label requirements for all steps of the pipeline.
**fit_paramsdict of string -> object: Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

y_predndarray: Result of calling fit_predict on the final estimator.

fit_transform(X, y=None, **fit_params)[source]¶

Fit the model and transform with the final estimator.

Fits all the transformers one after the other and transform the data. Then uses fit_transform on transformed data with the final estimator.

Parameters

Xiterable: Training data. Must fulfill input requirements of first step of the pipeline.
yiterable, default=None: Training targets. Must fulfill label requirements for all steps of the pipeline.
**fit_paramsdict of string -> object: Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

Xtndarray of shape (n_samples, n_transformed_features): Transformed samples.

static get_column_names(inplace: bool, columns: List[str], suffix: str)¶

Return the names of the modified columns.

Parameters

inplacebool: If True return columns. If False return columns__suffix.
columnsList[str]: List of columns.
suffixstr: Suffix used if inplace is False.

Returns

List[str]: List of column names.

get_feature_names_out(input_features=None)[source]¶

Get output feature names for transformation.

Transform input features using the pipeline.

Parameters

input_featuresarray-like of str or None, default=None: Input features.

Returns

feature_names_outndarray of str objects: Transformed feature names.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Returns the parameters given in the constructor as well as the estimators contained within the steps of the Pipeline.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any: Parameter names mapped to their values.

inverse_transform(Xt)[source]¶

Apply inverse_transform for each step in a reverse order.

All estimators in the pipeline must support inverse_transform.

Parameters

Xtarray-like of shape (n_samples, n_transformed_features): Data samples, where n_samples is the number of samples and n_features is the number of features. Must fulfill input requirements of last step of pipeline’s inverse_transform method.

Returns

Xtndarray of shape (n_samples, n_features): Inverse transformed data, that is, data in the original feature space.

property n_features_in_¶: Number of features seen during first step fit method.

property named_steps¶

Access the steps by name.

Read-only attribute to access any step by given name. Keys are steps names and values are the steps objects.

predict(X, **predict_params)[source]¶

Transform the data, and apply predict with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls predict method. Only valid if the final estimator implements predict.

Parameters

Xiterable: Data to predict on. Must fulfill input requirements of first step of the pipeline.
**predict_paramsdict of string -> object: Parameters to the predict called at the end of all transformations in the pipeline. Note that while this may be used to return uncertainties from some models with return_std or return_cov, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator.

New in version 0.20.

Returns

y_predndarray: Result of calling predict on the final estimator.

predict_log_proba(X, **predict_log_proba_params)[source]¶

Transform the data, and apply predict_log_proba with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls predict_log_proba method. Only valid if the final estimator implements predict_log_proba.

Parameters

Xiterable: Data to predict on. Must fulfill input requirements of first step of the pipeline.
**predict_log_proba_paramsdict of string -> object: Parameters to the predict_log_proba called at the end of all transformations in the pipeline.

Returns

y_log_probandarray of shape (n_samples, n_classes): Result of calling predict_log_proba on the final estimator.

predict_proba(X, **predict_proba_params)[source]¶

Transform the data, and apply predict_proba with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls predict_proba method. Only valid if the final estimator implements predict_proba.

Parameters

Xiterable: Data to predict on. Must fulfill input requirements of first step of the pipeline.
**predict_proba_paramsdict of string -> object: Parameters to the predict_proba called at the end of all transformations in the pipeline.

Returns

y_probandarray of shape (n_samples, n_classes): Result of calling predict_proba on the final estimator.

score(X, y=None, sample_weight=None)[source]¶

Transform the data, and apply score with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls score method. Only valid if the final estimator implements score.

Parameters

Xiterable: Data to predict on. Must fulfill input requirements of first step of the pipeline.
yiterable, default=None: Targets used for scoring. Must fulfill label requirements for all steps of the pipeline.
sample_weightarray-like, default=None: If not None, this argument is passed as sample_weight keyword argument to the score method of the final estimator.

Returns

scorefloat: Result of calling score on the final estimator.

score_samples(X)[source]¶

Transform the data, and apply score_samples with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls score_samples method. Only valid if the final estimator implements score_samples.

Parameters

Xiterable: Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_scorendarray of shape (n_samples,): Result of calling score_samples on the final estimator.

set_params(**kwargs)[source]¶

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in steps.

Parameters

**kwargsdict: Parameters of this estimator or parameters of estimators contained in steps. Parameters of the steps may be set using its name and the parameter name separated by a ‘__’.

Returns

selfobject: Pipeline class instance.

transform(X)[source]¶

Transform the data, and apply transform with the final estimator.

Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls transform method. Only valid if the final estimator implements transform.

This also works where final estimator is None in which case all prior transformations are applied.

Parameters

Xiterable: Data to transform. Must fulfill input requirements of first step of the pipeline.

Returns

Xtndarray of shape (n_samples, n_transformed_features): Transformed data.

Pipeline

Model Building