scikeras.utils.transformers

Classes

ClassifierLabelEncoder([loss, categories])	Default target transformer for KerasClassifier.
RegressorTargetEncoder()	Default target transformer for KerasRegressor.
TargetReshaper()	Convert 1D targets to 2D and back.

class scikeras.utils.transformers.ClassifierLabelEncoder(loss=None, categories='auto')

Default target transformer for KerasClassifier.

Parameters:

lossUnion[None, str, Loss], default None

Keras Model’s loss function. Used to automatically one-hot encode the target if the loss function is categorical crossentropy.

categoriesUnion[str, List[np.ndarray]], default “auto”

All of the categories present in the target for the entire dataset. “auto” will infer the categories from the data passed to fit.

Attributes:

classes_Iterable

The classes seen during fit.

n_classes_int

The number of classes seen during fit.

n_outputs_int

Dimensions of y that the transformer was trained on.

n_outputs_expected_int

Number of outputs the Keras Model is expected to have.

Parameters:

• loss (Union[None, str, Loss]) –

• categories (Union[str, List[ndarray]]) –

fit(y)

Fit the estimator to the target y.

For all targets, this transforms classes into ordinal numbers. If the loss function is categorical_crossentropy, the target will be one-hot encoded.

Parameters:

ynp.ndarray

The target data to be transformed.

Returns:

ClassifierLabelEncoder

A reference to the current instance of ClassifierLabelEncoder.

Parameters:

y (ndarray) –

Return type:

ClassifierLabelEncoder

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_metadata()

Returns a dictionary of meta-parameters generated when this transfromer was fitted.

Used by SciKeras to bind these parameters to the SciKeras estimator itself and make them available as inputs to the Keras model.

Returns:

Dict[str, Any]

Dictionary of meta-parameters generated when this transfromer was fitted.

Return type:

Dict[str, Any]

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

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

Returns:

paramsdict

Parameter names mapped to their values.

inverse_transform(y, return_proba=False)

Restore the data types, shape and classes of the input y to the output of the Keras Model.

Parameters:

ynp.ndarray

Raw probability predictions from the Keras Model.

return_probabool, default False

If True, return the prediction probabilites themselves. If False, return the class predictions.

Returns:

np.ndarray

Class predictions (of the same shape as the y to fit/transform), or class prediction probabilities.

Parameters:

• y (ndarray) –

• return_proba (bool) –

Return type:

ndarray

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

transform(y)

Transform the target y to the format expected by the Keras Model.

If the loss function is categorical_crossentropy, the target will be one-hot encoded. For other types of target, this transforms classes into ordinal numbers.

Returns:

np.ndarray

Transformed target.

Parameters:

y (ndarray) –

Return type:

ndarray

class scikeras.utils.transformers.RegressorTargetEncoder

Default target transformer for KerasRegressor.

Attributes:

n_outputs_int

Dimensions of y that the transformer was trained on.

n_outputs_expected_int

Number of outputs the Keras Model is expected to have.

fit(y)

Fit the transformer to the target y.

For RegressorTargetEncoder, this just records the dimensions of y as the expected number of outputs and saves the dtype.

Returns:

RegressorTargetEncoder

A reference to the current instance of RegressorTargetEncoder.

Parameters:

y (ndarray) –

Return type:

RegressorTargetEncoder

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_metadata()

Returns a dictionary of meta-parameters generated when this transfromer was fitted.

Used by SciKeras to bind these parameters to the SciKeras estimator itself and make them available as inputs to the Keras model.

Returns:

Dict[str, Any]

Dictionary of meta-parameters generated when this transfromer was fitted.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

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

Returns:

paramsdict

Parameter names mapped to their values.

inverse_transform(y)

Restore the data types and shape of the input y to the output of the Keras Model.

Parameters:

ynp.ndarray

Raw predictions from the Keras Model.

Returns:

np.ndarray

Keras Model predictions cast to the dtype and shape of the input targets.

Parameters:

y (ndarray) –

Return type:

ndarray

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

transform(y)

Transform the target y to the format expected by the Keras Model.

For RegressorTargetEncoder, this simply checks that the shape passed to fit matches the shape passed to transform.

Returns:

np.ndarray

Untouched input y.

Parameters:

y (ndarray) –

Return type:

ndarray

class scikeras.utils.transformers.TargetReshaper

Convert 1D targets to 2D and back.

For use in pipelines with transformers that only accept 2D inputs, like OneHotEncoder and OrdinalEncoder.

Attributes:

ndim_int

Dimensions of y that the transformer was trained on.

fit(y)

Fit the transformer to a target y.

Returns:

TargetReshaper

A reference to the current instance of TargetReshaper.

Parameters:

y (ndarray) –

Return type:

TargetReshaper

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

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

Returns:

paramsdict

Parameter names mapped to their values.

inverse_transform(y)

Revert the transformation of transform.

Parameters:

ynp.ndarray

Transformed numpy array.

Returns:

np.ndarray

If the transformer was fit to a 1D numpy array, and a 2D numpy array with a singleton second dimension is passed, it will be squeezed back to 1D. Otherwise, it will eb left untouched.

Parameters:

y (ndarray) –

Return type:

ndarray

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

static transform(y)

Makes 1D y 2D.

Parameters:

ynp.ndarray

Target y to be transformed.

Returns:

np.ndarray

A numpy array, of dimension at least 2.

Parameters:

y (ndarray) –

Return type:

ndarray