scikeras.utils.transformers¶
Classes
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Default dataset_transformer for KerasClassifier. |
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Default target transformer for KerasClassifier. |
Default target transformer for KerasRegressor. |
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Convert 1D targets to 2D and back. |
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class
scikeras.utils.transformers.ClassWeightDataTransformer(class_weight=None)[source]¶ Default dataset_transformer for KerasClassifier.
This transformer implements handling of the class_weight parameter for single output classifiers.
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fit_transform(X, y=None, **fit_params)[source]¶ 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
- Xnumpy array of shape [n_samples, n_features]
Training set.
- ynumpy array of shape [n_samples]
Target values.
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newnumpy array of shape [n_samples, n_features_new]
Transformed array.
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get_params(deep=True)[source]¶ 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
- paramsmapping of string to any
Parameter names mapped to their values.
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set_params(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
- selfobject
Estimator instance.
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class
scikeras.utils.transformers.ClassifierLabelEncoder(loss=None, categories='auto')[source]¶ 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.
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fit(y)[source]¶ 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 (numpy.ndarray) –
- Return type
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fit_transform(X, y=None, **fit_params)[source]¶ 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
- Xnumpy array of shape [n_samples, n_features]
Training set.
- ynumpy array of shape [n_samples]
Target values.
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newnumpy array of shape [n_samples, n_features_new]
Transformed array.
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get_metadata()[source]¶ 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]
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get_params(deep=True)[source]¶ 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
- paramsmapping of string to any
Parameter names mapped to their values.
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inverse_transform(y, return_proba=False)[source]¶ 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 (numpy.ndarray) –
return_proba (bool) –
- Return type
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set_params(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
- selfobject
Estimator instance.
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transform(y)[source]¶ 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 (numpy.ndarray) –
- Return type
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class
scikeras.utils.transformers.RegressorTargetEncoder[source]¶ 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.
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fit(y)[source]¶ 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.
- Parameters
- ynp.ndarray
The target data to be transformed.
- Returns
- RegressorTargetEncoder
A reference to the current instance of RegressorTargetEncoder.
- Parameters
y (numpy.ndarray) –
- Return type
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fit_transform(X, y=None, **fit_params)[source]¶ 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
- Xnumpy array of shape [n_samples, n_features]
Training set.
- ynumpy array of shape [n_samples]
Target values.
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newnumpy array of shape [n_samples, n_features_new]
Transformed array.
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get_metadata()[source]¶ 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)[source]¶ 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
- paramsmapping of string to any
Parameter names mapped to their values.
-
inverse_transform(y)[source]¶ 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 (numpy.ndarray) –
- Return type
-
set_params(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
- selfobject
Estimator instance.
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transform(y)[source]¶ 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 (numpy.ndarray) –
- Return type
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class
scikeras.utils.transformers.TargetReshaper[source]¶ 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.
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fit(y)[source]¶ Fit the transformer to a target y.
- Parameters
- ynp.ndarray
The target data to be transformed.
- Returns
- TargetReshaper
A reference to the current instance of TargetReshaper.
- Parameters
y (numpy.ndarray) –
- Return type
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fit_transform(X, y=None, **fit_params)[source]¶ 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
- Xnumpy array of shape [n_samples, n_features]
Training set.
- ynumpy array of shape [n_samples]
Target values.
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newnumpy array of shape [n_samples, n_features_new]
Transformed array.
-
get_params(deep=True)[source]¶ 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
- paramsmapping of string to any
Parameter names mapped to their values.
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inverse_transform(y)[source]¶ 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 (numpy.ndarray) –
- Return type
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set_params(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
- selfobject
Estimator instance.
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static
transform(y)[source]¶ 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 (numpy.ndarray) –
- Return type