onehot_encoder#

A transformer that encodes categorical features in a one-hot numeric array.

Module Contents#

Classes Summary#

OneHotEncoder

A transformer that encodes categorical features in a one-hot numeric array.

OneHotEncoderMeta

A version of the ComponentBaseMeta class which includes validation on an additional one-hot-encoder-specific method categories.

Contents#

class evalml.pipelines.components.transformers.encoders.onehot_encoder.OneHotEncoder(top_n=10, features_to_encode=None, categories=None, drop='if_binary', handle_unknown='ignore', handle_missing='error', random_seed=0, **kwargs)[source]#

A transformer that encodes categorical features in a one-hot numeric array.

Parameters
  • top_n (int) – Number of categories per column to encode. If None, all categories will be encoded. Otherwise, the n most frequent will be encoded and all others will be dropped. Defaults to 10.

  • features_to_encode (list[str]) – List of columns to encode. All other columns will remain untouched. If None, all appropriate columns will be encoded. Defaults to None.

  • categories (list) – A two dimensional list of categories, where categories[i] is a list of the categories for the column at index i. This can also be None, or “auto” if top_n is not None. Defaults to None.

  • drop (string, list) – Method (“first” or “if_binary”) to use to drop one category per feature. Can also be a list specifying which categories to drop for each feature. Defaults to ‘if_binary’.

  • handle_unknown (string) – Whether to ignore or error for unknown categories for a feature encountered during fit or transform. If either top_n or categories is used to limit the number of categories per column, this must be “ignore”. Defaults to “ignore”.

  • handle_missing (string) – Options for how to handle missing (NaN) values encountered during fit or transform. If this is set to “as_category” and NaN values are within the n most frequent, “nan” values will be encoded as their own column. If this is set to “error”, any missing values encountered will raise an error. Defaults to “error”.

  • random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges

{}

modifies_features

True

modifies_target

False

name

One Hot Encoder

training_only

False

Methods

categories

Returns a list of the unique categories to be encoded for the particular feature, in order.

clone

Constructs a new component with the same parameters and random state.

default_parameters

Returns the default parameters for this component.

describe

Describe a component and its parameters.

fit

Fits the one-hot encoder component.

fit_transform

Fits on X and transforms X.

get_feature_names

Return feature names for the categorical features after fitting.

load

Loads component at file path.

needs_fitting

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

parameters

Returns the parameters which were used to initialize the component.

save

Saves component at file path.

transform

One-hot encode the input data.

categories(self, feature_name)[source]#

Returns a list of the unique categories to be encoded for the particular feature, in order.

Parameters

feature_name (str) – The name of any feature provided to one-hot encoder during fit.

Returns

The unique categories, in the same dtype as they were provided during fit.

Return type

np.ndarray

Raises

ValueError – If feature was not provided to one-hot encoder as a training feature.

clone(self)#

Constructs a new component with the same parameters and random state.

Returns

A new instance of this component with identical parameters and random state.

default_parameters(cls)#

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns

Default parameters for this component.

Return type

dict

describe(self, print_name=False, return_dict=False)#

Describe a component and its parameters.

Parameters
  • print_name (bool, optional) – whether to print name of component

  • return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

fit(self, X, y=None)[source]#

Fits the one-hot encoder component.

Parameters
  • X (pd.DataFrame) – The input training data of shape [n_samples, n_features].

  • y (pd.Series, optional) – The target training data of length [n_samples].

Returns

self

Raises

ValueError – If encoding a column failed.

fit_transform(self, X, y=None)#

Fits on X and transforms X.

Parameters
  • X (pd.DataFrame) – Data to fit and transform.

  • y (pd.Series) – Target data.

Returns

Transformed X.

Return type

pd.DataFrame

Raises

MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.

get_feature_names(self)[source]#

Return feature names for the categorical features after fitting.

Feature names are formatted as {column name}_{category name}. In the event of a duplicate name, an integer will be added at the end of the feature name to distinguish it.

For example, consider a dataframe with a column called “A” and category “x_y” and another column called “A_x” with “y”. In this example, the feature names would be “A_x_y” and “A_x_y_1”.

Returns

The feature names after encoding, provided in the same order as input_features.

Return type

np.ndarray

static load(file_path)#

Loads component at file path.

Parameters

file_path (str) – Location to load file.

Returns

ComponentBase object

needs_fitting(self)#

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

Returns

True.

property parameters(self)#

Returns the parameters which were used to initialize the component.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#

Saves component at file path.

Parameters
  • file_path (str) – Location to save file.

  • pickle_protocol (int) – The pickle data stream format.

transform(self, X, y=None)[source]#

One-hot encode the input data.

Parameters
  • X (pd.DataFrame) – Features to one-hot encode.

  • y (pd.Series) – Ignored.

Returns

Transformed data, where each categorical feature has been encoded into numerical columns using one-hot encoding.

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.encoders.onehot_encoder.OneHotEncoderMeta[source]#

A version of the ComponentBaseMeta class which includes validation on an additional one-hot-encoder-specific method categories.

Attributes

FIT_METHODS

[‘fit’, ‘fit_transform’]

METHODS_TO_CHECK

None

PROPERTIES_TO_CHECK

[‘feature_importance’]

Methods

check_for_fit

check_for_fit wraps a method that validates if self._is_fitted is True.

register

Register a virtual subclass of an ABC.

set_fit

Wrapper for the fit method.

classmethod check_for_fit(cls, method)#

check_for_fit wraps a method that validates if self._is_fitted is True.

It raises an exception if False and calls and returns the wrapped method if True.

Parameters

method (callable) – Method to wrap.

Returns

The wrapped method.

Raises

ComponentNotYetFittedError – If component is not yet fitted.

register(cls, subclass)#

Register a virtual subclass of an ABC.

Returns the subclass, to allow usage as a class decorator.

classmethod set_fit(cls, method)#

Wrapper for the fit method.