encoders#

Components used to encode the input data.

Submodules#

Package Contents#

Classes Summary#

`LabelEncoder`	A transformer that encodes target labels using values between 0 and num_classes - 1.
`OneHotEncoder`	A transformer that encodes categorical features in a one-hot numeric array.
`OrdinalEncoder`	A transformer that encodes ordinal features as an array of ordinal integers representing the relative order of categories.
`TargetEncoder`	A transformer that encodes categorical features into target encodings.

Contents#

class evalml.pipelines.components.transformers.encoders.LabelEncoder(positive_label=None, random_seed=0, **kwargs)[source]#

A transformer that encodes target labels using values between 0 and num_classes - 1.

Parameters

positive_label (int, str) – The label for the class that should be treated as positive (1) for binary classification problems. Ignored for multiclass problems. Defaults to None.
random_seed (int) – Seed for the random number generator. Defaults to 0. Ignored.

Attributes

hyperparameter_ranges	{}
modifies_features	False
modifies_target	True
name	Label Encoder
training_only	False

Methods

`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 label encoder.
`fit_transform`	Fit and transform data using the label encoder.
`inverse_transform`	Decodes the target data.
`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`	Transform the target using the fitted label encoder.
`update_parameters`	Updates the parameter dictionary of the component.

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)[source]#

Fits the label encoder.

Parameters

X (pd.DataFrame) – The input training data of shape [n_samples, n_features]. Ignored.
y (pd.Series) – The target training data of length [n_samples].

Returns

self

Raises

ValueError – If input y is None.

fit_transform(self, X, y)[source]#

Fit and transform data using the label encoder.

Parameters

X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples].

Returns

The original features and an encoded version of the target.

Return type

pd.DataFrame, pd.Series

inverse_transform(self, y)[source]#

Decodes the target data.

Parameters: y (pd.Series) – Target data.
Returns: The decoded version of the target.
Return type: pd.Series
Raises: ValueError – If input y is None.

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]#

Transform the target using the fitted label encoder.

Parameters

X (pd.DataFrame) – The input training data of shape [n_samples, n_features]. Ignored.
y (pd.Series) – The target training data of length [n_samples].

Returns

The original features and an encoded version of the target.

Return type

pd.DataFrame, pd.Series

Raises

ValueError – If input y is None.

update_parameters(self, update_dict, reset_fit=True)#

Updates the parameter dictionary of the component.

Parameters

update_dict (dict) – A dict of parameters to update.
reset_fit (bool, optional) – If True, will set _is_fitted to False.

class evalml.pipelines.components.transformers.encoders.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.
`update_parameters`	Updates the parameter dictionary of the component.

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

update_parameters(self, update_dict, reset_fit=True)#

Updates the parameter dictionary of the component.

Parameters

update_dict (dict) – A dict of parameters to update.
reset_fit (bool, optional) – If True, will set _is_fitted to False.

class evalml.pipelines.components.transformers.encoders.OrdinalEncoder(features_to_encode=None, categories=None, handle_unknown='error', unknown_value=None, encoded_missing_value=None, random_seed=0, **kwargs)[source]#

A transformer that encodes ordinal features as an array of ordinal integers representing the relative order of categories.

Parameters

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. The order of columns does not matter.
categories (dict[str, list[str]]) – A dictionary mapping column names to their categories in the dataframes passed in at fit and transform. The order of categories specified for a column does not matter. Any category found in the data that is not present in categories will be handled as an unknown value. To not have unknown values raise an error, set handle_unknown to “use_encoded_value”. Defaults to None.
handle_unknown ("error" or "use_encoded_value") – Whether to ignore or error for unknown categories for a feature encountered during fit or transform. When set to “error”, an error will be raised when an unknown category is found. When set to “use_encoded_value”, unknown categories will be encoded as the value given for the parameter unknown_value. Defaults to “error.”
unknown_value (int or np.nan) – The value to use for unknown categories seen during fit or transform. Required when the parameter handle_unknown is set to “use_encoded_value.” The value has to be distinct from the values used to encode any of the categories in fit. Defaults to None.
encoded_missing_value (int or np.nan) – The value to use for missing (null) values seen during fit or transform. Defaults to np.nan.
random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges	{}
modifies_features	True
modifies_target	False
name	Ordinal 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 ordinal encoder component.
`fit_transform`	Fits on X and transforms X.
`get_feature_names`	Return feature names for the ordinal 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`	Ordinally encode the input data.
`update_parameters`	Updates the parameter dictionary of the component.

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 ordinal 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 ordinal 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 ordinal 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.
TypeError – If non-Ordinal columns are specified in features_to_encode.

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 ordinal features after fitting.

Feature names are formatted as {column name}_ordinal_encoding.

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]#

Ordinally encode the input data.

Parameters

X (pd.DataFrame) – Features to encode.
y (pd.Series) – Ignored.

Returns

Transformed data, where each ordinal feature has been encoded into a numerical column where ordinal integers represent the relative order of categories.

Return type

pd.DataFrame

update_parameters(self, update_dict, reset_fit=True)#

Updates the parameter dictionary of the component.

Parameters

update_dict (dict) – A dict of parameters to update.
reset_fit (bool, optional) – If True, will set _is_fitted to False.

class evalml.pipelines.components.transformers.encoders.TargetEncoder(cols=None, smoothing=1, handle_unknown='value', handle_missing='value', random_seed=0, **kwargs)[source]#

A transformer that encodes categorical features into target encodings.

Parameters

cols (list) – Columns to encode. If None, all string columns will be encoded, otherwise only the columns provided will be encoded. Defaults to None
smoothing (float) – The smoothing factor to apply. The larger this value is, the more influence the expected target value has on the resulting target encodings. Must be strictly larger than 0. Defaults to 1.0
handle_unknown (string) – Determines how to handle unknown categories for a feature encountered. Options are ‘value’, ‘error’, nd ‘return_nan’. Defaults to ‘value’, which replaces with the target mean
handle_missing (string) – Determines how to handle missing values encountered during fit or transform. Options are ‘value’, ‘error’, and ‘return_nan’. Defaults to ‘value’, which replaces with the target mean
random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges	{}
modifies_features	True
modifies_target	False
name	Target Encoder
training_only	False

Methods

`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 target encoder.
`fit_transform`	Fit and transform data using the target encoder.
`get_feature_names`	Return feature names for the input 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`	Transform data using the fitted target encoder.
`update_parameters`	Updates the parameter dictionary of the component.

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)[source]#

Fits the target encoder.

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

fit_transform(self, X, y)[source]#

Fit and transform data using the target encoder.

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

Transformed data.

Return type

pd.DataFrame

get_feature_names(self)[source]#

Return feature names for the input features after fitting.

Returns: The feature names after encoding.
Return type: np.array

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]#

Transform data using the fitted target encoder.

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

Transformed data.

Return type

pd.DataFrame

update_parameters(self, update_dict, reset_fit=True)#

Updates the parameter dictionary of the component.

Parameters

update_dict (dict) – A dict of parameters to update.
reset_fit (bool, optional) – If True, will set _is_fitted to False.