transformers

Subpackages

• dimensionality_reduction
  • lda
  • pca
• encoders
  • onehot_encoder
  • target_encoder
• feature_selection
  • feature_selector
  • rf_classifier_feature_selector
  • rf_regressor_feature_selector
• imputers
  • imputer
  • per_column_imputer
  • simple_imputer
  • target_imputer
• preprocessing
  • datetime_featurizer
  • delayed_feature_transformer
  • drop_null_columns
  • featuretools
  • log_transformer
  • lsa
  • polynomial_detrender
  • text_featurizer
  • text_transformer
  • transform_primitive_components
• samplers
  • base_sampler
  • oversamplers
  • undersampler
• scalers
  • standard_scaler

Submodules

• column_selectors
• transformer

Package Contents

Classes Summary

DateTimeFeaturizer	Transformer that can automatically extract features from datetime columns.
DelayedFeatureTransformer	Transformer that delays input features and target variable for time series problems.
DFSTransformer	Featuretools DFS component that generates features for the input features.
DropColumns	Drops specified columns in input data.
DropNullColumns	Transformer to drop features whose percentage of NaN values exceeds a specified threshold.
EmailFeaturizer	Transformer that can automatically extract features from emails.
FeatureSelector	Selects top features based on importance weights.
Imputer	Imputes missing data according to a specified imputation strategy.
LinearDiscriminantAnalysis	Reduces the number of features by using Linear Discriminant Analysis.
LogTransformer	Applies a log transformation to the target data.
LSA	Transformer to calculate the Latent Semantic Analysis Values of text input.
OneHotEncoder	A transformer that encodes categorical features in a one-hot numeric array.
PCA	Reduces the number of features by using Principal Component Analysis (PCA).
PerColumnImputer	Imputes missing data according to a specified imputation strategy per column.
PolynomialDetrender	Removes trends from time series by fitting a polynomial to the data.
RFClassifierSelectFromModel	Selects top features based on importance weights using a Random Forest classifier.
RFRegressorSelectFromModel	Selects top features based on importance weights using a Random Forest regressor.
SelectByType	Selects columns by specified Woodwork logical type or semantic tag in input data.
SelectColumns	Selects specified columns in input data.
SimpleImputer	Imputes missing data according to a specified imputation strategy.
SMOTENCSampler	SMOTENC Oversampler component. Uses SMOTENC to generate synthetic samples. Works on a mix of nomerical and categorical columns.
SMOTENSampler	SMOTEN Oversampler component. Uses SMOTEN to generate synthetic samples. Works for purely categorical datasets.
SMOTESampler	SMOTE Oversampler component. Works on numerical datasets only. This component is only run during training and not during predict.
StandardScaler	A transformer that standardizes input features by removing the mean and scaling to unit variance.
TargetEncoder	A transformer that encodes categorical features into target encodings.
TargetImputer	Imputes missing target data according to a specified imputation strategy.
TextFeaturizer	Transformer that can automatically featurize text columns using featuretools’ nlp_primitives.
Transformer	A component that may or may not need fitting that transforms data.
Undersampler	Initializes an undersampling transformer to downsample the majority classes in the dataset.
URLFeaturizer	Transformer that can automatically extract features from URL.

Contents

class evalml.pipelines.components.transformers.DateTimeFeaturizer(features_to_extract=None, encode_as_categories=False, date_index=None, random_seed=0, **kwargs)

Transformer that can automatically extract features from datetime columns.

Parameters

• features_to_extract (list) – List of features to extract. Valid options include “year”, “month”, “day_of_week”, “hour”. Defaults to None.

• encode_as_categories (bool) – Whether day-of-week and month features should be encoded as pandas “category” dtype. This allows OneHotEncoders to encode these features. Defaults to False.

• date_index (str) – Name of the column containing the datetime information used to order the data. Ignored.

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	DateTime Featurization Component

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 component to data
fit_transform	Fits on X and transforms X
get_feature_names	Gets the categories of each datetime feature.
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X by creating new features using existing DateTime columns, and then dropping those DateTime columns

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

get_feature_names(self)

Gets the categories of each datetime feature.

Returns

Dictionary, where each key-value pair is a column name and a dictionary mapping the unique feature values to their integer encoding.

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X by creating new features using existing DateTime columns, and then dropping those DateTime columns

Parameters

• X (pd.DataFrame) – Data to transform

• y (pd.Series, optional) – Ignored.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.DelayedFeatureTransformer(date_index=None, max_delay=2, delay_features=True, delay_target=True, gap=1, random_seed=0, **kwargs)

Transformer that delays input features and target variable for time series problems.

Parameters

• date_index (str) – Name of the column containing the datetime information used to order the data. Ignored.

• max_delay (int) – Maximum number of time units to delay each feature. Defaults to 2.

• delay_features (bool) – Whether to delay the input features. Defaults to True.

• delay_target (bool) – Whether to delay the target. Defaults to True.

• gap (int) – The number of time units between when the features are collected and when the target is collected. For example, if you are predicting the next time step’s target, gap=1. This is only needed because when gap=0, we need to be sure to start the lagging of the target variable at 1. Defaults to 1.

• random_seed (int) – Seed for the random number generator. This transformer performs the same regardless of the random seed provided.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Delayed Feature Transformer
needs_fitting	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 DelayFeatureTransformer.
fit_transform	Fits on X and transforms X
load	Loads component at file path
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Computes the delayed features for all features in X and y.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits the DelayFeatureTransformer.

Parameters

• X (pd.DataFrame or np.ndarray) – 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)

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

static load(file_path)

Loads component at file path

Parameters

file_path (str) – Location to load file

Returns

ComponentBase object

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.

Returns

None

transform(self, X, y=None)

Computes the delayed features for all features in X and y.

For each feature in X, it will add a column to the output dataframe for each delay in the (inclusive) range [1, max_delay]. The values of each delayed feature are simply the original feature shifted forward in time by the delay amount. For example, a delay of 3 units means that the feature value at row n will be taken from the n-3rd row of that feature

If y is not None, it will also compute the delayed values for the target variable.

Parameters

• X (pd.DataFrame or None) – Data to transform. None is expected when only the target variable is being used.

• y (pd.Series, or None) – Target.

Returns

Transformed X.

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.DFSTransformer(index='index', random_seed=0, **kwargs)

Featuretools DFS component that generates features for the input features.

Parameters

• index (string) – The name of the column that contains the indices. If no column with this name exists, then featuretools.EntitySet() creates a column with this name to serve as the index column. Defaults to ‘index’.

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	DFS Transformer

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 DFSTransformer Transformer component.
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Computes the feature matrix for the input X using featuretools’ dfs algorithm.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits the DFSTransformer Transformer component.

Parameters

• X (pd.DataFrame, np.array) – The input data to transform, of shape [n_samples, n_features]

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

Returns

self

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

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.

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.

Returns

None

transform(self, X, y=None)

Computes the feature matrix for the input X using featuretools’ dfs algorithm.

Parameters

• X (pd.DataFrame or np.ndarray) – The input training data to transform. Has shape [n_samples, n_features]

• y (pd.Series, optional) – Ignored.

Returns

Feature matrix

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.DropColumns(columns=None, random_seed=0, **kwargs)

Drops specified columns in input data.

Parameters

• columns (list(string)) – List of column names, used to determine which columns to drop.

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Drop Columns Transformer
needs_fitting	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 transformer by checking if column names are present in the dataset.
fit_transform	Fits on X and transforms X
load	Loads component at file path
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X by dropping columns.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits the transformer by checking if column names are present in the dataset.

Parameters

• X (pd.DataFrame) – Data to check.

• y (pd.Series, optional) – Targets.

Returns

self

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

static load(file_path)

Loads component at file path

Parameters

file_path (str) – Location to load file

Returns

ComponentBase object

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.

Returns

None

transform(self, X, y=None)

Transforms data X by dropping columns.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Targets.

Returns

Transformed X.

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.DropNullColumns(pct_null_threshold=1.0, random_seed=0, **kwargs)

Transformer to drop features whose percentage of NaN values exceeds a specified threshold.

Parameters

• pct_null_threshold (float) – The percentage of NaN values in an input feature to drop. Must be a value between [0, 1] inclusive. If equal to 0.0, will drop columns with any null values. If equal to 1.0, will drop columns with all null values. Defaults to 0.95.

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Drop Null Columns Transformer

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 component to data
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X by dropping columns that exceed the threshold of null values.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X by dropping columns that exceed the threshold of null values.

Parameters

• X (pd.DataFrame) – Data to transform

• y (pd.Series, optional) – Ignored.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.EmailFeaturizer(random_seed=0, **kwargs)

Transformer that can automatically extract features from emails.

Parameters

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Email Featurizer

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 component to data
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.FeatureSelector(parameters=None, component_obj=None, random_seed=0, **kwargs)

Selects top features based on importance weights.

Parameters

• parameters (dict) – Dictionary of parameters for the component. Defaults to None.

• component_obj (obj) – Third-party objects useful in component implementation. Defaults to None.

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

Attributes

model_family	ModelFamily.NONE
modifies_features	True
modifies_target	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 component to data
fit_transform	Fits on X and transforms X
get_names	Get names of selected features.
load	Loads component at file path
name	Returns string name of this component
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

get_names(self)

Get names of selected features.

Returns

List of the names of features selected

Return type

list[str]

static load(file_path)

Loads component at file path

Parameters

file_path (str) – Location to load file

Returns

ComponentBase object

property name(cls)

Returns string name of this component

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.

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.

Returns

None

transform(self, X, y=None)

Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data. Ignored.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.Imputer(categorical_impute_strategy='most_frequent', categorical_fill_value=None, numeric_impute_strategy='mean', numeric_fill_value=None, random_seed=0, **kwargs)

Imputes missing data according to a specified imputation strategy.

Parameters

• categorical_impute_strategy (string) – Impute strategy to use for string, object, boolean, categorical dtypes. Valid values include “most_frequent” and “constant”.

• numeric_impute_strategy (string) – Impute strategy to use for numeric columns. Valid values include “mean”, “median”, “most_frequent”, and “constant”.

• categorical_fill_value (string) – When categorical_impute_strategy == “constant”, fill_value is used to replace missing data. The default value of None will fill with the string “missing_value”.

• numeric_fill_value (int, float) – When numeric_impute_strategy == “constant”, fill_value is used to replace missing data. The default value of None will fill with 0.

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

Attributes

hyperparameter_ranges	{ “categorical_impute_strategy”: [“most_frequent”], “numeric_impute_strategy”: [“mean”, “median”, “most_frequent”],}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Imputer

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 imputer to data. ‘None’ values are converted to np.nan before imputation and are
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X by imputing missing values. ‘None’ values are converted to np.nan before imputation and are

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits imputer to data. ‘None’ values are converted to np.nan before imputation and are

treated as the same.

Parameters

• X (pd.DataFrame, np.ndarray) – 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=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

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X by imputing missing values. ‘None’ values are converted to np.nan before imputation and are

treated as the same.

Parameters

• X (pd.DataFrame) – Data to transform

• y (pd.Series, optional) – Ignored.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.LinearDiscriminantAnalysis(n_components=None, random_seed=0, **kwargs)

Reduces the number of features by using Linear Discriminant Analysis.

Parameters

• n_components (int) – The number of features to maintain after computation. Defaults to None.

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Linear Discriminant Analysis Transformer

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 component to data
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.LogTransformer(random_seed=0)

Applies a log transformation to the target data.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	False
modifies_target	True
name	Log Transformer

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 LogTransformer.
fit_transform	Log transforms the target variable.
inverse_transform	Inverts the transformation done by the transform method.
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Log transforms the target variable.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits the LogTransformer.

Parameters

• X (pd.DataFrame or np.ndarray) – Ignored.

• y (pd.Series, optional) – Ignored.

Returns

self

fit_transform(self, X, y=None)

Log transforms the target variable.

Parameters

• X (pd.DataFrame, optional) – Ignored.

• y (pd.Series) – Target variable to log transform.

Returns

The input features are returned without modification. The target

variable y is log transformed.

Return type

tuple of pd.DataFrame, pd.Series

inverse_transform(self, y)

Inverts the transformation done by the transform method.

Arguments:

y (pd.Series): Target transformed by this component.

Returns

Target without the transformation.

Return type

pd.Seriesø

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.

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.

Returns

None

transform(self, X, y=None)

Log transforms the target variable.

Parameters

• X (pd.DataFrame, optional) – Ignored.

• y (pd.Series) – Target data to log transform.

Returns

The input features are returned without modification. The target

variable y is log transformed.

Return type

tuple of pd.DataFrame, pd.Series

class evalml.pipelines.components.transformers.LSA(random_seed=0, **kwargs)

Transformer to calculate the Latent Semantic Analysis Values of text input.

Parameters

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	LSA Transformer

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 component to data
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X by applying the LSA pipeline.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X by applying the LSA pipeline.

Parameters

• X (pd.DataFrame) – The data to transform.

• y (pd.Series, optional) – Ignored.

Returns

Transformed X. The original column is removed and replaced with two columns of the

format LSA(original_column_name)[feature_number], where feature_number is 0 or 1.

Return type

pd.DataFrame

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

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	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	One Hot Encoder

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 component to data
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
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)

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

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

get_feature_names(self)

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.

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.

Returns

None

transform(self, X, y=None)

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.PCA(variance=0.95, n_components=None, random_seed=0, **kwargs)

Reduces the number of features by using Principal Component Analysis (PCA).

Parameters

• variance (float) – The percentage of the original data variance that should be preserved when reducing the number of features. Defaults to 0.95.

• n_components (int) – The number of features to maintain after computing SVD. Defaults to None, but will override variance variable if set.

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

Attributes

hyperparameter_ranges	Real(0.25, 1)}:type: {“variance”
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	PCA Transformer

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 component to data
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.PerColumnImputer(impute_strategies=None, default_impute_strategy='most_frequent', random_seed=0, **kwargs)

Imputes missing data according to a specified imputation strategy per column.

Parameters

• impute_strategies (dict) – Column and {“impute_strategy”: strategy, “fill_value”:value} pairings. Valid values for impute strategy include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types. Defaults to None, which uses “most_frequent” for all columns. When impute_strategy == “constant”, fill_value is used to replace missing data. When None, uses 0 when imputing numerical data and “missing_value” for strings or object data types.

• default_impute_strategy (str) – Impute strategy to fall back on when none is provided for a certain column. Valid values include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types. Defaults to “most_frequent”.

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Per Column Imputer

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 imputers on input data
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms input data by imputing missing values.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits imputers on input data

Parameters

• X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features] to fit.

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

Returns

self

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

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.

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.

Returns

None

transform(self, X, y=None)

Transforms input data by imputing missing values.

Parameters

• X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features] to transform.

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

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.PolynomialDetrender(degree=1, random_seed=0, **kwargs)

Removes trends from time series by fitting a polynomial to the data.

Parameters

• degree (int) – Degree for the polynomial. If 1, linear model is fit to the data. If 2, quadratic model is fit, etc. Defaults to 1.

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

Attributes

hyperparameter_ranges	{ “degree”: Integer(1, 3)}
model_family	ModelFamily.NONE
modifies_features	False
modifies_target	True
name	Polynomial Detrender

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 PolynomialDetrender.
fit_transform	Removes fitted trend from target variable.
inverse_transform	Adds back fitted trend to target variable.
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Removes fitted trend from target variable.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits the PolynomialDetrender.

Parameters

• X (pd.DataFrame, optional) – Ignored.

• y (pd.Series) – Target variable to detrend.

Returns

self

fit_transform(self, X, y=None)

Removes fitted trend from target variable.

Parameters

• X (pd.DataFrame, optional) – Ignored.

• y (pd.Series) – Target variable to detrend.

Returns

The first element are the input features returned without modification.

The second element is the target variable y with the fitted trend removed.

Return type

tuple of pd.DataFrame, pd.Series

inverse_transform(self, y)

Adds back fitted trend to target variable.

Parameters

• X (pd.DataFrame, optional) – Ignored.

• y (pd.Series) – Target variable.

Returns

The first element are the input features returned without modification.

The second element is the target variable y with the trend added back.

Return type

tuple of pd.DataFrame, pd.Series

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.

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.

Returns

None

transform(self, X, y=None)

Removes fitted trend from target variable.

Parameters

• X (pd.DataFrame, optional) – Ignored.

• y (pd.Series) – Target variable to detrend.

Returns

The input features are returned without modification. The target

variable y is detrended

Return type

tuple of pd.DataFrame, pd.Series

class evalml.pipelines.components.transformers.RFClassifierSelectFromModel(number_features=None, n_estimators=10, max_depth=None, percent_features=0.5, threshold=- np.inf, n_jobs=- 1, random_seed=0, **kwargs)

Selects top features based on importance weights using a Random Forest classifier.

Parameters

• number_features (int) – The maximum number of features to select. If both percent_features and number_features are specified, take the greater number of features. Defaults to 0.5. Defaults to None.

• n_estimators (float) – The number of trees in the forest. Defaults to 100.

• max_depth (int) – Maximum tree depth for base learners. Defaults to 6.

• percent_features (float) – Percentage of features to use. If both percent_features and number_features are specified, take the greater number of features. Defaults to 0.5.

• threshold (string or float) – The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median”, then the threshold value is the median of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. Defaults to -np.inf.

• n_jobs (int or None) – Number of jobs to run in parallel. -1 uses all processes. Defaults to -1.

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

Attributes

hyperparameter_ranges	{ “percent_features”: Real(0.01, 1), “threshold”: [“mean”, -np.inf],}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	RF Classifier Select From Model

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 component to data
fit_transform	Fits on X and transforms X
get_names	Get names of selected features.
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

get_names(self)

Get names of selected features.

Returns

List of the names of features selected

Return type

list[str]

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.

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.

Returns

None

transform(self, X, y=None)

Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data. Ignored.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.RFRegressorSelectFromModel(number_features=None, n_estimators=10, max_depth=None, percent_features=0.5, threshold=- np.inf, n_jobs=- 1, random_seed=0, **kwargs)

Selects top features based on importance weights using a Random Forest regressor.

Parameters

• number_features (int) – The maximum number of features to select. If both percent_features and number_features are specified, take the greater number of features. Defaults to 0.5. Defaults to None.

• n_estimators (float) – The number of trees in the forest. Defaults to 100.

• max_depth (int) – Maximum tree depth for base learners. Defaults to 6.

• percent_features (float) – Percentage of features to use. If both percent_features and number_features are specified, take the greater number of features. Defaults to 0.5.

• threshold (string or float) – The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median”, then the threshold value is the median of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. Defaults to -np.inf.

• n_jobs (int or None) – Number of jobs to run in parallel. -1 uses all processes. Defaults to -1.

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

Attributes

hyperparameter_ranges	{ “percent_features”: Real(0.01, 1), “threshold”: [“mean”, -np.inf],}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	RF Regressor Select From Model

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 component to data
fit_transform	Fits on X and transforms X
get_names	Get names of selected features.
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

get_names(self)

Get names of selected features.

Returns

List of the names of features selected

Return type

list[str]

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.

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.

Returns

None

transform(self, X, y=None)

Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data. Ignored.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.SelectByType(column_types=None, random_seed=0, **kwargs)

Selects columns by specified Woodwork logical type or semantic tag in input data.

Parameters

• column_types (string, ww.LogicalType, list(string), list(ww.LogicalType)) – List of Woodwork types or tags, used to determine which columns to select.

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Select Columns By Type Transformer
needs_fitting	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 transformer by checking if column names are present in the dataset.
fit_transform	Fits on X and transforms X
load	Loads component at file path
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits the transformer by checking if column names are present in the dataset.

Parameters

• X (pd.DataFrame) – Data to check.

• y (pd.Series, optional) – Targets.

Returns

self

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

static load(file_path)

Loads component at file path

Parameters

file_path (str) – Location to load file

Returns

ComponentBase object

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.

Returns

None

transform(self, X, y=None)

Transforms data X.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.SelectColumns(columns=None, random_seed=0, **kwargs)

Selects specified columns in input data.

Parameters

• columns (list(string)) – List of column names, used to determine which columns to select.

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Select Columns Transformer
needs_fitting	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 transformer by checking if column names are present in the dataset.
fit_transform	Fits on X and transforms X
load	Loads component at file path
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X by selecting columns.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits the transformer by checking if column names are present in the dataset.

Parameters

• X (pd.DataFrame) – Data to check.

• y (pd.Series, optional) – Targets.

Returns

self

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

static load(file_path)

Loads component at file path

Parameters

file_path (str) – Location to load file

Returns

ComponentBase object

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.

Returns

None

transform(self, X, y=None)

Transforms data X by selecting columns.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Targets.

Returns

Transformed X.

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.SimpleImputer(impute_strategy='most_frequent', fill_value=None, random_seed=0, **kwargs)

Imputes missing data according to a specified imputation strategy.

Parameters

• impute_strategy (string) – Impute strategy to use. Valid values include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types.

• fill_value (string) – When impute_strategy == “constant”, fill_value is used to replace missing data. Defaults to 0 when imputing numerical data and “missing_value” for strings or object data types.

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

Attributes

hyperparameter_ranges	{ “impute_strategy”: [“mean”, “median”, “most_frequent”]}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Simple Imputer

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 imputer to data. ‘None’ values are converted to np.nan before imputation and are
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms input by imputing missing values. ‘None’ and np.nan values are treated as the same.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits imputer to data. ‘None’ values are converted to np.nan before imputation and are

treated as the same.

Parameters

• X (pd.DataFrame or np.ndarray) – 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=None)

Fits on X and transforms X

Parameters

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

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

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.

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.

Returns

None

transform(self, X, y=None)

Transforms input by imputing missing values. ‘None’ and np.nan values are treated as the same.

Parameters

• X (pd.DataFrame) – Data to transform

• y (pd.Series, optional) – Ignored.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.SMOTENCSampler(sampling_ratio=0.25, k_neighbors_default=5, n_jobs=- 1, random_seed=0, **kwargs)

SMOTENC Oversampler component. Uses SMOTENC to generate synthetic samples. Works on a mix of nomerical and categorical columns. Input data must be Woodwork type, and this component is only run during training and not during predict.

Parameters

• sampling_ratio (float) – This is the goal ratio of the minority to majority class, with range (0, 1]. A value of 0.25 means we want a 1:4 ratio of the minority to majority class after oversampling. We will create the a sampling dictionary using this ratio, with the keys corresponding to the class and the values responding to the number of samples. Defaults to 0.25.

• k_neighbors_default (int) – The number of nearest neighbors used to construct synthetic samples. This is the default value used, but the actual k_neighbors value might be smaller if there are less samples. Defaults to 5.

• n_jobs (int) – The number of CPU cores to use. Defaults to -1.

• random_seed (int) – The seed to use for random sampling. Defaults to 0.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	True
name	SMOTENC Oversampler

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 Oversampler to the data.
fit_transform	Fit and transform the data using the data sampler. Used during training of the pipeline
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	No transformation needs to be done here.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y)

Fits the Oversampler to the data.

Parameters

• X (pd.DataFrame) – Training features

• y (pd.Series) – Target features

Returns

self

fit_transform(self, X, y)

Fit and transform the data using the data sampler. Used during training of the pipeline

Parameters

• X (pd.DataFrame) – Training features

• y – Target features

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.

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.

Returns

None

transform(self, X, y=None)

No transformation needs to be done here.

Parameters

• X (pd.DataFrame) – Training features. Ignored.

• y (pd.Series) – Target features. Ignored.

Returns

X and y data that was passed in.

Return type

pd.DataFrame, pd.Series

class evalml.pipelines.components.transformers.SMOTENSampler(sampling_ratio=0.25, k_neighbors_default=5, n_jobs=- 1, random_seed=0, **kwargs)

SMOTEN Oversampler component. Uses SMOTEN to generate synthetic samples. Works for purely categorical datasets. This component is only run during training and not during predict.

Parameters

• sampling_ratio (float) – This is the goal ratio of the minority to majority class, with range (0, 1]. A value of 0.25 means we want a 1:4 ratio of the minority to majority class after oversampling. We will create the a sampling dictionary using this ratio, with the keys corresponding to the class and the values responding to the number of samples. Defaults to 0.25.

• k_neighbors_default (int) – The number of nearest neighbors used to construct synthetic samples. This is the default value used, but the actual k_neighbors value might be smaller if there are less samples. Defaults to 5.

• n_jobs (int) – The number of CPU cores to use. Defaults to -1.

• random_seed (int) – The seed to use for random sampling. Defaults to 0.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	True
name	SMOTEN Oversampler

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 Oversampler to the data.
fit_transform	Fit and transform the data using the data sampler. Used during training of the pipeline
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	No transformation needs to be done here.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y)

Fits the Oversampler to the data.

Parameters

• X (pd.DataFrame) – Training features

• y (pd.Series) – Target features

Returns

self

fit_transform(self, X, y)

Fit and transform the data using the data sampler. Used during training of the pipeline

Parameters

• X (pd.DataFrame) – Training features

• y – Target features

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.

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.

Returns

None

transform(self, X, y=None)

No transformation needs to be done here.

Parameters

• X (pd.DataFrame) – Training features. Ignored.

• y (pd.Series) – Target features. Ignored.

Returns

X and y data that was passed in.

Return type

pd.DataFrame, pd.Series

class evalml.pipelines.components.transformers.SMOTESampler(sampling_ratio=0.25, k_neighbors_default=5, n_jobs=- 1, random_seed=0, **kwargs)

SMOTE Oversampler component. Works on numerical datasets only. This component is only run during training and not during predict.

Parameters

• sampling_ratio (float) – This is the goal ratio of the minority to majority class, with range (0, 1]. A value of 0.25 means we want a 1:4 ratio of the minority to majority class after oversampling. We will create the a sampling dictionary using this ratio, with the keys corresponding to the class and the values responding to the number of samples. Defaults to 0.25.

• k_neighbors_default (int) – The number of nearest neighbors used to construct synthetic samples. This is the default value used, but the actual k_neighbors value might be smaller if there are less samples. Defaults to 5.

• n_jobs (int) – The number of CPU cores to use. Defaults to -1.

• random_seed (int) – The seed to use for random sampling. Defaults to 0.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	True
name	SMOTE Oversampler

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 Oversampler to the data.
fit_transform	Fit and transform the data using the data sampler. Used during training of the pipeline
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	No transformation needs to be done here.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y)

Fits the Oversampler to the data.

Parameters

• X (pd.DataFrame) – Training features

• y (pd.Series) – Target features

Returns

self

fit_transform(self, X, y)

Fit and transform the data using the data sampler. Used during training of the pipeline

Parameters

• X (pd.DataFrame) – Training features

• y – Target features

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.

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.

Returns

None

transform(self, X, y=None)

No transformation needs to be done here.

Parameters

• X (pd.DataFrame) – Training features. Ignored.

• y (pd.Series) – Target features. Ignored.

Returns

X and y data that was passed in.

Return type

pd.DataFrame, pd.Series

class evalml.pipelines.components.transformers.StandardScaler(random_seed=0, **kwargs)

A transformer that standardizes input features by removing the mean and scaling to unit variance.

Parameters

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Standard Scaler

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 component to data
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.TargetEncoder(cols=None, smoothing=1.0, handle_unknown='value', handle_missing='value', random_seed=0, **kwargs)

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	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Target Encoder

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 component to data
fit_transform	Fits on X and transforms X
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
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

fit_transform(self, X, y)

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

get_feature_names(self)

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.TargetImputer(impute_strategy='most_frequent', fill_value=None, random_seed=0, **kwargs)

Imputes missing target data according to a specified imputation strategy.

Parameters

• impute_strategy (string) – Impute strategy to use. Valid values include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types. Defaults to “most_frequent”.

• fill_value (string) – When impute_strategy == “constant”, fill_value is used to replace missing data. Defaults to None which uses 0 when imputing numerical data and “missing_value” for strings or object data types.

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

Attributes

hyperparameter_ranges	{ “impute_strategy”: [“mean”, “median”, “most_frequent”]}
model_family	ModelFamily.NONE
modifies_features	False
modifies_target	True
name	Target Imputer

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 imputer to target data. ‘None’ values are converted to np.nan before imputation and are
fit_transform	Fits on and transforms the input target data.
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms input target data by imputing missing values. ‘None’ and np.nan values are treated as the same.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y)

Fits imputer to target data. ‘None’ values are converted to np.nan before imputation and are

treated as the same.

Parameters

• X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]. Ignored.

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

Returns

self

fit_transform(self, X, y)

Fits on and transforms the input target data.

Parameters

• X (pd.DataFrame) – Features. Ignored.

• y (pd.Series) – Target data to impute.

Returns

The original X, transformed y

Return type

(pd.DataFrame, pd.Series)

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.

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.

Returns

None

transform(self, X, y)

Transforms input target data by imputing missing values. ‘None’ and np.nan values are treated as the same.

Parameters

• X (pd.DataFrame) – Features. Ignored.

• y (pd.Series) – Target data to impute.

Returns

The original X, transformed y

Return type

(pd.DataFrame, pd.Series)

class evalml.pipelines.components.transformers.TextFeaturizer(random_seed=0, **kwargs)

Transformer that can automatically featurize text columns using featuretools’ nlp_primitives.

Since models cannot handle non-numeric data, any text must be broken down into features that provide useful information about that text. This component splits each text column into several informative features: Diversity Score, Mean Characters per Word, Polarity Score, and LSA (Latent Semantic Analysis). Calling transform on this component will replace any text columns in the given dataset with these numeric columns.

Parameters

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	Text Featurization Component

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 component to data
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X by creating new features using existing text columns

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

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

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

Returns

self

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

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X by creating new features using existing text columns

Parameters

• X (pd.DataFrame) – The data to transform.

• y (pd.Series, optional) – Ignored.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.Transformer(parameters=None, component_obj=None, random_seed=0, **kwargs)

A component that may or may not need fitting that transforms data. These components are used before an estimator.

To implement a new Transformer, define your own class which is a subclass of Transformer, including a name and a list of acceptable ranges for any parameters to be tuned during the automl search (hyperparameters). Define an __init__ method which sets up any necessary state and objects. Make sure your __init__ only uses standard keyword arguments and calls super().__init__() with a parameters dict. You may also override the fit, transform, fit_transform and other methods in this class if appropriate.

To see some examples, check out the definitions of any Transformer component.

Parameters

• parameters (dict) – Dictionary of parameters for the component. Defaults to None.

• component_obj (obj) – Third-party objects useful in component implementation. Defaults to None.

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

Attributes

model_family	ModelFamily.NONE
modifies_features	True
modifies_target	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 component to data
fit_transform	Fits on X and transforms X
load	Loads component at file path
name	Returns string name of this component
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

static load(file_path)

Loads component at file path

Parameters

file_path (str) – Location to load file

Returns

ComponentBase object

property name(cls)

Returns string name of this component

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.Undersampler(sampling_ratio=0.25, sampling_ratio_dict=None, min_samples=100, min_percentage=0.1, random_seed=0, **kwargs)

Initializes an undersampling transformer to downsample the majority classes in the dataset.

This component is only run during training and not during predict.

Parameters

• sampling_ratio (float) – The smallest minority:majority ratio that is accepted as ‘balanced’. For instance, a 1:4 ratio would be represented as 0.25, while a 1:1 ratio is 1.0. Must be between 0 and 1, inclusive. Defaults to 0.25.

• sampling_ratio_dict (dict) – A dictionary specifying the desired balanced ratio for each target value. For instance, in a binary case where class 1 is the minority, we could specify: sampling_ratio_dict={0: 0.5, 1: 1}, which means we would undersample class 0 to have twice the number of samples as class 1 (minority:majority ratio = 0.5), and don’t sample class 1. Overrides sampling_ratio if provided. Defaults to None.

• min_samples (int) – The minimum number of samples that we must have for any class, pre or post sampling. If a class must be downsampled, it will not be downsampled past this value. To determine severe imbalance, the minority class must occur less often than this and must have a class ratio below min_percentage. Must be greater than 0. Defaults to 100.

• min_percentage (float) – The minimum percentage of the minimum class to total dataset that we tolerate, as long as it is above min_samples. If min_percentage and min_samples are not met, treat this as severely imbalanced, and we will not resample the data. Must be between 0 and 0.5, inclusive. Defaults to 0.1.

• random_seed (int) – The seed to use for random sampling. Defaults to 0.

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	True
name	Undersampler

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	Resample the data using the sampler. Since our sampler doesn’t need to be fit, we do nothing here.
fit_transform	Fit and transform the data using the undersampler. Used during training of the pipeline
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	No transformation needs to be done here.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y)

Resample the data using the sampler. Since our sampler doesn’t need to be fit, we do nothing here.

Parameters

• X (pd.DataFrame) – Training features

• y (pd.Series) – Target features

Returns

self

fit_transform(self, X, y)

Fit and transform the data using the undersampler. Used during training of the pipeline

Parameters

• X (pd.DataFrame) – Training features

• y – Target features

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.

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.

Returns

None

transform(self, X, y=None)

No transformation needs to be done here.

Parameters

• X (pd.DataFrame) – Training features. Ignored.

• y (pd.Series) – Target features. Ignored.

Returns

X and y data that was passed in.

Return type

pd.DataFrame, pd.Series

class evalml.pipelines.components.transformers.URLFeaturizer(random_seed=0, **kwargs)

Transformer that can automatically extract features from URL.

Parameters

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

Attributes

hyperparameter_ranges	{}
model_family	ModelFamily.NONE
modifies_features	True
modifies_target	False
name	URL Featurizer

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 component to data
fit_transform	Fits on X and transforms X
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
parameters	Returns the parameters which were used to initialize the component
save	Saves component at file path
transform	Transforms data X.

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

prints and returns dictionary

Return type

None or dict

fit(self, X, y=None)

Fits component to data

Parameters

• X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]

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

Returns

self

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

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.

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.

Returns

None

transform(self, X, y=None)

Transforms data X.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

estimator dimensionality_reduction