preprocessing¶

Submodules¶

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.
`DropNullColumns`	Transformer to drop features whose percentage of NaN values exceeds a specified threshold.
`LSA`	Transformer to calculate the Latent Semantic Analysis Values of text input.
`PolynomialDetrender`	Removes trends from time series by fitting a polynomial to the data.
`TextFeaturizer`	Transformer that can automatically featurize text columns using featuretools’ nlp_primitives.
`TextTransformer`	Base class for all transformers working with text features.

Contents¶

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

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)[source]¶

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)[source]¶

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)[source]¶

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.preprocessing.DelayedFeatureTransformer(date_index=None, max_delay=2, delay_features=True, delay_target=True, gap=1, random_seed=0, **kwargs)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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.preprocessing.DFSTransformer(index='index', random_seed=0, **kwargs)[source]¶

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)[source]¶

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)[source]¶

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.preprocessing.DropNullColumns(pct_null_threshold=1.0, random_seed=0, **kwargs)[source]¶

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)[source]¶

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)[source]¶

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.preprocessing.LSA(random_seed=0, **kwargs)[source]¶

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)[source]¶

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)[source]¶

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.preprocessing.PolynomialDetrender(degree=1, random_seed=0, **kwargs)[source]¶

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)[source]¶

Fits the PolynomialDetrender.

Parameters

X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target variable to detrend.

Returns

self

fit_transform(self, X, y=None)[source]¶

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)[source]¶

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)[source]¶

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.preprocessing.TextFeaturizer(random_seed=0, **kwargs)[source]¶

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

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)[source]¶

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)[source]¶

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.preprocessing.TextTransformer(component_obj=None, random_seed=0, **kwargs)[source]¶

Base class for all transformers working with text features.

Parameters

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

target_imputer datetime_featurizer