column_selectors¶

Initalizes an transformer that selects specified columns in input data.

Module Contents¶

Classes Summary¶

`ColumnSelector`	Initalizes an transformer that selects specified columns in input data.
`DropColumns`	Drops specified columns in input data.
`SelectByType`	Selects columns by specified Woodwork logical type or semantic tag in input data.
`SelectColumns`	Selects specified columns in input data.

Contents¶

class evalml.pipelines.components.transformers.column_selectors.ColumnSelector(columns=None, random_seed=0, **kwargs)[source]¶

Initalizes an transformer that 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

modifies_features	True
modifies_target	False
training_only	False

Methods

`clone`	Constructs a new component with the same parameters and random state.
`default_parameters`	Returns the default parameters for this component.
`describe`	Describe a component and its parameters.
`fit`	Fits the 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.
`name`	Returns string name of this component.
`needs_fitting`	Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
`parameters`	Returns the parameters which were used to initialize the component.
`save`	Saves component at file path.
`transform`	Transform data using fitted column selector component.

clone(self)¶

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

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

default_parameters(cls)¶

Returns the default parameters for this component.

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

Returns: Default parameters for this component.
Return type: dict

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

Describe a component and its parameters.

Parameters

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

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

fit(self, X, y=None)[source]¶

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

Raises

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

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.

Returns: True.

property parameters(self)¶: Returns the parameters which were used to initialize the component.

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

Saves component at file path.

Parameters

file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.

transform(self, X, y=None)[source]¶

Transform data using fitted column selector component.

Parameters

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

Returns

Transformed data.

Return type

pd.DataFrame

class evalml.pipelines.components.transformers.column_selectors.DropColumns(columns=None, random_seed=0, **kwargs)[source]¶

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	{}
modifies_features	True
modifies_target	False
name	Drop Columns Transformer
needs_fitting	False
training_only	False

Methods

`clone`	Constructs a new component with the same parameters and random state.
`default_parameters`	Returns the default parameters for this component.
`describe`	Describe a component and its parameters.
`fit`	Fits the 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

Returns dictionary if return_dict is True, else None.

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

Raises

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

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.

transform(self, X, y=None)[source]¶

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.column_selectors.SelectByType(column_types=None, random_seed=0, **kwargs)[source]¶

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	{}
modifies_features	True
modifies_target	False
name	Select Columns By Type Transformer
needs_fitting	False
training_only	False

Methods

`clone`	Constructs a new component with the same parameters and random state.
`default_parameters`	Returns the default parameters for this component.
`describe`	Describe a component and its parameters.
`fit`	Fits the 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

Returns dictionary if return_dict is True, else None.

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

Raises

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

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.

transform(self, X, y=None)[source]¶

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.column_selectors.SelectColumns(columns=None, random_seed=0, **kwargs)[source]¶

Selects specified columns in input data.

Parameters

columns (list(string)) – List of column names, used to determine which columns to select. If columns are not present, they will not be selected.
random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges	{}
modifies_features	True
modifies_target	False
name	Select Columns Transformer
needs_fitting	False
training_only	False

Methods

`clone`	Constructs a new component with the same parameters and random state.
`default_parameters`	Returns the default parameters for this component.
`describe`	Describe a component and its parameters.
`fit`	Fits the 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`	Transform data using fitted column selector component.

clone(self)¶

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

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

default_parameters(cls)¶

Returns the default parameters for this component.

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

Returns: Default parameters for this component.
Return type: dict

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

Describe a component and its parameters.

Parameters

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

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

fit(self, X, y=None)[source]¶

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

Raises

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

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.

transform(self, X, y=None)¶

Transform data using fitted column selector component.

Parameters

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

Returns

Transformed data.

Return type

pd.DataFrame

standard_scaler

transformer