tubular.nominal.BaseNominalTransformer

class tubular.nominal.BaseNominalTransformer(columns=None, copy=True, verbose=False)

Bases: tubular.base.BaseTransformer

Base Transformer extension for nominal transformers.

Contains columns_set_or_check method which overrides the columns_set_or_check method in BaseTransformer if given primacy in inheritance. The difference being that BaseNominalTransformer’s columns_set_or_check only selects object and categorical columns from X, if the columns attribute is not set by the user.

__init__(columns=None, copy=True, verbose=False) → None

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([columns, copy, verbose])	Initialize self.
check_is_fitted(attribute)	Check if particular attributes are on the object.
check_mappable_rows(X)	Method to check that all the rows to apply the transformer to are able to be mapped according to the values in the mappings dict.
check_weights_column(X, weights_column)	Helper method for validating weights column in dataframe.
classname()	Method that returns the name of the current class when called.
columns_check(X)	Method to check that the columns attribute is set and all values are present in X.
columns_set_or_check(X)	Function to check or set columns attribute.
fit(X[, y])	Base transformer fit method, checks X and y types.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Base transformer transform method; checks X type (pandas DataFrame only) and copies data if requested.

check_is_fitted(attribute)

Check if particular attributes are on the object. This is useful to do before running transform to avoid trying to transform data without first running the fit method.

Wrapper for utils.validation.check_is_fitted function.

Parameters

attributes (List) – List of str values giving names of attribute to check exist on self.

check_mappable_rows(X)

Method to check that all the rows to apply the transformer to are able to be mapped according to the values in the mappings dict.

Raises

ValueError – If any of the rows in a column (c) to be mapped, could not be mapped according to the mapping dict in mappings[c].

static check_weights_column(X, weights_column)

Helper method for validating weights column in dataframe.

X (pd.DataFrame): df containing weight column weights_column (str): name of weight column

classname()

Method that returns the name of the current class when called.

columns_check(X)

Method to check that the columns attribute is set and all values are present in X.

Parameters

X (pd.DataFrame) – Data to check columns are in.

columns_set_or_check(X)

Function to check or set columns attribute.

If the columns attribute is None then set it to all object and category columns in X. Otherwise run the columns_check method.

Parameters

X (pd.DataFrame) – Data to check columns are in.

fit(X, y=None)

Base transformer fit method, checks X and y types. Currently only pandas DataFrames are allowed for X and DataFrames or Series for y.

Fit calls the columns_set_or_check method which will set the columns attribute to all columns in X, if it is None.

Parameters

• X (pd.DataFrame) – Data to fit the transformer on.

• y (None or pd.DataFrame or pd.Series, default = None) – Optional argument only required for the transformer to work with sklearn pipelines.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

• X (array-like of shape (n_samples, n_features)) – Input samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

• **fit_params (dict) – Additional fit parameters.

Returns

X_new – Transformed array.

Return type

ndarray array of shape (n_samples, n_features_new)

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

transform(X)

Base transformer transform method; checks X type (pandas DataFrame only) and copies data if requested.

Transform calls the columns_check method which will check columns in columns attribute are in X.

Parameters

X (pd.DataFrame) – Data to transform with the transformer.

Returns

X – Input X, copied if specified by user.

Return type

pd.DataFrame