tubular.mapping.CrossColumnMappingTransformer

class tubular.mapping.CrossColumnMappingTransformer(adjust_column, mappings, **kwargs)

Bases: tubular.mapping.BaseMappingTransformer

Transformer to adjust values in one column based on the values of another column.

Parameters

• adjust_column (str) – The column to be adjusted.

• mappings (dict or OrderedDict) – Dictionary containing adjustments. Each value in adjustments should be a dictionary of key (column to apply adjustment based on) value (adjustment dict for given columns) pairs. For example the following dict {‘a’: {1: ‘a’, 3: ‘b’}, ‘b’: {‘a’: 1, ‘b’: 2}} would replace the values in the adjustment column based off the values in column a using the mapping 1->’a’, 3->’b’ and also replace based off the values in column b using a mapping ‘a’->1, ‘b’->2. If more than one column is defined for this mapping, then this object must be an OrderedDict to ensure reproducibility.

• **kwargs – Arbitrary keyword arguments passed onto BaseTransformer.init method.

adjust_column

Column containing the values to be adjusted.

Type

str

mappings

Dictionary of mappings for each column individually to be applied to the adjust_column. The dict passed to mappings in init is set to the mappings attribute.

Type

dict

__init__(adjust_column, mappings, **kwargs)

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

Methods

__init__(adjust_column, mappings, **kwargs)	Initialize self.
check_is_fitted(attribute)	Check if particular attributes are on the object.
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)	Transforms values in given column using the values provided in the adjustments dictionary.

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.

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 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)

Transforms values in given column using the values provided in the adjustments dictionary.

Parameters

X (pd.DataFrame) – Data to apply adjustments to.

Returns

X – Transformed data X with adjustments applied to specified columns.

Return type

pd.DataFrame