tubular.numeric.LogTransformer¶

class tubular.numeric.LogTransformer(columns, base=None, add_1=False, drop=True, suffix='log', **kwargs)[source]¶

Bases: tubular.base.BaseTransformer

Transformer to apply log transformation.

Transformer has the option to add 1 to the columns to log and drop the original columns.

Parameters

columns (None or str or list) – Columns to log transform.
base (None or float/int) – Base for log transform. If None uses natural log.
add_1 (bool) – Should a constant of 1 be added to the columns to be transformed prior to applying the log transform?
drop (bool) – Should the original columns to be transformed be dropped after applying the log transform?
suffix (str, default = '_log') – The suffix to add onto the end of column names for new columns.
**kwargs – Arbitrary keyword arguments passed onto BaseTransformer.init method.

add_1¶

The name of the column or columns to be assigned to the output of running the pandas method in transform.

Type: bool

drop¶

The name of the pandas.DataFrame method to call.

Type: bool

suffix¶

The suffix to add onto the end of column names for new columns.

Type: str

__init__(columns, base=None, add_1=False, drop=True, suffix='log', **kwargs) → None[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(columns[, base, add_1, drop, suffix])	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)	Applies the log transform to the specified columns.

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)[source]¶

Applies the log transform to the specified columns.

If the drop attribute is True then the original columns are dropped. If the add_1 attribute is True then the original columns + 1 are logged.

Parameters: X (pd.DataFrame) – The dataframe to be transformed.
Returns: X – The dataframe with the specified columns logged, optionally dropping the original columns if self.drop is True.
Return type: pd.DataFrame