Scalers

Scale arrays

boxcox_lambda

boxcox_lambda(x, method, season_length=None, lower=-0.9, upper=2.0)

Find optimum lambda for the Box-Cox transformation

Parameters:

Name	Type	Description	Default
x	ndarray	Array with data to transform.	required
method	str	Method to use. Valid options are ‘guerrero’ and ‘loglik’. ‘guerrero’ minimizes the coefficient of variation for subseries of x and supports negative values. ‘loglik’ maximizes the log-likelihood function.	required
season_length	int	Length of the seasonal period. Only required if method=‘guerrero’.	None
lower	float	Lower bound for the lambda.	-0.9
upper	float	Upper bound for the lambda.	2.0

Returns:

Name	Type	Description
float	float	Optimum lambda.

boxcox

boxcox(x, lmbda)

Apply the Box-Cox transformation

Parameters:

Name	Type	Description	Default
x	ndarray	Array with data to transform.	required
lmbda	float	Lambda value to use.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the transformed data.

inv_boxcox

inv_boxcox(x, lmbda)

Invert the Box-Cox transformation

Parameters:

Name	Type	Description	Default
x	ndarray	Array with data to transform.	required
lmbda	float	Lambda value to use.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the inverted transformation.

LocalMinMaxScaler

LocalMinMaxScaler(skipna=False)

Bases: _BaseLocalScaler

Scale each group to the [0, 1] interval

Parameters:

Name	Type	Description	Default
skipna	bool	If True, exclude NaN values when computing statistics. When False (default), NaN values are included and may result in NaN statistics.	False

LocalMinMaxScaler.fit

fit(ga)

Compute the statistics for each group.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Name	Type	Description
self	_BaseLocalScaler	The fitted scaler object.

LocalMinMaxScaler.fit_transform

fit_transform(ga)

“Compute the statistics for each group and apply the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the transformed data.

LocalMinMaxScaler.inverse_transform

inverse_transform(ga)

Use the computed statistics to invert the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the inverted transformation.

LocalMinMaxScaler.stack

stack(scalers)

LocalMinMaxScaler.take

take(idxs)

LocalMinMaxScaler.transform

transform(ga)

Use the computed statistics to apply the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the transformed data.

LocalStandardScaler

LocalStandardScaler(skipna=False)

Bases: _BaseLocalScaler

Scale each group to have zero mean and unit variance

Parameters:

Name	Type	Description	Default
skipna	bool	If True, exclude NaN values when computing statistics. When False (default), NaN values are included and may result in NaN statistics.	False

LocalStandardScaler.fit

fit(ga)

Compute the statistics for each group.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Name	Type	Description
self	_BaseLocalScaler	The fitted scaler object.

LocalStandardScaler.fit_transform

fit_transform(ga)

“Compute the statistics for each group and apply the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the transformed data.

LocalStandardScaler.inverse_transform

inverse_transform(ga)

Use the computed statistics to invert the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the inverted transformation.

LocalStandardScaler.stack

stack(scalers)

LocalStandardScaler.take

take(idxs)

LocalStandardScaler.transform

transform(ga)

Use the computed statistics to apply the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the transformed data.

LocalRobustScaler

LocalRobustScaler(scale, skipna=False)

Bases: _BaseLocalScaler

Scale each group using robust statistics

Parameters:

Name	Type	Description	Default
scale	str	Type of robust scaling to use. Valid options are ‘iqr’ and ‘mad’. If ‘iqr’ will use the inter quartile range as the scale. If ‘mad’ will use median absolute deviation as the scale.	required
skipna	bool	If True, exclude NaN values when computing statistics. When False (default), NaN values are included and may result in NaN statistics.	False

LocalRobustScaler.fit

fit(ga)

Compute the statistics for each group.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Name	Type	Description
self	_BaseLocalScaler	The fitted scaler object.

LocalRobustScaler.fit_transform

fit_transform(ga)

“Compute the statistics for each group and apply the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the transformed data.

LocalRobustScaler.inverse_transform

inverse_transform(ga)

Use the computed statistics to invert the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the inverted transformation.

LocalRobustScaler.stack

stack(scalers)

LocalRobustScaler.take

take(idxs)

LocalRobustScaler.transform

transform(ga)

Use the computed statistics to apply the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the transformed data.

LocalBoxCoxScaler

LocalBoxCoxScaler(method, season_length=None, lower=-0.9, upper=2.0)

Bases: _BaseLocalScaler

Find the optimum lambda for the Box-Cox transformation by group and apply it

Parameters:

Name	Type	Description	Default
season_length	int	Length of the seasonal period. Only required if method=‘guerrero’.	None
lower	float	Lower bound for the lambda.	-0.9
upper	float	Upper bound for the lambda.	2.0
method	str	Method to use. Valid options are ‘guerrero’ and ‘loglik’. ‘guerrero’ minimizes the coefficient of variation for subseries of x and supports negative values. ‘loglik’ maximizes the log-likelihood function.	required

LocalBoxCoxScaler.fit

fit(ga)

Compute the statistics for each group.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Name	Type	Description
self	_BaseLocalScaler	The fitted scaler object.

LocalBoxCoxScaler.fit_transform

fit_transform(ga)

“Compute the statistics for each group and apply the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the transformed data.

LocalBoxCoxScaler.inverse_transform

inverse_transform(ga)

Use the computed lambdas to invert the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the inverted transformation.

LocalBoxCoxScaler.stack

stack(scalers)

LocalBoxCoxScaler.take

take(idxs)

LocalBoxCoxScaler.transform

transform(ga)

Use the computed lambdas to apply the transformation.

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the transformed data.

Difference

Difference(d)

Subtract a lag to each group

Parameters:

Name	Type	Description	Default
d	int	Lag to subtract.	required

Difference.fit_transform

fit_transform(ga)

Apply the transformation

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the transformed data.

Difference.inverse_transform

inverse_transform(ga)

Invert the transformation

Parameters:

Name	Type	Description	Default
ga	GroupedArray	Array with grouped data.	required

Returns:

Type	Description
ndarray	np.ndarray: Array with the inverted transformation.

Difference.stack

stack(scalers)

Difference.take

take(idxs)