Simple metric transformations

BoxCoxTransformer	Unit for a Box-Cox transformation of the pandas data.
LogTransformer	Unit for a logarithmic transformation of the pandas data.

class ambrosia.preprocessing.BoxCoxTransformer

Unit for a Box-Cox transformation of the pandas data.

A Box Cox transformation helps to transform non-normal dependent variables into a normal shape. All variables values must be positive.

Optimal transformation lambdas are selected automatically during the transformer fit process.

Attributes:

column_namesList

Names of column which will be selected for the transformation.

lambda_np.ndarray

Array of parameters using during the transformation of the selected columns.

fittedbool

Fit flag.

Examples

>>> boxcox = BoxCoxTransformer()
>>> boxcox.fit(dataframe, ['column1', 'column2'])
>>> boxcox.transform(dataframe, inplace=True)

fit(dataframe, column_names)

Fit to calculate transformation parameters for the selected columns.

Parameters:

dataframepd.DataFrame

Dataframe to calculate optimal transformation parameters.

column_namesColumnNamesType

One or number of columns in the dataframe.

Returns:

selfobject

Instance object.

transform(dataframe, inplace=False)

Apply Box-Cox transformation for the data.

Parameters:

dataframepd.DataFrame

Dataframe to transform.

inplacebool, default: False

If True transforms the given dataframe, otherwise copy and returns an another one.

Returns:

dfUnion[pd.DataFrame, None]

Transformed dataframe or None

fit_transform(dataframe, column_names, inplace=False)

Fit transformer parameters using given dataframe and transform it.

Parameters:

dataframepd.DataFrame

Dataframe for calculation of optimal parameters and further transformation.

column_namesColumnNamesType

One or number of columns in the dataframe.

inplacebool, default: False

If True transforms the given dataframe, otherwise copy and returns an another one.

Returns:

dfUnion[pd.DataFrame, None]

Transformed dataframe or None

store_params(store_path)

Parameters:

store_pathPath

Path where parameters will be stored in a json format.

load_params(load_path)

Parameters:

load_pathPath

Path to json file with parameters.

class ambrosia.preprocessing.LogTransformer

Unit for a logarithmic transformation of the pandas data.

A logarithmic transformation helps to transform some metrics distributions into a more normal shape and reduce the variance. All metrics values must be positive.

Attributes:

column_namesList

Names of column which will be selected for the transformation.

fittedbool

Fit flag.

Examples

>>> log = LogTransformer()
>>> log.fit(dataframe, ['column1', 'column2'])
>>> log.transform(dataframe, inplace=True)

fit(dataframe, column_names)

Fit names of the selected columns.

Parameters:

dataframepd.DataFrame

Dataframe with metrics.

column_namesColumnNamesType

One or number of columns in the dataframe.

Returns:

selfobject

Instance object.

transform(dataframe, inplace=False)

Apply log transformation for the data.

Parameters:

dataframepd.DataFrame

Dataframe to transform.

inplacebool, default: False

If True transforms the given dataframe, otherwise copy and returns an another one.

Returns:

dfUnion[pd.DataFrame, None]

Transformed dataframe or None

fit_transform(dataframe, column_names, inplace=False)

Fit transformer parameters using given dataframe and transform it.

Only column names are fittable.

Parameters:

dataframepd.DataFrame

Dataframe to transform.

column_namesColumnNamesType

One or number of columns in the dataframe.

inplacebool, default: False

If True transforms the given dataframe, otherwise copy and returns an another one.

Returns:

dfUnion[pd.DataFrame, None]

Transformed dataframe or None

store_params(store_path)

Parameters:

store_pathPath

Path where parameters will be stored in a json format.

load_params(load_path)

Parameters:

load_pathPath

Path to json file with parameters.