(self, scale=True, make_stationary=None, stationarity_lags=[1], missing="zeros", scaler="StandardScaler", logfile=None, **kwargs)
Initialize the TargetTransformer instance.
        
        scale is a boolean parameter to determine if the target will be scaled.
        
        make_stationary is a parameter to determine if the target will be made stationary. This should only be used for sequential data.
        Passing make_stationary='log' will apply a logarithm to the target and use an exponential for the inverse transform.
        Passing make_stationary='difference' will difference the values to make the target series stationary.
        By default the difference will be done with lag = 1. Alternate lags can be provided by passing a list using the stationarity_lags parameter.
        e.g. stationarity_lags=[1, 12]
        
        missing deteremines how missing values are dealt with before the scaling is applied. 
        Valid options specified through the missing parameter are: zeros, mean, median, mode

        Valid options for scaler are the scaler classes in sklearn.preprocessing

        Other kwargs are keyword arguments passed to the sklearn scaler instance.
Initialize the TargetTransformer instance.
        
        scale is a boolean parameter to determine if the target will be scaled.
        
        make_stationary is a parameter to determine if the target will be made stationary. This should only be used for sequential data.
        Passing make_stationary='log' will apply a logarithm to the target and use an exponential for the inverse transform.
        Passing make_stationary='difference' will difference the values to make the target series stationary.
        By default the difference will be done with lag = 1. Alternate lags can be provided by passing a list using the stationarity_lags parameter.
        e.g. stationarity_lags=[1, 12]
        
        missing deteremines how missing values are dealt with before the scaling is applied. 
        Valid options specified through the missing parameter are: zeros, mean, median, mode
Initialize the TargetTransformer instance . scale is a boolean parameter to determine if the target will be scaled . make_stationary is a parameter to determine if the target will be made stationary . This should only be used for sequential data . Passing make_stationary = log will apply a logarithm to the target and use an exponential for the inverse transform . Passing make_stationary = difference will difference the values to make the target series stationary . By default the difference will be done with lag = 1 . Alternate lags can be provided by passing a list using the stationarity_lags parameter . e . g . stationarity_lags = [ 1 12 ] missing deteremines how missing values are dealt with before the scaling is applied . Valid options specified through the missing parameter are : zeros mean median mode
def __init__(self, scale=True, make_stationary=None, stationarity_lags=[1], missing="zeros", scaler="StandardScaler", logfile=None, **kwargs):
        """
        Initialize the TargetTransformer instance.
        
        scale is a boolean parameter to determine if the target will be scaled.
        
        make_stationary is a parameter to determine if the target will be made stationary. This should only be used for sequential data.
        Passing make_stationary='log' will apply a logarithm to the target and use an exponential for the inverse transform.
        Passing make_stationary='difference' will difference the values to make the target series stationary.
        By default the difference will be done with lag = 1. Alternate lags can be provided by passing a list using the stationarity_lags parameter.
        e.g. stationarity_lags=[1, 12]
        
        missing deteremines how missing values are dealt with before the scaling is applied. 
        Valid options specified through the missing parameter are: zeros, mean, median, mode

        Valid options for scaler are the scaler classes in sklearn.preprocessing

        Other kwargs are keyword arguments passed to the sklearn scaler instance.
        """
        
        self.scale = scale
        self.make_stationary = make_stationary
        self.missing = missing
        self.scaler = scaler
        self.logfile = logfile
        self.lags = stationarity_lags
        self.kwargs = kwargs
def __init__ ( self , scale = True , make_stationary = None , stationarity_lags = [ 1 ] , missing = "zeros" , scaler = "StandardScaler" , logfile = None , * * kwargs ) : self . scale = scale self . make_stationary = make_stationary self . missing = missing self . scaler = scaler self . logfile = logfile self . lags = stationarity_lags self . kwargs = kwargs