fast_equal_frequency_binning Class Reference

Public Member Functions
def	__init__ (self, state=None)
def	fit (self, x, number_of_bins=100)
def	apply (self, x)
def	export_state (self)

Public Attributes
	state
	State of the class. More...

Detailed Description

This class provides a fast implementation of equal frequency binning.
In Equal frequency binning the binning is chosen in a way that every bin has the same number of entries.
An example with a Neural Network can be found in: mva/examples/keras/preprocessing.py

Definition at line 15 of file preprocessing.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			state = None
	)

Init the class.
If you saved a state before and wants to rebuild the class use the state parameter.

Definition at line 22 of file preprocessing.py.

    def __init__(self, state=None):
        """
        Init the class.
        If you saved a state before and wants to rebuild the class use the state parameter.
        """
        if state is None:
            
            self.state = {'binning_array': [], 'number_of_bins': 0}
        else:
            self.state = state
 

Member Function Documentation

apply()

def apply	(		self,
			x
	)

Bin a dataset

Definition at line 42 of file preprocessing.py.

export_state()

def export_state

(

self

)

Returns a pickable dictionary to save the state of the class in a mva weightfile

Definition at line 51 of file preprocessing.py.

fit()

def fit	(		self,
			x,
			number_of_bins = 100
	)

Do the fitting -> calculate binning boundaries

Definition at line 33 of file preprocessing.py.

Member Data Documentation

state

state

State of the class.

This will be saved

Definition at line 29 of file preprocessing.py.

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The documentation for this class was generated from the following file:

• mva/scripts/basf2_mva_extensions/preprocessing.py