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87 lines
2.2 KiB
Python
87 lines
2.2 KiB
Python
from math import log
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import numpy as np
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class Metrics:
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@staticmethod
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def conditional_entropy(x, y, base=2):
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"""quantifies the amount of information needed to describe the outcome
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of Y given that the value of X is known
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computes H(Y|X)
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Parameters
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----------
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x : np.array
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values of the variable
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y : np.array
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array of labels
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base : int, optional
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base of the logarithm, by default 2
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Returns
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-------
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float
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conditional entropy of y given x
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"""
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xy = np.c_[x, y]
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return Metrics.entropy(xy, base) - Metrics.entropy(x, base)
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@staticmethod
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def entropy(y, base=2):
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"""measure of the uncertainty in predicting the value of y
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Parameters
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----------
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y : np.array
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array of labels
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base : int, optional
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base of the logarithm, by default 2
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Returns
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-------
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float
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entropy of y
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"""
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_, count = np.unique(y, return_counts=True, axis=0)
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proba = count.astype(float) / len(y)
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proba = proba[proba > 0.0]
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return np.sum(proba * np.log(1.0 / proba)) / log(base)
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@staticmethod
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def information_gain(x, y, base=2):
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"""Measures the reduction in uncertainty about the value of y when the
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value of X is known (also called mutual information)
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(https://www.sciencedirect.com/science/article/pii/S0020025519303603)
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Parameters
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----------
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x : np.array
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values of the variable
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y : np.array
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array of labels
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base : int, optional
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base of the logarithm, by default 2
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Returns
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-------
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float
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Information gained
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"""
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return Metrics.entropy(y, base) - Metrics.conditional_entropy(
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x, y, base
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)
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@staticmethod
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def symmetrical_uncertainty(x, y):
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return (
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2.0
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* Metrics.information_gain(x, y)
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/ (Metrics.entropy(x) + Metrics.entropy(y))
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)
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class CFS:
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def __init__(self, a):
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self.a = a
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