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bin/measures.py
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''' This module is a part of my library. It aims to compute some measures for clustering. ''' import numpy as np def disequilibrium_(matrix1, matrix2, isGlobal=False, mod=None): ''' Compute disequilibrium for all the clusters. The disequilibrium is compute from the difference between two clustering sets. isGlobal permet à l'utilisateur de choisir le dénominateur de la fonction : - True : divise la valeur par le nombre d'élément du cluster - False : divise la valeur par le nombre d'élément total withPower permet à l'utilisateur de décider d'appliquer un carré 2 ou une valeur absolue. ''' def divide_line(a, divider): ''' Sub function used for dividing matrix by a vector line by line. ''' return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0) dividers1 = 0 dividers2 = 0 if isGlobal: dividers1 = matrix1.sum() dividers2 = matrix2.sum() else: dividers1 = matrix1.sum(axis=1) dividers2 = matrix2.sum(axis=1) matrix1_divided = np.apply_along_axis(divide_line, 0, np.asarray(matrix1, dtype=np.float), dividers1) matrix2_divided = np.apply_along_axis(divide_line, 0, np.asarray(matrix2, dtype=np.float), dividers2) diff = matrix1_divided - matrix2_divided |
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mask = np.logical_not(np.logical_and(matrix2==0, matrix1==0)) |
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result = diff if mod != None or mod == "": for word in mod.split(" "): if word == "power": result = np.power(result,2) elif word == "human": result = result * 100 elif word == "abs": result = np.absolute(result) else: raise Exception("Need to specify an accepted mod of the disequilibrium (\"power\", \"human\" or \"abs\"") return (mask, result) def disequilibrium_mean_by_cluster(mask, matrix): ''' Mean of disequilibrium matrix is the disequilibrium calculated from number of occurences belonging to a class, for each cluster. ''' nb_k = len(matrix) results = np.zeros((nb_k)) |
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for i in range(nb_k): results[i] = matrix[i].sum() / mask[i].sum() return results def disequilibrium(matrix1, matrix2, isGlobal=False): ''' Disequilibrium matrix And Disequilibrium value ''' mask, result = disequilibrium_(matrix1, matrix2, isGlobal) result_human = result * 100 result_power = np.power(result, 2) return ( mask, result_human, |
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disequilibrium_mean_by_cluster(mask, result_power).sum()/matrix1.shape[0] |
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) def entropy(count_matrix): def divide_line(a, divider): ''' Sub function used for dividing matrix by a vector line by line. ''' return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0) dividers = count_matrix.sum(axis=1) matrix_divided = np.apply_along_axis(divide_line, 0, np.asarray(count_matrix, dtype=np.float), dividers) result_matrix = -1 * matrix_divided * np.log2(matrix_divided, where=count_matrix != 0) result = result_matrix.sum(axis=1) * dividers / dividers.sum() result = result.sum() |
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return (result_matrix, result) |