Python与MATLAB小练习：计算准确度Accuracy

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• Python与MATLAB小练习：计算准确度Accuracy
• 1. Python程序
• 2. MATLAB程序

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Python与MATLAB小练习：计算准确度Accuracy

作者：凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/

分别使用Python与MATLAB编程，计算聚类准确度。思路为：首先利用匈牙利算法将训练后的标签进行调整，然后再计算准确度。

1. Python程序

# Python demo
#  -*- coding: utf-8 -*-
# Author：凯鲁嘎吉 Coral Gajic
# https://www.cnblogs.com/kailugaji/
# Python小练习：计算准确度Accuracy
# 先用匈牙利算法调整标签，然后再计算准确度
import numpy as np
# 已经调整过标签了
def cluster_acc(y_true, y_pred):
    y_true = y_true.astype(np.int64)
    assert y_pred.size == y_true.size
    D = max(y_pred.max(), y_true.max()) + 1
    w = np.zeros((D, D), dtype=np.int64)
    for i in range(y_pred.size):
        w[y_pred[i], y_true[i]] += 1
    from sklearn.utils.linear_assignment_ import linear_assignment
    # 匈牙利算法调整标签
    ind = linear_assignment(w.max() - w)
    return sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size

y_true = np.array([2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1])
y_pred_1 = np.array([1, 1, 2, 1, 1, 2, 2, 2, 3, 2, 2, 3, 1, 3, 3, 2, 3]) # 未调整的标签
y_pred_2 = np.array([2, 2, 3, 2, 2, 3, 3, 3, 1, 3, 3, 1, 2, 1, 1, 3, 1]) # 调整后的标签
result_1 = cluster_acc(y_true, y_pred_1)
result_2 = cluster_acc(y_true, y_pred_2)
print('1：', result_1)
print('2：', result_2)

结果：

1： 0.6470588235294118
2： 0.6470588235294118

2. MATLAB程序

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%% MATLAB demo clear clc y_true = [2 2 2 2 2 2 3 3 3 3 3 3 1 1 1 1 1 ]; y_pred_1 = [1 1 2 1 1 2 2 2 3 2 2 3 1 3 3 2 3]; results = Evaluate(y_true,y_pred_1); fprintf('未调整标签的准确度：%f\n', results(1)); % -------------------------------------------------- % 实际采用下面这个：先用匈牙利算法对标签进行调整，然后再计算准确度Accuracy y_pred_2 = label_map(y_pred_1, y_true); results = Evaluate(y_true,y_pred_2); fprintf('调整标签后的准确度：%f\n', results(1));   %% MATLAB实例：Munkres指派算法 - 凯鲁嘎吉 - 博客园 % 来自：https://www.cnblogs.com/kailugaji/p/11765596.html function [assignment,cost] = munkres(costMat) % MUNKRES   Munkres Assign Algorithm % % [ASSIGN,COST] = munkres(COSTMAT) returns the optimal assignment in ASSIGN % with the minimum COST based on the assignment problem represented by the % COSTMAT, where the (i,j)th element represents the cost to assign the jth % job to the ith worker. %    % This is vectorized implementation of the algorithm. It is the fastest % among all Matlab implementations of the algorithm.    % Examples % Example 1: a 5 x 5 example %{ [assignment,cost] = munkres(magic(5)); [assignedrows,dum]=find(assignment); disp(assignedrows'); % 3 2 1 5 4 disp(cost); %15 %} % Example 2: 400 x 400 random data %{ n=5; A=rand(n); tic [a,b]=munkres(A); toc                %}    % Reference: % "Munkres' Assignment Algorithm, Modified for Rectangular Matrices", % http://csclab.murraystate.edu/bob.pilgrim/445/munkres.html    % version 1.0 by Yi Cao at Cranfield University on 17th June 2008    assignment = false(size(costMat)); cost = 0;    costMat(costMat~=costMat)=Inf; validMat = costMat<Inf; validCol = any(validMat); validRow = any(validMat,2);    nRows = sum(validRow); nCols = sum(validCol); n = max(nRows,nCols); if ~n     return end        dMat = zeros(n); dMat(1:nRows,1:nCols) = costMat(validRow,validCol);    %************************************************* % Munkres' Assignment Algorithm starts here %*************************************************    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %   STEP 1: Subtract the row minimum from each row. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  dMat = bsxfun(@minus, dMat, min(dMat,[],2));    %************************************************************************** %   STEP 2: Find a zero of dMat. If there are no starred zeros in its %           column or row start the zero. Repeat for each zero %************************************************************************** zP = ~dMat; starZ = false(n); while any(zP(:))     [r,c]=find(zP,1);     starZ(r,c)=true;     zP(r,:)=false;     zP(:,c)=false; end    while 1 %************************************************************************** %   STEP 3: Cover each column with a starred zero. If all the columns are %           covered then the matching is maximum %**************************************************************************     primeZ = false(n);     coverColumn = any(starZ);     if ~any(~coverColumn)         break     end     coverRow = false(n,1);     while 1         %**************************************************************************         %   STEP 4: Find a noncovered zero and prime it.  If there is no starred         %           zero in the row containing this primed zero, Go to Step 5.         %           Otherwise, cover this row and uncover the column containing         %           the starred zero. Continue in this manner until there are no         %           uncovered zeros left. Save the smallest uncovered value and         %           Go to Step 6.         %**************************************************************************         zP(:) = false;         zP(~coverRow,~coverColumn) = ~dMat(~coverRow,~coverColumn);         Step = 6;         while any(any(zP(~coverRow,~coverColumn)))             [uZr,uZc] = find(zP,1);             primeZ(uZr,uZc) = true;             stz = starZ(uZr,:);             if ~any(stz)                 Step = 5;                 break;             end             coverRow(uZr) = true;             coverColumn(stz) = false;             zP(uZr,:) = false;             zP(~coverRow,stz) = ~dMat(~coverRow,stz);         end         if Step == 6             % *************************************************************************             % STEP 6: Add the minimum uncovered value to every element of each covered             %         row, and subtract it from every element of each uncovered column.             %         Return to Step 4 without altering any stars, primes, or covered lines.             %**************************************************************************             M=dMat(~coverRow,~coverColumn);             minval=min(min(M));             if minval==inf                 return             end             dMat(coverRow,coverColumn)=dMat(coverRow,coverColumn)+minval;             dMat(~coverRow,~coverColumn)=M-minval;         else             break         end     end     %**************************************************************************     % STEP 5:     %  Construct a series of alternating primed and starred zeros as     %  follows:     %  Let Z0 represent the uncovered primed zero found in Step 4.     %  Let Z1 denote the starred zero in the column of Z0 (if any).     %  Let Z2 denote the primed zero in the row of Z1 (there will always     %  be one).  Continue until the series terminates at a primed zero     %  that has no starred zero in its column.  Unstar each starred     %  zero of the series, star each primed zero of the series, erase     %  all primes and uncover every line in the matrix.  Return to Step 3.     %**************************************************************************     rowZ1 = starZ(:,uZc);     starZ(uZr,uZc)=true;     while any(rowZ1)         starZ(rowZ1,uZc)=false;         uZc = primeZ(rowZ1,:);         uZr = rowZ1;         rowZ1 = starZ(:,uZc);         starZ(uZr,uZc)=true;     end end    % Cost of assignment assignment(validRow,validCol) = starZ(1:nRows,1:nCols); cost = sum(costMat(assignment)); end   %% MATLAB实例：为匹配真实标签，对训练得到的标签进行调整 - 凯鲁嘎吉 - 博客园 % 来自：https://www.cnblogs.com/kailugaji/p/11771226.html function [ new_label ] = label_map( label, gnd ) %为匹配真实标签，对标签重新调整 K = length(unique(gnd)); cost_mat = zeros(K,K); for i=1:K     idx = find(label==i);     for j=1:K               cost_mat(i,j) = length(find(gnd(idx)~=j));     end end [assignment, ~] = munkres(cost_mat); [assignedrows, ~]=find(assignment'); new_label = label; for i=1:K     idx = find(label==i);     new_label(idx) = assignedrows(i); end end   %% MATLAB聚类有效性评价指标（外部 成对度量） - 凯鲁嘎吉 - 博客园 % 来自：https://www.cnblogs.com/kailugaji/p/11926253.html function result = Evaluate(real_label,pre_label) % This fucntion evaluates the performance of a classification model by % calculating the common performance measures: Accuracy, Sensitivity, % Specificity, Precision, Recall, F-Measure, G-mean. % Input: ACTUAL = Column matrix with actual class labels of the training %                 examples %        PREDICTED = Column matrix with predicted class labels by the %                    classification model % Output: EVAL = Row matrix with all the performance measures % https://www.mathworks.com/matlabcentral/fileexchange/37758-performance-measures-for-classification    idx = (real_label()==1);    p = length(real_label(idx)); n = length(real_label(~idx)); N = p+n;    tp = sum(real_label(idx)==pre_label(idx)); tn = sum(real_label(~idx)==pre_label(~idx)); fp = n-tn; fn = p-tp;    tp_rate = tp/p; tn_rate = tn/n;    accuracy = (tp+tn)/N; %准确度 sensitivity = tp_rate;  %敏感性：真阳性率 specificity = tn_rate; %特异性：真阴性率 precision = tp/(tp+fp);  %精度 recall = sensitivity;  %召回率 f_measure = 2*((precision*recall)/(precision + recall));  %F-measure gmean = sqrt(tp_rate*tn_rate); Jaccard=tp/(tp+fn+fp); %Jaccard系数    result = [accuracy sensitivity specificity precision recall f_measure gmean Jaccard]; end

结果：

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未调整标签的准确度：0.294118

调整标签后的准确度：0.647059

完成。