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cluster

无监督的cluster_acc计算
首先实现是分为两种形式，一种sklearn低版本的，一种是sklearn高版本的

实现1

from sklearn.utils.linear_assignment_ import linear_assignment
import sklearn
print(sklearn.__version__)
import numpy as npdef acc(ypred, y):"""Calculating the clustering accuracy. The predicted result must have the same number of clusters as the ground truth.ypred: 1-D numpy vector, predicted labelsy: 1-D numpy vector, ground truthThe problem of finding the best permutation to calculate the clustering accuracy is a linear assignment problem.This function construct a N-by-N cost matrix, then pass it to scipy.optimize.linear_sum_assignment to solve the assignment problem."""assert len(y) > 0assert len(np.unique(ypred)) == len(np.unique(y))s = np.unique(ypred)t = np.unique(y)N = len(np.unique(ypred))C = np.zeros((N, N), dtype = np.int32)for i in range(N):for j in range(N):idx = np.logical_and(ypred == s[i], y == t[j])C[i][j] = np.count_nonzero(idx)# convert the C matrix to the 'true' costCmax = np.amax(C)C = Cmax - C# indices = linear_assignment(C)row = indices[:][:, 0]col = indices[:][:, 1]# calculating the accuracy according to the optimal assignmentcount = 0for i in range(N):idx = np.logical_and(ypred == s[row[i]], y == t[col[i]] )count += np.count_nonzero(idx)return 1.0*count/len(y)if __name__ == '__main__':"""Using accuracy to evaluate clustering is usually not a good idea, the following example shows that even a completely wrong assignment yield accuracy of 0.5.Consider use more standard metrics, such as NMI or ARI."""              s = np.array([1, 2, 2 ,3, 1, 3,2,2,1,2,1,1,1])t = np.array([1, 1, 2, 2, 3, 3,1,1,2,2,2,2,2])   ac = acc(s, t)print(ac)

结果如下

实现2

from typing import List, Optional, Union
import numpy as np
import torch
from scipy.optimize import linear_sum_assignmentdef unsupervised_clustering_accuracy(y: Union[np.ndarray, torch.Tensor], y_pred: Union[np.ndarray, torch.Tensor]
) -> tuple:"""Unsupervised Clustering Accuracy"""assert len(y_pred) == len(y)u = np.unique(np.concatenate((y, y_pred)))n_clusters = len(u)mapping = dict(zip(u, range(n_clusters)))reward_matrix = np.zeros((n_clusters, n_clusters), dtype=np.int64)for y_pred_, y_ in zip(y_pred, y):if y_ in mapping:reward_matrix[mapping[y_pred_], mapping[y_]] += 1cost_matrix = reward_matrix.max() - reward_matrixrow_assign, col_assign = linear_sum_assignment(cost_matrix)# Construct optimal assignments matrixrow_assign = row_assign.reshape((-1, 1))  # (n,) to (n, 1) reshapecol_assign = col_assign.reshape((-1, 1))  # (n,) to (n, 1) reshapeassignments = np.concatenate((row_assign, col_assign), axis=1)optimal_reward = reward_matrix[row_assign, col_assign].sum() * 1.0return optimal_reward / y_pred.size, assignments 

结果如下

第三种实现(purity score即acc)

import sklearn.metrics as metrics
from sklearn.metrics import adjusted_rand_score as ari, normalized_mutual_info_score as nmidef purity_score(y_true, y_pred):"""A function to compute cluster purity"""# compute contingency matrix (also called confusion matrix)contingency_matrix = metrics.cluster.contingency_matrix(y_true, y_pred)return np.sum(np.amax(contingency_matrix, axis=0)) / np.sum(contingency_matrix)

from typing import List, Optional, Union
import numpy as np
import torch
from scipy.optimize import linear_sum_assignmentdef unsupervised_clustering_accuracy(y: Union[np.ndarray, torch.Tensor], y_pred: Union[np.ndarray, torch.Tensor]
) -> tuple:"""Unsupervised Clustering Accuracy"""assert len(y_pred) == len(y)u = np.unique(np.concatenate((y, y_pred)))n_clusters = len(u)mapping = dict(zip(u, range(n_clusters)))reward_matrix = np.zeros((n_clusters, n_clusters), dtype=np.int64)for y_pred_, y_ in zip(y_pred, y):if y_ in mapping:reward_matrix[mapping[y_pred_], mapping[y_]] += 1cost_matrix = reward_matrix.max() - reward_matrixrow_assign, col_assign = linear_sum_assignment(cost_matrix)# Construct optimal assignments matrixrow_assign = row_assign.reshape((-1, 1))  # (n,) to (n, 1) reshapecol_assign = col_assign.reshape((-1, 1))  # (n,) to (n, 1) reshapeassignments = np.concatenate((row_assign, col_assign), axis=1)optimal_reward = reward_matrix[row_assign, col_assign].sum() * 1.0return optimal_reward / y_pred.size, assignments 

测试1

y_true = np.array([1, 2, 2 ,3, 1, 3,2,2,1,2,1,1,1])
y_pred = np.array([1, 1, 2, 2, 3, 3,1,1,2,2,2,2,2])   ps=purity_score(y_true,y_pred)
uac = unsupervised_clustering_accuracy(y_true, y_pred)
print("purity socre={}".format(ps))
print("unsurpervised acc={}".format(uac[0]))

结果如下

测试2

本文标签： cluster