TICC.py 文件源码

def computeNetworkAccuracy(matching,train_cluster_inverse, num_clusters):
    """
    Takes in the matching for the clusters
    takes the computed clusters
    computes the average F1 score over the network
    """
    threshold = 1e-2
    f1 = 0
    for cluster in xrange(num_clusters):
        true_cluster_cov = np.loadtxt("Inverse Covariance cluster ="+ str(cluster) +".csv", delimiter = ",")
        matched_cluster = matching[cluster]
        matched_cluster_cov = train_cluster_inverse[matched_cluster] 
        (nrow,ncol) = true_cluster_cov.shape

        out_true = np.zeros([nrow,ncol])
        for i in xrange(nrow):
            for j in xrange(ncol):
                if np.abs(true_cluster_cov[i,j]) > threshold:
                    out_true[i,j] = 1
        out_matched = np.zeros([nrow,ncol])
        for i in xrange(nrow):
            for j in xrange(ncol):
                if np.abs(matched_cluster_cov[i,j]) > threshold:
                    out_matched[i,j] = 1
        np.savetxt("Network_true_cluster=" +str(cluster) + ".csv",true_cluster_cov, delimiter = ",")
        np.savetxt("Network_matched_cluster=" + str(matched_cluster)+".csv",matched_cluster_cov, delimiter = ",")


        ##compute the confusion matrix
        confusion_matrix = np.zeros([2,2])
        for i in xrange(nrow):
            for j in xrange(ncol):
                confusion_matrix[out_true[i,j],out_matched[i,j]] += 1
        f1 += computeF1_macro(confusion_matrix, [0,1],2)
    return f1/num_clusters

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