metric.py

from sklearn.metrics import v_measure_score, adjusted_rand_score, accuracy_score
from sklearn.cluster import KMeans
from scipy.optimize import linear_sum_assignment
from torch.utils.data import DataLoader
import numpy as np
import torch
from utils.utils import build_affinity_matrix
# import SGCNetwork as model

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
    u = linear_sum_assignment(w.max() - w)
    ind = np.concatenate([u[0].reshape(u[0].shape[0], 1), u[1].reshape([u[0].shape[0], 1])], axis=1)
    return sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size


def purity(y_true, y_pred):
    y_voted_labels = np.zeros(y_true.shape)
    labels = np.unique(y_true)
    ordered_labels = np.arange(labels.shape[0])
    for k in range(labels.shape[0]):
        y_true[y_true == labels[k]] = ordered_labels[k]
    labels = np.unique(y_true)
    bins = np.concatenate((labels, [np.max(labels)+1]), axis=0)

    for cluster in np.unique(y_pred):
        hist, _ = np.histogram(y_true[y_pred == cluster], bins=bins)
        winner = np.argmax(hist)
        y_voted_labels[y_pred == cluster] = winner

    return accuracy_score(y_true, y_voted_labels)


def evaluate(label, pred):
    nmi = v_measure_score(label, pred)
    ari = adjusted_rand_score(label, pred)
    acc = cluster_acc(label, pred)
    pur = purity(label, pred)
    return nmi, ari, acc, pur


def inference(loader, model, device, view, data_size):
    """
    :return:
    total_pred: prediction among all modalities
    pred_vectors: predictions of each modality, list
    labels_vector: true label
    Hs: high-level features
    Zs: low-level features
    """
    model.eval()
    soft_vector = []
    pred_vectors = []
    Hs = []
    Zs = []
    for v in range(view):
        pred_vectors.append([])
        Hs.append([])
        Zs.append([])
    labels_vector = []
    fusion_vector = []
    for step, (xs, y, _) in enumerate(loader):
        for v in range(view):
            xs[v] = xs[v].to(device)
        with torch.no_grad():
            hs, f, qs, tars, preds, tp = model.forward_fusion(xs)
        # adjs = []
        # for v in range(view):
        #     adj = build_affinity_matrix(hs[v], 10).to(device)
        #     adjs.append(adj)
        # hs, fusion_fea, qs, preds = model.forward_fusion(xs)
        # preds = model.subspace_affinity(fusion_fea)
        soft_vector.extend(preds.cpu().detach().numpy())
        labels_vector.extend(y.numpy())
        fusion_vector.extend(f.cpu().detach().numpy())
    num, class_num = preds.shape
    kmeans = KMeans(n_clusters=class_num, n_init=10) 
    kmeans_vectors = kmeans.fit_predict(fusion_vector)
    kmeans_vectors = kmeans_vectors.flatten()

    labels_vector = np.array(labels_vector).reshape(data_size)
    pred_vectors = np.argmax(np.array(soft_vector), axis=1)
    return pred_vectors, labels_vector, kmeans_vectors


def valid(model, device, dataset, view, data_size, class_num, eval_h=False):
    test_loader = DataLoader(
            dataset,
            batch_size=256,
            shuffle=False,
        )
    pred_vectors,  labels_vector, kmeans_vectors = inference(test_loader, model, device, view, data_size)

    # print("Clustering results on semantic labels: " + str(labels_vector.shape[0]))
    nmi, ari, acc, pur = evaluate(labels_vector, pred_vectors)
    # print('ACC = {:.4f} NMI = {:.4f} ARI = {:.4f} PUR={:.4f}'.format(acc, nmi, ari, pur))

    # print("Clustering results on kmeans clustering: " + str(kmeans_vectors.shape[0]))
    nmi_k, ari_k, acc_k, pur_k = evaluate(labels_vector, kmeans_vectors)
    # print('ACC = {:.4f} NMI = {:.4f} ARI = {:.4f} PUR={:.4f}'.format(acc_k, nmi_k, ari_k, pur_k))
    return acc, nmi, pur, ari, acc_k, nmi_k, ari_k,  pur_k