frontendnetwork.py 文件源码

def assort(self, model):
        #if not source:
        #    data = self.data
        #    sim_source = self.similarity_matrix('cos')
        data = self.data
        N = self.data.shape[0]
        sim_source = self.similarity_matrix(sim='cos')

        y = model.generate(N)
        #y = np.triu(y) + np.triu(y, 1).T
        sim_learn = model.similarity_matrix(sim='cos')

        np.fill_diagonal(indic_source, ma.masked)

        assert(N == y.shape[0])

        indic_source = ma.array(np.ones(sim_source.shape)*-1, mask=ma.masked)
        indic_source[(data == 1) & (sim_source > 0)] = 0
        indic_source[(data == 1) & (sim_source <= 0)] = 1
        indic_source[(data == 0) & (sim_source > 0)] = 2
        indic_source[(data == 0) & (sim_source <= 0)] = 3

        indic_learn = ma.array(np.ones(sim_learn.shape)*-1, mask=ma.masked)
        indic_learn[(y == 1) & (sim_learn > 0)] = 0
        indic_learn[(y == 1) & (sim_learn <= 0)] = 1
        indic_learn[(y == 0) & (sim_learn > 0)] = 2
        indic_learn[(y == 0) & (sim_learn <= 0)] = 3

        np.fill_diagonal(indic_learn, ma.masked)
        np.fill_diagonal(indic_source, ma.masked)
        indic_source[indic_source == -1] = ma.masked
        indic_learn[indic_learn == -1] = ma.masked

        ### Indicateur Homophily Christine
        homo_ind1_source = 1.0 * ( (indic_source==0).sum()+(indic_source==3).sum()-(indic_source==1).sum() - (indic_source==2).sum() ) / (N*(N-1))
        homo_ind1_learn = 1.0 * ( (indic_learn== 0).sum()+(indic_learn==3).sum()-(indic_learn==1).sum() - (indic_learn==2).sum() ) / (N*(N-1))

        # AMI / NMI
        from sklearn import metrics
        AMI = metrics.adjusted_mutual_info_score(indic_source.compressed(), indic_learn.compressed())
        NMI = metrics.normalized_mutual_info_score(indic_source.compressed(), indic_learn.compressed())

        print('homo_ind1 source: %f' % (homo_ind1_source))
        print('homo_ind1 learn: %f' % (homo_ind1_learn))
        print('AMI: %f, NMI: %f' % (AMI, NMI))

        d = {'NMI' : NMI, 'homo_ind1_source' : homo_ind1_source, 'homo_ind1_learn' : homo_ind1_learn}
        return d