Пример #1
0
    X_scaled = scale(X)

    #Declare list for t2 mean mean area under ROC curve (mma) values
    t2_mmas = []

    # Create initial variables to update
    max_t2_mma = 0
    opt_t2_gamma = 0

    print('\n### TIER 2 GRID SEARCH ###')

    for gamma in t2_gamma_list:

        amat_dict.update({gamma: []})

        t2_auc_mat, t2_kpcas, t2_models = p2f.m_test5_2_rocplot(
            X_scaled, y, gamma, dataset, filepath, 'tier2')

        amat_dict[gamma].append(t2_auc_mat)

        t2_mmas.append(np.mean(t2_auc_mat))

    amat_dict_list.append(amat_dict)

    # Select optimal t2 gamma
    for i in range(len(t2_mmas)):
        if t2_mmas[i] > max_t2_mma:
            max_t2_mma = t2_mmas[i]
            opt_t2_gamma = t2_gamma_list[i]

    # Record optimal gamma for given dataset
    opt_t2_gammas.append(opt_t2_gamma)
Пример #2
0
    #Scale initial data to centre
    toy_X_scaled = scale(toy_X)

    #Declare list for t2 mean mean area under ROC curve (mma) values
    t2_mmas = []

    # Create initial variables to update
    max_t2_mma = 0
    opt_t2_gamma = 0

    for gamma in t2_gamma_list:

        amat_dict.update({gamma: []})

        t2_auc_mat, t2_kpcas, t2_models = p2f.m_test5_2_rocplot(
            toy_X_scaled, toy_y, gamma, toy_label, filepath, plotpath, 'tier1')

        amat_dict[gamma].append(t2_auc_mat)

        p2f.plot_mpl_heatmap(
            t2_auc_mat,
            t2_kpcas,
            t2_models,
            cmap="autumn",
            cbarlabel=
            "Mean area under ROC curve after 10-fold cross validation",
            output='save',
            path='%s%s_tier2_heatmap_gamma_%s.png' %
            (filepath, nowtime, gamma))

        p2f.js_heatmap(t2_auc_mat, t2_kpcas, t2_models,
Пример #3
0
    plotpath = '%splotting/' % filepath

    if not os.path.exists(filepath):
        os.makedirs(filepath)
        os.makedirs(plotpath)

    X_rows, X_cols = X.shape

    def_gamma = 1 / X_cols

    gamma_list = [def_gamma / 100, def_gamma / 10, def_gamma]

    for gamma in gamma_list:

        auc_mat, kpcas, models = p2f.m_test5_2_rocplot(X, y, gamma, ds_label,
                                                       filepath, plotpath,
                                                       'gamma_%s' % gamma)

        p2f.plot_mpl_heatmap(
            auc_mat,
            kpcas,
            models,
            cmap="autumn",
            cbarlabel=
            "Mean area under ROC curve after 10-fold cross validation",
            output='save',
            path='%s%s_tier2_heatmap_gamma_%s.png' %
            (filepath, nowtime, gamma))

        p2f.js_heatmap(auc_mat, kpcas, models, "hmapgamma%s" % (gamma),
                       "%s%sheatmap_gamma%s.js" % (plotpath, nowtime, gamma))