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import numpy as npfrom sklearn.linear_model import LinearRegression, RidgeCV, LassoCV, ElasticNetCVfrom sklearn.preprocessing import PolynomialFeaturesfrom sklearn.pipeline import Pipelinefrom sklearn.exceptions import ConvergenceWarningimport matplotlib as mplimport matplotlib.pyplot as pltimport warningsdef xss(y, y_hat):    y = y.ravel()    y_hat = y_hat.ravel()    # Version 1    tss = ((y - np.average(y)) ** 2).sum()    rss = ((y_hat - y) ** 2).sum()    ess = ((y_hat - np.average(y)) ** 2).sum()    r2 = 1 - rss / tss    # print 'RSS:', rss, '\t ESS:', ess    # print 'TSS:', tss, 'RSS + ESS = ', rss + ess    # Version 2    # tss = np.var(y)    # rss = np.average((y_hat - y) ** 2)    # r2 = 1 - rss / tss    corr_coef = np.corrcoef(y, y_hat)[0, 1]    tss_list.append(tss)    rss_list.append(rss)    ess_list.append(ess)    ess_rss_list.append(rss + ess)    retur

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