Example #1
0
def test_plot_corr_grid():
    hie_data = randhie.load_pandas()
    corr_matrix = np.corrcoef(hie_data.data.values.T)

    fig = plot_corr_grid([corr_matrix] * 2, xnames=hie_data.names)
    plt.close(fig)

    fig = plot_corr_grid([corr_matrix] * 5, xnames=[], ynames=hie_data.names)
    plt.close(fig)

    fig = plot_corr_grid([corr_matrix] * 3, normcolor=True, titles='', cmap='jet')
    plt.close(fig)
Example #2
0
def test_plot_corr_grid():
    hie_data = randhie.load_pandas()
    corr_matrix = np.corrcoef(hie_data.data.values.T)

    fig = plot_corr_grid([corr_matrix] * 2, xnames=hie_data.names)
    plt.close(fig)

    fig = plot_corr_grid([corr_matrix] * 5, xnames=[], ynames=hie_data.names)
    plt.close(fig)

    fig = plot_corr_grid([corr_matrix] * 3,
                         normcolor=True,
                         titles='',
                         cmap='jet')
    plt.close(fig)
Example #3
0
    fig = plt.figure()
    for i, c in enumerate([rrcorr, corr_lw, corr_oas, corr_mcd]):
    #for i, c in enumerate([np.cov(rr, rowvar=0), cov_lw, cov_oas, cov_mcd]):
        ax = fig.add_subplot(2,2,i+1)
        plot_corr(c, xnames=None, title=titles[i],
              normcolor=normcolor, ax=ax)

    images = [c for ax in fig.axes for c in ax.get_children() if isinstance(c, mpl.image.AxesImage)]
    fig. subplots_adjust(bottom=0.1, right=0.9, top=0.9)
    cax = fig.add_axes([0.9, 0.1, 0.025, 0.8])
    fig.colorbar(images[0], cax=cax)

    corrli = [rrcorr, corr_lw, corr_oas, corr_mcd, pcacorr]
    diffssq = np.array([[((ci-cj)**2).sum() for ci in corrli]
                            for cj in corrli])
    diffsabs = np.array([[np.max(np.abs(ci-cj)) for ci in corrli]
                            for cj in corrli])
    print(diffssq)
    print('\nmaxabs')
    print(diffsabs)
    fig.savefig('corrmatrix_sklearn.png', dpi=120)

    fig2 = plot_corr_grid(corrli+[residcorr], ncols=3,
                          titles=titles+['pca', 'pca-residual'],
                          xnames=[], ynames=[])
    fig2.savefig('corrmatrix_sklearn_2.png', dpi=120)

#plt.show()
#plt.close('all')

Example #4
0
        ax = fig.add_subplot(2, 2, i + 1)
        plot_corr(c, xnames=None, title=titles[i], normcolor=normcolor, ax=ax)

    images = [
        c for ax in fig.axes for c in ax.get_children()
        if isinstance(c, mpl.image.AxesImage)
    ]
    fig.subplots_adjust(bottom=0.1, right=0.9, top=0.9)
    cax = fig.add_axes([0.9, 0.1, 0.025, 0.8])
    fig.colorbar(images[0], cax=cax)

    corrli = [rrcorr, corr_lw, corr_oas, corr_mcd, pcacorr]
    diffssq = np.array([[((ci - cj)**2).sum() for ci in corrli]
                        for cj in corrli])
    diffsabs = np.array([[np.max(np.abs(ci - cj)) for ci in corrli]
                         for cj in corrli])
    print diffssq
    print '\nmaxabs'
    print diffsabs
    fig.savefig('corrmatrix_sklearn.png', dpi=120)

    fig2 = plot_corr_grid(corrli + [residcorr],
                          ncols=3,
                          titles=titles + ['pca', 'pca-residual'],
                          xnames=[],
                          ynames=[])
    fig2.savefig('corrmatrix_sklearn_2.png', dpi=120)

#plt.show()
#plt.close('all')
    mcd.fit(rr, assume_centered=False)
    cov_mcd = mcd.covariance_
    corr_mcd = cov2corr(cov_mcd)

    titles = ["raw correlation", "lw", "oas", "mcd"]
    normcolor = None
    fig = plt.figure()
    for i, c in enumerate([rrcorr, corr_lw, corr_oas, corr_mcd]):
        # for i, c in enumerate([np.cov(rr, rowvar=0), cov_lw, cov_oas, cov_mcd]):
        ax = fig.add_subplot(2, 2, i + 1)
        plot_corr(c, xnames=None, title=titles[i], normcolor=normcolor, ax=ax)

    images = [c for ax in fig.axes for c in ax.get_children() if isinstance(c, mpl.image.AxesImage)]
    fig.subplots_adjust(bottom=0.1, right=0.9, top=0.9)
    cax = fig.add_axes([0.9, 0.1, 0.025, 0.8])
    fig.colorbar(images[0], cax=cax)

    corrli = [rrcorr, corr_lw, corr_oas, corr_mcd, pcacorr]
    diffssq = np.array([[((ci - cj) ** 2).sum() for ci in corrli] for cj in corrli])
    diffsabs = np.array([[np.max(np.abs(ci - cj)) for ci in corrli] for cj in corrli])
    print diffssq
    print "\nmaxabs"
    print diffsabs
    fig.savefig("corrmatrix_sklearn.png", dpi=120)

    fig2 = plot_corr_grid(corrli + [residcorr], ncols=3, titles=titles + ["pca", "pca-residual"], xnames=[], ynames=[])
    fig2.savefig("corrmatrix_sklearn_2.png", dpi=120)

# plt.show()
# plt.close('all')