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)
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)
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')
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')
