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- def plot_contour_trajectory(surf_file, dir_file, proj_file, surf_name='loss_vals',
- vmin=0.1, vmax=10, vlevel=0.5, show=False):
- """2D contour + trajectory"""
- assert exists(surf_file) and exists(proj_file) and exists(dir_file)
- # plot contours
- f = h5py.File(surf_file,'r')
- x = np.array(f['xcoordinates'][:])
- y = np.array(f['ycoordinates'][:])
- X, Y = np.meshgrid(x, y)
- if surf_name in f.keys():
- Z = np.array(f[surf_name][:])
- fig = plt.figure()
- CS1 = plt.contour(X, Y, Z, levels=np.arange(vmin, vmax, vlevel))
- CS2 = plt.contour(X, Y, Z, levels=np.logspace(1, 8, num=8))
- # plot trajectories
- pf = h5py.File(proj_file, 'r')
- plt.plot(pf['proj_xcoord'], pf['proj_ycoord'], marker='.')
- # plot red points when learning rate decays
- # for e in [150, 225, 275]:
- # plt.plot([pf['proj_xcoord'][e]], [pf['proj_ycoord'][e]], marker='.', color='r')
- # add PCA notes
- df = h5py.File(dir_file,'r')
- ratio_x = df['explained_variance_ratio_'][0]
- ratio_y = df['explained_variance_ratio_'][1]
- plt.xlabel('1st PC: %.2f %%' % (ratio_x*100), fontsize='xx-large')
- plt.ylabel('2nd PC: %.2f %%' % (ratio_y*100), fontsize='xx-large')
- df.close()
- plt.clabel(CS1, inline=1, fontsize=6)
- plt.clabel(CS2, inline=1, fontsize=6)
- fig.savefig(proj_file + '_' + surf_name + '_2dcontour_proj.pdf', dpi=300,
- bbox_inches='tight', format='pdf')
- pf.close()
- if show: plt.show()
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