def show_spec_in_graph(graph, vertex, spec, pos, weight, file_name):
dist = 1.0 - squareform(pdist(spec.T, 'cosine'))
plt.figure()
plt.stem(dist[vertex, :], markerfmt=' ')
rim = graph.new_vertex_property('vector<double>')
rim.set_2d_array(np.array([0, 0, 0, 1]))
rim[graph.vertex(vertex)] = [0.8941176471, 0.1019607843, 0.1098039216, 1]
rim_width = graph.new_vertex_property('float', vals=0.5)
rim_width.a[vertex] = 2
shape = graph.new_vertex_property('int', vals=0)
shape[graph.vertex(vertex)] = 2
size = graph.new_vertex_property('double', vals=10)
size.a[vertex] = 15
correlation = graph.new_vertex_property('double', vals=2)
correlation.a = dist[vertex, :]
vorder = graph.new_vertex_property('int', vals=0)
vorder.a[vertex] = 1
palette = sns.cubehelix_palette(256)
cmap = colors.ListedColormap(palette)
gt_draw.graph_draw(graph, pos=pos, vertex_color=rim, vorder=vorder,
vertex_pen_width=rim_width,
vertex_shape=shape, vertex_fill_color=correlation,
vcmap=cmap, vertex_size=size, edge_color=[0, 0, 0, 0.7],
edge_pen_width=weight, output=file_name + '.png',
output_size=(1200, 1200))
plt.figure()
utils.plot_colorbar(cmap, np.arange(0, 1.01, 0.2), file_name)
def make_polarization_map(self):
self.logger.info('Requested the creation of a polarization image.')
output_folder=os.path.dirname(os.path.realpath(__file__))+'/'
rescaled_flux=np.sqrt( np.power( self.stokes_Q,2) + np.power( self.stokes_U,2))
colormap=sns.cubehelix_palette(start=2.3, light=1,gamma=0.8, as_cmap=True,
reverse=True)
self.logger.info('Constructing the background of the polarization map')
# CONSTRUCTION OF THE BACKGROUND
#Get the figure axes
figure_axes=plt.gca()
#Construct the flux map as an array image
flux_map_background=figure_axes.imshow(rescaled_flux,origin='lower',aspect=None
,cmap=colormap)
self.logger.info('Constructing the lines of the polarization map')
# CONSTRUCTION OF THE VECTOR PLOT
#Get the polarization lines from the polarization angle
lines =construct_lines_from_angle(self.pol_angle,self.total_flux)
# Add the lines to the matplotlib collection lines with a good widths
lc = plt_collections.LineCollection(lines, linewidths=.5,colors='white')
# Add the lines to the current figure axes and scale
figure_axes.add_collection(lc)
figure_axes.autoscale()
# figure_axes.margins(0)
self.logger.info('Constructing the color bar of the polarization map')
# # CONSTRUCTION OF THE COLOR BAR
#Construct the colorbar and place it on the top
divider = make_axes_locatable(figure_axes)
cax = divider.append_axes("top", size="4%", pad=0)
image_colorbar=plt.colorbar(flux_map_background,orientation="horizontal",cax=cax)
image_colorbar.ax.xaxis.set_ticks_position('top')
#Get the figure, adjust it and save it in the folder
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
plt.savefig(output_folder+'results/Polarization_map.png', dpi=100)
self.logger.info('Polarization map correctly constructed.')
plt.close()
warnings.filterwarnings("always")
def make_polarization_background(self):
self.logger.info('Requested the creation of a background image.')
output_folder=os.path.dirname(os.path.realpath(__file__))+'/'
rescaled_flux=self.total_flux*1e7
colormap=sns.cubehelix_palette(start=2.3, light=1,gamma=0.8, as_cmap=True,reverse=True)
self.logger.info('Constructing the background.')
# CONSTRUCTION OF THE BACKGROUND
#Get the figure axes
figure_axes=plt.gca()
#Construct the flux map as an array image
flux_map_background=figure_axes.imshow(rescaled_flux,origin='lower',aspect=None
,cmap=colormap)
self.logger.info('Constructing the contours of the background image.')
# CONSTRUCTION OF THE CONTOURS
levels = np.linspace(np.max(rescaled_flux)*0.1,np.max(rescaled_flux)*0.6,5)
contour_plot= plt.contour(rescaled_flux, levels,
origin='lower',
linewidths=1,
colors='w')
# Construct labels for the contours
warnings.filterwarnings("ignore")
plt.clabel(contour_plot, fontsize=10)
self.logger.info('Constructing the color bar of the background image.')
# # CONSTRUCTION OF THE COLOR BAR
#Construct the colorbar and place it on the top
divider = make_axes_locatable(figure_axes)
cax = divider.append_axes("top", size="4%", pad=0)
image_colorbar=plt.colorbar(flux_map_background,orientation="horizontal",cax=cax)
image_colorbar.ax.xaxis.set_ticks_position('top')
#Get the figure, adjust it and save it in the folder
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
plt.savefig(output_folder+'results/Polarization_contours.png', dpi=100)
self.logger.info('Background image correctly constructed.')
warnings.filterwarnings("always")
def process_us():
name = 'US'
year = 2014
n_neigh = 6
graph = temperatures.country_network(name, 2014, n_neigh=6)
pos = graph.vertex_properties['pos']
station_values = graph.vertex_properties['station_values']
weight = graph.edge_properties['weights']
x_signal = station_values.a
n_eigs = 500
# n_eigs = graph.num_vertices() - 1
alpha = -1e-3
file_name = '{0}_{1}_k{2}'
file_name = file_name.format(name, year, n_neigh)
if os.path.exists(file_name + '_spec.pickle'):
with open(file_name + '_spec.pickle', 'r') as f:
spec_weighted = pickle.load(f)
factory = pickle.load(f)
else:
factory = spec.PageRankSGFT(graph, n_eigs, alpha, weight=weight)
spec_weighted = factory.compute(x_signal)
with open(file_name + '_spec.pickle', 'w') as f:
pickle.dump(spec_weighted, f)
pickle.dump(factory, f)
palette = sns.cubehelix_palette(256, start=2, rot=0, dark=0.15, light=1)
cmap = colors.ListedColormap(palette)
plt.figure()
show_spectrogram(spec_weighted[0:30, :], cmap=cmap)
plt.savefig(file_name + '_spec.pdf', dpi=300)
temperatures.plot(graph, weight, pos, station_values, file_name)
spec.show_window(factory, .5 * (graph.num_vertices() + 1), weight, pos,
file_name + '_window1.png')
spec.show_window(factory, .25 * (graph.num_vertices() + 1), weight, pos,
file_name + '_window2.png')
spec.show_window(factory, .75 * (graph.num_vertices() + 1), weight, pos,
file_name + '_window3.png')
show_spec_in_graph(graph, 417, spec_weighted, pos, weight,
file_name + '_florida')
def compare_spectrograms(factories, x_signal, graph, pos, weight=None,
file_name=None, show_ncomps=None):
palette = sns.cubehelix_palette(256, start=2, rot=0, dark=0.15, light=1)
cmap = colors.ListedColormap(palette)
stem_palette = sns.color_palette('Set1', n_colors=2)
def show_spectrogram(s):
s /= np.atleast_2d(np.max(s, axis=0))
s_range = np.max(s) - np.min(s)
s = utils.smoothstep(s,
min_edge=np.min(s) + s_range / 3,
max_edge=np.max(s) - s_range / 10)
if show_ncomps is None:
spec.plot(s, cmap)
else:
spec.plot(s[:show_ncomps, :], cmap)
def show_argmax_spectrogram_graph(s, vertex_size=20, amax_file_name=None):
amax = np.argmax(np.abs(s), axis=0)
n_values = np.unique(amax).size
assignment = graph.new_vertex_property('double', vals=amax)
if weight is None:
edge_pen_width = 1.0
else:
edge_pen_width = weight
palette = sns.color_palette('BuGn', n_colors=n_values)
cmap = colors.ListedColormap(palette)
gt_draw.graph_draw(graph, pos=pos, vertex_color=[0, 0, 0, 0.5],
vertex_fill_color=assignment, vcmap=cmap,
vertex_size=vertex_size, edge_color=[0, 0, 0, 0.7],
edge_pen_width=edge_pen_width, output=amax_file_name,
output_size=(1200, 1200))
def show_argmax_spectrogram_1d(s):
amax = np.argmax(np.abs(s), axis=0)
_, stemlines, baseline = plt.stem(amax, markerfmt=' ')
plt.setp(stemlines, 'color', stem_palette[1])
plt.setp(baseline, 'color','k')
plt.ylim((0, s.shape[0]))
if file_name is None:
plt.figure()
for i in range(len(factories)):
spectrogram = factories[i].compute(x_signal)
plt.subplot(2, 4, i + 1)
show_spectrogram(spectrogram)
plt.subplot(2, 4, 5 + i)
show_argmax_spectrogram_1d(spectrogram)
else:
file_name += '_{0}_{1}'
for i in range(len(factories)):
spectrogram = factories[i].compute(x_signal)
plt.figure()
show_spectrogram(spectrogram)
plt.savefig(file_name.format(i, 'spec.pdf'), dpi=300)
plt.figure()
show_argmax_spectrogram_1d(spectrogram)
plt.savefig(file_name.format(i, 'spec_amax.pdf'), dpi=300)
amax_file_name = file_name.format(i, 'spec_amax.png')
show_argmax_spectrogram_graph(spectrogram,
amax_file_name=amax_file_name)
def plot_learning_rate(output_dir=expanduser('~/output/recommender/learning_rate')):
with open(join(output_dir, 'results_netflix.json'), 'r') as f:
data_netflix = json.load(f)
with open(join(output_dir, 'results_10m.json'), 'r') as f:
data_10m = json.load(f)
min_time = 400
for i, learning_rate in enumerate(sorted(data_netflix.keys(), key=lambda t : float(t))):
this_data = data_netflix[learning_rate]
min_time = min(this_data['time'][0], min_time)
for i, learning_rate in enumerate(sorted(data_netflix.keys(), key=lambda t : float(t))):
this_data = data_netflix[learning_rate]
for j in range(len(this_data)):
this_data['time'][j] -= this_data['time'][0] - min_time
fig = plt.figure()
# fig.subplots_adjust(right=0.7)
fig.subplots_adjust(bottom=0.33)
fig.subplots_adjust(top=0.99)
fig.subplots_adjust(right=0.98)
fig.set_figwidth(3.25653379549)
fig.set_figheight(1.25)
ax = {}
gs = gridspec.GridSpec(1, 2)
palette = sns.cubehelix_palette(10, start=0, rot=3, hue=1, dark=.3,
light=.7,
reverse=False)
for j, data in enumerate([data_10m, data_netflix]):
ax[j] = fig.add_subplot(gs[j])
# palette = sns.hls_palette(10, l=.4, s=.7)
for i, learning_rate in enumerate(sorted(data.keys(), key=lambda t : float(t))):
if float(learning_rate) > .6:
this_data = data[learning_rate]
ax[j].plot(np.linspace(0., 20, len(this_data['rmse'])),
this_data['rmse'],
label='%.2f' % float(learning_rate),
color=palette[i],
zorder=int(100 * float(learning_rate)))
ax[j].set_xscale('log')
sns.despine(fig, ax)
ax[j].spines['left'].set_color((.6, .6, .6))
ax[j].spines['bottom'].set_color((.6, .6, .6))
ax[j].xaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax[j].yaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax[j].tick_params(axis='y', labelsize=6)
ax[0].set_ylabel('RMSE on test set')
ax[0].set_xlabel('Epoch', ha='left', va='top')
ax[0].xaxis.set_label_coords(-.18, -0.055)
ax[0].set_xlim([.1, 20])
ax[0].set_xticks([1, 10, 20])
ax[0].set_xticklabels(['1', '10', '20'])
ax[1].set_xlim([.1, 20])
ax[1].set_xticks([.1, 1, 10, 20])
ax[1].set_xticklabels(['.1', '1', '10', '20'])
ax[0].annotate('MovieLens 10M', xy=(.95, .8), ha='right', xycoords='axes fraction')
ax[1].annotate('Netflix', xy=(.95, .8), ha='right', xycoords='axes fraction')
ax[0].set_ylim([0.795, 0.863])
ax[1].set_ylim([0.93, 0.983])
ax[0].legend(ncol=4, loc='upper left', bbox_to_anchor=(0., -.13), fontsize=6, numpoints=1, columnspacing=.3, frameon=False)
ax[0].annotate('Learning rate $\\beta$', xy=(1.6, -.38), xycoords='axes fraction')
ltext = ax[0].get_legend().get_texts()
plt.setp(ltext, fontsize=7)
plt.savefig(expanduser('~/output/icml/learning_rate.pdf'))
T_0 = 0.00997
"""Plotting"""
# Font
rc('text', usetex=True)
matplotlib.rcParams['font.size'] = '15.0'
matplotlib.rcParams['xtick.labelsize'] = '15'
matplotlib.rcParams['ytick.labelsize'] = '15'
matplotlib.rcParams['legend.fontsize'] = '15'
matplotlib.rcParams['axes.linewidth'] = 0.6
# Colors
main_red = sns.color_palette("RdBu_r", 15)[12]
main_blue = sns.color_palette("RdBu_r", 15)[1]
soft_red = sns.color_palette("RdBu_r", 15)[10]
soft_blue = sns.color_palette("RdBu_r", 15)[4]
violet = sns.cubehelix_palette(8)[4]
green = sns.cubehelix_palette(8, start=.5, rot=-.75)[3]
fig, axes = plt.subplots(16, 7, figsize=(14., 19.5))
# Sort neurons, put neurons with max_val > 0.2 and max_val <0.3 in a separate group
smallR = []
mediumR = []
highR = []
veryhighR = []
setup = 'full_shuffling'
for neuron_index, neuron in enumerate(validNeurons):
ANALYSIS_DIR, analysis_num_str, R_tot, T_D, T, R, R_CI_lo, R_CI_hi = plots.load_analysis_results(
recorded_system,
rec_length,
rows = data.shape
t = range(0, rows[0])
errs = np.zeros((rows[0], 2))
t2 = np.linspace(0, 15)
for i in xrange(0, rows[0]):
errs[i, 0] = np.sqrt(
np.dot(data[i, 0:3] - data[i, 3:6], data[i, 0:3] - data[i, 3:6])) * 100
errs[i, 1] = np.sqrt(
np.dot(data[i, 0:3] - data[i, 6:9], data[i, 0:3] - data[i, 6:9])) * 100
errs[rows[0] - 1, 0] = 14
errs[rows[0] - 1, 1] = 14
colors = sns.cubehelix_palette(8,
start=2.5,
rot=0,
dark=0.3,
light=1.1,
reverse=False,
as_cmap=True)
colors.set_under(alpha=0.0)
#print np.mean(errs[:, 0]), np.mean(errs[:, 1])
#ss.plot(series=[(t, errs[:, 1]),
# (t, errs[:, 0])],
# series_colors=['red', 'blue'], y_grid=True,
#series_labels=['No SLAM', 'With SLAM'], plot_xlabel="Frame", plot_ylabel="Meters", savefile="./kinematic_error.pdf", savefile_size=(4, 3));
#plt.yscale('log')
boxprops = dict(linestyle='-', linewidth=1, color=(0.3, 0.3, 0.3))
flierprops = dict(marker='.',
markerfacecolor='black',
markersize=1,
def plot_benchs():
output_dir = join(trace_dir, 'benches')
fig = plt.figure()
fig.subplots_adjust(right=.9)
fig.subplots_adjust(top=.905)
fig.subplots_adjust(bottom=.12)
fig.subplots_adjust(left=.06)
fig.set_figheight(fig.get_figheight() * 0.66)
gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 1.5])
ylims = {
'100k': [.90, .96],
'1m': [.864, .915],
'10m': [.80, .868],
'netflix': [.93, .99]
}
xlims = {
'100k': [0.0001, 10],
'1m': [0.1, 20],
'10m': [1, 400],
'netflix': [30, 3000]
}
names = {
'dl_partial': 'Proposed \n(partial projection)',
'dl': 'Proposed \n(full projection)',
'cd': 'Coordinate descent'
}
zorder = {'cd': 10, 'dl': 1, 'dl_partial': 5}
for i, version in enumerate(['1m', '10m', 'netflix']):
try:
with open(join(output_dir, 'results_%s.json' % version), 'r') as f:
results = json.load(f)
except IOError:
continue
ax_time = fig.add_subplot(gs[0, i])
ax_time.grid()
sns.despine(fig, ax_time)
ax_time.spines['left'].set_color((.6, .6, .6))
ax_time.spines['bottom'].set_color((.6, .6, .6))
ax_time.xaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax_time.yaxis.set_tick_params(color=(.6, .6, .6), which='both')
for tick in ax_time.xaxis.get_major_ticks():
tick.label.set_fontsize(7)
tick.label.set_color('black')
for tick in ax_time.yaxis.get_major_ticks():
tick.label.set_fontsize(7)
tick.label.set_color('black')
if i == 0:
ax_time.set_ylabel('RMSE on test set')
if i == 2:
ax_time.set_xlabel('CPU time')
ax_time.xaxis.set_label_coords(1.14, -0.06)
ax_time.grid()
palette = sns.cubehelix_palette(3,
start=0,
rot=.5,
hue=1,
dark=.3,
light=.7,
reverse=False)
color = {'dl_partial': palette[2], 'dl': palette[1], 'cd': palette[0]}
for idx in sorted(OrderedDict(results).keys()):
this_result = results[idx]
ax_time.plot(this_result['timings'],
this_result['rmse'],
label=names[idx],
color=color[idx],
linewidth=2,
linestyle='-' if idx != 'cd' else '--',
zorder=zorder[idx])
if version == 'netflix':
ax_time.legend(loc='upper left',
bbox_to_anchor=(.65, 1.1),
numpoints=1,
frameon=False)
ax_time.set_xscale('log')
ax_time.set_ylim(ylims[version])
ax_time.set_xlim(xlims[version])
if version == '1m':
ax_time.set_xticks([.1, 1, 10])
ax_time.set_xticklabels(['0.1 s', '1 s', '10 s'])
elif version == '10m':
ax_time.set_xticks([1, 10, 100])
ax_time.set_xticklabels(['1 s', '10 s', '100 s'])
else:
ax_time.set_xticks([100, 1000])
ax_time.set_xticklabels(['100 s', '1000 s'])
ax_time.annotate(
'MovieLens %s' %
version.upper() if version != 'netflix' else 'Netflix (140M)',
xy=(.5 if version != 'netflix' else .4, 1),
xycoords='axes fraction',
ha='center',
va='bottom')
plt.savefig(join(trace_dir, 'bench.pdf'))
def display_explained_variance_density(output_dir):
dir_list = [join(output_dir, f) for f in os.listdir(output_dir) if
os.path.isdir(join(output_dir, f))]
fig = plt.figure(figsize=(fig_width * 0.73, fig_height))
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1])
fig.subplots_adjust(bottom=0.29)
fig.subplots_adjust(left=0.075)
fig.subplots_adjust(right=.92)
results = []
analyses = []
ref_time = 1000000
for dir_name in dir_list:
try:
analyses.append(
json.load(open(join(dir_name, 'analysis.json'), 'r')))
results.append(
json.load(open(join(dir_name, 'results.json'), 'r')))
if results[-1]['reduction'] == 12:
timings = np.array(results[-1]['timings'])
diff = timings[1:] - timings[:1]
ref_time = min(ref_time, np.min(diff))
except IOError:
pass
print(ref_time)
h_reductions = []
ax = {}
ylim = {1e-2: [2.455e8, 2.525e8], 1e-3: [2.3e8, 2.47e8],
1e-4: [2.16e8, 2.42e8]}
for i, alpha in enumerate([1e-3, 1e-4]):
ax[alpha] = fig.add_subplot(gs[:, i])
if i == 0:
ax[alpha].set_ylabel('Objective value on test set')
ax[alpha].annotate('$\\lambda = 10^{%.0f}$' % log(alpha, 10),
xy=(.65, .85),
fontsize=8,
xycoords='axes fraction')
ax[alpha].set_xlim([.05, 200])
ax[alpha].set_ylim(ylim[alpha])
for tick in ax[alpha].xaxis.get_major_ticks():
tick.label.set_fontsize(7)
ax[alpha].set_xscale('log')
ax[alpha].set_xticks([.1, 1, 10, 100])
ax[alpha].set_xticklabels(['.1 h', '1 h', '10 h', '100 h'])
sns.despine(fig=fig, ax=ax[alpha])
ax[alpha].spines['left'].set_color((.6, .6, .6))
ax[alpha].spines['bottom'].set_color((.6, .6, .6))
ax[alpha].xaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax[alpha].yaxis.set_tick_params(color=(.6, .6, .6), which='both')
for tick in ax[alpha].xaxis.get_major_ticks():
tick.label.set_color('black')
for tick in ax[alpha].yaxis.get_major_ticks():
tick.label.set_fontsize(6)
tick.label.set_color('black')
t = ax[alpha].yaxis.get_offset_text()
t.set_size(5)
ax[1e-4].set_xlabel('CPU\ntime', ha='right')
ax[1e-4].xaxis.set_label_coords(1.15, -0.05)
colormap = sns.cubehelix_palette(4, start=0, rot=0., hue=1, dark=.3,
light=.7,
reverse=False)
other_colormap = sns.cubehelix_palette(4, start=0, rot=.5, hue=1, dark=.3,
light=.7,
reverse=False)
colormap[0] = other_colormap[0]
colormap_dict = {reduction: color for reduction, color in
zip([1, 4, 8, 12],
colormap)}
x_bar = []
y_bar_objective = []
y_bar_density = []
hue_bar = []
for result, analysis in zip(results, analyses):
if result['alpha'] != 1e-2 and result['reduction'] != 2:
print("%s %s" % (result['alpha'], result['reduction']))
timings = (np.array(analysis['records']) + 1) / int(
result['reduction']) * 12 * ref_time / 3600
# timings = np.array(result['timings'])[np.array(analysis['records']) + 1] / 3600
s, = ax[result[
'alpha']].plot(
timings,
np.array(analysis['objectives']) / 4,
color=colormap_dict[int(result['reduction'])],
linewidth=2,
linestyle='--' if result[
'reduction'] == 1 else '-',
zorder=result['reduction'] if result[
'reduction'] != 1 else 100)
if result['alpha'] == 1e-3:
h_reductions.append(
(s, '%.0f' % result['reduction']))
handles, labels = list(zip(*h_reductions[::-1]))
argsort = sorted(range(len(labels)), key=lambda t: int(labels[t]))
handles = [handles[i] for i in argsort]
labels = [labels[i] for i in argsort]
offset = .3
yoffset = -.05
legend_vanilla = mlegend.Legend(ax[1e-3], handles[:1], ['No reduction'],
loc='lower left',
ncol=5,
numpoints=1,
handlelength=2,
markerscale=1.4,
bbox_to_anchor=(
0.3 + offset, -.39 + yoffset),
fontsize=8,
frameon=False
)
legend_ratio = mlegend.Legend(ax[1e-3], handles[1:], labels[1:],
loc='lower left',
ncol=5,
markerscale=1.4,
handlelength=2,
fontsize=8,
bbox_to_anchor=(
0.3 + offset, -.54 + yoffset),
frameon=False
)
ax[1e-3].annotate('Original online algorithm',
xy=(0.28 + offset, -.27 + yoffset),
xycoords='axes fraction',
horizontalalignment='right', verticalalignment='bottom',
fontsize=8)
ax[1e-3].annotate('Proposed reduction factor $r$',
xy=(0.28 + offset, -.42 + yoffset),
xycoords='axes fraction',
horizontalalignment='right', verticalalignment='bottom',
fontsize=8)
ax[1e-3].add_artist(legend_ratio)
ax[1e-3].add_artist(legend_vanilla)
ax[1e-3].annotate('(a) Convergence speed', xy=(0.7, 1.02), ha='center',
fontsize=9, va='bottom', xycoords='axes fraction')
fig.savefig(join(output_dir, 'hcp_bench.pdf'))
for result, analysis in zip(results, analyses):
if result['alpha'] != 1e-2 and result['reduction'] != 2:
x_bar.append(result['alpha'])
y_bar_objective.append(analysis['objectives'][-1])
y_bar_density.append(analysis['densities'][-1])
hue_bar.append(result['reduction'])
ref_objective = {}
for objective, alpha, reduction in zip(y_bar_objective, x_bar, hue_bar):
if reduction == 1:
ref_objective[alpha] = objective
for i, (objective, alpha) in enumerate(zip(y_bar_objective, x_bar)):
y_bar_objective[i] /= ref_objective[alpha]
y_bar_objective[i] -= 1
####################### Final objective
fig = plt.figure(figsize=(fig_width * 0.27, fig_height))
fig.subplots_adjust(bottom=0.29)
fig.subplots_adjust(left=0.05)
fig.subplots_adjust(right=1.2)
fig.subplots_adjust(top=0.85)
gs = gridspec.GridSpec(2, 1, width_ratios=[1, 1], height_ratios=[1.2, 0.8])
ax_bar_objective = fig.add_subplot(gs[0])
ax_bar_objective.set_ylim(-0.007, 0.007)
ax_bar_objective.set_yticks([-0.005, 0, 0.005])
ax_bar_objective.set_yticklabels(['-0.5\%', '0\%', '0.5\%'])
ax_bar_objective.tick_params(axis='y', labelsize=6)
sns.despine(fig=fig, ax=ax_bar_objective, left=True, right=False)
sns.barplot(x=x_bar, y=y_bar_objective, hue=hue_bar, ax=ax_bar_objective,
order=[1e-3, 1e-4],
palette=colormap)
plt.setp(ax_bar_objective.patches, linewidth=0.1)
ax_bar_objective.legend_ = None
ax_bar_objective.get_xaxis().set_visible(False)
ax_bar_objective.set_xlim([-.5, 1.6])
ax_bar_objective.annotate('Final\nobjective\ndeviation\n(relative)',
xy=(1.28, 0.45), fontsize=7, va='center',
xycoords='axes fraction')
ax_bar_objective.annotate('(Less is better)', xy=(.06, 0.1), fontsize=7,
va='center', xycoords='axes fraction')
ax_bar_objective.yaxis.set_label_position('right')
################################## Density
x_bar = []
y_bar_density = []
hue_bar = []
for result, analysis in zip(results, analyses):
if result['alpha'] != 1e-2 and result['reduction'] != 2:
x_bar.append(result['alpha'])
y_bar_density.append(analysis['densities'][-1])
hue_bar.append(result['reduction'])
ax_bar_density = fig.add_subplot(gs[1])
ax_bar_density.set_yscale('log')
ax_bar_density.set_ylim(100, 1000)
ax_bar_density.set_yticks([100, 1000])
ax_bar_density.set_yticklabels(['100', '1000'])
ax_bar_density.tick_params(axis='y', labelsize=6)
sns.barplot(x=x_bar, y=y_bar_density, hue=hue_bar, ax=ax_bar_density,
order=[1e-3, 1e-4],
palette=colormap)
ax_bar_density.set_xticklabels(['$10^{-2}$', '$10^{-3}$', '$10^{-4}$'])
sns.despine(fig=fig, ax=ax_bar_density, left=True, right=False)
# ax_bar_density.get_xaxis().set_ticks([])
ax_bar_density.set_xlim([-.5, 1.6])
ax_bar_density.set_xlabel('Regularization $\\lambda$')
ax_bar_density.annotate('$\\frac{\\ell_1}{\\ell_2}(\\mathbf D)$',
xy=(1.26, 0.45),
fontsize=7, va='center', xycoords='axes fraction')
ax_bar_density.yaxis.set_label_position('right')
plt.setp(ax_bar_density.patches, linewidth=0.1)
ax_bar_density.legend_ = None
for ax in [ax_bar_density, ax_bar_objective]:
ax.spines['right'].set_color((.6, .6, .6))
ax.spines['bottom'].set_color((.6, .6, .6))
ax.xaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax.yaxis.set_tick_params(color=(.6, .6, .6), which='both')
for tic in ax_bar_density.xaxis.get_major_ticks():
tic.tick1On = tic.tick2On = False
ax_bar_objective.spines['bottom'].set_position(('data', 0))
ax_bar_objective.spines['bottom'].set_linewidth(.3)
ax_bar_objective.annotate('(b) Decomposition quality', xy=(0.7, 1.21),
ha='center', va='bottom', fontsize=9,
xycoords='axes fraction')
fig.savefig(expanduser(join(output_dir, 'bar_plot.pdf')))
suppress_ticks=False)
m.drawmapboundary()
lat = Dataset(if_temp)['lat'][:]
lon = Dataset(if_temp)['lon'][:]
#rearrange matrix for plot
lon = shift_lon(lon)
arr_year = shift_lon(arr_year)
arr_year[np.isnan(arr_year)] = 150
x, y = np.meshgrid(lon, lat)
my_cmap = ListedColormap(sns.cubehelix_palette(24).as_hex()[0:24:3])
cs = m.pcolormesh(lon,
lat,
np.squeeze(arr_year),
cmap=my_cmap,
vmin=0,
vmax=80) #,legend_ticks)
cbar = m.colorbar(cs,
location='right',
pad="5%",
ticks=[10, 20, 30, 40, 50, 60, 70])
cbar.set_label('Year', fontsize=14, rotation=270, labelpad=18)
cbar.ax.set_yticklabels([10, 20, 30, 40, 50, 60, 70], size=14)
m.readshapefile(
G = nx.cubical_graph()
G = nx.relabel_nodes(G, {
0: "O",
1: "X",
2: "XZ",
3: "Z",
4: "Y",
5: "YZ",
6: "XYZ",
7: "XY"
})
pos = nx.spring_layout(G)
# Sequence of letters
sequence_of_letters = "".join(['X', 'Y', 'Z', 'Y', 'Y', 'Z'])
idx_colors = sns.cubehelix_palette(5, start=.5, rot=-.75)[::-1]
idx_weights = [3, 2, 1]
# Build plot
fig, ax = plt.subplots(figsize=(6, 4))
def update(num):
ax.clear()
i = num // 3
j = num % 3 + 1
triad = sequence_of_letters[i:i + 3]
path = ["O"] + ["".join(sorted(set(triad[:k + 1]))) for k in range(j)]
# Background nodes
nx.draw_networkx_edges(G, pos=pos, ax=ax, edge_color="gray")
sgd_data = sgd_data.iloc[[idxmin]]
var_data = data.query("method != 'sgd'")
data = pd.concat([sgd_data, var_data], axis=0)
data.reset_index(inplace=True)
data.to_csv(join(analysis_dir, 'data.csv'))
dump(data, join(analysis_dir, 'data.pkl'))
# Plot
fig, ax = plt.subplots(
1,
1,
)
fig.subplots_adjust(left=0.15, right=0.97, top=0.97, bottom=0.2)
colormap = sns.cubehelix_palette(6, rot=0.3, light=0.85, reverse=False)
reductions = [1, 4, 6, 8, 12, 24]
ref_colormap = sns.cubehelix_palette(6,
start=2,
rot=0.2,
light=0.7,
reverse=False)
sgd_colormap = sns.cubehelix_palette(6,
start=1,
rot=0.2,
light=0.7,
reverse=False)
color_dict = {
reduction: color
for reduction, color in zip(reductions, colormap)
}
def plot_learning_rate():
output_dir = join(trace_dir, 'learning_rate')
fig = plt.figure()
fig.subplots_adjust(bottom=0.33)
fig.subplots_adjust(top=0.99)
fig.subplots_adjust(right=0.98)
fig.set_figwidth(3.25653379549)
fig.set_figheight(1.25)
ax = {}
gs = gridspec.GridSpec(1, 2)
palette = sns.cubehelix_palette(10, start=0, rot=3, hue=1, dark=.3,
light=.7,
reverse=False)
for j, version in enumerate(['10m', 'netflix']):
with open(join(output_dir, 'results_%s.json' % version), 'r') as f:
data = json.load(f)
ax[j] = fig.add_subplot(gs[j])
learning_rates = sorted(data, key=lambda t: float(t))
for i, learning_rate in enumerate(learning_rates):
this_data = data[str(learning_rate)]
n_epochs = _get_hyperparams()['dl_partial'][version]['n_epochs']
ax[j].plot(np.linspace(0, n_epochs, len(this_data['rmse'])),
this_data['rmse'],
label='%.2f' % float(learning_rate),
color=palette[i],
zorder=int(100 * float(learning_rate)))
ax[j].set_xscale('log')
sns.despine(fig, ax)
ax[j].spines['left'].set_color((.6, .6, .6))
ax[j].spines['bottom'].set_color((.6, .6, .6))
ax[j].xaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax[j].yaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax[j].tick_params(axis='y', labelsize=6)
ax[0].set_ylabel('RMSE on test set')
ax[0].set_xlabel('Epoch', ha='left', va='top')
ax[0].xaxis.set_label_coords(-.18, -0.055)
ax[0].set_xlim([.1, 40])
ax[0].set_xticks([1, 10, 40])
ax[0].set_xticklabels(['1', '10', '40'])
ax[1].set_xlim([.1, 25])
ax[1].set_xticks([.1, 1, 10, 20])
ax[1].set_xticklabels(['.1', '1', '10', '20'])
ax[0].annotate('MovieLens 10M', xy=(.95, .9), ha='right',
xycoords='axes fraction', zorder=100)
ax[1].annotate('Netflix', xy=(.95, .9), ha='right',
xycoords='axes fraction', zorder=100)
ax[0].set_ylim([0.795, 0.877])
ax[1].set_ylim([0.93, .999])
ax[0].legend(ncol=4, loc='upper left', bbox_to_anchor=(-0.09, -.13),
fontsize=7, numpoints=1, columnspacing=.3, frameon=False)
ax[0].annotate('Learning rate $\\beta$', xy=(1.6, -.38),
xycoords='axes fraction')
ltext = ax[0].get_legend().get_texts()
plt.setp(ltext, fontsize=7)
plt.savefig(join(trace_dir, 'learning_rate.pdf'))
def display_explained_variance_epoch(output_dir):
dir_list = [join(output_dir, f) for f in os.listdir(output_dir) if
os.path.isdir(join(output_dir, f))]
fig = plt.figure()
gs = gridspec.GridSpec(1, 1, width_ratios=[1])
fig.set_figwidth(3.25653379549)
fig.set_figheight(1.3)
fig.subplots_adjust(bottom=0.105)
fig.subplots_adjust(top=0.9)
fig.subplots_adjust(left=0.12)
fig.subplots_adjust(right=.95)
results = []
analyses = []
ref_time = 1000000
for dir_name in dir_list:
try:
analyses.append(
json.load(open(join(dir_name, 'analysis.json'), 'r')))
results.append(
json.load(open(join(dir_name, 'results.json'), 'r')))
if results[-1]['reduction'] == 12:
timings = np.array(results[-1]['timings'])
diff = timings[1:] - timings[:1]
ref_time = min(ref_time, np.min(diff))
except IOError:
pass
h_reductions = []
ax = {}
ylim = {1e-2: [2.475e8, 2.522e8], 1e-3: [2.3e8, 2.335e8],
1e-4: [2.16e8, 2.24e8]}
for i, alpha in enumerate([1e-4]):
ax[alpha] = fig.add_subplot(gs[:, i])
if i == 0:
ax[alpha].set_ylabel('Objective value on test set')
ax[alpha].set_xlim([50, 4000])
for tick in ax[alpha].xaxis.get_major_ticks():
tick.label.set_fontsize(7)
ax[alpha].set_xscale('log')
ax[alpha].set_xticks([100, 1000, 1947, 4000])
ax[alpha].set_xticklabels(['100', '1000', 'Epoch', '4000'])
ax[alpha].set_ylim(ylim[alpha])
sns.despine(fig=fig, ax=ax[alpha])
ax[alpha].spines['left'].set_color((.6, .6, .6))
ax[alpha].spines['bottom'].set_color((.6, .6, .6))
ax[alpha].xaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax[alpha].yaxis.set_tick_params(color=(.6, .6, .6), which='both')
for tick in ax[alpha].xaxis.get_major_ticks():
tick.label.set_color('black')
for tick in ax[alpha].yaxis.get_major_ticks():
tick.label.set_fontsize(7)
tick.label.set_color('black')
t = ax[alpha].yaxis.get_offset_text()
t.set_size(6)
ax[1e-4].set_xlabel('Records')
ax[1e-4].xaxis.set_label_coords(-0.04, -0.047)
colormap = sns.cubehelix_palette(4, start=0, rot=0., hue=1, dark=.3,
light=.7,
reverse=False)
other_colormap = sns.cubehelix_palette(4, start=0, rot=.5, hue=1, dark=.3,
light=.7,
reverse=False)
colormap[0] = other_colormap[0]
colormap_dict = {reduction: color for reduction, color in
zip([1, 4, 8, 12],
colormap)}
for result, analysis in zip(results, analyses):
if result['alpha'] in [1e-4] and result['reduction'] in [1, 4, 8, 12]:
print("%s %s" % (result['alpha'], result['reduction']))
s, = ax[result[
'alpha']].plot(np.array(analysis['records']),
np.array(analysis['objectives']) / 4,
color=colormap_dict[result['reduction']],
linewidth=1.5,
linestyle='--' if result[
'reduction'] == 1 else '-',
zorder=result['reduction'] if result[
'reduction'] > 1 else 100)
h_reductions.append(
(s, result['reduction']))
handles, labels = list(zip(*h_reductions[::-1]))
argsort = sorted(range(len(labels)), key=lambda t: int(labels[t]))
handles = [handles[i] for i in argsort]
labels = [('$r=%i$' % labels[i]) for i in argsort]
labels[0] = 'No reduction\n(original alg.)'
ax[1e-4].annotate('$\\lambda = 10^{%.0f}$' % log(alpha, 10),
xy=(0.07, 0.07),
ha='left',
va='bottom',
fontsize=8,
xycoords='axes fraction')
legend_ratio = mlegend.Legend(ax[1e-4], handles[0:], labels[0:],
loc='upper right',
ncol=1,
numpoints=1,
handlelength=2,
frameon=False,
bbox_to_anchor=(1, 1.15)
)
ax[1e-4].add_artist(legend_ratio)
fig.savefig(join(output_dir, 'hcp_epoch.pdf'))
def plot_benchs():
output_dir = join(trace_dir, 'benches')
fig = plt.figure()
fig.subplots_adjust(right=.9)
fig.subplots_adjust(top=.905)
fig.subplots_adjust(bottom=.12)
fig.subplots_adjust(left=.06)
fig.set_figheight(fig.get_figheight() * 0.66)
gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 1.5])
ylims = {'100k': [.90, .96], '1m': [.864, .915], '10m': [.80, .868],
'netflix': [.93, .99]}
xlims = {'100k': [0.0001, 10], '1m': [0.1, 20], '10m': [1, 400],
'netflix': [30, 3000]}
names = {'dl_partial': 'Proposed \n(partial projection)',
'dl': 'Proposed \n(full projection)',
'cd': 'Coordinate descent'}
zorder = {'cd': 10,
'dl': 1,
'dl_partial': 5}
for i, version in enumerate(['1m', '10m', 'netflix']):
try:
with open(join(output_dir, 'results_%s.json' % version), 'r') as f:
results = json.load(f)
except IOError:
continue
ax_time = fig.add_subplot(gs[0, i])
ax_time.grid()
sns.despine(fig, ax_time)
ax_time.spines['left'].set_color((.6, .6, .6))
ax_time.spines['bottom'].set_color((.6, .6, .6))
ax_time.xaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax_time.yaxis.set_tick_params(color=(.6, .6, .6), which='both')
for tick in ax_time.xaxis.get_major_ticks():
tick.label.set_fontsize(7)
tick.label.set_color('black')
for tick in ax_time.yaxis.get_major_ticks():
tick.label.set_fontsize(7)
tick.label.set_color('black')
if i == 0:
ax_time.set_ylabel('RMSE on test set')
if i == 2:
ax_time.set_xlabel('CPU time')
ax_time.xaxis.set_label_coords(1.14, -0.06)
ax_time.grid()
palette = sns.cubehelix_palette(3, start=0, rot=.5, hue=1, dark=.3,
light=.7,
reverse=False)
color = {'dl_partial': palette[2], 'dl': palette[1], 'cd': palette[0]}
for idx in sorted(OrderedDict(results).keys()):
this_result = results[idx]
ax_time.plot(this_result['timings'], this_result['rmse'],
label=names[idx], color=color[idx],
linewidth=2,
linestyle='-' if idx != 'cd' else '--',
zorder=zorder[idx])
if version == 'netflix':
ax_time.legend(loc='upper left', bbox_to_anchor=(.65, 1.1),
numpoints=1,
frameon=False)
ax_time.set_xscale('log')
ax_time.set_ylim(ylims[version])
ax_time.set_xlim(xlims[version])
if version == '1m':
ax_time.set_xticks([.1, 1, 10])
ax_time.set_xticklabels(['0.1 s', '1 s', '10 s'])
elif version == '10m':
ax_time.set_xticks([1, 10, 100])
ax_time.set_xticklabels(['1 s', '10 s', '100 s'])
else:
ax_time.set_xticks([100, 1000])
ax_time.set_xticklabels(['100 s', '1000 s'])
ax_time.annotate(
'MovieLens %s' % version.upper() if version != 'netflix' else 'Netflix (140M)',
xy=(.5 if version != 'netflix' else .4, 1),
xycoords='axes fraction', ha='center', va='bottom')
plt.savefig(join(trace_dir, 'bench.pdf'))
def plot_learning_rate():
output_dir = join(trace_dir, 'learning_rate')
fig = plt.figure()
fig.subplots_adjust(bottom=0.33)
fig.subplots_adjust(top=0.99)
fig.subplots_adjust(right=0.98)
fig.set_figwidth(3.25653379549)
fig.set_figheight(1.25)
ax = {}
gs = gridspec.GridSpec(1, 2)
palette = sns.cubehelix_palette(10,
start=0,
rot=3,
hue=1,
dark=.3,
light=.7,
reverse=False)
for j, version in enumerate(['10m', 'netflix']):
with open(join(output_dir, 'results_%s.json' % version), 'r') as f:
data = json.load(f)
ax[j] = fig.add_subplot(gs[j])
learning_rates = sorted(data, key=lambda t: float(t))
for i, learning_rate in enumerate(learning_rates):
this_data = data[str(learning_rate)]
n_epochs = _get_hyperparams()['dl_partial'][version]['n_epochs']
ax[j].plot(np.linspace(0, n_epochs, len(this_data['rmse'])),
this_data['rmse'],
label='%.2f' % float(learning_rate),
color=palette[i],
zorder=int(100 * float(learning_rate)))
ax[j].set_xscale('log')
sns.despine(fig, ax)
ax[j].spines['left'].set_color((.6, .6, .6))
ax[j].spines['bottom'].set_color((.6, .6, .6))
ax[j].xaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax[j].yaxis.set_tick_params(color=(.6, .6, .6), which='both')
ax[j].tick_params(axis='y', labelsize=6)
ax[0].set_ylabel('RMSE on test set')
ax[0].set_xlabel('Epoch', ha='left', va='top')
ax[0].xaxis.set_label_coords(-.18, -0.055)
ax[0].set_xlim([.1, 40])
ax[0].set_xticks([1, 10, 40])
ax[0].set_xticklabels(['1', '10', '40'])
ax[1].set_xlim([.1, 25])
ax[1].set_xticks([.1, 1, 10, 20])
ax[1].set_xticklabels(['.1', '1', '10', '20'])
ax[0].annotate('MovieLens 10M',
xy=(.95, .9),
ha='right',
xycoords='axes fraction',
zorder=100)
ax[1].annotate('Netflix',
xy=(.95, .9),
ha='right',
xycoords='axes fraction',
zorder=100)
ax[0].set_ylim([0.795, 0.877])
ax[1].set_ylim([0.93, .999])
ax[0].legend(ncol=4,
loc='upper left',
bbox_to_anchor=(-0.09, -.13),
fontsize=7,
numpoints=1,
columnspacing=.3,
frameon=False)
ax[0].annotate('Learning rate $\\beta$',
xy=(1.6, -.38),
xycoords='axes fraction')
ltext = ax[0].get_legend().get_texts()
plt.setp(ltext, fontsize=7)
plt.savefig(join(trace_dir, 'learning_rate.pdf'))