def report_predstats(id_discdds, id_subset, id_distances, records):
r = Report('predistats-%s-%s' % (id_discdds, id_subset))
r.data('records', records)
f = r.figure()
colors = list(islice(cycle(['r', 'g', 'b', 'k', 'y', 'm']), 50))
delta = records['delta']
W = 0.2
# Save the raw values
for i, id_d in enumerate(id_distances):
r.data(id_d, records[id_d])
with f.plot('values_order', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
distance = records[id_d]
distance_order = scale_score(distance) / (float(distance.size) - 1)
step = float(i) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, distance_order,
colors[i], perc=10, label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('interval length')
pylab.ylabel('normalized distance')
pylab.xticks(ticks, ticks)
pylab.yticks((0, 1), (0, 1))
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, 1.2))
legend_put_below(ax)
with f.plot('values', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
distance = records[id_d]
step = float(i) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, distance,
colors[i], perc=10, label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('interval length')
pylab.ylabel('distance')
pylab.xticks(ticks, ticks)
# pylab.yticks((0, 1), (0, 1))
a = pylab.axis()
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, a[3]))
legend_put_below(ax)
return r
def report_uncert_stats(records, id_ddss):
# records = stats['records']
r = Report('uncert-statsall')
r.data('records', records)
f = r.figure()
id_distances = ['L2', 'L2w']
colors = list(islice(cycle(['r', 'g', 'b', 'k', 'y', 'm']), 50))
perc = 10
W = 0.2
with f.plot('distance', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_dds in enumerate(id_ddss):
which = records['id_discdds'] == id_dds
delta = records[which]['delta']
distance = records[which]['L2w']
if i == 0:
distance0 = records[which]['L2']
step = float(0) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, distance0,
colors[0], perc=perc,
label='L2')
step = float(i + 1) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, distance,
colors[i + 1], perc=perc, label='L2w' + id_dds)
legend_put_below(ax)
return r
def report_predstats(id_discdds, id_subset, id_distances, records):
r = Report('predistats-%s-%s' % (id_discdds, id_subset))
r.data('records', records)
f = r.figure()
colors = list(islice(cycle(['r', 'g', 'b', 'k', 'y', 'm']), 50))
delta = records['delta']
W = 0.2
# Save the raw values
for i, id_d in enumerate(id_distances):
r.data(id_d, records[id_d])
with f.plot('values_order', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
distance = records[id_d]
distance_order = scale_score(distance) / (float(distance.size) - 1)
step = float(i) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax,
delta + xstep,
distance_order,
colors[i],
perc=10,
label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('interval length')
pylab.ylabel('normalized distance')
pylab.xticks(ticks, ticks)
pylab.yticks((0, 1), (0, 1))
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, 1.2))
legend_put_below(ax)
with f.plot('values', **dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_d in enumerate(id_distances):
distance = records[id_d]
step = float(i) / max(len(id_distances) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax,
delta + xstep,
distance,
colors[i],
perc=10,
label=id_d)
ieee_spines(pylab)
ticks = sorted(list(set(list(delta))))
pylab.xlabel('interval length')
pylab.ylabel('distance')
pylab.xticks(ticks, ticks)
# pylab.yticks((0, 1), (0, 1))
a = pylab.axis()
pylab.axis((0.5, 0.5 + np.max(delta), -0.024, a[3]))
legend_put_below(ax)
return r
def report_stats(records, id_ddss, id_streams, id_distances):
r = Report('precision-stats')
r.data('records', records)
colors = list(islice(cycle(['r', 'g', 'b', 'k', 'y', 'm']), 50))
perc = 10
W = 0.2
for i, id_dds in enumerate(id_ddss):
r.text('dds%s' % i, id_dds)
streams_sets = generate_stream_sets(id_streams)
for stream_set, id_distance in itertools.product(streams_sets, id_distances):
f = r.figure(cols=2)
with f.plot('distance_legend', caption='Streams: %s, Distance: %s'
% (stream_set['label'], id_distance),
**dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_dds in enumerate(id_ddss):
which = (records['id_discdds'] == id_dds).astype('int')
for id_stream in stream_set['id_streams']:
which += (records['id_stream'] == id_stream).astype('int')
which = (which / (len(stream_set['id_streams']) + 1)).astype('bool')
delta = records[which]['delta']
distance = records[which][id_distance]
step = float(i) / max(len(id_ddss) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, distance,
colors[i], perc=perc, label='%s' % i)
with f.plot('distance', caption='treams: %s, Distance: %s'
% (stream_set['label'], id_distance),
**dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_dds in enumerate(id_ddss):
which = (records['id_discdds'] == id_dds).astype('int')
for id_stream in stream_set['id_streams']:
which += (records['id_stream'] == id_stream).astype('int')
which = (which / (len(stream_set['id_streams']) + 1)).astype('bool')
delta = records[which]['delta']
distance = records[which][id_distance]
step = float(i) / max(len(id_ddss) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, distance,
colors[i], perc=perc, label='%s' % i)
legend_put_below(ax)
with f.plot('difference', caption='Difference from learner_0, SStreams: %s, Distance: %s'
% (stream_set['label'], id_distance),
**dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
which0 = (records['id_discdds'] == id_ddss[0])
_ = records[which0]['delta'] # delta0
distance0 = records[which0][id_distance]
for i, id_dds in enumerate(id_ddss[1:]):
which = (records['id_discdds'] == id_dds).astype('int')
for id_stream in stream_set['id_streams']:
which += (records['id_stream'] == id_stream).astype('int')
which = (which / (len(stream_set['id_streams']) + 1)).astype('bool')
delta = records[which]['delta']
distance = records[which][id_distance]
difference = distance0 - distance
step = float(i) / max(len(id_ddss) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax, delta + xstep, difference,
colors[i + 1], perc=perc, label='%s' % i)
return r
def report_stats(records, id_ddss, id_streams, id_distances):
r = Report('precision-stats')
r.data('records', records)
colors = list(islice(cycle(['r', 'g', 'b', 'k', 'y', 'm']), 50))
perc = 10
W = 0.2
for i, id_dds in enumerate(id_ddss):
r.text('dds%s' % i, id_dds)
streams_sets = generate_stream_sets(id_streams)
for stream_set, id_distance in itertools.product(streams_sets,
id_distances):
f = r.figure(cols=2)
with f.plot('distance_legend',
caption='Streams: %s, Distance: %s' %
(stream_set['label'], id_distance),
**dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_dds in enumerate(id_ddss):
which = (records['id_discdds'] == id_dds).astype('int')
for id_stream in stream_set['id_streams']:
which += (records['id_stream'] == id_stream).astype('int')
which = (which /
(len(stream_set['id_streams']) + 1)).astype('bool')
delta = records[which]['delta']
distance = records[which][id_distance]
step = float(i) / max(len(id_ddss) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax,
delta + xstep,
distance,
colors[i],
perc=perc,
label='%s' % i)
with f.plot('distance',
caption='treams: %s, Distance: %s' %
(stream_set['label'], id_distance),
**dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
for i, id_dds in enumerate(id_ddss):
which = (records['id_discdds'] == id_dds).astype('int')
for id_stream in stream_set['id_streams']:
which += (records['id_stream'] == id_stream).astype('int')
which = (which /
(len(stream_set['id_streams']) + 1)).astype('bool')
delta = records[which]['delta']
distance = records[which][id_distance]
step = float(i) / max(len(id_ddss) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax,
delta + xstep,
distance,
colors[i],
perc=perc,
label='%s' % i)
legend_put_below(ax)
with f.plot(
'difference',
caption='Difference from learner_0, SStreams: %s, Distance: %s'
% (stream_set['label'], id_distance),
**dp_predstats_fig) as pylab:
ax = pylab.subplot(111)
which0 = (records['id_discdds'] == id_ddss[0])
_ = records[which0]['delta'] # delta0
distance0 = records[which0][id_distance]
for i, id_dds in enumerate(id_ddss[1:]):
which = (records['id_discdds'] == id_dds).astype('int')
for id_stream in stream_set['id_streams']:
which += (records['id_stream'] == id_stream).astype('int')
which = (which /
(len(stream_set['id_streams']) + 1)).astype('bool')
delta = records[which]['delta']
distance = records[which][id_distance]
difference = distance0 - distance
step = float(i) / max(len(id_ddss) - 1, 1)
xstep = W * 2 * (step - 0.5)
fancy_error_display(ax,
delta + xstep,
difference,
colors[i + 1],
perc=perc,
label='%s' % i)
return r
