def make_plot():
plt.figure(figsize=(15, 15), dpi=100)
plt.grid(False)
axes = plt.subplot(111, axisbg=(1,1,1))
axes.minorticks_off()
plotdata = get_plotdata()
plotvalues = plotdata.values()
plotvalues.sort(key=lambda x: -(x['freq']))
sizes = np.sqrt(np.sqrt([v['freq']-1 for v in plotvalues]))*2
texts = [v['colorname'] for v in plotvalues]
xy = np.array([rgb_to_wheel(lab_to_rgb(v['lab'])) for v in plotvalues])
x_components = xy[:,0]
y_components = xy[:,1]
rgb = np.array([v['rgb'] for v in plotvalues])
#rgba = np.zeros( (rgb.shape[0], rgb.shape[1]+1) )
#rgba[:,0:3] = rgb
#rgba[:,3] = 0.8
axes.scatter(x_components, y_components, s=sizes*2, c=rgb, edgecolors='none')
#plt.xlim(xmin=-1, xmax=1)
#plt.ylim(ymin=-1, ymax=1)
for x, y, text, size, color in zip(x_components, y_components, texts, sizes, rgb):
textcolor='black'
#print text
plt.text(x, y, text.decode('utf-8'), size=size/2.0, color=textcolor)
plt.savefig('concept_colors2.png', dpi=1000)
def get_plotdata():
colordata = make_lab_color_data()
plotdata = {}
for key, colors in colordata.items():
if len(colors) > 4:
lab = component_median(colors)
rgb = [x/255.0 for x in lab_to_rgb(lab)]
if key == 'rise': colorname = 'rose'
else: colorname = key
plotdata[key] = {
'colorname': colorname,
'rgb': rgb,
'lab': lab,
'freq': len(colors)
}
print key, lab
return plotdata
def run_leave_n_out():
from csc.divisi2 import examples
import colors
log.info('Loading ConceptNet matrix')
sa = examples.spreading_activation()
log.info('Loading test input')
train_input, test_input = training_and_test_data()
log.info('Loading test data')
test = make_test_data()
log.info('Building colorizer')
cmatrix = make_color_matrix()
colorizer = Colorizer(sa, cmatrix)
dist_dict = {}
baseline_dict = {}
distances = {
'baseline': [],
'weighted': [],
'inter_annotator': [],
}
test_concepts = set(test.keys()) & set(sa.row_labels)
for colorname in test_concepts:
try:
labout = tuple(colorizer.lab_color_for_concept(colorname)[:3])
except TypeError:
continue
labact = test[colorname]
rgbact = lab_to_rgb(labact)
dist = euclid(labout,labact)
distances['weighted'].append(dist)
baseline = euclid([50, 0, 0], labact)
distances['baseline'].append(baseline)
#try:
# wnout = tuple(colorizer.lab_color_for_concept_wordnet(colorname)[:3])
# wndist = euclid(wnout, labact)
# distances['wordnet'].append(wndist)
# rgbwnout = lab_to_rgb(wnout)
# print colorname, '(wordnet)', rgbact, rgbwnout, str(wndist)
#except TypeError:
# pass
#try:
# prismdata = [rgb_to_lab([color.__r, color.__g, color.__b]) for color in colors.prism(colorname)]
# prism_avg = numpy.mean(numpy.array(prismdata), axis=0)
# prism_dist = euclid(prism_avg, labact)
# distances['nodebox_prism'].append(prism_dist)
#except ZeroDivisionError:
# pass
inter_annotator = euclid(rgb_to_lab(test_input[colorname][0]),
labact)
if inter_annotator == 0:
inter_annotator = euclid(rgb_to_lab(test_input[colorname][1]),
labact)
distances['inter_annotator'].append(inter_annotator)
rgbout = lab_to_rgb(labout)
print colorname, rgbact, rgbout, str(dist)
totals = {}
for key, values in distances.items():
totals[key] = sum(values)/len(values)
totals['total'] = len(distances['weighted'])
print totals
return totals
def color_for_text(self, text):
l,a,b,c = self.lab_color_for_concept(text)
# hacky fix for dimness
l *= 1.25
r, g, b = lab_to_rgb((l,a,b))
return divisi2.DenseVector([r, g, b, c], OrderedSet(["red", "green", "blue", "colorful"]))
