def compare_model():
index = Index()
models = [Vectoriel(index, SimpleWeighter(index), normalized=True),
Vectoriel(index, BasicWeighter(index), normalized=True),
Vectoriel(index, ThirdWeighter(index), normalized=False),
Vectoriel(index, FourthWeighter(index), normalized=False),
Okapi(index),
LanguageModel(index, .7),
HITS(index, seeds=3, k=5),
PageRank(index, seeds=3, k=5)
]
names = ['Vectoriel1', 'Vectoriel2','Vectoriel3','Vectoriel4','Okapi', 'Language', 'HITS', 'PageRank']
models_scores = compare_models(names, models)
fig = plt.figure(figsize=(10,8))
colors=iter(cm.hot(np.linspace(0,.7,len(names))))
x = [s['AveragePrecision']['mean'] for s in scores.values()]
plt.xticks(x, scores.keys())
plt.bar(x, models_scores.values(), alpha=.4, color=c)
# for n, s in models_scores.items():
# color = next(colors)
# y = s['PrecisionRecall']['mean']
# x = list(range(len(y)))
# plt.plot(x, y, color=color, label=n)
# plt.legend()
plt.savefig('plot/models_comparison_precision_eval.png')
def optimize_pagerank_parameters(measures=['AveragePrecision']):
index = Index()
models, names, scores = [], [], {}
seeds_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 50]
k_values = [0, 1, 3, 5, 10, 20]
colors=iter(cm.hot(np.linspace(0,.7,len(k_values))))
fig = plt.figure(figsize=(10,8))
for k in k_values:
models_b, names_b = [], []
for seeds in seeds_values:
print('k : ', k, 'seeds : ', seeds)
name = 'PageRank_seeds=%d_k=%d'%(seeds, k)
models_b.append(PageRank(index, seeds=seeds, k=k))
names_b.append(name)
names.append(name)
scores_b = compare_models(names_b, models_b, measures)
scores.update(scores_b)
ap_b = [scores_b[model_name]['AveragePrecision']['eval_std'] for model_name in names_b]
color = next(colors)
label = 'k = '+str(k)
plt.plot(seeds_values, ap_b, c=color, label=label)
plt.title('PageRank model : average precision std depending on k and seeds values')
plt.xlabel('seeds')
plt.ylabel('Average Precision')
plt.legend()
plt.savefig('plot/PageRank_Model_ap_std.png')
def save_heatmap(heatmap, path='./image.png', colour=False):
'''
Saves a heatmap to the specified path, in colour if colour is True, or in greyscale if colour is False.
@param heatmap: A pixel matrix heatmap.
@type heatmap: Rectangular numpy matrix of floats
@param path: The file path to save the file into.
@type path: String
@param colour: Whether or not the heatmap should be saved in colour.
@type colour: Boolean
@rtype: Void
'''
from scipy import misc
import numpy as np
from time import time
import Image
heatmap = heatmap[::-1]
if colour:
#from matplotlib import colors
from matplotlib.pyplot import cm
print "Saving colour heatmap to " + path + "..."
print "\tMapping to colour scale..."
s = time()
print "\t\tRescaling..."
heatmap = heatmap / 5.
print "\t\tDone."
# excerpt from the registered 'hot' colormap:
# '_segmentdata': {'blue': ((0.0, 0.0, 0.0), (0.746032, 0.0, 0.0), (1.0, 1.0, 1.0)),
# 'green': ((0.0, 0.0, 0.0), (0.365079, 0.0, 0.0), (0.746032, 1.0, 1.0), (1.0, 1.0, 1.0)),
# 'red': ((0.0, 0.0416, 0.0416), (0.365079, 1.0, 1.0), (1.0, 1.0, 1.0))}
#cdict = [(0.0, (1.0, 0.0, 0.0, 0.0)),
# (0.365079, (1.0, 0.0, 0.0, 0.5)),
# (0.746032, (1.0, 1.0, 0.0, 0.5)),
# (1.0, (1.0, 1.0, 1.0, 0.5))]
#cmap_hot = colors.LinearSegmentedColormap.from_list(name='yes', colors=cdict)
#heatmap_color = Image.fromarray(np.uint8(cmap_hot(heatmap, bytes=True)))
heatmap_color = Image.fromarray(
np.uint8(cm.hot(heatmap, alpha=0.5, bytes=True)))
e = time()
print "\tTime to convert to colour: " + str(e - s)
misc.imsave(path, heatmap_color)
print "Done."
else:
print "Saving greyscale heatmap to " + path + "..."
misc.imsave(path, heatmap)
print "Done."
def save_heatmap(heatmap, path='./image.png', colour=False):
'''
Saves a heatmap to the specified path, in colour if colour is True, or in greyscale if colour is False.
@param heatmap: A pixel matrix heatmap.
@type heatmap: Rectangular numpy matrix of floats
@param path: The file path to save the file into.
@type path: String
@param colour: Whether or not the heatmap should be saved in colour.
@type colour: Boolean
@rtype: Void
'''
from scipy import misc
import numpy as np
from time import time
import Image
heatmap = heatmap[::-1]
if colour:
#from matplotlib import colors
from matplotlib.pyplot import cm
print "Saving colour heatmap to " + path + "..."
print "\tMapping to colour scale..."
s = time()
print "\t\tRescaling..."
heatmap = heatmap / 5.
print"\t\tDone."
# excerpt from the registered 'hot' colormap:
# '_segmentdata': {'blue': ((0.0, 0.0, 0.0), (0.746032, 0.0, 0.0), (1.0, 1.0, 1.0)),
# 'green': ((0.0, 0.0, 0.0), (0.365079, 0.0, 0.0), (0.746032, 1.0, 1.0), (1.0, 1.0, 1.0)),
# 'red': ((0.0, 0.0416, 0.0416), (0.365079, 1.0, 1.0), (1.0, 1.0, 1.0))}
#cdict = [(0.0, (1.0, 0.0, 0.0, 0.0)),
# (0.365079, (1.0, 0.0, 0.0, 0.5)),
# (0.746032, (1.0, 1.0, 0.0, 0.5)),
# (1.0, (1.0, 1.0, 1.0, 0.5))]
#cmap_hot = colors.LinearSegmentedColormap.from_list(name='yes', colors=cdict)
#heatmap_color = Image.fromarray(np.uint8(cmap_hot(heatmap, bytes=True)))
heatmap_color = Image.fromarray(np.uint8(cm.hot(heatmap, alpha=0.5, bytes=True)))
e = time()
print "\tTime to convert to colour: " + str(e - s)
misc.imsave(path, heatmap_color)
print "Done."
else:
print "Saving greyscale heatmap to " + path + "..."
misc.imsave(path, heatmap)
print "Done."
def optimize_okapi_parameters(measures=['AveragePrecision']):
index = Index()
k1_values = np.arange(1, 2.1, 0.1)
b_values = np.arange(0.65, 0.90, 0.05)
# k1_values = [1.1, 1.5]
# b_values = [.7, .8]
fig = plt.figure(figsize=(10,8))
scores, names = {}, []
colors=iter(cm.hot(np.linspace(0,.8,len(b_values))))
for b in b_values:
models_b, names_b = [], []
for k1 in k1_values:
name = 'Okapi_k1=%.2f_b=%.2f'%(k1, b)
models_b.append(Okapi(index, k1, b))
names_b.append(name)
names.append(name)
scores_b = compare_models(names_b, models_b, measures)
scores.update(scores_b)
ap_b = [scores_b[model_name]['AveragePrecision']['eval_std'] for model_name in names_b]
color = next(colors)
label = 'b = '+str(b)
plt.plot(k1_values, ap_b, c=color, label=label)
plt.title('Okapi model : average precision std depending on b values')
plt.xlabel('k1')
plt.ylabel('Average Precision std')
plt.legend()
plt.savefig('plot/Okapi_Model_ap_std.png')
ap = [scores[model_name]['AveragePrecision']['mean'] for model_name in names]
best_model = names[np.argmax(ap)]
print('best_model', best_model)
import numpy as np
import matplotlib
matplotlib.use("Qt5Agg")
from matplotlib.pyplot import cm
from spimagine import volshow
import OpenGL.GL as GL
if __name__ == '__main__':
data = np.einsum("ij,k", np.ones((100, ) * 2), np.linspace(0, 1, 100))
w = volshow(data)
w.glWidget._set_colormap_array(cm.hot(np.linspace(0, 1, 2**12))[:, :3])
print("maximal texture size: ", GL.glGetIntegerv(GL.GL_MAX_TEXTURE_SIZE))
import numpy as np
import matplotlib
matplotlib.use("Qt5Agg")
from matplotlib.pyplot import cm
from spimagine import volshow
import OpenGL.GL as GL
if __name__ == '__main__':
data = np.einsum("ij,k",np.ones((100,)*2), np.linspace(0,1,100))
w = volshow(data)
w.glWidget._set_colormap_array(cm.hot(np.linspace(0,1,2**12))[:,:3])
print("maximal texture size: ", GL.glGetIntegerv(GL.GL_MAX_TEXTURE_SIZE))