def calculate_plot_metrics(self, number_of_recommendations=1):
"""
This method calculates and plots (currently precision and recall) metrics.
"""
precision = []
precision_set = []
recall = []
recall_set = []
for i in range(number_of_recommendations):
print "In loop: ", i, "\r",
sys.stdout.flush()
#event_ids = [e.id for e in ml.recommend_events(self.user)]
#event_category_id_dict = ml.get_categories(event_ids,'C')
#event_category_ids = [event_category_id_dict[e] for e in event_ids]
# just get category ids directly
cats = ml.recommend_categories(self.user)
event_categories = ml.sample_category_distribution(
cats.items(), settings.N)
event_categories = [[a.id] for a in event_categories]
#print map(lambda l: map(self.get_category_string, l),event_category_ids)
p, pres = self.calculate_precision_value(event_categories)
precision.append(p)
precision_set.append(pres)
r, rres = self.calculate_recall_value(event_categories)
recall.append(r)
recall_set.append(rres)
self.update_behavior(event_categories)
#print "Events: ", events
#print "precision: ", precision
#print "recall: ", recall
#print [[self.get_category_string(c) for c in clst] for clst in event_category_ids][0]
print "precision: ", precision
#print "Precision: ", precision_set
print "recall: ", recall
print "recall: ", [
map(self.get_category_string, a) for a in map(list, recall_set)
]
#plt.figure(1)
#plt.plot(precision,color=color,label=label)
#plt.figure(2)
#plt.plot(recall,color=color, label=label)
return (precision, recall)
def test_convergence(self):
categories = ml.recommend_categories(self.user)
#print "Categories: ", categories
picked_category = ml.sample_distribution(categories.items())[0]
#print "picked category: ", picked_category
picked_aggr = EventActionAggregate(user=self.user, category=picked_category)
lst = []
ctree = CachedCategoryTree()
parents = ctree.parents(picked_category)
count = 0
while count < 100:
count +=1
print "Round: %d\r"%count,
sys.stdout.flush()
# recommend a new set of categories
recommendation_scores = ml.recommend_categories(self.user)
cats = ml.sample_category_distribution(recommendation_scores.items(),
settings.N)
found_count = cats.count(picked_category)
#print "Categories: ",cats
#print "ID: ", picked_category.id
cats = set(cats)
cats.discard(picked_category.id)
# # G(oto) picked category
picked_aggr.g += found_count
picked_aggr.save()
# X all other categories
for c in cats:
if c in parents:
continue
try:
eaa = EventActionAggregate.objects.get(user=self.user, category=c)
except EventActionAggregate.DoesNotExist:
eaa = EventActionAggregate(user=self.user, category=c)
eaa.x += 1
eaa.save()
lst.append(found_count*100.0/settings.N)
plt.plot(lst,color="blue")
plt.title("Rate of learning one category")
plt.xlabel("Trials")
plt.ylabel("% of all Recommendations")
plt.savefig("learning/test_results/test.pdf")
plt.cla()
self.assertTrue(True)
def calculate_plot_metrics(self, number_of_recommendations=1):
"""
This method calculates and plots (currently precision and recall) metrics.
"""
precision = []
precision_set = []
recall = []
recall_set = []
for i in range(number_of_recommendations):
print "In loop: ", i, "\r",
sys.stdout.flush()
#event_ids = [e.id for e in ml.recommend_events(self.user)]
#event_category_id_dict = ml.get_categories(event_ids,'C')
#event_category_ids = [event_category_id_dict[e] for e in event_ids]
# just get category ids directly
cats = ml.recommend_categories(self.user)
event_categories = ml.sample_category_distribution(cats.items(),
settings.N)
event_categories= [[a.id] for a in event_categories]
#print map(lambda l: map(self.get_category_string, l),event_category_ids)
p,pres =self.calculate_precision_value(event_categories)
precision.append(p)
precision_set.append(pres)
r,rres = self.calculate_recall_value(event_categories)
recall.append(r)
recall_set.append(rres)
self.update_behavior(event_categories)
#print "Events: ", events
#print "precision: ", precision
#print "recall: ", recall
#print [[self.get_category_string(c) for c in clst] for clst in event_category_ids][0]
print "precision: ", precision
#print "Precision: ", precision_set
print "recall: ", recall
print "recall: ", [map(self.get_category_string,a) for a in map(list,recall_set)]
#plt.figure(1)
#plt.plot(precision,color=color,label=label)
#plt.figure(2)
#plt.plot(recall,color=color, label=label)
return (precision, recall)
