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)
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_multi_category_recall(self,user=None):
if not user:
user = self.user
for c in Category.objects.all():
EventActionAggregate(user=user,category=c).save()
# Each color corresponds to the number of categories (position in list + 1) selected during the iteration.
colors = ["r","b","g","k","c","m","y"]
# k is the number of categories selected.
for k in range(1,8):
categories = set(ml.recommend_categories(user))
picked_categories = set(random.sample(categories,k))
print "User picked categories: ", picked_categories
# Generate mapping between categories and User's event action aggregate (GVIX store)
picked_cat_aggregates = dict([(c,EventActionAggregate.objects.get(user=user, category=c)) for c in picked_categories])
trials = count()
lst = []
while trials.next() < 50:
print "Loop: ", trials
mean_loop_count = count()
recall = 0
iterations = 10
G = {} #temporary dictionary that store G counts for selected categories during iterations.
while mean_loop_count.next() < iterations:
cats = set(ml.recommend_categories(user))
correct_recommendations = cats.intersection(picked_categories)
for c in correct_recommendations:
try:
G[c] += 1
except:
G[c] = 1
#picked_cat_aggregates[x].save()
cats.discard(picked_cat_aggregates[c])
#end of looping over correct_recommendations
recall += len(correct_recommendations)*100.0/len(picked_categories)
#end of looping over iterations to calculate means.
#####
#print "Recall: ", recall
#set and save the x values of discarded categories to the average number of times the category was G'd over iterations.
for key,value in G.iteritems():
picked_cat_aggregates[key].g += min(round(value/iterations),2)
picked_cat_aggregates[key].save()
#end of looping over temporary dictionary.
lst.append(recall*1.0/iterations)
#Variant 1: All items in the last iterations that were not in the picked category have been X'd once.
for c in cats:
try:
eaa = EventActionAggregate.objects.get(user=user, category=c)
except EventActionAggregate.DoesNotExist:
eaa = EventActionAggregate(user=user, category=c)
eaa.x += 1
eaa.save()
#end of adding X's
#end
print "Recall: ",lst
plt.plot(lst,color=colors[k-1],label=k)
for c in Category.objects.all():
#import pdb; pdb.set_trace()
try:
eaa = EventActionAggregate.objects.get(user=user, category=c)
eaa.g = 0
eaa.v = 0
eaa.i = 0
eaa.x = 0
eaa.save()
except:
pass
plt.title("Recall")
plt.xlabel("Trials")
plt.ylabel("% of User preferred categories")
#plt.legend()
plt.savefig("learning/test_results/recall.pdf")
plt.cla()
#import pdb; pdb.set_trace()
self.assertTrue(True)
