def __recurse_ct(self, ct, n, d):
"""given a cached category tree, a current node, and a dictionary of
category->preference mappings"""
current_pref = d[n.id]
for child in ct.children(n):
d[child.id] = ml.sample_category_distribution(self.transition_matrix[current_pref])[0]
self.__recurse_ct(ct, child, d)
def __recurse_ct(self, ct, n, d):
"""given a cached category tree, a current node, and a dictionary of
category->preference mappings"""
current_pref = d[n.id]
for child in ct.children(n):
d[child.id] = ml.sample_category_distribution(
self.transition_matrix[current_pref])[0]
self.__recurse_ct(ct, child, d)
def get_preference_dictionary(self, ct):
"""passed a CachedCategoryTree, return category->preference dict"""
# pass around a dictionary to add
ret = {}
# start at the concrete node's children
top_level = ct.children(ct.concrete_node)
for n in top_level:
# assign randomly
ret[n.id] = ml.sample_category_distribution(self.original_distribution)[0]
self.__recurse_ct(ct, n, ret)
return ret
def get_preference_dictionary(self, ct):
"""passed a CachedCategoryTree, return category->preference dict"""
# pass around a dictionary to add
ret = {}
# start at the concrete node's children
top_level = ct.children(ct.concrete_node)
for n in top_level:
# assign randomly
ret[n.id] = ml.sample_category_distribution(
self.original_distribution)[0]
self.__recurse_ct(ct, n, ret)
return ret
def run_round(self, num_recommendations=settings.N):
"""
run a single round with the given number of recommendations, adding
to the behavior database and returning the Round object
"""
cats = ml.recommend_categories(self.user, ctree=self.ctree,
db=self.db).items()
categories = [c.id for c in
ml.sample_category_distribution(cats, settings.N)]
actions = map(self.get_action, categories)
r = Round(categories, actions)
# add behavior and to rounds
self.db.update_from_round(self.user, r)
return r
def run_round(self, num_recommendations=settings.N):
"""
run a single round with the given number of recommendations, adding
to the behavior database and returning the Round object
"""
cats = ml.recommend_categories(self.user, ctree=self.ctree,
db=self.db).items()
categories = [
c.id for c in ml.sample_category_distribution(cats, settings.N)
]
actions = map(self.get_action, categories)
r = Round(categories, actions)
# add behavior and to rounds
self.db.update_from_round(self.user, r)
return r
def sample_action(self):
"""get one random from this preference distribution"""
return ml.sample_category_distribution(self.distribution)[0]
def sample_action(self):
"""get one random from this preference distribution"""
return ml.sample_category_distribution(self.distribution)[0]
