def compare_items_rank(items_pop1, items_pop2, topk=1000):
arr1 = []
arr2 = []
rankdiff = []
rank = []
arr1 = sorted(items_pop1.iteritems(), key=operator.itemgetter(1), reverse=True)
arr2 = sorted(items_pop2.iteritems(), key=operator.itemgetter(1), reverse=True)
rankdict1 = {}
rankdict2 = {}
i = 1
for k, v in arr1:
rankdict1[k] = i
i += 1
i = 1
for k, v in arr2:
rankdict2[k] = i
i += 1
count = 0
t1 = []
t2 = []
for k, v in arr1[:topk]:
if k in rankdict2:
rankdiff.append(abs(rankdict1[k] - rankdict2[k])) # /float(rankdict1[k]))
rank.append(rankdict1[k])
t1.append(rankdict1[k])
t2.append(rankdict2[k])
if rankdict2[k] > topk * 2:
count += 1
else:
print "This rank is not even listed in top", topk, "loved artists", rankdict1[k]
print count
plotter.plotLinesXY(t1, t2, "listen", "love", ylim_val=[0, 5000])
return max(rankdiff), np.mean(rankdiff), np.median(rankdiff), min(rankdiff)
def plot_early_late_adoption(self, interact_type):
num_interacts = self.find_early_late_adopters(interact_type)
prev_users = []; freq=[]
for k,v in sorted(num_interacts.iteritems(), key=operator.itemgetter(0)):
prev_users.append(k)
freq.append(v)
plotter.plotLinesXY(prev_users, freq, labelx="Fraction of users who have interacted before with an item",
labely="Number of interactions", logyscale=True)
def temporal(ta):
x = ta.create_interactions_stream(interact_type)
ta.plot_early_late_adoption(interact_type)
num_friends = ta.count_friends_before_interaction(0)
prev_users = []; freq=[]
for k,v in num_friends.iteritems():
prev_users.append(k)
freq.append(v)
plotter.plotLinesXY(prev_users, freq, labelx="Number of friends who have interacted before with an item",
labely="Number of interactions", xlim_val=(0,20))
def plot_early_late_adoption(self, interact_type):
num_interacts = self.find_early_late_adopters(interact_type)
prev_users = []
freq = []
for k, v in sorted(num_interacts.iteritems(),
key=operator.itemgetter(0)):
prev_users.append(k)
freq.append(v)
plotter.plotLinesXY(
prev_users,
freq,
labelx="Fraction of users who have interacted before with an item",
labely="Number of interactions",
logyscale=True)
def temporal(ta):
x = ta.create_interactions_stream(interact_type)
ta.plot_early_late_adoption(interact_type)
num_friends = ta.count_friends_before_interaction(0)
prev_users = []
freq = []
for k, v in num_friends.iteritems():
prev_users.append(k)
freq.append(v)
plotter.plotLinesXY(
prev_users,
freq,
labelx="Number of friends who have interacted before with an item",
labely="Number of interactions",
xlim_val=(0, 20))
def compare_items_rank(items_pop1, items_pop2, topk=1000):
arr1 = []
arr2 = []
rankdiff = []
rank = []
arr1 = sorted(items_pop1.iteritems(),
key=operator.itemgetter(1),
reverse=True)
arr2 = sorted(items_pop2.iteritems(),
key=operator.itemgetter(1),
reverse=True)
rankdict1 = {}
rankdict2 = {}
i = 1
for k, v in arr1:
rankdict1[k] = i
i += 1
i = 1
for k, v in arr2:
rankdict2[k] = i
i += 1
count = 0
t1 = []
t2 = []
for k, v in arr1[:topk]:
if k in rankdict2:
rankdiff.append(abs(rankdict1[k] -
rankdict2[k])) #/float(rankdict1[k]))
rank.append(rankdict1[k])
t1.append(rankdict1[k])
t2.append(rankdict2[k])
if rankdict2[k] > topk * 2:
count += 1
else:
print "This rank is not even listed in top", topk, "loved artists", rankdict1[
k]
print count
plotter.plotLinesXY(t1, t2, "listen", "love", ylim_val=[0, 5000])
return max(rankdiff), np.mean(rankdiff), np.median(rankdiff), min(rankdiff)
labely="Frequency of Users",
xlim_val=[0, 3])
listen_dict = {}
for val1, val2 in promisc:
listen_dict[val1] = listen_dict.get(val1, [])
listen_dict[val1].append(val2)
mean_promisc = []
listen_values = []
for key, val_arr in listen_dict.iteritems():
listen_values.append(key)
mean_promisc.append(np.mean(val_arr))
plotter.plotLinesXY(listen_values,
mean_promisc,
labelx="Number of listen interactions",
labely="Ratio of love/listen interactions",
display=True,
logxscale=False,
logyscale=False)
items_pop_listen = na.get_items_popularity2(
interact_type=na.netdata.interact_types_dict["listen"],
count_duplicates=True)
items_pop_love = na.get_items_popularity2(
interact_type=na.netdata.interact_types_dict["love"],
count_duplicates=True)
plotter.plotHist(items_pop_listen.values(),
labelx="Total number of Listens for an item",
labely="Frequency")
plotter.plotHist(items_pop_love.values(),
labelx="Total number of Loves for an item",
)
listen_dict = {}
for val1, val2 in promisc:
listen_dict[val1] = listen_dict.get(val1, [])
listen_dict[val1].append(val2)
mean_promisc = []
listen_values = []
for key, val_arr in listen_dict.iteritems():
listen_values.append(key)
mean_promisc.append(np.mean(val_arr))
plotter.plotLinesXY(
listen_values,
mean_promisc,
labelx="Number of listen interactions",
labely="Ratio of love/listen interactions",
display=True,
logxscale=False,
logyscale=False,
)
items_pop_listen = na.get_items_popularity2(
interact_type=na.netdata.interact_types_dict["listen"], count_duplicates=True
)
items_pop_love = na.get_items_popularity2(
interact_type=na.netdata.interact_types_dict["love"], count_duplicates=True
)
plotter.plotHist(items_pop_listen.values(), labelx="Total number of Listens for an item", labely="Frequency")
plotter.plotHist(items_pop_love.values(), labelx="Total number of Loves for an item", labely="Frequency")
sorted_items_listen = sorted(items_pop_listen.iteritems(), key=operator.itemgetter(1), reverse=True)
sorted_items_love = sorted(items_pop_love.iteritems(), key=operator.itemgetter(1), reverse=True)
