def locality(la):
items_cov_list, items_popularity, cov_ratio_list, degree_distr = la.compare_items_edge_coverage(1, minimum_interactions=1)
print utils.mean_sd(items_cov_list)
print utils.mean_sd(items_popularity)
plotter.plotHist(sorted([val for val in cov_ratio_list]), "Ratio of Common Likes with friends to total popularity", "Frequency (Number of items)", logyscale=True, bins=20)
#####plotter.plotHist(sorted([val for val in cov_ratio_list]), "Ratio of Edge coverage to total popularity", "Frequency", logyscale=True)
#plotter.plotHist(sorted(items_popularity), "Item", "total popularity")
plotter.plotCumulativePopularity(items_popularity, labelx="Item percentile", labely="Cum. percent of number of likes")
f_out = open('plots/data/influenced_loves_ratio.tsv', 'w')
for i in range(len(items_cov_list)):
f_out.write(str(items_cov_list[i])+' '+str(items_popularity[i])+' '+str(cov_ratio_list[i])+'\n')
f_out.close()
def locality(la):
items_cov_list, items_popularity, cov_ratio_list, degree_distr = la.compare_items_edge_coverage(
1, minimum_interactions=1)
print utils.mean_sd(items_cov_list)
print utils.mean_sd(items_popularity)
plotter.plotHist(sorted([val for val in cov_ratio_list]),
"Ratio of Common Likes with friends to total popularity",
"Frequency (Number of items)",
logyscale=True,
bins=20)
#####plotter.plotHist(sorted([val for val in cov_ratio_list]), "Ratio of Edge coverage to total popularity", "Frequency", logyscale=True)
#plotter.plotHist(sorted(items_popularity), "Item", "total popularity")
plotter.plotCumulativePopularity(items_popularity,
labelx="Item percentile",
labely="Cum. percent of number of likes")
f_out = open('plots/data/influenced_loves_ratio.tsv', 'w')
for i in range(len(items_cov_list)):
f_out.write(
str(items_cov_list[i]) + ' ' + str(items_popularity[i]) + ' ' +
str(cov_ratio_list[i]) + '\n')
f_out.close()
na = AdoptShareComparer(data)
x = na.get_sum_interactions_by_type()
listen_arr, love_arr = compare_interact_types_byuser(na,
"listen",
"love",
binwidth=100,
duplicates=False,
min_exposure=2,
logyscale=False,
logxscale=True,
plot_type="xy")
promisc = [(listen_arr[i], float(love_arr[i]) / listen_arr[i])
for i in range(len(listen_arr))]
plotter.plotHist([val2 for val1, val2 in promisc if val2 <= 3],
labelx="Ratio of love to listen interactions for a user",
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",
listen_arr, love_arr = compare_interact_types_byuser(
na,
"listen",
"love",
binwidth=100,
duplicates=False,
min_exposure=2,
logyscale=False,
logxscale=True,
plot_type="xy",
)
promisc = [(listen_arr[i], float(love_arr[i]) / listen_arr[i]) for i in range(len(listen_arr))]
plotter.plotHist(
[val2 for val1, val2 in promisc if val2 <= 3],
labelx="Ratio of love to listen interactions for a user",
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,
