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,