def main(): timer = Timer() timer.start() cornflower_blue = ColorPalette.BLUE tomato = ColorPalette.TOMATO color_cycle_4 = ColorPalette.CC4 label_fs = ColorPalette.LABELFS title_fs = ColorPalette.TITLEFS tick_style = ColorPalette.TICKSTYLE bar_text_style = ColorPalette.BARTEXTSTYLE data_loader = DataLoader() data_loader.load_embed_content_dict() embed_cid_dict = data_loader.embed_cid_dict embed_genre_dict = data_loader.embed_genre_dict fig, axes = plt.subplots(ncols=3, nrows=2, figsize=(12, 4)) gs = axes[0, 0].get_gridspec() for ax in axes[:, 0]: ax.remove() ax_left = fig.add_subplot(gs[:, 0]) for ax in axes[:, 1]: ax.remove() ax_mid = fig.add_subplot(gs[:, 1]) axes = [ax_left, ax_mid, axes[0, 2], axes[1, 2]] # == == == == == == Part 1: Plot the probability of forming a persistent link == == == == == == # p_form_list = [] p_persistent_list = [] with open('./justify_persistent_link.log', 'r') as fin: for line in fin: _, p_form, _, p_persistent = re.split(',|:', line) p_form = float(p_form.strip()) p_persistent = float(p_persistent.strip()) p_form_list.append(p_form) p_persistent_list.append(p_persistent) axes[0].plot(p_form_list, p_persistent_list, color=cornflower_blue) for p_form in [0.5, 0.7, 0.8, 0.9]: p_persistent = p_persistent_list[int(p_form * 100)] axes[0].scatter(p_form, p_persistent, s=15, c=tomato, edgecolors='k', zorder=30) axes[0].text(p_form - 0.01, p_persistent, '({0:.2f}, {1:.2f})'.format(p_form, p_persistent), ha='right', va='bottom') axes[0].set_xlabel('prob. of forming a link', fontsize=label_fs) axes[0].set_ylabel('prob. of being persistent link', fontsize=label_fs) axes[0].tick_params(**tick_style) axes[0].set_title('(a)', fontsize=title_fs) # == == == == == == Part 2: Plot the portion of persistent links that pass statistics test == == == == == == # log_files_list = [ './random_pearsonr.log', './ephemeral_pearsonr.log', './persistent_pearsonr.log', './reciprocal_pearsonr.log' ] link_cnt_list = [] sign_ratio_list = [] same_artist_list = [] sign_ratio_same_artist_list = [] same_genre_list = [] sign_ratio_same_genre_list = [] for log_file in log_files_list: cnt = 0 same_artist_cnt = 0 same_genre_cnt = 0 sign_cnt = 0 sign_cnt_same_artist = 0 sign_cnt_same_genre = 0 with open(log_file, 'r') as fin: for line in fin: src_embed, tar_embed, r, p = line.rstrip().split(',') src_embed = int(src_embed) tar_embed = int(tar_embed) r = float(r) p = float(p) if p < 0.05: sign_cnt += 1 cnt += 1 if embed_cid_dict[src_embed] == embed_cid_dict[tar_embed]: same_artist_cnt += 1 if p < 0.05: sign_cnt_same_artist += 1 if is_same_genre(embed_genre_dict[src_embed], embed_genre_dict[tar_embed]): same_genre_cnt += 1 if p < 0.05: sign_cnt_same_genre += 1 sign_ratio_list.append(sign_cnt / cnt) same_artist_list.append(same_artist_cnt / cnt) sign_ratio_same_artist_list.append(sign_cnt_same_artist / cnt) same_genre_list.append(same_genre_cnt / cnt) sign_ratio_same_genre_list.append(sign_cnt_same_genre / cnt) link_cnt_list.append(cnt) print( '#links: {0}, #sign links: {1}, #sign same artist: {2}, #sign same genre: {3}' .format(cnt, sign_cnt, sign_cnt_same_artist, sign_cnt_same_genre)) ind = np.arange(len(log_files_list)) axes[1].bar(ind, sign_ratio_list, 0.6, edgecolor=['k'] * 4, color=color_cycle_4, lw=1.5, alpha=0.6) axes[1].set_ylim([0, axes[0].get_ylim()[1]]) axes[1].set_ylabel('percentage of links with p<0.05', fontsize=label_fs) axes[1].set_xticklabels( ('', 'random' + r'$^{}$' + '\n({0:,})'.format(link_cnt_list[0]), 'ephemeral' + r'$^{}$' + '\n({0:,})'.format(link_cnt_list[1]), 'persistent' + r'$^{-}$' + '\n({0:,})'.format(link_cnt_list[2]), 'reciprocal' + r'$^{}$' + '\n({0:,})'.format(link_cnt_list[3]))) for tick in ind: axes[1].text(tick, sign_ratio_list[tick] + 0.01, '{0:.3f}'.format(sign_ratio_list[tick]), **bar_text_style) axes[1].tick_params(**tick_style) axes[1].set_title('(b)', fontsize=title_fs) # == == == == == == Part 3: Plot the percentage of significant persistent links belong to the same artist or contain the same genre == == == == == == # axes[2].bar(ind, np.array(same_artist_list) - np.array(sign_ratio_same_artist_list), 0.6, bottom=sign_ratio_same_artist_list, edgecolor=color_cycle_4, color=['w'] * 4, hatch='//', lw=1.5, alpha=0.6) axes[2].bar(ind, sign_ratio_same_artist_list, 0.6, edgecolor=['k'] * 4, color=color_cycle_4, lw=1.5, alpha=0.6) axes[2].set_ylim([0, axes[0].get_ylim()[1]]) axes[2].set_ylabel('same artist', fontsize=label_fs) axes[2].text(0, same_artist_list[0] + 0.01, '{0:.3f}'.format(same_artist_list[0]), **bar_text_style) for tick in ind[1:]: axes[2].text(tick, same_artist_list[tick] + 0.01, '{0:.3f}'.format(same_artist_list[tick]), **bar_text_style) axes[2].text(tick, sign_ratio_same_artist_list[tick] + 0.01, '{0:.3f}'.format(sign_ratio_same_artist_list[tick]), **bar_text_style) axes[2].tick_params(**tick_style) axes[2].get_xaxis().set_visible(False) axes[2].set_title('(c)', fontsize=title_fs) axes[3].bar(ind, np.array(same_genre_list) - np.array(sign_ratio_same_genre_list), 0.6, bottom=sign_ratio_same_genre_list, edgecolor=color_cycle_4, color=['w'] * 4, hatch='//', lw=1.5, alpha=0.6) axes[3].bar(ind, sign_ratio_same_genre_list, 0.6, edgecolor=['k'] * 4, color=color_cycle_4, lw=1.5, alpha=0.6) axes[3].set_ylim([0, axes[0].get_ylim()[1]]) axes[3].set_ylabel('same genre', fontsize=label_fs) for tick in ind: axes[3].text(tick, same_genre_list[tick] + 0.01, '{0:.3f}'.format(same_genre_list[tick]), **bar_text_style) axes[3].text(tick, sign_ratio_same_genre_list[tick] + 0.01, '{0:.3f}'.format(sign_ratio_same_genre_list[tick]), **bar_text_style) axes[3].tick_params(**tick_style) axes[3].set_xticklabels( ('', 'random' + r'$^{}$' + '\n({0:,})'.format(link_cnt_list[0]), 'ephemeral' + r'$^{}$' + '\n({0:,})'.format(link_cnt_list[1]), 'persistent' + r'$^{-}$' + '\n({0:,})'.format(link_cnt_list[2]), 'reciprocal' + r'$^{}$' + '\n({0:,})'.format(link_cnt_list[3]))) hide_spines(axes) timer.stop() plt.tight_layout() plt.savefig('../images/model_persistent_links.pdf', bbox_inches='tight') if not platform.system() == 'Linux': plt.show()
def main(): timer = Timer() timer.start() cornflower_blue = ColorPalette.BLUE tomato = ColorPalette.TOMATO color_cycle_4 = ColorPalette.CC4 label_fs = ColorPalette.LABELFS title_fs = ColorPalette.TITLEFS tick_style = ColorPalette.TICKSTYLE data_loader = DataLoader() data_loader.load_video_views() embed_view_dict = data_loader.embed_view_dict embed_avg_train_view_dict = { embed: np.mean(embed_view_dict[embed][:-NUM_OUTPUT]) for embed in embed_view_dict.keys() } data_loader.load_embed_content_dict() embed_cid_dict = data_loader.embed_cid_dict embed_genre_dict = data_loader.embed_genre_dict cid_artist_dict = {} cid_tag_dict = {} with open('../data/artist_details.json', 'r') as fin: for line in fin: artist_json = json.loads(line.rstrip()) cid_artist_dict[ artist_json['channel_id']] = artist_json['artist_name'] cid_tag_dict[artist_json['channel_id']] = artist_json['tag-dict'] cid_views_dict = defaultdict(int) cid_views_wo_network_dict = defaultdict(int) arnet_smape_list = [] net_ratio_list = [] same_artist_net_ratio_list = [] same_genre_net_ratio_list = [] total_views = 0 network_explained_views = 0 with open('./embed_prediction.json', 'r') as fin: for line in fin: result_json = json.loads(line.rstrip()) tar_embed = result_json['embed'] avg_train_views = embed_avg_train_view_dict[tar_embed] true_value = result_json['true_value'] arnet_pred = result_json['arnet_pred'] arnet_smape_list.append(smape(true_value, arnet_pred)[0]) incoming_embeds = result_json['incoming_embeds'] link_weights = result_json['link_weights'] same_artist_contributed_views = 0 same_genre_contributed_views = 0 for edge_inx, src_embed in enumerate(incoming_embeds): if embed_cid_dict[tar_embed] == embed_cid_dict[src_embed]: same_artist_contributed_views += link_weights[ edge_inx] * embed_avg_train_view_dict[src_embed] if is_same_genre(embed_genre_dict[tar_embed], embed_genre_dict[src_embed]): same_genre_contributed_views += link_weights[ edge_inx] * embed_avg_train_view_dict[src_embed] # analyse network contribution arnet_net_ratio = result_json['net_ratio'] net_ratio_list.append(arnet_net_ratio) # rounding issue can make the value slightly larger than 1 same_artist_net_ratio_list.append( min(same_artist_contributed_views / avg_train_views, 1)) same_genre_net_ratio_list.append( min(same_genre_contributed_views / avg_train_views, 1)) cid_views_dict[embed_cid_dict[tar_embed]] += avg_train_views cid_views_wo_network_dict[embed_cid_dict[ tar_embed]] += avg_train_views * (1 - arnet_net_ratio) total_views += avg_train_views network_explained_views += avg_train_views * arnet_net_ratio print( '\nFor an average video in our dataset, we estimate {0:.1f}% of the views come from the network.' .format(100 * np.mean(net_ratio_list))) print( 'In particular, {0:.1f}% ({1:.1f}%) of the views come from the same artist.' .format( 100 * np.mean(same_artist_net_ratio_list), 100 * np.mean(same_artist_net_ratio_list) / np.mean(net_ratio_list))) print( 'In total, our model estimates that the recommendation network contributes {0:.1f}% of popularity in the Vevo network.' .format(100 * network_explained_views / total_views)) print('total views for 13K: {0:.1f}M'.format(total_views / 1000000)) print('explained views for 13K: {0:.1f}M'.format(network_explained_views / 1000000)) print('total views for 60K: {0:.1f}M'.format( np.sum(list(embed_avg_train_view_dict.values())) / 1000000)) print('Gini coef with network: {0:.4f}'.format( gini(list(cid_views_dict.values())))) print('Gini coef without network: {0:.4f}\n'.format( gini(list(cid_views_wo_network_dict.values())))) fig, axes = plt.subplots(ncols=3, nrows=2, figsize=(12, 4.2)) gs = axes[0, 0].get_gridspec() for ax in axes[:, 1]: ax.remove() ax_mid = fig.add_subplot(gs[:, 1]) for ax in axes[:, 2]: ax.remove() ax_right = fig.add_subplot(gs[:, 2]) axes = [axes[0, 0], axes[1, 0], ax_mid, ax_right] # == == == == == == Part 1: Plot SMAPE vs. traffic composition == == == == == == # num_bin = 10 sorted_same_artist_tuple_list = sorted( [(x, y) for x, y in zip(same_artist_net_ratio_list, arnet_smape_list)], key=lambda x: x[0]) same_artist_split_values = [ np.percentile(same_artist_net_ratio_list, x) for x in np.arange(10, 101, 10) ] same_artist_bins = [[] for _ in range(num_bin)] for same_artist_net_ratio, arnet_smape in sorted_same_artist_tuple_list: slice_idx = int( np.floor( percentileofscore(same_artist_net_ratio_list, same_artist_net_ratio) / 10)) if slice_idx >= num_bin: slice_idx = num_bin - 1 same_artist_bins[slice_idx].append(arnet_smape) sorted_same_genre_tuple_list = sorted( [(x, y) for x, y in zip(same_genre_net_ratio_list, arnet_smape_list)], key=lambda x: x[0]) same_genre_split_values = [ np.percentile(same_genre_net_ratio_list, x) for x in np.arange(10, 101, 10) ] same_genre_bins = [[] for _ in range(num_bin)] for same_genre_net_ratio, arnet_smape in sorted_same_genre_tuple_list: slice_idx = int( np.floor( percentileofscore(same_genre_net_ratio_list, same_genre_net_ratio) / 10)) if slice_idx >= num_bin: slice_idx = num_bin - 1 same_genre_bins[slice_idx].append(arnet_smape) axes[0].plot(range(1, 11, 1), [np.mean(x) for x in same_artist_bins], color=cornflower_blue, label='same artist', mfc='none', marker='o', markersize=4) axes[1].plot(range(1, 11, 1), [np.mean(x) for x in same_genre_bins], color=tomato, label='same genre', mfc='none', marker='o', markersize=4) for ax in [axes[0], axes[1]]: ax.set_xlim([0.5, 10.5]) ax.set_ylim([7, 10.5]) ax.set_ylabel('SMAPE', fontsize=label_fs) ax.xaxis.set_ticks(np.arange(1, 10, 2)) ax.tick_params(**tick_style) ax.legend(frameon=False) axes[0].xaxis.set_major_formatter( FuncFormatter(lambda x, _: '({0:.3f})'.format(same_artist_split_values[ int(x) - 1]))) axes[1].xaxis.set_major_formatter( FuncFormatter(lambda x, _: '{0:.0f}%\n({1:.3f})'.format( 10 * x, same_genre_split_values[int(x) - 1]))) # axes[0].xaxis.set_major_formatter( # FuncFormatter(lambda x, _: '({0:.3f})'.format(10 * x))) # axes[1].xaxis.set_major_formatter( # FuncFormatter(lambda x, _: '{0:.0f}%\n({1:.3f})'.format(10 * x, 10 * x))) axes[1].set_xlabel('$\eta_v$ percentile', fontsize=label_fs) axes[0].set_title('(a)', fontsize=title_fs) # == == == == == == Part 2: Plot who can utilize the network better? == == == == == == # artist_views_list = list(cid_views_dict.values()) wo_network_artist_views_list = list(cid_views_wo_network_dict.values()) cid_list = sorted(cid_views_dict.keys()) artist_true_percentile = [ percentileofscore(artist_views_list, cid_views_dict[cid]) for cid in cid_list ] wo_network_artist_percentile = [ percentileofscore(wo_network_artist_views_list, cid_views_wo_network_dict[cid]) for cid in cid_list ] percentile_change = np.array([ artist_true_percentile[i] - wo_network_artist_percentile[i] for i in range(len(cid_list)) ]) num_popularity_loss = sum(percentile_change < 0) num_popularity_equal = sum(percentile_change == 0) num_popularity_gain = sum(percentile_change > 0) print('{0} ({1:.2f}%) artists lose popularity with network'.format( num_popularity_loss, num_popularity_loss / len(cid_list) * 100)) print('{0} ({1:.2f}%) artists with no popularity change'.format( num_popularity_equal, num_popularity_equal / len(cid_list) * 100)) print('{0} ({1:.2f}%) artists gain popularity with network\n'.format( num_popularity_gain, num_popularity_gain / len(cid_list) * 100)) artist_percentile_mat = [[] for _ in range(10)] artist_cid_mat = [[] for _ in range(10)] for idx, percentile_value in enumerate(wo_network_artist_percentile): bin_idx = min(int(np.floor(percentile_value / 10)), 9) artist_percentile_mat[bin_idx].append(artist_true_percentile[idx] - percentile_value) artist_cid_mat[bin_idx].append(cid_list[idx]) red_circle = dict(markerfacecolor=tomato, marker='o', markersize=4) axes[2].boxplot(artist_percentile_mat, showfliers=True, widths=0.5, flierprops=red_circle) axes[2].axhline(y=0, color=cornflower_blue, linestyle='--', lw=1, zorder=0) axes[2].set_xlabel('artist popularity percentile without network', fontsize=label_fs) axes[2].set_ylabel('percentile change with network', fontsize=label_fs) axes[2].tick_params(**tick_style) axes[2].set_xticks(axes[2].get_xticks()[::2]) axes[2].xaxis.set_major_formatter( FuncFormatter(lambda x, _: '{0:.0f}%'.format(10 * x))) axes[2].yaxis.set_major_formatter( FuncFormatter(lambda x, _: '{0:.0f}%'.format(x))) axes[2].set_title('(b)', fontsize=12) # find outliers whis = 1.5 top_outliers_list = [] bottom_outliers_list = [] for box_idx, box in enumerate(artist_percentile_mat): q1 = np.percentile(box, 25) q3 = np.percentile(box, 75) iq = q3 - q1 hi_val = q3 + whis * iq lo_val = q1 - whis * iq for idx, val in enumerate(box): if val > hi_val: top_outliers_list.append((artist_cid_mat[box_idx][idx], val)) elif val < lo_val: bottom_outliers_list.append( (artist_cid_mat[box_idx][idx], val)) sorted_top_outliers_list = sorted( [(cid_artist_dict[x[0]], cid_tag_dict[x[0]], int( cid_views_dict[x[0]]), x[1]) for x in top_outliers_list], key=lambda t: t[2], reverse=True) for t in sorted_top_outliers_list: print(t) print('-------------------') sorted_bottom_outliers_list = sorted( [(cid_artist_dict[x[0]], cid_tag_dict[x[0]], int( cid_views_dict[x[0]]), x[1]) for x in bottom_outliers_list], key=lambda t: t[2], reverse=True) for t in sorted_bottom_outliers_list: print(t) indie_xaxis, indie_yaxis = [], [] rap_xaxis, rap_yaxis = [], [] other_xaxis, other_yaxis = [], [] lose_xaxis, lose_yaxis = [], [] for top_outlier, _ in top_outliers_list: if 'indie' in ','.join(cid_tag_dict[top_outlier].keys()) or \ 'alternative' in ','.join(cid_tag_dict[top_outlier].keys()) or \ 'new wave' in ','.join(cid_tag_dict[top_outlier].keys()): indie_xaxis.append(cid_views_dict[top_outlier]) indie_yaxis.append((cid_views_dict[top_outlier] - cid_views_wo_network_dict[top_outlier]) / cid_views_dict[top_outlier]) elif 'rap' in ','.join(cid_tag_dict[top_outlier].keys()) or \ 'hip hop' in ','.join(cid_tag_dict[top_outlier].keys()) or \ 'rhythm and blues' in ','.join(cid_tag_dict[top_outlier].keys()) or \ 'reggae' in ','.join(cid_tag_dict[top_outlier].keys()) or \ 'punk' in ','.join(cid_tag_dict[top_outlier].keys()) or \ 'funk' in ','.join(cid_tag_dict[top_outlier].keys()) or \ 'r&b' in ','.join(cid_tag_dict[top_outlier].keys()): rap_xaxis.append(cid_views_dict[top_outlier]) rap_yaxis.append((cid_views_dict[top_outlier] - cid_views_wo_network_dict[top_outlier]) / cid_views_dict[top_outlier]) else: other_xaxis.append(cid_views_dict[top_outlier]) other_yaxis.append((cid_views_dict[top_outlier] - cid_views_wo_network_dict[top_outlier]) / cid_views_dict[top_outlier]) for bottom_outlier, _ in bottom_outliers_list: lose_xaxis.append(cid_views_dict[bottom_outlier]) lose_yaxis.append((cid_views_dict[bottom_outlier] - cid_views_wo_network_dict[bottom_outlier]) / cid_views_dict[bottom_outlier]) axes[3].scatter(indie_xaxis, indie_yaxis, marker='^', facecolors='none', edgecolors=color_cycle_4[0], s=20, label='Indie: {0}'.format(len(indie_xaxis))) axes[3].scatter(rap_xaxis, rap_yaxis, marker='o', facecolors='none', edgecolors=color_cycle_4[1], s=20, label='Hip hop: {0}'.format(len(rap_xaxis))) axes[3].scatter(other_xaxis, other_yaxis, marker='s', facecolors='none', edgecolors=color_cycle_4[2], s=20, label='Other: {0}'.format(len(other_xaxis))) # axes[3].scatter(lose_xaxis, lose_yaxis, marker='x', color=color_cycle_4[3], s=20, label='artists lose popularity: {0}'.format(len(bad_xaxis))) axes[3].set_ylim((-0.02, 1.02)) axes[3].set_xscale('log') axes[3].set_xlabel('artist average daily views', fontsize=label_fs) axes[3].set_ylabel('network contribution ratio ' + '$\eta_v$', fontsize=label_fs) axes[3].tick_params(**tick_style) axes[3].legend(frameon=False, loc='lower left') axes[3].set_title('(c)', fontsize=title_fs) hide_spines(axes) timer.stop() plt.tight_layout(w_pad=0.2) plt.savefig('../images/model_prediction_analysis.pdf', bbox_inches='tight') plt.show()
def main(): # == == == == == == Part 1: Set up environment == == == == == == # timer = Timer() timer.start() data_prefix = '../data/' target_day_indices = [0, 15, 30, 45] color_cycle_4 = ColorPalette.CC4 date_labels = [ 'Sep 01, 2018', 'Sep 16, 2018', 'Oct 01, 2018', 'Oct 16, 2018' ] # == == == == == == Part 2: Load video views == == == == == == # data_loader = DataLoader() data_loader.load_video_views() embed_view_dict = data_loader.embed_view_dict embed_avg_view_dict = data_loader.embed_avg_view_dict num_videos = data_loader.num_videos target_day_view_list = [[], [], [], []] for embed in range(num_videos): for target_idx, target_day in enumerate(target_day_indices): target_day_view_list[target_idx].append( embed_view_dict[embed][target_day]) # == == == == == == Part 3: Load dynamic network snapshot == == == == == == # embed_indegree_dict = { embed: np.zeros((T, )) for embed in np.arange(num_videos) } # daily indegree for each embed zero_indegree_list = [] # percentage of zero indegree for each day num_edges_list = [] # number of total edges for each day for t in range(T): filename = 'network_{0}.p'.format( (datetime(2018, 9, 1) + timedelta(days=t)).strftime('%Y-%m-%d')) indegree_list = [] with open(os.path.join(data_prefix, 'network_pickle', filename), 'rb') as fin: network_dict = pickle.load(fin) # embed_tar: [(embed_src, pos_src, view_src), ...] for tar_embed in range(num_videos): indegree_value = len( [1 for x in network_dict[tar_embed] if x[1] < NUM_REL]) embed_indegree_dict[tar_embed][t] = indegree_value indegree_list.append(indegree_value) indegree_counter = Counter(indegree_list) zero_indegree_list.append(indegree_counter[0] / num_videos) num_edges_list.append(sum(indegree_list)) print('>>> Finish loading day {0}...'.format(t + 1)) print('>>> Network structure has been loaded!') print('\n>>> Average number of edges: {0:.0f}, max: {1:.0f}, min: {2:.0f}'. format( sum(num_edges_list) / len(num_edges_list), max(num_edges_list), min(num_edges_list))) fig, axes = plt.subplots(1, 3, figsize=(12, 4.5)) ax1, ax2, ax3 = axes.ravel() # == == == == == == Part 4: Plot ax1 indegree CCDF == == == == == == # embed_avg_indegree_dict = defaultdict(float) for t in range(T): for embed in range(num_videos): embed_avg_indegree_dict[embed] += embed_indegree_dict[embed][t] / T indegree_ranked_embed_list = [ x[0] for x in sorted(embed_avg_indegree_dict.items(), key=lambda kv: kv[1], reverse=True) ] top_20_indegree_embeds = indegree_ranked_embed_list[:20] popular_ranked_embed_list = [ x[0] for x in sorted( embed_avg_view_dict.items(), key=lambda kv: kv[1], reverse=True) ] top_20_popular_embeds = popular_ranked_embed_list[:20] for target_idx, target_day in enumerate(target_day_indices): indegree_list = [] for embed in range(num_videos): indegree_list.append(embed_indegree_dict[embed][target_day]) print( 'video with 10 indegree has more in-links than {0:.2f}% videos on date {1}' .format(percentileofscore(indegree_list, 10), date_labels[target_idx])) print( 'video with 20 indegree has more in-links than {0:.2f}% videos on date {1}' .format(percentileofscore(indegree_list, 20), date_labels[target_idx])) plot_ccdf(indegree_list, ax=ax1, color=color_cycle_4[target_idx], label=date_labels[target_idx]) # compute the powerlaw fit powerlaw_fit = Fit(list(embed_avg_indegree_dict.values())) infer_alpha = powerlaw_fit.power_law.alpha p = powerlaw_fit.power_law.ccdf() ins_x_axis = powerlaw_fit.power_law.__dict__['parent_Fit'].__dict__[ 'data'][:int(0.9 * len(p))] ins_y_axis = 0.1 * p[:int(0.9 * len(p))] ax1.plot(ins_x_axis, ins_y_axis, 'k:') ax1.text(0.4, 0.6, r'$x^{{{0:.2f}}}$'.format(-infer_alpha + 1), size=12, ha='right', va='bottom', transform=ax1.transAxes) ax1.set_xscale('log') ax1.set_yscale('log') ax1.set_xlabel('indegree', fontsize=11) ax1.set_ylabel('$P(X) \geq x$', fontsize=11) ax1.tick_params(axis='both', which='major', labelsize=10) ax1.set_title('(a) indegree distribution', fontsize=12) ax1.legend(frameon=False, fontsize=11, ncol=1, fancybox=False, shadow=True) mean_zero_indegree = sum(zero_indegree_list) / len(zero_indegree_list) ax1.axhline(y=1 - mean_zero_indegree, color='k', linestyle='--', zorder=30) ax1.text(0.96, 0.9, '{0:.0f}% with 0 indegree'.format(mean_zero_indegree * 100), size=11, transform=ax1.transAxes, ha='right', va='top') # == == == == == == Part 5: Plot ax2 views distribution == == == == == == # for target_idx, views_list in enumerate(target_day_view_list): x_values = range(100) y_values = [np.percentile(views_list, x) for x in x_values] ax2.plot(x_values, y_values, color=color_cycle_4[target_idx], label=date_labels[target_idx]) ax2.set_yscale('log') ax2.set_xlabel('views percentile', fontsize=11) ax2.set_ylabel('num of views', fontsize=11) ax2.tick_params(axis='both', which='major', labelsize=10) ax2.set_title('(b) daily views vs. its percentile', fontsize=12) avg_views_list = sorted(list(embed_avg_view_dict.values()), reverse=True) gini_coef = gini(avg_views_list) print('top 1% videos occupy {0:.2f}% views'.format( sum(avg_views_list[:int(0.01 * num_videos)]) / sum(avg_views_list) * 100)) print('top 10% videos occupy {0:.2f}% views'.format( sum(avg_views_list[:int(0.1 * num_videos)]) / sum(avg_views_list) * 100)) print('Gini coef: {0:.3f}'.format(gini_coef)) spearman_degree = [ embed_avg_indegree_dict[embed] for embed in range(num_videos) ] spearman_views = [ embed_avg_view_dict[embed] for embed in range(num_videos) ] print( 'Spearman correlation between views and indegree: {0:.4f}, pvalue: {1:.2f}' .format(*spearmanr(spearman_views, spearman_degree))) median_views = np.median(avg_views_list) top_views_90th = np.percentile(avg_views_list, 90) top_views_99th = np.percentile(avg_views_list, 99) ax2_xmin = ax2.get_xlim()[0] ax2_ymin = ax2.get_ylim()[0] ax2.plot((50, 50), (ax2_ymin, median_views), color='k', linestyle='--', zorder=30) ax2.plot((ax2_xmin, 50), (median_views, median_views), color='k', linestyle='--', zorder=30) ax2.text(0.49, 0.45, 'median views {0:,.0f}'.format(median_views), size=11, transform=ax2.transAxes, ha='right', va='bottom') ax2.plot((90, 90), (ax2_ymin, top_views_90th), color='k', linestyle='--', zorder=30) ax2.plot((ax2_xmin, 90), (top_views_90th, top_views_90th), color='k', linestyle='--', zorder=30) ax2.text(0.88, 0.75, '90th views {0:,.0f}'.format(top_views_90th), size=11, transform=ax2.transAxes, ha='right', va='bottom') ax2.plot((99, 99), (ax2_ymin, top_views_99th), color='k', linestyle='--', zorder=30) ax2.plot((ax2_xmin, 99), (top_views_99th, top_views_99th), color='k', linestyle='--', zorder=30) ax2.text(0.91, 0.95, '99th views {0:,.0f}'.format(top_views_99th), size=11, transform=ax2.transAxes, ha='right', va='bottom') # == == == == == == Part 7: Plot ax3 video uploading trend == == == == == == # x_axis = range(2009, 2018) x_labels = ["'09", "'10", "'11", "'12", "'13", "'14", "'15", "'16", "'17"] upload_mat = np.zeros((len(x_axis), 8)) target_topics = [ 'Pop_music', 'Rock_music', 'Hip_hop_music', 'Independent_music', 'Country_music', 'Electronic_music', 'Soul_music', 'Others' ] topic_labels = [ 'Pop', 'Rock', 'Hip hop', 'Independent', 'Country', 'Electronic', 'Soul', 'Others' ] color_cycle_8 = ColorPalette.CC8 data_loader.load_embed_content_dict() embed_title_dict = data_loader.embed_title_dict embed_uploadtime_dict = data_loader.embed_uploadtime_dict embed_genre_dict = data_loader.embed_genre_dict for embed in range(num_videos): upload_year = int(embed_uploadtime_dict[embed][:4]) if 2009 <= upload_year <= 2017: year_idx = upload_year - 2009 genres = embed_genre_dict[embed] if len(genres) == 0: # add one to "Others" genre upload_mat[year_idx, 7] += 1 else: for genre in genres: upload_mat[year_idx, target_topics.index(genre)] += 1 / len(genres) print() print([ '{0}: {1}'.format(topic, int(num)) for topic, num in zip(target_topics, np.sum(upload_mat, axis=0)) ]) stackedBarPlot(ax=ax3, data=upload_mat, cols=color_cycle_8, edgeCols=['#000000'] * 8, xlabel='uploaded year', ylabel='num of videos', scale=False, endGaps=True) ax3.tick_params(axis='both', which='major', labelsize=9) ax3.set_xticks(np.arange(len(x_axis))) ax3.set_xticklabels(x_labels) ax3.yaxis.set_major_formatter(FuncFormatter(concise_fmt)) ax3.legend([ plt.Rectangle((0, 0), 1, 1, fc=c, ec='k', alpha=0.6) for c in color_cycle_8 ], topic_labels, fontsize=9, frameon=False, handletextpad=0.2, columnspacing=0.3, ncol=4, bbox_to_anchor=(1, -0.12), bbox_transform=ax3.transAxes, fancybox=False, shadow=True) ax3.set_title('(c) VEVO videos uploading trend', fontsize=12) union_top_set = set(top_20_indegree_embeds).union(top_20_popular_embeds) print('\n>>> Size of the union set at cutoff 15:', len(union_top_set)) print('{0:>24} | {1:>17} | {2:>5} | {3:>8} | {4:>6} | {5:>10} | {6:>5}'. format('Video title', 'Artist', 'Age', 'Indegree', '-rank', 'Views', '-rank')) for embed in top_20_indegree_embeds: print( '{0:>24} & {1:>17} & {2:>5} & {3:>8} & {4:>6} & {5:>10} & {6:>5} \\\\' .format( embed_title_dict[embed].split( ' - ', 1)[1].split('(')[0].split('ft')[0].strip(), embed_title_dict[embed].split( ' - ', 1)[0].split('&')[0].split(',')[0].strip(), '{0:,}'.format( (datetime(2018, 11, 2) - str2obj(embed_uploadtime_dict[embed])).days), '{0:,}'.format(int(embed_avg_indegree_dict[embed])), '{0:,}'.format(top_20_indegree_embeds.index(embed) + 1), '{0:,}'.format(int(embed_avg_view_dict[embed])), '{0:,}'.format(popular_ranked_embed_list.index(embed) + 1))) print('\n{0:>24} | {1:>17} | {2:>5} | {3:>8} | {4:>6} | {5:>10} | {6:>5}'. format('Video title', 'Artist', 'Age', 'Indegree', '-rank', 'Views', '-rank')) for embed in top_20_popular_embeds: print( '{0:>24} & {1:>17} & {2:>5} & {3:>8} & {4:>6} & {5:>10} & {6:>5} \\\\' .format( embed_title_dict[embed].split( ' - ', 1)[1].split('(')[0].split('ft')[0].strip(), embed_title_dict[embed].split( ' - ', 1)[0].split('&')[0].split(',')[0].strip(), '{0:,}'.format( (datetime(2018, 11, 2) - str2obj(embed_uploadtime_dict[embed])).days), '{0:,}'.format(int(embed_avg_indegree_dict[embed])), '{0:,}'.format(indegree_ranked_embed_list.index(embed) + 1), '{0:,}'.format(int(embed_avg_view_dict[embed])), '{0:,}'.format(top_20_popular_embeds.index(embed) + 1))) hide_spines(axes) timer.stop() plt.tight_layout() plt.savefig('../images/measure_basic_statistics.pdf', bbox_inches='tight') if not platform.system() == 'Linux': plt.show()
def main(): # == == == == == == Part 1: Set up environment == == == == == == # timer = Timer() timer.start() data_prefix = '../data/' # == == == == == == Part 2: Load video views == == == == == == # data_loader = DataLoader() data_loader.load_video_views() embed_avg_view_dict = data_loader.embed_avg_view_dict num_videos = data_loader.num_videos data_loader.load_embed_content_dict() embed_cid_dict = data_loader.embed_cid_dict embed_genre_dict = data_loader.embed_genre_dict # == == == == == == Part 3: Load dynamic network snapshot == == == == == == # network_dict_list = [] for t in range(T): target_date_str = obj2str(datetime(2018, 9, 1) + timedelta(days=t)) filename = 'network_{0}.p'.format(target_date_str) network_dict = pickle.load( open(os.path.join(data_prefix, 'network_pickle', filename), 'rb')) for embed in network_dict: network_dict[embed] = [ x[0] for x in network_dict[embed] if x[1] < NUM_REL ] network_dict_list.append(network_dict) persistent_src_embed_set = set() persistent_tar_embed_set = set() existing_edges = set() num_reciprocal_edges = 0 num_same_artist = 0 num_same_genre = 0 with open(os.path.join(data_prefix, 'persistent_network.csv'), 'w') as fout: fout.write('Source,Target\n') for tar_embed in range(num_videos): src_union_set = set() for t in range(T): src_union_set.update(set(network_dict_list[t][tar_embed])) for src_embed in src_union_set: linkage_list = [0] * T for t in range(T): if src_embed in network_dict_list[t][tar_embed]: linkage_list[t] = 1 if is_persistent_link(linkage_list): # filter: at least 100 daily views for target video, # and the mean daily views of source video is at least 1% of the target video src_mean = embed_avg_view_dict[src_embed] tar_mean = embed_avg_view_dict[tar_embed] if tar_mean >= 100 and src_mean >= 0.01 * tar_mean: fout.write('{0},{1}\n'.format(src_embed, tar_embed)) persistent_src_embed_set.add(src_embed) persistent_tar_embed_set.add(tar_embed) if '{1}-{0}'.format(src_embed, tar_embed) in existing_edges: num_reciprocal_edges += 1 if embed_cid_dict[src_embed] == embed_cid_dict[ tar_embed]: num_same_artist += 1 if is_same_genre(embed_genre_dict[src_embed], embed_genre_dict[tar_embed]): num_same_genre += 1 existing_edges.add('{0}-{1}'.format( src_embed, tar_embed)) print('{0} edges in the persistent network'.format(len(existing_edges))) print( '{0} source videos, {1} target videos, {2} videos appear in both set'. format( len(persistent_src_embed_set), len(persistent_tar_embed_set), len(persistent_src_embed_set.intersection( persistent_tar_embed_set)))) print('{0} pairs of reciprocal edges'.format(num_reciprocal_edges)) print('{0} ({1:.1f}%) edges belong to the same artist'.format( num_same_artist, 100 * num_same_artist / len(existing_edges))) print('{0} ({1:.1f}%) edges belong to the same genre'.format( num_same_genre, 100 * num_same_genre / len(existing_edges))) timer.stop()
def main(): # == == == == == == Part 1: Set up environment == == == == == == # timer = Timer() timer.start() data_prefix = '../data/' year_labels = [ "all years", "'09", "'10", "'11", "'12", "'13", "'14", "'15", "'16", "'17", "'18" ] num_year = len(year_labels) - 1 # == == == == == == Part 2: Load video views == == == == == == # data_loader = DataLoader() data_loader.load_video_views() data_loader.load_embed_content_dict() embed_avg_view_dict = data_loader.embed_avg_view_dict embed_uploadtime_dict = data_loader.embed_uploadtime_dict num_videos = data_loader.num_videos for embed in range(num_videos): upload_year = int(embed_uploadtime_dict[embed][:4]) if upload_year >= 2009: year_idx = upload_year - 2009 else: year_idx = 0 embed_uploadtime_dict[embed] = year_idx views_by_years_list = [[] for _ in range(num_year)] indegrees_by_years_list = [[] for _ in range(num_year)] # == == == == == == Part 3: Load dynamic network snapshot == == == == == == # embed_indegree_dict_15 = { embed: np.zeros((T, )) for embed in np.arange(num_videos) } for t in range(T): filename = 'network_{0}.p'.format( obj2str(datetime(2018, 9, 1) + timedelta(days=t))) with open(os.path.join(data_prefix, 'network_pickle', filename), 'rb') as fin: network_dict = pickle.load(fin) # embed_tar: [(embed_src, pos_src, view_src)] for embed in range(num_videos): embed_indegree_dict_15[embed][t] = len( [1 for x in network_dict[embed] if x[1] < NUM_REL_15]) print('>>> Finish loading day {0}...'.format(t + 1)) print('>>> Network structure has been loaded!') for embed in range(num_videos): views_by_years_list[embed_uploadtime_dict[embed]].append( embed_avg_view_dict[embed]) indegrees_by_years_list[embed_uploadtime_dict[embed]].append( np.mean(embed_indegree_dict_15[embed])) spearman_traces = [] all_views, all_indegrees = [], [] for i in range(num_year): all_views.extend(views_by_years_list[i]) all_indegrees.extend(indegrees_by_years_list[i]) print('\n>>> {0}'.format(year_labels[0]), spearmanr(all_views, all_indegrees)) spearman_traces.append(spearmanr(all_views, all_indegrees)[0]) for i in range(num_year): spearman_traces.append( spearmanr(views_by_years_list[i], indegrees_by_years_list[i])[0]) print('>>> {0} year'.format(year_labels[1 + i]), spearmanr(views_by_years_list[i], indegrees_by_years_list[i])) # == == == == == == Part 4: Plotting script == == == == == == # fig, ax1 = plt.subplots(1, 1, figsize=(8, 2)) tomato = ColorPalette.TOMATO blue = ColorPalette.BLUE bar1 = ax1.bar(range(num_year + 1), spearman_traces, edgecolor=['k'] * (num_year + 1), color=[tomato] + [blue] * num_year, lw=1) for rect in bar1: height = rect.get_height() plt.text(rect.get_x() + rect.get_width() / 2.0, height, '{0:.3f}'.format(height), ha='center', va='bottom') ax1.set_xticks(np.arange(11)) ax1.set_xticklabels(year_labels) ax1.set_ylabel(r'spearman $\rho$') hide_spines(ax1) timer.stop() plt.tight_layout() plt.savefig('../images/measure_spearmanr.pdf', bbox_inches='tight') if not platform.system() == 'Linux': plt.show()