def plot_comments_count():
all = load_obj('comments_count_by_month')
reply = load_obj('comments_reply_count_by_month')
noreply = load_obj('comments_noreply_count_by_month')
dates = []
for i in range(2008, 2014):
for j in range(1, 13):
if i == 2013 and j == 7:
break
date = "0{}-{}".format(j, i)
dates.append(date)
fig, ax = plt.subplots(figsize=(10, 6))
# plt.title('Liczba zamieszczanych komentarzy')
plt.ylabel('Liczba komentarzy')
plt.plot(dates, all)
plt.plot(dates, reply)
plt.plot(dates, noreply)
plt.legend(['wszystkie komentarze', 'odpowiedzi na komentarze', 'bezpośrednie komentarze do posta'], loc='upper left')
# plt.yscale('log', nonposy='clip')
plt.xticks(rotation=45)
every_nth = 6
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.savefig('charts/comment_frequency')
plt.show()
def plot_popular_users_posts_avg(dict):
big = load_obj('avg_posts_count_active_popularity')
avg = load_obj('avg_posts_count_average_active_popularity')
dates = []
for i in range(2008, 2014):
for j in range(1, 13):
if i == 2013 and j == 7:
break
date = "0{}-{}".format(j, i)
dates.append(date)
fig, ax = plt.subplots(figsize=(10, 6))
plt.title('Częstotliwość zameszczania postów')
plt.ylabel('Liczba postów')
plt.plot(dates, big)
plt.plot(dates, avg)
plt.legend(['duża popularność', 'średnia popularność'], loc='upper right')
# plt.yscale('log', nonposy='clip')
plt.xticks(rotation=45)
every_nth = 6
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.savefig('charts/{}_user_post_frequency'.format(dict))
plt.show()
def create_post_user_measures_dict():
post_user_measures = {}
authors = load_obj("authors")
popularity = load_obj("user_popularity_all")
for id in authors:
if id in popularity:
post_user_measures[id] = {}
post_user_measures[id]["influence"] = calculate_user_influence(id)
post_user_measures[id]["popularity"] = popularity[id]
save_obj(post_user_measures, "post_user_measures")
def add_percentage_of_commented_posts_to_post_user_dict():
post_user_dict = load_obj("post_user_dict")
user_post = load_obj("user_post")
for post_author in user_post:
post_count = post_user_dict[post_author]["post_count"]
for comment_author in user_post[post_author]:
commented_posts = user_post[post_author][comment_author][
"commented_posts"]
user_post[post_author][comment_author][
"percentage_of_commented_posts"] = round(
(commented_posts / post_count) * 100, 2)
save_obj(user_post, "user_post")
def add_percentage_of_commented_posts_to_dict():
post_user_dict = load_obj("post_user_dict")
user_relation = load_obj("user_relation")
for comment_author in user_relation:
for post_user in user_relation[comment_author]:
post_count = post_user_dict[post_user]["post_count"]
commented_posts = user_relation[comment_author][post_user][
"commented_posts"]
user_relation[comment_author][post_user][
"percentage_of_commented_posts"] = round(
(commented_posts / post_count) * 100, 2)
save_obj(user_relation, "user_relation")
def user_first_interaction():
users = {}
comment = load_obj("first_comment_date_by_user")
post = load_obj("first_post_date_by_user")
for user in comment:
users[user] = comment[user]
for user in post:
if user not in users:
users[user] = post[user]
elif post[user] < users[user]:
users[user] = post[user]
save_obj(users, "user_first_interaction_date")
def create_user_relation_more_than_100_posts_dict():
user_relation = load_obj("user_relation")
post_user_dict = load_obj("post_user_dict")
user_relation_100_posts = {}
for comment_author in list(user_relation):
user_relation_100_posts[comment_author] = {}
for post_author in list(user_relation[comment_author]):
if post_user_dict[post_author]["post_count"] >= 100:
user_relation_100_posts[comment_author][post_author] = {}
user_relation_100_posts[comment_author][post_author]["percentage_of_commented_posts"] = \
user_relation[comment_author][post_author]["percentage_of_commented_posts"]
save_obj(user_relation_100_posts, "user_relation_100_posts")
print_dict(user_relation_100_posts)
def sowiniec_chart():
s = load_obj("sowiniec_godziemba")
g = load_obj("godziemba_sowiniec")
sp = load_obj("sowiniec_posts")
gp = load_obj("godziemba_posts")
s_ratio = []
g_ratio = []
for i in range(len(s)):
if sp[i] == 0:
s_ratio.append(0)
else:
s_ratio.append(s[i]/sp[i])
if gp[i] == 0:
g_ratio.append(0)
else:
g_ratio.append(g[i]/gp[i])
if s_ratio[i] > 1:
s_ratio[i] = 1
if g_ratio[i] > 1:
g_ratio[i] = 1
dates = []
for i in range(2008, 2014):
for j in range(1, 13):
if i == 2013 and j == 7:
break
date = "0{}-{}".format(j, i)
dates.append(date)
fig, ax = plt.subplots(figsize=(10, 6))
# plt.title('Relacja użytkowników Sowiniec - Godziemba')
plt.ylabel('Stosunek liczby komentarzy do liczby postów użytkownika')
plt.plot(dates, s_ratio)
plt.plot(dates, g_ratio)
plt.legend(['komentarze otrzymane przez Sowińca od Godziemby', 'komentarze otrzymane przez Godziembę od Sownińca'],
loc='lower right')
# plt.yscale('log', nonposy='clip')
plt.xticks(rotation=45)
every_nth = 6
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.savefig('charts/sowiniec_godziemba.png')
plt.show()
def plot_user_relation_hist(id):
a = load_obj("authors")
d = load_obj('user_post_100')
l = []
for u in d[id]:
l.append(d[id][u]['percentage_of_commented_posts'])
num_bins = 20
plt.title("Histogram - % skomentowanych postów - {}".format(a[id]))
plt.xlabel("% skomentowanych postów")
plt.ylabel("Liczba użytkowników")
# plt.yscale('log', nonposy='clip')
plt.hist(l, num_bins, facecolor='blue', alpha=0.5)
plt.savefig('charts/user_relation_{}'.format(a[id]))
plt.show()
def calculate_user_influence(user_id):
post_user_dict = load_obj("post_user_dict")
authors = load_obj("authors")
if user_id in post_user_dict:
if post_user_dict[user_id]['followers_10'] != 0:
ir = post_user_dict[user_id][
'unique_users_counter'] / post_user_dict[user_id][
'followers_10']
else:
ir = 0
rmr = post_user_dict[user_id]['all_inter_sum'] / post_user_dict[
user_id]['post_count']
snp = round(((ir + rmr) / 2), 4)
print(str(authors[user_id]) + " & " + str(snp) + " &")
return snp
def plot_popularity_max_and_avg(dict):
max_vals = []
avg_vals = []
d2 = load_obj(dict)
for i in range(0, 66):
d = d2[i]
m_val = max(user['popularity_weight'] for user in d.values())
avg_val = sum(user['popularity_weight'] for user in d.values()) / len(d)
max_vals.append(m_val)
avg_vals.append(avg_val)
dates = []
for i in range(2008, 2014):
for j in range(1, 13):
if i == 2013 and j == 7:
break
date = "0{}-{}".format(j, i)
dates.append(date)
fig, ax = plt.subplots(figsize=(10, 6))
plt.title('Średnia i maksymalna wartość miary popularności w zależności od miesiąca')
plt.ylabel('Popularność')
plt.plot(dates, max_vals)
plt.plot(dates, avg_vals)
plt.legend(['max', 'średnia'], loc='upper right')
plt.yscale('log', nonposy='clip')
plt.xticks(rotation=45)
every_nth = 6
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.savefig('charts/{}_max_avg'.format(dict))
plt.show()
def find_max_interaction_of_post_user():
post_author = load_obj("user_post")
a = load_obj("authors")
d = {}
for user in post_author:
maxval = find_max_val_and_key(post_author[user])
d[user] = maxval
s = list(sorted(d.keys(), key=lambda x: d[x][1], reverse=True))
# print_dict(s)
for i in s:
print(
str(a[i]) + " & " + str(a[d[i][0]]) + " & " + str(d[i][1]) +
"\\\\")
def return_sorted_relation_user_post_author(user_id):
user_relation = load_obj("user_post")
post_users_dict = user_relation[user_id]
res = sorted(post_users_dict.items(),
key=lambda x: x[1]['percentage_of_commented_posts'],
reverse=True)
print_dict(res)
def plot_users_by_month():
dates = []
for i in range(2008, 2014):
for j in range(1, 13):
if i == 2013 and j == 7:
break
date = "0{}-{}".format(j, i)
dates.append(date)
print(len(dates))
users = load_obj("users_by_month")
print(len(users))
fig, ax = plt.subplots(figsize=(10, 6))
# plt.title("Liczba aktywnych użytkowników portalu Salon24 na przestrzeni 5 lat")
plt.ylabel("Liczba użytkowników")
plt.xticks(rotation=45)
plt.plot(dates, users)
print(ax.xaxis.get_ticklabels())
every_nth = 6
for n, label in enumerate(ax.xaxis.get_ticklabels()):
print(label)
if n % every_nth != 0:
label.set_visible(False)
plt.savefig("charts/users_by_month.png")
plt.show()
def plot_popularuty_histogram():
user_popularity = load_obj("user_popularity_without_comment_replies")
num_bins = 100
vals = user_popularity.values()
v = [float(x) for x in vals if float(x) >= 0.01]
plt.title("Popularność użytkowników - histogram")
plt.xlabel("Popularity")
plt.ylabel("Liczba użytkowników")
plt.yscale('log', nonposy='clip')
plt.hist(v, num_bins, facecolor='blue', alpha=0.5)
plt.show()
def plot_relation_count():
d = load_obj("comment_relation_count")
plt.yscale('log', nonposy='clip')
bars = plt.bar(list(d.keys()), d.values(), color='g')
plt.gca().set_xticks(list(d.keys()))
plt.title("Liczba relacji między użytkownikami wg podziału 0-10")
plt.xlabel("Poziom relacji")
plt.ylabel("Liczba relacji")
for bar in bars:
yval = bar.get_height()
plt.text(bar.get_x() + 0.4, yval + 0.005, yval, ha='center')
plt.savefig("charts/comment_relation_count")
plt.show()
def plot_influence_count():
data = load_obj("influence_rmi_by_month")
weak_p = []
avg_p = []
big_p = []
for i in range(0, 66):
c_w = 0
c_a = 0
c_b = 0
d = data[i]
for user in d:
if d[user]['infuence_w'] >= 15:
c_b += 1
elif 5 <= d[user]['infuence_w'] < 15:
c_a += 1
elif 2 <= d[user]['infuence_w'] < 5:
c_w += 1
weak_p.append(c_w)
avg_p.append(c_a)
big_p.append(c_b)
dates = []
for i in range(2008, 2014):
for j in range(1, 13):
if i == 2013 and j == 7:
break
date = "0{}-{}".format(j, i)
dates.append(date)
fig, ax = plt.subplots(figsize=(10, 6))
# plt.title('Liczba użytkowników wg progów wpływowości w zależności od miesiąca')
plt.ylabel('Liczba użytkowników')
plt.plot(dates, weak_p)
plt.plot(dates, avg_p)
plt.plot(dates, big_p)
plt.legend(['słaba wpływowość', 'średnia wpływowość', 'duża wpływowość'], loc='upper left')
# plt.yscale('log', nonposy='clip')
plt.xticks(rotation=45)
every_nth = 6
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.savefig('charts/influence_count.png')
plt.show()
print(sum(weak_p) / len(weak_p))
print(sum(avg_p) / len(avg_p))
print(sum(big_p) / len(big_p))
def plot_influence_hist():
d = load_obj("influence_static")
l = []
for u in d:
l.append(d[u])
# num_bins = 20
plt.title("Histogram - wpływowość użytkowników")
plt.xlabel("Miara wpływowości")
plt.ylabel("Liczba użytkowników")
plt.yscale('log', nonposy='clip')
plt.hist(l, 200, facecolor='blue', alpha=0.5)
plt.savefig('charts/influence_static')
plt.show()
def plot_influence_histogram():
post_user_measures = load_obj("post_user_measures")
num_bins = 100
influence = []
for user in post_user_measures:
if post_user_measures[user]["influence"] is not None:
influence.append(post_user_measures[user]["influence"])
print(influence)
plt.title("Wpływowość użytkowników - histogram")
plt.xlabel("Influence")
plt.ylabel("Liczba użytkowników")
plt.yscale('log', nonposy='clip')
plt.hist(influence, num_bins, facecolor='blue', alpha=0.5)
plt.show()
def plot_popularity_count(dict):
weak_p = []
avg_p = []
big_p = []
d2 = load_obj(dict)
for i in range(0, 66):
c_w = 0
c_a = 0
c_b = 0
d = d2[i]
for user in d:
if d[user]['popularity_weight'] >= 0.3:
c_b += 1
elif 0.12 <= d[user]['popularity_weight'] < 0.3:
c_a += 1
elif 0.05 <= d[user]['popularity_weight'] < 0.12:
c_w += 1
weak_p.append(c_w)
avg_p.append(c_a)
big_p.append(c_b)
dates = []
for i in range(2008, 2014):
for j in range(1, 13):
if i == 2013 and j == 7:
break
date = "0{}-{}".format(j, i)
dates.append(date)
fig, ax = plt.subplots(figsize=(10, 6))
# plt.title('Liczba użytkowników wg progów popularności w zależności od miesiąca')
plt.ylabel('Liczba użytkowników')
plt.plot(dates, weak_p)
plt.plot(dates, avg_p)
plt.plot(dates, big_p)
plt.legend(['słaba popularność', 'średnia popularność', 'duża popularność'], loc='upper left')
# plt.yscale('log', nonposy='clip')
plt.xticks(rotation=45)
print(sum(weak_p) / len(weak_p))
print(sum(avg_p) / len(avg_p))
print(sum(big_p) / len(big_p))
every_nth = 6
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.savefig('charts/{}_user_count'.format(dict))
plt.show()
def plot_user_interactions_histogram():
d = load_obj("all_users_posts_comment_count")
l = []
for u in d:
l.append(d[u])
# num_bins = 20
plt.title("Histogram - liczba wpisów użytkowników")
plt.xlabel("Liczba wpisów")
plt.ylabel("Liczba użytkowników")
plt.yscale('log', nonposy='clip')
plt.hist(l, 200, facecolor='blue', alpha=0.5)
plt.savefig('charts/user_interactions')
plt.show()
def plot_active_popular_users_weights(dict):
dates = []
for i in range(2008, 2014):
for j in range(1, 13):
if i == 2013 and j == 7:
break
date = "0{}-{}".format(j, i)
dates.append(date)
fig, ax = plt.subplots(figsize=(10, 6))
plt.ylabel("Popularność")
plt.xticks(rotation=45)
all_time_popular_ids = [657, 783, 11926, 675, 1991]
popular_users = {}
for i in all_time_popular_ids:
popular_users[i] = []
# print_dict(popular_users)
dict = load_obj(dict)
for i in range(0, 66):
print(i)
active_popularity = dict[i]
# print_dict(active_popularity)
for j in all_time_popular_ids:
if j in active_popularity:
# print("{} - {} - {}".format())
popular_users[j].append(active_popularity[j]['popularity_weight'])
else:
popular_users[j].append(0)
for i in popular_users:
plt.plot(dates, popular_users[i], linewidth=0.7)
plt.legend(['FREE YOUR MIND', 'RENATA RUDECKA-KALINOWSKA', 'KRZYSZTOF LESKI', 'CEZARY KRYSZTOPA', 'SOWINIEC'],
loc='upper right')
every_nth = 6
for n, label in enumerate(ax.xaxis.get_ticklabels()):
# print(label)
if n % every_nth != 0:
label.set_visible(False)
plt.savefig("charts/{}_weighted_fix.png".format("all"))
plt.show()
def calculate_influence():
post_user_dict = load_obj("post_user_dict")
users = []
snp_list = []
for user in post_user_dict:
if post_user_dict[user]['followers_20'] != 0:
ir = post_user_dict[user]['unique_users_counter'] / post_user_dict[
user]['followers_20']
else:
ir = 0
rmr = post_user_dict[user]['all_inter_sum'] / post_user_dict[user][
'post_count']
snp = round(((ir + rmr) / 2), 4)
# print(str(user) + " - " + str(snp))
users.append(user)
snp_list.append(snp)
list1, list2 = zip(*sorted(zip(snp_list, users), reverse=True))
return list1, list2
def create_huge_dictionary3(self):
d = load_obj("huge_dict")
for row in self.records:
author_id = row[0]
date = row[1]
interactedWith = row[2]
if author_id not in d:
d[author_id] = {}
d[author_id]["posts"] = {}
d[author_id]["posts"]["dates"] = []
d[author_id]["posts"]["interactions"] = []
d[author_id]["commnets"] = {}
d[author_id]["commnets"]["dates"] = []
d[author_id]["commnets"]["interactedWith"] = []
d[author_id]["commnets"]["interactions"] = []
d[author_id]["commnets"]["dates"].append(date)
d[author_id]["commnets"]["interactedWith"].append(interactedWith)
d[author_id]["commnets"]["interactions"].append(0)
save_obj(d, "huge_dict")
def plot_post_count_by_month():
dates = []
for i in range(2008, 2014):
for j in range(1, 13):
if i == 2013 and j == 7:
break
date = "0{}-{}".format(j, i)
dates.append(date)
fig, ax = plt.subplots(figsize=(10, 6))
plt.title("Liczba postów zamieszczonych w ciągu miesiąca przez danego użytkownika")
plt.ylabel("Liczba postów")
plt.xticks(rotation=45)
all_time_popular_ids = [657, 11926, 675, 440, 783] #, 496, 797, 11, 440, 66]
popular_users_post_count = {}
for i in all_time_popular_ids:
popular_users_post_count[i] = []
for i in range(0, 66):
print(i)
post_count = load_obj("post_count_by_month_{}".format(i))
for j in all_time_popular_ids:
if j in post_count:
popular_users_post_count[j].append(post_count[j])
else:
popular_users_post_count[j].append(0)
for i in popular_users_post_count:
plt.plot(dates, popular_users_post_count[i], linewidth=0.7)
plt.legend(['FREE YOUR MIND', 'KRZYSZTOF LESKI', 'CEZARY KRYSZTOPA', 'MAREK MIGALSKI', 'RENATA RUDECKA-KALINOWSKA'],
loc='upper right')
every_nth = 6
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.savefig("charts/popular_users_post_by_month.png")
plt.show()
def plot_popularity_histograms(dict):
d = load_obj(dict)
popularity_dicts = {2009: d[12],
2010: d[24],
2011: d[36],
2012: d[48]}
num_bins = 100
for year in popularity_dicts:
plt.title("Histogram - popularność - styczeń {}".format(year))
plt.xlabel("Popularity")
plt.ylabel("Liczba użytkowników")
plt.yscale('log', nonposy='clip')
popularity = []
for user in popularity_dicts[year]:
popularity.append(float(popularity_dicts[year][user]['popularity_weight']))
plt.hist(popularity, num_bins, facecolor='blue', alpha=0.5)
plt.savefig('charts/{}_{}'.format(dict, year))
plt.show()
j01 = [i for i in popularity if i >= 0.1]
j02 = [i for i in popularity if i >= 0.2]
def plot_active_users_by_month():
dates = []
for i in range(2008, 2014):
for j in range(1, 13):
if i == 2013 and j == 7:
break
date = "0{}-{}".format(j, i)
dates.append(date)
users_count = load_obj("active_users_by_month")
fig, ax = plt.subplots(figsize=(10, 6))
# plt.title("Aktywni użytkownicy w danym miesiącu")
plt.ylabel("Liczba użytkowników")
plt.xticks(rotation=45)
plt.plot(dates, users_count)
print(ax.xaxis.get_ticklabels())
every_nth = 6
for n, label in enumerate(ax.xaxis.get_ticklabels()):
print(label)
if n % every_nth != 0:
label.set_visible(False)
plt.savefig("charts/active_users_by_month.png")
plt.show()
