def decide_posts_to_keep(raw_data_file_path, anonymous_coward_name):
# Read raw data.
document_gen = document_generator([raw_data_file_path])
post_to_targets = dict()
for document in document_gen:
comment_gen = comment_generator(document=document)
################################################################################################################
# Within-discussion comment and user anonymization.
################################################################################################################
comment_name_set,\
user_name_set,\
within_discussion_comment_anonymize,\
within_discussion_user_anonymize,\
within_discussion_anonymous_coward = within_discussion_comment_and_user_anonymization(comment_gen=comment_gen,
extract_comment_name=extract_comment_name,
extract_user_name=extract_user_name,
anonymous_coward_name=anonymous_coward_name)
################################################################################################################
# Calculate prediction targets.
################################################################################################################
try:
targets = calculate_targets(document, comment_name_set,
user_name_set,
within_discussion_anonymous_coward)
except KeyError as e:
continue
if targets["comments"] > 1:
post_id = document["post_id"]
post_to_targets[post_id] = targets
post_id_list = list()
comments_list = list()
users_list = list()
score_list = list()
controversiality_list = list()
for post_id, targets in post_to_targets.items():
post_id_list.append(post_id)
comments_list.append(targets["comments"])
users_list.append(targets["users"])
score_list.append(targets["score_wilson"])
controversiality_list.append(targets["controversiality_wilson"])
n = len(post_id_list)
post_id_list = np.array(post_id_list)
comments_list = np.array(comments_list)
users_list = np.array(users_list)
score_list = np.array(score_list)
controversiality_list = np.array(controversiality_list)
# Rank according to comments.
comments_rank = rankdata(-comments_list)
i_comments = np.argsort(comments_list)
post_id_list_comments = post_id_list[i_comments]
comments_list = comments_list[i_comments]
print(np.max(comments_list))
# Rank according to users.
users_rank = rankdata(-users_list)
i_users = np.argsort(users_list)
post_id_list_users = post_id_list[i_users]
users_list = users_list[i_users]
print(np.max(users_list))
# Rank according to score_wilson.
score_rank = rankdata(-score_list)
i_score = np.argsort(score_list)
post_id_list_score = post_id_list[i_score]
score_list = score_list[i_score]
print(np.max(score_list))
# Rank according to controversiality_wilson.
controversiality_rank = rankdata(-controversiality_list)
i_controversiality = np.argsort(controversiality_list)
post_id_list_controversiality = post_id_list[i_controversiality]
controversiality_list = controversiality_list[i_controversiality]
print(np.max(controversiality_list))
# Rank according to all.
all_rank = comments_rank + users_rank + score_rank + controversiality_rank
i = np.argsort(all_rank)
post_id_list_new = post_id_list[i][::-1]
# Select 500 posts.
post_id_chunk_list = [
chunk[-1] for chunk in split_list(list(post_id_list_new), 500)
]
for post_id in post_id_chunk_list:
print(post_to_targets[post_id])
return set(post_id_chunk_list)
def decide_posts_to_keep(raw_data_file_path, anonymous_coward_name):
# Read raw data.
document_gen = document_generator([raw_data_file_path])
post_to_targets = dict()
for document in document_gen:
comment_gen = comment_generator(document=document)
################################################################################################################
# Within-discussion comment and user anonymization.
################################################################################################################
comment_name_set,\
user_name_set,\
within_discussion_comment_anonymize,\
within_discussion_user_anonymize,\
within_discussion_anonymous_coward = within_discussion_comment_and_user_anonymization(comment_gen=comment_gen,
extract_comment_name=extract_comment_name,
extract_user_name=extract_user_name,
anonymous_coward_name=anonymous_coward_name)
################################################################################################################
# Calculate prediction targets.
################################################################################################################
try:
targets = calculate_targets(document,
comment_name_set,
user_name_set,
within_discussion_anonymous_coward)
except KeyError as e:
continue
if targets["comments"] > 1:
post_id = document["post_id"]
post_to_targets[post_id] = targets
post_id_list = list()
comments_list = list()
users_list = list()
score_list = list()
controversiality_list = list()
for post_id, targets in post_to_targets.items():
post_id_list.append(post_id)
comments_list.append(targets["comments"])
users_list.append(targets["users"])
score_list.append(targets["score_wilson"])
controversiality_list.append(targets["controversiality_wilson"])
n = len(post_id_list)
post_id_list = np.array(post_id_list)
comments_list = np.array(comments_list)
users_list = np.array(users_list)
score_list = np.array(score_list)
controversiality_list = np.array(controversiality_list)
# Rank according to comments.
comments_rank = rankdata(- comments_list)
i_comments = np.argsort(comments_list)
post_id_list_comments = post_id_list[i_comments]
comments_list = comments_list[i_comments]
print(np.max(comments_list))
# Rank according to users.
users_rank = rankdata(- users_list)
i_users = np.argsort(users_list)
post_id_list_users = post_id_list[i_users]
users_list = users_list[i_users]
print(np.max(users_list))
# Rank according to score_wilson.
score_rank = rankdata(- score_list)
i_score = np.argsort(score_list)
post_id_list_score = post_id_list[i_score]
score_list = score_list[i_score]
print(np.max(score_list))
# Rank according to controversiality_wilson.
controversiality_rank = rankdata(- controversiality_list)
i_controversiality = np.argsort(controversiality_list)
post_id_list_controversiality = post_id_list[i_controversiality]
controversiality_list = controversiality_list[i_controversiality]
print(np.max(controversiality_list))
# Rank according to all.
all_rank = comments_rank + users_rank + score_rank + controversiality_rank
i = np.argsort(all_rank)
post_id_list_new = post_id_list[i][::-1]
# Select 500 posts.
post_id_chunk_list = [chunk[-1] for chunk in split_list(list(post_id_list_new), 500)]
for post_id in post_id_chunk_list:
print(post_to_targets[post_id])
return set(post_id_chunk_list)
def make_discussion_json(document, timestamp_df, handcrafted_df, lifetime_list,
anonymous_coward_name):
discussion_json = dict()
discussion_json["post_url"] = get_post_url(document)
discussion_json["post_title"] = get_post_title(document)
# discussion_json["snapshot_timestamps"] = [repr(float(snapshot_timestamp)) for snapshot_timestamp in lifetime_list]
discussion_json["graph_snapshots"] = list()
comment_gen = comment_generator(document=document)
comment_name_set,\
user_name_set,\
within_discussion_comment_anonymize,\
within_discussion_user_anonymize,\
within_discussion_anonymous_coward = within_discussion_comment_and_user_anonymization(comment_gen=comment_gen,
extract_comment_name=extract_comment_name,
extract_user_name=extract_user_name,
anonymous_coward_name=anonymous_coward_name)
try:
discussion_json["prediction_targets"] = calculate_targets(
document, comment_name_set, user_name_set,
within_discussion_anonymous_coward)
except KeyError as e:
return None
try:
safe_comment_gen = safe_comment_generator(
document=document,
comment_generator=comment_generator,
within_discussion_comment_anonymize=
within_discussion_comment_anonymize,
extract_comment_name=extract_comment_name,
extract_parent_comment_name=extract_parent_comment_name,
extract_timestamp=extract_timestamp,
safe=True)
except TypeError:
return None
try:
initial_post = next(safe_comment_gen)
except TypeError:
return None
try:
timestamp = extract_timestamp(initial_post)
except TypeError:
return None
op_raw_id = extract_user_name(initial_post)
op_id = within_discussion_user_anonymize[op_raw_id]
if op_id == within_discussion_anonymous_coward:
op_is_anonymous = True
else:
op_is_anonymous = False
comment_counter = 0
timestamp_column_names_list,\
timestamp_array = initialize_timestamp_array(discussion_json["prediction_targets"]["comments"] + 1,
cascade_source_timestamp=timestamp)
intermediate_dict = initialize_intermediate(
comment_name_set,
user_name_set,
timestamp,
within_discussion_anonymous_coward,
op_is_anonymous=op_is_anonymous)
comment_tree = spsp.dok_matrix(
(len(comment_name_set), len(comment_name_set)), dtype=np.int8)
user_graph = spsp.dok_matrix((len(user_name_set), len(user_name_set)),
dtype=np.int32)
current_lifetime = 0.0
# lifetime_list.append(np.inf)
for lifetime_counter, lifetime in enumerate(lifetime_list):
while True:
try:
comment = next(safe_comment_gen)
except TypeError:
return None
except StopIteration:
handcrafted_df_row = handcrafted_df.iloc[comment_counter]
time_step_json = make_time_step_json(
current_lifetime, comment_tree, user_graph,
timestamp_array[comment_counter, 1], handcrafted_df_row)
discussion_json["graph_snapshots"].append(time_step_json)
break
if comment is None:
return None
comment_counter += 1
commenter_name = extract_user_name(comment)
if commenter_name is None:
commenter_is_anonymous = True
else:
commenter_is_anonymous = False
try:
discussion_tree,\
user_graph,\
comment_id,\
parent_comment_id,\
commenter_id,\
parent_commenter_id,\
user_graph_modified,\
parent_commenter_is_anonymous,\
comment_id_to_user_id = update_discussion_and_user_graphs(comment=comment,
extract_comment_name=extract_comment_name,
extract_parent_comment_name=extract_parent_comment_name,
extract_user_name=extract_user_name,
discussion_tree=comment_tree,
user_graph=user_graph,
within_discussion_comment_anonymize=within_discussion_comment_anonymize,
within_discussion_user_anonymize=within_discussion_user_anonymize,
within_discussion_anonymous_coward=within_discussion_anonymous_coward,
comment_id_to_user_id=intermediate_dict["comment_id_to_user_id"])
intermediate_dict[
"comment_id_to_user_id"] = comment_id_to_user_id
except RuntimeError:
return None
try:
timestamp = extract_timestamp(comment)
except TypeError:
return None
update_timestamp_array(timestamp_column_names_list,
timestamp_array, timestamp, comment_counter)
timestamp_difference = timestamp_array[
comment_counter, 1] - timestamp_array[comment_counter - 1, 1]
try:
intermediate_dict,\
comment_depth = update_intermediate(discussion_tree,
user_graph,
intermediate_dict,
commenter_is_anonymous,
parent_commenter_is_anonymous,
comment_id,
parent_comment_id,
commenter_id,
parent_commenter_id,
user_graph_modified,
timestamp,
timestamp_difference)
except RuntimeError:
return None
current_lifetime = timestamp_array[comment_counter,
1] - timestamp_array[0, 1]
if current_lifetime >= lifetime:
# Read features.
# handcrafted_df_row = handcrafted_df[feature_list]
handcrafted_df_row = handcrafted_df.iloc[comment_counter]
time_step_json = make_time_step_json(
current_lifetime, comment_tree, user_graph,
timestamp_array[comment_counter, 1], handcrafted_df_row)
discussion_json["graph_snapshots"].append(time_step_json)
break
discussion_json["post_timestamp"] = timestamp_array[0, 1]
# discussion_json["final_comment_tree_size"] = discussion_json["prediction_targets"]["comments"] + 1
# discussion_json["final_user_graph_size"] = discussion_json["prediction_targets"]["users"]
return discussion_json
def make_discussion_json(document, timestamp_df, handcrafted_df, lifetime_list, anonymous_coward_name):
discussion_json = dict()
discussion_json["post_url"] = get_post_url(document)
discussion_json["post_title"] = get_post_title(document)
# discussion_json["snapshot_timestamps"] = [repr(float(snapshot_timestamp)) for snapshot_timestamp in lifetime_list]
discussion_json["graph_snapshots"] = list()
comment_gen = comment_generator(document=document)
comment_name_set,\
user_name_set,\
within_discussion_comment_anonymize,\
within_discussion_user_anonymize,\
within_discussion_anonymous_coward = within_discussion_comment_and_user_anonymization(comment_gen=comment_gen,
extract_comment_name=extract_comment_name,
extract_user_name=extract_user_name,
anonymous_coward_name=anonymous_coward_name)
try:
discussion_json["prediction_targets"] = calculate_targets(document,
comment_name_set,
user_name_set,
within_discussion_anonymous_coward)
except KeyError as e:
return None
try:
safe_comment_gen = safe_comment_generator(document=document,
comment_generator=comment_generator,
within_discussion_comment_anonymize=within_discussion_comment_anonymize,
extract_comment_name=extract_comment_name,
extract_parent_comment_name=extract_parent_comment_name,
extract_timestamp=extract_timestamp,
safe=True)
except TypeError:
return None
try:
initial_post = next(safe_comment_gen)
except TypeError:
return None
try:
timestamp = extract_timestamp(initial_post)
except TypeError:
return None
op_raw_id = extract_user_name(initial_post)
op_id = within_discussion_user_anonymize[op_raw_id]
if op_id == within_discussion_anonymous_coward:
op_is_anonymous = True
else:
op_is_anonymous = False
comment_counter = 0
timestamp_column_names_list,\
timestamp_array = initialize_timestamp_array(discussion_json["prediction_targets"]["comments"] + 1,
cascade_source_timestamp=timestamp)
intermediate_dict = initialize_intermediate(comment_name_set,
user_name_set,
timestamp,
within_discussion_anonymous_coward,
op_is_anonymous=op_is_anonymous)
comment_tree = spsp.dok_matrix((len(comment_name_set),
len(comment_name_set)),
dtype=np.int8)
user_graph = spsp.dok_matrix((len(user_name_set),
len(user_name_set)),
dtype=np.int32)
current_lifetime = 0.0
# lifetime_list.append(np.inf)
for lifetime_counter, lifetime in enumerate(lifetime_list):
while True:
try:
comment = next(safe_comment_gen)
except TypeError:
return None
except StopIteration:
handcrafted_df_row = handcrafted_df.iloc[comment_counter]
time_step_json = make_time_step_json(current_lifetime,
comment_tree,
user_graph,
timestamp_array[comment_counter, 1],
handcrafted_df_row)
discussion_json["graph_snapshots"].append(time_step_json)
break
if comment is None:
return None
comment_counter += 1
commenter_name = extract_user_name(comment)
if commenter_name is None:
commenter_is_anonymous = True
else:
commenter_is_anonymous = False
try:
discussion_tree,\
user_graph,\
comment_id,\
parent_comment_id,\
commenter_id,\
parent_commenter_id,\
user_graph_modified,\
parent_commenter_is_anonymous,\
comment_id_to_user_id = update_discussion_and_user_graphs(comment=comment,
extract_comment_name=extract_comment_name,
extract_parent_comment_name=extract_parent_comment_name,
extract_user_name=extract_user_name,
discussion_tree=comment_tree,
user_graph=user_graph,
within_discussion_comment_anonymize=within_discussion_comment_anonymize,
within_discussion_user_anonymize=within_discussion_user_anonymize,
within_discussion_anonymous_coward=within_discussion_anonymous_coward,
comment_id_to_user_id=intermediate_dict["comment_id_to_user_id"])
intermediate_dict["comment_id_to_user_id"] = comment_id_to_user_id
except RuntimeError:
return None
try:
timestamp = extract_timestamp(comment)
except TypeError:
return None
update_timestamp_array(timestamp_column_names_list,
timestamp_array,
timestamp,
comment_counter)
timestamp_difference = timestamp_array[comment_counter, 1] - timestamp_array[comment_counter-1, 1]
try:
intermediate_dict,\
comment_depth = update_intermediate(discussion_tree,
user_graph,
intermediate_dict,
commenter_is_anonymous,
parent_commenter_is_anonymous,
comment_id,
parent_comment_id,
commenter_id,
parent_commenter_id,
user_graph_modified,
timestamp,
timestamp_difference)
except RuntimeError:
return None
current_lifetime = timestamp_array[comment_counter, 1] - timestamp_array[0, 1]
if current_lifetime >= lifetime:
# Read features.
# handcrafted_df_row = handcrafted_df[feature_list]
handcrafted_df_row = handcrafted_df.iloc[comment_counter]
time_step_json = make_time_step_json(current_lifetime,
comment_tree,
user_graph,
timestamp_array[comment_counter, 1],
handcrafted_df_row)
discussion_json["graph_snapshots"].append(time_step_json)
break
discussion_json["post_timestamp"] = timestamp_array[0, 1]
# discussion_json["final_comment_tree_size"] = discussion_json["prediction_targets"]["comments"] + 1
# discussion_json["final_user_graph_size"] = discussion_json["prediction_targets"]["users"]
return discussion_json
