def update_user_histories(active_users):
"""
"""
## Get Today's Date
today = datetime.now().date()
## Initialize Connection
user_history_con = sql.connect(USER_HISTORY_DB_PATH)
cursor = user_history_con.cursor()
## Get User Query Dates
query_dates = {}
max_date_command = """
SELECT USER, QUERY_END_DATE
FROM HISTORY
WHERE USER='******'
ORDER BY QUERY_END_DATE
LIMIT 1;"""
for user in tqdm(active_users,
total=len(active_users),
desc="Query Periods",
file=sys.stdout):
res = cursor.execute(max_date_command.format(user))
result = res.fetchall()
if len(result) == 0:
query_dates[user] = (GLOBAL_START_DATE, today.isoformat())
else:
if result[0][1] != today.isoformat():
query_dates[user] = (result[0][1], today.isoformat())
## Initialize Reddit Wrapper
reddit = RedditData()
## Query Comment History
user_comment_histories = []
for user, (start, stop) in tqdm(query_dates.items(),
total=len(query_dates),
file=sys.stdout,
desc="User Histories"):
df = reddit.retrieve_author_comments(user,
start_date=start,
end_date=stop)
try:
subreddit_counts = df.groupby(
["author"])["subreddit"].value_counts().rename(
"COMMENT_COUNT").reset_index()
except:
print('failed user:', user)
import pdb
pdb.set_trace()
subreddit_counts["QUERY_START_DATE"] = start
subreddit_counts["QUERY_END_DATE"] = stop
subreddit_counts.rename(columns={
"author": "USER",
"subreddit": "SUBREDDIT"
},
inplace=True)
user_comment_histories.append(subreddit_counts)
## Update Database
if len(user_comment_histories) > 0:
user_comment_histories = pd.concat(user_comment_histories).reset_index(
drop=True)
user_comment_histories.to_sql(
name="HISTORY",
con=user_history_con,
if_exists="append",
index=False,
)
## Close Connection
user_history_con.commit()
user_history_con.close()
subreddit_mask = np.nonzero((X_masked > 0).sum(axis=1) >= MIN_SUPPORT)[0]
X_masked = X_masked[subreddit_mask]
rows_masked = [rows[i] for i in subreddit_mask]
## Weight Using BM25
if BM25_WEIGHTING:
X_masked = bm25_weight(X_masked).tocsr()
## Fit Model
cf = CollaborativeFiltering(factors=N_FACTORS,
regularization=REGULARIZATION,
iterations=ITERATIONS,
num_threads=NUM_THREADS,
random_state=RANDOM_STATE)
cf = cf.fit(X_masked, rows=rows_masked, columns=columns_masked)
#####################
### Testing
#####################
## Test Recommendations
reddit = RedditData()
keith = reddit.retrieve_author_comments("HuskyKeith")
keith_counts = keith["subreddit"].tolist()
keith_recs = cf.recommend(keith_counts, 20)
## Test Similarity
cf.get_similar_item("movies")
## Dump Model
cf.dump(f"{MODEL_DIR}{MODEL_NAME}")