def save_matches(df_matched, track=track):
if track == "python":
# Connecting to the database
matches_db = df_matched[[
'id_user_1', 'id_user_2', 'score_u1', 'score_u2'
]]
matches_db.columns = [
'fk_user_1_id', 'fk_user_2_id', 'algo_score_u1', 'algo_score_u2'
]
matches_db["fk_round_id"] = conf_data["round_num_id"]
save = "db"
conn = dbq.connect()
dbq.m_single_insert(conn, matches_db)
else:
save = ".csv"
to_path = conf_data["filepath"][track] + str(
conf_data["dates"]["round_num"]
) + "/" + dt_today + conf_data["file_name"]["buddy_results"]
df_matched.to_csv(to_path,
sep=',',
decimal=".",
encoding='utf-8',
index=False,
header=True)
return print("saved {} to {}".format(track, save))
def prep_db_data(track="python"):
"""
Function that reads data stored in db
:return: pd.df, ready to be transformed (transform_data)
"""
conn = dbq.connect()
df_raw = pd.read_sql_query(dbq.signup_info, conn)
conn.close()
if df_raw.shape[0] == 0:
print("no data in local db, referring to .csv instead")
file_path = conf_data["filepath"][track] + str(conf_data["round_num"]) + \
conf_data["file_name"]["signups"]
df_raw = pd.read_csv(file_path, usecols=dbq.var)
return make_df(df_raw)
def prior_buddy_check(idx_dict, track):
"""
THIS FUNCTION NEEDS TESTING!
Retrieving prior buddy-combinations in order to set prior combinations to zero.
This function is run in scoring_alg()
:return: dictionary with all prior combinations by indeces
"""
if track == "python":
connection = dbq.connect()
df_m = pd.read_sql_query(dbq.prior_part, connection)
connection.close()
# df_m = pd.read_csv("datafiles_sens/" + track + "/"+ conf_data["file_name"]["prior_matches"], usecols=["fk_round_id", "fk_user_1_id", "fk_user_2_id"]) # aka db "matches" table
# df_m.dropna(how="all", inplace=True)
# print("retrieving matches for {} from .csv file. Fix db connection (or make sure .csv files are up to date)!".format(track))
else:
#read prior matches
df_m = pd.read_csv("datafiles_sens/" + track + "/" +
conf_data["file_name"]["prior_matches"],
usecols=[
"fk_round_id", "fk_user_1_id", "fk_user_2_id"
]) # aka db "matches" table
df_m.dropna(how="all", inplace=True)
# get lists of all participants prior this round, all participants of this round that have been in prior rounds too
df_prev = df_m[df_m["fk_round_id"] != conf_data["round_num_id"]]
userid_current = idx_dict.values()
userid_past = {
*list(df_prev["fk_user_1_id"]), *list(df_prev["fk_user_2_id"])
}
userid_repeating = set(list(set(userid_current) & set(userid_past)))
# get a dictionary with all indices of prior combinations
prior_combinations = {}
for p_userid in userid_repeating: # for each of repeating signups get the previous buddy/buddies that have also signed up for this round
comb1 = set(df_m.loc[(df_m['fk_user_1_id'] == p_userid),
"fk_user_2_id"]) & set(userid_repeating)
comb2 = set(df_m.loc[(df_m['fk_user_2_id'] == p_userid),
"fk_user_1_id"]) & set(userid_repeating)
past_buddies = {*list(comb1), *list(comb2)}
for pb in past_buddies:
prior_combinations[pb] = p_userid
return prior_combinations