team/yrtv
4
0
Fork 0
Code Issues 8 Pull Requests Actions Packages Projects 3 Releases Wiki Activity

1.2.2: Profile Upgraded

Browse Source
This commit is contained in:
Jacky Yang
2026-01-27 00:57:35 +08:00
parent 1b9cab5628
commit b9c1af5d70
17 changed files with 655 additions and 186 deletions
Download Patch File Download Diff File Expand all files Collapse all files

340
web/services/feature_service.py
View File

@@ -166,7 +166,7 @@ class FeatureService:
merged.sort(key=lambda x: x.get(order_col, 0) or 0, reverse=True)
return merged, total
# Normal L3 browse
sql = f"SELECT * FROM dm_player_features ORDER BY {order_col} DESC LIMIT ? OFFSET ?"
features = query_db('l3', sql, [per_page, offset])
@@ -199,15 +199,34 @@ class FeatureService:
Refreshes the L3 Data Mart with full feature calculations.
"""
from web.config import Config
from web.services.web_service import WebService
import json
l3_db_path = Config.DB_L3_PATH
l2_db_path = Config.DB_L2_PATH
# Get Team Players
lineups = WebService.get_lineups()
team_player_ids = set()
for lineup in lineups:
if lineup['player_ids_json']:
try:
ids = json.loads(lineup['player_ids_json'])
# Ensure IDs are strings
team_player_ids.update([str(i) for i in ids])
except:
pass
if not team_player_ids:
print("No players found in any team lineup. Skipping L3 rebuild.")
return 0
conn_l2 = sqlite3.connect(l2_db_path)
conn_l2.row_factory = sqlite3.Row
try:
print("Loading L2 data...")
df = FeatureService._load_and_calculate_dataframe(conn_l2, min_matches)
print(f"Loading L2 data for {len(team_player_ids)} players...")
df = FeatureService._load_and_calculate_dataframe(conn_l2, list(team_player_ids))
if df is None or df.empty:
print("No data to process.")
@@ -231,6 +250,7 @@ class FeatureService:
df_to_save['updated_at'] = pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')
# Generate Insert SQL
print(f"DEBUG: Saving {len(df_to_save.columns)} columns to L3. Sample side_kd_ct: {df_to_save.get('side_kd_ct', pd.Series([0])).iloc[0]}")
placeholders = ','.join(['?'] * len(df_to_save.columns))
cols_str = ','.join(df_to_save.columns)
sql = f"INSERT OR REPLACE INTO dm_player_features ({cols_str}) VALUES ({placeholders})"
@@ -251,9 +271,14 @@ class FeatureService:
conn_l2.close()
@staticmethod
def _load_and_calculate_dataframe(conn, min_matches):
def _load_and_calculate_dataframe(conn, player_ids):
if not player_ids:
return None
placeholders = ','.join(['?'] * len(player_ids))
# 1. Basic Stats
query_basic = """
query_basic = f"""
SELECT
steam_id_64,
COUNT(*) as matches_played,
@@ -298,10 +323,10 @@ class FeatureService:
SUM(util_he_usage) as sum_util_he,
SUM(util_decoy_usage) as sum_util_decoy
FROM fact_match_players
WHERE steam_id_64 IN ({placeholders})
GROUP BY steam_id_64
HAVING COUNT(*) >= ?
"""
df = pd.read_sql_query(query_basic, conn, params=(min_matches,))
df = pd.read_sql_query(query_basic, conn, params=player_ids)
if df.empty: return None
# Basic Derived
@@ -492,6 +517,9 @@ class FeatureService:
# Force overwrite winner_side with calculated winner since DB data is unreliable (mostly NULL)
df_rounds['winner_side'] = df_rounds['calculated_winner']
# Ensure winner_side is string type to match side ('CT', 'T')
df_rounds['winner_side'] = df_rounds['winner_side'].astype(str)
# Fallback for Round 1 if still None (e.g. if prev is 0 and score is 1)
# Logic above handles Round 1 correctly (prev is 0).
@@ -533,6 +561,10 @@ class FeatureService:
# Merge Scores
df_events = df_events.merge(df_rounds, on=['match_id', 'round_num'], how='left')
# --- BAT: Win Rate vs All ---
# Removed as per request (Difficult to calculate / All Zeros)
df['bat_win_rate_vs_all'] = 0
# --- HPS: Match Point & Comeback ---
# Match Point Win Rate
mp_rounds = df_rounds[((df_rounds['ct_score'] == 12) | (df_rounds['t_score'] == 12) |
@@ -584,6 +616,85 @@ class FeatureService:
kd_stats.index.name = 'steam_id_64'
df = df.merge(kd_stats[['hps_comeback_kd_diff']], on='steam_id_64', how='left')
# HPS: Losing Streak KD Diff
# Logic: KD in rounds where team has lost >= 3 consecutive rounds vs Global KD
# 1. Identify Streak Rounds
if not df_rounds.empty:
# Ensure sorted
df_rounds = df_rounds.sort_values(['match_id', 'round_num'])
# Shift to check previous results
# We need to handle match boundaries. Groupby match_id is safer.
# CT Loss Streak
g = df_rounds.groupby('match_id')
df_rounds['ct_lost_1'] = g['t_win'].shift(1).fillna(False)
df_rounds['ct_lost_2'] = g['t_win'].shift(2).fillna(False)
df_rounds['ct_lost_3'] = g['t_win'].shift(3).fillna(False)
df_rounds['ct_in_loss_streak'] = (df_rounds['ct_lost_1'] & df_rounds['ct_lost_2'] & df_rounds['ct_lost_3'])
# T Loss Streak
df_rounds['t_lost_1'] = g['ct_win'].shift(1).fillna(False)
df_rounds['t_lost_2'] = g['ct_win'].shift(2).fillna(False)
df_rounds['t_lost_3'] = g['ct_win'].shift(3).fillna(False)
df_rounds['t_in_loss_streak'] = (df_rounds['t_lost_1'] & df_rounds['t_lost_2'] & df_rounds['t_lost_3'])
# Merge into events
# df_events already has 'match_id', 'round_num', 'attacker_side'
# We need to merge streak info
streak_cols = df_rounds[['match_id', 'round_num', 'ct_in_loss_streak', 't_in_loss_streak']]
df_events = df_events.merge(streak_cols, on=['match_id', 'round_num'], how='left')
# Determine if attacker is in streak
df_events['att_is_loss_streak'] = np.where(
df_events['attacker_side'] == 'CT', df_events['ct_in_loss_streak'],
np.where(df_events['attacker_side'] == 'T', df_events['t_in_loss_streak'], False)
)
# Determine if victim is in streak (for deaths)
df_events['vic_is_loss_streak'] = np.where(
df_events['victim_side'] == 'CT', df_events['ct_in_loss_streak'],
np.where(df_events['victim_side'] == 'T', df_events['t_in_loss_streak'], False)
)
# Calculate KD in Streak
ls_k = df_events[df_events['att_is_loss_streak']].groupby('attacker_steam_id').size()
ls_d = df_events[df_events['vic_is_loss_streak']].groupby('victim_steam_id').size()
ls_stats = pd.DataFrame({'ls_k': ls_k, 'ls_d': ls_d}).fillna(0)
ls_stats['ls_kd'] = ls_stats['ls_k'] / ls_stats['ls_d'].replace(0, 1)
# Compare with Global KD (from df_sides or recomputed)
# Recompute global KD from events to be consistent
g_k = df_events.groupby('attacker_steam_id').size()
g_d = df_events.groupby('victim_steam_id').size()
g_stats = pd.DataFrame({'g_k': g_k, 'g_d': g_d}).fillna(0)
g_stats['g_kd'] = g_stats['g_k'] / g_stats['g_d'].replace(0, 1)
ls_stats = ls_stats.join(g_stats[['g_kd']], how='outer').fillna(0)
ls_stats['hps_losing_streak_kd_diff'] = ls_stats['ls_kd'] - ls_stats['g_kd']
ls_stats.index.name = 'steam_id_64'
df = df.merge(ls_stats[['hps_losing_streak_kd_diff']], on='steam_id_64', how='left')
else:
df['hps_losing_streak_kd_diff'] = 0
# HPS: Momentum Multi-kill Rate
# Team won 3+ rounds -> 2+ kills
# Need sequential win info.
# Hard to vectorise fully without accurate round sequence reconstruction including missing rounds.
# Placeholder: 0
df['hps_momentum_multikill_rate'] = 0
# HPS: Tilt Rating Drop
df['hps_tilt_rating_drop'] = 0
# HPS: Clutch Rating Rise
df['hps_clutch_rating_rise'] = 0
# HPS: Undermanned Survival
df['hps_undermanned_survival_time'] = 0
# --- PTL: Pistol Stats ---
pistol_rounds = [1, 13]
@@ -606,70 +717,164 @@ class FeatureService:
df['ptl_pistol_kd'] = 1.0
df['ptl_pistol_util_efficiency'] = 0.0
# --- T/CT Stats ---
ct_k = df_events[df_events['attacker_side'] == 'CT'].groupby('attacker_steam_id').size()
ct_d = df_events[df_events['victim_side'] == 'CT'].groupby('victim_steam_id').size()
t_k = df_events[df_events['attacker_side'] == 'T'].groupby('attacker_steam_id').size()
t_d = df_events[df_events['victim_side'] == 'T'].groupby('victim_steam_id').size()
# --- T/CT Stats (Directly from L2 Side Tables) ---
query_sides_l2 = f"""
SELECT
steam_id_64,
'CT' as side,
COUNT(*) as matches,
SUM(round_total) as rounds,
AVG(rating2) as rating,
SUM(kills) as kills,
SUM(deaths) as deaths,
SUM(assists) as assists,
AVG(CAST(is_win as FLOAT)) as win_rate,
SUM(first_kill) as fk,
SUM(first_death) as fd,
AVG(kast) as kast,
AVG(rws) as rws,
SUM(kill_2 + kill_3 + kill_4 + kill_5) as multi_kill_rounds,
SUM(headshot_count) as hs
FROM fact_match_players_ct
WHERE steam_id_64 IN ({placeholders})
GROUP BY steam_id_64
side_stats = pd.DataFrame({'ct_k': ct_k, 'ct_d': ct_d, 't_k': t_k, 't_d': t_d}).fillna(0)
side_stats['side_rating_ct'] = side_stats['ct_k'] / side_stats['ct_d'].replace(0, 1)
side_stats['side_rating_t'] = side_stats['t_k'] / side_stats['t_d'].replace(0, 1)
side_stats['side_kd_diff_ct_t'] = side_stats['side_rating_ct'] - side_stats['side_rating_t']
UNION ALL
side_stats.index.name = 'steam_id_64'
df = df.merge(side_stats[['side_rating_ct', 'side_rating_t', 'side_kd_diff_ct_t']], on='steam_id_64', how='left')
SELECT
steam_id_64,
'T' as side,
COUNT(*) as matches,
SUM(round_total) as rounds,
AVG(rating2) as rating,
SUM(kills) as kills,
SUM(deaths) as deaths,
SUM(assists) as assists,
AVG(CAST(is_win as FLOAT)) as win_rate,
SUM(first_kill) as fk,
SUM(first_death) as fd,
AVG(kast) as kast,
AVG(rws) as rws,
SUM(kill_2 + kill_3 + kill_4 + kill_5) as multi_kill_rounds,
SUM(headshot_count) as hs
FROM fact_match_players_t
WHERE steam_id_64 IN ({placeholders})
GROUP BY steam_id_64
"""
# Side First Kill Rate
# Need total rounds per side for denominator
# Use df_player_rounds calculated in Match Point section
# If not calculated there (no MP rounds), calc now
if 'df_player_rounds' not in locals():
q_all_rounds = f"SELECT match_id, round_num FROM fact_rounds WHERE match_id IN (SELECT match_id FROM fact_match_players WHERE steam_id_64 IN ({placeholders}))"
df_all_rounds = pd.read_sql_query(q_all_rounds, conn, params=valid_ids)
df_player_rounds = df_all_rounds.merge(df_fh_sides, on='match_id')
mask_fh = df_player_rounds['round_num'] <= df_player_rounds['halftime_round']
df_player_rounds['side'] = np.where(mask_fh, df_player_rounds['fh_side'],
np.where(df_player_rounds['fh_side'] == 'CT', 'T', 'CT'))
df_sides = pd.read_sql_query(query_sides_l2, conn, params=valid_ids + valid_ids)
rounds_per_side = df_player_rounds.groupby(['steam_id_64', 'side']).size().unstack(fill_value=0)
if 'CT' not in rounds_per_side.columns: rounds_per_side['CT'] = 0
if 'T' not in rounds_per_side.columns: rounds_per_side['T'] = 0
# First Kills (Earliest event in round)
# Group by match, round -> min time.
fk_events = df_events.sort_values('event_time').drop_duplicates(['match_id', 'round_num'])
fk_ct = fk_events[fk_events['attacker_side'] == 'CT'].groupby('attacker_steam_id').size()
fk_t = fk_events[fk_events['attacker_side'] == 'T'].groupby('attacker_steam_id').size()
fk_stats = pd.DataFrame({'fk_ct': fk_ct, 'fk_t': fk_t}).fillna(0)
fk_stats = fk_stats.join(rounds_per_side, how='outer').fillna(0)
fk_stats['side_first_kill_rate_ct'] = fk_stats['fk_ct'] / fk_stats['CT'].replace(0, 1)
fk_stats['side_first_kill_rate_t'] = fk_stats['fk_t'] / fk_stats['T'].replace(0, 1)
fk_stats.index.name = 'steam_id_64'
df = df.merge(fk_stats[['side_first_kill_rate_ct', 'side_first_kill_rate_t']], on='steam_id_64', how='left')
if not df_sides.empty:
# Calculate Derived Rates per row before pivoting
df_sides['rounds'] = df_sides['rounds'].replace(0, 1) # Avoid div by zero
# KD Calculation (Sum of Kills / Sum of Deaths)
df_sides['kd'] = df_sides['kills'] / df_sides['deaths'].replace(0, 1)
# KAST Proxy (if KAST is 0)
# KAST ~= (Kills + Assists + Survived) / Rounds
# Survived = Rounds - Deaths
if df_sides['kast'].mean() == 0:
df_sides['survived'] = df_sides['rounds'] - df_sides['deaths']
df_sides['kast'] = (df_sides['kills'] + df_sides['assists'] + df_sides['survived']) / df_sides['rounds'] * 100
df_sides['fk_rate'] = df_sides['fk'] / df_sides['rounds']
df_sides['fd_rate'] = df_sides['fd'] / df_sides['rounds']
df_sides['mk_rate'] = df_sides['multi_kill_rounds'] / df_sides['rounds']
df_sides['hs_rate'] = df_sides['hs'] / df_sides['kills'].replace(0, 1)
# Pivot
# We want columns like side_rating_ct, side_rating_t, etc.
pivoted = df_sides.pivot(index='steam_id_64', columns='side').reset_index()
# Flatten MultiIndex columns
new_cols = ['steam_id_64']
for col_name, side in pivoted.columns[1:]:
# Map L2 column names to Feature names
# rating -> side_rating_{side}
# kd -> side_kd_{side}
# win_rate -> side_win_rate_{side}
# fk_rate -> side_first_kill_rate_{side}
# fd_rate -> side_first_death_rate_{side}
# kast -> side_kast_{side}
# rws -> side_rws_{side}
# mk_rate -> side_multikill_rate_{side}
# hs_rate -> side_headshot_rate_{side}
target_map = {
'rating': 'side_rating',
'kd': 'side_kd',
'win_rate': 'side_win_rate',
'fk_rate': 'side_first_kill_rate',
'fd_rate': 'side_first_death_rate',
'kast': 'side_kast',
'rws': 'side_rws',
'mk_rate': 'side_multikill_rate',
'hs_rate': 'side_headshot_rate'
}
if col_name in target_map:
new_cols.append(f"{target_map[col_name]}_{side.lower()}")
else:
new_cols.append(f"{col_name}_{side.lower()}") # Fallback for intermediate cols if needed
pivoted.columns = new_cols
# Select only relevant columns to merge
cols_to_merge = [c for c in new_cols if c.startswith('side_')]
cols_to_merge.append('steam_id_64')
df = df.merge(pivoted[cols_to_merge], on='steam_id_64', how='left')
# Fill NaN with 0 for side stats
for c in cols_to_merge:
if c != 'steam_id_64':
df[c] = df[c].fillna(0)
# Add calculated diffs for scoring/display if needed (or just let template handle it)
# KD Diff for L3 Score calculation
if 'side_rating_ct' in df.columns and 'side_rating_t' in df.columns:
df['side_kd_diff_ct_t'] = df['side_rating_ct'] - df['side_rating_t']
else:
df['side_kd_diff_ct_t'] = 0
# --- Obj Override from Main Table (sum_plants, sum_defuses) ---
# side_obj_t = sum_plants / matches_played
# side_obj_ct = sum_defuses / matches_played
df['side_obj_t'] = df['sum_plants'] / df['matches_played'].replace(0, 1)
df['side_obj_ct'] = df['sum_defuses'] / df['matches_played'].replace(0, 1)
df['side_obj_t'] = df['side_obj_t'].fillna(0)
df['side_obj_ct'] = df['side_obj_ct'].fillna(0)
else:
# Fallbacks
cols = ['hps_match_point_win_rate', 'hps_comeback_kd_diff', 'ptl_pistol_kd', 'ptl_pistol_util_efficiency',
'side_rating_ct', 'side_rating_t', 'side_first_kill_rate_ct', 'side_first_kill_rate_t', 'side_kd_diff_ct_t']
'side_rating_ct', 'side_rating_t', 'side_first_kill_rate_ct', 'side_first_kill_rate_t', 'side_kd_diff_ct_t',
'bat_win_rate_vs_all', 'hps_losing_streak_kd_diff', 'hps_momentum_multikill_rate',
'hps_tilt_rating_drop', 'hps_clutch_rating_rise', 'hps_undermanned_survival_time',
'side_win_rate_ct', 'side_win_rate_t', 'side_kd_ct', 'side_kd_t',
'side_kast_ct', 'side_kast_t', 'side_rws_ct', 'side_rws_t',
'side_first_death_rate_ct', 'side_first_death_rate_t',
'side_multikill_rate_ct', 'side_multikill_rate_t',
'side_headshot_rate_ct', 'side_headshot_rate_t',
'side_obj_ct', 'side_obj_t']
for c in cols:
df[c] = 0
df['hps_match_point_win_rate'] = df['hps_match_point_win_rate'].fillna(0.5)
df['bat_win_rate_vs_all'] = df['bat_win_rate_vs_all'].fillna(0.5)
df['hps_losing_streak_kd_diff'] = df['hps_losing_streak_kd_diff'].fillna(0)
# HPS Pressure Entry Rate (Entry Kills in Losing Matches)
q_mp_team = f"SELECT match_id, steam_id_64, is_win, entry_kills FROM fact_match_players WHERE steam_id_64 IN ({placeholders})"
# HPS Pressure Entry Rate (Entry Kills per Round in Losing Matches)
q_mp_team = f"SELECT match_id, steam_id_64, is_win, entry_kills, round_total FROM fact_match_players WHERE steam_id_64 IN ({placeholders})"
df_mp_team = pd.read_sql_query(q_mp_team, conn, params=valid_ids)
if not df_mp_team.empty:
losing_matches = df_mp_team[df_mp_team['is_win'] == 0]
if not losing_matches.empty:
# Average entry kills per losing match
pressure_entry = losing_matches.groupby('steam_id_64')['entry_kills'].mean().reset_index()
pressure_entry.rename(columns={'entry_kills': 'hps_pressure_entry_rate'}, inplace=True)
df = df.merge(pressure_entry, on='steam_id_64', how='left')
# Sum Entry Kills / Sum Rounds
pressure_entry = losing_matches.groupby('steam_id_64')[['entry_kills', 'round_total']].sum().reset_index()
pressure_entry['hps_pressure_entry_rate'] = pressure_entry['entry_kills'] / pressure_entry['round_total'].replace(0, 1)
df = df.merge(pressure_entry[['steam_id_64', 'hps_pressure_entry_rate']], on='steam_id_64', how='left')
if 'hps_pressure_entry_rate' not in df.columns:
df['hps_pressure_entry_rate'] = 0
@@ -720,15 +925,23 @@ class FeatureService:
df_player_rounds['side'] = np.where(mask_fh, df_player_rounds['fh_side'],
np.where(df_player_rounds['fh_side'] == 'CT', 'T', 'CT'))
# Filter for Pistol Rounds (1, 13)
player_pistol = df_player_rounds[df_player_rounds['round_num'].isin([1, 13])].copy()
# Filter for Pistol Rounds (1 and after halftime)
# Use halftime_round logic (MR12: 13, MR15: 16)
player_pistol = df_player_rounds[
(df_player_rounds['round_num'] == 1) |
(df_player_rounds['round_num'] == df_player_rounds['halftime_round'] + 1)
].copy()
# Merge with df_rounds to get calculated winner_side
# Note: df_rounds has the fixed 'winner_side' column
df_rounds['winner_side'] = df_rounds['winner_side'].astype(str) # Ensure string for merge safety
player_pistol = player_pistol.merge(df_rounds[['match_id', 'round_num', 'winner_side']], on=['match_id', 'round_num'], how='left')
# Calculate Win
player_pistol['is_win'] = (player_pistol['side'] == player_pistol['winner_side']).astype(int)
# Ensure winner_side is in player_pistol columns after merge
if 'winner_side' in player_pistol.columns:
player_pistol['is_win'] = (player_pistol['side'] == player_pistol['winner_side']).astype(int)
else:
player_pistol['is_win'] = 0
ptl_wins = player_pistol.groupby('steam_id_64')['is_win'].agg(['sum', 'count']).reset_index()
ptl_wins.rename(columns={'sum': 'pistol_wins', 'count': 'pistol_rounds'}, inplace=True)
@@ -800,18 +1013,19 @@ class FeatureService:
# HPS (20%)
df['score_hps'] = (
0.30 * n('sum_1v3p') +
0.25 * n('sum_1v3p') +
0.20 * n('hps_match_point_win_rate') +
0.20 * n('hps_comeback_kd_diff') +
0.15 * n('hps_pressure_entry_rate') +
0.15 * n('basic_avg_rating')
0.20 * n('basic_avg_rating')
)
# PTL (10%)
df['score_ptl'] = (
0.40 * n('ptl_pistol_kills') +
0.40 * n('ptl_pistol_win_rate') +
0.20 * n('basic_avg_headshot_kills') # Pistol rounds rely on HS
0.30 * n('ptl_pistol_kills') +
0.30 * n('ptl_pistol_win_rate') +
0.20 * n('ptl_pistol_kd') +
0.20 * n('ptl_pistol_util_efficiency')
)
# T/CT (10%)

14
web/services/stats_service.py
View File

@@ -638,10 +638,16 @@ class StatsService:
'basic_avg_perfect_kill', 'basic_avg_revenge_kill',
# L3 Advanced Dimensions
'sta_last_30_rating', 'sta_win_rating', 'sta_loss_rating', 'sta_rating_volatility', 'sta_time_rating_corr',
'bat_kd_diff_high_elo', 'bat_avg_duel_win_rate',
'hps_clutch_win_rate_1v1', 'hps_clutch_win_rate_1v3_plus', 'hps_match_point_win_rate', 'hps_pressure_entry_rate', 'hps_comeback_kd_diff',
'ptl_pistol_kills', 'ptl_pistol_win_rate', 'ptl_pistol_kd',
'side_rating_ct', 'side_rating_t', 'side_first_kill_rate_ct', 'side_first_kill_rate_t', 'side_kd_diff_ct_t',
'bat_kd_diff_high_elo', 'bat_avg_duel_win_rate', 'bat_win_rate_vs_all',
'hps_clutch_win_rate_1v1', 'hps_clutch_win_rate_1v3_plus', 'hps_match_point_win_rate', 'hps_pressure_entry_rate', 'hps_comeback_kd_diff', 'hps_losing_streak_kd_diff',
'ptl_pistol_kills', 'ptl_pistol_win_rate', 'ptl_pistol_kd', 'ptl_pistol_util_efficiency',
'side_rating_ct', 'side_rating_t', 'side_first_kill_rate_ct', 'side_first_kill_rate_t', 'side_kd_diff_ct_t', 'side_hold_success_rate_ct', 'side_entry_success_rate_t',
'side_win_rate_ct', 'side_win_rate_t', 'side_kd_ct', 'side_kd_t',
'side_kast_ct', 'side_kast_t', 'side_rws_ct', 'side_rws_t',
'side_first_death_rate_ct', 'side_first_death_rate_t',
'side_multikill_rate_ct', 'side_multikill_rate_t',
'side_headshot_rate_ct', 'side_headshot_rate_t',
'side_defuses_ct', 'side_plants_t',
'util_avg_nade_dmg', 'util_avg_flash_time', 'util_avg_flash_enemy', 'util_usage_rate'
]