def test_get_sf_evaluation_cloud_mate_in_x():
# this specific FEN is already evaluated by lichess
fen = '8/8/6k1/8/6KP/6P1/8/8 b - - 0 1'
# loc/depth don't matter
rating = transforms.get_sf_evaluation(fen, '', 1)
assert rating == 9999
def test_get_sf_evaluation_cloud():
# this specific FEN is already evaluated by lichess
fen = 'r1bqkb1r/pp1ppppp/2n2n2/2p5/8/1P3NP1/PBPPPP1P/RN1QKB1R b KQkq - 0 1'
# loc/depth don't matter
rating = transforms.get_sf_evaluation(fen, '', 1)
assert rating == -0.29
def test_get_sf_evaluation_double_checkmate():
fen = '6k1/4pppp/6r1/3b4/4r3/8/1Q5P/1R5K w - - 0 0'
cfg = ConfigParser()
cfg.read('luigi.cfg')
stockfish_loc = cfg['stockfish_cfg']['location']
depth = 20
rating = transforms.get_sf_evaluation(fen, stockfish_loc, depth)
assert rating == 9999
fen = '6k1/4pppp/6r1/3b4/4r3/8/1Q5P/1R5K b - - 0 0'
rating = transforms.get_sf_evaluation(fen, stockfish_loc, depth)
assert rating == -9999
def test_get_sf_evaluation_in_checkmate():
fen = '4Rb1k/7Q/8/1p4N1/p7/8/1P4PK/8 b - - 4 0'
cfg = ConfigParser()
cfg.read('luigi.cfg')
stockfish_loc = cfg['stockfish_cfg']['location']
depth = 20
rating = transforms.get_sf_evaluation(fen, stockfish_loc, depth)
assert rating is None
def test_get_sf_evaluation_checkmate_white():
fen = '5rk1/4Q1b1/8/pp6/8/7N/1P2R1PK/8 w - - 1 0'
cfg = ConfigParser()
cfg.read('luigi.cfg')
stockfish_loc = cfg['stockfish_cfg']['location']
depth = 20
rating = transforms.get_sf_evaluation(fen, stockfish_loc, depth)
assert rating == 9999
def test_get_sf_evaluation_checkmate_black():
fen = '8/5q1k/7p/4Q2r/P3P3/4R1P1/7p/3R1r1K w - - 3 0'
cfg = ConfigParser()
cfg.read('luigi.cfg')
stockfish_loc = cfg['stockfish_cfg']['location']
depth = 20
rating = transforms.get_sf_evaluation(fen, stockfish_loc, depth)
assert rating == -9999
def test_get_sf_evaluation_deep():
fen = 'r1bq1rk1/1pp3b1/3p2np/nP2P1p1/4Pp2/PN3NP1/1B3PBP/R2Q1RK1 b - - 2 0'
cfg = ConfigParser()
cfg.read('luigi.cfg')
stockfish_loc = cfg['stockfish_cfg']['location']
depth = 20
rating = transforms.get_sf_evaluation(fen, stockfish_loc, depth)
if '10' in stockfish_loc:
assert rating == -0.89
elif '11' in stockfish_loc:
assert rating == -0.89
elif '12' in stockfish_loc:
assert rating == -0.89
elif '13' in stockfish_loc:
assert rating == -0.89
def run(self):
from pandas import read_pickle, to_numeric, concat, DataFrame
self.output().makedirs()
with self.input().open('r') as f:
df = read_pickle(f, compression=None)
if df.empty:
def complete(self):
return True
with self.output().temporary_path() as temp_output_path:
df.to_pickle(temp_output_path, compression=None)
return
stockfish_params = stockfish_cfg()
df = df[['evaluations', 'eval_depths', 'positions']]
# explode the two different list-likes separately, then concat
no_evals = df[~df['evaluations'].astype(bool)]
df = df[df['evaluations'].astype(bool)]
no_evals = DataFrame(no_evals['positions'].explode())
no_evals['positions'] = get_clean_fens(no_evals['positions'])
evals = df['evaluations'].explode().reset_index(drop=True)
depths = df['eval_depths'].explode().reset_index(drop=True)
positions = df['positions'].explode().reset_index(drop=True)
positions = get_clean_fens(positions)
sql = """SELECT fen, evaluation, eval_depth
FROM position_evals
WHERE fen IN %(positions)s;
"""
db_evaluations = run_remote_sql_query(sql,
positions=tuple(positions.tolist() + no_evals['positions'].tolist()), # noqa
)
positions_evaluated = db_evaluations['fen'].drop_duplicates()
df = concat([positions, evals, depths], axis=1)
if self.local_stockfish:
local_evals = []
counter = 0
position_count = len(no_evals['positions'])
for position in no_evals['positions'].tolist():
if position in positions_evaluated.values:
# position will be dropped later if evaluation is None
evaluation = None
else:
sf_eval = get_sf_evaluation(position + ' 0',
stockfish_params.location,
stockfish_params.depth)
if sf_eval is not None:
evaluation = sf_eval
local_evals.append(evaluation)
# progress bar stuff
counter += 1
current_progress = counter / position_count
self.set_status_message(f'Analyzed :: '
f'{counter} / {position_count}')
self.set_progress_percentage(round(current_progress * 100, 2))
self.set_status_message(f'Analyzed all {position_count} positions')
self.set_progress_percentage(100)
no_evals['evaluations'] = local_evals
no_evals['eval_depths'] = stockfish_params.depth
no_evals.dropna(inplace=True)
df = concat([df, no_evals], axis=0, ignore_index=True)
df = df[~df['positions'].isin(positions_evaluated)]
df.rename(columns={'evaluations': 'evaluation',
'eval_depths': 'eval_depth',
'positions': 'fen'},
inplace=True)
df['evaluation'] = to_numeric(df['evaluation'],
errors='coerce')
df.dropna(inplace=True)
df = concat([df, db_evaluations], axis=0, ignore_index=True)
with self.output().temporary_path() as temp_output_path:
df.to_pickle(temp_output_path, compression=None)