File size: 5,565 Bytes
ab8be8f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from pathlib import Path
from concurrent.futures import ProcessPoolExecutor
import qlib
from qlib.data import D
import fire
import datacompy
import pandas as pd
from tqdm import tqdm
from loguru import logger
class CheckBin:
NOT_IN_FEATURES = "not in features"
COMPARE_FALSE = "compare False"
COMPARE_TRUE = "compare True"
COMPARE_ERROR = "compare error"
def __init__(
self,
qlib_dir: str,
csv_path: str,
check_fields: str = None,
freq: str = "day",
symbol_field_name: str = "symbol",
date_field_name: str = "date",
file_suffix: str = ".csv",
max_workers: int = 16,
):
"""
Parameters
----------
qlib_dir : str
qlib dir
csv_path : str
origin csv path
check_fields : str, optional
check fields, by default None, check qlib_dir/features/<first_dir>/*.<freq>.bin
freq : str, optional
freq, value from ["day", "1m"]
symbol_field_name: str, optional
symbol field name, by default "symbol"
date_field_name: str, optional
date field name, by default "date"
file_suffix: str, optional
csv file suffix, by default ".csv"
max_workers: int, optional
max workers, by default 16
"""
self.qlib_dir = Path(qlib_dir).expanduser()
bin_path_list = list(self.qlib_dir.joinpath("features").iterdir())
self.qlib_symbols = sorted(map(lambda x: x.name.lower(), bin_path_list))
qlib.init(
provider_uri=str(self.qlib_dir.resolve()),
mount_path=str(self.qlib_dir.resolve()),
auto_mount=False,
redis_port=-1,
)
csv_path = Path(csv_path).expanduser()
self.csv_files = sorted(csv_path.glob(f"*{file_suffix}") if csv_path.is_dir() else [csv_path])
if check_fields is None:
check_fields = list(map(lambda x: x.name.split(".")[0], bin_path_list[0].glob(f"*.bin")))
else:
check_fields = check_fields.split(",") if isinstance(check_fields, str) else check_fields
self.check_fields = list(map(lambda x: x.strip(), check_fields))
self.qlib_fields = list(map(lambda x: f"${x}", self.check_fields))
self.max_workers = max_workers
self.symbol_field_name = symbol_field_name
self.date_field_name = date_field_name
self.freq = freq
self.file_suffix = file_suffix
def _compare(self, file_path: Path):
symbol = file_path.name.strip(self.file_suffix)
if symbol.lower() not in self.qlib_symbols:
return self.NOT_IN_FEATURES
# qlib data
qlib_df = D.features([symbol], self.qlib_fields, freq=self.freq)
qlib_df.rename(columns={_c: _c.strip("$") for _c in qlib_df.columns}, inplace=True)
# csv data
origin_df = pd.read_csv(file_path)
origin_df[self.date_field_name] = pd.to_datetime(origin_df[self.date_field_name])
if self.symbol_field_name not in origin_df.columns:
origin_df[self.symbol_field_name] = symbol
origin_df.set_index([self.symbol_field_name, self.date_field_name], inplace=True)
origin_df.index.names = qlib_df.index.names
origin_df = origin_df.reindex(qlib_df.index)
try:
compare = datacompy.Compare(
origin_df,
qlib_df,
on_index=True,
abs_tol=1e-08, # Optional, defaults to 0
rel_tol=1e-05, # Optional, defaults to 0
df1_name="Original", # Optional, defaults to 'df1'
df2_name="New", # Optional, defaults to 'df2'
)
_r = compare.matches(ignore_extra_columns=True)
return self.COMPARE_TRUE if _r else self.COMPARE_FALSE
except Exception as e:
logger.warning(f"{symbol} compare error: {e}")
return self.COMPARE_ERROR
def check(self):
"""Check whether the bin file after ``dump_bin.py`` is executed is consistent with the original csv file data"""
logger.info("start check......")
error_list = []
not_in_features = []
compare_false = []
with tqdm(total=len(self.csv_files)) as p_bar:
with ProcessPoolExecutor(max_workers=self.max_workers) as executor:
for file_path, _check_res in zip(self.csv_files, executor.map(self._compare, self.csv_files)):
symbol = file_path.name.strip(self.file_suffix)
if _check_res == self.NOT_IN_FEATURES:
not_in_features.append(symbol)
elif _check_res == self.COMPARE_ERROR:
error_list.append(symbol)
elif _check_res == self.COMPARE_FALSE:
compare_false.append(symbol)
p_bar.update()
logger.info("end of check......")
if error_list:
logger.warning(f"compare error: {error_list}")
if not_in_features:
logger.warning(f"not in features: {not_in_features}")
if compare_false:
logger.warning(f"compare False: {compare_false}")
logger.info(
f"total {len(self.csv_files)}, {len(error_list)} errors, {len(not_in_features)} not in features, {len(compare_false)} compare false"
)
if __name__ == "__main__":
fire.Fire(CheckBin)
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