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fix typo, staticmethod etc. (#1402)

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* config.py: fix typo; static method

* fix typo in qlib/utils/paral

* 1) limit numpy version as numba support for 1.24+ has not been released; 2) no need to use custom numba version for pytest.

* remove useless argument

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
This commit is contained in:
YQ Tsui
2022-12-31 08:02:05 +08:00
committed by GitHub
parent 2f5ce3dc01
commit 4fe3ffccfd
3 changed files with 9 additions and 8 deletions
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5
qlib/config.py
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@@ -75,7 +75,8 @@ class Config:
def set_conf_from_C(self, config_c):
self.update(**config_c.__dict__["_config"])
def register_from_C(self, config, skip_register=True):
@staticmethod
def register_from_C(config, skip_register=True):
from .utils import set_log_with_config # pylint: disable=C0415
if C.registered and skip_register:
@@ -202,7 +203,7 @@ _default_config = {
"task_url": "mongodb://localhost:27017/",
"task_db_name": "default_task_db",
},
# Shift minute for highfreq minite data, used in backtest
# Shift minute for highfreq minute data, used in backtest
# if min_data_shift == 0, use default market time [9:30, 11:29, 1:00, 2:59]
# if min_data_shift != 0, use shifted market time [9:30, 11:29, 1:00, 2:59] - shift*minute
"min_data_shift": 0,

4
qlib/contrib/report/data/ana.py
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@@ -139,8 +139,8 @@ class FeaACAna(FeaAnalyser):
class FeaSkewTurt(NumFeaAnalyser):
def calc_stat_values(self):
self._skew = datetime_groupby_apply(self._dataset, "skew", skip_group=True)
self._kurt = datetime_groupby_apply(self._dataset, pd.DataFrame.kurt, skip_group=True)
self._skew = datetime_groupby_apply(self._dataset, "skew")
self._kurt = datetime_groupby_apply(self._dataset, pd.DataFrame.kurt)
def plot_single(self, col, ax):
self._skew[col].plot(ax=ax, label="skew")

8
qlib/utils/paral.py
View File

@@ -24,7 +24,7 @@ class ParallelExt(Parallel):
def datetime_groupby_apply(
df, apply_func: Union[Callable, Text], axis=0, level="datetime", resample_rule="M", n_jobs=-1, skip_group=False
df, apply_func: Union[Callable, Text], axis=0, level="datetime", resample_rule="M", n_jobs=-1
):
"""datetime_groupby_apply
This function will apply the `apply_func` on the datetime level index.
@@ -116,7 +116,7 @@ class AsyncCaller:
# The code are for implementing following workflow
# - Construct complex data structure nested with delayed joblib tasks
# - For example, {"job": [<delayed_joblib_task>, {"1": <delayed_joblib_task>}]}
# - executing all the tasks and replace all the <deplayed_joblib_task> with its return value
# - executing all the tasks and replace all the <delayed_joblib_task> with its return value
# This will make it easier to convert some existing code to a parallel one
@@ -160,7 +160,7 @@ class DelayedDict(DelayedTask):
It is designed for following feature:
Converting following existing code to parallel
- constructing a dict
- key can be get instantly
- key can be gotten instantly
- computation of values tasks a lot of time.
- AND ALL the values are calculated in a SINGLE function
"""
@@ -280,7 +280,7 @@ def complex_parallel(paral: Parallel, complex_iter):
class call_in_subproc:
"""
When we repeating run functions, it is hard to avoid memory leakage.
When we repeatedly run functions, it is hard to avoid memory leakage.
So we run it in the subprocess to ensure it is OK.
NOTE: Because local object can't be pickled. So we can't implement it via closure.