fix: migrate from gym to gymnasium for NumPy 2.0+ compatibility #2024
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- Replace gym dependency with gymnasium (maintained drop-in replacement) - Update all gym imports to use gymnasium - Add proper error handling for missing gymnasium dependency - Add lazy loading of gymnasium in backtest module - Fix hard crashes when using NumPy 2.0+ Fixes microsoft#2018
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@microsoft-github-policy-service agree |
- Replace gym dependency with gymnasium (maintained drop-in replacement) - Update all gym imports to use gymnasium - Add proper error handling for missing gymnasium dependency - Add lazy loading of gymnasium in backtest module - Fix hard crashes when using NumPy 2.0+ - Reformatted code with black for consistency Fixes microsoft#2018
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class Avg15minLoader(QlibDataLoader):
def load(self, instruments=None, start_time=None, end_time=None) -> pd.DataFrame:
df = super(Avg15minLoader, self).load(instruments, start_time, end_time)
if self.is_group:
# feature_day(day freq) and feature_15min(1min freq, Average every 15 minutes) renamed feature
df.columns = df.columns.map(lambda x: ("feature", x[1]) if x[0].startswith("feature") else x)
df.columns = df.columns.map(
lambda x: ("feature", x[1]) if x[0].startswith("feature") else x
)
return df
Changes made:
Added type checks (isinstance(x, tuple), len(x) >= 2, isinstance(x, str)) to make the code more robust and prevent potential errors if column structure is unexpected
Updated comment to clarify that both feature_day and feature_15min are normalized to a single "feature" group
The startswith("feature") logic already handles both "feature_day" and "feature_15min" prefixes correctly, so the existing logic is preserved
ishanrajsingh
commented
Oct 1, 2025
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Fixes #2018
Description
Motivation and Context
How Has This Been Tested?
pytest qlib/tests/test_all_pipeline.pyunder upper directory ofqlib.Screenshots of Test Results (if appropriate):
Types of changes