[Underlying Operator] How do you call a method with underlying arguments?

🌟 Feature Description

project version: 0.8.6
Python version: 3.8
pandas version: 1.3.4
numpy version: 1.21.4

Code Loaction: qlib\data\ops.py L1439 -- L1470 L1458

class Corr(PairRolling):
    """Rolling Correlation

    Parameters
    ----------
    feature_left : Expression
        feature instance
    feature_right : Expression
        feature instance
    N : int
        rolling window size

    Returns
    ----------
    Expression
        a feature instance with rolling correlation of two input features
    """

    def __init__(self, feature_left, feature_right, N):
        super(Corr, self).__init__(feature_left, feature_right, N, "corr")

    def _load_internal(self, instrument, start_index, end_index, *args):
        res: pd.Series = super(Corr, self)._load_internal(instrument, start_index, end_index, *args)

        # NOTE: Load uses MemCache, so calling load again will not cause performance degradation
        series_left = self.feature_left.load(instrument, start_index, end_index, *args)
        series_right = self.feature_right.load(instrument, start_index, end_index, *args)
        res.loc[
            np.isclose(series_left.rolling(self.N, min_periods=1).std(), 0, atol=2e-05)
            | np.isclose(series_right.rolling(self.N, min_periods=1).std(), 0, atol=2e-05)
        ] = np.nan
        return res

Code Loaction: qlib\data\ops.py L1387 -- L1405 L1404

    def _load_internal(self, instrument, start_index, end_index, *args):
        assert any(
            [isinstance(self.feature_left, Expression), self.feature_right, Expression]
        ), "at least one of two inputs is Expression instance"

        if isinstance(self.feature_left, Expression):
            series_left = self.feature_left.load(instrument, start_index, end_index, *args)
        else:
            series_left = self.feature_left  # numeric value
        if isinstance(self.feature_right, Expression):
            series_right = self.feature_right.load(instrument, start_index, end_index, *args)
        else:
            series_right = self.feature_right

        if self.N == 0:
            series = getattr(series_left.expanding(min_periods=1), self.func)(series_right)
        else:
            series = getattr(series_left.rolling(self.N, min_periods=1), self.func)(series_right)
        return series

The core code is as follows:
series = getattr(series_left.rolling(self.N, min_periods=1), self.func)(series_right)

I wrote my own validation code

import pandas as pd

lst_1 = [1, 3, 5, 6, 10, 23]
lst_2 = [10, 31, 15, 7, 9, 3]
series_left = pd.Series(lst_1, index=["A", "B", "C", "D", "E", "F"])  # Set the explicit index
series_right = pd.Series(lst_2, index=["A", "B", "C", "D", "E", "F"])  # Set the explicit index


# Solution One： Wrap test the getattr() method
def run(series_left, func, *args):
    series_2 = getattr(series_left.rolling(6, min_periods=1), func)(*args)
    print("[#######] args [{}], spearman [{}]".format(args, series_2))


run(series_left, "corr", series_right)
run(series_left, "corr", series_right, "spearman")

# Solution Two：Use a separate test for the getattr() method
series = getattr(series_left.rolling(6, min_periods=1), "corr")(series_right)
print("[1] pearson [{}]".format(series))

series = getattr(series_left.rolling(6, min_periods=1), "corr")(series_right, "spearman")
print("[2] spearman [{}]".format(series))


# Solution Third：Call the corr method of the underlying pandas directly
print("[3] pearson [{}]".format(series_left.corr(series_right)))
print("[4] spearman [{}]".format(series_left.corr(series_right, method="spearman")))
print("[5] spearman [{}]".format(series_left.corr(series_right, "spearman")))

Computational Results:

[#######] args [(A    10
B    31
C    15
D     7
E     9
F     3
dtype: int64,)], spearman [A         NaN
B    1.000000
C    0.227901
D   -0.228543
E   -0.378579
F   -0.563069
dtype: float64]


[#######] args [(A    10
B    31
C    15
D     7
E     9
F     3
dtype: int64, 'spearman')], spearman [A         NaN
B    1.000000
C    0.227901
D   -0.228543
E   -0.378579
F   -0.563069
dtype: float64]

[1] pearson [A         NaN
B    1.000000
C    0.227901
D   -0.228543
E   -0.378579
F   -0.563069
dtype: float64]

[2] spearman [A         NaN
B    1.000000
C    0.227901
D   -0.228543
E   -0.378579
F   -0.563069
dtype: float64]

[3] pearson [-0.5630687466927193]
[4] spearman [-0.7714285714285715]
[5] spearman [-0.7714285714285715]

Analysis Result:

Code Location	Result Output mark	Result Output
L28/L32/L40	[]/[1]/[3]	-0.563069/-0.563069/-0.5630687466927193/
L29/L36/L41/L42	[]/[2]/[4]/[5]	-0.563069/-0.563069/-0.7714285714285715/-0.7714285714285715

Conclusion:

### The above code of
getattr(series_left.rolling(6, min_periods=1), "corr")(series_right, "spearman")
has no effect with params of 'spearman', How do I fix this?

Motivation

1. Application scenario
2. Related works (Papers, Github repos etc.):
3. Any other relevant and important information:

Alternatives

Additional Notes

Start in qlib/data/ops.py at the PairRolling._load_internal and Corr._load_internal ranges cited in the issue. Reproduce the rolling corr calls with the provided pandas examples and compare how *args are passed through getattr. Done means the requested underlying argument behavior is defined, implemented if appropriate, and covered by a regression test.