microsoft/qlib · error · TypeError

invalid argument type for `target`

Error message

invalid argument type for `target`

What it means

ShrinkRiskModel.__init__ (qlib/model/riskmodel/shrink.py:82) validates the `target` argument (the shrinking target matrix F): it must be a string in {'const_var', 'const_corr', 'single_factor'} or an np.ndarray supplied directly. Anything else (None, list, pd.DataFrame, int) raises TypeError('invalid argument type for `target`'). Note lists and DataFrames are not auto-converted.

Source

Thrown at qlib/model/riskmodel/shrink.py:82

        if isinstance(alpha, str):
            assert alpha in [self.SHR_LW, self.SHR_OAS], f"shrinking method `{alpha}` is not supported"
        elif isinstance(alpha, (float, np.floating)):
            assert 0 <= alpha <= 1, "alpha should be between [0, 1]"
        else:
            raise TypeError("invalid argument type for `alpha`")
        self.alpha = alpha

        # target
        if isinstance(target, str):
            assert target in [
                self.TGT_CONST_VAR,
                self.TGT_CONST_CORR,
                self.TGT_SINGLE_FACTOR,
            ], f"shrinking target `{target} is not supported"
        elif isinstance(target, np.ndarray):
            pass
        else:
            raise TypeError("invalid argument type for `target`")
        if alpha == self.SHR_OAS and target != self.TGT_CONST_VAR:
            raise NotImplementedError("currently `oas` can only support `const_var` as target")
        self.target = target

    def _predict(self, X: np.ndarray) -> np.ndarray:
        # sample covariance
        S = super()._predict(X)

        # shrinking target
        F = self._get_shrink_target(X, S)

        # get shrinking parameter
        alpha = self._get_shrink_param(X, S, F)

        # shrink covariance
        if alpha > 0:
            S *= 1 - alpha
            F *= alpha

View on GitHub (pinned to 79633dd950)

Solutions

  1. Use one of the built-in target strings: 'const_var', 'const_corr', or 'single_factor'
  2. Convert custom targets to np.ndarray first: target=df.to_numpy() or np.asarray(list_target)
  3. Ensure the config value for target is a string; remove null values

Example fix

# before
model = ShrinkRiskModel(target=cov_df)  # pd.DataFrame -> TypeError

# after
model = ShrinkRiskModel(target=cov_df.to_numpy())
# or
model = ShrinkRiskModel(target='const_corr')
Defensive patterns

Strategy: validation

Validate before calling

import numpy as np
assert isinstance(target, str) and target in ('const_var', 'const_corr', 'single_factor') or isinstance(target, np.ndarray), f'invalid target: {target!r}'

Type guard

def is_valid_target(t) -> bool:
    return (isinstance(t, str) and t in ('const_var', 'const_corr', 'single_factor')) or isinstance(t, np.ndarray)

Try / catch

try:
    model = ShrinkRiskModel(target=target)
except TypeError as e:
    raise ValueError('target must be const_var/const_corr/single_factor or np.ndarray') from e

Prevention

When it happens

Trigger: Constructing ShrinkRiskModel(target=None); passing target as a Python list or pandas DataFrame instead of np.ndarray; passing an int/float thinking it scales the target.

Common situations: Supplying a custom shrinkage target computed as a DataFrame from a prior covariance step; configs omitting target or setting it to null; assuming any array-like is accepted.

Related errors

AI-assisted analysis of microsoft/qlib@79633dd950 (2026-08-15). Data as JSON: /api/errors/4853653916a6d5fa. Report an issue: GitHub.