microsoft/qlib · error · NotImplementedError

This type of input is not supported

Error message

This type of input is not supported

What it means

CombFeaAna (qlib/contrib/report/data/ana.py) combines several feature analysers and plots them in one figure. It requires at least two analyser classes to combine; passing zero or one class makes combination meaningless, so the constructor raises NotImplementedError.

Source

Thrown at qlib/contrib/report/data/ana.py:35

import numpy as np
from qlib.contrib.report.data.base import FeaAnalyser
from qlib.contrib.report.utils import sub_fig_generator
from qlib.utils.paral import datetime_groupby_apply
from qlib.contrib.eva.alpha import pred_autocorr_all
from loguru import logger
import seaborn as sns

DT_COL_NAME = "datetime"


class CombFeaAna(FeaAnalyser):
    """
    Combine the sub feature analysers and plot then in a single graph
    """

    def __init__(self, dataset: pd.DataFrame, *fea_ana_cls):
        if len(fea_ana_cls) <= 1:
            raise NotImplementedError(f"This type of input is not supported")
        self._fea_ana_l = [fcls(dataset) for fcls in fea_ana_cls]
        super().__init__(dataset=dataset)

    def skip(self, col):
        return np.all(list(map(lambda fa: fa.skip(col), self._fea_ana_l)))

    def calc_stat_values(self):
        """The statistics of features are finished in the underlying analysers"""

    def plot_all(self, *args, **kwargs):
        ax_gen = iter(sub_fig_generator(row_n=len(self._fea_ana_l), *args, **kwargs))

        for col in self._dataset:
            if not self.skip(col):
                axes = next(ax_gen)
                for fa, ax in zip(self._fea_ana_l, axes):
                    if not fa.skip(col):
                        fa.plot_single(col, ax)

View on GitHub (pinned to 79633dd950)

Solutions

1. Pass at least two analyser classes: CombFeaAna(df, AnaA, AnaB)
2. If you only need one analyser, instantiate that analyser directly instead of CombFeaAna
3. When spreading a config-driven list, check its length before delegating to CombFeaAna

Example fix

# before
comb = CombFeaAna(df, *[SciFeaAna])

# after
if len(analyser_classes) > 1:
    comb = CombFeaAna(df, *analyser_classes)
else:
    comb = analyser_classes[0](df)

analyser_classes = [A, B]
assert len(analyser_classes) > 1, 'CombFeaAna needs >= 2 analyser classes'
comb = CombFeaAna(df, *analyser_classes) if len(analyser_classes) > 1 else analyser_classes[0](df)

try:
    comb = CombFeaAna(df, *classes)
except NotImplementedError:
    comb = classes[0](df)  # single analyser: use it directly

When it happens

Trigger: Instantiating CombFeaAna(df, SingleFeaAna) (only one analyser class) or CombFeaAna(df) (none). The check `len(fea_ana_cls) <= 1` fires in __init__.

Common situations: Programmatically building a list of analysers from config and accidentally passing an empty or single-element list, e.g. CombFeaAna(df, *analyser_list) where analyser_list has one item.

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AI-assisted analysis of microsoft/qlib@79633dd950 (2026-08-15). Data as JSON: /api/errors/0d0a8e5c180d3bd8. Report an issue: GitHub.