Missing constant annotation when the same value is sometimes converted to string

[Aclrian]

URL Hash

#/sklearn/sklearn.preprocessing._data/scale/with_mean

Actual Annotation Type

@optional

Actual Annotation Inputs

{
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    "authors": [
        "$autogen$"
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    "defaultType": "boolean",
    "defaultValue": true
}

Expected Annotation Type

@constant

Expected Annotation Inputs

with value True (boolean)

Minimal API Data (optional)

Minimal API Data for `sklearn/sklearn.preprocessing._data/scale/with_mean`

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    "schemaVersion": 1,
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    "version": "1.1.1",
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        {
            "id": "sklearn/sklearn.preprocessing",
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            ],
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                "sklearn/sklearn.preprocessing._data/scale"
            ]
        }
    ],
    "classes": [],
    "functions": [
        {
            "id": "sklearn/sklearn.preprocessing._data/scale",
            "name": "scale",
            "qname": "sklearn.preprocessing._data.scale",
            "decorators": [],
            "parameters": [
                {
                    "id": "sklearn/sklearn.preprocessing._data/scale/with_mean",
                    "name": "with_mean",
                    "qname": "sklearn.preprocessing._data.scale.with_mean",
                    "default_value": "True",
                    "assigned_by": "NAME_ONLY",
                    "is_public": true,
                    "docstring": {
                        "type": "bool, default=True",
                        "description": "If True, center the data before scaling."
                    },
                    "type": {}
                }
            ],
            "results": [],
            "is_public": true,
            "reexported_by": [
                "sklearn/sklearn.preprocessing"
            ],
            "description": "Standardize a dataset along any axis.\n\nCenter to the mean and component wise scale to unit variance.\n\nRead more in the :ref:`User Guide <preprocessing_scaler>`.",
            "docstring": "Standardize a dataset along any axis.\n\n    Center to the mean and component wise scale to unit variance.\n\n    Read more in the :ref:`User Guide <preprocessing_scaler>`.\n\n    Parameters\n    ----------\n    X : {array-like, sparse matrix} of shape (n_samples, n_features)\n        The data to center and scale.\n\n    axis : int, default=0\n        axis used to compute the means and standard deviations along. If 0,\n        independently standardize each feature, otherwise (if 1) standardize\n        each sample.\n\n    with_mean : bool, default=True\n        If True, center the data before scaling.\n\n    with_std : bool, default=True\n        If True, scale the data to unit variance (or equivalently,\n        unit standard deviation).\n\n    copy : bool, default=True\n        set to False to perform inplace row normalization and avoid a\n        copy (if the input is already a numpy array or a scipy.sparse\n        CSC matrix and if axis is 1).\n\n    Returns\n    -------\n    X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)\n        The transformed data.\n\n    Notes\n    -----\n    This implementation will refuse to center scipy.sparse matrices\n    since it would make them non-sparse and would potentially crash the\n    program with memory exhaustion problems.\n\n    Instead the caller is expected to either set explicitly\n    `with_mean=False` (in that case, only variance scaling will be\n    performed on the features of the CSC matrix) or to call `X.toarray()`\n    if he/she expects the materialized dense array to fit in memory.\n\n    To avoid memory copy the caller should pass a CSC matrix.\n\n    NaNs are treated as missing values: disregarded to compute the statistics,\n    and maintained during the data transformation.\n\n    We use a biased estimator for the standard deviation, equivalent to\n    `numpy.std(x, ddof=0)`. Note that the choice of `ddof` is unlikely to\n    affect model performance.\n\n    For a comparison of the different scalers, transformers, and normalizers,\n    see :ref:`examples/preprocessing/plot_all_scaling.py\n    <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.\n\n    .. warning:: Risk of data leak\n\n        Do not use :func:`~sklearn.preprocessing.scale` unless you know\n        what you are doing. A common mistake is to apply it to the entire data\n        *before* splitting into training and test sets. This will bias the\n        model evaluation because information would have leaked from the test\n        set to the training set.\n        In general, we recommend using\n        :class:`~sklearn.preprocessing.StandardScaler` within a\n        :ref:`Pipeline <pipeline>` in order to prevent most risks of data\n        leaking: `pipe = make_pipeline(StandardScaler(), LogisticRegression())`.\n\n    See Also\n    --------\n    StandardScaler : Performs scaling to unit variance using the Transformer\n        API (e.g. as part of a preprocessing\n        :class:`~sklearn.pipeline.Pipeline`).\n\n    "
        }
    ]
}

Minimal Usage Store (optional)

Minimal Usage Store for `sklearn/sklearn.preprocessing._data/scale/with_mean`

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Suggested Solution (optional)

No response

Additional Context (optional)

True: 311
'True': 3

[lars-reimann]

More generally: Invalid values should be filtered out. In this case it works because a non-empty string is considered "truthy". Even passing 'False' would have the same effect as passing True.