This repository was archived by the owner on Nov 23, 2024. It is now read-only.
This repository was archived by the owner on Nov 23, 2024. It is now read-only.
Missing constant annotation when the same value is sometimes converted to string #34
Description
URL Hash
#/sklearn/sklearn.preprocessing._data/scale/with_mean
Actual Annotation Type
@optional
Actual Annotation Inputs
{
"target": "sklearn/sklearn.preprocessing._data/scale/with_mean",
"authors": [
"$autogen$"
],
"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`
{
"schemaVersion": 1,
"distribution": "scikit-learn",
"package": "sklearn",
"version": "1.1.1",
"modules": [
{
"id": "sklearn/sklearn.preprocessing",
"name": "sklearn.preprocessing",
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{
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"declaration": "add_dummy_feature",
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{
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{
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{
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},
{
"module": "sklearn.preprocessing._data",
"declaration": "RobustScaler",
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{
"module": "sklearn.preprocessing._data",
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},
{
"module": "sklearn.preprocessing._data",
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{
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{
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{
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{
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{
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{
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},
{
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"alias": null
}
],
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"functions": [
"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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"'True'": 3
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}
}Suggested Solution (optional)
No response
Additional Context (optional)
True: 311
'True': 3