Missing keyword 'between' for boundary annotation

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#/sklearn/sklearn.cluster._mean_shift/estimate_bandwidth/quantile

Expected Annotation Type

@boundary

Expected Annotation Inputs

[0, 1]

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Minimal API Data for `sklearn/sklearn.cluster._mean_shift/estimate_bandwidth/quantile`

```json5 { "schemaVersion": 1, "distribution": "scikit-learn", "package": "sklearn", "version": "1.1.1", "modules": [ { "id": "sklearn/sklearn.cluster", "name": "sklearn.cluster", "imports": [], "from_imports": [ { "module": "sklearn.cluster._affinity_propagation", "declaration": "affinity_propagation", "alias": null }, { "module": "sklearn.cluster._affinity_propagation", "declaration": "AffinityPropagation", "alias": null }, { "module": "sklearn.cluster._agglomerative", "declaration": "AgglomerativeClustering", "alias": null }, { "module": "sklearn.cluster._agglomerative", "declaration": "FeatureAgglomeration", "alias": null }, { "module": "sklearn.cluster._agglomerative", "declaration": "linkage_tree", "alias": null }, { "module": "sklearn.cluster._agglomerative", "declaration": "ward_tree", "alias": null }, { "module": "sklearn.cluster._bicluster", "declaration": "SpectralBiclustering", "alias": null }, { "module": "sklearn.cluster._bicluster", "declaration": "SpectralCoclustering", "alias": null }, { "module": "sklearn.cluster._birch", "declaration": "Birch", "alias": null }, { "module": "sklearn.cluster._bisect_k_means", "declaration": "BisectingKMeans", "alias": null }, { "module": "sklearn.cluster._dbscan", "declaration": "dbscan", "alias": null }, { "module": "sklearn.cluster._dbscan", "declaration": "DBSCAN", "alias": null }, { "module": "sklearn.cluster._kmeans", "declaration": "k_means", "alias": null }, { "module": "sklearn.cluster._kmeans", "declaration": "KMeans", "alias": null }, { "module": "sklearn.cluster._kmeans", "declaration": "kmeans_plusplus", "alias": null }, { "module": "sklearn.cluster._kmeans", "declaration": "MiniBatchKMeans", "alias": null }, { "module": "sklearn.cluster._mean_shift", "declaration": "estimate_bandwidth", "alias": null }, { "module": "sklearn.cluster._mean_shift", "declaration": "get_bin_seeds", "alias": null }, { "module": "sklearn.cluster._mean_shift", "declaration": "mean_shift", "alias": null }, { "module": "sklearn.cluster._mean_shift", "declaration": "MeanShift", "alias": null }, { "module": "sklearn.cluster._optics", "declaration": "cluster_optics_dbscan", "alias": null }, { "module": "sklearn.cluster._optics", "declaration": "cluster_optics_xi", "alias": null }, { "module": "sklearn.cluster._optics", "declaration": "compute_optics_graph", "alias": null }, { "module": "sklearn.cluster._optics", "declaration": "OPTICS", "alias": null }, { "module": "sklearn.cluster._spectral", "declaration": "spectral_clustering", "alias": null }, { "module": "sklearn.cluster._spectral", "declaration": "SpectralClustering", "alias": null } ], "classes": [], "functions": [ "sklearn/sklearn.cluster._mean_shift/estimate_bandwidth" ] } ], "classes": [], "functions": [ { "id": "sklearn/sklearn.cluster._mean_shift/estimate_bandwidth", "name": "estimate_bandwidth", "qname": "sklearn.cluster._mean_shift.estimate_bandwidth", "decorators": [], "parameters": [ { "id": "sklearn/sklearn.cluster._mean_shift/estimate_bandwidth/quantile", "name": "quantile", "qname": "sklearn.cluster._mean_shift.estimate_bandwidth.quantile", "default_value": "0.3", "assigned_by": "NAME_ONLY", "is_public": true, "docstring": { "type": "float, default=0.3", "description": "Should be between [0, 1]\n0.5 means that the median of all pairwise distances is used." }, "type": {} } ], "results": [], "is_public": true, "reexported_by": [ "sklearn/sklearn.cluster" ], "description": "Estimate the bandwidth to use with the mean-shift algorithm.\n\nThat this function takes time at least quadratic in n_samples. For large\ndatasets, it's wise to set that parameter to a small value.", "docstring": "Estimate the bandwidth to use with the mean-shift algorithm.\n\n That this function takes time at least quadratic in n_samples. For large\n datasets, it's wise to set that parameter to a small value.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Input points.\n\n quantile : float, default=0.3\n Should be between [0, 1]\n 0.5 means that the median of all pairwise distances is used.\n\n n_samples : int, default=None\n The number of samples to use. If not given, all samples are used.\n\n random_state : int, RandomState instance, default=None\n The generator used to randomly select the samples from input points\n for bandwidth estimation. Use an int to make the randomness\n deterministic.\n See :term:`Glossary `.\n\n n_jobs : int, default=None\n The number of parallel jobs to run for neighbors search.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary `\n for more details.\n\n Returns\n -------\n bandwidth : float\n The bandwidth parameter.\n " } ] } ```

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Minimal Usage Store for `sklearn/sklearn.cluster._mean_shift/estimate_bandwidth/quantile`

```json5 { "schemaVersion": 1, "module_counts": { "sklearn/sklearn.cluster": 2237 }, "class_counts": {}, "function_counts": { "sklearn/sklearn.cluster._mean_shift/estimate_bandwidth": 3 }, "parameter_counts": { "sklearn/sklearn.cluster._mean_shift/estimate_bandwidth/quantile": 3 }, "value_counts": { "sklearn/sklearn.cluster._mean_shift/estimate_bandwidth/quantile": { "0.1": 1, "0.2": 1, "0.001": 1 } } } ```

Additional Context (optional)

Documentation: Should be between [0, 1] 0.5 means that the median of all pairwise distances is used.

At #/sklearn/sklearn.cluster._optics/OPTICS/__init__/xi is the same problem.

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