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        "author": "Jakub Svehla",
        "author_email": "jakub.svehla@datamole.cz",
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        "description": "# active-semi-supervised-clustering\n\nActive semi-supervised clustering algorithms for scikit-learn.\n\n## Algorithms\n\n### Semi-supervised clustering\n\n* Seeded-KMeans\n* Constrainted-KMeans\n* COP-KMeans\n* Pairwise constrained K-Means (PCK-Means)\n* Metric K-Means (MK-Means)\n* Metric pairwise constrained K-Means (MPCK-Means)\n\n### Active learning of pairwise clustering\n\n* Explore & Consolidate\n* Min-max\n* Normalized point-based uncertainty (NPU) method\n\n## Installation\n\n```\npip install active-semi-supervised-clustering\n```\n\n## Usage\n\n```python\nfrom sklearn import datasets, metrics\nfrom active_semi_clustering.semi_supervised.pairwise_constraints import PCKMeans\nfrom active_semi_clustering.active.pairwise_constraints import ExampleOracle, ExploreConsolidate, MinMax\n```\n\n```python\nX, y = datasets.load_iris(return_X_y=True)\n```\n\nFirst, obtain some pairwise constraints from an oracle.\n\n```python\n# TODO implement your own oracle that will, for example, query a domain expert via GUI or CLI\noracle = ExampleOracle(y, max_queries_cnt=10)\n\nactive_learner = MinMax(n_clusters=3)\nactive_learner.fit(X, oracle=oracle)\npairwise_constraints = active_learner.pairwise_constraints_\n```\n\nThen, use the constraints to do the clustering.\n\n```python\nclusterer = PCKMeans(n_clusters=3)\nclusterer.fit(X, ml=pairwise_constraints[0], cl=pairwise_constraints[1])\n```\n\nEvaluate the clustering using Adjusted Rand Score.\n\n```python\nmetrics.adjusted_rand_score(y, clusterer.labels_)\n```\n\n\n",
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