"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"em.plot_distribution_of_scores()"
]
},
{
"cell_type": "markdown",
"id": "07649cc9-b7e6-4acf-876b-ce5f97c00772",
"metadata": {},
"source": [
"## Entity Clustering\n",
"\n",
"It takes as input the similarity graph produced by Entity Matching and partitions it into a set of equivalence clusters, with every cluster corresponding to a distinct real-world object."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5a232214-58fc-4a70-8069-37f3fe974f9e",
"metadata": {},
"outputs": [],
"source": [
"from pyjedai.clustering import UniqueMappingClustering\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "6f8efe1d-b981-4fba-b5cf-2b28fb189ee2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"***************************************************************************************************************************\n",
" Μethod: Unique Mapping Clustering\n",
"***************************************************************************************************************************\n",
"Method name: Unique Mapping Clustering\n",
"Parameters: \n",
"Runtime: 0.2460 seconds\n",
"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
"Performance:\n",
"\tPrecision: 92.56% \n",
"\tRecall: 87.92%\n",
"\tF1-score: 90.18%\n",
"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
"Classification report:\n",
"\tTrue positives: 946\n",
"\tFalse positives: 76\n",
"\tTrue negatives: 1156570\n",
"\tFalse negatives: 130\n",
"\tTotal comparisons: 1022\n",
"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
]
},
{
"data": {
"text/plain": [
"{'Precision %': 92.56360078277886,\n",
" 'Recall %': 87.9182156133829,\n",
" 'F1 %': 90.1811248808389,\n",
" 'True Positives': 946,\n",
" 'False Positives': 76,\n",
" 'True Negatives': 1156570,\n",
" 'False Negatives': 130}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"umc = UniqueMappingClustering()\n",
"clusters = umc.process(pairs_graph, data)\n",
"umc.evaluate(clusters, with_classification_report=True)"
]
},
{
"cell_type": "markdown",
"id": "ac01eed9-25f4-41b2-8c18-e126901acd2a",
"metadata": {},
"source": [
"
\n",
"\n",
"K. Nikoletos, J. Maciejewski, G. Papadakis & M. Koubarakis\n",
"
\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3537920-78f1-4333-8448-04a584edc152",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
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"vscode": {
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![](\"https://scikit-learn.org/stable/_static/scikit-learn-logo-small.png\"){kind=logo}
\n",
"