{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# KCenters"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"This notebook illustrates graph clustering with the KCenters algorithm."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"from IPython.display import SVG"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2019-11-25T09:04:07.335572Z",
"start_time": "2019-11-25T09:04:07.330695Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
},
"scrolled": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2019-11-25T09:03:43.045120Z",
"start_time": "2019-11-25T09:03:43.041227Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"from sknetwork.data import karate_club, painters, movie_actor\n",
"from sknetwork.clustering import get_modularity\n",
"from sknetwork.clustering import KCenters\n",
"from sknetwork.visualization import svg_graph, svg_bigraph"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Graphs"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2019-11-25T09:03:43.582333Z",
"start_time": "2019-11-25T09:03:43.572000Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"graph = karate_club(metadata=True)\n",
"adjacency = graph.adjacency\n",
"position = graph.position"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2019-11-25T09:03:44.149657Z",
"start_time": "2019-11-25T09:03:44.128059Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
},
"scrolled": true
},
"outputs": [],
"source": [
"kcenters = KCenters(n_clusters=2)\n",
"kcenters.fit(adjacency)\n",
"labels = kcenters.fit_predict(adjacency)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([33, 0])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# cluster centers\n",
"kcenters.centers_"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2019-11-25T09:03:44.631654Z",
"start_time": "2019-11-25T09:03:44.623873Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1] [18 16]\n"
]
}
],
"source": [
"labels_unique, counts = np.unique(labels, return_counts=True)\n",
"print(labels_unique, counts)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2019-11-25T09:03:45.166971Z",
"start_time": "2019-11-25T09:03:45.160098Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image = svg_graph(adjacency, position, labels=labels)\n",
"SVG(image)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-30T14:37:17.935102Z",
"start_time": "2019-10-30T14:37:17.923771Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.37146614069690986"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# metric\n",
"get_modularity(adjacency, labels)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Directed graphs"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-30T14:37:49.296788Z",
"start_time": "2019-10-30T14:37:49.286527Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"graph = painters(metadata=True)\n",
"adjacency = graph.adjacency\n",
"names = graph.names\n",
"position = graph.position"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-30T14:37:50.496413Z",
"start_time": "2019-10-30T14:37:50.486975Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
},
"scrolled": true
},
"outputs": [],
"source": [
"# clustering\n",
"kcenters = KCenters(n_clusters=3, directed=True)\n",
"labels = kcenters.fit_predict(adjacency)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-30T14:38:09.086642Z",
"start_time": "2019-10-30T14:38:09.080982Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2] [4 6 4]\n"
]
}
],
"source": [
"labels_unique, counts = np.unique(labels, return_counts=True)\n",
"print(labels_unique, counts)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Vincent van Gogh', 'Edouard Manet', 'Peter Paul Rubens'],\n",
" dtype='\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Pablo PicassoClaude MonetMichel AngeloEdouard ManetPeter Paul RubensRembrandtGustav KlimtEdgar DegasVincent van GoghLeonardo da VinciHenri MatissePaul CezannePierre-Auguste RenoirEgon Schiele"
],
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image = svg_graph(adjacency, position, names=names, labels=labels)\n",
"SVG(image)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-30T14:38:13.415879Z",
"start_time": "2019-10-30T14:38:13.406435Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.2816"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_modularity(adjacency, labels)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Bipartite graphs"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-30T14:38:14.429988Z",
"start_time": "2019-10-30T14:38:14.422543Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"graph = movie_actor(metadata=True)\n",
"biadjacency = graph.biadjacency\n",
"names_row = graph.names_row\n",
"names_col = graph.names_col"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-30T14:38:34.386474Z",
"start_time": "2019-10-30T14:38:34.372114Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
},
"scrolled": true
},
"outputs": [],
"source": [
"# clustering\n",
"kcenters = KCenters(n_clusters=3, center_position=\"both\")\n",
"kcenters.fit(biadjacency)\n",
"labels_row = kcenters.labels_row_\n",
"labels_col = kcenters.labels_col_"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"centers row: ['Inception' 'The Grand Budapest Hotel']\n",
"centers col: ['Ryan Gosling']\n"
]
}
],
"source": [
"# cluster centers\n",
"centers_row = kcenters.centers_row_\n",
"centers_col = kcenters.centers_col_\n",
"\n",
"print(\"centers row:\", names_row[centers_row])\n",
"print(\"centers col:\", names_col[centers_col])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image = svg_bigraph(biadjacency, names_row, names_col, labels_row, labels_col)\n",
"SVG(image)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-30T14:46:02.572278Z",
"start_time": "2019-10-30T14:46:02.558375Z"
},
"pycharm": {
"is_executing": false,
"name": "#%%\n"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.5099206349206349"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# metric\n",
"get_modularity(biadjacency, labels_row, labels_col)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
"0a13fb35a34143c5ac61884113699f99": {
"model_module": "cytoscape-jupyter-widget",
"model_module_version": "^0.1.0",
"model_name": "CytoscapeModel",
"state": {
"_model_module_version": "^0.1.0",
"_view_module_version": "^0.1.0",
"background": "#FFFFFF",
"data": {
"directed": false,
"elements": {
"edges": [
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