testing.ipynb

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   "source": [
    "import pandas as pd\n",
    "from skquery.pairwise import RandomMLCL\n",
    "from skquery.oracle import MLCLOracle\n",
    "from active_semi_clustering.semi_supervised.pairwise_constraints import COPKMeans\n",
    "from sklearn.datasets import *\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import adjusted_rand_score\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go"
   ]
  },
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    "#Plots data partition, optionally with ML/CL constraints\n",
    "def plot(dataset, partition, constraints=None, filename=None):\n",
    "    viz_dataset = pd.DataFrame(PCA(n_components=2).fit_transform(dataset)) if dataset.shape[1] > 3 else pd.DataFrame(dataset)\n",
    "    fig = None\n",
    "    match viz_dataset.shape[1]:\n",
    "        case 2:\n",
    "            fig = px.scatter(viz_dataset, x=0, y=1, template=\"simple_white\",\n",
    "                             color=partition, symbol=partition,\n",
    "                             hover_data={'index': viz_dataset.index.astype(str)})\n",
    "        case 3:\n",
    "            fig = px.scatter_3d(viz_dataset, x=0, y=1, z=2, template=\"simple_white\",\n",
    "                                color=partition, symbol=partition,\n",
    "                                hover_data={'index': viz_dataset.index.astype(str)})\n",
    "\n",
    "    if constraints:\n",
    "        for key in constraints:\n",
    "            for cst in constraints[key]:\n",
    "                points = viz_dataset.iloc[list(cst)]\n",
    "                match viz_dataset.shape[1]:\n",
    "                    case 2:\n",
    "                        fig.add_trace(go.Scatter(name=str(cst), x=[points.iloc[0, 0], points.iloc[1, 0]],\n",
    "                                                 mode=\"lines\", y=[points.iloc[0, 1], points.iloc[1, 1]]))\n",
    "                    case 3:\n",
    "                        fig.add_trace(go.Scatter3d(name=str(cst), x=[points.iloc[0, 0], points.iloc[1, 0]],\n",
    "                                                   mode=\"lines\", y=[points.iloc[0, 1], points.iloc[1, 1]],\n",
    "                                                   z=[points.iloc[0, 2], points.iloc[1, 2]]))\n",
    "                if key == \"ml\":\n",
    "                    fig['data'][-1]['line']['color'] = \"#ff0000\"\n",
    "                else:\n",
    "                    fig['data'][-1]['line']['color'] = \"#0000ff\"\n",
    "                    fig['data'][-1]['line']['dash'] = \"dash\"\n",
    "\n",
    "    fig.update_layout(showlegend=False)\n",
    "    fig.update(layout_coloraxis_showscale=False)\n",
    "    if not filename:\n",
    "        fig.show()\n",
    "    else:\n",
    "        fig.write_html(filename)"
   ],
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   "source": [
    "X, y = load_iris(return_X_y=True)"
   ],
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     "text": [
      "Initial ARI with GT : 0.7163421126838476\n"
     ]
    }
   ],
   "source": [
    "cc_alg = COPKMeans(n_clusters=len(set(y)))\n",
    "cc_alg.fit(X)\n",
    "init_partition = cc_alg.labels_\n",
    "plot(X, init_partition)\n",
    "print(f\"Initial ARI with GT : {adjusted_rand_score(y, init_partition)}\")"
   ],
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    "ExecuteTime": {
     "end_time": "2023-07-06T09:45:49.368274557Z",
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     "text": [
      "ML :  [(123, 116), (69, 57)]\n",
      "CL :  [(103, 29), (114, 97), (53, 17), (15, 59), (18, 78), (14, 140), (148, 9), (101, 62)]\n"
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   ],
   "source": [
    "#Test run\n",
    "qs = RandomMLCL()\n",
    "oracle = MLCLOracle(truth=y, budget=10)\n",
    "constraints = qs.fit(X, oracle, partition=init_partition)\n",
    "\n",
    "plot(X, init_partition, constraints)\n",
    "print(\"ML : \", constraints[\"ml\"])\n",
    "print(\"CL : \", constraints[\"cl\"])\n",
    "#qs.csts_to_file(constraints)"
   ],
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    "ExecuteTime": {
     "end_time": "2023-07-06T09:45:28.365694281Z",
     "start_time": "2023-07-06T09:45:28.239685041Z"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final ARI with GT : 0.7429751965483791\n"
     ]
    }
   ],
   "source": [
    "cc_alg.fit(X, ml=constraints[\"ml\"], cl=constraints[\"cl\"])\n",
    "plot(X, cc_alg.labels_)\n",
    "print(f\"Final ARI with GT : {adjusted_rand_score(y, cc_alg.labels_)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T09:45:31.783704976Z",
     "start_time": "2023-07-06T09:45:31.426512687Z"
    }
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