notebooks/Optimizations/Two_Moons_ROCK_Hyperparameter_Search.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizing Rock's hyperparameter $t_{max}$ on the Two Moons dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
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     },
     "user_tz": -120
    },
    "id": "UbvqVKulcAwD"
   },
   "outputs": [],
   "source": [
    "import sys  \n",
    "sys.path.insert(0, '../..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1204,
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    "id": "lWMfMVRSeO88",
    "outputId": "35201e78-8252-4721-b183-62d022b8fa7d"
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 1053,
     "status": "ok",
     "timestamp": 1621262750073,
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    "id": "EXvc2zQ-csmQ"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.datasets import make_blobs, make_moons, make_circles, load_iris, load_wine, load_breast_cancer, load_digits, fetch_covtype    \n",
    "from sklearn.cluster import KMeans, DBSCAN, SpectralClustering, MeanShift, estimate_bandwidth\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import adjusted_mutual_info_score as AMI\n",
    "from rock import ROCK\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import logging\n",
    "import sys\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 961,
     "status": "ok",
     "timestamp": 1621262755373,
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     "user_tz": -120
    },
    "id": "4OLzvGn2sVLX"
   },
   "outputs": [],
   "source": [
    "plt.rcParams['axes.grid'] = True\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.bottom'] = False\n",
    "plt.rcParams['axes.spines.left'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 794,
     "status": "ok",
     "timestamp": 1621262757340,
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     "user_tz": -120
    },
    "id": "sfM20A23iHzF"
   },
   "outputs": [],
   "source": [
    "experiment = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 515,
     "status": "ok",
     "timestamp": 1621262758997,
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    "id": "FLtgfMjY5C_j"
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   "outputs": [],
   "source": [
    "seed = 0\n",
    "datasets = []\n",
    "for s in range(seed, seed+10):\n",
    "      datasets.append(make_moons(n_samples=1000, noise=0.15,\n",
    "            shuffle=True, random_state=s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "executionInfo": {
     "elapsed": 533,
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     "timestamp": 1621262760952,
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    "id": "SpOu6jOr43i3"
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   "outputs": [],
   "source": [
    "tmaxs = range(1, 31)"
   ]
  },
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   "source": [
    "for t in tqdm(range(0, len(tmaxs))):\n",
    "  for d in tqdm(range(0, len(datasets))):\n",
    "      dataset = datasets[d]\n",
    "      run = {}\n",
    "      scaler = StandardScaler()\n",
    "      data = scaler.fit_transform(dataset[0])\n",
    "      rock = ROCK(tmax=tmaxs[t]).fit(data).labels_\n",
    "      gt = dataset[1]\n",
    "      run['AMI'] = AMI(rock, gt)\n",
    "      run['tmax'] = tmaxs[t]\n",
    "\n",
    "      experiment.append(run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 745,
     "status": "ok",
     "timestamp": 1621263341049,
     "user": {
      "displayName": "Isdm Hueni",
      "photoUrl": "",
      "userId": "05072038424951783895"
     },
     "user_tz": -120
    },
    "id": "U3FnCVEum_Vi"
   },
   "outputs": [],
   "source": [
    "results = pd.DataFrame(experiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 538,
     "status": "ok",
     "timestamp": 1621263342202,
     "user": {
      "displayName": "Isdm Hueni",
      "photoUrl": "",
      "userId": "05072038424951783895"
     },
     "user_tz": -120
    },
    "id": "3l6fW2qXx_vy"
   },
   "outputs": [],
   "source": [
    "results.to_csv('../../results/optimization/Rock_two_moons_HPO.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 1092,
     "status": "ok",
     "timestamp": 1621263381083,
     "user": {
      "displayName": "Isdm Hueni",
      "photoUrl": "",
      "userId": "05072038424951783895"
     },
     "user_tz": -120
    },
    "id": "DkL5J08Dnava",
    "outputId": "599425fb-ec85-480b-82ac-6221d9041295"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tmax'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "results.groupby('tmax').mean().plot()\n",
    "#plt.savefig('Rock_moon_hypersearch_scaled.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 905,
     "status": "ok",
     "timestamp": 1621263934787,
     "user": {
      "displayName": "Isdm Hueni",
      "photoUrl": "",
      "userId": "05072038424951783895"
     },
     "user_tz": -120
    },
    "id": "ygyIvPm51UXI",
    "outputId": "86a249ee-b449-4887-d37a-959c28a972f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tmax'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.groupby('tmax').max().plot()\n",
    "#plt.savefig('Rock_moon_hypersearch_max.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 1049,
     "status": "ok",
     "timestamp": 1621263978137,
     "user": {
      "displayName": "Isdm Hueni",
      "photoUrl": "",
      "userId": "05072038424951783895"
     },
     "user_tz": -120
    },
    "id": "myT7FWE53Nsb",
    "outputId": "46463609-5d8a-4250-868f-0e95896628aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tmax'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.groupby('tmax').std().plot()\n",
    "#plt.savefig('Rock_moon_hypersearch_std.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following table shows that Rock's optimal AMI is obtained at $t_{max}$ = 15. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 987
    },
    "executionInfo": {
     "elapsed": 435,
     "status": "ok",
     "timestamp": 1621260337059,
     "user": {
      "displayName": "Isdm Hueni",
      "photoUrl": "",
      "userId": "05072038424951783895"
     },
     "user_tz": -120
    },
    "id": "oM28byBypWDb",
    "outputId": "0ab64505-976c-4e29-e5f2-58511ac26988"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tmax</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.155898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.319799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.419308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.491158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.440662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.441496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.494020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.528716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.683343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.645449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.731953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.739148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.760449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.760041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.788123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.746232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.738681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.745535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.652142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.679275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.659699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.635706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.639437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.636006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.639261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.632833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.637283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.633970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.670437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.677149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AMI\n",
       "tmax          \n",
       "1     0.155898\n",
       "2     0.319799\n",
       "3     0.419308\n",
       "4     0.491158\n",
       "5     0.440662\n",
       "6     0.441496\n",
       "7     0.494020\n",
       "8     0.528716\n",
       "9     0.683343\n",
       "10    0.645449\n",
       "11    0.731953\n",
       "12    0.739148\n",
       "13    0.760449\n",
       "14    0.760041\n",
       "15    0.788123\n",
       "16    0.746232\n",
       "17    0.738681\n",
       "18    0.745535\n",
       "19    0.652142\n",
       "20    0.679275\n",
       "21    0.659699\n",
       "22    0.635706\n",
       "23    0.639437\n",
       "24    0.636006\n",
       "25    0.639261\n",
       "26    0.632833\n",
       "27    0.637283\n",
       "28    0.633970\n",
       "29    0.670437\n",
       "30    0.677149"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.groupby('tmax').mean()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyOnKYv9c9rNpb6HvpexNZ0O",
   "collapsed_sections": [],
   "mount_file_id": "1BEarC5Yzn2sKxVwSpF8UBlkh2xmwfzz2",
   "name": "ROCK_Hyperparameter_Search_Moons_2.ipynb",
   "provenance": [
    {
     "file_id": "1vi3vjOwPDsYrLGNN2_krA5SkV1Wty3bn",
     "timestamp": 1621258755453
    },
    {
     "file_id": "1yZZ4GfxaXLw7kQQ-ufKegMhT-sh93n-d",
     "timestamp": 1620305717278
    },
    {
     "file_id": "1ovm-C9cOxmlH44OzWKrLSfVgtP3HqT18",
     "timestamp": 1620113188233
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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