notebooks/Comparisons/Two_Moons_Analysis-num_samples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Moons: Varying the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys  \n",
    "sys.path.insert(0, '../../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import optuna\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.datasets import make_blobs, make_moons\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 logging\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 0\n",
    "n_samples = 1000\n",
    "jitter = 15 * 0.01\n",
    "datasets = []\n",
    "\n",
    "n_samples_grid = [50, 100, 200, 400, 600, 800, 1000, 1200, 1400, 1600]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = []\n",
    "n_centers = 2\n",
    "rock_results = []\n",
    "other_results = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in n_samples_grid:\n",
    "    step = 0\n",
    "    for s in range(seed, seed+10):\n",
    "        #print(f'jitter: {j}, seed: {s}')\n",
    "        dataset = make_moons(n_samples=n, noise=jitter, shuffle=True, random_state=s) \n",
    "        run = {}\n",
    "        run['step'] = step\n",
    "        run['n_samples'] = n\n",
    "        run['n_centers'] = n_centers\n",
    "        run['jitter'] = jitter\n",
    "        scaler = StandardScaler()\n",
    "        data = scaler.fit_transform(dataset[0])\n",
    "        kmeans = KMeans(n_clusters=n_centers, random_state=seed).fit(data).labels_\n",
    "        eps, min_pts = (0.2, 4)\n",
    "        dbscan = DBSCAN(eps=eps, min_samples=min_pts).fit(data).labels_\n",
    "        run['eps'] = eps\n",
    "        run['min_pts'] = min_pts\n",
    "        spectral = SpectralClustering(n_clusters=2, random_state=seed).fit(data).labels_\n",
    "        bandwidth = estimate_bandwidth(data)\n",
    "\n",
    "        run['bandwidth'] = bandwidth\n",
    "        mean_shift = MeanShift(bandwidth=bandwidth).fit(data).labels_\n",
    "\n",
    "        rock = ROCK(tmax=15).fit(data).labels_\n",
    "\n",
    "        gt = dataset[1]\n",
    "        rock_results.append(AMI(rock, gt))\n",
    "        other_results.append(np.max([AMI(kmeans, gt), AMI(dbscan, gt), AMI(spectral, gt), AMI(mean_shift, gt)]))\n",
    "\n",
    "        run['ROCK'] = AMI(rock, gt)\n",
    "        run['K_MEANS'] = AMI(kmeans, gt)\n",
    "        run['DBSCAN'] = AMI(dbscan, gt)\n",
    "        run['SPECTRAL'] = AMI(spectral, gt)\n",
    "        run['MEAN_SHIFT'] = AMI(mean_shift, gt)\n",
    "\n",
    "        step += 1\n",
    "\n",
    "        experiment.append(run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(experiment).to_csv('../../results/analysis/two_moons_analysis_samples.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
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      ],
      "text/plain": [
       "    step  n_samples  n_centers  jitter  eps  min_pts  bandwidth      ROCK  \\\n",
       "0      0         50          2    0.15  0.2        4   1.256490  0.470339   \n",
       "1      1         50          2    0.15  0.2        4   1.206272  0.234534   \n",
       "2      2         50          2    0.15  0.2        4   1.246717  0.724580   \n",
       "3      3         50          2    0.15  0.2        4   1.240091  0.337154   \n",
       "4      4         50          2    0.15  0.2        4   1.181165  0.356112   \n",
       "..   ...        ...        ...     ...  ...      ...        ...       ...   \n",
       "95     5       1600          2    0.15  0.2        4   1.246459  0.449125   \n",
       "96     6       1600          2    0.15  0.2        4   1.244771  0.865812   \n",
       "97     7       1600          2    0.15  0.2        4   1.252444  0.862723   \n",
       "98     8       1600          2    0.15  0.2        4   1.246762  0.480228   \n",
       "99     9       1600          2    0.15  0.2        4   1.243589  0.357066   \n",
       "\n",
       "     K_MEANS    DBSCAN  SPECTRAL  MEAN_SHIFT  \n",
       "0   0.356112  0.098771  0.408445    0.526333  \n",
       "1   0.310651  0.000000  0.408445    0.280944  \n",
       "2   0.361061  0.098771  0.361061    0.361061  \n",
       "3   0.310651  0.000000  0.310651    0.310651  \n",
       "4   0.356112  0.000000  0.356112    0.356112  \n",
       "..       ...       ...       ...         ...  \n",
       "95  0.369921 -0.000495  0.410721    0.418788  \n",
       "96  0.396441 -0.000667  0.427418    0.437379  \n",
       "97  0.390140  0.000509  0.432613    0.436253  \n",
       "98  0.366998 -0.000816  0.415503    0.428755  \n",
       "99  0.361029  0.001794  0.423758    0.433799  \n",
       "\n",
       "[100 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(experiment)"
   ]
  }
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