notebooks/Comparisons/Den_Blobs_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blobs: varying the number of dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys  \n",
    "sys.path.insert(0, '../../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 0\n",
    "n_samples = 300\n",
    "n_features = 3\n",
    "n_centers = 2\n",
    "datasets = []\n",
    "\n",
    "feature_grid = [2, 3, 4, 5, 10, 15, 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = []\n",
    "rock_results = []\n",
    "other_results = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getBlobDensities(n_centers):\n",
    "  return np.round([3/n_centers * x for x in range(1, n_centers + 1)], decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f in feature_grid:\n",
    "    step = 0\n",
    "    for s in range(seed, seed+10):\n",
    "        dataset = make_blobs(n_samples=n_samples, centers=n_centers, n_features=f, \n",
    "                             cluster_std=getBlobDensities(n_centers), random_state=s)\n",
    "        run = {}\n",
    "        run['step'] = step\n",
    "        run['n_samples'] = n_samples\n",
    "        run['n_centers'] = n_centers\n",
    "        run['features'] = f\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, f * 2)\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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(experiment).to_csv('../../results/analysis/den_blobs_analysis_dim.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "<p>70 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "    step  n_samples  n_centers  features  eps  min_pts  bandwidth      ROCK  \\\n",
       "0      0        300          2         2  0.2        4   1.245013  0.005039   \n",
       "1      1        300          2         2  0.2        4   0.896702  0.971025   \n",
       "2      2        300          2         2  0.2        4   1.175592  0.721544   \n",
       "3      3        300          2         2  0.2        4   1.160315  0.583578   \n",
       "4      4        300          2         2  0.2        4   1.256347  0.000000   \n",
       "..   ...        ...        ...       ...  ...      ...        ...       ...   \n",
       "65     5        300          2        20  0.2       40   3.891290  1.000000   \n",
       "66     6        300          2        20  0.2       40   4.246561  1.000000   \n",
       "67     7        300          2        20  0.2       40   4.530091  1.000000   \n",
       "68     8        300          2        20  0.2       40   4.217402  1.000000   \n",
       "69     9        300          2        20  0.2       40   4.206570  1.000000   \n",
       "\n",
       "     K_MEANS    DBSCAN  SPECTRAL  MEAN_SHIFT  \n",
       "0   0.298242  0.217132  0.299603    0.000000  \n",
       "1   0.948730  0.618514  0.948730    0.957038  \n",
       "2   0.585288  0.402353  0.545164    0.757600  \n",
       "3   0.566263  0.331601  0.483559    0.586298  \n",
       "4   0.332521  0.198305  0.268162    0.000000  \n",
       "..       ...       ...       ...         ...  \n",
       "65  1.000000  0.000000  1.000000    0.985680  \n",
       "66  1.000000  0.000000  1.000000    0.934729  \n",
       "67  1.000000  0.000000  1.000000    0.985680  \n",
       "68  1.000000  0.000000  1.000000    0.864755  \n",
       "69  1.000000  0.000000  1.000000    0.961244  \n",
       "\n",
       "[70 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(experiment)"
   ]
  }
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