make_classification-make_blobs.ipynb

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     "text": [
      "Using matplotlib backend: MacOSX\n"
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   "source": [
    "%matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.datasets import make_gaussian_quantiles\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplots_adjust(bottom=.05, top=.9, left=.05, right=.95)\n",
    "\n",
    "plt.subplot(321)\n",
    "plt.title(\"Une caractéristique donnant de l'information , un cluster par classe\", fontsize='small')\n",
    "X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=1,\n",
    "                             n_clusters_per_class=1)\n",
    "plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n",
    "            s=25, edgecolor='k')\n",
    "\n",
    "plt.subplot(322)\n",
    "plt.title(\"deux caractéristiques donnant de l'information, un cluster par classe\", fontsize='small')\n",
    "X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,\n",
    "                             n_clusters_per_class=1)\n",
    "plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n",
    "            s=25, edgecolor='k')\n",
    "\n",
    "plt.subplot(323)\n",
    "plt.title(\"deux caractéristiques donnant de l'information, deux clusters par classe\",\n",
    "          fontsize='small')\n",
    "X2, Y2 = make_classification(n_features=2, n_redundant=0, n_informative=2)\n",
    "plt.scatter(X2[:, 0], X2[:, 1], marker='o', c=Y2,\n",
    "            s=25, edgecolor='k')\n",
    "\n",
    "plt.subplot(324)\n",
    "plt.title(\"Multi-class, deux caractéristiques donnant de l'information, un  cluster\",\n",
    "          fontsize='small')\n",
    "X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,\n",
    "                             n_clusters_per_class=1, n_classes=3)\n",
    "plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n",
    "            s=25, edgecolor='k')\n",
    "\n",
    "plt.subplot(325)\n",
    "plt.title(\"3 blobs\", fontsize='small')\n",
    "X1, Y1 = make_blobs(n_features=2, centers=3)\n",
    "plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n",
    "            s=25, edgecolor='k')\n",
    "\n",
    "plt.subplot(326)\n",
    "plt.title(\"Gaussian divisés en trois quantiles\", fontsize='small')\n",
    "X1, Y1 = make_gaussian_quantiles(n_features=2, n_classes=3)\n",
    "plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n",
    "            s=25, edgecolor='k')\n",
    "\n",
    "plt.show()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-13T13:24:34.641197Z",
     "start_time": "2020-04-13T13:24:34.449854Z"
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   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "X1, Y1 = make_classification(n_features=3, n_redundant=0, n_informative=2,\n",
    "                             n_clusters_per_class=2)\n",
    "plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,\n",
    "            s=25, edgecolor='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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