exemple 1 ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "if not os.path.exists('figures'):\n",
    "    os.makedirs('figures')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sklearn\n",
      "  Downloading sklearn-0.0.tar.gz (1.1 kB)\n",
      "Collecting scikit-learn\n",
      "  Downloading scikit_learn-0.23.2-cp37-cp37m-manylinux1_x86_64.whl (6.8 MB)\n",
      "\u001b[K     |████████████████████████████████| 6.8 MB 3.2 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scipy>=0.19.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from scikit-learn->sklearn) (1.5.2)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from scikit-learn->sklearn) (1.19.2)\n",
      "Collecting threadpoolctl>=2.0.0\n",
      "  Downloading threadpoolctl-2.1.0-py3-none-any.whl (12 kB)\n",
      "Collecting joblib>=0.11\n",
      "  Downloading joblib-0.16.0-py3-none-any.whl (300 kB)\n",
      "\u001b[K     |████████████████████████████████| 300 kB 66.9 MB/s eta 0:00:01\n",
      "\u001b[?25hBuilding wheels for collected packages: sklearn\n",
      "  Building wheel for sklearn (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1315 sha256=a0be569e99b0d0d76fbf9235298814098da577af984c36dc9813420e3c30c9ad\n",
      "  Stored in directory: /home/jovyan/.cache/pip/wheels/46/ef/c3/157e41f5ee1372d1be90b09f74f82b10e391eaacca8f22d33e\n",
      "Successfully built sklearn\n",
      "Installing collected packages: threadpoolctl, joblib, scikit-learn, sklearn\n",
      "Successfully installed joblib-0.16.0 scikit-learn-0.23.2 sklearn-0.0 threadpoolctl-2.1.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_plot(ax, title):\n",
    "    ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
    "    ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "    ax.set_xlabel('feature 1', color='gray')\n",
    "    ax.set_ylabel('feature 2', color='gray')\n",
    "    ax.set_title(title, color='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.datasets.samples_generator module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# create 50 separable points\n",
    "X, y = make_blobs(n_samples=50, centers=2,\n",
    "                  random_state=0, cluster_std=0.60)\n",
    "\n",
    "# fit the support vector classifier model\n",
    "clf = SVC(kernel='linear')\n",
    "clf.fit(X, y)\n",
    "\n",
    "# create some new points to predict\n",
    "X2, _ = make_blobs(n_samples=80, centers=2,\n",
    "                   random_state=0, cluster_std=0.80)\n",
    "X2 = X2[50:]\n",
    "\n",
    "# predict the labels\n",
    "y2 = clf.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "point_style = dict(cmap='Paired', s=50)\n",
    "ax.scatter(X[:, 0], X[:, 1], c=y, **point_style)\n",
    "\n",
    "# format plot\n",
    "format_plot(ax, 'Input Data')\n",
    "ax.axis([-1, 4, -2, 7])\n",
    "\n",
    "fig.savefig('figures/05.01-classification-1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}