2_ML_supervised_classifier.ipynb

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     "text": [
      "Collecting wget\n",
      "  Downloading https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip\n",
      "Building wheels for collected packages: wget\n",
      "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for wget: filename=wget-3.2-cp36-none-any.whl size=9681 sha256=0dabde0aa594129b4b225d19739f1a9823d66926764de4fa1bd40bdb7510bb58\n",
      "  Stored in directory: /root/.cache/pip/wheels/40/15/30/7d8f7cea2902b4db79e3fea550d7d7b85ecb27ef992b618f3f\n",
      "Successfully built wget\n",
      "Installing collected packages: wget\n",
      "Successfully installed wget-3.2\n"
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   ],
   "source": [
    "!pip install wget\n",
    "import wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://github.com/IALeMans/Meetup_ai-basics_2019-2/raw/master/datasets.p'\n",
    "filename = wget.download(url)\n",
    "\n",
    "datasets_name = ['X_train', 'X_test', 'y_train', 'y_test']\n",
    "f = open(filename, 'rb')\n",
    "dataset = {}\n",
    "for dataset_name in datasets_name:\n",
    "    dataset[dataset_name]=(pickle.load(f))\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8000, 11)\n",
      "(2000, 11)\n",
      "(8000,)\n",
      "(2000,)\n"
     ]
    }
   ],
   "source": [
    "print(dataset['X_train'].shape)\n",
    "print(dataset['X_test'].shape)\n",
    "print(dataset['y_train'].shape)\n",
    "print(dataset['y_test'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.5698444 ,  1.74309049,  0.16958176, -1.09168714, -0.46460796,\n",
       "         0.00666099, -1.21571749,  0.8095029 ,  0.64259497, -1.03227043,\n",
       "         1.10643166],\n",
       "       [ 1.75486502, -0.57369368, -2.30455945,  0.91601335,  0.30102557,\n",
       "        -1.37744033, -0.00631193, -0.92159124,  0.64259497,  0.9687384 ,\n",
       "        -0.74866447],\n",
       "       [-0.5698444 , -0.57369368, -1.19119591, -1.09168714, -0.94312892,\n",
       "        -1.031415  ,  0.57993469, -0.92159124,  0.64259497, -1.03227043,\n",
       "         1.48533467],\n",
       "       [-0.5698444 ,  1.74309049,  0.03556578,  0.91601335,  0.10961719,\n",
       "         0.00666099,  0.47312769, -0.92159124,  0.64259497, -1.03227043,\n",
       "         1.27652776],\n",
       "       [-0.5698444 ,  1.74309049,  2.05611444, -1.09168714,  1.73658844,\n",
       "         1.04473698,  0.8101927 ,  0.8095029 ,  0.64259497,  0.9687384 ,\n",
       "         0.55837842]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "dataset['X_train'][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construction du modèle : Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='rbf', max_iter=-1, probability=False, random_state=0,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
    "classifier.fit(dataset['X_train'], dataset['y_train'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tester le modele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(dataset['X_test'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matrice de confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(dataset['y_test'], y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8635"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(cm[0,0]+cm[1,1])/cm.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1547   48]\n",
      " [ 225  180]]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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