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mooc-rr
Commit 08345ce0 authored by 335f5f369755e7a506491c0327a7d5ee's avatar 335f5f369755e7a506491c0327a7d5ee
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plotting logistic regression

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module2/exo5/exo5_fr.ipynb
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......@@ -40,7 +40,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 23,
"metadata": {},
"outputs": [
{
......@@ -261,33 +261,33 @@
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1"
" Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1"
]
},
"execution_count": 1,
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
......@@ -295,6 +295,8 @@
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels as sm\n",
"from statsmodels.tools import add_constant\n",
"data = pd.read_csv(\"shuttle.csv\")\n",
"data"
]
......@@ -313,147 +315,131 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inspection graphique des données\n",
"Les vols où aucun incident n'est relevé n'apportant aucun information\n",
"sur l'influence de la température ou de la pression sur les\n",
"dysfonctionnements, nous nous concentrons sur les expériences où au\n",
"moins un joint a été défectueux.\n"
"## Regression logistique"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Count</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Malfunction</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11/12/81</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2/03/84</td>\n",
" <td>6</td>\n",
" <td>57</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4/06/84</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8/30/84</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1/24/85</td>\n",
" <td>6</td>\n",
" <td>53</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10/30/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1/12/86</td>\n",
" <td>6</td>\n",
" <td>58</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction\n",
"1 11/12/81 6 70 50 1\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"13 1/24/85 6 53 200 2\n",
"20 10/30/85 6 75 200 2\n",
"22 1/12/86 6 58 200 1"
"<matplotlib.axes._subplots.AxesSubplot at 0x7f68967aec18>"
]
},
"execution_count": 2,
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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E3JdwUV8HMKNseDqwtY++S+i1OyoitubftwNryHZxHSQiVkZEKSJKLS0tVUoyM7OB6jcwIuKNiLg7Is4DTgHagZ9K+vOEea8HZkuaJekwslA4aBeXpDcDpwF3lLUdIWliz2vgTOCxxGUyM7MCVD3oLWkc2W3OlwIzgauB26pNFxFd+Sm495CdVrsqIh6XdGE+fkXe9ePAuoh4tWzyo4E1+Q0PxwI3RcTdqQtlZmZDTxF9HVYASd8n2x11F3BLRIzo//JLpVK0tbXVuwwzs4YhaUPqpQvVtjDOAV4F/gD4i7JbnAuIiHjTgKs0M7OGUu1utakX9pmZ2SjnQDAzsyQODDMzS+LAMDOzJA4MMzNL4sAwM7MkDgwzM0viwDAzsyQODDMzS+LAMDOzJA4MMzNL4sAwM7MkDgwzM0viwDAzsySFBoakhZKeltQu6bIK40+X9IqkjfnX5anTmpnZ8Kr6xL2BkjQGuIbsOeAdwHpJayPiiV5dfxYRHx3gtGZmNkyK3MKYD7RHxOaIeB24BVg0DNOamVkBigyMacCWsuGOvK23BZIekXSXpHfUOK2ZmQ2TwnZJkT3GtbfeDxB/GHhrROyR9BHgdmB24rTZm0jLgGUAra2tA6/WzMz6VeQWRgcwo2x4OrC1vENE/CYi9uSv7wSaJU1NmbZsHisjohQRpZaWlqGs38zMyhQZGOuB2ZJmSToMWAKsLe8g6RhJyl/Pz+vpTJnWzMyGV2G7pCKiS9JFwD3AGGBVRDwu6cJ8/ArgE8BnJXUBe4ElERFAxWmLqtXMzKpT9vd5dCiVStHW1lbvMszMGoakDRFRSunrK73NzCyJA8PMzJI4MMzMLIkDw8zMkjgwzMwsiQPDzMySODDMzCyJA8PMzJI4MMzMLIkDw8zMkjgwzMwsiQPDzMySODDMzCyJA8PMzJI4MMzMLIkDw8zMkhQaGJIWSnpaUrukyyqM/7SkTfnXg5JOLBv3rKRHJW2U5KcimZnVWWGPaJU0BrgG+DDQAayXtDYinijr9gxwWkTslHQWsBI4uWz8GRHxUlE1mplZuiK3MOYD7RGxOSJeB24BFpV3iIgHI2JnPvgQML3AeszMbBCKDIxpwJay4Y68rS+fAe4qGw5gnaQNkpb1NZGkZZLaJLXt2LFjUAWbmVnfCtslBahCW1TsKJ1BFhjvK2s+NSK2SjoKuFfSUxFx/0EzjFhJtiuLUqlUcf5mZjZ4RW5hdAAzyoanA1t7d5I0F7gOWBQRnT3tEbE1/74dWEO2i8vMzOqkyMBYD8yWNEvSYcASYG15B0mtwG3AORHxr2XtR0ia2PMaOBN4rMBazcysisJ2SUVEl6SLgHuAMcCqiHhc0oX5+BXA5cAU4NuSALoiogQcDazJ28YCN0XE3UXVamZm1Sli9Oz2L5VK0dbmSzbMzFJJ2pD/o16Vr/Q2M7MkDgwzM0viwDAzsyQODDMzS+LAMDOzJA4MMzNL4sAwM7MkDgwzM0viwDAzsyQODDMzS+LAMDOzJA4MMzNL4sAwM7MkDgwzM0viwDAzsySFBoakhZKeltQu6bIK4yXp6nz8JkknpU5rZmbDq7DAkDQGuAY4C5gDLJU0p1e3s4DZ+dcy4NoapjWrm849+3hkyy469+yr2rftmU6+ue5p2p7prNq3lvnW0rd9225ubdtC+7bdVfumKqrWomrwz2DwCntEKzAfaI+IzQCSbgEWAU+U9VkE3BDZY/8ekjRJ0rHAzIRpzerijo0vcOnqTTQ3NbG/u5vli+dy9rxpFfv+2XUP8UB7FhRX/7id9x8/hRsvOGXQ862l7+W3P8oNDz3/u+FzF7Ry1aJ31bLIw1ZrUTX4ZzA0itwlNQ3YUjbckbel9EmZ1mzYde7Zx6WrN/Ha/m527+vitf3dfGH1por/3bU90/m7sOjxs/bOilsatcy3lr7t23Yf8IcS4IZfPD+o/7KLqrWoGvwzGDpFBoYqtPV+gHhffVKmzWYgLZPUJqltx44dNZZoVpuOnXtpbjrwY9Pc1ETHzr0H9b3/1y9VnEel9lrmW0vfjVt2Vayhr/YURdVaVA3+GQydIgOjA5hRNjwd2JrYJ2VaACJiZUSUIqLU0tIy6KLN+jN98gT2d3cf0La/u5vpkycc1PcDs6dWnEel9lrmW0vfeTMmVayhr/YURdVaVA3+GQydIgNjPTBb0ixJhwFLgLW9+qwFzs3PljoFeCUiXkyc1mzYTTlyHMsXz2V8cxMTx41lfHMTyxfPZcqR4w7qW5o1hfcfP+WAtvcfP4XSrCkH9a1lvrX0Pf7oiZy7oPWAtnMXtHL80RNrXfTCay2qBv8Mho6y480FzVz6CPC3wBhgVUR8TdKFABGxQpKAbwELgd8C50dEW1/TVnu/UqkUbW1txSyMWZnOPfvo2LmX6ZMnVP2Atj3Tyf2/fokPzJ5aMSwGOt9a+rZv283GLbuYN2PSoP5QDketRdXgn0FlkjZERCmpb5GBMdwcGGZmtaklMHylt5mZJXFgmJlZEgeGmZklcWCYmVkSB4aZmSVxYJiZWZJRdVqtpB3Ac/Wuo5epQOV7RDQ2L1fjGa3L5uUanLdGRNJtMkZVYIxEktpSz3FuJF6uxjNal83LNXy8S8rMzJI4MMzMLIkDo3gr611AQbxcjWe0LpuXa5j4GIaZmSXxFoaZmSVxYAwhSc9KelTSRkk9t2m/UtILedvG/LbtDSV/1vqtkp6S9KSkBZLeIuleSb/Ov0+ud50D0ceyNfQ6k3RCWe0bJf1G0sWNvs76Wa6GXl8Akv6rpMclPSbpZknjR+L68i6pISTpWaAUES+VtV0J7ImIb9SrrsGS9H3gZxFxXf5Aq8OBLwEvR8RfS7oMmBwRl9a10AHoY9kupsHXWQ9JY4AXgJOBzzMK1hkctFzn08DrS9I04AFgTkTslfRPwJ3AHEbY+vIWhvVL0puADwB/DxARr0fELmAR8P282/eBP65PhQPXz7KNJh8C/l9EPMcoWGdlypdrNBgLTJA0luyflq2MwPXlwBhaAayTtEHSsrL2iyRtkrRqJGxW1ug4YAfwPUn/Iuk6SUcAR+eP0yX/flQ9ixygvpYNGnudlVsC3Jy/Hg3rrEf5ckEDr6+IeAH4BvA88CLZo6rXMQLXlwNjaJ0aEScBZwGfl/QB4FrgbcA8sl+Gv6ljfQMxFjgJuDYi3g28ClxW35KGTF/L1ujrDIB8F9vZwA/qXctQqrBcDb2+8oBbBMwC/h1whKQ/q29VlTkwhlBEbM2/bwfWAPMjYltEvBER3cB3gfn1rHEAOoCOiPhlPnwr2R/ZbZKOBci/b69TfYNRcdlGwTrrcRbwcERsy4dHwzqDXss1CtbXHwHPRMSOiNgP3Aa8lxG4vhwYQ0TSEZIm9rwGzgQe61nhuY8Dj9WjvoGKiH8Dtkg6IW/6EPAEsBY4L287D7ijDuUNSl/L1ujrrMxSDtxt0/DrLHfAco2C9fU8cIqkwyWJ7PfwSUbg+vJZUkNE0nFkWxWQ7eq4KSK+JulGsk3lAJ4F/kvPfslGIWkecB1wGLCZ7KyUJuCfgFayX/hPRsTLdStygPpYtqtp/HV2OLAFOC4iXsnbptDg66yP5RoNn7EvA58CuoB/AS4AjmSErS8HhpmZJfEuKTMzS+LAMDOzJA4MMzNL4sAwM7MkDgwzM0sytt4FmBUtP530R/ngMcAbZLcEgeziytfrUlg/JP0n4M78WhGzEcGn1dohZSTdPVjSmIh4o49xDwAXRcTGGuY3NiK6hqxAs168S8oOaZLOk/Sr/DkK35bUJGmspF2Svi7pYUn3SDpZ0n2SNvc8b0HSBZLW5OOflvQ/E+f7VUm/AuZL+rKk9flzEFYo8ymyC9H+MZ/+MEkdkibl8z5F0g/z11+V9B1J95LdRHGspG/m771J0gXD/1O10cqBYYcsSe8ku5XEeyNiHtku2iX56DcD6/KbSb4OXEl2y4ZPAleVzWZ+Ps1JwJ9Kmpcw34cjYn5E/AL4u4h4D/CufNzCiPhHYCPwqYiYl7DL7N3AxyLiHGAZsD0i5gPvIbsJZutAfj5mvfkYhh3K/ojsj2pbdgsfJpDddgJgb0Tcm79+lOyW012SHgVmls3jnojYCSDpduB9ZJ+rvub7Or+/hQzAhyRdAowHpgIbgLtqXI47IuK1/PWZwNsllQfUbLJbS5gNigPDDmUCVkXE/zqgMXuITfl/9d3AvrLX5Z+b3gcBo8p890Z+4DC/L9K3yO6Q+4Kkr5IFRyVd/H6PQO8+r/Zaps9FxI8wG2LeJWWHsh8C/1HSVMjOphrA7pszlT0X/HCyZxr8vIb5TiALoJfyOx0vLhu3G5hYNvws8If56/J+vd0DfC4Pp57nYE+ocZnMKvIWhh2yIuLR/C6hP5TUBOwHLiR7PGaqB4CbyB7gc2PPWU0p842ITmXPFH8MeA74Zdno7wHXSdpLdpzkSuC7kv4N+FU/9XyH7O6mG/PdYdvJgsxs0HxardkA5WcgvTMiLq53LWbDwbukzMwsibcwzMwsibcwzMwsiQPDzMySODDMzCyJA8PMzJI4MMzMLIkDw8zMkvx/GY+fU3UQosUAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data = data[data.Malfunction>0]\n",
"data"
"data['problem'] = np.where(data['Malfunction']>0, 1, 0)\n",
"data.plot(x=\"Temperature\",y=\"Malfunction\",kind=\"scatter\")\n",
"data.plot(x=\"Temperature\",y=\"problem\",kind=\"scatter\")"
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.441635\n",
" Iterations 7\n",
" Logit Regression Results \n",
"==============================================================================\n",
"Dep. Variable: problem No. Observations: 23\n",
"Model: Logit Df Residuals: 21\n",
"Method: MLE Df Model: 1\n",
"Date: Sun, 29 Nov 2020 Pseudo R-squ.: 0.2813\n",
"Time: 20:53:49 Log-Likelihood: -10.158\n",
"converged: True LL-Null: -14.134\n",
" LLR p-value: 0.004804\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"const 15.0429 7.379 2.039 0.041 0.581 29.505\n",
"Temperature -0.2322 0.108 -2.145 0.032 -0.444 -0.020\n",
"===============================================================================\n"
]
}
],
"source": [
"Très bien, nous avons une variabilité de température importante mais\n",
"la pression est quasiment toujours égale à 200, ce qui devrait\n",
"simplifier l'analyse.\n",
"#data[\"Malfunction\"] = data[\"Malfunction\"].astype('category')\n",
"\n",
"Comment la fréquence d'échecs varie-t-elle avec la température ?\n"
"y = data[\"problem\"]\n",
"\n",
"# on ne prend que les colonnes quantitatives\n",
"x = data[\"Temperature\"]\n",
"# on ajoute une colonne pour la constante\n",
"x_stat = sm.tools.add_constant(x)\n",
"# on ajuste le modèle\n",
"model = sm.api.Logit(y, x_stat)\n",
"result = model.fit()\n",
"print(result.summary())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[9.99969558e-01 9.99951569e-01 9.99922951e-01 9.99877426e-01\n",
" 9.99805006e-01 9.99689811e-01 9.99506599e-01 9.99215256e-01\n",
" 9.98752098e-01 9.98016125e-01 9.96847468e-01 9.94993835e-01\n",
" 9.92058981e-01 9.87425318e-01 9.80141989e-01 9.68773521e-01\n",
" 9.51220648e-01 9.24569286e-01 8.85115205e-01 8.28844843e-01\n",
" 7.52713482e-01 6.56742588e-01 5.45991136e-01 4.30493132e-01\n",
" 3.22094054e-01 2.29968258e-01 1.58049102e-01 1.05538936e-01\n",
" 6.90440720e-02 4.45405463e-02 2.84673270e-02 1.80846183e-02\n",
" 1.14441228e-02 7.22401354e-03 4.55293511e-03 2.86663594e-03\n",
" 1.80377025e-03 1.13453604e-03 7.13423407e-04 4.48547518e-04]\n"
]
},
{
"data": {
"image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -464,6 +450,52 @@
"output_type": "display_data"
}
],
"source": [
"X1= range(20,100,2)\n",
"X1_test = sm.tools.add_constant(X1)\n",
"ypred = result.predict(X1_test)\n",
"print(ypred)\n",
"ax1 = data.plot(x=\"Temperature\",y=\"problem\",kind=\"scatter\")\n",
"ax1 = plt.plot(X1, ypred ,'r--')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inspection graphique des données\n",
"Les vols où aucun incident n'est relevé n'apportant aucun information\n",
"sur l'influence de la température ou de la pression sur les\n",
"dysfonctionnements, nous nous concentrons sur les expériences où au\n",
"moins un joint a été défectueux.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = data[data.Malfunction>0]\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Très bien, nous avons une variabilité de température importante mais\n",
"la pression est quasiment toujours égale à 200, ce qui devrait\n",
"simplifier l'analyse.\n",
"\n",
"Comment la fréquence d'échecs varie-t-elle avec la température ?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
......@@ -500,78 +532,9 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 7</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 5</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5250</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 13 Apr 2019</td> <th> Deviance: </th> <td> 0.22231</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>19:11:24</td> <th> Pearson chi2: </th> <td> 0.236</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> -1.3895</td> <td> 7.828</td> <td> -0.178</td> <td> 0.859</td> <td> -16.732</td> <td> 13.953</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> 0.0014</td> <td> 0.122</td> <td> 0.012</td> <td> 0.991</td> <td> -0.238</td> <td> 0.240</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 7\n",
"Model: GLM Df Residuals: 5\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -2.5250\n",
"Date: Sat, 13 Apr 2019 Deviance: 0.22231\n",
"Time: 19:11:24 Pearson chi2: 0.236\n",
"No. Iterations: 4 Covariance Type: nonrobust\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953\n",
"Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n",
"===============================================================================\n",
"\"\"\""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"import statsmodels.api as sm\n",
"\n",
......@@ -593,6 +556,15 @@
"estimations avec des pincettes.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data[\"Success\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
......@@ -605,22 +577,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"%matplotlib inline\n",
"data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
......@@ -648,17 +607,9 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.06521739130434782\n"
]
}
],
"outputs": [],
"source": [
"data = pd.read_csv(\"shuttle.csv\")\n",
"print(np.sum(data.Malfunction)/np.sum(data.Count))"
......@@ -705,7 +656,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.6.4"
}
},
"nbformat": 4,
......
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