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mooc-rr
Commit 1e89ab46 authored by e6a2acf6b1e71323e580d5754b297b60's avatar e6a2acf6b1e71323e580d5754b297b60
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Annulez la mission.

parent 35c33965
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Showing with 652 additions and 40 deletions
module2/exo5/exo5_fr.ipynb
View file @ 1e89ab46
...@@ -40,7 +40,7 @@ ...@@ -40,7 +40,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -287,7 +287,7 @@ ...@@ -287,7 +287,7 @@
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 1, "execution_count": 14,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -322,7 +322,7 @@ ...@@ -322,7 +322,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -425,7 +425,7 @@ ...@@ -425,7 +425,7 @@
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 2, "execution_count": 15,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -448,12 +448,12 @@ ...@@ -448,12 +448,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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\n", "image/png": 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -500,7 +500,7 @@ ...@@ -500,7 +500,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -524,10 +524,10 @@ ...@@ -524,10 +524,10 @@
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5250</td> \n", " <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5250</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Date:</th> <td>Sat, 13 Apr 2019</td> <th> Deviance: </th> <td> 0.22231</td> \n", " <th>Date:</th> <td>Wed, 25 Mar 2020</td> <th> Deviance: </th> <td> 0.22231</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Time:</th> <td>19:11:24</td> <th> Pearson chi2: </th> <td> 0.236</td> \n", " <th>Time:</th> <td>18:06:02</td> <th> Pearson chi2: </th> <td> 0.236</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n", " <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
...@@ -555,8 +555,8 @@ ...@@ -555,8 +555,8 @@
"Model Family: Binomial Df Model: 1\n", "Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n", "Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -2.5250\n", "Method: IRLS Log-Likelihood: -2.5250\n",
"Date: Sat, 13 Apr 2019 Deviance: 0.22231\n", "Date: Wed, 25 Mar 2020 Deviance: 0.22231\n",
"Time: 19:11:24 Pearson chi2: 0.236\n", "Time: 18:06:02 Pearson chi2: 0.236\n",
"No. Iterations: 4 Covariance Type: nonrobust\n", "No. Iterations: 4 Covariance Type: nonrobust\n",
"===============================================================================\n", "===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n", " coef std err z P>|z| [0.025 0.975]\n",
...@@ -567,7 +567,7 @@ ...@@ -567,7 +567,7 @@
"\"\"\"" "\"\"\""
] ]
}, },
"execution_count": 4, "execution_count": 17,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -605,12 +605,12 @@ ...@@ -605,12 +605,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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\n", "image/png": 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -648,7 +648,7 @@ ...@@ -648,7 +648,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -686,6 +686,618 @@ ...@@ -686,6 +686,618 @@
"analyse et de regarder ce jeu de données sous tous les angles afin\n", "analyse et de regarder ce jeu de données sous tous les angles afin\n",
"d'expliquer ce qui ne va pas." "d'expliquer ce qui ne va pas."
] ]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### réponse \n",
"\n",
"La régression a été effectué sur le jeu de données complet (pression = 50 et 200 bar).\n",
"\n",
"### hyp. 1\n",
"Il faut retirer la données correspondant à la pression de 50 bars pour voire l'influence de la température.\n",
"\n",
"ccl = ce n'est pas la pression qui pose problème."
]
},
{
"cell_type": "code",
"execution_count": 28,
"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>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",
"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"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"shuttle.csv\")\n",
"data = data[data.Malfunction>0]\n",
"data = data[data.Pressure>50]\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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"
}
],
"source": [
"data[\"Frequency\"]=data.Malfunction/data.Count\n",
"data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### hyp. 2\n",
"La fréquence de malfonction doit être calculée sur l'ensemble des données (pas de filtre sur data.malfunction) car on masque tous les test pour lesquelles on n'as pas eu de pb.\n",
"\n",
"La fréquence doit être calculée comme la somme des malfunction / somme des counts pour une température donnée. "
]
},
{
"cell_type": "code",
"execution_count": 44,
"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>0</th>\n",
" <td>4/12/81</td>\n",
" <td>6</td>\n",
" <td>66</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\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>2</th>\n",
" <td>3/22/82</td>\n",
" <td>6</td>\n",
" <td>69</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11/11/82</td>\n",
" <td>6</td>\n",
" <td>68</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4/04/83</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6/18/82</td>\n",
" <td>6</td>\n",
" <td>72</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8/30/83</td>\n",
" <td>6</td>\n",
" <td>73</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11/28/83</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>100</td>\n",
" <td>0</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>11</th>\n",
" <td>10/05/84</td>\n",
" <td>6</td>\n",
" <td>78</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11/08/84</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</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>14</th>\n",
" <td>4/12/85</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>4/29/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6/17/85</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7/29/85</td>\n",
" <td>6</td>\n",
" <td>81</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>8/27/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>10/03/85</td>\n",
" <td>6</td>\n",
" <td>79</td>\n",
" <td>200</td>\n",
" <td>0</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>21</th>\n",
" <td>11/26/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</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",
"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": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"shuttle.csv\")\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"# data = data[data.Pressure == 200]"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"nb_count = []\n",
"nb_malfunction = []\n",
"\n",
"unique_temp = data.Temperature.unique()\n",
"for temp in unique_temp:\n",
" nb_count.append(data['Count'][data['Temperature'] == temp].sum())\n",
" nb_malfunction.append(data['Malfunction'][data['Temperature'] == temp].sum())\n"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"df_data = pd.DataFrame()\n",
"df_data['Temperature'] = unique_temp\n",
"df_data['Count'] = nb_count\n",
"df_data['Malfunction'] = nb_malfunction\n",
"df_data['Frequency'] = df_data['Malfunction']/df_data['Count']"
]
},
{
"cell_type": "code",
"execution_count": 84,
"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"
}
],
"source": [
"df_data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"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> 16</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 14</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> -2.4880</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 25 Mar 2020</td> <th> Deviance: </th> <td> 1.1965</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>18:42:18</td> <th> Pearson chi2: </th> <td> 1.82</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>6</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> 6.8667</td> <td> 8.822</td> <td> 0.778</td> <td> 0.436</td> <td> -10.424</td> <td> 24.157</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> -0.1458</td> <td> 0.143</td> <td> -1.023</td> <td> 0.306</td> <td> -0.425</td> <td> 0.134</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: 16\n",
"Model: GLM Df Residuals: 14\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -2.4880\n",
"Date: Wed, 25 Mar 2020 Deviance: 1.1965\n",
"Time: 18:42:18 Pearson chi2: 1.82\n",
"No. Iterations: 6 Covariance Type: nonrobust\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept 6.8667 8.822 0.778 0.436 -10.424 24.157\n",
"Temperature -0.1458 0.143 -1.023 0.306 -0.425 0.134\n",
"===============================================================================\n",
"\"\"\""
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statsmodels.api as sm\n",
"\n",
"df_data[\"Success\"]=df_data.Count-df_data.Malfunction\n",
"df_data[\"Intercept\"]=1\n",
"\n",
"logmodel=sm.GLM(df_data['Frequency'], df_data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n",
"logmodel.summary()"
]
},
{
"cell_type": "code",
"execution_count": 87,
"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"
}
],
"source": [
"%matplotlib inline\n",
"data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
"data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n",
"data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
"plt.scatter(x=df_data[\"Temperature\"],y=df_data[\"Frequency\"])\n",
"plt.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Probabilité de défaillance d'un joint: 0.9126150661497261\n",
"Probabilité de défaillance de deux joints: 0.8328662589634689\n",
"Probabilité de défaillance d'un lanceur sur trois: 0.9953313383250852\n"
]
}
],
"source": [
"proba = logmodel.predict([1, 31])\n",
"print(\"Probabilité de défaillance d'un joint: \", str(proba[0]))\n",
"print(\"Probabilité de défaillance de deux joints: \", str(proba[0]**2))\n",
"proba3 = 1.-(1-proba**2)**3\n",
"print(\"Probabilité de défaillance d'un lanceur sur trois: \", str(proba3[0]))"
]
} }
], ],
"metadata": { "metadata": {
...@@ -705,7 +1317,7 @@ ...@@ -705,7 +1317,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
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