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Commit 59c33bb5 authored by b4c7ed5a2ec09ed38e4e0aa7d0a61152's avatar b4c7ed5a2ec09ed38e4e0aa7d0a61152
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module 2 exo 5

parent 6bb2e4d3
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Showing with 227 additions and 40 deletions
module2/exo5/exo5_fr.ipynb
View file @ 59c33bb5
...@@ -40,7 +40,7 @@ ...@@ -40,7 +40,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 65,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -261,33 +261,33 @@ ...@@ -261,33 +261,33 @@
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" Date Count Temperature Pressure Malfunction\n", " Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n", "0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n", "1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n", "2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n", "3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n", "4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n", "5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n", "6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n", "7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n", "8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n", "9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n", "10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n", "11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n", "12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n", "13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n", "14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n", "15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n", "16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n", "17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n", "18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n", "19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n", "20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n", "21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 1, "execution_count": 65,
"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": 66,
"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": 66,
"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": 67,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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E8S1bW1luMbMnw7Xp/p/Ma77aYSjs41YfupIdk5O7zdsxOcnqQ1e2stxiZk+G66QjD5nXfLXDUNjHHXbQ/lx6zokcsGKEg/dfzgErRrj0nBPnPEG6t8stZvZkuI5bdTBveMlRu817w0uO8mTzgHmieRFYe9IRnH7c4fO+YmZvl1vM7MlwXXL2C3jDi4/mtm/dxI1/8GIDYQgMhUXisIP236s3sL1dbjGzJ8N13KqDGT9whYEwJB4+kiQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUqPVUEhyVpK7k2xOcuEMz++f5DPd57+Z5Og265Ekza61UEiyDLgMeAVwAnBekhOmDHszsKWqjgM+ALyvrXokSXNrc0/hVGBzVd1TVduBK4Gzp4w5G/hk9/E1wMuSpMWaJEmzWN7iuo8A7u+ZHgdO29OYqtqZ5KfAYcCPegclWQes605OJLm7lYoX3uFM+V1kT2ZgT6azJzN7Kn15Tj+D2gyFmf7ir70YQ1WtB9YvRFGDlGRjVY0Ou46nE3synT2Zzp7MbBB9afPw0ThwZM/0auDBPY1Jshx4FvDjFmuSJM2izVC4BTg+yTFJ9gPOBTZMGbMBeGP38WuAL1fVtD0FSdJgtHb4qHuO4ALgemAZ8PGquiPJJcDGqtoA/HfgiiSb6ewhnNtWPUOyzx3yGgB7Mp09mc6ezKz1vsQ/zCVJu/iJZklSw1CQJDUMhQWS5N4ktyXZlGRjd97FSR7oztuU5JXDrnPQkhyS5Jok/5zkriQvSfILSW5I8i/dfw8ddp2DtIeeLNltJclze37vTUl+luTtS3k7maUnrW8nnlNYIEnuBUar6kc98y4GJqrqL4dV17Al+STwj1X1se5VaAcC7wZ+XFX/rXtPrEOr6l1DLXSA9tCTt7PEtxVobo/zAJ0Pup7PEt5OdpnSkzfR8nbinoJak+SZwK/RucqMqtpeVT9h99ubfBL4D8OpcPBm6Yk6Xgb8v6r6Pkt4O5mityetMxQWTgFfTPKt7m05drkgya1JPr6Udn+7jgV+CHwiyXeSfCzJM4BVVfUDgO6/vzjMIgdsTz2Bpb2t7HIu8Onu46W8nfTq7Qm0vJ0YCgvn9Ko6hc5dYc9P8mvAh4BfBk4CfgC8f4j1DcNy4BTgQ1V1MvAYMO0W6kvMnnqy1LcVuofS1gJXD7uWp4sZetL6dmIoLJCqerD778PAZ4FTq+qhqnqyqiaBj9K5c+xSMg6MV9U3u9PX0HlDfCjJswG6/z48pPqGYcaeuK0AnT+ovl1VD3Wnl/J2sstuPRnEdmIoLIAkz0hy8K7HwMuB23dt0F2vBm4fRn3DUlX/Ctyf5LndWS8D7mT325u8Efj8EMobij31ZKlvK13nsfthkiW7nfTYrSeD2E68+mgBJDmWzt4BdA4PfKqq/jzJFXR28wq4F/i9XcdIl4okJwEfA/YD7qFz9cQIcBVwFHAf8NqqWjI3QtxDTz7IEt5WkhxI5zb6x1bVT7vzDmNpbycz9aT19xRDQZLU8PCRJKlhKEiSGoaCJKlhKEiSGoaCJKnR2jevSYPWvYTxS93JXwKepHNLCeh8mHD7UAqbRZLfBa7rfn5BGjovSdWi9HS6Q22SZVX15B6e+xpwQVVtmsf6llfVzgUrUOrh4SMtCUnemOTm7j3o/y7JSJLlSX6S5C+SfDvJ9UlOS/KVJPfsuld9krck+Wz3+buTvKfP9b43yc3AqUn+LMktSW5P8uF0vJ7OB5E+011+vyTjSQ7prvvFSW7sPn5vko8kuYHOzfSWJ/mr7mvfmuQtg++qFiNDQYtekufTuSXAr1bVSXQOm57bffpZwBe7NzPcDlxM59YTrwUu6VnNqd1lTgH+Y5KT+ljvt6vq1Kr6BvA3VfUi4AXd586qqs8Am4DXV9VJfRzeOhl4VVX9NrAOeLiqTgVeROcmjEftTX+kXp5T0FJwJp03zo1JAFbSuX0AwNaquqH7+Dbgp1W1M8ltwNE967i+qrYAJPkc8FI6///sab3b+fmtTwBeluSdwAHA4cC3gC/M8/f4fFU90X38cuB5SXpD6Hg6t4OQ9pqhoKUgwMer6r/sNjNZTufNe5dJYFvP497/P6aefKs51ru1uifsuvew+Vs6d0N9IMl76YTDTHby8z34qWMem/I7va2qvoS0gDx8pKXgRuB1SQ6HzlVKe3Go5eXpfLfygXS+Eezr81jvSjoh86Pu3XTP6XnuUeDgnul7gRd2H/eOm+p64G3dANr1nb4r5/k7SdO4p6BFr6puS/JnwI1JRoAdwH8CHpzHar4GfIrOF5xcsetqoX7WW1WPpPO9zLcD3we+2fP0J4CPJdlK57zFxcBHk/wrcPMs9XyEzt1DN3UPXT1MJ6ykp8RLUqU5dK/seX5VvX3YtUht8/CRJKnhnoIkqeGegiSpYShIkhqGgiSpYShIkhqGgiSp8f8B+Q9eu+sB8EwAAAAASUVORK5CYII=\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": 68,
"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, 18 Sep 2024</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>16:24:13</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, 18 Sep 2024 Deviance: 0.22231\n",
"Time: 19:11:24 Pearson chi2: 0.236\n", "Time: 16:24:13 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": 68,
"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": 69,
"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": 70,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -686,6 +686,193 @@ ...@@ -686,6 +686,193 @@
"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": [
"# Modification de l'étude"
]
},
{
"cell_type": "code",
"execution_count": 71,
"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",
"pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
"import matplotlib.pyplot as plt\n",
"\n",
"data[\"Frequency\"]=data.Malfunction/data.Count\n",
"data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"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> 23</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 21</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> -3.9210</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 18 Sep 2024</td> <th> Deviance: </th> <td> 3.0144</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>16:24:16</td> <th> Pearson chi2: </th> <td> 5.00</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> 5.0850</td> <td> 7.477</td> <td> 0.680</td> <td> 0.496</td> <td> -9.570</td> <td> 19.740</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> -0.1156</td> <td> 0.115</td> <td> -1.004</td> <td> 0.316</td> <td> -0.341</td> <td> 0.110</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: 23\n",
"Model: GLM Df Residuals: 21\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -3.9210\n",
"Date: Wed, 18 Sep 2024 Deviance: 3.0144\n",
"Time: 16:24:16 Pearson chi2: 5.00\n",
"No. Iterations: 6 Covariance Type: nonrobust\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n",
"Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n",
"===============================================================================\n",
"\"\"\""
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statsmodels.api as sm\n",
"\n",
"data[\"Success\"]=data.Count-data.Malfunction\n",
"data[\"Intercept\"]=1\n",
"\n",
"logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n",
"logmodel.summary()"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f96b29bc630>"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\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": [
"%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=data[\"Temperature\"],y=data[\"Frequency\"])\n",
"plt.grid(True)\n",
"#predictions = logmodel.get_prediction(data_pred[['Intercept','Temperature']])\n",
"#pred_summary = predictions.summary_frame()\n",
"## Extract the lower, and upper confidence intervals\n",
"#data_pred[\"ci_lower\"] = pred_summary[\"mean_ci_lower\"]\n",
"#data_pred[\"ci_upper\"] = pred_summary[\"mean_ci_upper\"]\n",
"#plt.fill_between(data_pred[\"Temperature\"], data_pred[\"ci_lower\"], data_pred[\"ci_upper\"], color=\"blue\", alpha=0.2, label=\"95% CI\")\n",
"import seaborn as sns\n",
"plt.figure()\n",
"plt.xlim(30,90)\n",
"plt.ylim(0,1)\n",
"sns.regplot(x='Temperature',y='Frequency',data=data,logistic=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
...@@ -705,7 +892,7 @@ ...@@ -705,7 +892,7 @@
"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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