diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 26ad6d94fa840f788a57621b06dc6af83a848391..ad30402d160f6326553e9aaf34b4d721f50ece05 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -686,6 +686,587 @@ "analyse et de regarder ce jeu de données sous tous les angles afin\n", "d'expliquer ce qui ne va pas." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ajout de la pression" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'pd' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mode.chained_assignment'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# this removes a useless warning from pandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Frequency\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMalfunction\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined" + ] + } + ], + "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=\"Pressure\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Frequency\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMalfunction\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Pressure\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Frequency\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"scatter\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" + ] + } + ], + "source": [ + "data[\"Frequency\"]=data.Malfunction/data.Count\n", + "data.plot(x=\"Pressure\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateCountTemperaturePressureMalfunction
04/12/81666500
111/12/81670501
23/22/82669500
311/11/82668500
44/04/83667500
56/18/82672500
68/30/836731000
711/28/836701000
82/03/846572001
94/06/846632001
108/30/846702001
1110/05/846782000
1211/08/846672000
131/24/856532002
144/12/856672000
154/29/856752000
166/17/856702000
177/29/856812000
188/27/856762000
1910/03/856792000
2010/30/856752002
2111/26/856762000
221/12/866582001
\n", + "
" + ], + "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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "data = pd.read_csv(\"shuttle.csv\")\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateCountTemperaturePressureMalfunction
111/12/81670501
82/03/846572001
94/06/846632001
108/30/846702001
131/24/856532002
2010/30/856752002
221/12/866582001
\n", + "
" + ], + "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" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data[data.Malfunction>0]\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il faut voir maintenant comment la pression peut agir sur les résultats" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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=\"Pressure\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimation de l'influence de la pression" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 7
Model: GLM Df Residuals: 5
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -2.5168
Date: Fri, 05 Mar 2021 Deviance: 0.20593
Time: 08:48:11 Pearson chi2: 0.214
No. Iterations: 4 Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
Intercept -1.7283 3.593 -0.481 0.630 -8.770 5.313
Pressure 0.0024 0.019 0.125 0.901 -0.035 0.040
" + ], + "text/plain": [ + "\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: -2.5168\n", + "Date: Fri, 05 Mar 2021 Deviance: 0.20593\n", + "Time: 08:48:11 Pearson chi2: 0.214\n", + "No. Iterations: 4 Covariance Type: nonrobust\n", + "==============================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "Intercept -1.7283 3.593 -0.481 0.630 -8.770 5.313\n", + "Pressure 0.0024 0.019 0.125 0.901 -0.035 0.040\n", + "==============================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 8, + "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','Pressure']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n", + "\n", + "logmodel.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'estimateur le plus probable du paramètre de température est 0.0024 et l'erreur standard de cet estimateur est de 0.019, autrement dit on ne peut pas distinguer d'impact particulier et il faut prendre nos estimations avec des pincettes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimation du dysfonctionnement des joints toriques" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -705,7 +1286,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.4" } }, "nbformat": 4,