diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 26ad6d94fa840f788a57621b06dc6af83a848391..54b644f837742f737c3e466fd59a495cd0dd91a2 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -261,30 +261,30 @@ "" ], "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, @@ -431,7 +431,7 @@ } ], "source": [ - "data = data[data.Malfunction>0]\n", + "data = data[data.Malfunction>0] # Comment this to see big differences\n", "data" ] }, @@ -453,7 +453,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ "
" ] @@ -524,10 +524,10 @@ " Method: IRLS Log-Likelihood: -2.5250 \n", "\n", "\n", - " Date: Sat, 13 Apr 2019 Deviance: 0.22231 \n", + " Date: Wed, 23 Jun 2021 Deviance: 0.22231 \n", "\n", "\n", - " Time: 19:11:24 Pearson chi2: 0.236 \n", + " Time: 12:48:54 Pearson chi2: 0.236 \n", "\n", "\n", " No. Iterations: 4 Covariance Type: nonrobust\n", @@ -555,8 +555,8 @@ "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", + "Date: Wed, 23 Jun 2021 Deviance: 0.22231\n", + "Time: 12:48:54 Pearson chi2: 0.236\n", "No. Iterations: 4 Covariance Type: nonrobust\n", "===============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", @@ -610,7 +610,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -650,18 +650,10 @@ "cell_type": "code", "execution_count": 6, "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))" + "#data = pd.read_csv(\"shuttle.csv\")\n", + "#print(np.sum(data.Malfunction)/np.sum(data.Count))" ] }, { @@ -686,6 +678,149 @@ "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": [ + "# Impact de la pression" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data.plot(x=\"Pressure\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", + "plt.grid(True)" + ] + }, + { + "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: Wed, 23 Jun 2021 Deviance: 0.20593
Time: 12:48:55 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: Wed, 23 Jun 2021 Deviance: 0.20593\n", + "Time: 12:48:55 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": [ + "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Pressure']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n", + "\n", + "logmodel.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data_pred = pd.DataFrame({'Pressure': np.linspace(start=25, stop=200, num=121), 'Intercept': 1})\n", + "data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Pressure']])\n", + "data_pred.plot(x=\"Pressure\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", + "plt.scatter(x=data[\"Pressure\"],y=data[\"Frequency\"])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -705,7 +840,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.4" } }, "nbformat": 4,