diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 26ad6d94fa840f788a57621b06dc6af83a848391..8e67d10a0a2c714a356c46268700c988a8bf8a1d 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -261,33 +261,33 @@ "" ], "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": 13, "metadata": {}, "output_type": "execute_result" } @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -351,6 +351,9 @@ " Temperature\n", " Pressure\n", " Malfunction\n", + " Frequency\n", + " Success\n", + " Intercept\n", " \n", " \n", " \n", @@ -361,6 +364,9 @@ " 70\n", " 50\n", " 1\n", + " 0.166667\n", + " 5\n", + " 1\n", " \n", " \n", " 8\n", @@ -369,6 +375,9 @@ " 57\n", " 200\n", " 1\n", + " 0.166667\n", + " 5\n", + " 1\n", " \n", " \n", " 9\n", @@ -377,6 +386,9 @@ " 63\n", " 200\n", " 1\n", + " 0.166667\n", + " 5\n", + " 1\n", " \n", " \n", " 10\n", @@ -385,6 +397,9 @@ " 70\n", " 200\n", " 1\n", + " 0.166667\n", + " 5\n", + " 1\n", " \n", " \n", " 13\n", @@ -393,6 +408,9 @@ " 53\n", " 200\n", " 2\n", + " 0.333333\n", + " 4\n", + " 1\n", " \n", " \n", " 20\n", @@ -401,6 +419,9 @@ " 75\n", " 200\n", " 2\n", + " 0.333333\n", + " 4\n", + " 1\n", " \n", " \n", " 22\n", @@ -409,30 +430,42 @@ " 58\n", " 200\n", " 1\n", + " 0.166667\n", + " 5\n", + " 1\n", " \n", " \n", "\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" + " Date Count Temperature Pressure Malfunction Frequency Success \\\n", + "1 11/12/81 6 70 50 1 0.166667 5 \n", + "8 2/03/84 6 57 200 1 0.166667 5 \n", + "9 4/06/84 6 63 200 1 0.166667 5 \n", + "10 8/30/84 6 70 200 1 0.166667 5 \n", + "13 1/24/85 6 53 200 2 0.333333 4 \n", + "20 10/30/85 6 75 200 2 0.333333 4 \n", + "22 1/12/86 6 58 200 1 0.166667 5 \n", + "\n", + " Intercept \n", + "1 1 \n", + "8 1 \n", + "9 1 \n", + "10 1 \n", + "13 1 \n", + "20 1 \n", + "22 1 " ] }, - "execution_count": 2, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data = data[data.Malfunction>0]\n", - "data" + "data2 = data[data.Malfunction>0]\n", + "data2" ] }, { @@ -448,12 +481,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -470,7 +503,7 @@ "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", + "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[-1,1])\n", "plt.grid(True)" ] }, @@ -500,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -509,28 +542,28 @@ "\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", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 7Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 5Model: GLM Df Residuals: 20
Model Family: Binomial Df Model: 1Model Family: Binomial Df Model: 2
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -2.5250Method: IRLS Log-Likelihood: -3.7926
Date: Sat, 13 Apr 2019 Deviance: 0.22231Date: Fri, 17 Apr 2020 Deviance: 2.7576
Time: 19:11:24 Pearson chi2: 0.236Time: 15:26:14 Pearson chi2: 4.19
No. Iterations: 4 Covariance Type: nonrobustNo. Iterations: 6 Covariance Type: nonrobust
\n", "\n", @@ -538,10 +571,13 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", + "\n", + "\n", + " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953Intercept 2.5202 8.541 0.295 0.768 -14.220 19.260
Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240Temperature -0.0983 0.110 -0.894 0.371 -0.314 0.117
Pressure 0.0085 0.019 0.451 0.652 -0.028 0.045
" ], @@ -550,24 +586,25 @@ "\"\"\"\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", + "Dep. Variable: Frequency No. Observations: 23\n", + "Model: GLM Df Residuals: 20\n", + "Model Family: Binomial Df Model: 2\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", + "Method: IRLS Log-Likelihood: -3.7926\n", + "Date: Fri, 17 Apr 2020 Deviance: 2.7576\n", + "Time: 15:26:14 Pearson chi2: 4.19\n", + "No. Iterations: 6 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", + "Intercept 2.5202 8.541 0.295 0.768 -14.220 19.260\n", + "Temperature -0.0983 0.110 -0.894 0.371 -0.314 0.117\n", + "Pressure 0.0085 0.019 0.451 0.652 -0.028 0.045\n", "===============================================================================\n", "\"\"\"" ] }, - "execution_count": 4, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -578,7 +615,7 @@ "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", + "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature',\"Pressure\",\"Pressure\"]], family=sm.families.Binomial(sm.families.links.logit)).fit()\n", "\n", "logmodel.summary()" ] @@ -605,31 +642,387 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 40, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "ename": "ValueError", + "evalue": "shapes (121,4) and (3,) not aligned: 4 (dim 1) != 3 (dim 0)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\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[1;32m 2\u001b[0m \u001b[0mdata_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'Temperature'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m121\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Intercept'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Pressure_min\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Pressure_max\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m 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"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/statsmodels/genmod/generalized_linear_model.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, params, exog, exposure, offset, linear)\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[0mexog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 852\u001b[0;31m \u001b[0mlinpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mexposure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 853\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlinear\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlinpred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: shapes (121,4) and (3,) not aligned: 4 (dim 1) != 3 (dim 0)" + ] } ], "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 = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1,\"Pressure_min\":50,\"Pressure_max\":200})\n", + "data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature',\"Pressure_min\",\"Pressure_max\"]])\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)" ] }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateCountTemperaturePressureMalfunctionFrequencySuccessIntercept
04/12/816665000.00000061
111/12/816705010.16666751
23/22/826695000.00000061
311/11/826685000.00000061
44/04/836675000.00000061
56/18/826725000.00000061
68/30/8367310000.00000061
711/28/8367010000.00000061
82/03/8465720010.16666751
94/06/8466320010.16666751
108/30/8467020010.16666751
1110/05/8467820000.00000061
1211/08/8466720000.00000061
131/24/8565320020.33333341
144/12/8566720000.00000061
154/29/8567520000.00000061
166/17/8567020000.00000061
177/29/8568120000.00000061
188/27/8567620000.00000061
1910/03/8567920000.00000061
2010/30/8567520020.33333341
2111/26/8567620000.00000061
221/12/8665820010.16666751
\n", + "
" + ], + "text/plain": [ + " Date Count Temperature Pressure Malfunction Frequency Success \\\n", + "0 4/12/81 6 66 50 0 0.000000 6 \n", + "1 11/12/81 6 70 50 1 0.166667 5 \n", + "2 3/22/82 6 69 50 0 0.000000 6 \n", + "3 11/11/82 6 68 50 0 0.000000 6 \n", + "4 4/04/83 6 67 50 0 0.000000 6 \n", + "5 6/18/82 6 72 50 0 0.000000 6 \n", + "6 8/30/83 6 73 100 0 0.000000 6 \n", + "7 11/28/83 6 70 100 0 0.000000 6 \n", + "8 2/03/84 6 57 200 1 0.166667 5 \n", + "9 4/06/84 6 63 200 1 0.166667 5 \n", + "10 8/30/84 6 70 200 1 0.166667 5 \n", + "11 10/05/84 6 78 200 0 0.000000 6 \n", + "12 11/08/84 6 67 200 0 0.000000 6 \n", + "13 1/24/85 6 53 200 2 0.333333 4 \n", + "14 4/12/85 6 67 200 0 0.000000 6 \n", + "15 4/29/85 6 75 200 0 0.000000 6 \n", + "16 6/17/85 6 70 200 0 0.000000 6 \n", + "17 7/29/85 6 81 200 0 0.000000 6 \n", + "18 8/27/85 6 76 200 0 0.000000 6 \n", + "19 10/03/85 6 79 200 0 0.000000 6 \n", + "20 10/30/85 6 75 200 2 0.333333 4 \n", + "21 11/26/85 6 76 200 0 0.000000 6 \n", + "22 1/12/86 6 58 200 1 0.166667 5 \n", + "\n", + " Intercept \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 \n", + "5 1 \n", + "6 1 \n", + "7 1 \n", + "8 1 \n", + "9 1 \n", + "10 1 \n", + "11 1 \n", + "12 1 \n", + "13 1 \n", + "14 1 \n", + "15 1 \n", + "16 1 \n", + "17 1 \n", + "18 1 \n", + "19 1 \n", + "20 1 \n", + "21 1 \n", + "22 1 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, { "cell_type": "markdown", "metadata": { @@ -648,7 +1041,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -705,7 +1098,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..4933e016e5d0241312815f3bee24f6b3e7bfad67 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -364,7 +364,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4,