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Commit 79075a68 authored by 28e1f4de90ce29a7aec6f2ae83fdfffc's avatar 28e1f4de90ce29a7aec6f2ae83fdfffc
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test de prendre que les températures faible, la proba réaugmente sur les température élevée

parent c40d830e
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Showing with 679 additions and 83 deletions
module2/exo5/exo5_fr.ipynb
View file @ 79075a68
{ {
"cells": [ "cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- 2025/08/13 début du travail sur cet exercice -->"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
...@@ -355,14 +362,6 @@ ...@@ -355,14 +362,6 @@
" </thead>\n", " </thead>\n",
" <tbody>\n", " <tbody>\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>8</th>\n", " <th>8</th>\n",
" <td>2/03/84</td>\n", " <td>2/03/84</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -379,14 +378,6 @@ ...@@ -379,14 +378,6 @@
" <td>1</td>\n", " <td>1</td>\n",
" </tr>\n", " </tr>\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", " <th>13</th>\n",
" <td>1/24/85</td>\n", " <td>1/24/85</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -395,14 +386,6 @@ ...@@ -395,14 +386,6 @@
" <td>2</td>\n", " <td>2</td>\n",
" </tr>\n", " </tr>\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", " <th>22</th>\n",
" <td>1/12/86</td>\n", " <td>1/12/86</td>\n",
" <td>6</td>\n", " <td>6</td>\n",
...@@ -416,12 +399,9 @@ ...@@ -416,12 +399,9 @@
], ],
"text/plain": [ "text/plain": [
" Date Count Temperature Pressure Malfunction\n", " Date Count Temperature Pressure Malfunction\n",
"1 11/12/81 6 70 50 1\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",
"13 1/24/85 6 53 200 2\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" "22 1/12/86 6 58 200 1"
] ]
}, },
...@@ -431,8 +411,9 @@ ...@@ -431,8 +411,9 @@
} }
], ],
"source": [ "source": [
"data = data[data.Malfunction>0]\n", "#data = data[data.Malfunction>0] \n",
"data" "data2 = data[data.Temperature <= 65]\n",
"data2"
] ]
}, },
{ {
...@@ -453,7 +434,7 @@ ...@@ -453,7 +434,7 @@
"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>"
] ]
...@@ -469,8 +450,8 @@ ...@@ -469,8 +450,8 @@
"pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n", "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"\n", "\n",
"data[\"Frequency\"]=data.Malfunction/data.Count\n", "data2[\"Frequency\"]=data2.Malfunction/data2.Count\n",
"data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", "data2.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)" "plt.grid(True)"
] ]
}, },
...@@ -509,10 +490,10 @@ ...@@ -509,10 +490,10 @@
"<table class=\"simpletable\">\n", "<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n", "<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n", "<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 7</td> \n", " <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 4</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 5</td> \n", " <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 2</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n", " <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
...@@ -521,13 +502,13 @@ ...@@ -521,13 +502,13 @@
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n", " <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <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> -1.3845</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, 13 Aug 2025</td> <th> Deviance: </th> <td>0.040847</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>16:05:21</td> <th> Pearson chi2: </th> <td>0.0407</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",
...@@ -538,10 +519,10 @@ ...@@ -538,10 +519,10 @@
" <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", " <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",
"<tr>\n", "<tr>\n",
" <th>Intercept</th> <td> -1.3895</td> <td> 7.828</td> <td> -0.178</td> <td> 0.859</td> <td> -16.732</td> <td> 13.953</td>\n", " <th>Intercept</th> <td> 4.3201</td> <td> 20.789</td> <td> 0.208</td> <td> 0.835</td> <td> -36.425</td> <td> 45.066</td>\n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Temperature</th> <td> 0.0014</td> <td> 0.122</td> <td> 0.012</td> <td> 0.991</td> <td> -0.238</td> <td> 0.240</td>\n", " <th>Temperature</th> <td> -0.0985</td> <td> 0.364</td> <td> -0.271</td> <td> 0.787</td> <td> -0.812</td> <td> 0.615</td>\n",
"</tr>\n", "</tr>\n",
"</table>" "</table>"
], ],
...@@ -550,19 +531,19 @@ ...@@ -550,19 +531,19 @@
"\"\"\"\n", "\"\"\"\n",
" Generalized Linear Model Regression Results \n", " Generalized Linear Model Regression Results \n",
"==============================================================================\n", "==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 7\n", "Dep. Variable: Frequency No. Observations: 4\n",
"Model: GLM Df Residuals: 5\n", "Model: GLM Df Residuals: 2\n",
"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: -1.3845\n",
"Date: Sat, 13 Apr 2019 Deviance: 0.22231\n", "Date: Wed, 13 Aug 2025 Deviance: 0.040847\n",
"Time: 19:11:24 Pearson chi2: 0.236\n", "Time: 16:05:21 Pearson chi2: 0.0407\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",
"-------------------------------------------------------------------------------\n", "-------------------------------------------------------------------------------\n",
"Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953\n", "Intercept 4.3201 20.789 0.208 0.835 -36.425 45.066\n",
"Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n", "Temperature -0.0985 0.364 -0.271 0.787 -0.812 0.615\n",
"===============================================================================\n", "===============================================================================\n",
"\"\"\"" "\"\"\""
] ]
...@@ -575,10 +556,10 @@ ...@@ -575,10 +556,10 @@
"source": [ "source": [
"import statsmodels.api as sm\n", "import statsmodels.api as sm\n",
"\n", "\n",
"data[\"Success\"]=data.Count-data.Malfunction\n", "data2[\"Success\"]=data2.Count-data2.Malfunction\n",
"data[\"Intercept\"]=1\n", "data2[\"Intercept\"]=1\n",
"\n", "\n",
"logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n", "logmodel=sm.GLM(data2['Frequency'], data2[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n", "\n",
"logmodel.summary()" "logmodel.summary()"
] ]
...@@ -605,12 +586,148 @@ ...@@ -605,12 +586,148 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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5KgqSDz7YzSHPPLnf9RSHS1tfE2awbHy57KYS1ErUSNTUQI1ErYQENTUdt0WNoLZmb7u9j8nuqylaRo0K5teI2hoxvOPxUr4sAFFbky1b+bq7PK5omWv68HSUFfSSLgC+B9QCd0fEXxbd/3VgPtAKvAH8SUT8qg/19Nk+r/qljgAKjhpKvcruEzzFO0vnEUL2uP0dtaz/TSvvvbRjnzCIotvdBl3Rq3rbfu7b35HBPvOLQ6rLkcq+Ry7dHYE1N3/AyFVPdd7X2ZcSwVNYb9fneyD/Qnpp3UsH9PDC8FBhQOThURwEtTXZziyU7/D7Pr62RojC8MkeO6y2hkOG7Q2cjmDqWEbn8vL17vztbzn6qCP3CaWOdvvczutUPr+wPqlrqGX9UNewLFFDTan11AhREKI1RcuVgL3PRZdw7QjfGvHsM88we/YZBbUL1RQ9FwXPrfLlVotb+vCYHoNeUi1wB3Ae0ASskrQsIjYWNFsDnBoRH0j6r8BfAVfsb7nbm9s596//uXQQlArtff716Ro8Q9Lavrz2ltbxh93lFb9oR+g86ug4AqnpLmz2hkoWGnuPHrJ1lA6O2jwUJHizpoWjfv/wLjt58Tpqa9Sl9pJHPwV9KWyX9TN7LHmfawtrrum6jNqa7LkR+4ZNjYAuobdvUBTWvnrV85x+2qx9gqXrkV/R9ig6EhzK4dHY2EhDw8mDXUa/+djIGj4+ZuRglzGklHNEPxPYGhGvAkh6ELgY6Az6iHiqoP2zwBd6WujwGlH/e2M6d8CSQdBNKOzdWfNQoGvoFR/xFP6bpOLbRUc1xUddxQGyN/T2hlvx0UiNxIurVzNr1oyCYN7fkR37HAWpKJiGmiwspg12Gf2iaXQNnxg3erDLMKuYcoJ+AvB6wXQTMGs/7b8E/GOpOyQtABYAjB8/nssmvFdmmX0Q+U8FteU/5RirD2ja+EJlCxhCmpubaWxsHOwy+kXKfQP372BUTtCXOpwsGaGSvgCcCvxhqfsjYjGwGKC+vj4aGhrKq7IKZUe8DYNdRr9JuX8p9w3cv4NROUHfBBxTMD0R2FHcSNK5wP8E/jAiPqxMeWZmdqDK+cDUKmCKpMmSRgBXAssKG0iaBtwFzI2InZUv08zM+qrHoI+IVuBaYAWwCVgSERsk3Sxpbt5sEXAo8BNJayUt62ZxZmY2wMq6jj4ilgPLi+Z9q+D2uRWuy6zXlq7ZzqIVm9nxTgtHH17Hwjn1APvMu2TahAGtoT/X1xvfXLqeB557na+dsIcv3bCcebOO4ZZLThzssmwA+JOxloSla7ZzwyPradmTXRe1/Z0WFv7kJRDsaYvOeTc8sh6gX8K3VA39ub7e+ObS9fzw2dc6p9siOqcd9unzoGaWhEUrNncGbIc97dEZ8h1a9rSxaMXmAauhP9fXGw8893qv5ltaHPSWhB3vtPRL20rU0F/r6422bsab6G6+pcVBb0k4+vC6fmlbiRr6a329UdvNp6u7m29pcdBbEhbOqadueG2XecNrxPDarkFWN7y2803agaihP9fXG/NmHdOr+ZYWvxlrSeh4s3Mwr7rprobBfiMW9r7h2nFOvlbyVTcHEQe9JeOSaRNKhupABm13NQwFt1xyIrdcciKNjY3821UNg12ODSCfujEzS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBJXVtBLukDSZklbJX2jxP2HSHoov/85SZMqXaiZmfVNj0EvqRa4A7gQOB6YJ+n4omZfAt6OiH8HfBf4X5Uu1MzM+qacI/qZwNaIeDUiPgIeBC4uanMx8A/57YeBz0hS5co0M7O+GlZGmwnA6wXTTcCs7tpERKukd4FxwJuFjSQtABbkkx9KerkvRVeJIynqf2JS7l/KfQP3r9rV9/YB5QR9qSPz6EMbImIxsBhA0uqIOLWM9Vcl9696pdw3cP+qnaTVvX1MOadumoBjCqYnAju6ayNpGDAW+F1vizEzs8orJ+hXAVMkTZY0ArgSWFbUZhnwx/ntS4F/ioh9jujNzGzg9XjqJj/nfi2wAqgF7o2IDZJuBlZHxDLgHuB+SVvJjuSvLGPdiw+g7mrg/lWvlPsG7l+163X/5ANvM7O0+ZOxZmaJc9CbmSVuQIJe0khJz0t6SdIGSd/O50/Oh0zYkg+hMGIg6ukPkmolrZH0s3w6pb5tk7Re0tqOS7skHSHp8bx/j0v62GDX2VeSDpf0sKT/J2mTpNNT6Z+k+ny7dfy8J+lrCfXvv+WZ8rKkB/KsSWnf+/O8bxskfS2f1+ttN1BH9B8C50TEycBU4AJJp5ENlfDdiJgCvE02lEK1+nNgU8F0Sn0DODsiphZcn/wN4Mm8f0/m09Xqe8DPI+I44GSy7ZhE/yJic77dpgKnAB8Aj5JA/yRNAK4DTo2IE8guFrmSRPY9SScAXyYbneBk4HOSptCXbRcRA/oDjAJeJPt07ZvAsHz+6cCKga6nQn2amD/h5wA/I/sAWRJ9y+vfBhxZNG8zcFR++yhg82DX2ce+HQb8kvzChNT6V9Sn84GnU+kfez+RfwTZFYQ/A+aksu8BlwF3F0z/BfA/+rLtBuwcfX5qYy2wE3gc+DfgnYhozZs0kW24anQ72QZoz6fHkU7fIPuU80pJL+TDWAD8XkT8GiD//fFBq+7AfBJ4A/j7/NTb3ZJGk07/Cl0JPJDfrvr+RcR24DbgNeDXwLvAC6Sz770MnCVpnKRRwGfJPpja6203YEEfEW2R/fs4kexfkU+XajZQ9VSKpM8BOyPihcLZJZpWXd8KzI6I6WQjmP6ZpLMGu6AKGgZMB+6MiGnA+1ThaYye5Oep5wI/GexaKiU/N30xMBk4GhhN9jdarCr3vYjYRHYa6nHg58BLQOt+H9SNAb/qJiLeARqB04DD8yEToPTQCtVgNjBX0jaykT3PITvCT6FvAETEjvz3TrLzuzOB30o6CiD/vXPwKjwgTUBTRDyXTz9MFvyp9K/DhcCLEfHbfDqF/p0L/DIi3oiIPcAjwBmkte/dExHTI+Issg+jbqEP226grroZL+nw/HYd2QbaBDxFNmQCZEMoPDYQ9VRSRNwQERMjYhLZv8b/FBFXkUDfACSNljSm4zbZed6X6TrsRdX2LyJ+A7wuqWNEwM8AG0mkfwXmsfe0DaTRv9eA0ySNyodF79h2Sex7AJI+nv8+FviPZNuw19tuQD4ZK+kksvHqa8leXJZExM2SPkl2FHwEsAb4QkR82O8F9RNJDcD1EfG5VPqW9+PRfHIY8OOIuFXSOGAJcCzZDndZRFTlQHaSpgJ3AyOAV4Evkv+dkkb/RpG9afnJiHg3n5fE9ssv1b6C7JTGGmA+2Tn5qt/3ACT9C9l7fnuAr0fEk33Zdh4Cwcwscf5krJlZ4hz0ZmaJc9CbmSXOQW9mljgHvZlZ4sr5cnCzAZVfPvZkPvn7QBvZMAUAMyPio0EpbD8k/QmwPL8u32xI8eWVNqRJuglojojbhkAttRHR1s19/wpcGxFre7G8YQVjspj1G5+6saoi6Y+VfbfBWkl/I6lG0jBJ70haJOlFSSskzZL0z5JelfTZ/LHzJT2a379Z0jfLXO4tkp4HZkr6tqRV+Rjhf6vMFWTDbz+UP36EpKaCT4OfJumJ/PYtku6S9DjZQGrDJP11vu51kuYP/LNqqXPQW9XIx+f+PHBGPkDeMPZ+Ef1YYGU++NpHwE1kH4m/DLi5YDEz88dMB/6zpKllLPfFiJgZEc8A34uIGcCJ+X0XRMRDwFrgisjGfu/p1NI04KKI+CNgAdmgeDOBGWSDxh3bl+fHrDs+R2/V5FyyMFydDW1CHdlH+wFaIuLx/PZ64N2IaJW0HphUsIwVEfE2gKSlwH8g2w+6W+5H7B0CAuAzkhYCI4EjyYbF/cde9uOxiNid3z4f+LSkwheWKWQfbTerCAe9VRMB90bEX3SZmY1UWHgU3U72rWYdtwv/zovflIoeltsS+RtZ+Zgx/xeYHhHbJd1CFviltLL3P+biNu8X9elPI+JJzPqJT91YNXkCuFzSkZBdndOH0xznK/uO2FFkY5k/3Yvl1pG9cLyZj+j5nwru2wWMKZjeRvbVfRS1K7YC+NOOYXWVfcdrXS/7ZLZfPqK3qhER6/PRCp+QVEM2ot9/oXfjjf8r8GPgD4D7O66SKWe5EfGWpH8gG6b5V8BzBXf/PXC3pBay9wFuAv5O0m+A5/dTz11koxCuzU8b7SR7ATKrGF9eaQeN/IqWEyLia4Ndi9lA8qkbM7PE+YjezCxxPqI3M0ucg97MLHEOejOzxDnozcwS56A3M0vc/wcowwoTqhaBUgAAAABJRU5ErkJggg==\n", "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",
" <th>Frequency</th>\n",
" <th>Success</th>\n",
" <th>Intercept</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",
" <td>0.166667</td>\n",
" <td>5</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",
" <td>0.166667</td>\n",
" <td>5</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",
" <td>0.333333</td>\n",
" <td>4</td>\n",
" <td>1</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",
" <td>0.166667</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction Frequency Success \\\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",
"13 1/24/85 6 53 200 2 0.333333 4 \n",
"22 1/12/86 6 58 200 1 0.166667 5 \n",
"\n",
" Intercept \n",
"8 1 \n",
"9 1 \n",
"13 1 \n",
"22 1 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'Frequency'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'Frequency'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-768b7e088d43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdata_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Frequency'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_pred\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;34m'Temperature'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdata_pred\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\"Temperature\"\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\"line\"\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[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Temperature\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\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;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2137\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2141\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2144\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2145\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2148\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1840\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1843\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1844\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m 3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3842\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3844\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3845\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2525\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2529\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'Frequency'"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -623,13 +740,492 @@ ...@@ -623,13 +740,492 @@
], ],
"source": [ "source": [
"%matplotlib inline\n", "%matplotlib inline\n",
"data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n", "data_pred = pd.DataFrame({'Temperature': np.linspace(start=25, stop=60, num=121), 'Intercept': 1})\n",
"data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n", "data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n",
"data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
"plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n", "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n",
"plt.grid(True)" "plt.grid(True)"
] ]
}, },
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Intercept</th>\n",
" <th>Temperature</th>\n",
" <th>Frequency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>30.000000</td>\n",
" <td>0.796399</td>\n",
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" <td>1</td>\n",
" <td>30.333333</td>\n",
" <td>0.791021</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>30.666667</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>31.000000</td>\n",
" <td>0.779954</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>31.333333</td>\n",
" <td>0.774265</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>1</td>\n",
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" <td>0.768472</td>\n",
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" <td>1</td>\n",
" <td>32.333333</td>\n",
" <td>0.756578</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>32.666667</td>\n",
" <td>0.750478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>33.000000</td>\n",
" <td>0.744277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1</td>\n",
" <td>33.333333</td>\n",
" <td>0.737975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" <td>33.666667</td>\n",
" <td>0.731574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1</td>\n",
" <td>34.000000</td>\n",
" <td>0.725075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1</td>\n",
" <td>34.333333</td>\n",
" <td>0.718479</td>\n",
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" <td>34.666667</td>\n",
" <td>0.711788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1</td>\n",
" <td>35.000000</td>\n",
" <td>0.705003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1</td>\n",
" <td>35.333333</td>\n",
" <td>0.698126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1</td>\n",
" <td>35.666667</td>\n",
" <td>0.691159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1</td>\n",
" <td>36.000000</td>\n",
" <td>0.684104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1</td>\n",
" <td>36.333333</td>\n",
" <td>0.676963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1</td>\n",
" <td>36.666667</td>\n",
" <td>0.669738</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1</td>\n",
" <td>37.000000</td>\n",
" <td>0.662433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1</td>\n",
" <td>37.333333</td>\n",
" <td>0.655049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1</td>\n",
" <td>37.666667</td>\n",
" <td>0.647590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1</td>\n",
" <td>38.000000</td>\n",
" <td>0.640058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1</td>\n",
" <td>38.333333</td>\n",
" <td>0.632456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1</td>\n",
" <td>38.666667</td>\n",
" <td>0.624788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1</td>\n",
" <td>39.000000</td>\n",
" <td>0.617057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1</td>\n",
" <td>39.333333</td>\n",
" <td>0.609266</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1</td>\n",
" <td>39.666667</td>\n",
" <td>0.601419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>1</td>\n",
" <td>60.333333</td>\n",
" <td>0.164500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>1</td>\n",
" <td>60.666667</td>\n",
" <td>0.160035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>1</td>\n",
" <td>61.000000</td>\n",
" <td>0.155669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>1</td>\n",
" <td>61.333333</td>\n",
" <td>0.151400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>1</td>\n",
" <td>61.666667</td>\n",
" <td>0.147228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>1</td>\n",
" <td>62.000000</td>\n",
" <td>0.143152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>1</td>\n",
" <td>62.333333</td>\n",
" <td>0.139170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>1</td>\n",
" <td>62.666667</td>\n",
" <td>0.135281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>1</td>\n",
" <td>63.000000</td>\n",
" <td>0.131485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>1</td>\n",
" <td>63.333333</td>\n",
" <td>0.127779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>1</td>\n",
" <td>63.666667</td>\n",
" <td>0.124163</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>1</td>\n",
" <td>64.000000</td>\n",
" <td>0.120635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>1</td>\n",
" <td>64.333333</td>\n",
" <td>0.117193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>1</td>\n",
" <td>64.666667</td>\n",
" <td>0.113838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>1</td>\n",
" <td>65.000000</td>\n",
" <td>0.110566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>1</td>\n",
" <td>65.333333</td>\n",
" <td>0.107377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>1</td>\n",
" <td>65.666667</td>\n",
" <td>0.104269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>1</td>\n",
" <td>66.000000</td>\n",
" <td>0.101241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>1</td>\n",
" <td>66.333333</td>\n",
" <td>0.098291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>1</td>\n",
" <td>66.666667</td>\n",
" <td>0.095418</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>1</td>\n",
" <td>67.000000</td>\n",
" <td>0.092620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>1</td>\n",
" <td>67.333333</td>\n",
" <td>0.089896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>1</td>\n",
" <td>67.666667</td>\n",
" <td>0.087245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>1</td>\n",
" <td>68.000000</td>\n",
" <td>0.084664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>1</td>\n",
" <td>68.333333</td>\n",
" <td>0.082153</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>1</td>\n",
" <td>68.666667</td>\n",
" <td>0.079710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>1</td>\n",
" <td>69.000000</td>\n",
" <td>0.077334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>1</td>\n",
" <td>69.333333</td>\n",
" <td>0.075022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>1</td>\n",
" <td>69.666667</td>\n",
" <td>0.072775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>1</td>\n",
" <td>70.000000</td>\n",
" <td>0.070589</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>121 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Intercept Temperature Frequency\n",
"0 1 30.000000 0.796399\n",
"1 1 30.333333 0.791021\n",
"2 1 30.666667 0.785539\n",
"3 1 31.000000 0.779954\n",
"4 1 31.333333 0.774265\n",
"5 1 31.666667 0.768472\n",
"6 1 32.000000 0.762576\n",
"7 1 32.333333 0.756578\n",
"8 1 32.666667 0.750478\n",
"9 1 33.000000 0.744277\n",
"10 1 33.333333 0.737975\n",
"11 1 33.666667 0.731574\n",
"12 1 34.000000 0.725075\n",
"13 1 34.333333 0.718479\n",
"14 1 34.666667 0.711788\n",
"15 1 35.000000 0.705003\n",
"16 1 35.333333 0.698126\n",
"17 1 35.666667 0.691159\n",
"18 1 36.000000 0.684104\n",
"19 1 36.333333 0.676963\n",
"20 1 36.666667 0.669738\n",
"21 1 37.000000 0.662433\n",
"22 1 37.333333 0.655049\n",
"23 1 37.666667 0.647590\n",
"24 1 38.000000 0.640058\n",
"25 1 38.333333 0.632456\n",
"26 1 38.666667 0.624788\n",
"27 1 39.000000 0.617057\n",
"28 1 39.333333 0.609266\n",
"29 1 39.666667 0.601419\n",
".. ... ... ...\n",
"91 1 60.333333 0.164500\n",
"92 1 60.666667 0.160035\n",
"93 1 61.000000 0.155669\n",
"94 1 61.333333 0.151400\n",
"95 1 61.666667 0.147228\n",
"96 1 62.000000 0.143152\n",
"97 1 62.333333 0.139170\n",
"98 1 62.666667 0.135281\n",
"99 1 63.000000 0.131485\n",
"100 1 63.333333 0.127779\n",
"101 1 63.666667 0.124163\n",
"102 1 64.000000 0.120635\n",
"103 1 64.333333 0.117193\n",
"104 1 64.666667 0.113838\n",
"105 1 65.000000 0.110566\n",
"106 1 65.333333 0.107377\n",
"107 1 65.666667 0.104269\n",
"108 1 66.000000 0.101241\n",
"109 1 66.333333 0.098291\n",
"110 1 66.666667 0.095418\n",
"111 1 67.000000 0.092620\n",
"112 1 67.333333 0.089896\n",
"113 1 67.666667 0.087245\n",
"114 1 68.000000 0.084664\n",
"115 1 68.333333 0.082153\n",
"116 1 68.666667 0.079710\n",
"117 1 69.000000 0.077334\n",
"118 1 69.333333 0.075022\n",
"119 1 69.666667 0.072775\n",
"120 1 70.000000 0.070589\n",
"\n",
"[121 rows x 3 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_pred\n"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
...@@ -648,7 +1244,7 @@ ...@@ -648,7 +1244,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -705,7 +1301,7 @@ ...@@ -705,7 +1301,7 @@
"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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