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mooc-rr
Commit 71c31986 authored by Waad ALMASRI's avatar Waad ALMASRI
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Merge branch 'master' of... 

Merge branch 'master' of https://app-learninglab.inria.fr/moocrr/gitlab/da2bcc7bce9e6c165b2e2847d19f3af0/mooc-rr
parents 3485b5d6 b6dc6f87
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module2/exo5/exo5_fr.ipynb
View file @ 71c31986
...@@ -40,7 +40,7 @@ ...@@ -40,7 +40,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 79,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -287,7 +287,7 @@ ...@@ -287,7 +287,7 @@
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 1, "execution_count": 79,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -317,12 +317,13 @@ ...@@ -317,12 +317,13 @@
"Les vols où aucun incident n'est relevé n'apportant aucun information\n", "Les vols où aucun incident n'est relevé n'apportant aucun information\n",
"sur l'influence de la température ou de la pression sur les\n", "sur l'influence de la température ou de la pression sur les\n",
"dysfonctionnements, nous nous concentrons sur les expériences où au\n", "dysfonctionnements, nous nous concentrons sur les expériences où au\n",
"moins un joint a été défectueux.\n" "moins un joint a été défectueux.\n",
"\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 80,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -425,7 +426,7 @@ ...@@ -425,7 +426,7 @@
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 2, "execution_count": 80,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -448,12 +449,12 @@ ...@@ -448,12 +449,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 81,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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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>"
] ]
...@@ -500,7 +501,7 @@ ...@@ -500,7 +501,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 82,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -524,10 +525,10 @@ ...@@ -524,10 +525,10 @@
" <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, 26 Aug 2020</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>16:53:28</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 +556,8 @@ ...@@ -555,8 +556,8 @@
"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, 26 Aug 2020 Deviance: 0.22231\n",
"Time: 19:11:24 Pearson chi2: 0.236\n", "Time: 16:53:28 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 +568,7 @@ ...@@ -567,7 +568,7 @@
"\"\"\"" "\"\"\""
] ]
}, },
"execution_count": 4, "execution_count": 82,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -578,6 +579,7 @@ ...@@ -578,6 +579,7 @@
"data[\"Success\"]=data.Count-data.Malfunction\n", "data[\"Success\"]=data.Count-data.Malfunction\n",
"data[\"Intercept\"]=1\n", "data[\"Intercept\"]=1\n",
"\n", "\n",
"data[\"Frequency\"]=data.Malfunction/data.Count\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']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n", "\n",
"logmodel.summary()" "logmodel.summary()"
...@@ -605,12 +607,12 @@ ...@@ -605,12 +607,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 16,
"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 +650,7 @@ ...@@ -648,7 +650,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -664,6 +666,27 @@ ...@@ -664,6 +666,27 @@
"print(np.sum(data.Malfunction)/np.sum(data.Count))" "print(np.sum(data.Malfunction)/np.sum(data.Count))"
] ]
}, },
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.01270572944054793"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = np.sum(data.Malfunction)/np.sum(data.Count)\n",
"1-(1-p**2)**3"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
...@@ -672,7 +695,7 @@ ...@@ -672,7 +695,7 @@
"un joint primaire un joint secondaire sur chacune des trois parties du\n", "un joint primaire un joint secondaire sur chacune des trois parties du\n",
"lançeur, la probabilité de défaillance des deux joints d'un lançeur\n", "lançeur, la probabilité de défaillance des deux joints d'un lançeur\n",
"est de $p^2 \\approx 0.00425$. La probabilité de défaillance d'un des\n", "est de $p^2 \\approx 0.00425$. La probabilité de défaillance d'un des\n",
"lançeur est donc de $1-(1-p^2)^3 \\approx 1.2%$. Ça serait vraiment\n", "lançeur est donc de $1-(1-p^2)^3 \\approx$ 1.2\\%. Ça serait vraiment\n",
"pas de chance... Tout est sous contrôle, le décollage peut donc avoir\n", "pas de chance... Tout est sous contrôle, le décollage peut donc avoir\n",
"lieu demain comme prévu.\n", "lieu demain comme prévu.\n",
"\n", "\n",
...@@ -686,6 +709,305 @@ ...@@ -686,6 +709,305 @@
"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": [
"# Nouvelle perspective"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"data = pd.read_csv(\"shuttle.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tout d'abord, nous allons inspecter graphiquement la relation entre le dysfonctionnement des joints toriques et la température."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
"/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fce15975198>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import seaborn as sns\n",
"sns.distplot(data.Temperature.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fce13783710>"
]
},
"execution_count": 3,
"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"
}
],
"source": [
"sns.distplot(data.Malfunction.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fce13693c88>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAFNNJREFUeJzt3W+MXXd95/H3pxNHNWm2KXigxLFxQJa1dAshuorJpoJE3SQOKusgtVpHLFSo1EtFpKVaWZtUCLq7XWkld/cBu2kik3oDW3DULYnxA8DhwWrDQhN5TNI4CbhrQsDjYclAmlDAUmLvdx/MmezN+I7nzMwdz5/zfklXc8/v/M6539+cuR8fnzl3fqkqJEnd8QvLXYAk6cIy+CWpYwx+SeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjrlouQsYZMOGDbVly5blLkOSVo2jR4/+qKpG2/RdkcG/ZcsWxsbGlrsMSVo1knyvbV8v9UhSxxj8ktQxBr8kdYzBL0kdY/BLUscY/JLUMXMGf5JNSf5Hkm8leSrJvxzQJ0k+leREkieSXN23bkeS4826O4Y9AEnS/LS5j/8M8K+q6ptJLgWOJvlqVT3d1+cWYGvz2A7cDWxPMgLcBdwIjANHkhyase1QHHzsFHsPH2fihdNcftl69ty8jVvfsXHYLyNpyHzvXnhzBn9V/QD4QfP875N8C9gI9If3TuCzNTWB7yNJLkvyRmALcKKqngFIcn/Td6jBf/CxU9z5wDFOv3wWgFMvnObOB44B+AMkrWC+d5fHvK7xJ9kCvAN4dMaqjcDJvuXxpm229qHae/j4Kz84006/fJa9h48P+6UkDZHv3eXROviT/BLwBeBjVfWTmasHbFLnaR+0/91JxpKMTU5Oti0LgIkXTs+rXdLK4Ht3ebQK/iTrmAr9z1XVAwO6jAOb+pavACbO036OqtpXVb2q6o2Otvo7Q6+4/LL182qXtDL43l0ebe7qCfDnwLeq6j/N0u0Q8MHm7p53Ai82vxs4AmxNcmWSi4FdTd+h2nPzNtavG3lV2/p1I+y5eduwX0rSEPneXR5t7uq5DvgAcCzJ403bHwGbAarqHuBLwHuAE8DPgQ81684kuR04DIwA+6vqqaGOgP//SyDvDJBWF9+7yyNTN+KsLL1er/yzzJLUXpKjVdVr09dP7kpSxxj8ktQxBr8kdYzBL0kdY/BLUscY/JLUMQa/JHWMwS9JHWPwS1LHGPyS1DEGvyR1jMEvSR1j8EtSxxj8ktQxBr8kdcycE7Ek2Q/8FvBcVf2jAev3AO/v298/BEar6vkkzwJ/D5wFzrT9W9GSpKXT5oz/PmDHbCuram9VXVVVVwF3Av+zqp7v63JDs97Ql6QVYM7gr6qHgefn6te4DTiwqIokSUtqaNf4k7yGqf8ZfKGvuYCHkhxNsntYryVJWrg2k6239V7g6zMu81xXVRNJXg98Ncm3m/9BnKP5h2E3wObNm4dYliSp3zDv6tnFjMs8VTXRfH0OeBC4ZraNq2pfVfWqqjc6OjrEsiRJ/YYS/El+GXg38MW+tkuSXDr9HLgJeHIYrydJWrg2t3MeAK4HNiQZBz4JrAOoqnuabu8DHqqqn/Vt+gbgwSTTr/P5qvrK8EqXJC3EnMFfVbe16HMfU7d99rc9A7x9oYVJkpaGn9yVpI4x+CWpYwx+SeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpYwx+SeoYg1+SOmbO4E+yP8lzSQZOm5jk+iQvJnm8eXyib92OJMeTnEhyxzALlyQtTJsz/vuAHXP0+VpVXdU8/i1AkhHgLuAW4K3AbUneuphiJUmLN2fwV9XDwPML2Pc1wImqeqaqXgLuB3YuYD+SpCEa1jX+a5P8TZIvJ/m1pm0jcLKvz3jTNlCS3UnGkoxNTk4OqSxJ0kzDCP5vAm+qqrcD/xk42LRnQN+abSdVta+qelXVGx0dHUJZkqRBFh38VfWTqvpp8/xLwLokG5g6w9/U1/UKYGKxrydJWpxFB3+SX02S5vk1zT5/DBwBtia5MsnFwC7g0GJfT5K0OBfN1SHJAeB6YEOSceCTwDqAqroH+G3gD5KcAU4Du6qqgDNJbgcOAyPA/qp6aklGIUlqLVMZvbL0er0aGxtb7jIkadVIcrSqem36+sldSeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpYwx+SeoYg1+SOsbgl6SOmTP4k+xP8lySJ2dZ//4kTzSPbyR5e9+6Z5McS/J4Ev/AviStAG3O+O8Ddpxn/XeBd1fV24B/B+ybsf6Gqrqq7QQBkqSlNefUi1X1cJIt51n/jb7FR5iaVF2StEIN+xr/7wFf7lsu4KEkR5PsHvJrSZIWYM4z/raS3MBU8P9GX/N1VTWR5PXAV5N8u6oenmX73cBugM2bNw+rLEnSDEM540/yNuBeYGdV/Xi6vaommq/PAQ8C18y2j6raV1W9quqNjo4OoyxJ0gCLDv4km4EHgA9U1d/2tV+S5NLp58BNwMA7gyRJF86cl3qSHACuBzYkGQc+CawDqKp7gE8ArwP+LAnAmeYOnjcADzZtFwGfr6qvLMEYJEnz0OauntvmWP9h4MMD2p8B3n7uFpKk5eQndyWpYwx+SeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpYwx+SeoYg1+SOmbO4E+yP8lzSQbOnpUpn0pyIskTSa7uW7cjyfFm3R3DLFyStDBtJlu/D/gvwGdnWX8LsLV5bAfuBrYnGQHuAm4ExoEjSQ5V1dOLLXq1O/jYKfYePs7EC6e5/LL17Ll5G7e+Y+Oi+642a3lsS+HjB49x4NGTnK1iJOG27Zv4k1t/fbnL0irUZgauh5NsOU+XncBnq6qAR5JcluSNwBbgRDMTF0nub/p2OvgPPnaKOx84xumXzwJw6oXT3PnAMYBzQm8+fVebtTy2pfDxg8f4i0e+/8ry2apXlg1/zdcwrvFvBE72LY83bbO1d9rew8dfCbtpp18+y97DxxfVd7VZy2NbCgcePTmvdul8hhH8GdBW52kfvJNkd5KxJGOTk5NDKGtlmnjhdOv2+fRdbdby2JbC2Rr81pmtXTqfYQT/OLCpb/kKYOI87QNV1b6q6lVVb3R0dAhlrUyXX7a+dft8+q42a3lsS2Ekg86jZm+XzmcYwX8I+GBzd887gRer6gfAEWBrkiuTXAzsavp22p6bt7F+3cir2tavG2HPzdsW1Xe1WctjWwq3bd80r3bpfOb85W6SA8D1wIYk48AngXUAVXUP8CXgPcAJ4OfAh5p1Z5LcDhwGRoD9VfXUEoxhVZn+xWWbu1nm03e1WctjWwrTv8D1rh4NQ2oFXiPs9Xo1Nja23GVI0qqR5GhV9dr09ZO7ktQxBr8kdYzBL0kdY/BLUscY/JLUMQa/JHWMwS9JHWPwS1LHGPyS1DEGvyR1jMEvSR1j8EtSxxj8ktQxBr8kdYzBL0kdY/BLUse0Cv4kO5IcT3IiyR0D1u9J8njzeDLJ2SSvbdY9m+RYs87ZVSRpmbWZenEEuAu4kakJ1I8kOVRVT0/3qaq9wN6m/3uBP6yq5/t2c0NV/WiolUuSFqTNGf81wImqeqaqXgLuB3aep/9twIFhFCdJGr42wb8RONm3PN60nSPJa4AdwBf6mgt4KMnRJLtne5Eku5OMJRmbnJxsUZYkaSHaBH8GtM02Q/t7ga/PuMxzXVVdDdwCfDTJuwZtWFX7qqpXVb3R0dEWZUmSFqJN8I8Dm/qWrwAmZum7ixmXeapqovn6HPAgU5eOJEnLpE3wHwG2JrkyycVMhfuhmZ2S/DLwbuCLfW2XJLl0+jlwE/DkMAqXJC3MnHf1VNWZJLcDh4ERYH9VPZXkI836e5qu7wMeqqqf9W3+BuDBJNOv9fmq+sowByBJmp9UzXa5fvn0er0aG/OWf0lqK8nRquq16esndyWpYwx+SeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpYwx+SeoYg1+SOqZV8CfZkeR4khNJ7hiw/vokLyZ5vHl8ou22kqQLa84ZuJKMAHcBNzI1/+6RJIeq6ukZXb9WVb+1wG0lSRdImzP+a4ATVfVMVb0E3A/sbLn/xWwrSVoCbYJ/I3Cyb3m8aZvp2iR/k+TLSX5tnttKki6QOS/1ABnQNnOi3m8Cb6qqnyZ5D3AQ2Npy26kXSXYDuwE2b97coixJ0kK0OeMfBzb1LV8BTPR3qKqfVNVPm+dfAtYl2dBm27597KuqXlX1RkdH5zEESdJ8tAn+I8DWJFcmuRjYBRzq75DkV5OkeX5Ns98ft9lWknRhzXmpp6rOJLkdOAyMAPur6qkkH2nW3wP8NvAHSc4Ap4FdVVXAwG2XaCySpBYylc8rS6/Xq7GxseUuQ5JWjSRHq6rXpq+f3JWkjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpYwx+SeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4JaljDH5J6hiDX5I6plXwJ9mR5HiSE0nuGLD+/UmeaB7fSPL2vnXPJjmW5PEkzq4iSctszqkXk4wAdwE3MjV5+pEkh6rq6b5u3wXeXVV/l+QWYB+wvW/9DVX1oyHWLUlaoDZn/NcAJ6rqmap6Cbgf2Nnfoaq+UVV/1yw+Alwx3DIlScPSJvg3Aif7lsebttn8HvDlvuUCHkpyNMnu2TZKsjvJWJKxycnJFmVJkhZizks9QAa0DZyhPckNTAX/b/Q1X1dVE0leD3w1yber6uFzdli1j6lLRPR6vZU3A7wkrRFtzvjHgU19y1cAEzM7JXkbcC+ws6p+PN1eVRPN1+eAB5m6dCRJWiZtgv8IsDXJlUkuBnYBh/o7JNkMPAB8oKr+tq/9kiSXTj8HbgKeHFbxkqT5m/NST1WdSXI7cBgYAfZX1VNJPtKsvwf4BPA64M+SAJypqh7wBuDBpu0i4PNV9ZUlGYkkqZVUrbzL6b1er8bGvOVfktpKcrQ54Z6Tn9yVpI4x+CWpYwx+SeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpYwx+SeqYVsGfZEeS40lOJLljwPok+VSz/okkV7fdVpJ0Yc05A1eSEeAu4Eam5t89kuRQVT3d1+0WYGvz2A7cDWxvua20LA4+doq9h48z8cJpLr9sPXtu3sat79g4sO/7P/3XfP07z7+yfN1bXsvnfv/aRe93Pn0/fvAYBx49ydkqRhJu276JP7n11+c56gtT61LV4PdgOOYMfqYmRz9RVc8AJLkf2An0h/dO4LM1NZ3XI0kuS/JGYEuLbaUL7uBjp7jzgWOcfvksAKdeOM2dDxwDOOcNNzP0Ab7+ned5/6f/+pzwn89+59P34weP8RePfP+V5bNVrywvNPiWqtalqsHvwfC0udSzETjZtzzetLXp02Zb6YLbe/j4K2+0aadfPsvew8fP6Tsz9M/XPp/9zqfvgUdPntN2vvY2lqrWparB78HwtAn+DGibOVHvbH3abDu1g2R3krEkY5OTky3KkhZu4oXT82pfiv3Op+/ZWebGnq29jaWqdalq8HswPG2CfxzY1Ld8BTDRsk+bbQGoqn1V1auq3ujoaIuypIW7/LL182pfiv3Op+9IBp1Dzd7exlLVulQ1+D0YnjbBfwTYmuTKJBcDu4BDM/ocAj7Y3N3zTuDFqvpBy22lC27PzdtYv27kVW3r142w5+Zt5/S97i2vHbiPQe3z2e98+t62fdM5bedrb2Opal2qGvweDM+cv9ytqjNJbgcOAyPA/qp6KslHmvX3AF8C3gOcAH4OfOh82y7JSKR5mP6lWZs7KT73+9e2vqtnPvudT9/pX14O846Wpap1qWrwezA8qUVcH1sqvV6vxsbGlrsMSVo1khytql6bvn5yV5I6xuCXpI4x+CWpYwx+SeoYg1+SOsbgl6SOWZG3cyaZBL633HXMsAH40XIXsQQc1+qzVsfmuBbnTVXV6s8erMjgX4mSjLW9R3Y1cVyrz1odm+O6cLzUI0kdY/BLUscY/O3tW+4ClojjWn3W6tgc1wXiNX5J6hjP+CWpYwz+AZI8m+RYkseTjDVtf5zkVNP2eJL3LHed89XMhfxXSb6d5FtJrk3y2iRfTfK/m6+/stx1LsQsY1vVxyzJtr7aH0/ykyQfW+3H7DzjWtXHCyDJHyZ5KsmTSQ4k+cWVeLy81DNAkmeBXlX9qK/tj4GfVtWfLlddi5XkM8DXqureZmKc1wB/BDxfVf8hyR3Ar1TVv17WQhdglrF9jFV+zKYlGQFOAduBj7IGjhmcM64PsYqPV5KNwP8C3lpVp5P8JVNzlbyVFXa8POPviCT/AHgX8OcAVfVSVb0A7AQ+03T7DHDr8lS4cOcZ21rym8B3qup7rIFj1qd/XGvBRcD6JBcxdfIxwQo8Xgb/YAU8lORokt197bcneSLJ/pXw37V5ejMwCfzXJI8luTfJJcAbmmkyab6+fjmLXKDZxgar+5j12wUcaJ6vhWM2rX9csIqPV1WdAv4U+D7wA6amoH2IFXi8DP7Brquqq4FbgI8meRdwN/AW4CqmDup/XMb6FuIi4Grg7qp6B/Az4I7lLWloZhvbaj9mADSXrv4p8N+Xu5ZhGjCuVX28mn+odgJXApcDlyT558tb1WAG/wBVNdF8fQ54ELimqn5YVWer6v8CnwauWc4aF2AcGK+qR5vlv2IqLH+Y5I0Azdfnlqm+xRg4tjVwzKbdAnyzqn7YLK+FYwYzxrUGjtc/Ab5bVZNV9TLwAPCPWYHHy+CfIcklSS6dfg7cBDw5feAa7wOeXI76Fqqq/g9wMsm2puk3gaeBQ8DvNm2/C3xxGcpblNnGttqPWZ/bePXlkFV/zBqvGtcaOF7fB96Z5DVJwtTP4bdYgcfLu3pmSPJmps7yYeoSwuer6t8n+W9M/Re0gGeBfzF93W61SHIVcC9wMfAMU3dR/ALwl8Bmpn5wf6eqnl+2IhdolrF9itV/zF4DnATeXFUvNm2vY5Ufs1nGtRbeY/8G+GfAGeAx4MPAL7HCjpfBL0kd46UeSeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4Jalj/h9PxBfh4dyEbAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.scatter(x=data.Temperature, y=data.Malfunction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous remarquons que des températures:\n",
" - inférieures à 65 F causent toujours un dysfonctionnement\n",
" - entre 65 et 75 F causent rarement un dysfonctionnement\n",
" - supérieures à 75 F ne causent pas de dysfonctionnement\n",
" \n",
"Essayons de transformer le problème en binaire, ç.à.d ajouter une nouvelle colonne *Malfunction2* telle que:\n",
" - Malfunction2 = 1 si Malfunction >1\n",
" - 0 autrement"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fce13689550>"
]
},
"execution_count": 5,
"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"
}
],
"source": [
"data['Malfunction2'] = [1 if m>=1 else 0 for m in data.Malfunction]\n",
"plt.scatter(x=data.Temperature, y=data.Malfunction2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Malfunction2</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> -10.158</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 26 Aug 2020</td> <th> Deviance: </th> <td> 20.315</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>16:57:48</td> <th> Pearson chi2: </th> <td> 23.2</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>5</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> 15.0429</td> <td> 7.379</td> <td> 2.039</td> <td> 0.041</td> <td> 0.581</td> <td> 29.505</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> -0.2322</td> <td> 0.108</td> <td> -2.145</td> <td> 0.032</td> <td> -0.444</td> <td> -0.020</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Malfunction2 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: -10.158\n",
"Date: Wed, 26 Aug 2020 Deviance: 20.315\n",
"Time: 16:57:48 Pearson chi2: 23.2\n",
"No. Iterations: 5 Covariance Type: nonrobust\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept 15.0429 7.379 2.039 0.041 0.581 29.505\n",
"Temperature -0.2322 0.108 -2.145 0.032 -0.444 -0.020\n",
"===============================================================================\n",
"\"\"\""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statsmodels.api as sm\n",
"data['Intercept']=1\n",
"logmodel=sm.GLM(data['Malfunction2'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n",
"logmodel.summary()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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": [
"import numpy as np\n",
"data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
"data_pred['Malfunction2'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n",
"data_pred.plot(x=\"Temperature\",y=\"Malfunction2\",kind=\"line\",ylim=[0,1])\n",
"plt.scatter(x=data[\"Temperature\"],y=data[\"Malfunction2\"])\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Conclusion\n",
"Avec cette nouvelle perspective, on peut déduire qu'à une température de 31 F, un dysfonctionnement est extrèmement probable."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
...@@ -705,7 +1027,7 @@ ...@@ -705,7 +1027,7 @@
"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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