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mooc-rr
Commit eae7fefc authored by Agathe Schmider's avatar Agathe Schmider
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Merge branch 'master' of... 

Merge branch 'master' of https://app-learninglab.inria.fr/moocrr/gitlab/a308dc99373eb1db581156a44d010769/mooc-rr
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parents 8078421b 2568722b
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Showing with 1100 additions and 309 deletions
module2/exo4/exercice.ipynb
View file @ eae7fefc
......@@ -27,6 +27,8 @@
],
"source": [
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"mypath=os.getcwd()\n",
"print(mypath)"
]
......@@ -52,13 +54,15 @@
" 'exercice_fr.ipynb',\n",
" 'exercice.ipynb',\n",
" 'exercice_fr.Rmd',\n",
" 'spokes_calc.ipynb',\n",
" 'exercice_python_fr.org',\n",
" 'exercice_R_en.org',\n",
" 'exercice_R_fr.org',\n",
" 'exercice_en.Rmd',\n",
" 'exercice_en.ipynb',\n",
" '.ipynb_checkpoints',\n",
" 'donnees.csv']"
" 'donnees.csv',\n",
" 'Untitled.ipynb']"
]
},
"execution_count": 2,
......@@ -156,9 +160,9 @@
" <th>date</th>\n",
" <th>sport</th>\n",
" <th>durée</th>\n",
" <th>FC moy</th>\n",
" <th>FC max</th>\n",
" <th>intensité ressentie</th>\n",
" <th>FCmoy</th>\n",
" <th>FCmax</th>\n",
" <th>intensitéressentie</th>\n",
" <th>Unnamed: 7</th>\n",
" </tr>\n",
" </thead>\n",
......@@ -223,19 +227,19 @@
"</div>"
],
"text/plain": [
" Unnamed: 0 date sport durée FC moy FC max \\\n",
"0 NaN ----- ----- ------ ------ ------- \n",
"1 NaN 18/03/2020 vélo 1:09:16 128 176 \n",
"2 NaN 19/03/2020 vélo 2:29:58 151 188 \n",
"3 NaN 20/03/2020 vélo 0:44:05 144 176 \n",
"4 NaN 25/03/2020 crossfit 0:51:25 128 182 \n",
" Unnamed: 0 date sport durée FCmoy FCmax \\\n",
"0 NaN ----- ----- ------ ------ ------- \n",
"1 NaN 18/03/2020 vélo 1:09:16 128 176 \n",
"2 NaN 19/03/2020 vélo 2:29:58 151 188 \n",
"3 NaN 20/03/2020 vélo 0:44:05 144 176 \n",
"4 NaN 25/03/2020 crossfit 0:51:25 128 182 \n",
"\n",
" intensité ressentie Unnamed: 7 \n",
"0 ------------------- NaN \n",
"1 facile NaN \n",
"2 mod+ NaN \n",
"3 facile NaN \n",
"4 mod+ NaN "
" intensitéressentie Unnamed: 7 \n",
"0 ------------------- NaN \n",
"1 facile NaN \n",
"2 mod+ NaN \n",
"3 facile NaN \n",
"4 mod+ NaN "
]
},
"execution_count": 6,
......@@ -278,9 +282,9 @@
" <th>date</th>\n",
" <th>sport</th>\n",
" <th>durée</th>\n",
" <th>FC moy</th>\n",
" <th>FC max</th>\n",
" <th>intensité ressentie</th>\n",
" <th>FCmoy</th>\n",
" <th>FCmax</th>\n",
" <th>intensitéressentie</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
......@@ -289,8 +293,8 @@
" <td>18/03/2020</td>\n",
" <td>vélo</td>\n",
" <td>1:09:16</td>\n",
" <td>128</td>\n",
" <td>176</td>\n",
" <td>128.0</td>\n",
" <td>176.0</td>\n",
" <td>facile</td>\n",
" </tr>\n",
" <tr>\n",
......@@ -298,8 +302,8 @@
" <td>19/03/2020</td>\n",
" <td>vélo</td>\n",
" <td>2:29:58</td>\n",
" <td>151</td>\n",
" <td>188</td>\n",
" <td>151.0</td>\n",
" <td>188.0</td>\n",
" <td>mod+</td>\n",
" </tr>\n",
" <tr>\n",
......@@ -307,8 +311,8 @@
" <td>20/03/2020</td>\n",
" <td>vélo</td>\n",
" <td>0:44:05</td>\n",
" <td>144</td>\n",
" <td>176</td>\n",
" <td>144.0</td>\n",
" <td>176.0</td>\n",
" <td>facile</td>\n",
" </tr>\n",
" <tr>\n",
......@@ -316,8 +320,8 @@
" <td>25/03/2020</td>\n",
" <td>crossfit</td>\n",
" <td>0:51:25</td>\n",
" <td>128</td>\n",
" <td>182</td>\n",
" <td>128.0</td>\n",
" <td>182.0</td>\n",
" <td>mod+</td>\n",
" </tr>\n",
" <tr>\n",
......@@ -325,8 +329,8 @@
" <td>26/03/2020</td>\n",
" <td>vélo</td>\n",
" <td>0:45:29</td>\n",
" <td>162</td>\n",
" <td>193</td>\n",
" <td>162.0</td>\n",
" <td>193.0</td>\n",
" <td>mod++</td>\n",
" </tr>\n",
" </tbody>\n",
......@@ -334,12 +338,12 @@
"</div>"
],
"text/plain": [
" date sport durée FC moy FC max intensité ressentie \n",
"0 18/03/2020 vélo 1:09:16 128 176 facile \n",
"1 19/03/2020 vélo 2:29:58 151 188 mod+ \n",
"2 20/03/2020 vélo 0:44:05 144 176 facile \n",
"3 25/03/2020 crossfit 0:51:25 128 182 mod+ \n",
"4 26/03/2020 vélo 0:45:29 162 193 mod++ "
" date sport durée FCmoy FCmax intensitéressentie\n",
"0 18/03/2020 vélo 1:09:16 128.0 176.0 facile\n",
"1 19/03/2020 vélo 2:29:58 151.0 188.0 mod+\n",
"2 20/03/2020 vélo 0:44:05 144.0 176.0 facile\n",
"3 25/03/2020 crossfit 0:51:25 128.0 182.0 mod+\n",
"4 26/03/2020 vélo 0:45:29 162.0 193.0 mod++"
]
},
"execution_count": 7,
......@@ -363,15 +367,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
"[['18/03/2020 ' 'vélo ' '1:09:16 ' 128 176 'facile ']\n",
" ['19/03/2020 ' 'vélo ' '2:29:58 ' 151 188 'mod+ ']\n",
" ['20/03/2020 ' 'vélo ' '0:44:05 ' 144 176 'facile ']\n",
" ['25/03/2020 ' 'crossfit ' '0:51:25 ' 128 182 'mod+ ']\n",
" ['26/03/2020 ' 'vélo ' '0:45:29 ' 162 193 'mod++ ']\n",
" ['30/03/2020 ' 'cap ' '0:39:04 ' 158 189 'mod++ ']\n",
" ['30/03/2020 ' 'crossfit ' '0:29:14 ' 130 169 'mod+ ']\n",
" ['31/03/2020 ' 'vélo ' '0:41:52 ' 156 181 'mod+ ']\n",
" ['01/04/2020 ' 'vélo ' '0:39:06 ' 168 190 'mod++ ']]\n"
"[['18/03/2020' 'vélo' '1:09:16' 128.0 176.0 'facile']\n",
" ['19/03/2020' 'vélo' '2:29:58' 151.0 188.0 'mod+']\n",
" ['20/03/2020' 'vélo' '0:44:05' 144.0 176.0 'facile']\n",
" ['25/03/2020' 'crossfit' '0:51:25' 128.0 182.0 'mod+']\n",
" ['26/03/2020' 'vélo' '0:45:29' 162.0 193.0 'mod++']\n",
" ['30/03/2020' 'cap' '0:39:04' 158.0 189.0 'mod++']\n",
" ['30/03/2020' 'crossfit' '0:29:14' 130.0 169.0 'mod+']\n",
" ['31/03/2020' 'vélo' '0:41:52' 156.0 181.0 'mod+']\n",
" ['01/04/2020' 'vélo' '0:39:06' 168.0 190.0 'mod++']\n",
" ['04/04/2020' 'slack' '1:30:00' nan nan 'facile']\n",
" ['05/04/2020' 'vélo' '1:03:41' 152.0 189.0 'mod++']\n",
" ['05/04/2020' 'slack' '1:00:00' nan nan 'facile']]\n"
]
}
],
......@@ -389,7 +396,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"9\n"
"12\n"
]
}
],
......@@ -406,9 +413,10 @@
{
"data": {
"text/plain": [
"array(['18/03/2020 ', '19/03/2020 ', '20/03/2020 ', '25/03/2020 ',\n",
" '26/03/2020 ', '30/03/2020 ', '30/03/2020 ', '31/03/2020 ',\n",
" '01/04/2020 '], dtype=object)"
"array(['18/03/2020', '19/03/2020', '20/03/2020', '25/03/2020',\n",
" '26/03/2020', '30/03/2020', '30/03/2020', '31/03/2020',\n",
" '01/04/2020', '04/04/2020', '05/04/2020', '05/04/2020'],\n",
" dtype=object)"
]
},
"execution_count": 10,
......@@ -428,7 +436,7 @@
{
"data": {
"text/plain": [
"'18/03/2020 '"
"'18/03/2020'"
]
},
"execution_count": 11,
......@@ -442,7 +450,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 12,
"metadata": {},
"outputs": [
{
......@@ -451,14 +459,201 @@
"datetime.datetime(2020, 3, 18, 0, 0)"
]
},
"execution_count": 16,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datetime import datetime, date, time, timezone\n",
"datetime.strptime(mat[0,0],\"%d/%m/%Y \")"
"datetime.strptime(mat[0,0],\"%d/%m/%Y\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'datetime.time'>\n",
"13:55:26\n"
]
}
],
"source": [
"time_str = '13:55:26'\n",
"time_object = datetime.strptime(time_str, '%H:%M:%S').time()\n",
"print(type(time_object))\n",
"print(time_object)"
]
},
{
"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": [
"sport=mat[:,1]\n",
"pd.Series(sport).value_counts().plot('bar')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f4983c805f8>]"
]
},
"execution_count": 15,
"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": [
"dates = mat[:,0]\n",
"dates_list = [datetime.strptime(date, '%d/%m/%Y').date() for date in dates]\n",
"plt.plot(dates)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"mat[:,0]=dates_list"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[datetime.date(2020, 3, 18) datetime.date(2020, 3, 19)\n",
" datetime.date(2020, 3, 20) datetime.date(2020, 3, 25)\n",
" datetime.date(2020, 3, 26) datetime.date(2020, 3, 30)\n",
" datetime.date(2020, 3, 30) datetime.date(2020, 3, 31)\n",
" datetime.date(2020, 4, 1) datetime.date(2020, 4, 4)\n",
" datetime.date(2020, 4, 5) datetime.date(2020, 4, 5)]\n"
]
}
],
"source": [
"print(mat[:,0])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"durees=mat[:,2]\n",
"mat[:,2]=[datetime.strptime(time, '%H:%M:%S').time() for time in durees]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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": [
"plt.scatter(dates,durees)\n",
"plt.gcf().autofmt_xdate()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[datetime.date(2020, 3, 18), 'vélo', datetime.time(1, 9, 16),\n",
" 128.0, 176.0, 'facile'],\n",
" [datetime.date(2020, 3, 19), 'vélo', datetime.time(2, 29, 58),\n",
" 151.0, 188.0, 'mod+'],\n",
" [datetime.date(2020, 3, 20), 'vélo', datetime.time(0, 44, 5),\n",
" 144.0, 176.0, 'facile'],\n",
" [datetime.date(2020, 3, 25), 'crossfit', datetime.time(0, 51, 25),\n",
" 128.0, 182.0, 'mod+'],\n",
" [datetime.date(2020, 3, 26), 'vélo', datetime.time(0, 45, 29),\n",
" 162.0, 193.0, 'mod++'],\n",
" [datetime.date(2020, 3, 30), 'cap', datetime.time(0, 39, 4),\n",
" 158.0, 189.0, 'mod++'],\n",
" [datetime.date(2020, 3, 30), 'crossfit', datetime.time(0, 29, 14),\n",
" 130.0, 169.0, 'mod+'],\n",
" [datetime.date(2020, 3, 31), 'vélo', datetime.time(0, 41, 52),\n",
" 156.0, 181.0, 'mod+'],\n",
" [datetime.date(2020, 4, 1), 'vélo', datetime.time(0, 39, 6),\n",
" 168.0, 190.0, 'mod++'],\n",
" [datetime.date(2020, 4, 4), 'slack', datetime.time(1, 30), nan,\n",
" nan, 'facile'],\n",
" [datetime.date(2020, 4, 5), 'vélo', datetime.time(1, 3, 41),\n",
" 152.0, 189.0, 'mod++'],\n",
" [datetime.date(2020, 4, 5), 'slack', datetime.time(1, 0), nan,\n",
" nan, 'facile']], dtype=object)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mat"
]
},
{
......
module2/exo4/spokes_calc.ipynb
View file @ eae7fefc
This source diff could not be displayed because it is too large. You can view the blob instead.
module2/exo5/exo5_fr.ipynb
View file @ eae7fefc
......@@ -2,14 +2,20 @@
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"# Analyse du risque de défaillance des joints toriques de la navette Challenger"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"Le 27 Janvier 1986, veille du décollage de la navette *Challenger*, eu\n",
"lieu une télé-conférence de trois heures entre les ingénieurs de la\n",
......@@ -32,7 +38,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"## Chargement des données\n",
"Nous commençons donc par charger ces données:"
......@@ -41,7 +50,10 @@
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
......@@ -261,30 +273,30 @@
"</div>"
],
"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,
......@@ -301,7 +313,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"Le jeu de données nous indique la date de l'essai, le nombre de joints\n",
"toriques mesurés (il y en a 6 sur le lançeur principal), la\n",
......@@ -311,19 +326,27 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"## Inspection graphique des données\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",
"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",
"**/!\\ c'est FAUX**, il faut prendre en compte toutes les expériences, même s'il n'y a pas eu de dysfonctionnement."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
......@@ -355,6 +378,14 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4/12/81</td>\n",
" <td>6</td>\n",
" <td>66</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11/12/81</td>\n",
" <td>6</td>\n",
......@@ -363,6 +394,54 @@
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3/22/82</td>\n",
" <td>6</td>\n",
" <td>69</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11/11/82</td>\n",
" <td>6</td>\n",
" <td>68</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4/04/83</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6/18/82</td>\n",
" <td>6</td>\n",
" <td>72</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8/30/83</td>\n",
" <td>6</td>\n",
" <td>73</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11/28/83</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2/03/84</td>\n",
" <td>6</td>\n",
......@@ -387,6 +466,22 @@
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>10/05/84</td>\n",
" <td>6</td>\n",
" <td>78</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11/08/84</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1/24/85</td>\n",
" <td>6</td>\n",
......@@ -395,6 +490,54 @@
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>4/12/85</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>4/29/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6/17/85</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7/29/85</td>\n",
" <td>6</td>\n",
" <td>81</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>8/27/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>10/03/85</td>\n",
" <td>6</td>\n",
" <td>79</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10/30/85</td>\n",
" <td>6</td>\n",
......@@ -403,6 +546,14 @@
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>11/26/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1/12/86</td>\n",
" <td>6</td>\n",
......@@ -416,12 +567,28 @@
],
"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"
]
},
......@@ -431,13 +598,16 @@
}
],
"source": [
"data = data[data.Malfunction>0]\n",
"#data = data[data.Malfunction>0]\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"Très bien, nous avons une variabilité de température importante mais\n",
"la pression est quasiment toujours égale à 200, ce qui devrait\n",
......@@ -449,11 +619,14 @@
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -476,7 +649,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"À première vue, ce n'est pas flagrant mais bon, essayons quand même\n",
"d'estimer l'impact de la température $t$ sur la probabilité de\n",
......@@ -485,7 +661,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"## Estimation de l'influence de la température\n",
"\n",
......@@ -501,7 +680,10 @@
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
......@@ -509,10 +691,10 @@
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\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> 23</td> \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> 21</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
......@@ -521,16 +703,16 @@
" <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> -2.5250</td> \n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -3.9210</td> \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>Mon, 06 Apr 2020</td> <th> Deviance: </th> <td> 3.0144</td> \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>14:14:59</td> <th> Pearson chi2: </th> <td> 5.00</td> \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>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
......@@ -538,10 +720,10 @@
" <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",
"</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> 5.0850</td> <td> 7.477</td> <td> 0.680</td> <td> 0.496</td> <td> -9.570</td> <td> 19.740</td>\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> -0.1156</td> <td> 0.115</td> <td> -1.004</td> <td> 0.316</td> <td> -0.341</td> <td> 0.110</td>\n",
"</tr>\n",
"</table>"
],
......@@ -550,19 +732,19 @@
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 7\n",
"Model: GLM Df Residuals: 5\n",
"Dep. Variable: Frequency 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: -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: -3.9210\n",
"Date: Mon, 06 Apr 2020 Deviance: 3.0144\n",
"Time: 14:14:59 Pearson chi2: 5.00\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 5.0850 7.477 0.680 0.496 -9.570 19.740\n",
"Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n",
"===============================================================================\n",
"\"\"\""
]
......@@ -585,7 +767,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"L'estimateur le plus probable du paramètre de température est 0.0014\n",
"et l'erreur standard de cet estimateur est de 0.122, autrement dit on\n",
......@@ -595,7 +780,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"## Estimation de la probabilité de dysfonctionnant des joints toriques\n",
"La température prévue le jour du décollage est de 31°F. Essayons\n",
......@@ -606,11 +794,15 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false,
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -631,65 +823,528 @@
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": 6,
"metadata": {
"hideCode": false,
"hidePrompt": false,
"scrolled": true
"hidePrompt": false
},
"source": [
"Comme on pouvait s'attendre au vu des données initiales, la\n",
"température n'a pas d'impact notable sur la probabilité d'échec des\n",
"joints toriques. Elle sera d'environ 0.2, comme dans les essais\n",
"précédents où nous il y a eu défaillance d'au moins un joint. Revenons\n",
"à l'ensemble des données initiales pour estimer la probabilité de\n",
"défaillance d'un joint:\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.06521739130434782\n"
"2 0.817774\n",
"Name: Frequency, dtype: float64\n"
]
}
],
"source": [
"data = pd.read_csv(\"shuttle.csv\")\n",
"print(np.sum(data.Malfunction)/np.sum(data.Count))"
"f=data_pred.Frequency[data_pred.Temperature==31]\n",
"print(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cette probabilité est donc d'environ $p=0.065$, sachant qu'il existe\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",
"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",
"pas de chance... Tout est sous contrôle, le décollage peut donc avoir\n",
"lieu demain comme prévu.\n",
"\n",
"Seulement, le lendemain, la navette Challenger explosera et emportera\n",
"avec elle ses sept membres d'équipages. L'opinion publique est\n",
"fortement touchée et lors de l'enquête qui suivra, la fiabilité des\n",
"joints toriques sera directement mise en cause. Au delà des problèmes\n",
"de communication interne à la NASA qui sont pour beaucoup dans ce\n",
"fiasco, l'analyse précédente comporte (au moins) un petit\n",
"problème... Saurez-vous le trouver ? Vous êtes libre de modifier cette\n",
"analyse et de regarder ce jeu de données sous tous les angles afin\n",
"d'expliquer ce qui ne va pas."
"cell_type": "code",
"execution_count": 7,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
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" <th>Count</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Malfunction</th>\n",
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" <td>6</td>\n",
" <td>1</td>\n",
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" <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",
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],
"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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"metadata": {
"hideCode": false,
"hidePrompt": false,
"scrolled": true
},
"source": [
"Comme on pouvait s'attendre au vu des données initiales, la\n",
"température n'a pas d'impact notable sur la probabilité d'échec des\n",
"joints toriques. Elle sera d'environ 0.2, comme dans les essais\n",
"précédents où nous il y a eu défaillance d'au moins un joint. Revenons\n",
"à l'ensemble des données initiales pour estimer la probabilité de\n",
"défaillance d'un joint:\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"hideCode": false,
"hidePrompt": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.21428571428571427\n"
]
}
],
"source": [
"data = pd.read_csv(\"shuttle.csv\")\n",
"data = data[data.Malfunction>0] #On avait oublié de reprendre les même données ! pas bien\n",
"p=np.sum(data.Malfunction)/np.sum(data.Count)\n",
"print(np.sum(data.Malfunction)/np.sum(data.Count))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, si on prend le raisonnement de cette analyse avec les données tronquées utilisées à la base, on a :"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"La probabilité qu'un joint soit défaillant est p = 0.214\n",
"La probabilité que deux joints soient défaillants est p^2 = 0.04592\n",
"La probabilité de défaillance d'un des lançeur est = 0.13153, soit de 13.2 %\n"
]
}
],
"source": [
"print('La probabilité qu\\'un joint soit défaillant est p = %.3f' %p)\n",
"p2=p**2\n",
"print('La probabilité que deux joints soient défaillants est p^2 = %.5f' %p2)\n",
"p_lanceur = 1-(1-p**2)**3\n",
"print('La probabilité de défaillance d\\'un des lançeur est = %.5f, soit de %.1f %%' %(p_lanceur,p_lanceur*100) )"
]
},
{
"cell_type": "markdown",
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"Cette probabilité est donc d'environ $p=0.065$, sachant qu'il existe\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",
"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",
"pas de chance... Tout est sous contrôle, le décollage peut donc avoir\n",
"lieu demain comme prévu.\n",
"\n",
"Seulement, le lendemain, la navette Challenger explosera et emportera\n",
"avec elle ses sept membres d'équipages. L'opinion publique est\n",
"fortement touchée et lors de l'enquête qui suivra, la fiabilité des\n",
"joints toriques sera directement mise en cause. Au delà des problèmes\n",
"de communication interne à la NASA qui sont pour beaucoup dans ce\n",
"fiasco, l'analyse précédente comporte (au moins) un petit\n",
"problème... Saurez-vous le trouver ? Vous êtes libre de modifier cette\n",
"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": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"Si on prend la prédiction du modèle :"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"La probabilité qu'un joint soit défaillant est p = 0.818\n",
"La probabilité que deux joints soient défaillants est p^2 = 0.66875\n",
"La probabilité de défaillance d'un des lançeur est = 0.96365, soit de 96.4 %\n"
]
}
],
"source": [
"p=f\n",
"print('La probabilité qu\\'un joint soit défaillant est p = %.3f' %p)\n",
"p2=p**2\n",
"print('La probabilité que deux joints soient défaillants est p^2 = %.5f' %p2)\n",
"p_lanceur = 1-(1-p**2)**3\n",
"print('La probabilité de défaillance d\\'un des lançeur est = %.5f, soit de %.1f %%' %(p_lanceur,p_lanceur*100) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On voit beaucoup mieux comment ça pouvait être une catatrophe ! Cependant, l'incertitude n'est pas du tout prise en compte encore une fois dans ce calcul."
]
}
],
"metadata": {
"celltoolbar": "Hide code",
"hide_code_all_hidden": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
......@@ -705,7 +1360,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.6.4"
}
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......
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