diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 596057ccb467b35a913f058ab41dbe103163fc7c..d1b8680ef29b6d9c533cafdee431dc1e601ed659 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -324,12 +324,14 @@ "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", + "* secon test avec uniquement les tempéraures <65" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -405,13 +407,13 @@ "22 1/12/86 6 58 200 1" ] }, - "execution_count": 2, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#data = data[data.Malfunction>0] \n", + "#data = data[data.Malfunction>0] data.Temperature <= 65\n", "data2 = data[data.Temperature <= 65]\n", "data2" ] @@ -429,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -481,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -505,10 +507,10 @@ " Method: IRLS Log-Likelihood: -1.3845 \n", "\n", "\n", - " Date: Wed, 13 Aug 2025 Deviance: 0.040847 \n", + " Date: Mon, 18 Aug 2025 Deviance: 0.040847 \n", "\n", "\n", - " Time: 16:05:21 Pearson chi2: 0.0407 \n", + " Time: 15:47:24 Pearson chi2: 0.0407 \n", "\n", "\n", " No. Iterations: 4 Covariance Type: nonrobust\n", @@ -536,8 +538,8 @@ "Model Family: Binomial Df Model: 1\n", "Link Function: logit Scale: 1.0000\n", "Method: IRLS Log-Likelihood: -1.3845\n", - "Date: Wed, 13 Aug 2025 Deviance: 0.040847\n", - "Time: 16:05:21 Pearson chi2: 0.0407\n", + "Date: Mon, 18 Aug 2025 Deviance: 0.040847\n", + "Time: 15:47:24 Pearson chi2: 0.0407\n", "No. Iterations: 4 Covariance Type: nonrobust\n", "===============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", @@ -548,7 +550,7 @@ "\"\"\"" ] }, - "execution_count": 4, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -586,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -683,7 +685,7 @@ "22 1 " ] }, - "execution_count": 6, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -694,40 +696,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "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\u001b[0m in \u001b[0;36m\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", + "image/png": "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\n", "text/plain": [ "
" ] @@ -743,13 +717,13 @@ "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.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", - "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n", + "plt.scatter(x=data2[\"Temperature\"],y=data2[\"Frequency\"])\n", "plt.grid(True)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -782,182 +756,182 @@ " \n", " 0\n", " 1\n", - " 30.000000\n", - " 0.796399\n", + " 25.000000\n", + " 0.864905\n", " \n", " \n", " 1\n", " 1\n", - " 30.333333\n", - " 0.791021\n", + " 25.291667\n", + " 0.861511\n", " \n", " \n", " 2\n", " 1\n", - " 30.666667\n", - " 0.785539\n", + " 25.583333\n", + " 0.858046\n", " \n", " \n", " 3\n", " 1\n", - " 31.000000\n", - " 0.779954\n", + " 25.875000\n", + " 0.854509\n", " \n", " \n", " 4\n", " 1\n", - " 31.333333\n", - " 0.774265\n", + " 26.166667\n", + " 0.850900\n", " \n", " \n", " 5\n", " 1\n", - " 31.666667\n", - " 0.768472\n", + " 26.458333\n", + " 0.847216\n", " \n", " \n", " 6\n", " 1\n", - " 32.000000\n", - " 0.762576\n", + " 26.750000\n", + " 0.843459\n", " \n", " \n", " 7\n", " 1\n", - " 32.333333\n", - " 0.756578\n", + " 27.041667\n", + " 0.839627\n", " \n", " \n", " 8\n", " 1\n", - " 32.666667\n", - " 0.750478\n", + " 27.333333\n", + " 0.835719\n", " \n", " \n", " 9\n", " 1\n", - " 33.000000\n", - " 0.744277\n", + " 27.625000\n", + " 0.831734\n", " \n", " \n", " 10\n", " 1\n", - " 33.333333\n", - " 0.737975\n", + " 27.916667\n", + " 0.827674\n", " \n", " \n", " 11\n", " 1\n", - " 33.666667\n", - " 0.731574\n", + " 28.208333\n", + " 0.823536\n", " \n", " \n", " 12\n", " 1\n", - " 34.000000\n", - " 0.725075\n", + " 28.500000\n", + " 0.819320\n", " \n", " \n", " 13\n", " 1\n", - " 34.333333\n", - " 0.718479\n", + " 28.791667\n", + " 0.815026\n", " \n", " \n", " 14\n", " 1\n", - " 34.666667\n", - " 0.711788\n", + " 29.083333\n", + " 0.810654\n", " \n", " \n", " 15\n", " 1\n", - " 35.000000\n", - " 0.705003\n", + " 29.375000\n", + " 0.806203\n", " \n", " \n", " 16\n", " 1\n", - " 35.333333\n", - " 0.698126\n", + " 29.666667\n", + " 0.801673\n", " \n", " \n", " 17\n", " 1\n", - " 35.666667\n", - " 0.691159\n", + " 29.958333\n", + " 0.797064\n", " \n", " \n", " 18\n", " 1\n", - " 36.000000\n", - " 0.684104\n", + " 30.250000\n", + " 0.792375\n", " \n", " \n", " 19\n", " 1\n", - " 36.333333\n", - " 0.676963\n", + " 30.541667\n", + " 0.787607\n", " \n", " \n", " 20\n", " 1\n", - " 36.666667\n", - " 0.669738\n", + " 30.833333\n", + " 0.782759\n", " \n", " \n", " 21\n", " 1\n", - " 37.000000\n", - " 0.662433\n", + " 31.125000\n", + " 0.777832\n", " \n", " \n", " 22\n", " 1\n", - " 37.333333\n", - " 0.655049\n", + " 31.416667\n", + " 0.772826\n", " \n", " \n", " 23\n", " 1\n", - " 37.666667\n", - " 0.647590\n", + " 31.708333\n", + " 0.767741\n", " \n", " \n", " 24\n", " 1\n", - " 38.000000\n", - " 0.640058\n", + " 32.000000\n", + " 0.762576\n", " \n", " \n", " 25\n", " 1\n", - " 38.333333\n", - " 0.632456\n", + " 32.291667\n", + " 0.757333\n", " \n", " \n", " 26\n", " 1\n", - " 38.666667\n", - " 0.624788\n", + " 32.583333\n", + " 0.752012\n", " \n", " \n", " 27\n", " 1\n", - " 39.000000\n", - " 0.617057\n", + " 32.875000\n", + " 0.746614\n", " \n", " \n", " 28\n", " 1\n", - " 39.333333\n", - " 0.609266\n", + " 33.166667\n", + " 0.741138\n", " \n", " \n", " 29\n", " 1\n", - " 39.666667\n", - " 0.601419\n", + " 33.458333\n", + " 0.735586\n", " \n", " \n", " ...\n", @@ -968,182 +942,182 @@ " \n", " 91\n", " 1\n", - " 60.333333\n", - " 0.164500\n", + " 51.541667\n", + " 0.318910\n", " \n", " \n", " 92\n", " 1\n", - " 60.666667\n", - " 0.160035\n", + " 51.833333\n", + " 0.312700\n", " \n", " \n", " 93\n", " 1\n", - " 61.000000\n", - " 0.155669\n", + " 52.125000\n", + " 0.306557\n", " \n", " \n", " 94\n", " 1\n", - " 61.333333\n", - " 0.151400\n", + " 52.416667\n", + " 0.300481\n", " \n", " \n", " 95\n", " 1\n", - " 61.666667\n", - " 0.147228\n", + " 52.708333\n", + " 0.294475\n", " \n", " \n", " 96\n", " 1\n", - " 62.000000\n", - " 0.143152\n", + " 53.000000\n", + " 0.288539\n", " \n", " \n", " 97\n", " 1\n", - " 62.333333\n", - " 0.139170\n", + " 53.291667\n", + " 0.282675\n", " \n", " \n", " 98\n", " 1\n", - " 62.666667\n", - " 0.135281\n", + " 53.583333\n", + " 0.276884\n", " \n", " \n", " 99\n", " 1\n", - " 63.000000\n", - " 0.131485\n", + " 53.875000\n", + " 0.271166\n", " \n", " \n", " 100\n", " 1\n", - " 63.333333\n", - " 0.127779\n", + " 54.166667\n", + " 0.265524\n", " \n", " \n", " 101\n", " 1\n", - " 63.666667\n", - " 0.124163\n", + " 54.458333\n", + " 0.259956\n", " \n", " \n", " 102\n", " 1\n", - " 64.000000\n", - " 0.120635\n", + " 54.750000\n", + " 0.254466\n", " \n", " \n", " 103\n", " 1\n", - " 64.333333\n", - " 0.117193\n", + " 55.041667\n", + " 0.249052\n", " \n", " \n", " 104\n", " 1\n", - " 64.666667\n", - " 0.113838\n", + " 55.333333\n", + " 0.243715\n", " \n", " \n", " 105\n", " 1\n", - " 65.000000\n", - " 0.110566\n", + " 55.625000\n", + " 0.238457\n", " \n", " \n", " 106\n", " 1\n", - " 65.333333\n", - " 0.107377\n", + " 55.916667\n", + " 0.233277\n", " \n", " \n", " 107\n", " 1\n", - " 65.666667\n", - " 0.104269\n", + " 56.208333\n", + " 0.228176\n", " \n", " \n", " 108\n", " 1\n", - " 66.000000\n", - " 0.101241\n", + " 56.500000\n", + " 0.223154\n", " \n", " \n", " 109\n", " 1\n", - " 66.333333\n", - " 0.098291\n", + " 56.791667\n", + " 0.218211\n", " \n", " \n", " 110\n", " 1\n", - " 66.666667\n", - " 0.095418\n", + " 57.083333\n", + " 0.213348\n", " \n", " \n", " 111\n", " 1\n", - " 67.000000\n", - " 0.092620\n", + " 57.375000\n", + " 0.208564\n", " \n", " \n", " 112\n", " 1\n", - " 67.333333\n", - " 0.089896\n", + " 57.666667\n", + " 0.203859\n", " \n", " \n", " 113\n", " 1\n", - " 67.666667\n", - " 0.087245\n", + " 57.958333\n", + " 0.199234\n", " \n", " \n", " 114\n", " 1\n", - " 68.000000\n", - " 0.084664\n", + " 58.250000\n", + " 0.194689\n", " \n", " \n", " 115\n", " 1\n", - " 68.333333\n", - " 0.082153\n", + " 58.541667\n", + " 0.190222\n", " \n", " \n", " 116\n", " 1\n", - " 68.666667\n", - " 0.079710\n", + " 58.833333\n", + " 0.185834\n", " \n", " \n", " 117\n", " 1\n", - " 69.000000\n", - " 0.077334\n", + " 59.125000\n", + " 0.181525\n", " \n", " \n", " 118\n", " 1\n", - " 69.333333\n", - " 0.075022\n", + " 59.416667\n", + " 0.177294\n", " \n", " \n", " 119\n", " 1\n", - " 69.666667\n", - " 0.072775\n", + " 59.708333\n", + " 0.173141\n", " \n", " \n", " 120\n", " 1\n", - " 70.000000\n", - " 0.070589\n", + " 60.000000\n", + " 0.169064\n", " \n", " \n", "\n", @@ -1152,72 +1126,72 @@ ], "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", + "0 1 25.000000 0.864905\n", + "1 1 25.291667 0.861511\n", + "2 1 25.583333 0.858046\n", + "3 1 25.875000 0.854509\n", + "4 1 26.166667 0.850900\n", + "5 1 26.458333 0.847216\n", + "6 1 26.750000 0.843459\n", + "7 1 27.041667 0.839627\n", + "8 1 27.333333 0.835719\n", + "9 1 27.625000 0.831734\n", + "10 1 27.916667 0.827674\n", + "11 1 28.208333 0.823536\n", + "12 1 28.500000 0.819320\n", + "13 1 28.791667 0.815026\n", + "14 1 29.083333 0.810654\n", + "15 1 29.375000 0.806203\n", + "16 1 29.666667 0.801673\n", + "17 1 29.958333 0.797064\n", + "18 1 30.250000 0.792375\n", + "19 1 30.541667 0.787607\n", + "20 1 30.833333 0.782759\n", + "21 1 31.125000 0.777832\n", + "22 1 31.416667 0.772826\n", + "23 1 31.708333 0.767741\n", + "24 1 32.000000 0.762576\n", + "25 1 32.291667 0.757333\n", + "26 1 32.583333 0.752012\n", + "27 1 32.875000 0.746614\n", + "28 1 33.166667 0.741138\n", + "29 1 33.458333 0.735586\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", + "91 1 51.541667 0.318910\n", + "92 1 51.833333 0.312700\n", + "93 1 52.125000 0.306557\n", + "94 1 52.416667 0.300481\n", + "95 1 52.708333 0.294475\n", + "96 1 53.000000 0.288539\n", + "97 1 53.291667 0.282675\n", + "98 1 53.583333 0.276884\n", + "99 1 53.875000 0.271166\n", + "100 1 54.166667 0.265524\n", + "101 1 54.458333 0.259956\n", + "102 1 54.750000 0.254466\n", + "103 1 55.041667 0.249052\n", + "104 1 55.333333 0.243715\n", + "105 1 55.625000 0.238457\n", + "106 1 55.916667 0.233277\n", + "107 1 56.208333 0.228176\n", + "108 1 56.500000 0.223154\n", + "109 1 56.791667 0.218211\n", + "110 1 57.083333 0.213348\n", + "111 1 57.375000 0.208564\n", + "112 1 57.666667 0.203859\n", + "113 1 57.958333 0.199234\n", + "114 1 58.250000 0.194689\n", + "115 1 58.541667 0.190222\n", + "116 1 58.833333 0.185834\n", + "117 1 59.125000 0.181525\n", + "118 1 59.416667 0.177294\n", + "119 1 59.708333 0.173141\n", + "120 1 60.000000 0.169064\n", "\n", "[121 rows x 3 columns]" ] }, - "execution_count": 8, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1234,30 +1208,48 @@ "scrolled": true }, "source": [ - "Comme on pouvait s'attendre au vu des données initiales, la\n", + "\n", + "\n", + "En ne prenant que la partie des température basse, on voit une influence notable de la baisse de température avec une probabilité d'échec de 0.77\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.06521739130434782\n" + "data p : 0.06521739130434782\n", + "data p^2 : 0.004253308128544423\n", + "data 1−(1−𝑝2)3 : 0.01270572944054793 soit 1.27%\n", + "\n", + "data2 p : 0.20833333333333334\n", + "data2 p^2 : 0.04340277777777778\n", + "data2 1−(1−𝑝2)3 : 0.1246386921781899 soit 12.46%\n", + "\n" ] } ], "source": [ "data = pd.read_csv(\"shuttle.csv\")\n", - "print(np.sum(data.Malfunction)/np.sum(data.Count))" + "p_data = np.sum(data.Malfunction)/np.sum(data.Count)\n", + "p_data2 = np.sum(data2.Malfunction)/np.sum(data2.Count)\n", + "print(\"data p : \",p_data)\n", + "print('data p^2 : ',p_data**2)\n", + "print('data 1−(1−𝑝2)3 : ',1-(1-p_data**2)**3,f' soit {(1-(1-p_data**2)**3)*100:.2f}%')\n", + "print()\n", + "print(\"data2 p : \",p_data2)\n", + "print('data2 p^2 : ',p_data2**2)\n", + "print('data2 1−(1−𝑝2)3 : ',1-(1-p_data2**2)**3,f' soit {(1-(1-p_data2**2)**3)*100:.2f}%')\n", + "print (\"\")" ] }, {