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Commit b6dc6f87 authored by da2bcc7bce9e6c165b2e2847d19f3af0's avatar da2bcc7bce9e6c165b2e2847d19f3af0
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exo5 jupyter notebook submitted

parent 9413cd2d
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Showing with 21 additions and 42 deletions
module2/exo5/exo5_fr.ipynb
View file @ b6dc6f87
...@@ -719,10 +719,11 @@ ...@@ -719,10 +719,11 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 36, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"import pandas as pd\n",
"data = pd.read_csv(\"shuttle.csv\")" "data = pd.read_csv(\"shuttle.csv\")"
] ]
}, },
...@@ -735,7 +736,7 @@ ...@@ -735,7 +736,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 48, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -751,24 +752,12 @@ ...@@ -751,24 +752,12 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f55dd684f98>" "<matplotlib.axes._subplots.AxesSubplot at 0x7fce15975198>"
] ]
}, },
"execution_count": 48, "execution_count": 2,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "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": [ "source": [
...@@ -778,15 +767,13 @@ ...@@ -778,15 +767,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 49, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "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UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", "/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been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" " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
] ]
...@@ -794,10 +781,10 @@ ...@@ -794,10 +781,10 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f55dd65e278>" "<matplotlib.axes._subplots.AxesSubplot at 0x7fce13783710>"
] ]
}, },
"execution_count": 49, "execution_count": 3,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -820,16 +807,16 @@ ...@@ -820,16 +807,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 37, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.collections.PathCollection at 0x7f55dd626f98>" "<matplotlib.collections.PathCollection at 0x7fce13693c88>"
] ]
}, },
"execution_count": 37, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -867,16 +854,16 @@ ...@@ -867,16 +854,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 41, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.collections.PathCollection at 0x7f55dd5c2e48>" "<matplotlib.collections.PathCollection at 0x7fce13689550>"
] ]
}, },
"execution_count": 41, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -900,16 +887,7 @@ ...@@ -900,16 +887,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"sm.families.links.nbinom"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -936,7 +914,7 @@ ...@@ -936,7 +914,7 @@
" <th>Date:</th> <td>Wed, 26 Aug 2020</td> <th> Deviance: </th> <td> 20.315</td> \n", " <th>Date:</th> <td>Wed, 26 Aug 2020</td> <th> Deviance: </th> <td> 20.315</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Time:</th> <td>15:51:23</td> <th> Pearson chi2: </th> <td> 23.2</td> \n", " <th>Time:</th> <td>16:57:48</td> <th> Pearson chi2: </th> <td> 23.2</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>No. Iterations:</th> <td>5</td> <th> Covariance Type: </th> <td>nonrobust</td>\n", " <th>No. Iterations:</th> <td>5</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
...@@ -965,7 +943,7 @@ ...@@ -965,7 +943,7 @@
"Link Function: logit Scale: 1.0000\n", "Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -10.158\n", "Method: IRLS Log-Likelihood: -10.158\n",
"Date: Wed, 26 Aug 2020 Deviance: 20.315\n", "Date: Wed, 26 Aug 2020 Deviance: 20.315\n",
"Time: 15:51:23 Pearson chi2: 23.2\n", "Time: 16:57:48 Pearson chi2: 23.2\n",
"No. Iterations: 5 Covariance Type: nonrobust\n", "No. Iterations: 5 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",
...@@ -976,12 +954,13 @@ ...@@ -976,12 +954,13 @@
"\"\"\"" "\"\"\""
] ]
}, },
"execution_count": 55, "execution_count": 13,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"import statsmodels.api as sm\n",
"data['Intercept']=1\n", "data['Intercept']=1\n",
"logmodel=sm.GLM(data['Malfunction2'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n", "logmodel=sm.GLM(data['Malfunction2'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n", "\n",
...@@ -990,7 +969,7 @@ ...@@ -990,7 +969,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 44, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1007,7 +986,7 @@ ...@@ -1007,7 +986,7 @@
} }
], ],
"source": [ "source": [
"%matplotlib inline\n", "import numpy as np\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=30, stop=90, num=121), 'Intercept': 1})\n",
"data_pred['Malfunction2'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n", "data_pred['Malfunction2'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n",
"data_pred.plot(x=\"Temperature\",y=\"Malfunction2\",kind=\"line\",ylim=[0,1])\n", "data_pred.plot(x=\"Temperature\",y=\"Malfunction2\",kind=\"line\",ylim=[0,1])\n",
......
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