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moocrr-reproducibility-study
Commit 5b0e7c50 authored by Marie-Gabrielle Dondon's avatar Marie-Gabrielle Dondon
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Replace challenger_Python_ipynb.ipynb

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src/Python3/challenger_Python_ipynb.ipynb
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...@@ -33,7 +33,7 @@ ...@@ -33,7 +33,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -171,7 +171,7 @@ ...@@ -171,7 +171,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -418,7 +418,7 @@ ...@@ -418,7 +418,7 @@
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 2, "execution_count": 3,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -437,7 +437,7 @@ ...@@ -437,7 +437,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -472,7 +472,7 @@ ...@@ -472,7 +472,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -499,7 +499,7 @@ ...@@ -499,7 +499,7 @@
" <th>Date:</th> <td>Mon, 12 Nov 2018</td> <th> Deviance: </th> <td> 3.0144</td> \n", " <th>Date:</th> <td>Mon, 12 Nov 2018</td> <th> Deviance: </th> <td> 3.0144</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Time:</th> <td>20:18:55</td> <th> Pearson chi2: </th> <td> 5.00</td> \n", " <th>Time:</th> <td>21:08:43</td> <th> Pearson chi2: </th> <td> 5.00</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n", " <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
...@@ -528,7 +528,7 @@ ...@@ -528,7 +528,7 @@
"Link Function: logit Scale: 1.0000\n", "Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -3.9210\n", "Method: IRLS Log-Likelihood: -3.9210\n",
"Date: Mon, 12 Nov 2018 Deviance: 3.0144\n", "Date: Mon, 12 Nov 2018 Deviance: 3.0144\n",
"Time: 20:18:55 Pearson chi2: 5.00\n", "Time: 21:08:43 Pearson chi2: 5.00\n",
"No. Iterations: 6 Covariance Type: nonrobust\n", "No. Iterations: 6 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",
...@@ -539,7 +539,7 @@ ...@@ -539,7 +539,7 @@
"\"\"\"" "\"\"\""
] ]
}, },
"execution_count": 4, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -565,7 +565,7 @@ ...@@ -565,7 +565,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -592,7 +592,7 @@ ...@@ -592,7 +592,7 @@
" <th>Date:</th> <td>Mon, 12 Nov 2018</td> <th> Deviance: </th> <td> 18.086</td> \n", " <th>Date:</th> <td>Mon, 12 Nov 2018</td> <th> Deviance: </th> <td> 18.086</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Time:</th> <td>20:18:55</td> <th> Pearson chi2: </th> <td> 30.0</td> \n", " <th>Time:</th> <td>21:08:47</td> <th> Pearson chi2: </th> <td> 30.0</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n", " <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
...@@ -621,7 +621,7 @@ ...@@ -621,7 +621,7 @@
"Link Function: logit Scale: 1.0000\n", "Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -23.526\n", "Method: IRLS Log-Likelihood: -23.526\n",
"Date: Mon, 12 Nov 2018 Deviance: 18.086\n", "Date: Mon, 12 Nov 2018 Deviance: 18.086\n",
"Time: 20:18:55 Pearson chi2: 30.0\n", "Time: 21:08:47 Pearson chi2: 30.0\n",
"No. Iterations: 6 Covariance Type: nonrobust\n", "No. Iterations: 6 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",
...@@ -632,7 +632,7 @@ ...@@ -632,7 +632,7 @@
"\"\"\"" "\"\"\""
] ]
}, },
"execution_count": 5, "execution_count": 6,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -665,7 +665,7 @@ ...@@ -665,7 +665,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -679,7 +679,20 @@ ...@@ -679,7 +679,20 @@
"3 31.5 1 1.0\n", "3 31.5 1 1.0\n",
"4 32.0 1 1.0\n" "4 32.0 1 1.0\n"
] ]
}, }
],
"source": [
"data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121),\n",
" 'Intercept': 1})\n",
"data_pred['Frequency'] = logmodel.predict(data_pred)\n",
"print(data_pred.head())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{ {
"data": { "data": {
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\n", 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\n",
...@@ -693,11 +706,6 @@ ...@@ -693,11 +706,6 @@
], ],
"source": [ "source": [
"%matplotlib inline\n", "%matplotlib inline\n",
"data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121),\n",
" 'Intercept': 1})\n",
"data_pred['Frequency'] = logmodel.predict(data_pred)\n",
"print(data_pred.head())\n",
"\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)"
...@@ -714,7 +722,7 @@ ...@@ -714,7 +722,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -728,7 +736,23 @@ ...@@ -728,7 +736,23 @@
"3 31.5 1 1.0 0.809002\n", "3 31.5 1 1.0 0.809002\n",
"4 32.0 1 1.0 0.799911\n" "4 32.0 1 1.0 0.799911\n"
] ]
}, }
],
"source": [
"# Inspiring from http://blog.yhat.com/posts/logistic-regression-and-python.html\n",
"def logit_inv(x):\n",
" return(np.exp(x)/(np.exp(x)+1))\n",
"\n",
"data_pred['Prob']=logit_inv(data_pred['Temperature'] * logmodel.params['Temperature'] + \n",
" logmodel.params['Intercept'])\n",
"print(data_pred.head())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{ {
"data": { "data": {
"image/png": 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\n", 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\n",
...@@ -741,14 +765,7 @@ ...@@ -741,14 +765,7 @@
} }
], ],
"source": [ "source": [
"# Inspiring from http://blog.yhat.com/posts/logistic-regression-and-python.html\n", "%matplotlib inline\n",
"def logit_inv(x):\n",
" return(np.exp(x)/(np.exp(x)+1))\n",
"\n",
"data_pred['Prob']=logit_inv(data_pred['Temperature'] * logmodel.params['Temperature'] + \n",
" logmodel.params['Intercept'])\n",
"print(data_pred.head())\n",
"\n",
"data_pred.plot(x=\"Temperature\",y=\"Prob\",kind=\"line\",ylim=[0,1])\n", "data_pred.plot(x=\"Temperature\",y=\"Prob\",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)"
...@@ -781,7 +798,7 @@ ...@@ -781,7 +798,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -794,7 +811,7 @@ ...@@ -794,7 +811,7 @@
}, },
{ {
"data": { "data": {
"image/png": 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\n", 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -804,6 +821,7 @@ ...@@ -804,6 +821,7 @@
} }
], ],
"source": [ "source": [
"%matplotlib inline\n",
"sns.set(color_codes=True)\n", "sns.set(color_codes=True)\n",
"plt.xlim(30,90)\n", "plt.xlim(30,90)\n",
"plt.ylim(0,1)\n", "plt.ylim(0,1)\n",
......
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