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Bayesian Logistic Regression on the Kaggle Titanic dataset via PyMC3
Raw
pymc3
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bayesian Logistic Regression on the Kaggle titanic dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import seaborn\n",
"import numpy as np\n",
"pd.set_option('display.float_format', lambda x: '%.3f' % x)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import qgrid\n",
"qgrid.nbinstall()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pymc3 import Model, Normal, Bernoulli, find_MAP, NUTS, sample, sum, traceplot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load dataset"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train_data = pd.read_csv('input/train.csv')\n",
"test_data = pd.read_csv('input/test.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fill in missing values in Age column"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mean_age = np.mean(train_data.loc[~np.isnan(train_data['Age']), 'Age'])\n",
"train_data.loc[np.isnan(train_data['Age']), 'Age'] = mean_age\n",
"test_data.loc[np.isnan(test_data['Age']), 'Age'] = mean_age"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"output_type": "display_data"
},
{
"data": {
"application/javascript": [
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George Sibley\",\"Sex\":\"male\",\"Age\":0.83,\"SibSp\":1,\"Parch\":1,\"Ticket\":\"29106\",\"Fare\":18.75,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":832,\"PassengerId\":833,\"Survived\":0,\"Pclass\":3,\"Name\":\"Saad, Mr. Amin\",\"Sex\":\"male\",\"Age\":29.699118,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"2671\",\"Fare\":7.2292,\"Cabin\":null,\"Embarked\":\"C\"},{\"null\":833,\"PassengerId\":834,\"Survived\":0,\"Pclass\":3,\"Name\":\"Augustsson, Mr. Albert\",\"Sex\":\"male\",\"Age\":23.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"347468\",\"Fare\":7.8542,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":834,\"PassengerId\":835,\"Survived\":0,\"Pclass\":3,\"Name\":\"Allum, Mr. Owen George\",\"Sex\":\"male\",\"Age\":18.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"2223\",\"Fare\":8.3,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":835,\"PassengerId\":836,\"Survived\":1,\"Pclass\":1,\"Name\":\"Compton, Miss. Sara Rebecca\",\"Sex\":\"female\",\"Age\":39.0,\"SibSp\":1,\"Parch\":1,\"Ticket\":\"PC 17756\",\"Fare\":83.1583,\"Cabin\":\"E49\",\"Embarked\":\"C\"},{\"null\":836,\"PassengerId\":837,\"Survived\":0,\"Pclass\":3,\"Name\":\"Pasic, Mr. Jakob\",\"Sex\":\"male\",\"Age\":21.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"315097\",\"Fare\":8.6625,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":837,\"PassengerId\":838,\"Survived\":0,\"Pclass\":3,\"Name\":\"Sirota, Mr. Maurice\",\"Sex\":\"male\",\"Age\":29.699118,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"392092\",\"Fare\":8.05,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":838,\"PassengerId\":839,\"Survived\":1,\"Pclass\":3,\"Name\":\"Chip, Mr. Chang\",\"Sex\":\"male\",\"Age\":32.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"1601\",\"Fare\":56.4958,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":839,\"PassengerId\":840,\"Survived\":1,\"Pclass\":1,\"Name\":\"Marechal, Mr. Pierre\",\"Sex\":\"male\",\"Age\":29.699118,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"11774\",\"Fare\":29.7,\"Cabin\":\"C47\",\"Embarked\":\"C\"},{\"null\":840,\"PassengerId\":841,\"Survived\":0,\"Pclass\":3,\"Name\":\"Alhomaki, Mr. Ilmari Rudolf\",\"Sex\":\"male\",\"Age\":20.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"SOTON\\/O2 3101287\",\"Fare\":7.925,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":841,\"PassengerId\":842,\"Survived\":0,\"Pclass\":2,\"Name\":\"Mudd, Mr. Thomas Charles\",\"Sex\":\"male\",\"Age\":16.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"S.O.\\/P.P. 3\",\"Fare\":10.5,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":842,\"PassengerId\":843,\"Survived\":1,\"Pclass\":1,\"Name\":\"Serepeca, Miss. Augusta\",\"Sex\":\"female\",\"Age\":30.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"113798\",\"Fare\":31.0,\"Cabin\":null,\"Embarked\":\"C\"},{\"null\":843,\"PassengerId\":844,\"Survived\":0,\"Pclass\":3,\"Name\":\"Lemberopolous, Mr. Peter L\",\"Sex\":\"male\",\"Age\":34.5,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"2683\",\"Fare\":6.4375,\"Cabin\":null,\"Embarked\":\"C\"},{\"null\":844,\"PassengerId\":845,\"Survived\":0,\"Pclass\":3,\"Name\":\"Culumovic, Mr. Jeso\",\"Sex\":\"male\",\"Age\":17.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"315090\",\"Fare\":8.6625,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":845,\"PassengerId\":846,\"Survived\":0,\"Pclass\":3,\"Name\":\"Abbing, Mr. Anthony\",\"Sex\":\"male\",\"Age\":42.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"C.A. 5547\",\"Fare\":7.55,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":846,\"PassengerId\":847,\"Survived\":0,\"Pclass\":3,\"Name\":\"Sage, Mr. Douglas Bullen\",\"Sex\":\"male\",\"Age\":29.699118,\"SibSp\":8,\"Parch\":2,\"Ticket\":\"CA. 2343\",\"Fare\":69.55,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":847,\"PassengerId\":848,\"Survived\":0,\"Pclass\":3,\"Name\":\"Markoff, Mr. Marin\",\"Sex\":\"male\",\"Age\":35.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"349213\",\"Fare\":7.8958,\"Cabin\":null,\"Embarked\":\"C\"},{\"null\":848,\"PassengerId\":849,\"Survived\":0,\"Pclass\":2,\"Name\":\"Harper, Rev. John\",\"Sex\":\"male\",\"Age\":28.0,\"SibSp\":0,\"Parch\":1,\"Ticket\":\"248727\",\"Fare\":33.0,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":849,\"PassengerId\":850,\"Survived\":1,\"Pclass\":1,\"Name\":\"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)\",\"Sex\":\"female\",\"Age\":29.699118,\"SibSp\":1,\"Parch\":0,\"Ticket\":\"17453\",\"Fare\":89.1042,\"Cabin\":\"C92\",\"Embarked\":\"C\"},{\"null\":850,\"PassengerId\":851,\"Survived\":0,\"Pclass\":3,\"Name\":\"Andersson, Master. Sigvard Harald Elias\",\"Sex\":\"male\",\"Age\":4.0,\"SibSp\":4,\"Parch\":2,\"Ticket\":\"347082\",\"Fare\":31.275,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":851,\"PassengerId\":852,\"Survived\":0,\"Pclass\":3,\"Name\":\"Svensson, Mr. Johan\",\"Sex\":\"male\",\"Age\":74.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"347060\",\"Fare\":7.775,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":852,\"PassengerId\":853,\"Survived\":0,\"Pclass\":3,\"Name\":\"Boulos, Miss. Nourelain\",\"Sex\":\"female\",\"Age\":9.0,\"SibSp\":1,\"Parch\":1,\"Ticket\":\"2678\",\"Fare\":15.2458,\"Cabin\":null,\"Embarked\":\"C\"},{\"null\":853,\"PassengerId\":854,\"Survived\":1,\"Pclass\":1,\"Name\":\"Lines, Miss. Mary Conover\",\"Sex\":\"female\",\"Age\":16.0,\"SibSp\":0,\"Parch\":1,\"Ticket\":\"PC 17592\",\"Fare\":39.4,\"Cabin\":\"D28\",\"Embarked\":\"S\"},{\"null\":854,\"PassengerId\":855,\"Survived\":0,\"Pclass\":2,\"Name\":\"Carter, Mrs. Ernest Courtenay (Lilian Hughes)\",\"Sex\":\"female\",\"Age\":44.0,\"SibSp\":1,\"Parch\":0,\"Ticket\":\"244252\",\"Fare\":26.0,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":855,\"PassengerId\":856,\"Survived\":1,\"Pclass\":3,\"Name\":\"Aks, Mrs. Sam (Leah Rosen)\",\"Sex\":\"female\",\"Age\":18.0,\"SibSp\":0,\"Parch\":1,\"Ticket\":\"392091\",\"Fare\":9.35,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":856,\"PassengerId\":857,\"Survived\":1,\"Pclass\":1,\"Name\":\"Wick, Mrs. George Dennick (Mary Hitchcock)\",\"Sex\":\"female\",\"Age\":45.0,\"SibSp\":1,\"Parch\":1,\"Ticket\":\"36928\",\"Fare\":164.8667,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":857,\"PassengerId\":858,\"Survived\":1,\"Pclass\":1,\"Name\":\"Daly, Mr. Peter Denis \",\"Sex\":\"male\",\"Age\":51.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"113055\",\"Fare\":26.55,\"Cabin\":\"E17\",\"Embarked\":\"S\"},{\"null\":858,\"PassengerId\":859,\"Survived\":1,\"Pclass\":3,\"Name\":\"Baclini, Mrs. Solomon (Latifa Qurban)\",\"Sex\":\"female\",\"Age\":24.0,\"SibSp\":0,\"Parch\":3,\"Ticket\":\"2666\",\"Fare\":19.2583,\"Cabin\":null,\"Embarked\":\"C\"},{\"null\":859,\"PassengerId\":860,\"Survived\":0,\"Pclass\":3,\"Name\":\"Razi, Mr. Raihed\",\"Sex\":\"male\",\"Age\":29.699118,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"2629\",\"Fare\":7.2292,\"Cabin\":null,\"Embarked\":\"C\"},{\"null\":860,\"PassengerId\":861,\"Survived\":0,\"Pclass\":3,\"Name\":\"Hansen, Mr. Claus Peter\",\"Sex\":\"male\",\"Age\":41.0,\"SibSp\":2,\"Parch\":0,\"Ticket\":\"350026\",\"Fare\":14.1083,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":861,\"PassengerId\":862,\"Survived\":0,\"Pclass\":2,\"Name\":\"Giles, Mr. Frederick Edward\",\"Sex\":\"male\",\"Age\":21.0,\"SibSp\":1,\"Parch\":0,\"Ticket\":\"28134\",\"Fare\":11.5,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":862,\"PassengerId\":863,\"Survived\":1,\"Pclass\":1,\"Name\":\"Swift, Mrs. Frederick Joel (Margaret Welles Barron)\",\"Sex\":\"female\",\"Age\":48.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"17466\",\"Fare\":25.9292,\"Cabin\":\"D17\",\"Embarked\":\"S\"},{\"null\":863,\"PassengerId\":864,\"Survived\":0,\"Pclass\":3,\"Name\":\"Sage, Miss. Dorothy Edith \\\"Dolly\\\"\",\"Sex\":\"female\",\"Age\":29.699118,\"SibSp\":8,\"Parch\":2,\"Ticket\":\"CA. 2343\",\"Fare\":69.55,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":864,\"PassengerId\":865,\"Survived\":0,\"Pclass\":2,\"Name\":\"Gill, Mr. John William\",\"Sex\":\"male\",\"Age\":24.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"233866\",\"Fare\":13.0,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":865,\"PassengerId\":866,\"Survived\":1,\"Pclass\":2,\"Name\":\"Bystrom, Mrs. (Karolina)\",\"Sex\":\"female\",\"Age\":42.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"236852\",\"Fare\":13.0,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":866,\"PassengerId\":867,\"Survived\":1,\"Pclass\":2,\"Name\":\"Duran y More, Miss. Asuncion\",\"Sex\":\"female\",\"Age\":27.0,\"SibSp\":1,\"Parch\":0,\"Ticket\":\"SC\\/PARIS 2149\",\"Fare\":13.8583,\"Cabin\":null,\"Embarked\":\"C\"},{\"null\":867,\"PassengerId\":868,\"Survived\":0,\"Pclass\":1,\"Name\":\"Roebling, Mr. Washington Augustus II\",\"Sex\":\"male\",\"Age\":31.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"PC 17590\",\"Fare\":50.4958,\"Cabin\":\"A24\",\"Embarked\":\"S\"},{\"null\":868,\"PassengerId\":869,\"Survived\":0,\"Pclass\":3,\"Name\":\"van Melkebeke, Mr. Philemon\",\"Sex\":\"male\",\"Age\":29.699118,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"345777\",\"Fare\":9.5,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":869,\"PassengerId\":870,\"Survived\":1,\"Pclass\":3,\"Name\":\"Johnson, Master. Harold Theodor\",\"Sex\":\"male\",\"Age\":4.0,\"SibSp\":1,\"Parch\":1,\"Ticket\":\"347742\",\"Fare\":11.1333,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":870,\"PassengerId\":871,\"Survived\":0,\"Pclass\":3,\"Name\":\"Balkic, Mr. Cerin\",\"Sex\":\"male\",\"Age\":26.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"349248\",\"Fare\":7.8958,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":871,\"PassengerId\":872,\"Survived\":1,\"Pclass\":1,\"Name\":\"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)\",\"Sex\":\"female\",\"Age\":47.0,\"SibSp\":1,\"Parch\":1,\"Ticket\":\"11751\",\"Fare\":52.5542,\"Cabin\":\"D35\",\"Embarked\":\"S\"},{\"null\":872,\"PassengerId\":873,\"Survived\":0,\"Pclass\":1,\"Name\":\"Carlsson, Mr. Frans Olof\",\"Sex\":\"male\",\"Age\":33.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"695\",\"Fare\":5.0,\"Cabin\":\"B51 B53 B55\",\"Embarked\":\"S\"},{\"null\":873,\"PassengerId\":874,\"Survived\":0,\"Pclass\":3,\"Name\":\"Vander Cruyssen, Mr. Victor\",\"Sex\":\"male\",\"Age\":47.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"345765\",\"Fare\":9.0,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":874,\"PassengerId\":875,\"Survived\":1,\"Pclass\":2,\"Name\":\"Abelson, Mrs. Samuel (Hannah Wizosky)\",\"Sex\":\"female\",\"Age\":28.0,\"SibSp\":1,\"Parch\":0,\"Ticket\":\"P\\/PP 3381\",\"Fare\":24.0,\"Cabin\":null,\"Embarked\":\"C\"},{\"null\":875,\"PassengerId\":876,\"Survived\":1,\"Pclass\":3,\"Name\":\"Najib, Miss. Adele Kiamie \\\"Jane\\\"\",\"Sex\":\"female\",\"Age\":15.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"2667\",\"Fare\":7.225,\"Cabin\":null,\"Embarked\":\"C\"},{\"null\":876,\"PassengerId\":877,\"Survived\":0,\"Pclass\":3,\"Name\":\"Gustafsson, Mr. Alfred Ossian\",\"Sex\":\"male\",\"Age\":20.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"7534\",\"Fare\":9.8458,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":877,\"PassengerId\":878,\"Survived\":0,\"Pclass\":3,\"Name\":\"Petroff, Mr. Nedelio\",\"Sex\":\"male\",\"Age\":19.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"349212\",\"Fare\":7.8958,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":878,\"PassengerId\":879,\"Survived\":0,\"Pclass\":3,\"Name\":\"Laleff, Mr. Kristo\",\"Sex\":\"male\",\"Age\":29.699118,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"349217\",\"Fare\":7.8958,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":879,\"PassengerId\":880,\"Survived\":1,\"Pclass\":1,\"Name\":\"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)\",\"Sex\":\"female\",\"Age\":56.0,\"SibSp\":0,\"Parch\":1,\"Ticket\":\"11767\",\"Fare\":83.1583,\"Cabin\":\"C50\",\"Embarked\":\"C\"},{\"null\":880,\"PassengerId\":881,\"Survived\":1,\"Pclass\":2,\"Name\":\"Shelley, Mrs. William (Imanita Parrish Hall)\",\"Sex\":\"female\",\"Age\":25.0,\"SibSp\":0,\"Parch\":1,\"Ticket\":\"230433\",\"Fare\":26.0,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":881,\"PassengerId\":882,\"Survived\":0,\"Pclass\":3,\"Name\":\"Markun, Mr. Johann\",\"Sex\":\"male\",\"Age\":33.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"349257\",\"Fare\":7.8958,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":882,\"PassengerId\":883,\"Survived\":0,\"Pclass\":3,\"Name\":\"Dahlberg, Miss. Gerda Ulrika\",\"Sex\":\"female\",\"Age\":22.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"7552\",\"Fare\":10.5167,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":883,\"PassengerId\":884,\"Survived\":0,\"Pclass\":2,\"Name\":\"Banfield, Mr. Frederick James\",\"Sex\":\"male\",\"Age\":28.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"C.A.\\/SOTON 34068\",\"Fare\":10.5,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":884,\"PassengerId\":885,\"Survived\":0,\"Pclass\":3,\"Name\":\"Sutehall, Mr. Henry Jr\",\"Sex\":\"male\",\"Age\":25.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"SOTON\\/OQ 392076\",\"Fare\":7.05,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":885,\"PassengerId\":886,\"Survived\":0,\"Pclass\":3,\"Name\":\"Rice, Mrs. William (Margaret Norton)\",\"Sex\":\"female\",\"Age\":39.0,\"SibSp\":0,\"Parch\":5,\"Ticket\":\"382652\",\"Fare\":29.125,\"Cabin\":null,\"Embarked\":\"Q\"},{\"null\":886,\"PassengerId\":887,\"Survived\":0,\"Pclass\":2,\"Name\":\"Montvila, Rev. Juozas\",\"Sex\":\"male\",\"Age\":27.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"211536\",\"Fare\":13.0,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":887,\"PassengerId\":888,\"Survived\":1,\"Pclass\":1,\"Name\":\"Graham, Miss. Margaret Edith\",\"Sex\":\"female\",\"Age\":19.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"112053\",\"Fare\":30.0,\"Cabin\":\"B42\",\"Embarked\":\"S\"},{\"null\":888,\"PassengerId\":889,\"Survived\":0,\"Pclass\":3,\"Name\":\"Johnston, Miss. Catherine Helen \\\"Carrie\\\"\",\"Sex\":\"female\",\"Age\":29.699118,\"SibSp\":1,\"Parch\":2,\"Ticket\":\"W.\\/C. 6607\",\"Fare\":23.45,\"Cabin\":null,\"Embarked\":\"S\"},{\"null\":889,\"PassengerId\":890,\"Survived\":1,\"Pclass\":1,\"Name\":\"Behr, Mr. Karl Howell\",\"Sex\":\"male\",\"Age\":26.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"111369\",\"Fare\":30.0,\"Cabin\":\"C148\",\"Embarked\":\"C\"},{\"null\":890,\"PassengerId\":891,\"Survived\":0,\"Pclass\":3,\"Name\":\"Dooley, Mr. Patrick\",\"Sex\":\"male\",\"Age\":32.0,\"SibSp\":0,\"Parch\":0,\"Ticket\":\"370376\",\"Fare\":7.75,\"Cabin\":null,\"Embarked\":\"Q\"}], [{\"type\": \"Integer\", \"field\": null}, {\"type\": \"Integer\", \"field\": \"PassengerId\"}, {\"type\": \"Integer\", \"field\": \"Survived\"}, {\"type\": \"Integer\", \"field\": \"Pclass\"}, {\"field\": \"Name\"}, {\"field\": \"Sex\"}, {\"type\": \"Float\", \"field\": \"Age\"}, {\"type\": \"Integer\", \"field\": \"SibSp\"}, {\"type\": \"Integer\", \"field\": \"Parch\"}, {\"field\": \"Ticket\"}, {\"type\": \"Float\", \"field\": \"Fare\"}, {\"field\": \"Cabin\"}, {\"field\": \"Embarked\"}]);\n",
" grid.initialize_slick_grid();\n",
" });\n",
" });\n",
"});\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qgrid.show_grid(train_data, remote_js=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare the training data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = train_data[\"Survived\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/dist-packages/pandas/core/indexing.py:249: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[key] = _infer_fill_value(value)\n",
"/usr/local/lib/python3.4/dist-packages/pandas/core/indexing.py:415: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[item] = s\n"
]
}
],
"source": [
"x = train_data[[\"Pclass\", \"Age\"]]\n",
"x.loc[:, \"Intercept\"] = 1\n",
"x.loc[:, \"Sex\"] = ((train_data[\"Sex\"] == \"male\") * 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import theano.tensor as t\n",
"def tinvlogit(x):\n",
" return t.exp(x) / (1 + t.exp(x))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"logistic_model = Model()\n",
"\n",
"with logistic_model:\n",
" effects = Normal('effects', mu=0, tau=2. ** -2, shape=(1, x.shape[1]))\n",
" \n",
" # expected parameter\n",
" p = tinvlogit(t.sum(effects * x, axis=1))\n",
" \n",
" # Likelihood (sampling distribution) of observations\n",
" Y_obs = Bernoulli('Y_obs', p=p, observed=y)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 5000 of 5000 complete in 61.5 sec"
]
}
],
"source": [
"with logistic_model:\n",
" # move the chain to the MAP which should be a good starting point\n",
" start = find_MAP()\n",
" step = NUTS(scaling=start)\n",
" trace = sample(5000, step, start)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fa8eb2e3ac8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7fa8eb0b2d30>]], dtype=object)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wHMy+XuxUivTbBzCqjA9KvLGTyMarSe56c9KxKdbwEKn9+0eDA8Ac6Cd98CDBNWvHjdlw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\n4wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa901371c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"traceplot(trace)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[[-1.09676879, -0.02903348, 4.48974061, -2.45753634]],\n",
"\n",
" [[-1.09676879, -0.02903348, 4.48974061, -2.45753634]],\n",
"\n",
" [[-1.16002106, -0.0338922 , 4.51968149, -2.55373352]],\n",
"\n",
" ..., \n",
" [[-1.24548209, -0.03337827, 5.10623622, -3.00418852]],\n",
"\n",
" [[-1.13716367, -0.03495873, 4.4685661 , -2.53877428]],\n",
"\n",
" [[-1.13443239, -0.02604646, 4.49673206, -2.44640603]]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace['effects']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare the testing data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"new_x = test_data[[\"Pclass\", \"Age\"]]\n",
"new_x.loc[:, \"Intercept\"] = 1\n",
"new_x.loc[:, \"Sex\"] = (test_data[\"Sex\"] == \"male\") * 1\n",
"# new_x = new_x.drop(\"Pclass\", 1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def invlogit(x):\n",
" return np.exp(x) / (1 + np.exp(x))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Take average of all the eta's implied by the samples"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"eta = np.zeros(new_x.shape[0])\n",
"for row in trace['effects']:\n",
" row=row[0]\n",
" eta = np.add(eta, np.dot(new_x, row.transpose()))\n",
"eta = np.divide(eta, trace['effects'].shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.07725205, 0.42751563, 0.09950293, 0.09527585, 0.61603883,\n",
" 0.13550032, 0.5567653 , 0.24945878, 0.64454149, 0.11231489,\n",
" 0.0883926 , 0.35559176, 0.93581827, 0.09679533, 0.87495842,\n",
" 0.8220575 , 0.20152091, 0.11231489, 0.57927939, 0.44255144])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = invlogit(eta)\n",
"p[:20]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare Submission"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prediction = pd.DataFrame({'Survived_p':p})"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"prediction[\"PassengerId\"] = test_data[\"PassengerId\"]\n",
"prediction['Survived'] = (prediction['Survived_p']>0.5) * 1"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prediction.drop('Survived_p', 1).to_csv('output.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prediction['Sex'] = test_data[\"Sex\"]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived_p</th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.077</td>\n",
" <td>892</td>\n",
" <td>0</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.428</td>\n",
" <td>893</td>\n",
" <td>0</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.100</td>\n",
" <td>894</td>\n",
" <td>0</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.095</td>\n",
" <td>895</td>\n",
" <td>0</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.616</td>\n",
" <td>896</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived_p PassengerId Survived Sex\n",
"0 0.077 892 0 male\n",
"1 0.428 893 0 female\n",
"2 0.100 894 0 male\n",
"3 0.095 895 0 male\n",
"4 0.616 896 1 female"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prediction.head()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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