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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn import ensemble, preprocessing, cross_validation\n",
"from sklearn.metrics import roc_auc_score as auc\n",
"from time import time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# PREPARE DATA\n",
"data = pd.read_csv('train_Spring.csv').set_index(\"ID\")\n",
"test = pd.read_csv('test_Spring.csv').set_index(\"ID\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# remove constants\n",
"nunique = pd.Series([data[col].nunique() for col in data.columns], index = data.columns)\n",
"constants = nunique[nunique<2].index.tolist()\n",
"data = data.drop(constants,axis=1)\n",
"test = test.drop(constants,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\numpy\\lib\\arraysetops.py:198: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n",
" flag = np.concatenate(([True], aux[1:] != aux[:-1]))\n",
"C:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\numpy\\lib\\arraysetops.py:251: FutureWarning: numpy equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n",
" return aux[:-1][aux[1:] == aux[:-1]]\n",
"C:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\numpy\\lib\\arraysetops.py:384: FutureWarning: numpy equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n",
" bool_ar = (sar[1:] == sar[:-1])\n"
]
}
],
"source": [
"# encode string\n",
"strings = data.dtypes == 'object'; strings = strings[strings].index.tolist(); encoders = {}\n",
"for col in strings:\n",
" encoders[col] = preprocessing.LabelEncoder()\n",
" data[col] = encoders[col].fit_transform(data[col])\n",
" try:\n",
" test[col] = encoders[col].transform(test[col])\n",
" except:\n",
" # lazy way to incorporate the feature only if can be encoded in the test set\n",
" del test[col]\n",
" del data[col]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# DATA ready\n",
"X = data.drop('target',1).fillna(0); y = data.target"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# RF FTW :)\n",
"rf = ensemble.RandomForestClassifier(n_jobs=4, n_estimators = 20, random_state = 11)\n",
"#rf = ensemble.RandomForestClassifier(n_jobs=500, n_estimators = 1000, random_state = 15)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# CROSS VALIDATE AND PRINT TRAIN AND TEST SCORE\n",
"kf = cross_validation.StratifiedKFold(y, n_folds=5, shuffle=True, random_state=11)\n",
"trscores, cvscores, times = [], [], []\n",
"for itr, icv in kf:\n",
" t = time()\n",
" trscore = auc(y.iloc[itr], rf.fit(X.iloc[itr], y.iloc[itr]).predict_proba(X.iloc[itr])[:,1])\n",
" cvscore = auc(y.iloc[icv], rf.predict_proba(X.iloc[icv])[:,1])\n",
" trscores.append(trscore); cvscores.append(cvscore); times.append(time()-t)\n",
"print \"TRAIN %.4f | TEST %.4f | TIME %.2fm (1-fold)\" % (np.mean(trscores), np.mean(cvscores), np.mean(times)/60)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# MAKING SUBMISSION\n",
"submission = pd.DataFrame(rf.fit(X,y).predict_proba(test.fillna(0))[:,1], index=test.index, columns=['target'])\n",
"submission.index.name = 'ID'\n",
"submission.to_csv('Springleaf5.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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