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simple demo-the-concept notebook #27

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649 changes: 649 additions & 0 deletions boruta/examples/boruta_with_more.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from boruta import BorutaPy\n",
"\n",
"# load X and y\n",
"# NOTE BorutaPy accepts numpy arrays only, hence the .values attribute\n",
"X = pd.read_csv('examples/test_X.csv', index_col=0).values\n",
"y = pd.read_csv('examples/test_y.csv', header=None, index_col=0).values\n",
"y = y.ravel()\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# simple encapsulating class to demonstrate the concept\n",
"# BorutaPy would be more sklearn consistent if it implemented these routines directly\n",
"class BorutaWithMore(BorutaPy):\n",
" def fit(self, X, y):\n",
" rtn = super(BorutaWithMore, self).fit(X, y)\n",
" self.estimator.fit(self.transform(X), y)\n",
" return rtn\n",
" def score(self, X, y, sample_weight=None):\n",
" return self.estimator.score(self.transform(X), y, sample_weight)\n",
" def predict(self, X):\n",
" return self.estimator.predict(self.transform(X))\n",
" \n",
"\n",
"rf = RandomForestClassifier(n_jobs=-1, class_weight='auto', max_depth=5)\n",
"\n",
"# define Boruta feature selection method\n",
"feat_selector = BorutaWithMore(rf, n_estimators='auto', verbose=2, random_state=1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.25)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\n",
"\n",
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/petercacioppi/anaconda/lib/python2.7/site-packages/boruta/boruta_py.py:418: RuntimeWarning: invalid value encountered in greater\n",
" hits = np.where(cur_imp[0] > imp_sha_max)[0]\n"
]
},
{
"data": {
"text/plain": [
"BorutaWithMore(alpha=0.05,\n",
" estimator=RandomForestClassifier(bootstrap=True, class_weight='auto', criterion='gini',\n",
" max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=69, n_jobs=-1, oob_score=False,\n",
" random_state=<mtrand.RandomState object at 0x1156183c0>,\n",
" verbose=0, warm_start=False),\n",
" max_iter=100, n_estimators='auto', perc=100,\n",
" random_state=<mtrand.RandomState object at 0x1156183c0>,\n",
" two_step=True, verbose=2)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feat_selector.fit(train_X, train_y)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.98799999999999999"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feat_selector.score(test_X, test_y)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1,\n",
" 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0,\n",
" 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
" 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0,\n",
" 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
" 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
" 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
" 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
" 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
" 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feat_selector.predict(test_X)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\n",
"\n",
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"\n",
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"BorutaPy finished running.\n",
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"\n",
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]
}
],
"source": [
"from sklearn import model_selection\n",
"cv_score = model_selection.cross_val_score(feat_selector, X=X, y=y, cv=5)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1. , 0.985, 0.98 , 0.99 , 0.985])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_score"
]
}
],
"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": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}