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Created January 9, 2018 08:25
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gistfile1.txt
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train = pd.read_csv('./train.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"enc = preprocessing.LabelEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>sex</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" <th>native_country</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>39</td>\n",
" <td>State-gov</td>\n",
" <td>77516</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Never-married</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>2174</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>Self-emp-not-inc</td>\n",
" <td>83311</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Exec-managerial</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>Private</td>\n",
" <td>215646</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Divorced</td>\n",
" <td>Handlers-cleaners</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education_num \\\n",
"0 39 State-gov 77516 Bachelors 13 \n",
"1 50 Self-emp-not-inc 83311 Bachelors 13 \n",
"2 38 Private 215646 HS-grad 9 \n",
"\n",
" marital_status occupation relationship race sex \\\n",
"0 Never-married Adm-clerical Not-in-family White Male \n",
"1 Married-civ-spouse Exec-managerial Husband White Male \n",
"2 Divorced Handlers-cleaners Not-in-family White Male \n",
"\n",
" capital_gain capital_loss hours_per_week native_country income \n",
"0 2174 0 40 United-States <=50K \n",
"1 0 0 13 United-States <=50K \n",
"2 0 0 40 United-States <=50K "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train['income'] = enc.fit_transform(train['income'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train =pd.read_csv('./X_train')\n",
"\n",
"y_train = train['income']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"from xgboost import XGBClassifier\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"X_test = pd.read_csv('./X_test')\n",
"y_test = pd.read_csv('./correct_answer.csv')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = XGBClassifier()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"param_grid = dict({'max_depth':(6,7,8,9,10), \n",
" 'n_estimators':(list(range(200,300,1)))})"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"grid = GridSearchCV(cv=10,estimator=model, param_grid=param_grid,refit=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best: 0.874297 using {'max_depth': 6, 'n_estimators': 203}\n",
"0.874021 (0.003667) with: {'max_depth': 6, 'n_estimators': 200}\n",
"0.874113 (0.003366) with: {'max_depth': 6, 'n_estimators': 201}\n",
"0.874175 (0.003395) with: {'max_depth': 6, 'n_estimators': 202}\n",
"0.874298 (0.003439) with: {'max_depth': 6, 'n_estimators': 203}\n",
"0.873990 (0.003502) with: {'max_depth': 6, 'n_estimators': 204}\n",
"0.874052 (0.003351) with: {'max_depth': 6, 'n_estimators': 205}\n",
"0.874083 (0.003400) with: {'max_depth': 6, 'n_estimators': 206}\n",
"0.874113 (0.003462) with: {'max_depth': 6, 'n_estimators': 207}\n",
"0.873775 (0.003525) with: {'max_depth': 6, 'n_estimators': 208}\n",
"0.874021 (0.003723) with: {'max_depth': 6, 'n_estimators': 209}\n",
"0.873929 (0.003816) with: {'max_depth': 6, 'n_estimators': 210}\n",
"0.873929 (0.003798) with: {'max_depth': 6, 'n_estimators': 211}\n",
"0.874021 (0.003899) with: {'max_depth': 6, 'n_estimators': 212}\n",
"0.874052 (0.003840) with: {'max_depth': 6, 'n_estimators': 213}\n",
"0.874021 (0.003657) with: {'max_depth': 6, 'n_estimators': 214}\n",
"0.874113 (0.003628) with: {'max_depth': 6, 'n_estimators': 215}\n",
"0.873990 (0.003585) with: {'max_depth': 6, 'n_estimators': 216}\n",
"0.874021 (0.003379) with: {'max_depth': 6, 'n_estimators': 217}\n",
"0.873898 (0.003416) with: {'max_depth': 6, 'n_estimators': 218}\n",
"0.873990 (0.003443) with: {'max_depth': 6, 'n_estimators': 219}\n",
"0.873868 (0.003407) with: {'max_depth': 6, 'n_estimators': 220}\n",
"0.873714 (0.003412) with: {'max_depth': 6, 'n_estimators': 221}\n",
"0.873745 (0.003419) with: {'max_depth': 6, 'n_estimators': 222}\n",
"0.873806 (0.003346) with: {'max_depth': 6, 'n_estimators': 223}\n",
"0.873806 (0.003388) with: {'max_depth': 6, 'n_estimators': 224}\n",
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"0.873745 (0.003503) with: {'max_depth': 6, 'n_estimators': 227}\n",
"0.873775 (0.003488) with: {'max_depth': 6, 'n_estimators': 228}\n",
"0.873591 (0.003511) with: {'max_depth': 6, 'n_estimators': 229}\n",
"0.873653 (0.003415) with: {'max_depth': 6, 'n_estimators': 230}\n",
"0.873622 (0.003462) with: {'max_depth': 6, 'n_estimators': 231}\n",
"0.873530 (0.003458) with: {'max_depth': 6, 'n_estimators': 232}\n",
"0.873407 (0.003370) with: {'max_depth': 6, 'n_estimators': 233}\n",
"0.873438 (0.003335) with: {'max_depth': 6, 'n_estimators': 234}\n",
"0.873407 (0.003320) with: {'max_depth': 6, 'n_estimators': 235}\n",
"0.873407 (0.003291) with: {'max_depth': 6, 'n_estimators': 236}\n",
"0.873468 (0.003451) with: {'max_depth': 6, 'n_estimators': 237}\n",
"0.873591 (0.003479) with: {'max_depth': 6, 'n_estimators': 238}\n",
"0.873591 (0.003476) with: {'max_depth': 6, 'n_estimators': 239}\n",
"0.873591 (0.003366) with: {'max_depth': 6, 'n_estimators': 240}\n",
"0.873591 (0.003465) with: {'max_depth': 6, 'n_estimators': 241}\n",
"0.873530 (0.003548) with: {'max_depth': 6, 'n_estimators': 242}\n",
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"0.873560 (0.003709) with: {'max_depth': 6, 'n_estimators': 244}\n",
"0.873468 (0.003792) with: {'max_depth': 6, 'n_estimators': 245}\n",
"0.873345 (0.003900) with: {'max_depth': 6, 'n_estimators': 246}\n",
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"0.873284 (0.003886) with: {'max_depth': 6, 'n_estimators': 248}\n",
"0.873376 (0.003972) with: {'max_depth': 6, 'n_estimators': 249}\n",
"0.873468 (0.003917) with: {'max_depth': 6, 'n_estimators': 250}\n",
"0.873438 (0.003906) with: {'max_depth': 6, 'n_estimators': 251}\n",
"0.873499 (0.003887) with: {'max_depth': 6, 'n_estimators': 252}\n",
"0.873376 (0.003842) with: {'max_depth': 6, 'n_estimators': 253}\n",
"0.873253 (0.003893) with: {'max_depth': 6, 'n_estimators': 254}\n",
"0.873284 (0.003847) with: {'max_depth': 6, 'n_estimators': 255}\n",
"0.873223 (0.003805) with: {'max_depth': 6, 'n_estimators': 256}\n",
"0.873223 (0.003740) with: {'max_depth': 6, 'n_estimators': 257}\n",
"0.873591 (0.003920) with: {'max_depth': 6, 'n_estimators': 258}\n",
"0.873407 (0.004045) with: {'max_depth': 6, 'n_estimators': 259}\n",
"0.873499 (0.004076) with: {'max_depth': 6, 'n_estimators': 260}\n",
"0.873407 (0.003981) with: {'max_depth': 6, 'n_estimators': 261}\n",
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"0.873376 (0.004160) with: {'max_depth': 6, 'n_estimators': 264}\n",
"0.873315 (0.004257) with: {'max_depth': 6, 'n_estimators': 265}\n",
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"0.873253 (0.004468) with: {'max_depth': 6, 'n_estimators': 269}\n",
"0.873284 (0.004498) with: {'max_depth': 6, 'n_estimators': 270}\n",
"0.873192 (0.004343) with: {'max_depth': 6, 'n_estimators': 271}\n",
"0.873223 (0.004184) with: {'max_depth': 6, 'n_estimators': 272}\n",
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"0.873284 (0.004120) with: {'max_depth': 6, 'n_estimators': 275}\n",
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"0.873376 (0.003637) with: {'max_depth': 6, 'n_estimators': 282}\n",
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"0.873161 (0.003627) with: {'max_depth': 6, 'n_estimators': 285}\n",
"0.873253 (0.003625) with: {'max_depth': 6, 'n_estimators': 286}\n",
"0.873284 (0.003648) with: {'max_depth': 6, 'n_estimators': 287}\n",
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"0.869230 (0.003888) with: {'max_depth': 9, 'n_estimators': 299}\n",
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"0.871319 (0.003986) with: {'max_depth': 10, 'n_estimators': 211}\n",
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"0.871288 (0.004285) with: {'max_depth': 10, 'n_estimators': 247}\n",
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"0.871011 (0.004167) with: {'max_depth': 10, 'n_estimators': 253}\n",
"0.870981 (0.004060) with: {'max_depth': 10, 'n_estimators': 254}\n",
"0.871073 (0.004108) with: {'max_depth': 10, 'n_estimators': 255}\n",
"0.871103 (0.004206) with: {'max_depth': 10, 'n_estimators': 256}\n",
"0.870889 (0.004178) with: {'max_depth': 10, 'n_estimators': 257}\n",
"0.870796 (0.004236) with: {'max_depth': 10, 'n_estimators': 258}\n",
"0.870919 (0.004178) with: {'max_depth': 10, 'n_estimators': 259}\n",
"0.870735 (0.004009) with: {'max_depth': 10, 'n_estimators': 260}\n",
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"0.870305 (0.004320) with: {'max_depth': 10, 'n_estimators': 262}\n",
"0.870428 (0.004277) with: {'max_depth': 10, 'n_estimators': 263}\n",
"0.870274 (0.003989) with: {'max_depth': 10, 'n_estimators': 264}\n",
"0.870029 (0.004141) with: {'max_depth': 10, 'n_estimators': 265}\n",
"0.870059 (0.004264) with: {'max_depth': 10, 'n_estimators': 266}\n",
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"0.870059 (0.003746) with: {'max_depth': 10, 'n_estimators': 269}\n",
"0.870121 (0.003913) with: {'max_depth': 10, 'n_estimators': 270}\n",
"0.870182 (0.003977) with: {'max_depth': 10, 'n_estimators': 271}\n",
"0.870151 (0.003836) with: {'max_depth': 10, 'n_estimators': 272}\n",
"0.870090 (0.003847) with: {'max_depth': 10, 'n_estimators': 273}\n",
"0.870059 (0.003811) with: {'max_depth': 10, 'n_estimators': 274}\n",
"0.870029 (0.003767) with: {'max_depth': 10, 'n_estimators': 275}\n",
"0.870029 (0.003807) with: {'max_depth': 10, 'n_estimators': 276}\n",
"0.869844 (0.003943) with: {'max_depth': 10, 'n_estimators': 277}\n",
"0.870121 (0.003949) with: {'max_depth': 10, 'n_estimators': 278}\n",
"0.870274 (0.003884) with: {'max_depth': 10, 'n_estimators': 279}\n",
"0.870213 (0.004089) with: {'max_depth': 10, 'n_estimators': 280}\n",
"0.870182 (0.003965) with: {'max_depth': 10, 'n_estimators': 281}\n",
"0.869998 (0.003908) with: {'max_depth': 10, 'n_estimators': 282}\n",
"0.869691 (0.004029) with: {'max_depth': 10, 'n_estimators': 283}\n",
"0.869568 (0.003986) with: {'max_depth': 10, 'n_estimators': 284}\n",
"0.869568 (0.003917) with: {'max_depth': 10, 'n_estimators': 285}\n",
"0.869384 (0.003982) with: {'max_depth': 10, 'n_estimators': 286}\n",
"0.869384 (0.004068) with: {'max_depth': 10, 'n_estimators': 287}\n",
"0.869384 (0.004123) with: {'max_depth': 10, 'n_estimators': 288}\n",
"0.869445 (0.004172) with: {'max_depth': 10, 'n_estimators': 289}\n",
"0.869629 (0.004230) with: {'max_depth': 10, 'n_estimators': 290}\n",
"0.869599 (0.004322) with: {'max_depth': 10, 'n_estimators': 291}\n",
"0.869691 (0.004368) with: {'max_depth': 10, 'n_estimators': 292}\n",
"0.869783 (0.004413) with: {'max_depth': 10, 'n_estimators': 293}\n",
"0.869629 (0.004456) with: {'max_depth': 10, 'n_estimators': 294}\n",
"0.869476 (0.004394) with: {'max_depth': 10, 'n_estimators': 295}\n",
"0.869476 (0.004401) with: {'max_depth': 10, 'n_estimators': 296}\n",
"0.869445 (0.004159) with: {'max_depth': 10, 'n_estimators': 297}\n",
"0.869445 (0.004391) with: {'max_depth': 10, 'n_estimators': 298}\n",
"0.869414 (0.004419) with: {'max_depth': 10, 'n_estimators': 299}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_search.py:747: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
" DeprecationWarning)\n"
]
}
],
"source": [
"grid_result = grid.fit(X_train,y_train)\n",
"\n",
"# summarize results\n",
"print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
"\n",
"for params, mean_score, scores in grid_result.grid_scores_:\n",
" \n",
" print(\"%f (%f) with: %r\" % (scores.mean(), scores.std(), params))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.87556046925864506"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test = pd.read_csv('./X_test')\n",
"\n",
"y_test = pd.read_csv('./correct_answer.csv')\n",
"\n",
"test_label = grid.predict(X_test)\n",
"\n",
"accuracy_score(test_label,y_test['label'])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"res = pd.DataFrame({\n",
" 'id':range(1,len(test_label)+1)\n",
" ,'label':test_label\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"res.to_csv('110.csv',index=None)"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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