{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 데모"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 라이브러리 import 및 설정"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:21:33.648315Z",
"start_time": "2020-10-05T07:21:33.376477Z"
}
},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:21:34.453802Z",
"start_time": "2020-10-05T07:21:33.650466Z"
}
},
"outputs": [],
"source": [
"from hyperopt import STATUS_OK, Trials, hp, space_eval, tpe, fmin\n",
"import lightgbm as lgb\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import rcParams\n",
"import numpy as np\n",
"from pathlib import Path\n",
"import pandas as pd\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.model_selection import StratifiedKFold, train_test_split\n",
"import seaborn as sns\n",
"import warnings"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:21:34.485818Z",
"start_time": "2020-10-05T07:21:34.458213Z"
}
},
"outputs": [],
"source": [
"rcParams['figure.figsize'] = (16, 8)\n",
"plt.style.use('fivethirtyeight')\n",
"pd.set_option('max_columns', 100)\n",
"pd.set_option(\"display.precision\", 4)\n",
"warnings.simplefilter('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 학습데이터 로드"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[03-pandas-eda.ipynb](https://github.com/kaggler-tv/dku-kaggle-class/blob/master/notebook/03-pandas-eda.ipynb)에서 생성한 `feature.csv` 피처파일 사용"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:21:34.516990Z",
"start_time": "2020-10-05T07:21:34.489212Z"
}
},
"outputs": [],
"source": [
"data_dir = Path('../data/dacon-dku')\n",
"feature_dir = Path('../build/feature')\n",
"val_dir = Path('../build/val')\n",
"tst_dir = Path('../build/tst')\n",
"sub_dir = Path('../build/sub')\n",
"\n",
"trn_file = data_dir / 'train.csv'\n",
"tst_file = data_dir / 'test.csv'\n",
"sample_file = data_dir / 'sample_submission.csv'\n",
"\n",
"target_col = 'class'\n",
"n_fold = 5\n",
"n_class = 3\n",
"seed = 42"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:21:34.546003Z",
"start_time": "2020-10-05T07:21:34.519228Z"
}
},
"outputs": [],
"source": [
"algo_name = 'lgb_hyperopt'\n",
"feature_name = 'feature'\n",
"model_name = f'{algo_name}_{feature_name}'\n",
"\n",
"feature_file = feature_dir / f'{feature_name}.csv'\n",
"p_val_file = val_dir / f'{model_name}.val.csv'\n",
"p_tst_file = tst_dir / f'{model_name}.tst.csv'\n",
"sub_file = sub_dir / f'{model_name}.csv'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:21:35.926340Z",
"start_time": "2020-10-05T07:21:34.548114Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400000, 20)\n"
]
},
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" z | \n",
" redshift | \n",
" dered_u | \n",
" dered_g | \n",
" dered_r | \n",
" dered_i | \n",
" dered_z | \n",
" nObserve | \n",
" airmass_u | \n",
" class | \n",
" d_dered_u | \n",
" d_dered_g | \n",
" d_dered_r | \n",
" d_dered_i | \n",
" d_dered_z | \n",
" d_dered_ig | \n",
" d_dered_zg | \n",
" d_dered_rz | \n",
" d_dered_iz | \n",
" d_obs_det | \n",
"
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" \n",
" id | \n",
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" 0 | \n",
" 16.9396 | \n",
" -8.1086e-05 | \n",
" 23.1243 | \n",
" 20.2578 | \n",
" 18.9551 | \n",
" 17.6321 | \n",
" 16.9089 | \n",
" 2.9444 | \n",
" 1.1898 | \n",
" 0.0 | \n",
" -0.1397 | \n",
" -0.0790 | \n",
" -0.0544 | \n",
" -0.0403 | \n",
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" 2.0462 | \n",
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\n",
" \n",
" 1 | \n",
" 13.1689 | \n",
" 4.5061e-03 | \n",
" 14.9664 | \n",
" 14.0045 | \n",
" 13.4114 | \n",
" 13.2363 | \n",
" 13.1347 | \n",
" 0.6931 | \n",
" 1.2533 | \n",
" 1.0 | \n",
" -0.0857 | \n",
" -0.0574 | \n",
" -0.0410 | \n",
" -0.0322 | \n",
" -0.0343 | \n",
" -0.7683 | \n",
" -0.8698 | \n",
" 0.2767 | \n",
" 0.1016 | \n",
" -0.3069 | \n",
"
\n",
" \n",
" 2 | \n",
" 15.3500 | \n",
" 4.7198e-04 | \n",
" 16.6076 | \n",
" 15.6866 | \n",
" 15.4400 | \n",
" 15.3217 | \n",
" 15.2961 | \n",
" 1.0986 | \n",
" 1.0225 | \n",
" 0.0 | \n",
" -0.1787 | \n",
" -0.1388 | \n",
" -0.0963 | \n",
" -0.0718 | \n",
" -0.0540 | \n",
" -0.3649 | \n",
" -0.3905 | \n",
" 0.1440 | \n",
" 0.0257 | \n",
" -0.9014 | \n",
"
\n",
" \n",
" 3 | \n",
" 19.6346 | \n",
" 5.8143e-06 | \n",
" 25.3536 | \n",
" 20.9947 | \n",
" 20.0873 | \n",
" 19.7947 | \n",
" 19.5552 | \n",
" 1.6094 | \n",
" 1.2054 | \n",
" 0.0 | \n",
" -0.3070 | \n",
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" 0.5321 | \n",
" 0.2395 | \n",
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\n",
" \n",
" 4 | \n",
" 17.9826 | \n",
" -3.3247e-05 | \n",
" 23.7714 | \n",
" 20.4338 | \n",
" 18.8630 | \n",
" 18.1903 | \n",
" 17.8759 | \n",
" 2.6391 | \n",
" 1.1939 | \n",
" 0.0 | \n",
" -0.6820 | \n",
" -0.2653 | \n",
" -0.1794 | \n",
" -0.1339 | \n",
" -0.1067 | \n",
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"text/plain": [
" z redshift dered_u dered_g dered_r dered_i dered_z \\\n",
"id \n",
"0 16.9396 -8.1086e-05 23.1243 20.2578 18.9551 17.6321 16.9089 \n",
"1 13.1689 4.5061e-03 14.9664 14.0045 13.4114 13.2363 13.1347 \n",
"2 15.3500 4.7198e-04 16.6076 15.6866 15.4400 15.3217 15.2961 \n",
"3 19.6346 5.8143e-06 25.3536 20.9947 20.0873 19.7947 19.5552 \n",
"4 17.9826 -3.3247e-05 23.7714 20.4338 18.8630 18.1903 17.8759 \n",
"\n",
" nObserve airmass_u class d_dered_u d_dered_g d_dered_r d_dered_i \\\n",
"id \n",
"0 2.9444 1.1898 0.0 -0.1397 -0.0790 -0.0544 -0.0403 \n",
"1 0.6931 1.2533 1.0 -0.0857 -0.0574 -0.0410 -0.0322 \n",
"2 1.0986 1.0225 0.0 -0.1787 -0.1388 -0.0963 -0.0718 \n",
"3 1.6094 1.2054 0.0 -0.3070 -0.1941 -0.1339 -0.1003 \n",
"4 2.6391 1.1939 0.0 -0.6820 -0.2653 -0.1794 -0.1339 \n",
"\n",
" d_dered_z d_dered_ig d_dered_zg d_dered_rz d_dered_iz d_obs_det \n",
"id \n",
"0 -0.0307 -2.6257 -3.3488 2.0462 0.7232 -15.0556 \n",
"1 -0.0343 -0.7683 -0.8698 0.2767 0.1016 -0.3069 \n",
"2 -0.0540 -0.3649 -0.3905 0.1440 0.0257 -0.9014 \n",
"3 -0.0795 -1.2000 -1.4395 0.5321 0.2395 -1.3906 \n",
"4 -0.1067 -2.2436 -2.5579 0.9871 0.3144 -9.3609 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(feature_file, index_col=0)\n",
"print(df.shape)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:21:35.990814Z",
"start_time": "2020-10-05T07:21:35.928126Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(320000,) (320000, 19) (80000, 19)\n"
]
}
],
"source": [
"y = df[target_col].values[:320000]\n",
"df.drop(target_col, axis=1, inplace=True)\n",
"trn = df.iloc[:320000].values\n",
"tst = df.iloc[320000:].values\n",
"feature_name = df.columns.tolist()\n",
"print(y.shape, trn.shape, tst.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hyperparameter Tuning"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:21:36.126505Z",
"start_time": "2020-10-05T07:21:35.992940Z"
}
},
"outputs": [],
"source": [
"X_trn, X_val, y_trn, y_val = train_test_split(trn, y, test_size=.2, random_state=seed)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:21:36.159475Z",
"start_time": "2020-10-05T07:21:36.129498Z"
}
},
"outputs": [],
"source": [
"params = {\n",
" \"objective\": \"multiclass\",\n",
" \"n_estimators\": 1000,\n",
" \"subsample_freq\": 1,\n",
" \"random_state\": seed,\n",
" \"n_jobs\": -1,\n",
"}\n",
"\n",
"space = {\n",
" \"learning_rate\": hp.loguniform(\"learning_rate\", np.log(0.01), np.log(0.3)),\n",
" \"num_leaves\": hp.choice(\"num_leaves\", [15, 31, 63, 127]),\n",
" \"colsample_bytree\": hp.quniform(\"colsample_bytree\", .5, .9, 0.1),\n",
" \"subsample\": hp.quniform(\"subsample\", .5, .9, 0.1),\n",
" \"min_child_samples\": hp.choice('min_child_samples', [10, 25, 100])\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`hp`는 `hyperopt`에서 불러온 모듈이며 `hp`의 각 함수가 뜻하는 바는 아래와 같습니다. \n",
"\n",
"- `hp.loguniform(\"learning_rate\", np.log(0.01), np.log(0.3))`: learning_rate를 log(0.01)과 log(0.3) 사이의 임의의 값으로 선택\n",
"- `hp.choice(\"num_leaves\", [15, 31, 63, 127])`: num_leaves를 15, 31, 63, 127 중 하나의 값으로 선택\n",
"- `hp.quniform(\"colsample_bytree\", .5, .9, 0.1)`: 0.5와 0.9사이의 0.1의 간격을 갖는 값중 하나로 colsample_bytree를 선택"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:24:21.669204Z",
"start_time": "2020-10-05T07:21:36.162569Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100%|██████████| 10/10 [02:45<00:00, 16.55s/trial, best loss: 0.161683208975629] \n",
"{'objective': 'multiclass', 'n_estimators': 1000, 'subsample_freq': 1, 'random_state': 42, 'n_jobs': -1, 'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.03108434204266342, 'min_child_samples': 10, 'num_leaves': 127, 'subsample': 0.6000000000000001}\n"
]
}
],
"source": [
"def objective(hyperparams):\n",
" model = lgb.LGBMClassifier(**params, **hyperparams)\n",
" model.fit(X=X_trn, y=y_trn,\n",
" eval_set=[(X_val, y_val)],\n",
" eval_metric=\"multi_logloss\",\n",
" early_stopping_rounds=10,\n",
" verbose=False)\n",
" score = model.best_score_[\"valid_0\"][\"multi_logloss\"]\n",
"\n",
" return {'loss': score, 'status': STATUS_OK, 'model': model}\n",
"\n",
"trials = Trials()\n",
"best = fmin(fn=objective, space=space, trials=trials,\n",
" algo=tpe.suggest, max_evals=10, verbose=1)\n",
"\n",
"hyperparams = space_eval(space, best)\n",
"n_best = trials.best_trial['result']['model'].best_iteration_\n",
"params.update(hyperparams)\n",
"print(params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stratified K-Fold Cross Validation"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:24:21.720344Z",
"start_time": "2020-10-05T07:24:21.673155Z"
}
},
"outputs": [],
"source": [
"cv = StratifiedKFold(n_splits=n_fold, shuffle=True, random_state=seed)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LightGBM 모델 학습"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:26:55.847662Z",
"start_time": "2020-10-05T07:24:21.723827Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"training model for CV #1\n",
"[1]\tvalid_0's multi_logloss: 0.954233\n",
"Training until validation scores don't improve for 10 rounds\n",
"[2]\tvalid_0's multi_logloss: 0.924743\n",
"[3]\tvalid_0's multi_logloss: 0.894827\n",
"[4]\tvalid_0's multi_logloss: 0.866741\n",
"[5]\tvalid_0's multi_logloss: 0.839951\n",
"[6]\tvalid_0's multi_logloss: 0.816908\n",
"[7]\tvalid_0's multi_logloss: 0.793714\n",
"[8]\tvalid_0's multi_logloss: 0.772796\n",
"[9]\tvalid_0's multi_logloss: 0.753847\n",
"[10]\tvalid_0's multi_logloss: 0.73288\n",
"[11]\tvalid_0's multi_logloss: 0.71286\n",
"[12]\tvalid_0's multi_logloss: 0.693807\n",
"[13]\tvalid_0's multi_logloss: 0.675524\n",
"[14]\tvalid_0's multi_logloss: 0.65779\n",
"[15]\tvalid_0's multi_logloss: 0.641412\n",
"[16]\tvalid_0's multi_logloss: 0.626447\n",
"[17]\tvalid_0's multi_logloss: 0.611459\n",
"[18]\tvalid_0's multi_logloss: 0.596917\n",
"[19]\tvalid_0's multi_logloss: 0.583272\n",
"[20]\tvalid_0's multi_logloss: 0.570976\n",
"[21]\tvalid_0's multi_logloss: 0.557878\n",
"[22]\tvalid_0's multi_logloss: 0.545354\n",
"[23]\tvalid_0's multi_logloss: 0.533336\n",
"[24]\tvalid_0's multi_logloss: 0.52288\n",
"[25]\tvalid_0's multi_logloss: 0.511876\n",
"[26]\tvalid_0's multi_logloss: 0.502915\n",
"[27]\tvalid_0's multi_logloss: 0.492674\n",
"[28]\tvalid_0's multi_logloss: 0.482539\n",
"[29]\tvalid_0's multi_logloss: 0.47385\n",
"[30]\tvalid_0's multi_logloss: 0.464822\n",
"[31]\tvalid_0's multi_logloss: 0.457011\n",
"[32]\tvalid_0's multi_logloss: 0.448649\n",
"[33]\tvalid_0's multi_logloss: 0.439989\n",
"[34]\tvalid_0's multi_logloss: 0.431633\n",
"[35]\tvalid_0's multi_logloss: 0.423759\n",
"[36]\tvalid_0's multi_logloss: 0.416499\n",
"[37]\tvalid_0's multi_logloss: 0.409675\n",
"[38]\tvalid_0's multi_logloss: 0.402948\n",
"[39]\tvalid_0's multi_logloss: 0.396482\n",
"[40]\tvalid_0's multi_logloss: 0.390272\n",
"[41]\tvalid_0's multi_logloss: 0.384193\n",
"[42]\tvalid_0's multi_logloss: 0.378325\n",
"[43]\tvalid_0's multi_logloss: 0.372325\n",
"[44]\tvalid_0's multi_logloss: 0.367487\n",
"[45]\tvalid_0's multi_logloss: 0.362428\n",
"[46]\tvalid_0's multi_logloss: 0.357493\n",
"[47]\tvalid_0's multi_logloss: 0.352895\n",
"[48]\tvalid_0's multi_logloss: 0.348047\n",
"[49]\tvalid_0's multi_logloss: 0.34296\n",
"[50]\tvalid_0's multi_logloss: 0.338691\n",
"[51]\tvalid_0's multi_logloss: 0.334022\n",
"[52]\tvalid_0's multi_logloss: 0.329819\n",
"[53]\tvalid_0's multi_logloss: 0.325557\n",
"[54]\tvalid_0's multi_logloss: 0.321717\n",
"[55]\tvalid_0's multi_logloss: 0.317862\n",
"[56]\tvalid_0's multi_logloss: 0.314433\n",
"[57]\tvalid_0's multi_logloss: 0.311109\n",
"[58]\tvalid_0's multi_logloss: 0.307696\n",
"[59]\tvalid_0's multi_logloss: 0.303858\n",
"[60]\tvalid_0's multi_logloss: 0.300642\n",
"[61]\tvalid_0's multi_logloss: 0.296929\n",
"[62]\tvalid_0's multi_logloss: 0.293582\n",
"[63]\tvalid_0's multi_logloss: 0.290824\n",
"[64]\tvalid_0's multi_logloss: 0.287785\n",
"[65]\tvalid_0's multi_logloss: 0.284729\n",
"[66]\tvalid_0's multi_logloss: 0.281985\n",
"[67]\tvalid_0's multi_logloss: 0.278864\n",
"[68]\tvalid_0's multi_logloss: 0.275776\n",
"[69]\tvalid_0's multi_logloss: 0.273325\n",
"[70]\tvalid_0's multi_logloss: 0.270471\n",
"[71]\tvalid_0's multi_logloss: 0.268078\n",
"[72]\tvalid_0's multi_logloss: 0.26569\n",
"[73]\tvalid_0's multi_logloss: 0.263856\n",
"[74]\tvalid_0's multi_logloss: 0.261546\n",
"[75]\tvalid_0's multi_logloss: 0.259072\n",
"[76]\tvalid_0's multi_logloss: 0.257032\n",
"[77]\tvalid_0's multi_logloss: 0.254743\n",
"[78]\tvalid_0's multi_logloss: 0.252826\n",
"[79]\tvalid_0's multi_logloss: 0.250495\n",
"[80]\tvalid_0's multi_logloss: 0.248533\n",
"[81]\tvalid_0's multi_logloss: 0.246417\n",
"[82]\tvalid_0's multi_logloss: 0.244279\n",
"[83]\tvalid_0's multi_logloss: 0.242394\n",
"[84]\tvalid_0's multi_logloss: 0.240407\n",
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"Early stopping, best iteration is:\n",
"[514]\tvalid_0's multi_logloss: 0.159596\n",
"training model for CV #2\n",
"[1]\tvalid_0's multi_logloss: 0.954226\n",
"Training until validation scores don't improve for 10 rounds\n",
"[2]\tvalid_0's multi_logloss: 0.924784\n",
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"Early stopping, best iteration is:\n",
"[489]\tvalid_0's multi_logloss: 0.160048\n",
"training model for CV #3\n",
"[1]\tvalid_0's multi_logloss: 0.954266\n",
"Training until validation scores don't improve for 10 rounds\n",
"[2]\tvalid_0's multi_logloss: 0.924936\n",
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"[460]\tvalid_0's multi_logloss: 0.161166\n",
"Early stopping, best iteration is:\n",
"[450]\tvalid_0's multi_logloss: 0.161157\n",
"training model for CV #4\n",
"[1]\tvalid_0's multi_logloss: 0.954189\n",
"Training until validation scores don't improve for 10 rounds\n",
"[2]\tvalid_0's multi_logloss: 0.924709\n",
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"[556]\tvalid_0's multi_logloss: 0.161285\n",
"Early stopping, best iteration is:\n",
"[546]\tvalid_0's multi_logloss: 0.16127\n",
"training model for CV #5\n",
"[1]\tvalid_0's multi_logloss: 0.954304\n",
"Training until validation scores don't improve for 10 rounds\n",
"[2]\tvalid_0's multi_logloss: 0.924878\n",
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"[485]\tvalid_0's multi_logloss: 0.160091\n",
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"[489]\tvalid_0's multi_logloss: 0.160088\n",
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"[494]\tvalid_0's multi_logloss: 0.160079\n",
"[495]\tvalid_0's multi_logloss: 0.160081\n",
"[496]\tvalid_0's multi_logloss: 0.160077\n",
"[497]\tvalid_0's multi_logloss: 0.160074\n",
"Early stopping, best iteration is:\n",
"[487]\tvalid_0's multi_logloss: 0.160073\n"
]
}
],
"source": [
"p_val = np.zeros((trn.shape[0], n_class))\n",
"p_tst = np.zeros((tst.shape[0], n_class))\n",
"for i, (i_trn, i_val) in enumerate(cv.split(trn, y), 1):\n",
" print(f'training model for CV #{i}')\n",
" clf = lgb.LGBMClassifier(**params)\n",
" clf.fit(trn[i_trn], y[i_trn],\n",
" eval_set=[(trn[i_val], y[i_val])],\n",
" eval_metric='multiclass',\n",
" early_stopping_rounds=10)\n",
" \n",
" p_val[i_val, :] = clf.predict_proba(trn[i_val])\n",
" p_tst += clf.predict_proba(tst) / n_fold"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:26:55.933675Z",
"start_time": "2020-10-05T07:26:55.850561Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"93.2728%\n"
]
}
],
"source": [
"print(f'{accuracy_score(y, np.argmax(p_val, axis=1)) * 100:.4f}%')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:26:55.985419Z",
"start_time": "2020-10-05T07:26:55.936866Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(320000, 3) (80000, 3)\n"
]
}
],
"source": [
"print(p_val.shape, p_tst.shape)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:26:57.151112Z",
"start_time": "2020-10-05T07:26:55.989541Z"
}
},
"outputs": [],
"source": [
"np.savetxt(p_val_file, p_val, fmt='%.6f', delimiter=',')\n",
"np.savetxt(p_tst_file, p_tst, fmt='%.6f', delimiter=',')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 피처 중요도 시각화"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:26:57.632154Z",
"start_time": "2020-10-05T07:26:57.153597Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"imp = pd.DataFrame({'feature': df.columns, 'importance': clf.feature_importances_})\n",
"imp = imp.sort_values('importance').set_index('feature')\n",
"imp.plot(kind='barh')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 제출 파일 생성"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2020-10-05T07:26:57.697203Z",
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