diff --git a/code/Ensemble/power_ensemble.ipynb b/code/Ensemble/power_ensemble.ipynb
new file mode 100644
index 0000000..af41d1e
--- /dev/null
+++ b/code/Ensemble/power_ensemble.ipynb
@@ -0,0 +1,68 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "343ee127-66d1-48cd-a207-3f5ddba915cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['/opt/ml/code/output/pjh_0.7312_0.7911.csv', '/opt/ml/code/output/output6_19_17_30.csv', '/opt/ml/code/output/hk_auc8285_acc7554.csv', '/opt/ml/code/output/output_7975.csv']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 평균\n",
+    "from glob import glob\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "output_path = \"/opt/ml/code/output/cross_validation/output.csv\"\n",
+    "csv_file_path_list = glob(\"/opt/ml/code/output/*.csv\")\n",
+    "print(csv_file_path_list)\n",
+    "\n",
+    "POWER = 1/4\n",
+    "\n",
+    "# concat result dataframe\n",
+    "result = pd.read_csv(csv_file_path_list[0])[\"prediction\"]\n",
+    "result = result ** POWER\n",
+    "for csv_file_path in csv_file_path_list[1:]:\n",
+    "    temp_result = pd.read_csv(csv_file_path)[\"prediction\"]\n",
+    "    temp_result = temp_result ** POWER\n",
+    "    result = pd.concat([result, temp_result], axis=1)\n",
+    "\n",
+    "# mean result dataframe\n",
+    "result = pd.DataFrame(result.mean(axis=1)).reset_index().rename(columns = {0:\"prediction\", \"index\":\"id\"})\n",
+    "result.to_csv(output_path, index=False)"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/code/Ensemble/stacking.ipynb b/code/Ensemble/stacking.ipynb
new file mode 100644
index 0000000..b0456b0
--- /dev/null
+++ b/code/Ensemble/stacking.ipynb
@@ -0,0 +1,2116 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3f275051-6f26-4e07-bdd6-0a03c50f8789",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "from datetime import datetime\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "from sklearn.decomposition import PCA, KernelPCA, TruncatedSVD\n",
+    "tqdm.pandas()\n",
+    "\n",
+    "\n",
+    "def timestamp(df):\n",
+    "    # year, month\n",
+    "    df[\"year\"] = df[\"Timestamp\"].apply(lambda x: x.year)\n",
+    "    df[\"month\"] = df[\"Timestamp\"].apply(lambda x: x.month)\n",
+    "    df[\"year\"] = df[\"year\"].astype(\"category\")\n",
+    "    df[\"month\"] = df[\"month\"].astype(\"category\")\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def assessmentItemID(df):\n",
+    "    df[\"assessmentItemID\"] = df[\"assessmentItemID\"].astype(\"category\")\n",
+    "    df[\"question_num\"] = df[\"assessmentItemID\"].apply(lambda x: int(x[-2:]))\n",
+    "#     df[\"question_num\"] = df[\"question_num\"].astype(\"category\")\n",
+    "    df[\"question_class\"] = df[\"assessmentItemID\"].apply(lambda x: x[2])\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def KnowledgeTag_relative(df):\n",
+    "    # KnowledgeTag별 누적 풀이 수, 정답 수, 정답률\n",
+    "    df_KnowledgeTag = df.sort_values(by=[\"KnowledgeTag\", \"Timestamp\"])\n",
+    "    df[\"KnowledgeTag_total_answer\"] = df_KnowledgeTag.groupby(\"KnowledgeTag\")[\"answercode\"].cumcount()\n",
+    "    df[\"KnowledgeTag_correct_answer\"] = df_KnowledgeTag.groupby(\"KnowledgeTag\")[\"answercode\"].transform(lambda x: x.cumsum().shift(1)).fillna(0)\n",
+    "    df[\"KnowledgeTag_acc\"] = (df[\"KnowledgeTag_correct_answer\"] / df[\"KnowledgeTag_total_answer\"]).fillna(0)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def userID_KnowledgeTag_relative(df):\n",
+    "    # userID, KnowledgeTag별 누적 풀이 수, 정답 수, 정답률\n",
+    "    df_userID_KnowledgeTag = df.sort_values(by=[\"userID\", \"Timestamp\"]).reset_index(drop=True)\n",
+    "    df[\"userID_KnowledgeTag_total_answer\"] = df_userID_KnowledgeTag.groupby(\"KnowledgeTag\")[\"answercode\"].cumcount()\n",
+    "    df[\"userID_KnowledgeTag_correct_answer\"] = df_userID_KnowledgeTag.groupby(\"KnowledgeTag\")[\"answercode\"].transform(lambda x: x.cumsum().shift(1)).fillna(0)\n",
+    "    df[\"userID_KnowledgeTag_acc\"] = (df[\"userID_KnowledgeTag_correct_answer\"] / df[\"userID_KnowledgeTag_total_answer\"]).fillna(0)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def assessmentItemID_relative(df):\n",
+    "    # assessmentItemID별 누적 풀이 수, 정답 수, 정답률\n",
+    "    df_assessmentItemID = df.sort_values(by=[\"assessmentItemID\", \"Timestamp\"])\n",
+    "    df[\"assessmentItemID_total_answer\"] = df_assessmentItemID.groupby(\"assessmentItemID\")[\"answercode\"].cumcount()\n",
+    "    df[\"assessmentItemID_correct_answer\"] = df_assessmentItemID.groupby(\"assessmentItemID\")[\"answercode\"].transform(lambda x: x.cumsum().shift(1)).fillna(0)\n",
+    "    df[\"assessmentItemID_acc\"] = (df[\"assessmentItemID_correct_answer\"] / df[\"assessmentItemID_total_answer\"]).fillna(0)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def question_class_relative(df):\n",
+    "    if \"question_class\" not in df.columns:\n",
+    "        df = question_class(df)\n",
+    "    # Question Class 별 누적 풀이 수, 정답 수, 정답률\n",
+    "    df.sort_values(by=[\"question_class\", \"Timestamp\"], inplace=True)\n",
+    "    df[\"question_class_correct_answer\"] = df.groupby(\"question_class\")[\"answercode\"].transform(lambda x: x.cumsum().shift(1)).fillna(0)\n",
+    "    df[\"question_class_total_answer\"] = df.groupby(\"question_class\")[\"answercode\"].cumcount()\n",
+    "    df[\"question_class_acc\"] = (df[\"question_class_correct_answer\"] / df[\"question_class_total_answer\"]).fillna(0)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def userID_question_class_relative(df):\n",
+    "    # question_class 있어야 계산 가능\n",
+    "    if \"question_class\" not in df.columns:\n",
+    "        df = question_class(df)\n",
+    "    # userID_question_class 키값 생성(temp)\n",
+    "    df[\"userID_question_class\"] = df[[\"userID\", \"question_class\"]].apply(lambda data: str(data[\"userID\"]) + \"_\" + data[\"question_class\"], axis=1)\n",
+    "    # userID_question_class별 시간 순으로 정렬\n",
+    "    df.sort_values(by=[\"userID_question_class\", \"Timestamp\"], inplace=True)\n",
+    "    # userID_question_class별 누적 풀이 수, 정답 수, 정답률\n",
+    "    df[\"userID_question_class_correct_answer\"] = df.groupby(\"userID_question_class\")[\"answercode\"].transform(lambda x: x.cumsum().shift(1)).fillna(0)\n",
+    "    df[\"userID_question_class_total_answer\"] = df.groupby(\"userID_question_class\")[\"answercode\"].cumcount()\n",
+    "    df[\"userID_question_class_acc\"] = (df[\"userID_question_class_correct_answer\"] / df[\"userID_question_class_total_answer\"]).fillna(0)\n",
+    "    # userID_question_class 키값 삭제(temp)\n",
+    "    df.drop(\"userID_question_class\", axis=1, inplace=True)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def question_num_relative(df):\n",
+    "    if \"question_num\" not in df.columns:\n",
+    "        df = question_class(df)\n",
+    "    # Question Class 별 누적 풀이 수, 정답 수, 정답률\n",
+    "    df.sort_values(by=[\"question_num\", \"Timestamp\"], inplace=True)\n",
+    "    df[\"question_num_correct_answer\"] = df.groupby(\"question_num\")[\"answercode\"].transform(lambda x: x.cumsum().shift(1)).fillna(0)\n",
+    "    df[\"question_num_total_answer\"] = df.groupby(\"question_num\")[\"answercode\"].cumcount()\n",
+    "    df[\"question_num_acc\"] = (df[\"question_num_correct_answer\"] / df[\"question_num_total_answer\"]).fillna(0)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def userID_question_num_relative(df):\n",
+    "    # question_class 있어야 계산 가능\n",
+    "    if \"question_num\" not in df.columns:\n",
+    "        df = question_class(df)\n",
+    "    # userID_question_class 키값 생성(temp)\n",
+    "    df[\"userID_question_num\"] = df[[\"userID\", \"question_num\"]].apply(lambda data: str(data[\"userID\"]) + \"_\" + str(data[\"question_num\"]), axis=1)\n",
+    "    # userID_question_class별 시간 순으로 정렬\n",
+    "    df.sort_values(by=[\"userID_question_num\", \"Timestamp\"], inplace=True)\n",
+    "    # userID_question_class별 누적 풀이 수, 정답 수, 정답률\n",
+    "    df[\"userID_question_num_correct_answer\"] = df.groupby(\"userID_question_num\")[\"answercode\"].transform(lambda x: x.cumsum().shift(1)).fillna(0)\n",
+    "    df[\"userID_question_num_total_answer\"] = df.groupby(\"userID_question_num\")[\"answercode\"].cumcount()\n",
+    "    df[\"userID_question_num_acc\"] = (df[\"userID_question_num_correct_answer\"] / df[\"userID_question_num_total_answer\"]).fillna(0)\n",
+    "    # userID_question_class 키값 삭제(temp)\n",
+    "    df.drop(\"userID_question_num\", axis=1, inplace=True)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def userID_relative(df):\n",
+    "    # userID별 시간 순으로 정렬\n",
+    "    df.sort_values(by=[\"userID\", \"Timestamp\"], inplace=True)\n",
+    "    # user 별 누적 풀이 수, 정답 수, 정답률\n",
+    "    df[\"userID_correct_answer\"] = df.groupby(\"userID\")[\"answercode\"].transform(lambda x: x.cumsum().shift(1)).fillna(0)\n",
+    "    df[\"userID_total_answer\"] = df.groupby(\"userID\")[\"answercode\"].cumcount()\n",
+    "    df[\"userID_acc\"] = (df[\"userID_correct_answer\"] / df[\"userID_total_answer\"]).fillna(0)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def userID_acc_rolling(df, window=5):\n",
+    "    # user_acc 있어야 이동평균 계산 가능\n",
+    "    if \"userID_acc\" not in df.columns:\n",
+    "        df = userID_relative(df)\n",
+    "    # userID별 시간 순으로 정렬\n",
+    "    df.sort_values(by=[\"userID\", \"Timestamp\"], inplace=True)\n",
+    "    \n",
+    "    # userID별 정답률(user_acc)의 이동 평균\n",
+    "    df[\"userID_acc_rolling\"] = df.groupby([\"userID\"])[\"userID_acc\"].rolling(window).mean().values\n",
+    "    # userID별 window-1만큼 N/A data가 생김(rolling의 특성상 앞데이터에 생김)\n",
+    "    # userID별 user_acc_rolling의 중앙값으로 대체\n",
+    "    def changed_user_acc_rolling(data):\n",
+    "        return data[\"userID_acc_rolling_x\"] if data[\"userID_acc_rolling_x\"] != \"missing\" else data[\"userID_acc_rolling_y\"]\n",
+    "    user_median = df.groupby(\"userID\")[\"userID_acc_rolling\"].median()\n",
+    "    df = pd.merge(df, user_median, on=[\"userID\"], how=\"left\")\n",
+    "    # 결측치 중앙값 변환 및 임시 열 삭제\n",
+    "    df[\"userID_acc_rolling_x\"] = df[\"userID_acc_rolling_x\"].fillna(\"missing\")\n",
+    "    df[\"userID_acc_rolling_\" + str(window)] = df.progress_apply(changed_user_acc_rolling, axis=1)\n",
+    "    df.drop(\"userID_acc_rolling_x\", axis=1, inplace=True)\n",
+    "    df.drop(\"userID_acc_rolling_y\", axis=1, inplace=True)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def feature_dimension_reduction(df, kind=\"lda\"):\n",
+    "    if \"assessmentItemID_total_answer\" not in df.columns:\n",
+    "        df = assessmentItemID_relative(df)\n",
+    "    if \"KnowledgeTag_total_answer\" not in df.columns:\n",
+    "        df = KnowledgeTag_relative(df)\n",
+    "    if \"question_class_correct_answer\" not in df.columns:\n",
+    "        df = question_class_relative(df)\n",
+    "    if \"userID_question_class_correct_answer\" not in df.columns:\n",
+    "        df = userID_question_class_relative(df)\n",
+    "    \n",
+    "    if kind == \"lda\":\n",
+    "        model = LDA(n_components=1)\n",
+    "    elif kind == \"pca\":\n",
+    "        model = PCA(n_components=1)\n",
+    "    elif kind == \"kpca\":\n",
+    "        model = KernelPCA(n_components=1)\n",
+    "    elif kind == \"kpca_rbf\":\n",
+    "        model = KernelPCA(n_components=1, kernel=\"rbf\")\n",
+    "    elif kind == \"kpca_poly\":\n",
+    "        model = KernelPCA(n_components=1, kernel=\"poly\")\n",
+    "    elif kind == \"svd\":\n",
+    "        model = TruncatedSVD(n_components=1)\n",
+    "    else:\n",
+    "        return df\n",
+    "        \n",
+    "    y = df[\"answercode\"]\n",
+    "    \n",
+    "    # KnowledgeTag_dimension_reduction\n",
+    "    X = df[[\"KnowledgeTag_total_answer\", \"KnowledgeTag_correct_answer\", \"KnowledgeTag_acc\"]].fillna(0)\n",
+    "    df[\"KnowledgeTag_\" + kind] = model.fit_transform(X, y)\n",
+    "    # userID_KnowledgeTag_dimension_reduction\n",
+    "    X = df[[\"userID_KnowledgeTag_total_answer\", \"userID_KnowledgeTag_correct_answer\",\"userID_KnowledgeTag_acc\"]].fillna(0)\n",
+    "    df[\"userID_KnowledgeTag_\" + kind] = model.fit_transform(X, y)\n",
+    "    # assessmentItemID_dimension_reduction\n",
+    "    X = df[[\"assessmentItemID_total_answer\", \"assessmentItemID_correct_answer\",\"assessmentItemID_acc\"]].fillna(0)\n",
+    "    df[\"assessmentItemID_\" + kind] = model.fit_transform(X, y)\n",
+    "    # question_class_dimension_reduction\n",
+    "    X = df[[\"question_class_correct_answer\", \"question_class_total_answer\",\"question_class_acc\"]].fillna(0)\n",
+    "    df[\"question_class_\" + kind] = model.fit_transform(X, y)\n",
+    "    # user_question_class_dimension_reductio\n",
+    "    X = df[[\"userID_question_class_correct_answer\", \"userID_question_class_total_answer\",\"userID_question_class_acc\"]].fillna(0)\n",
+    "    df[\"userID_question_class_\" + kind] = model.fit_transform(X, y)\n",
+    "    # question_num_dimension_reduction\n",
+    "    X = df[[\"question_num_correct_answer\", \"question_num_total_answer\",\"question_num_acc\"]].fillna(0)\n",
+    "    df[\"question_num_\" + kind] = model.fit_transform(X, y)\n",
+    "    # user_question_num_dimension_reduction\n",
+    "    X = df[[\"userID_question_num_correct_answer\", \"userID_question_num_total_answer\",\"userID_question_num_acc\"]].fillna(0)\n",
+    "    df[\"userID_question_num_\" + kind] = model.fit_transform(X, y)\n",
+    "    # userID_dimension_reduction\n",
+    "    X = df[[\"userID_correct_answer\", \"userID_total_answer\", \"userID_acc\"]].fillna(0)\n",
+    "    df[\"userID_\" + kind] = model.fit_transform(X, y)\n",
+    "    # all_data_dimension_reduction\n",
+    "    X = df.iloc[:, -8:]\n",
+    "    df[\"all_data_\" + kind] = model.fit_transform(X, y)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def userID_elapsed_median(df, max_time=600):\n",
+    "    # 약 1m 50s 소요(Progress bar 2개 생김)\n",
+    "    # userID별 시간 순으로 정렬\n",
+    "    df.sort_values(by=[\"userID\", \"Timestamp\"], inplace=True)\n",
+    "\n",
+    "    # sample별 elapsed time \n",
+    "    diff = df.loc[:, [\"userID\", \"Timestamp\"]].groupby(\"userID\").diff().shift(-1)\n",
+    "    elapsed = diff[\"Timestamp\"].progress_apply(lambda x: x.total_seconds() if max_time > x.total_seconds() else None)\n",
+    "    df[\"userID_elapsed_median\"] = elapsed\n",
+    "    \n",
+    "    # userID별 마지막 문제의 풀이 시간(데이터에서 알 수 없는)을\n",
+    "    # userID별 문제 풀이 시간의 \"중앙값\"으로 반환하기 위한 Aggregation\n",
+    "    user_median = df.groupby(\"userID\")[\"userID_elapsed_median\"].median()\n",
+    "    df = pd.merge(df, user_median, on=[\"userID\"], how=\"left\")\n",
+    "    \n",
+    "    # 결측치 중앙값 변환 및 임시 열 삭제\n",
+    "    df[\"userID_elapsed_median_x\"] = df[\"userID_elapsed_median_x\"].fillna(\"missing\")\n",
+    "    def changed_elapsed(data):\n",
+    "        return data[\"userID_elapsed_median_x\"] if data[\"userID_elapsed_median_x\"] != \"missing\" else data[\"userID_elapsed_median_y\"]\n",
+    "    df[\"userID_elapsed_median\"] = df.progress_apply(changed_elapsed, axis=1)\n",
+    "    df.drop(\"userID_elapsed_median_x\", axis=1, inplace=True)\n",
+    "    df.drop(\"userID_elapsed_median_y\", axis=1, inplace=True)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def userID_elapsed_median_rolling(df, window=10):\n",
+    "    # userID_elapsed_median이 있어야 이동평균 계산 가능\n",
+    "    if 'userID_elapsed_median' not in df.columns:\n",
+    "        df = userID_elapsed_median(df)\n",
+    "    # userID별 시간 순으로 정렬\n",
+    "    df.sort_values(by=[\"userID\", \"Timestamp\"], inplace=True)\n",
+    "    \n",
+    "    # userID별 문제 풀이 시간의 이동평균\n",
+    "    df['userID_elapsed_median_rolling'] = df.groupby(['userID'])['userID_elapsed_median'].rolling(window).mean().values\n",
+    "    # 유저별 window-1만큼 N/A data가 생김(rolling의 특성상 앞데이터에 생김)\n",
+    "    # 유저별 userID_elapsed_median_rolling의 중앙값으로 대체\n",
+    "    def changed_mean_time(data):\n",
+    "        return data[\"userID_elapsed_median_rolling_x\"] if data[\"userID_elapsed_median_rolling_x\"] != 'missing' else data[\"userID_elapsed_median_rolling_y\"]\n",
+    "    user_median = df.groupby('userID')['userID_elapsed_median_rolling'].median()\n",
+    "    df = pd.merge(df, user_median, on=[\"userID\"], how=\"left\")\n",
+    "    \n",
+    "    # 결측치 중앙값 변환 및 임시 열 삭제\n",
+    "    df['userID_elapsed_median_rolling_x'] = df['userID_elapsed_median_rolling_x'].fillna('missing')\n",
+    "    df['userID_elapsed_median_rolling_' + str(window)] = df.progress_apply(changed_mean_time, axis=1)\n",
+    "    df.drop('userID_elapsed_median_rolling_x', axis=1, inplace=True)\n",
+    "    df.drop('userID_elapsed_median_rolling_y', axis=1, inplace=True)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def assessmentItemID_time_relative(df):\n",
+    "    # 문제별 풀이 시간의 중앙값&평균값\n",
+    "    # userID_elapsed_median 있어야 assessmentItemID_time 계산 가능\n",
+    "    if 'userID_elapsed_median' not in df.columns:\n",
+    "        df = userID_elapsed_median(df)\n",
+    "    # assessmentItemID별 풀이 시간의 중앙값&평균값\n",
+    "    df_total_agg = df.copy()\n",
+    "    agg_df = df_total_agg.groupby('assessmentItemID')['userID_elapsed_median'].agg(['median', 'mean'])\n",
+    "    # mapping을 위해 pandas DataFrame을 dictionary형태로 변환\n",
+    "    agg_dict = agg_df.to_dict()\n",
+    "    # 구한 통계량을 각 사용자에게 mapping\n",
+    "    df['assessmentItemID_time_median'] = df_total_agg['assessmentItemID'].map(agg_dict['median'])\n",
+    "    df['assessmentItemID_time_mean'] = df_total_agg['assessmentItemID'].map(agg_dict['mean'])\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def assessmentItemID_elapsed_median(df, max_time=600):\n",
+    "    # 약 1m 50s 소요(Progress bar 2개 생김)\n",
+    "    # userID별 시간 순으로 정렬\n",
+    "    df.sort_values(by=[\"assessmentItemID\", \"Timestamp\"], inplace=True)\n",
+    "\n",
+    "    # sample별 elapsed time \n",
+    "    diff = df.loc[:, [\"assessmentItemID\", \"Timestamp\"]].groupby(\"assessmentItemID\").diff().shift(-1)\n",
+    "    elapsed = diff[\"Timestamp\"].progress_apply(lambda x: x.total_seconds() if max_time > x.total_seconds() else None)\n",
+    "    df[\"assessmentItemID_elapsed_median\"] = elapsed\n",
+    "    \n",
+    "    # userID별 마지막 문제의 풀이 시간(데이터에서 알 수 없는)을\n",
+    "    # userID별 문제 풀이 시간의 \"중앙값\"으로 반환하기 위한 Aggregation\n",
+    "    user_median = df.groupby(\"assessmentItemID\")[\"assessmentItemID_elapsed_median\"].median()\n",
+    "    df = pd.merge(df, user_median, on=[\"assessmentItemID\"], how=\"left\")\n",
+    "    \n",
+    "    # 결측치 중앙값 변환 및 임시 열 삭제\n",
+    "    df[\"assessmentItemID_elapsed_median_x\"] = df[\"assessmentItemID_elapsed_median_x\"].fillna(\"missing\")\n",
+    "    def changed_elapsed(data):\n",
+    "        return data[\"assessmentItemID_elapsed_median_x\"] if data[\"assessmentItemID_elapsed_median_x\"] != \"missing\" else data[\"assessmentItemID_elapsed_median_y\"]\n",
+    "    df[\"assessmentItemID_elapsed_median\"] = df.progress_apply(changed_elapsed, axis=1)\n",
+    "    df.drop(\"assessmentItemID_elapsed_median_x\", axis=1, inplace=True)\n",
+    "    df.drop(\"assessmentItemID_elapsed_median_y\", axis=1, inplace=True)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def assessmentItemID_elapsed_median_rolling(df, window=10):\n",
+    "    # userID_elapsed_median이 있어야 이동평균 계산 가능\n",
+    "    if 'assessmentItemID_elapsed_median' not in df.columns:\n",
+    "        df = assessmentItemID_elapsed_median(df)\n",
+    "    # userID별 시간 순으로 정렬\n",
+    "    df.sort_values(by=[\"assessmentItemID\", \"Timestamp\"], inplace=True)\n",
+    "    \n",
+    "    # userID별 문제 풀이 시간의 이동평균\n",
+    "    df['assessmentItemID_elapsed_median_rolling'] = df.groupby(['assessmentItemID'])['assessmentItemID_elapsed_median'].rolling(window).mean().values\n",
+    "    # 유저별 window-1만큼 N/A data가 생김(rolling의 특성상 앞데이터에 생김)\n",
+    "    # 유저별 userID_elapsed_median_rolling의 중앙값으로 대체\n",
+    "    def changed_mean_time(data):\n",
+    "        return data[\"assessmentItemID_elapsed_median_rolling_x\"] if data[\"assessmentItemID_elapsed_median_rolling_x\"] != 'missing' else data[\"assessmentItemID_elapsed_median_rolling_y\"]\n",
+    "    user_median = df.groupby('assessmentItemID')['assessmentItemID_elapsed_median_rolling'].median()\n",
+    "    df = pd.merge(df, user_median, on=[\"assessmentItemID\"], how=\"left\")\n",
+    "    \n",
+    "    # 결측치 중앙값 변환 및 임시 열 삭제\n",
+    "    df['assessmentItemID_elapsed_median_rolling_x'] = df['assessmentItemID_elapsed_median_rolling_x'].fillna('missing')\n",
+    "    df['assessmentItemID_elapsed_median_rolling_' + str(window)] = df.progress_apply(changed_mean_time, axis=1)\n",
+    "    df.drop('assessmentItemID_elapsed_median_rolling_x', axis=1, inplace=True)\n",
+    "    df.drop('assessmentItemID_elapsed_median_rolling_y', axis=1, inplace=True)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "# User가 해당 문제를 풀어본 경험 Feature\n",
+    "def userID_assessmentItemID_experience(df):\n",
+    "    # userID별 시간 순으로 정렬\n",
+    "    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)\n",
+    "    \n",
+    "    # userID 별로 assessmentItemID를 풀어본 적 있는지\n",
+    "    df[\"userID_assessmentItemID_experience\"] = df.groupby([\"userID\", \"assessmentItemID\"])['assessmentItemID'].cumcount()\n",
+    "    df['userID_assessmentItemID_experience'] = df['userID_assessmentItemID_experience'].apply(lambda x : 1 if x > 0 else 0)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "# User가 해당 test를 풀어본 경험 Feature\n",
+    "def userID_testid_experience(df):\n",
+    "    # userID별 시간 순으로 정렬\n",
+    "    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)\n",
+    "    \n",
+    "    # userID 별로 testid를 풀어본 적 있는지\n",
+    "    df[\"userID_testid_experience\"] = df.groupby([\"userID\", \"testId\"])['testId'].cumcount()\n",
+    "    df['userID_testid_experience'] = df['userID_testid_experience'].apply(lambda x : 1 if x > 0 else 0)\n",
+    "    return df\n",
+    "    \n",
+    "\n",
+    "def feature_engineering(df):    \n",
+    "    print(\"assessmentItemID 관련 feature\")\n",
+    "    df = assessmentItemID(df)\n",
+    "    \n",
+    "    print(\"KnowledgeTag별 누적 풀이 수, 정답 수, 정답률\")\n",
+    "    df = KnowledgeTag_relative(df)\n",
+    "    \n",
+    "    print(\"userID, KnowledgeTag별 누적 풀이 수, 정답 수, 정답률\")\n",
+    "    df = userID_KnowledgeTag_relative(df)\n",
+    "    \n",
+    "    print(\"assessmentItemID별 누적 풀이 수, 정답 수, 정답률\")\n",
+    "    df = assessmentItemID_relative(df)\n",
+    "    \n",
+    "    print(\"question class별 누적 풀이 수, 정답 수, 정답률\")\n",
+    "    df = question_class_relative(df)\n",
+    "    \n",
+    "    print(\"userID_question_class별 누적 풀이 수, 정답 수, 정답률\")\n",
+    "    df = userID_question_class_relative(df)\n",
+    "    \n",
+    "    print(\"question num별 누적 풀이 수, 정답 수, 정답률\")\n",
+    "    df = question_num_relative(df)\n",
+    "    \n",
+    "    print(\"userID_question_num별 누적 풀이 수, 정답 수, 정답률\")\n",
+    "    df = userID_question_num_relative(df)\n",
+    "    \n",
+    "    print(\"user 별 누적 풀이 수, 정답 수, 정답률\")\n",
+    "    df = userID_relative(df)\n",
+    "    \n",
+    "    print(\"userID별 정답률(user_acc)의 이동 평균 및 중앙값\")\n",
+    "    window_list = [5, 10, 15, 30]\n",
+    "    window_list = [5]\n",
+    "    for window in window_list:\n",
+    "        print(window)\n",
+    "        df = userID_elapsed_median_rolling(df, window=window)\n",
+    "        \n",
+    "    print(\"feature_dimension_reduction\")\n",
+    "    dimension_reduction_type = [\"lda\"]\n",
+    "    for kind in dimension_reduction_type:\n",
+    "        print(kind)\n",
+    "        df = feature_dimension_reduction(df, kind=kind)\n",
+    "        \n",
+    "    print(\"User가 해당 문제를 풀어본 경험 Feature\")\n",
+    "    df = userID_assessmentItemID_experience(df)\n",
+    "    print(\"User가 해당 test를 풀어본 경험 Feature\")\n",
+    "    df = userID_testid_experience(df)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "361973a7-1012-42e3-a373-aecf34fda7cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import pickle\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "import random\n",
+    "\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import random\n",
+    "import warnings\n",
+    "import lightgbm as lgb\n",
+    "from wandb.lightgbm import wandb_callback\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "import numpy as np\n",
+    "import random\n",
+    "from matplotlib import pylab as plt\n",
+    "from datetime import datetime\n",
+    "import wandb\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "\n",
+    "%matplotlib inline\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e025187d-7857-457e-8ae8-74d9e514e83c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2266586, 6)\n",
+      "(260114, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 기존\n",
+    "data_dir = '/opt/ml/input/data/train_dataset'\n",
+    "train_csv_file_path = os.path.join(data_dir, 'train_data.csv')\n",
+    "train_df = pd.read_csv(train_csv_file_path, parse_dates=['Timestamp'])\n",
+    "print(train_df.shape)\n",
+    "\n",
+    "test_csv_file_path = os.path.join(data_dir, 'test_data.csv')\n",
+    "test_df = pd.read_csv(test_csv_file_path, parse_dates=['Timestamp'])\n",
+    "# test = test_df[test_df[\"answerCode\"] == -1]\n",
+    "# test_df = test_df[test_df[\"answerCode\"] > -1]\n",
+    "print(test_df.shape)\n",
+    "\n",
+    "# inference\n",
+    "ikyo = pd.read_csv(\"/opt/ml/code/output/cross_validation/output_8253_1.csv\")\n",
+    "ikyo[\"userID\"] = test_df[test_df[\"userID\"] != test_df['userID'].shift(-1)].reset_index()[\"userID\"]\n",
+    "\n",
+    "df = pd.concat([train_df, test_df], ignore_index=True)\n",
+    "df[\"answercode\"] = df[\"answerCode\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "02dbf323-0c49-408d-a0b6-7b34cecc767d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.iloc[df[df[\"answercode\"] == -1].index, -1] = ikyo[\"prediction\"]\n",
+    "\n",
+    "df[\"answercode\"] = df[\"answercode\"].apply(lambda data: 1 if data >= 0.5 else 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "365e3097-5b0d-42b0-ac89-c533c36ccc40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "assessmentItemID 관련 feature\n",
+      "KnowledgeTag별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID, KnowledgeTag별 누적 풀이 수, 정답 수, 정답률\n",
+      "assessmentItemID별 누적 풀이 수, 정답 수, 정답률\n",
+      "question class별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID_question_class별 누적 풀이 수, 정답 수, 정답률\n",
+      "question num별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID_question_num별 누적 풀이 수, 정답 수, 정답률\n",
+      "user 별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID별 정답률(user_acc)의 이동 평균 및 중앙값\n",
+      "5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2526700/2526700 [00:25<00:00, 101013.30it/s]\n",
+      "100%|██████████| 2526700/2526700 [00:52<00:00, 48185.37it/s]\n",
+      "100%|██████████| 2526700/2526700 [00:52<00:00, 48056.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "feature_dimension_reduction\n",
+      "lda\n",
+      "User가 해당 문제를 풀어본 경험 Feature\n",
+      "User가 해당 test를 풀어본 경험 Feature\n",
+      "CPU times: user 5min 27s, sys: 35.2 s, total: 6min 2s\n",
+      "Wall time: 5min 54s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>userID</th>\n",
+       "      <th>assessmentItemID</th>\n",
+       "      <th>testId</th>\n",
+       "      <th>answerCode</th>\n",
+       "      <th>Timestamp</th>\n",
+       "      <th>KnowledgeTag</th>\n",
+       "      <th>answercode</th>\n",
+       "      <th>question_num</th>\n",
+       "      <th>question_class</th>\n",
+       "      <th>KnowledgeTag_total_answer</th>\n",
+       "      <th>...</th>\n",
+       "      <th>userID_KnowledgeTag_lda</th>\n",
+       "      <th>assessmentItemID_lda</th>\n",
+       "      <th>question_class_lda</th>\n",
+       "      <th>userID_question_class_lda</th>\n",
+       "      <th>question_num_lda</th>\n",
+       "      <th>userID_question_num_lda</th>\n",
+       "      <th>userID_lda</th>\n",
+       "      <th>all_data_lda</th>\n",
+       "      <th>userID_assessmentItemID_experience</th>\n",
+       "      <th>userID_testid_experience</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001001</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:11</td>\n",
+       "      <td>7224</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>365</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.898323</td>\n",
+       "      <td>-1.767781</td>\n",
+       "      <td>-1.035837</td>\n",
+       "      <td>2.586445</td>\n",
+       "      <td>-1.474883</td>\n",
+       "      <td>1.89388</td>\n",
+       "      <td>3.207732</td>\n",
+       "      <td>0.922117</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001002</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:14</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1743</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.898323</td>\n",
+       "      <td>-1.666476</td>\n",
+       "      <td>-1.035911</td>\n",
+       "      <td>-1.372008</td>\n",
+       "      <td>-1.063197</td>\n",
+       "      <td>1.89388</td>\n",
+       "      <td>-1.728488</td>\n",
+       "      <td>-1.931817</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   userID assessmentItemID      testId  answerCode           Timestamp  \\\n",
+       "0       0       A060001001  A060000001           1 2020-03-24 00:17:11   \n",
+       "1       0       A060001002  A060000001           1 2020-03-24 00:17:14   \n",
+       "\n",
+       "   KnowledgeTag  answercode  question_num question_class  \\\n",
+       "0          7224           1             1              6   \n",
+       "1          7225           1             2              6   \n",
+       "\n",
+       "   KnowledgeTag_total_answer  ...  userID_KnowledgeTag_lda  \\\n",
+       "0                        365  ...                 0.898323   \n",
+       "1                       1743  ...                 0.898323   \n",
+       "\n",
+       "   assessmentItemID_lda  question_class_lda  userID_question_class_lda  \\\n",
+       "0             -1.767781           -1.035837                   2.586445   \n",
+       "1             -1.666476           -1.035911                  -1.372008   \n",
+       "\n",
+       "   question_num_lda  userID_question_num_lda  userID_lda  all_data_lda  \\\n",
+       "0         -1.474883                  1.89388    3.207732      0.922117   \n",
+       "1         -1.063197                  1.89388   -1.728488     -1.931817   \n",
+       "\n",
+       "   userID_assessmentItemID_experience  userID_testid_experience  \n",
+       "0                                   0                         0  \n",
+       "1                                   0                         1  \n",
+       "\n",
+       "[2 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "test = feature_engineering(df)\n",
+    "test.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "16521383-1ef6-4a99-a717-93f200c1f4a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kpca\n",
+      "kpca_rbf\n",
+      "kpca_poly\n"
+     ]
+    }
+   ],
+   "source": [
+    "test = test[test[\"answerCode\"] == -1]\n",
+    "\n",
+    "dimension_reduction_type = [\"kpca\", \"kpca_rbf\", \"kpca_poly\"]\n",
+    "for kind in dimension_reduction_type:\n",
+    "    print(kind)\n",
+    "    test = feature_dimension_reduction(test, kind=kind)\n",
+    "\n",
+    "def type_change(df):\n",
+    "    df[\"userID\"] = df[\"userID\"].astype(\"category\")\n",
+    "    df[\"testId\"] = df[\"testId\"].astype(\"category\")\n",
+    "    df[\"question_class\"] = df[\"question_class\"].astype(\"category\")\n",
+    "    df[\"KnowledgeTag\"] = df[\"KnowledgeTag\"].astype(\"category\")\n",
+    "    return df\n",
+    "\n",
+    "test = type_change(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d4a0ea6a-4c2f-48c3-a2b7-4b076ff8564d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2266586, 6)\n",
+      "(259370, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 기존\n",
+    "data_dir = '/opt/ml/input/data/train_dataset'\n",
+    "train_csv_file_path = os.path.join(data_dir, 'train_data.csv')\n",
+    "train_df = pd.read_csv(train_csv_file_path, parse_dates=['Timestamp'])\n",
+    "print(train_df.shape)\n",
+    "\n",
+    "test_csv_file_path = os.path.join(data_dir, 'test_data.csv')\n",
+    "test_df = pd.read_csv(test_csv_file_path, parse_dates=['Timestamp'])\n",
+    "# test = test_df[test_df[\"answerCode\"] == -1]\n",
+    "test_df = test_df[test_df[\"answerCode\"] > -1]\n",
+    "print(test_df.shape)\n",
+    "\n",
+    "df = pd.concat([train_df, test_df], ignore_index=True)\n",
+    "df[\"answercode\"] = df[\"answerCode\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b7862dbd-83c3-4d1a-a488-89f8096f633c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "assessmentItemID 관련 feature\n",
+      "KnowledgeTag별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID, KnowledgeTag별 누적 풀이 수, 정답 수, 정답률\n",
+      "assessmentItemID별 누적 풀이 수, 정답 수, 정답률\n",
+      "question class별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID_question_class별 누적 풀이 수, 정답 수, 정답률\n",
+      "question num별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID_question_num별 누적 풀이 수, 정답 수, 정답률\n",
+      "user 별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID별 정답률(user_acc)의 이동 평균 및 중앙값\n",
+      "5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2525956/2525956 [00:24<00:00, 101797.44it/s]\n",
+      "100%|██████████| 2525956/2525956 [00:53<00:00, 47070.07it/s]\n",
+      "100%|██████████| 2525956/2525956 [00:52<00:00, 47690.07it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "feature_dimension_reduction\n",
+      "lda\n",
+      "User가 해당 문제를 풀어본 경험 Feature\n",
+      "User가 해당 test를 풀어본 경험 Feature\n",
+      "CPU times: user 5min 29s, sys: 34.6 s, total: 6min 3s\n",
+      "Wall time: 5min 55s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>userID</th>\n",
+       "      <th>assessmentItemID</th>\n",
+       "      <th>testId</th>\n",
+       "      <th>answerCode</th>\n",
+       "      <th>Timestamp</th>\n",
+       "      <th>KnowledgeTag</th>\n",
+       "      <th>answercode</th>\n",
+       "      <th>question_num</th>\n",
+       "      <th>question_class</th>\n",
+       "      <th>KnowledgeTag_total_answer</th>\n",
+       "      <th>...</th>\n",
+       "      <th>userID_KnowledgeTag_lda</th>\n",
+       "      <th>assessmentItemID_lda</th>\n",
+       "      <th>question_class_lda</th>\n",
+       "      <th>userID_question_class_lda</th>\n",
+       "      <th>question_num_lda</th>\n",
+       "      <th>userID_question_num_lda</th>\n",
+       "      <th>userID_lda</th>\n",
+       "      <th>all_data_lda</th>\n",
+       "      <th>userID_assessmentItemID_experience</th>\n",
+       "      <th>userID_testid_experience</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001001</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:11</td>\n",
+       "      <td>7224</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>365</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.833508</td>\n",
+       "      <td>-1.767737</td>\n",
+       "      <td>-1.035569</td>\n",
+       "      <td>2.586450</td>\n",
+       "      <td>-1.474702</td>\n",
+       "      <td>1.894006</td>\n",
+       "      <td>3.208150</td>\n",
+       "      <td>0.935909</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001002</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:14</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1743</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.833508</td>\n",
+       "      <td>-1.666415</td>\n",
+       "      <td>-1.035643</td>\n",
+       "      <td>-1.371945</td>\n",
+       "      <td>-1.062984</td>\n",
+       "      <td>1.894006</td>\n",
+       "      <td>-1.728447</td>\n",
+       "      <td>-1.917765</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   userID assessmentItemID      testId  answerCode           Timestamp  \\\n",
+       "0       0       A060001001  A060000001           1 2020-03-24 00:17:11   \n",
+       "1       0       A060001002  A060000001           1 2020-03-24 00:17:14   \n",
+       "\n",
+       "   KnowledgeTag  answercode  question_num question_class  \\\n",
+       "0          7224           1             1              6   \n",
+       "1          7225           1             2              6   \n",
+       "\n",
+       "   KnowledgeTag_total_answer  ...  userID_KnowledgeTag_lda  \\\n",
+       "0                        365  ...                 1.833508   \n",
+       "1                       1743  ...                 1.833508   \n",
+       "\n",
+       "   assessmentItemID_lda  question_class_lda  userID_question_class_lda  \\\n",
+       "0             -1.767737           -1.035569                   2.586450   \n",
+       "1             -1.666415           -1.035643                  -1.371945   \n",
+       "\n",
+       "   question_num_lda  userID_question_num_lda  userID_lda  all_data_lda  \\\n",
+       "0         -1.474702                 1.894006    3.208150      0.935909   \n",
+       "1         -1.062984                 1.894006   -1.728447     -1.917765   \n",
+       "\n",
+       "   userID_assessmentItemID_experience  userID_testid_experience  \n",
+       "0                                   0                         0  \n",
+       "1                                   0                         1  \n",
+       "\n",
+       "[2 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "df = feature_engineering(df)\n",
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f5782c50-4c8f-465d-bfce-c0b4bb045363",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[df[\"userID\"] != df['userID'].shift(-1)].reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9a5468b7-d36c-4d32-b814-8b69b8385e78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kpca\n",
+      "kpca_rbf\n",
+      "kpca_poly\n"
+     ]
+    }
+   ],
+   "source": [
+    "dimension_reduction_type = [\"kpca\", \"kpca_rbf\", \"kpca_poly\"]\n",
+    "for kind in dimension_reduction_type:\n",
+    "    print(kind)\n",
+    "    df = feature_dimension_reduction(df, kind=kind)\n",
+    "\n",
+    "def type_change(df):\n",
+    "    df[\"userID\"] = df[\"userID\"].astype(\"category\")\n",
+    "    df[\"testId\"] = df[\"testId\"].astype(\"category\")\n",
+    "    df[\"question_class\"] = df[\"question_class\"].astype(\"category\")\n",
+    "    df[\"KnowledgeTag\"] = df[\"KnowledgeTag\"].astype(\"category\")\n",
+    "    return df\n",
+    "\n",
+    "df = type_change(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2a22d996-a7cf-4f54-a2c6-916a25932794",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "# save\n",
+    "with open('train_data.pickle', 'wb') as f:\n",
+    "    pickle.dump(df, f, pickle.HIGHEST_PROTOCOL)\n",
+    "    \n",
+    "# save\n",
+    "with open('test_data.pickle', 'wb') as f:\n",
+    "    pickle.dump(test, f, pickle.HIGHEST_PROTOCOL)\n",
+    "\n",
+    "# load\n",
+    "with open('train_data.pickle', 'rb') as f:\n",
+    "    df = pickle.load(f)\n",
+    "\n",
+    "# load\n",
+    "with open('test_data.pickle', 'rb') as f:\n",
+    "    test = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "be9bb2d7-a276-4773-b5bd-af3e1d3ac3e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7438\n",
+      "744\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "7439"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 종헌\n",
+    "train_df = pd.read_csv(\"/opt/ml/code/output/oof/pjh_valid_proba.csv\").rename(columns={\"userid\": \"userID\", \"prediction\": \"pjh_pred\"})\n",
+    "test_df = pd.read_csv(\"/opt/ml/code/output/oof/pjh_test_proba.csv\").rename(columns={\"userid\": \"userID\", \"prediction\": \"pjh_pred\"})\n",
+    "\n",
+    "train = train_df.merge(df, on=\"userID\", how=\"left\")\n",
+    "test = test_df.merge(test, on=\"userID\", how=\"left\")\n",
+    "\n",
+    "print(len(set(train_df[\"userID\"].unique())))\n",
+    "print(len(set(test_df[\"userID\"].unique())))\n",
+    "len(set(train_df[\"userID\"].unique()) | set(test_df[\"userID\"].unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b88ce8a7-aa0c-40ca-97d3-b76f8a1de049",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7442\n",
+      "744\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "7442"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 익효\n",
+    "train_df = pd.read_csv(\"/opt/ml/code/output/oof/stacking_jih.csv\").rename(columns={\"id\": \"userID\", \"pred\": \"jik_pred\"})\n",
+    "test_df = pd.read_csv(\"/opt/ml/code/output/oof/test.csv\").rename(columns={\"id\": \"userID\", \"prediction\": \"jik_pred\"})\n",
+    "\n",
+    "train = train_df.merge(train, on=\"userID\", how=\"right\")\n",
+    "test = pd.concat([test, test_df], axis=1)\n",
+    "\n",
+    "print(len(set(train_df[\"userID\"].unique())))\n",
+    "print(len(set(test_df[\"userID\"].unique())))\n",
+    "len(set(train_df[\"userID\"].unique()) | set(test_df[\"userID\"].unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e0a7b524-ef54-454c-bdce-8d8a003ce59c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 태양\n",
+    "import pickle\n",
+    "\n",
+    "for i in range(5):\n",
+    "    if i:\n",
+    "        with open('/opt/ml/code/output/oof/7975/oof_sun_' + str(i) + '.pickle', 'rb') as f:\n",
+    "            a = pickle.load(f)\n",
+    "            result = pd.concat([result, pd.DataFrame(a[1], a[0]).reset_index().rename(columns = {\"index\": \"userID\", 0: \"sun_pred\"})])\n",
+    "    else:\n",
+    "        with open('/opt/ml/code/output/oof/7975/oof_sun_' + str(i) + '.pickle', 'rb') as f:\n",
+    "            a = pickle.load(f)\n",
+    "            result = pd.DataFrame(a[1], a[0]).reset_index().rename(columns = {\"index\": \"userID\", 0: \"sun_pred\"})\n",
+    "            \n",
+    "test_df = pd.read_csv(\"/opt/ml/code/output/oof/sun_test.csv\").rename(columns={\"id\": \"userID\", \"prediction\": \"sun_pred\"})\n",
+    "\n",
+    "train = train.merge(result, on=\"userID\", how=\"left\")\n",
+    "test = pd.concat([test, test_df], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "98ca0434-b228-40a9-97d4-3309de7f28c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7438\n",
+      "744\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "7438"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 재희\n",
+    "train_df = pd.read_csv(\"/opt/ml/code/output/oof/LGBM_8073_valid_proba.csv\").rename(columns={\"id\": \"userID\", \"prediction\": \"rjh_pred\"})\n",
+    "test_df = pd.read_csv(\"/opt/ml/code/output/oof/LGBM_8073_test_proba.csv\").rename(columns={\"id\": \"userID\", \"prediction\": \"rjh_pred\"})\n",
+    "\n",
+    "train = train_df.merge(train, on=\"userID\", how=\"left\")\n",
+    "test = pd.concat([test, test_df], axis=1)\n",
+    "\n",
+    "print(len(set(train_df[\"userID\"].unique())))\n",
+    "print(len(set(test_df[\"userID\"].unique())))\n",
+    "len(set(train_df[\"userID\"].unique()) | set(test_df[\"userID\"].unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "afcd91e8-da50-4f72-91e8-8388b2f57f0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7442\n",
+      "744\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "7442"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 수지\n",
+    "train_df = pd.read_csv(\"/opt/ml/code/output/oof/suz_0611_valid_proba.csv\").rename(columns={\"id\": \"userID\", \"prediction\": \"osj_pred\"})\n",
+    "test_df = pd.read_csv(\"/opt/ml/code/output/oof/suz_0611_test_proba.csv\").rename(columns={\"id\": \"userID\", \"prediction\": \"osj_pred\"})\n",
+    "\n",
+    "train = train_df.merge(train, on=\"userID\", how=\"right\")\n",
+    "test = pd.concat([test, test_df], axis=1)\n",
+    "\n",
+    "print(len(set(train_df[\"userID\"].unique())))\n",
+    "print(len(set(test_df[\"userID\"].unique())))\n",
+    "len(set(train_df[\"userID\"].unique()) | set(test_df[\"userID\"].unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "474f051a-50a8-4dd1-846b-d2b6ef4fe43c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train[\"sun_jik_pred\"] = train[[\"sun_pred\", \"jik_pred\"]].mean(axis=1)\n",
+    "train[\"sun_jik_jik_pred\"] = (train[\"sun_pred\"] + train[\"jik_pred\"] * 2) / 3\n",
+    "train[\"sun_jik_power_4_pred\"] = (train[\"sun_pred\"]**4 + train[\"jik_pred\"] ** 4) / 2\n",
+    "train[\"sun_jik_power_025_pred\"] = (train[\"sun_pred\"]**0.25 + train[\"jik_pred\"] ** 0.25) / 2\n",
+    "\n",
+    "test[\"sun_jik_pred\"] = test[[\"sun_pred\", \"jik_pred\"]].mean(axis=1)\n",
+    "test[\"sun_jik_jik_pred\"] = (test[\"sun_pred\"] + test[\"jik_pred\"] * 2) / 3\n",
+    "test[\"sun_jik_power_4_pred\"] = (test[\"sun_pred\"]**4 + train[\"jik_pred\"] ** 4) / 2\n",
+    "test[\"sun_jik_power_025_pred\"] = (test[\"sun_pred\"]**0.25 + train[\"jik_pred\"] ** 0.25) / 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bb8522ca-30ac-4625-b1fd-7a82cfcefd33",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "jik_pred\n",
+      "0.8312157477051288\n",
+      "pjh_pred\n",
+      "0.8135325617865522\n",
+      "sun_pred\n",
+      "0.798833670827161\n",
+      "rjh_pred\n",
+      "0.8342890834350734\n",
+      "osj_pred\n",
+      "0.8435961414556462\n",
+      "sun_jik_pred\n",
+      "0.8325791098846328\n",
+      "sun_jik_jik_pred\n",
+      "0.8348436616285727\n"
+     ]
+    }
+   ],
+   "source": [
+    "columns = [\"jik_pred\", \"pjh_pred\", \"sun_pred\", \"rjh_pred\", \"osj_pred\", \"sun_jik_pred\", \"sun_jik_jik_pred\"]\n",
+    "\n",
+    "for column in columns:\n",
+    "    print(column)\n",
+    "    print(roc_auc_score(train[\"answerCode\"], train[column].fillna(0.5)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "840161bd-7513-4533-9678-1308734bd16b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['userID', 'osj_pred', 'Timestamp_x', 'rjh_pred', 'Unnamed: 0_x',\n",
+       "       'timestamp', 'jik_pred', 'next_userID', 'Unnamed: 0_y', 'pjh_pred',\n",
+       "       'index', 'assessmentItemID', 'testId', 'answerCode', 'Timestamp_y',\n",
+       "       'KnowledgeTag', 'answercode', 'question_num', 'question_class',\n",
+       "       'KnowledgeTag_total_answer', 'KnowledgeTag_correct_answer',\n",
+       "       'KnowledgeTag_acc', 'userID_KnowledgeTag_total_answer',\n",
+       "       'userID_KnowledgeTag_correct_answer', 'userID_KnowledgeTag_acc',\n",
+       "       'assessmentItemID_total_answer', 'assessmentItemID_correct_answer',\n",
+       "       'assessmentItemID_acc', 'question_class_correct_answer',\n",
+       "       'question_class_total_answer', 'question_class_acc',\n",
+       "       'userID_question_class_correct_answer',\n",
+       "       'userID_question_class_total_answer', 'userID_question_class_acc',\n",
+       "       'question_num_correct_answer', 'question_num_total_answer',\n",
+       "       'question_num_acc', 'userID_question_num_correct_answer',\n",
+       "       'userID_question_num_total_answer', 'userID_question_num_acc',\n",
+       "       'userID_correct_answer', 'userID_total_answer', 'userID_acc',\n",
+       "       'userID_elapsed_median', 'userID_elapsed_median_rolling_5',\n",
+       "       'KnowledgeTag_lda', 'userID_KnowledgeTag_lda', 'assessmentItemID_lda',\n",
+       "       'question_class_lda', 'userID_question_class_lda', 'question_num_lda',\n",
+       "       'userID_question_num_lda', 'userID_lda', 'all_data_lda',\n",
+       "       'userID_assessmentItemID_experience', 'userID_testid_experience',\n",
+       "       'KnowledgeTag_kpca', 'userID_KnowledgeTag_kpca',\n",
+       "       'assessmentItemID_kpca', 'question_class_kpca',\n",
+       "       'userID_question_class_kpca', 'question_num_kpca',\n",
+       "       'userID_question_num_kpca', 'userID_kpca', 'all_data_kpca',\n",
+       "       'KnowledgeTag_kpca_rbf', 'userID_KnowledgeTag_kpca_rbf',\n",
+       "       'assessmentItemID_kpca_rbf', 'question_class_kpca_rbf',\n",
+       "       'userID_question_class_kpca_rbf', 'question_num_kpca_rbf',\n",
+       "       'userID_question_num_kpca_rbf', 'userID_kpca_rbf', 'all_data_kpca_rbf',\n",
+       "       'KnowledgeTag_kpca_poly', 'userID_KnowledgeTag_kpca_poly',\n",
+       "       'assessmentItemID_kpca_poly', 'question_class_kpca_poly',\n",
+       "       'userID_question_class_kpca_poly', 'question_num_kpca_poly',\n",
+       "       'userID_question_num_kpca_poly', 'userID_kpca_poly',\n",
+       "       'all_data_kpca_poly', 'sun_pred', 'sun_jik_pred', 'sun_jik_jik_pred',\n",
+       "       'sun_jik_power_4_pred', 'sun_jik_power_025_pred'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cf57df92-4dbe-4afc-a0ef-1207aa316fbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'userID', 'pjh_pred', 'assessmentItemID', 'testId', 'answerCode',\n",
+       "       'Timestamp', 'KnowledgeTag', 'answercode', 'question_num',\n",
+       "       'question_class', 'KnowledgeTag_total_answer',\n",
+       "       'KnowledgeTag_correct_answer', 'KnowledgeTag_acc',\n",
+       "       'userID_KnowledgeTag_total_answer',\n",
+       "       'userID_KnowledgeTag_correct_answer', 'userID_KnowledgeTag_acc',\n",
+       "       'assessmentItemID_total_answer', 'assessmentItemID_correct_answer',\n",
+       "       'assessmentItemID_acc', 'question_class_correct_answer',\n",
+       "       'question_class_total_answer', 'question_class_acc',\n",
+       "       'userID_question_class_correct_answer',\n",
+       "       'userID_question_class_total_answer', 'userID_question_class_acc',\n",
+       "       'question_num_correct_answer', 'question_num_total_answer',\n",
+       "       'question_num_acc', 'userID_question_num_correct_answer',\n",
+       "       'userID_question_num_total_answer', 'userID_question_num_acc',\n",
+       "       'userID_correct_answer', 'userID_total_answer', 'userID_acc',\n",
+       "       'userID_elapsed_median', 'userID_elapsed_median_rolling_5',\n",
+       "       'KnowledgeTag_lda', 'userID_KnowledgeTag_lda', 'assessmentItemID_lda',\n",
+       "       'question_class_lda', 'userID_question_class_lda', 'question_num_lda',\n",
+       "       'userID_question_num_lda', 'userID_lda', 'all_data_lda',\n",
+       "       'userID_assessmentItemID_experience', 'userID_testid_experience',\n",
+       "       'KnowledgeTag_kpca', 'userID_KnowledgeTag_kpca',\n",
+       "       'assessmentItemID_kpca', 'question_class_kpca',\n",
+       "       'userID_question_class_kpca', 'question_num_kpca',\n",
+       "       'userID_question_num_kpca', 'userID_kpca', 'all_data_kpca',\n",
+       "       'KnowledgeTag_kpca_rbf', 'userID_KnowledgeTag_kpca_rbf',\n",
+       "       'assessmentItemID_kpca_rbf', 'question_class_kpca_rbf',\n",
+       "       'userID_question_class_kpca_rbf', 'question_num_kpca_rbf',\n",
+       "       'userID_question_num_kpca_rbf', 'userID_kpca_rbf', 'all_data_kpca_rbf',\n",
+       "       'KnowledgeTag_kpca_poly', 'userID_KnowledgeTag_kpca_poly',\n",
+       "       'assessmentItemID_kpca_poly', 'question_class_kpca_poly',\n",
+       "       'userID_question_class_kpca_poly', 'question_num_kpca_poly',\n",
+       "       'userID_question_num_kpca_poly', 'userID_kpca_poly',\n",
+       "       'all_data_kpca_poly', 'userID', 'jik_pred', 'userID', 'sun_pred',\n",
+       "       'userID', 'rjh_pred', 'userID', 'osj_pred', 'sun_jik_pred',\n",
+       "       'sun_jik_jik_pred', 'sun_jik_power_4_pred', 'sun_jik_power_025_pred'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8933cd38-a186-4420-99bb-20f5bc7cdf7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>assessmentItemID_total_answer</th>\n",
+       "      <th>assessmentItemID_correct_answer</th>\n",
+       "      <th>assessmentItemID_acc</th>\n",
+       "      <th>userID_question_class_correct_answer</th>\n",
+       "      <th>userID_question_class_total_answer</th>\n",
+       "      <th>userID_question_class_acc</th>\n",
+       "      <th>jik_pred</th>\n",
+       "      <th>sun_pred</th>\n",
+       "      <th>sun_jik_pred</th>\n",
+       "      <th>sun_jik_jik_pred</th>\n",
+       "      <th>...</th>\n",
+       "      <th>assessmentItemID_kpca_poly</th>\n",
+       "      <th>question_class_kpca_poly</th>\n",
+       "      <th>userID_question_class_kpca_poly</th>\n",
+       "      <th>question_num_kpca_poly</th>\n",
+       "      <th>userID_question_num_kpca_poly</th>\n",
+       "      <th>userID_kpca_poly</th>\n",
+       "      <th>userID_assessmentItemID_experience</th>\n",
+       "      <th>userID_testid_experience</th>\n",
+       "      <th>userID_elapsed_median_rolling_5</th>\n",
+       "      <th>answerCode</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>372</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0.150538</td>\n",
+       "      <td>170.0</td>\n",
+       "      <td>362</td>\n",
+       "      <td>0.469613</td>\n",
+       "      <td>3.760731e-02</td>\n",
+       "      <td>0.142654</td>\n",
+       "      <td>0.090131</td>\n",
+       "      <td>0.072623</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.455014e+06</td>\n",
+       "      <td>2.618637e+15</td>\n",
+       "      <td>1.758173e+06</td>\n",
+       "      <td>-4.844229e+15</td>\n",
+       "      <td>159301.609307</td>\n",
+       "      <td>6.685003e+07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>56.8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>317.0</td>\n",
+       "      <td>351</td>\n",
+       "      <td>0.903134</td>\n",
+       "      <td>9.827468e-01</td>\n",
+       "      <td>0.843784</td>\n",
+       "      <td>0.913266</td>\n",
+       "      <td>0.936426</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.107819e+06</td>\n",
+       "      <td>6.979407e+15</td>\n",
+       "      <td>1.008129e+07</td>\n",
+       "      <td>-5.357891e+15</td>\n",
+       "      <td>70712.012754</td>\n",
+       "      <td>2.877639e+08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>200</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0.130000</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.134021</td>\n",
+       "      <td>2.442704e-09</td>\n",
+       "      <td>0.041673</td>\n",
+       "      <td>0.020836</td>\n",
+       "      <td>0.013891</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-3.975562e+06</td>\n",
+       "      <td>-6.092281e+14</td>\n",
+       "      <td>-9.804004e+06</td>\n",
+       "      <td>-7.394932e+15</td>\n",
+       "      <td>-108789.834221</td>\n",
+       "      <td>-5.691442e+07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>249</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>0.425703</td>\n",
+       "      <td>564.0</td>\n",
+       "      <td>860</td>\n",
+       "      <td>0.655814</td>\n",
+       "      <td>7.093197e-01</td>\n",
+       "      <td>0.427248</td>\n",
+       "      <td>0.568284</td>\n",
+       "      <td>0.615296</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.502617e+06</td>\n",
+       "      <td>-1.918543e+15</td>\n",
+       "      <td>1.987392e+08</td>\n",
+       "      <td>-7.330267e+15</td>\n",
+       "      <td>156034.046756</td>\n",
+       "      <td>3.195926e+08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>40.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>145</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>0.668966</td>\n",
+       "      <td>298.0</td>\n",
+       "      <td>424</td>\n",
+       "      <td>0.702830</td>\n",
+       "      <td>9.948128e-01</td>\n",
+       "      <td>0.761487</td>\n",
+       "      <td>0.878150</td>\n",
+       "      <td>0.917037</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-4.086328e+06</td>\n",
+       "      <td>-5.063882e+14</td>\n",
+       "      <td>1.684814e+07</td>\n",
+       "      <td>-7.092266e+15</td>\n",
+       "      <td>-89662.971786</td>\n",
+       "      <td>4.041229e+07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 51 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   assessmentItemID_total_answer  assessmentItemID_correct_answer  \\\n",
+       "0                            372                             56.0   \n",
+       "1                              4                              3.0   \n",
+       "2                            200                             26.0   \n",
+       "3                            249                            106.0   \n",
+       "4                            145                             97.0   \n",
+       "\n",
+       "   assessmentItemID_acc  userID_question_class_correct_answer  \\\n",
+       "0              0.150538                                 170.0   \n",
+       "1              0.750000                                 317.0   \n",
+       "2              0.130000                                  13.0   \n",
+       "3              0.425703                                 564.0   \n",
+       "4              0.668966                                 298.0   \n",
+       "\n",
+       "   userID_question_class_total_answer  userID_question_class_acc  \\\n",
+       "0                                 362                   0.469613   \n",
+       "1                                 351                   0.903134   \n",
+       "2                                  97                   0.134021   \n",
+       "3                                 860                   0.655814   \n",
+       "4                                 424                   0.702830   \n",
+       "\n",
+       "       jik_pred  sun_pred  sun_jik_pred  sun_jik_jik_pred  ...  \\\n",
+       "0  3.760731e-02  0.142654      0.090131          0.072623  ...   \n",
+       "1  9.827468e-01  0.843784      0.913266          0.936426  ...   \n",
+       "2  2.442704e-09  0.041673      0.020836          0.013891  ...   \n",
+       "3  7.093197e-01  0.427248      0.568284          0.615296  ...   \n",
+       "4  9.948128e-01  0.761487      0.878150          0.917037  ...   \n",
+       "\n",
+       "   assessmentItemID_kpca_poly  question_class_kpca_poly  \\\n",
+       "0                2.455014e+06              2.618637e+15   \n",
+       "1               -5.107819e+06              6.979407e+15   \n",
+       "2               -3.975562e+06             -6.092281e+14   \n",
+       "3               -1.502617e+06             -1.918543e+15   \n",
+       "4               -4.086328e+06             -5.063882e+14   \n",
+       "\n",
+       "   userID_question_class_kpca_poly  question_num_kpca_poly  \\\n",
+       "0                     1.758173e+06           -4.844229e+15   \n",
+       "1                     1.008129e+07           -5.357891e+15   \n",
+       "2                    -9.804004e+06           -7.394932e+15   \n",
+       "3                     1.987392e+08           -7.330267e+15   \n",
+       "4                     1.684814e+07           -7.092266e+15   \n",
+       "\n",
+       "   userID_question_num_kpca_poly  userID_kpca_poly  \\\n",
+       "0                  159301.609307      6.685003e+07   \n",
+       "1                   70712.012754      2.877639e+08   \n",
+       "2                 -108789.834221     -5.691442e+07   \n",
+       "3                  156034.046756      3.195926e+08   \n",
+       "4                  -89662.971786      4.041229e+07   \n",
+       "\n",
+       "   userID_assessmentItemID_experience  userID_testid_experience  \\\n",
+       "0                                   0                         1   \n",
+       "1                                   0                         1   \n",
+       "2                                   0                         1   \n",
+       "3                                   0                         1   \n",
+       "4                                   0                         1   \n",
+       "\n",
+       "   userID_elapsed_median_rolling_5  answerCode  \n",
+       "0                             56.8           0  \n",
+       "1                             75.0           1  \n",
+       "2                              7.4           0  \n",
+       "3                             40.2           0  \n",
+       "4                             30.2           1  \n",
+       "\n",
+       "[5 rows x 51 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "FEATS = ['assessmentItemID_total_answer', 'assessmentItemID_correct_answer', 'assessmentItemID_acc',\n",
+    "         'userID_question_class_correct_answer', 'userID_question_class_total_answer', 'userID_question_class_acc',\n",
+    "         \"jik_pred\", \"sun_pred\", \"sun_jik_pred\", 'sun_jik_jik_pred', 'sun_jik_power_4_pred', 'sun_jik_power_025_pred', \n",
+    "         'KnowledgeTag_lda', 'userID_KnowledgeTag_lda', 'assessmentItemID_lda', \n",
+    "         'question_class_lda', 'userID_question_class_lda', 'question_num_lda',\n",
+    "         'userID_question_num_lda', 'userID_lda', 'all_data_lda',\n",
+    "         'KnowledgeTag_kpca', 'userID_KnowledgeTag_kpca',\n",
+    "         'assessmentItemID_kpca', 'question_class_kpca',\n",
+    "         'userID_question_class_kpca', 'question_num_kpca',\n",
+    "         'userID_question_num_kpca', 'userID_kpca', 'all_data_kpca',\n",
+    "         'KnowledgeTag_kpca_rbf', 'userID_KnowledgeTag_kpca_rbf',\n",
+    "         'assessmentItemID_kpca_rbf', 'question_class_kpca_rbf',\n",
+    "         'userID_question_class_kpca_rbf', 'question_num_kpca_rbf',\n",
+    "         'userID_question_num_kpca_rbf', 'userID_kpca_rbf', 'all_data_kpca_rbf',\n",
+    "         'KnowledgeTag_kpca_poly', 'userID_KnowledgeTag_kpca_poly',\n",
+    "         'assessmentItemID_kpca_poly', 'question_class_kpca_poly',\n",
+    "         'userID_question_class_kpca_poly', 'question_num_kpca_poly',\n",
+    "         'userID_question_num_kpca_poly', 'userID_kpca_poly',\n",
+    "         'userID_assessmentItemID_experience', 'userID_testid_experience',\n",
+    "         'userID_elapsed_median_rolling_5',\n",
+    "         \"answerCode\"]\n",
+    "train[FEATS].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "663e7453-5611-4bca-89d0-a5d8f038ea79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>assessmentItemID_total_answer</th>\n",
+       "      <th>assessmentItemID_correct_answer</th>\n",
+       "      <th>assessmentItemID_acc</th>\n",
+       "      <th>userID_question_class_correct_answer</th>\n",
+       "      <th>userID_question_class_total_answer</th>\n",
+       "      <th>userID_question_class_acc</th>\n",
+       "      <th>jik_pred</th>\n",
+       "      <th>sun_pred</th>\n",
+       "      <th>sun_jik_pred</th>\n",
+       "      <th>sun_jik_jik_pred</th>\n",
+       "      <th>...</th>\n",
+       "      <th>assessmentItemID_kpca_poly</th>\n",
+       "      <th>question_class_kpca_poly</th>\n",
+       "      <th>userID_question_class_kpca_poly</th>\n",
+       "      <th>question_num_kpca_poly</th>\n",
+       "      <th>userID_question_num_kpca_poly</th>\n",
+       "      <th>userID_kpca_poly</th>\n",
+       "      <th>userID_assessmentItemID_experience</th>\n",
+       "      <th>userID_testid_experience</th>\n",
+       "      <th>userID_elapsed_median_rolling_5</th>\n",
+       "      <th>answerCode</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>249</td>\n",
+       "      <td>133.0</td>\n",
+       "      <td>0.534137</td>\n",
+       "      <td>564.0</td>\n",
+       "      <td>861</td>\n",
+       "      <td>0.655052</td>\n",
+       "      <td>0.805032</td>\n",
+       "      <td>0.533959</td>\n",
+       "      <td>0.669495</td>\n",
+       "      <td>0.714674</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.002197e+06</td>\n",
+       "      <td>-1.805907e+15</td>\n",
+       "      <td>1.998720e+08</td>\n",
+       "      <td>-7.105437e+15</td>\n",
+       "      <td>-92059.906678</td>\n",
+       "      <td>3.206844e+08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27.6</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>145</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>0.613793</td>\n",
+       "      <td>299.0</td>\n",
+       "      <td>425</td>\n",
+       "      <td>0.703529</td>\n",
+       "      <td>0.982029</td>\n",
+       "      <td>0.786134</td>\n",
+       "      <td>0.884081</td>\n",
+       "      <td>0.916730</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-4.318186e+06</td>\n",
+       "      <td>-3.626281e+14</td>\n",
+       "      <td>1.826659e+07</td>\n",
+       "      <td>-7.051564e+15</td>\n",
+       "      <td>-78865.563502</td>\n",
+       "      <td>4.107684e+07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29.8</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>248</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>0.370968</td>\n",
+       "      <td>191.0</td>\n",
+       "      <td>489</td>\n",
+       "      <td>0.390593</td>\n",
+       "      <td>0.112553</td>\n",
+       "      <td>0.243223</td>\n",
+       "      <td>0.177888</td>\n",
+       "      <td>0.156110</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.990065e+06</td>\n",
+       "      <td>-3.676959e+14</td>\n",
+       "      <td>1.623048e+07</td>\n",
+       "      <td>-7.051510e+15</td>\n",
+       "      <td>-68835.666071</td>\n",
+       "      <td>7.288832e+08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.193548</td>\n",
+       "      <td>381.0</td>\n",
+       "      <td>412</td>\n",
+       "      <td>0.924757</td>\n",
+       "      <td>0.920797</td>\n",
+       "      <td>0.824487</td>\n",
+       "      <td>0.872642</td>\n",
+       "      <td>0.888694</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.258445e+06</td>\n",
+       "      <td>7.097086e+15</td>\n",
+       "      <td>2.478480e+07</td>\n",
+       "      <td>-4.803164e+15</td>\n",
+       "      <td>638936.156149</td>\n",
+       "      <td>7.610829e+08</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>72.6</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>236</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>0.317797</td>\n",
+       "      <td>273.0</td>\n",
+       "      <td>334</td>\n",
+       "      <td>0.817365</td>\n",
+       "      <td>0.070350</td>\n",
+       "      <td>0.375657</td>\n",
+       "      <td>0.223003</td>\n",
+       "      <td>0.172119</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.676155e+06</td>\n",
+       "      <td>-1.184679e+15</td>\n",
+       "      <td>6.711042e+06</td>\n",
+       "      <td>-6.647451e+15</td>\n",
+       "      <td>-84211.032967</td>\n",
+       "      <td>-4.139170e+07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21.1</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 51 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   assessmentItemID_total_answer  assessmentItemID_correct_answer  \\\n",
+       "0                            249                            133.0   \n",
+       "1                            145                             89.0   \n",
+       "2                            248                             92.0   \n",
+       "3                             31                              6.0   \n",
+       "4                            236                             75.0   \n",
+       "\n",
+       "   assessmentItemID_acc  userID_question_class_correct_answer  \\\n",
+       "0              0.534137                                 564.0   \n",
+       "1              0.613793                                 299.0   \n",
+       "2              0.370968                                 191.0   \n",
+       "3              0.193548                                 381.0   \n",
+       "4              0.317797                                 273.0   \n",
+       "\n",
+       "   userID_question_class_total_answer  userID_question_class_acc  jik_pred  \\\n",
+       "0                                 861                   0.655052  0.805032   \n",
+       "1                                 425                   0.703529  0.982029   \n",
+       "2                                 489                   0.390593  0.112553   \n",
+       "3                                 412                   0.924757  0.920797   \n",
+       "4                                 334                   0.817365  0.070350   \n",
+       "\n",
+       "   sun_pred  sun_jik_pred  sun_jik_jik_pred  ...  assessmentItemID_kpca_poly  \\\n",
+       "0  0.533959      0.669495          0.714674  ...               -1.002197e+06   \n",
+       "1  0.786134      0.884081          0.916730  ...               -4.318186e+06   \n",
+       "2  0.243223      0.177888          0.156110  ...               -1.990065e+06   \n",
+       "3  0.824487      0.872642          0.888694  ...               -5.258445e+06   \n",
+       "4  0.375657      0.223003          0.172119  ...               -2.676155e+06   \n",
+       "\n",
+       "   question_class_kpca_poly  userID_question_class_kpca_poly  \\\n",
+       "0             -1.805907e+15                     1.998720e+08   \n",
+       "1             -3.626281e+14                     1.826659e+07   \n",
+       "2             -3.676959e+14                     1.623048e+07   \n",
+       "3              7.097086e+15                     2.478480e+07   \n",
+       "4             -1.184679e+15                     6.711042e+06   \n",
+       "\n",
+       "   question_num_kpca_poly  userID_question_num_kpca_poly  userID_kpca_poly  \\\n",
+       "0           -7.105437e+15                  -92059.906678      3.206844e+08   \n",
+       "1           -7.051564e+15                  -78865.563502      4.107684e+07   \n",
+       "2           -7.051510e+15                  -68835.666071      7.288832e+08   \n",
+       "3           -4.803164e+15                  638936.156149      7.610829e+08   \n",
+       "4           -6.647451e+15                  -84211.032967     -4.139170e+07   \n",
+       "\n",
+       "   userID_assessmentItemID_experience  userID_testid_experience  \\\n",
+       "0                                   0                         1   \n",
+       "1                                   0                         1   \n",
+       "2                                   0                         1   \n",
+       "3                                   0                         1   \n",
+       "4                                   0                         1   \n",
+       "\n",
+       "   userID_elapsed_median_rolling_5  answerCode  \n",
+       "0                             27.6          -1  \n",
+       "1                             29.8          -1  \n",
+       "2                              9.4          -1  \n",
+       "3                             72.6          -1  \n",
+       "4                             21.1          -1  \n",
+       "\n",
+       "[5 rows x 51 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test[FEATS].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "3acb66b8-bb65-42c9-8cbf-f86c91baadec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = train[FEATS]\n",
+    "test = test[FEATS]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c8561608-c1f9-4eff-bd9e-4ef787c8e577",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "# xgboost 관련\n",
+    "from xgboost import XGBClassifier\n",
+    "from xgboost import plot_importance\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from bayes_opt import BayesianOptimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "21cbcbe2-f111-4417-ab31-6f76e80b4660",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==============================\n",
+      "{'booster': 'dart', 'learning_rate': 0.08837, 'objective': 'binary:logistic', 'eval_metric': 'auc', 'max_depth': 3, 'min_child_weight': 5.7, 'gamma': 4.7, 'subsample': 0.8, 'colsample_bytree': 0.65, 'random_state': 2021}\n",
+      "==============================\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def set_params():\n",
+    "    params = {}\n",
+    "    params[\"booster\"] = \"dart\" # gbdt, dart, goss\n",
+    "    params[\"learning_rate\"] = 0.08837 # 1e-1, 5e-2, 1e-2, 5e-3, 1e-3\n",
+    "    params[\"objective\"] = \"binary:logistic\"\n",
+    "    params[\"eval_metric\"] = \"auc\" # binary_logloss, rmse, huber, auc\n",
+    "    params[\"max_depth\"] = 3 # -1\n",
+    "    params[\"min_child_weight\"] = 5.7 # 20 100 ~ 1000 수백 또는 수천 개로 정하는 것\n",
+    "    params[\"gamma\"] = 4.7 # 0.0\n",
+    "    params[\"subsample\"] = 0.8 # 0.0\n",
+    "    params[\"colsample_bytree\"] = 0.65 # 0.0\n",
+    "    params[\"random_state\"] = 2021\n",
+    "    \n",
+    "    print(\"=\"*30)\n",
+    "    print(params)\n",
+    "    print(\"=\"*30)\n",
+    "    print()\n",
+    "    \n",
+    "    return params\n",
+    "\n",
+    "params = set_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "735474d8-860f-4cb2-8e69-674d6786455b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==============================\n",
+      "{'booster': 'dart', 'learning_rate': 0.08837, 'objective': 'binary:logistic', 'eval_metric': 'auc', 'max_depth': 3, 'min_child_weight': 5.7, 'gamma': 4.7, 'subsample': 0.8, 'colsample_bytree': 0.65, 'random_state': 2021}\n",
+      "==============================\n",
+      "\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "VALID AUC : 0.8424596602127828 ACC : 0.7627688172043011\n",
+      "\n",
+      "VALID AUC : 0.8551062656505664 ACC : 0.7762096774193549\n",
+      "\n",
+      "VALID AUC : 0.8385358753921116 ACC : 0.7614247311827957\n",
+      "\n",
+      "VALID AUC : 0.8485941375626889 ACC : 0.7659717552118359\n",
+      "\n",
+      "VALID AUC : 0.8427353212753246 ACC : 0.7558843308675185\n",
+      "\n",
+      "0.8454862520186948\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import xgboost as xgb\n",
+    "\n",
+    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=2021)\n",
+    "\n",
+    "params = set_params()\n",
+    "\n",
+    "auc_score = 0\n",
+    "\n",
+    "final_preds = []\n",
+    "\n",
+    "print(\"@\"*50)\n",
+    "print(\"start\")\n",
+    "print(\"@\"*50)\n",
+    "\n",
+    "for fold, (train_index, test_index) in enumerate(skf.split(train, train[\"answerCode\"])):\n",
+    "\n",
+    "    temp_train = train.iloc[train_index,:]\n",
+    "    temp_valid = train.iloc[test_index,:]\n",
+    "\n",
+    "    # X, y 값 분리\n",
+    "    y_train = temp_train[\"answerCode\"]\n",
+    "    train_df = temp_train.drop([\"answerCode\"], axis=1)\n",
+    "\n",
+    "    y_test = temp_valid[\"answerCode\"]\n",
+    "    test_df = temp_valid.drop([\"answerCode\"], axis=1)\n",
+    "\n",
+    "    D_train = xgb.DMatrix(train_df, label=y_train)\n",
+    "    D_test = xgb.DMatrix(test_df, label=y_test)\n",
+    "    \n",
+    "    y_final = test[\"answerCode\"]\n",
+    "    final = test.drop([\"answerCode\"], axis=1)\n",
+    "    D_final = xgb.DMatrix(final, label=y_final)\n",
+    "\n",
+    "    model = xgb.train(params, D_train, num_boost_round=100)\n",
+    "\n",
+    "    preds = model.predict(D_test)\n",
+    "\n",
+    "    acc = accuracy_score(y_test, np.where(preds >= 0.5, 1, 0))\n",
+    "    auc = roc_auc_score(y_test, preds)\n",
+    "\n",
+    "    print(f'VALID AUC : {auc} ACC : {acc}\\n')\n",
+    "    \n",
+    "    final_preds.append(model.predict(D_final))\n",
+    "    \n",
+    "    fig,ax = plt.subplots(figsize=(10,8))\n",
+    "    plot_importance(model, ax=ax, max_num_features = 50, height=.4)\n",
+    "\n",
+    "    auc_score += auc\n",
+    "\n",
+    "print(auc_score / 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1142144d-fc11-45fb-bf1b-f6ebbd6be3f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = pd.DataFrame(np.array(final_preds).mean(axis=0)).reset_index().rename(columns = {0:\"prediction\", \"index\":\"id\"})\n",
+    "result.to_csv(\"stacking.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "0b348f3a-0d8a-45da-af18-3b7602645578",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result[\"prediction\"].hist(bins=50)"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/code/LGBM/lgbm_baseline.ipynb b/code/LGBM/lgbm_baseline.ipynb
new file mode 100644
index 0000000..01c8578
--- /dev/null
+++ b/code/LGBM/lgbm_baseline.ipynb
@@ -0,0 +1,2771 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LGBM Baseline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-24T09:49:29.375544Z",
+     "start_time": "2021-05-24T09:49:28.999092Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import random\n",
+    "import warnings\n",
+    "import lightgbm as lgb\n",
+    "from wandb.lightgbm import wandb_callback\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from tqdm import tqdm\n",
+    "import numpy as np\n",
+    "import random\n",
+    "from matplotlib import pylab as plt\n",
+    "from lgbm_function import inference, set_params, custom_train_test_split\n",
+    "from feature_engineering import feature_engineering_sun\n",
+    "from bayes_opt import BayesianOptimization\n",
+    "from datetime import datetime\n",
+    "import wandb\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "\n",
+    "%matplotlib inline\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Load & Preprocessing\n",
+    "- validation data answer의 정보가 모델에 학습되는 것을 방지\n",
+    "- inference 단계와 동일하게 user mean을 기준으로 random하게 값을 지정\n",
+    "- train할 때 마지막 random하게 지정한 값은 제외\n",
+    "- test, validation의 마지막 값을 제외한 모든 행을 학습에 사용"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-24T09:49:29.678737Z",
+     "start_time": "2021-05-24T09:49:29.376581Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2266586, 8)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>userID</th>\n",
+       "      <th>assessmentItemID</th>\n",
+       "      <th>testId</th>\n",
+       "      <th>answerCode</th>\n",
+       "      <th>Timestamp</th>\n",
+       "      <th>KnowledgeTag</th>\n",
+       "      <th>next_userID</th>\n",
+       "      <th>is_test_data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001001</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:11</td>\n",
+       "      <td>7224</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001002</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:14</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001003</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:22</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001004</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:29</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001005</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:36</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   userID assessmentItemID      testId  answerCode           Timestamp  \\\n",
+       "0       0       A060001001  A060000001           1 2020-03-24 00:17:11   \n",
+       "1       0       A060001002  A060000001           1 2020-03-24 00:17:14   \n",
+       "2       0       A060001003  A060000001           1 2020-03-24 00:17:22   \n",
+       "3       0       A060001004  A060000001           1 2020-03-24 00:17:29   \n",
+       "4       0       A060001005  A060000001           1 2020-03-24 00:17:36   \n",
+       "\n",
+       "   KnowledgeTag  next_userID  is_test_data  \n",
+       "0          7224          0.0         False  \n",
+       "1          7225          0.0         False  \n",
+       "2          7225          0.0         False  \n",
+       "3          7225          0.0         False  \n",
+       "4          7225          0.0         False  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dir = '/opt/ml/input/data/train_dataset'\n",
+    "train_csv_file_path = os.path.join(data_dir, 'train_data.csv')\n",
+    "train_df = pd.read_csv(train_csv_file_path, parse_dates=['Timestamp'])\n",
+    "train_df[\"next_userID\"] = train_df['userID'].shift(-1)\n",
+    "train_df[\"is_test_data\"] = False\n",
+    "print(train_df.shape)\n",
+    "train_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(259370, 8)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>userID</th>\n",
+       "      <th>assessmentItemID</th>\n",
+       "      <th>testId</th>\n",
+       "      <th>answerCode</th>\n",
+       "      <th>Timestamp</th>\n",
+       "      <th>KnowledgeTag</th>\n",
+       "      <th>next_userID</th>\n",
+       "      <th>is_test_data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A050023001</td>\n",
+       "      <td>A050000023</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-01-09 10:56:31</td>\n",
+       "      <td>2626</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A050023002</td>\n",
+       "      <td>A050000023</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-01-09 10:56:57</td>\n",
+       "      <td>2626</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A050023003</td>\n",
+       "      <td>A050000023</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2020-01-09 10:58:31</td>\n",
+       "      <td>2625</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A050023004</td>\n",
+       "      <td>A050000023</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2020-01-09 10:58:36</td>\n",
+       "      <td>2625</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A050023006</td>\n",
+       "      <td>A050000023</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2020-01-09 10:58:43</td>\n",
+       "      <td>2623</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   userID assessmentItemID      testId  answerCode           Timestamp  \\\n",
+       "0       3       A050023001  A050000023           1 2020-01-09 10:56:31   \n",
+       "1       3       A050023002  A050000023           1 2020-01-09 10:56:57   \n",
+       "2       3       A050023003  A050000023           0 2020-01-09 10:58:31   \n",
+       "3       3       A050023004  A050000023           0 2020-01-09 10:58:36   \n",
+       "4       3       A050023006  A050000023           0 2020-01-09 10:58:43   \n",
+       "\n",
+       "   KnowledgeTag  next_userID  is_test_data  \n",
+       "0          2626          3.0         False  \n",
+       "1          2626          3.0         False  \n",
+       "2          2625          3.0         False  \n",
+       "3          2625          3.0         False  \n",
+       "4          2623          3.0         False  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_csv_file_path = os.path.join(data_dir, 'test_data.csv')\n",
+    "test_df = pd.read_csv(test_csv_file_path, parse_dates=['Timestamp'])\n",
+    "test_df = test_df[test_df[\"answerCode\"] > -1]\n",
+    "test_df[\"next_userID\"] = test_df['userID'].shift(-5)\n",
+    "test_df[\"is_test_data\"] = test_df[[\"userID\", \"next_userID\"]].apply(lambda data: False if data[\"userID\"] == data[\"next_userID\"] else True, axis=1)\n",
+    "print(test_df.shape)\n",
+    "test_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2525956, 8)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([train_df, test_df], ignore_index=True)\n",
+    "del(train_df)\n",
+    "del(test_df)\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 43.2 s, sys: 836 ms, total: 44 s\n",
+      "Wall time: 44.1 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "def random_answering(data):\n",
+    "    if data[\"is_test_data\"]:\n",
+    "        return 1 if random.random() < 0.5 else 0\n",
+    "    else:\n",
+    "        return data[\"answerCode\"]\n",
+    "\n",
+    "df[\"answercode\"] = df.apply(random_answering, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-24T09:49:29.683739Z",
+     "start_time": "2021-05-24T09:49:28.981Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Timestamp 관련 feature\n",
+      "assessmentItemID 관련 feature\n",
+      "KnowledgeTag별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID, KnowledgeTag별 누적 풀이 수, 정답 수, 정답률\n",
+      "assessmentItemID별 누적 풀이 수, 정답 수, 정답률\n",
+      "question class별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID_question_class별 누적 풀이 수, 정답 수, 정답률\n",
+      "question num별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID_question_num별 누적 풀이 수, 정답 수, 정답률\n",
+      "user 별 누적 풀이 수, 정답 수, 정답률\n",
+      "userID별 timestamp 중앙값\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2525956/2525956 [00:26<00:00, 95144.91it/s] \n",
+      "100%|██████████| 2525956/2525956 [00:55<00:00, 45765.68it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "문제별 풀이 시간의 중앙값&평균값\n",
+      "userID별 정답률(user_acc)의 이동 평균 및 중앙값\n",
+      "5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2525956/2525956 [00:55<00:00, 45491.33it/s]\n",
+      "100%|██████████| 2525956/2525956 [00:55<00:00, 45293.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2525956/2525956 [00:55<00:00, 45780.82it/s]\n",
+      "100%|██████████| 2525956/2525956 [00:56<00:00, 44982.34it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2525956/2525956 [00:54<00:00, 46207.33it/s]\n",
+      "100%|██████████| 2525956/2525956 [00:58<00:00, 43198.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2525956/2525956 [00:57<00:00, 43599.19it/s]\n",
+      "100%|██████████| 2525956/2525956 [00:59<00:00, 42402.29it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "feature_dimension_reduction\n",
+      "lda\n",
+      "svd\n",
+      "User가 해당 문제를 풀어본 경험 Feature\n",
+      "User가 해당 test를 풀어본 경험 Feature\n",
+      "CPU times: user 17min 58s, sys: 2min 32s, total: 20min 31s\n",
+      "Wall time: 19min 41s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>userID</th>\n",
+       "      <th>assessmentItemID</th>\n",
+       "      <th>testId</th>\n",
+       "      <th>answerCode</th>\n",
+       "      <th>Timestamp</th>\n",
+       "      <th>KnowledgeTag</th>\n",
+       "      <th>next_userID</th>\n",
+       "      <th>is_test_data</th>\n",
+       "      <th>answercode</th>\n",
+       "      <th>year</th>\n",
+       "      <th>...</th>\n",
+       "      <th>userID_KnowledgeTag_svd</th>\n",
+       "      <th>assessmentItemID_svd</th>\n",
+       "      <th>question_class_svd</th>\n",
+       "      <th>userID_question_class_svd</th>\n",
+       "      <th>question_num_svd</th>\n",
+       "      <th>userID_question_num_svd</th>\n",
+       "      <th>userID_svd</th>\n",
+       "      <th>all_data_svd</th>\n",
+       "      <th>userID_assessmentItemID_experience</th>\n",
+       "      <th>userID_testid_experience</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001001</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:11</td>\n",
+       "      <td>7224</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>225.635792</td>\n",
+       "      <td>39868.971351</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>55488.827014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>68304.216652</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001002</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:14</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>223.963244</td>\n",
+       "      <td>39870.359260</td>\n",
+       "      <td>1.395334</td>\n",
+       "      <td>54423.735554</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.392196</td>\n",
+       "      <td>67470.716893</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 68 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  userID assessmentItemID      testId  answerCode           Timestamp  \\\n",
+       "0      0       A060001001  A060000001           1 2020-03-24 00:17:11   \n",
+       "1      0       A060001002  A060000001           1 2020-03-24 00:17:14   \n",
+       "\n",
+       "  KnowledgeTag  next_userID  is_test_data  answercode  year  ...  \\\n",
+       "0         7224          0.0         False           1  2020  ...   \n",
+       "1         7225          0.0         False           1  2020  ...   \n",
+       "\n",
+       "  userID_KnowledgeTag_svd  assessmentItemID_svd question_class_svd  \\\n",
+       "0                     0.0            225.635792       39868.971351   \n",
+       "1                     0.0            223.963244       39870.359260   \n",
+       "\n",
+       "   userID_question_class_svd  question_num_svd  userID_question_num_svd  \\\n",
+       "0                   0.000000      55488.827014                      0.0   \n",
+       "1                   1.395334      54423.735554                      0.0   \n",
+       "\n",
+       "   userID_svd  all_data_svd  userID_assessmentItemID_experience  \\\n",
+       "0    0.000000  68304.216652                                   0   \n",
+       "1    1.392196  67470.716893                                   0   \n",
+       "\n",
+       "   userID_testid_experience  \n",
+       "0                         0  \n",
+       "1                         1  \n",
+       "\n",
+       "[2 rows x 68 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "df = feature_engineering_sun(df)\n",
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['userID', 'assessmentItemID', 'testId', 'answerCode', 'Timestamp',\n",
+       "       'KnowledgeTag', 'next_userID', 'is_test_data', 'answercode', 'year',\n",
+       "       'month', 'question_num', 'question_class', 'KnowledgeTag_total_answer',\n",
+       "       'KnowledgeTag_correct_answer', 'KnowledgeTag_acc',\n",
+       "       'userID_KnowledgeTag_total_answer',\n",
+       "       'userID_KnowledgeTag_correct_answer', 'userID_KnowledgeTag_acc',\n",
+       "       'assessmentItemID_total_answer', 'assessmentItemID_correct_answer',\n",
+       "       'assessmentItemID_acc', 'question_class_correct_answer',\n",
+       "       'question_class_total_answer', 'question_class_acc',\n",
+       "       'userID_question_class_total_answer',\n",
+       "       'userID_question_class_correct_answer', 'userID_question_class_acc',\n",
+       "       'question_num_correct_answer', 'question_num_total_answer',\n",
+       "       'question_num_acc', 'userID_question_num_total_answer',\n",
+       "       'userID_question_num_correct_answer', 'userID_question_num_acc',\n",
+       "       'userID_correct_answer', 'userID_total_answer', 'userID_acc',\n",
+       "       'userID_elapsed_median', 'assessmentItemID_time_median',\n",
+       "       'assessmentItemID_time_mean', 'userID_acc_rolling_5',\n",
+       "       'userID_elapsed_median_rolling_5', 'userID_acc_rolling_10',\n",
+       "       'userID_elapsed_median_rolling_10', 'userID_acc_rolling_15',\n",
+       "       'userID_elapsed_median_rolling_15', 'userID_acc_rolling_30',\n",
+       "       'userID_elapsed_median_rolling_30', 'KnowledgeTag_lda',\n",
+       "       'userID_KnowledgeTag_lda', 'assessmentItemID_lda', 'question_class_lda',\n",
+       "       'userID_question_class_lda', 'question_num_lda',\n",
+       "       'userID_question_num_lda', 'userID_lda', 'all_data_lda',\n",
+       "       'KnowledgeTag_svd', 'userID_KnowledgeTag_svd', 'assessmentItemID_svd',\n",
+       "       'question_class_svd', 'userID_question_class_svd', 'question_num_svd',\n",
+       "       'userID_question_num_svd', 'userID_svd', 'all_data_svd',\n",
+       "       'userID_assessmentItemID_experience', 'userID_testid_experience'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>userID</th>\n",
+       "      <th>assessmentItemID</th>\n",
+       "      <th>testId</th>\n",
+       "      <th>answerCode</th>\n",
+       "      <th>Timestamp</th>\n",
+       "      <th>KnowledgeTag</th>\n",
+       "      <th>next_userID</th>\n",
+       "      <th>is_test_data</th>\n",
+       "      <th>answercode</th>\n",
+       "      <th>year</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pred_userID_KnowledgeTag_svd</th>\n",
+       "      <th>pred_assessmentItemID_svd</th>\n",
+       "      <th>pred_question_class_svd</th>\n",
+       "      <th>pred_userID_question_class_svd</th>\n",
+       "      <th>pred_question_num_svd</th>\n",
+       "      <th>pred_userID_question_num_svd</th>\n",
+       "      <th>pred_userID_svd</th>\n",
+       "      <th>pred_all_data_svd</th>\n",
+       "      <th>pred_userID_assessmentItemID_experience</th>\n",
+       "      <th>pred_userID_testid_experience</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001001</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:11</td>\n",
+       "      <td>7224</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001002</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:14</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>225.635792</td>\n",
+       "      <td>39868.971351</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>55488.827014</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>68304.216652</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001003</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:22</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>223.963244</td>\n",
+       "      <td>39870.359260</td>\n",
+       "      <td>1.395334</td>\n",
+       "      <td>54423.735554</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.392196</td>\n",
+       "      <td>67470.716893</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001004</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:29</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.471256</td>\n",
+       "      <td>220.618148</td>\n",
+       "      <td>39871.747169</td>\n",
+       "      <td>2.788897</td>\n",
+       "      <td>53791.418093</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.783401</td>\n",
+       "      <td>66968.120221</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>A060001005</td>\n",
+       "      <td>A060000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-24 00:17:36</td>\n",
+       "      <td>7225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.865134</td>\n",
+       "      <td>225.635792</td>\n",
+       "      <td>39873.135078</td>\n",
+       "      <td>4.182461</td>\n",
+       "      <td>52641.660567</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.174606</td>\n",
+       "      <td>66053.545087</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2525951</th>\n",
+       "      <td>7441</td>\n",
+       "      <td>A030071005</td>\n",
+       "      <td>A030000071</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2020-06-05 06:50:21</td>\n",
+       "      <td>438</td>\n",
+       "      <td>7441.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.025986</td>\n",
+       "      <td>106.779681</td>\n",
+       "      <td>112921.194514</td>\n",
+       "      <td>3.028496</td>\n",
+       "      <td>148815.437899</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.036800</td>\n",
+       "      <td>186821.500958</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2525952</th>\n",
+       "      <td>7441</td>\n",
+       "      <td>A040165001</td>\n",
+       "      <td>A040000165</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-08-21 01:06:39</td>\n",
+       "      <td>8836</td>\n",
+       "      <td>7441.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.822695</td>\n",
+       "      <td>98.974257</td>\n",
+       "      <td>112922.024215</td>\n",
+       "      <td>3.845519</td>\n",
+       "      <td>141310.766711</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.859350</td>\n",
+       "      <td>180846.830330</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2525953</th>\n",
+       "      <td>7441</td>\n",
+       "      <td>A040165002</td>\n",
+       "      <td>A040000165</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-08-21 01:06:50</td>\n",
+       "      <td>8836</td>\n",
+       "      <td>7441.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>117.348169</td>\n",
+       "      <td>219522.603095</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>309990.171028</td>\n",
+       "      <td>0.810791</td>\n",
+       "      <td>4.681933</td>\n",
+       "      <td>379630.758839</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2525954</th>\n",
+       "      <td>7441</td>\n",
+       "      <td>A040165003</td>\n",
+       "      <td>A040000165</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-08-21 01:07:36</td>\n",
+       "      <td>8836</td>\n",
+       "      <td>7441.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.471256</td>\n",
+       "      <td>116.233111</td>\n",
+       "      <td>219523.991004</td>\n",
+       "      <td>1.395334</td>\n",
+       "      <td>305243.442158</td>\n",
+       "      <td>0.810791</td>\n",
+       "      <td>6.073270</td>\n",
+       "      <td>375852.292570</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2525955</th>\n",
+       "      <td>7441</td>\n",
+       "      <td>A040165004</td>\n",
+       "      <td>A040000165</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-08-21 01:08:49</td>\n",
+       "      <td>8836</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.865134</td>\n",
+       "      <td>120.693342</td>\n",
+       "      <td>219526.766822</td>\n",
+       "      <td>2.788897</td>\n",
+       "      <td>301028.514945</td>\n",
+       "      <td>1.402592</td>\n",
+       "      <td>7.464570</td>\n",
+       "      <td>372498.086417</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2525956 rows × 136 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        userID assessmentItemID      testId  answerCode           Timestamp  \\\n",
+       "0            0       A060001001  A060000001           1 2020-03-24 00:17:11   \n",
+       "1            0       A060001002  A060000001           1 2020-03-24 00:17:14   \n",
+       "2            0       A060001003  A060000001           1 2020-03-24 00:17:22   \n",
+       "3            0       A060001004  A060000001           1 2020-03-24 00:17:29   \n",
+       "4            0       A060001005  A060000001           1 2020-03-24 00:17:36   \n",
+       "...        ...              ...         ...         ...                 ...   \n",
+       "2525951   7441       A030071005  A030000071           0 2020-06-05 06:50:21   \n",
+       "2525952   7441       A040165001  A040000165           1 2020-08-21 01:06:39   \n",
+       "2525953   7441       A040165002  A040000165           1 2020-08-21 01:06:50   \n",
+       "2525954   7441       A040165003  A040000165           1 2020-08-21 01:07:36   \n",
+       "2525955   7441       A040165004  A040000165           1 2020-08-21 01:08:49   \n",
+       "\n",
+       "        KnowledgeTag  next_userID  is_test_data  answercode  year  ...  \\\n",
+       "0               7224          0.0         False           1  2020  ...   \n",
+       "1               7225          0.0         False           1  2020  ...   \n",
+       "2               7225          0.0         False           1  2020  ...   \n",
+       "3               7225          0.0         False           1  2020  ...   \n",
+       "4               7225          0.0         False           1  2020  ...   \n",
+       "...              ...          ...           ...         ...   ...  ...   \n",
+       "2525951          438       7441.0         False           0  2020  ...   \n",
+       "2525952         8836       7441.0         False           1  2020  ...   \n",
+       "2525953         8836       7441.0         False           1  2020  ...   \n",
+       "2525954         8836       7441.0         False           1  2020  ...   \n",
+       "2525955         8836          NaN         False           1  2020  ...   \n",
+       "\n",
+       "        pred_userID_KnowledgeTag_svd  pred_assessmentItemID_svd  \\\n",
+       "0                                NaN                        NaN   \n",
+       "1                           0.000000                 225.635792   \n",
+       "2                           0.000000                 223.963244   \n",
+       "3                           1.471256                 220.618148   \n",
+       "4                           2.865134                 225.635792   \n",
+       "...                              ...                        ...   \n",
+       "2525951                     3.025986                 106.779681   \n",
+       "2525952                     3.822695                  98.974257   \n",
+       "2525953                     0.000000                 117.348169   \n",
+       "2525954                     1.471256                 116.233111   \n",
+       "2525955                     2.865134                 120.693342   \n",
+       "\n",
+       "        pred_question_class_svd  pred_userID_question_class_svd  \\\n",
+       "0                           NaN                             NaN   \n",
+       "1                  39868.971351                        0.000000   \n",
+       "2                  39870.359260                        1.395334   \n",
+       "3                  39871.747169                        2.788897   \n",
+       "4                  39873.135078                        4.182461   \n",
+       "...                         ...                             ...   \n",
+       "2525951           112921.194514                        3.028496   \n",
+       "2525952           112922.024215                        3.845519   \n",
+       "2525953           219522.603095                        0.000000   \n",
+       "2525954           219523.991004                        1.395334   \n",
+       "2525955           219526.766822                        2.788897   \n",
+       "\n",
+       "         pred_question_num_svd  pred_userID_question_num_svd  pred_userID_svd  \\\n",
+       "0                          NaN                           NaN              NaN   \n",
+       "1                 55488.827014                      0.000000         0.000000   \n",
+       "2                 54423.735554                      0.000000         1.392196   \n",
+       "3                 53791.418093                      0.000000         2.783401   \n",
+       "4                 52641.660567                      0.000000         4.174606   \n",
+       "...                        ...                           ...              ...   \n",
+       "2525951          148815.437899                      0.000000         3.036800   \n",
+       "2525952          141310.766711                      0.000000         3.859350   \n",
+       "2525953          309990.171028                      0.810791         4.681933   \n",
+       "2525954          305243.442158                      0.810791         6.073270   \n",
+       "2525955          301028.514945                      1.402592         7.464570   \n",
+       "\n",
+       "         pred_all_data_svd  pred_userID_assessmentItemID_experience  \\\n",
+       "0                      NaN                                      NaN   \n",
+       "1             68304.216652                                      0.0   \n",
+       "2             67470.716893                                      0.0   \n",
+       "3             66968.120221                                      0.0   \n",
+       "4             66053.545087                                      0.0   \n",
+       "...                    ...                                      ...   \n",
+       "2525951      186821.500958                                      0.0   \n",
+       "2525952      180846.830330                                      0.0   \n",
+       "2525953      379630.758839                                      0.0   \n",
+       "2525954      375852.292570                                      0.0   \n",
+       "2525955      372498.086417                                      0.0   \n",
+       "\n",
+       "         pred_userID_testid_experience  \n",
+       "0                                  NaN  \n",
+       "1                                  0.0  \n",
+       "2                                  1.0  \n",
+       "3                                  1.0  \n",
+       "4                                  1.0  \n",
+       "...                                ...  \n",
+       "2525951                            1.0  \n",
+       "2525952                            1.0  \n",
+       "2525953                            0.0  \n",
+       "2525954                            1.0  \n",
+       "2525955                            1.0  \n",
+       "\n",
+       "[2525956 rows x 136 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df = df.shift(1)\n",
+    "new_df.columns = [\"pred_\" + i for i in new_df.columns]\n",
+    "final_df = pd.concat([df, new_df], axis=1)\n",
+    "\n",
+    "del(new_df)\n",
+    "del(df)\n",
+    "\n",
+    "final_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FEATS = ['assessmentItemID_acc', 'assessmentItemID_correct_answer', 'assessmentItemID_total_answer',\n",
+    "         'userID_question_class_acc', 'userID_question_class_correct_answer', 'userID_question_class_total_answer',\n",
+    "         'question_class_acc', 'question_class_correct_answer', 'question_class_total_answer',\n",
+    "         'userID_testid_experience', 'userID_assessmentItemID_experience',\n",
+    "         'assessmentItemID_lda', 'userID_question_class_lda', 'question_class_lda', 'question_num_lda', 'userID_lda', \n",
+    "         'KnowledgeTag_lda', 'userID_KnowledgeTag_lda', 'all_data_lda',\n",
+    "         'assessmentItemID_svd', 'userID_question_class_svd', 'question_class_svd', 'question_num_svd', 'userID_svd', \n",
+    "         'KnowledgeTag_svd', 'userID_KnowledgeTag_svd', 'all_data_svd',\n",
+    "         'userID_elapsed_median_rolling_5', 'userID_elapsed_median_rolling_10',\n",
+    "         'userID_elapsed_median_rolling_15', 'userID_elapsed_median_rolling_30',\n",
+    "         'assessmentItemID', 'testId', 'question_class', 'question_num', 'userID', 'KnowledgeTag']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 15.5 s, sys: 18.8 s, total: 34.3 s\n",
+      "Wall time: 34.3 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# 유저별 분리\n",
+    "train_lst, test_lst = custom_train_test_split(final_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==============================\n",
+      "{'boosting_type': 'dart', 'learning_rate': 0.05, 'objective': 'binary', 'metric': 'auc', 'num_iterations': 100, 'max_depth': -1, 'num_leaves': 127, 'min_data_in_leaf': 100, 'max_bin': 256, 'bagging_fraction': 0.7, 'feature_fraction': 0.7, 'lambda_l1': 0.1, 'lambda_l2': 0.1}\n",
+      "==============================\n",
+      "\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "0 번째 fold\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "==============================\n",
+      "train, test shape\n",
+      "(2524526, 135) (1430, 135)\n",
+      "==============================\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33msunnight9507\u001b[0m (use `wandb login --relogin` to force relogin)\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.10.32 is available!  To upgrade, please run:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                Tracking run with wandb version 0.10.30<br/>\n",
+       "                Syncing run <strong style=\"color:#cdcd00\">logical-plasma-2576</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
+       "                Project page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT</a><br/>\n",
+       "                Run page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/y4gklll1\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/y4gklll1</a><br/>\n",
+       "                Run data is saved locally in <code>/opt/ml/code/wandb/run-20210619_172258-y4gklll1</code><br/><br/>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Info] Number of positive: 1652912, number of negative: 871614\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.588170 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 25009\n",
+      "[LightGBM] [Info] Number of data points in the train set: 2524526, number of used features: 37\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654742 -> initscore=0.639947\n",
+      "[LightGBM] [Info] Start training from score 0.639947\n",
+      "[100]\ttraining's auc: 0.848456\tvalid_1's auc: 0.800057\n",
+      "VALID AUC : 0.8000565033823552 ACC : 0.727972027972028\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "0 번째 fold\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "==============================\n",
+      "train, test shape\n",
+      "(2524412, 135) (1544, 135)\n",
+      "==============================\n",
+      "\n"
+     ]
+    },
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+     "data": {
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+      "text/html": [
+       "<br/>Waiting for W&B process to finish, PID 18230<br/>Program ended successfully."
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+     },
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find user logs for this run at: <code>/opt/ml/code/wandb/run-20210619_172258-y4gklll1/logs/debug.log</code>"
+      ],
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+       "<IPython.core.display.HTML object>"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+       "Find internal logs for this run at: <code>/opt/ml/code/wandb/run-20210619_172258-y4gklll1/logs/debug-internal.log</code>"
+      ],
+      "text/plain": [
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+       "<h3>Run summary:</h3><br/><style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
+       "    </style><table class=\"wandb\">\n",
+       "<tr><td>training_auc</td><td>0.84846</td></tr><tr><td>valid_1_auc</td><td>0.80006</td></tr><tr><td>_runtime</td><td>101</td></tr><tr><td>_timestamp</td><td>1624123479</td></tr><tr><td>_step</td><td>99</td></tr></table>"
+      ],
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+     "data": {
+      "text/html": [
+       "<h3>Run history:</h3><br/><style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
+       "    </style><table class=\"wandb\">\n",
+       "<tr><td>training_auc</td><td>▁▂▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇█████████</td></tr><tr><td>valid_1_auc</td><td>▁▃▄▄▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇▇████████████████</td></tr><tr><td>_runtime</td><td>▁▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇▇▇██</td></tr><tr><td>_timestamp</td><td>▁▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇▇▇██</td></tr><tr><td>_step</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr></table><br/>"
+      ],
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+    {
+     "data": {
+      "text/html": [
+       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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+     "data": {
+      "text/html": [
+       "\n",
+       "                    <br/>Synced <strong style=\"color:#cdcd00\">logical-plasma-2576</strong>: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/y4gklll1\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/y4gklll1</a><br/>\n",
+       "                "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "...Successfully finished last run (ID:y4gklll1). Initializing new run:<br/><br/>"
+      ],
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.10.32 is available!  To upgrade, please run:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                Tracking run with wandb version 0.10.30<br/>\n",
+       "                Syncing run <strong style=\"color:#cdcd00\">solar-meadow-2577</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
+       "                Project page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT</a><br/>\n",
+       "                Run page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/sfd7mank\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/sfd7mank</a><br/>\n",
+       "                Run data is saved locally in <code>/opt/ml/code/wandb/run-20210619_172446-sfd7mank</code><br/><br/>\n",
+       "            "
+      ],
+      "text/plain": [
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Info] Number of positive: 1652830, number of negative: 871582\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.483986 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 24994\n",
+      "[LightGBM] [Info] Number of data points in the train set: 2524412, number of used features: 37\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654739 -> initscore=0.639934\n",
+      "[LightGBM] [Info] Start training from score 0.639934\n",
+      "[100]\ttraining's auc: 0.848449\tvalid_1's auc: 0.804981\n",
+      "VALID AUC : 0.8049809663840158 ACC : 0.7299222797927462\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
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LLgFgr7324pVXXmHx4sVceeWVjBs3bov2xo8fT9u2bVMxlHLhSYxzJZCU7KUIc8yss5ntC4wAbpd0eCWH5pzbwey222506dIFgPr169O2bVtWrlyJJL7//nsA1q1bx+677w7AwQcfzG9+8xsADjzwQL755puCtj7//HOeffZZhgwZUsmjKD/xfq2bc0lISgOeMbP24fFFQD3gO2AYkAcsNbNTJNUFbgPaAzWB0WY2Q9Ig4IRwXXXgjKL6M7MsSdcAw4GXKmpczjmXKDs7m4ULF3LAAQdw6623cuSRR3LRRRexefNmXn/99a3q33fffXTv3r3g8XnnncdNN93EDz/8UJlhlyufiXE7klFAZzPrSJTMAFwOzDaz7kAvYGxIbAC6ACea2WGlaHsB0Ka8A3bOuWRycnLo168ft956K7vssgt33XUXt9xyCytWrOCWW27hrLPO2qL+yy+/zH333cfQoUMBeOaZZ2jSpAldu3ZNRfjlxt/szm13ipmJORDIAaYD080sR9I7QC2i2RmARsCRwAHAYWZ2ZuE2JWUAF5lZwT4YSZ2BR81sq8VlSUOBoQCNG+/a9apb7y3nEVeeprXhqw2pjmLbefypFff4IbVj6NCsAQB5eXlceuml7L///px88skA9O3bl//85z9Iwszo27cvzz77LACffPIJV111FWPGjOE3v/kN9erV495772XWrFlUr16dn3/+mR9//JFDDz2Uyy+/PDWDK0GvXr2SvtmdLye57VEeW84y1grfjwZ6AscAl0vqAAjoZ2bLEhuQdACwvgx9dgbeT3bCzCYCEyF6x944v2Np3N9x1eNPrbjHDyl+x96BGZgZZ5xxBocccgi33nprwbk99tgDSWRkZPDSSy/Rpk0bMjIy+OyzzxgyZAhPPPEEBx98cME79ia+a29mZiY333wzzzzzTOUP6leK92+Tc8l9BTSR9FuimZe+wCxgDzN7WdJrwClEszPPA+dIOsfMTFJnM1tYls4kdQSuBOK7O845Fwtz587loYceokOHDqSnpwNw/fXXc++993LuueeSl5dHrVq1mDhxIgDXXHMN3377LX/9618B2LBhA8uWLSuq+djxJMZtd8wsN2y0nQesBD4g2pz7sKQGRLMvE8xsraRrgVuBdyVVA5YTJT0lOVTSQqAOsBoYYWa+qdc5V6F69OhBUdtA5s+fv1XZv/71L/71r38VPM7MzNyqTuGZmTjxJMZtl8xsAjChFPU2AGcnKZ8ETEp4nE30CibMLBNoUC6BOuec22b+6iTnnHPOxZInMc4555yLJU9inHPOORdLvifGuUpUu2Z1lo05OtVhbLPMzEyyB2akOoxt5vGnVtzjh+1jDNsTn4lxzjnnXCx5EuOcc865WPIkxjnnnHOx5HtinKtEG3I3kTbq2VSHsc0u7JDHII8/ZSoz/uwY791yOw6fiXHOOedcLHkS45xzzrlY8iTGOeecc7HkSYxzzrmkBg8eTJMmTWjfvn1BWf/+/UlPTyc9PZ20tLSCT1J+5JFHCsrT09OpVq0aWVlZQPQBg/vuuy9DhgwhPT2d1atXp2A0bnvkG3vddk9SQ+BPZnbnNlx7HjDRzH4Mj7OBbmb2TaF6o4EcM7v518brXFUxaNAghg8fzumnn15QNnXq1ILjCy+8kAYNos9CHThwIAMHDgRg8eLFHHfccQUJDkRJTk5OTmw/LdlVTT4T43YEDYG/buO15wF1yi0S52KkZ8+eNGrUKOk5M+Pxxx9nwIABW52bMmUKp5xySkWH55zPxLgdwhhgb0lZwAvAauBkYGdgmpldLaku8DjQHKgOXAs0BXYHXpb0jZn1SmxU0uXAGaG9FcD8yhmOc6k3Z84cmjZtSuvWrbc6N3XqVGbMmLFF2ZlnnsmGDRs444wzuOKKK5BUWaG67ZjPxLgdwSjgEzNLJ0piWgPdgXSgq6SewO+BL8ysk5m1B2aa2QTgC6BXkgSmK3BKaOMoYP/KGYpzVcOUKVOSzsK89dZb1KlTZ4t9NI888giLFy9mwoQJzJkzh4ceeqgyQ3XbMZ+JcTuaPuFrYXhcjyipmQOMk3Qj8IyZzSmhnUOJZnHy98o8XVRFSUOBoQCNG+/KVR3yft0IUqhp7egN1+LK4y+9zMxMAL788kvWr19f8Bhg06ZNTJ06lXvuuWeLcoA77riDAw44YKvyjz76iM2bN9OlSxemTZtGixYtKnYAFSQnJ2erscVJ3OMvzJMYt6MRcIOZ3bPVCakL0azKPyS9ZGbXlEeHZjYRmAjQomUrG7c4vv/sLuyQh8efOpUZf/4nNWdnZ1O3bt0tNuTOnDmTDh06cNJJJ21xzebNmxk4cCBz5syhZcuWAOTl5bF27VoaN27Miy++yMcff8yRRx4Z2w2+mZmZsY0d4h9/Yb6c5HYEPwD1w/HzwGBJ9QAkNZPURNLuwI9m9jAwFuiS5NpErwLHSaotqT5wTIWOwLkUGDBgAAcddBDLli2jefPm3HfffQA89thjSZeSXn31VfbYY4+CBAZg48aNHHnkkXTs2JEhQ4bQrFkz/vznP1faGNz2Lb5/kjhXSmb2raS5kpYA/wUeBd4IGwtzgFOBVsBYSZuBXOAv4fKJwExJXyTuizGzBZKmAouINva+XWkDcq6STJkyJWn5pEmTkpZnZGTw5ptvblFWt25d5s+P9rxvb7MALvU8iXE7BDP7U6Gi8YUef0I0S1P4utuA2xIepyUcXwdcV35ROuecKwtfTnLOOedcLHkS45xzzrlY8iTGOeecc7Hke2Kcq0S1a1Zn2ZijUx3GNsvMzCx46W0cefzObV98JsY555xzseRJjHPOOediyZMY55xzzsWS74lxrhJtyN1E2qhnUx3GNruwQx6DqlD82THeX+Sc+/V8JsY555xzseRJjHPOOediyZMY55xzzsWSJzHOudi75ZZbaNeuHe3bt2fAgAH89NNPHHrooaSnp5Oens7uu+/OcccdB8AHH3zAQQcdxM4778zNN9+c2sCdc7+Kb+x120RSjpnVC8dHAbcCvc3s04rsq5T1RwM5ZlamZyhJHYCHwsMWwLrw9Y2ZHVGWtlzlWblyJRMmTGDp0qXUrl2bk08+mccee4w5c+YU1OnXrx/HHnssAI0aNWLChAlMnz49RRE758qLz8S4X0XS4cAE4A8VkcBUJjNbbGbpZpYOPA1cHB57AlPF5eXlsWHDBvLy8vjxxx/ZfffdC859//33zJ49u2AmpkmTJuy///7UrFkzRdE658qLJzFum0nqCdwL9DWzT0LZJEkTJL0u6X+STgzlkjRW0hJJiyX1D+V3SPpjOJ4m6f5wPFjSdUn6vFjS25LelfT3hPLLJX0o6TVg34Ty/UPdrPz+Q3n18Di/rbOLGedVod4SSRMlqbi2XeVq1qwZF110ES1atGC33XajQYMG9OnTp+D89OnTOfzww9lll11SGKVzriJ4EuO21c7AdOA4M/ug0LndgB5AX2BMKDsBSAc6AUcAYyXtBswBDg11mgH7heNDgVcTG5XUB2gNdA9tdZXUU1JX4JRQdhSwf8JlDwBnh9mVTQnlZwHrzGz/UP/PkvYqYqy3m9n+ZtYeqB3GVVzbrhKtWbOGGTNmsHz5cr744gvWr1/Pww8/XHB+ypQpDBgwIIUROucqiu+JcdsqF3idKBk4t9C56Wa2GVgqqWko6wFMMbNNwFeSXiFKHuYA50naD1gK/CYkNwcBIwq12yd8LQyP6xElNfWBaWb2I4Ckp8P3hkB9M3sj1H+UXxKQPkDH/JkioEFoa3mSsfaSdAlQB2gEvCdpTjFtb0HSUGAoQOPGu3JVh7xk1WKhae3oDe+qiszMTDIzM6lVqxbvvfceAG3btuWJJ56gefPmrFu3jtdff53zzz+fzMxMcnJyyMzMBCA7O5vatWsXPI6DxPjjKO7xQ/zHEPf4C/Mkxm2rzcDJwEuSLjOz6xPObUw4VnGNmNnKkGz8nmjmpVFoN8fMfihUXcANZnbPFoXSedsQv4BzzOz5YitJtYA7gW5mtiJsGq5Vlo7MbCIwEaBFy1Y2bnF8/9ld2CGPqhR/9sAMateuzRNPPEH37t2pXbs2DzzwAEcccQQZGRncfffdHHfccQXLS5mZmWRkZBQc16tXr+BxHCTGH0dxjx/iP4a4x1+YLye5bRZmPo4GBko6q4Tqc4D+YS/KrkBPYF449yZwHlESMwe4KHwv7HlgsKT8V0U1k9QkXHecpNqS6gPHhPjWAj9IOiBcf0qhtv4iqWZoax9JdZP0mZ+wfBP6PbEUbbtKdMABB3DiiSfSpUsXOnTowObNmxk6dCgAjz322FZLSV9++SXNmzfnn//8J//4xz9o3rw533//fSpCd879SlXnTyoXS2b2naTfA69K+rqYqtOIlogWAQZcYmZfhnNzgD5m9rGkT4lmY7ZKYsxslqS2wBthb20OcKqZLZA0NbS9Gng74bKzgHslbQZeIXrJNMC/gDRgQdio+zVwXJI+10q6F1gCfFnKtl0l+/vf/87f//73rcqTTZv/3//9H59//nklROWcq2iexLhtkvi+LWa2AsjfFPt0snpmZsDF4atwW/cB94XjXKBusjbC8XhgfJI2rgO2ejUT8J6ZdQSQNAp4J9TfDFwWvpKNb1DC8RXAFaVt2znnXOXwJMZt746WdCnR7/qnwKCYtO2cc64EnsS47ZqZTQWmxq1t55xzJfONvc4555yLJZ+Jca4S1a5ZnWVjjk51GNssMzOT7IEZqQ7DOecAn4lxzjnnXEx5EuOcc865WPIkxjnnnHOx5HtinKtEG3I3kTbq2VSHsc0u7JDHoHKOPzvGe4Scc6nlMzHOOeeciyVPYpxzzjkXS57EOOeccy6WPIlxzqXcsmXLSE9PL/jaZZdduPXWW7n44otp06YNHTt25Pjjj2ft2rUAfPvtt/Tq1Yt69eoxfPjw1AbvnEsZT2Kccym37777kpWVRVZWFvPnz6dOnTocf/zx9O7dmyVLlvDuu++yzz77cMMNNwBQq1Ytrr32Wm6++eYUR+6cSyVPYlxKScqW1Dgc55ThukmSTiyhziBJu29jXEnbl5Qh6ZltadOVzksvvcTee+/NnnvuSZ8+fahRI3oR5YEHHsjnn38OQN26denRowe1atVKZajOuRTzJMZtzwYB25TEuNR57LHHGDBgwFbl999/P3/4wx9SEJFzrqryJMZVGknTJc2X9J6koWW8VpJul7RM0otAk4RzV0l6W9ISSRND3ROBbsAjkrIk1U5Wr5R9/17SB5IWACcklHeX9IakhZJel7RvWcbktvbzzz/z9NNPc9JJJ21Rft1111GjRg0GDhyYosicc1WRzCzVMbgdhKRGZvadpNrA28BhwHygm5l9IynHzOoVce0JwF+A3wNNgaXAEDN7Mr/dUO8h4HEz+4+kTOAiM3snsf/C9YrobxLwTPj6CPgd8DEwFahjZn0l7QL8aGZ5ko4A/mJm/ZK0NRQYCtC48a5dr7r13rLeuiqjaW34akP5ttmhWYOC49dee40ZM2YwduzYgrKZM2fyn//8h3Hjxm21fDRz5kyWLVvGueeeW6q+cnJyqFcv6a9YLHj8qRf3McQ1/l69es03s26Fy/0de11lGiHp+HC8B9C6DNf2BKaY2SbgC0mzE871knQJUAdoBLwHJEtOSlsvURtguZl9BCDpYUJCAjQAJktqDRhQM1kDZjYRmAjQomUrG7c4vv/sLuyQR3nHn/ip2HfffTd//etfyciIymbOnMnTTz/NK6+8wq677rr1tdnZ5OTkFNQvSWZmZqnrVkUef+rFfQxxj7+w+P5v6mJFUgZwBHCQmf0YZkl+9a5MSbWAO4lmc1ZIGp2s3dLWK6NrgZfN7HhJaUDmr2xvh7Z+/XpeeOEF7rnnnoKy4cOHs3HjRnr37g1Em3vvvvtuANLS0vj+++/5+eefmT59OrNmzWK//fZLSezOudTwJMZVlgbAmpDAtAEOLOP1rwJnS5pMtB+mF/AovyQi30iqB5wIPBnKfgDqh+Pi6hXnAyBN0t5m9gmQuOO0AbAyHA8q43hcIXXr1uXbb7/douzjjz8usn52dnYFR+Scq+p8Y6+rLDOBGpLeB8YAb5bx+mlEe1OWAg8CbwCY2VrgXmAJ8DzRXpt8k4C7JWUBG4upVyQz+4lo+ejZsLF3dcLpm4AbJC3E/yBwzrlK5//xukphZhuBZK+PTUuoU+RuM4t2oCd9a1YzuwK4Ikn5U8BTCUVJ6xXR5qCE45lEe2MK13kD2KdQ+8455yqJz8Q455xzLpZ8JsZVKZI6AA8VKt5oZgdUUH93AIcUKh5vZg9URH/OOefKjycxrkoxs8VAeiX297fK6gugds3qLBtzdGV2Wa4yMzO3eEm0c86lki8nOeeccy6WPIlxzjnnXCx5EuOcc865WPI9Mc5Vog25m0gb9Wyqw9hmF3bIY1CIPzvGe3ucc9sHn4lxzjnnXCx5EuOcc865WPIkxjnnnHOx5EmMc+5XSUtLo0OHDqSnp9OtWzcArrzySjp27Eh6ejp9+vThiy++AKL3mWnQoAHp6emkp6dzzTXXpDJ051zM+cZe59yv9vLLL9O4ceOCxxdffDHXXnstABMmTOCaa67h7rvvBuDQQw/lmWeeSUmczrnti8/EFCIpTdKScJwhaZ2khZKWSXpVUt9tbHeQpNvLN9pS9ZshqVKeMQrdu26SJlRGv0niyKmMeCSNlrRSUlb4Oqo824+zXXbZpeB4/fr1SEphNM657ZXPxCSQlOx+zDGzvuF8OjBd0gYze6lSg4sZM3sHeKei2pdU3cw2VYF4bjGzmyug3diQRJ8+fZDE2WefzdChQwG4/PLLefDBB2nQoAEvv/xyQf033niDTp06sfvuu3PzzTfTrl27VIXunIu5WM/EJP6lHR5fFP46HiFpqaR3JT0WztWVdL+keWFm5dhQPkjS05JmA8UmJmaWBVwDDC8mpl0lPSXp7fBV+MMFkXSMpLdCHC9KahrKR0t6SNIbkj6S9OdQvluYBcqStETSoaG8T6i7QNITkuqF8t9L+kDSAuCEEu7haEmTJc2R9KmkEyTdJGmxpJmSaoZ6XSW9Imm+pOcl7ZZQvkjSIuBvCe0WzABJ6h7iXCjpdUn7Jtz7f4d+PpJ0Uwmx5kgaF/o6SNIF4X4skXReCdcmxjM6/C5kSvqfpBEJ9a4Ms26vSZoi6aLi2nXw2muvsWDBAv773/9yxx138OqrrwJw3XXXsWLFCgYOHMjtt0eTkF26dOHTTz9l0aJFnHPOORx33HEpjNw5F3fb60zMKGAvM9soqWEouxyYbWaDQ9k8SS+Gc12Ajmb2naS0EtpeAFxczPnxRH+dvyapBfA80LZQndeAA83MJA0BLgEuDOc6AgcCdYGFkp4FBgDPm9l1kqoDdSQ1Bq4AjjCz9ZJGAheEROBe4HfAx8DUEsYDsDfQC9gPeAPoZ2aXSJoGHB1iuA041sy+ltQfuA4YDDwADDezVyWNLaL9D4BDzSxP0hHA9UC/cC4d6AxsBJZJus3MVhTRTl3gLTO7UFJX4EzgAEDAW5JeMbOFpRgvQJsw5vqh37tCLP2ATkBNop/1/BLaGS7pdKJZngvNbE3hCpKGAkMBGjfelas65JUyxKqnae3oDe8g2qSb76OPPgKgc+fOTJkyhc2bNxeca9myJaNGjaJXr15btFWnTh1++OEHZsyYQYMGDSo+eCAnJ2eLuOPG40+9uI8h7vEXtr0mMe8Cj0iaDkwPZX2APyb8ZV0LaBGOXzCz70rZdkmL+0cA+yXsAdglf4YkQXNgapjN2AlYnnBuhpltADZIehnoDrwN3B9mRaabWZakw4iSjrmhr52IEpA2wHIz+whA0sOEJ9Bi/NfMciUtBqoDM0P5YiAN2BdoD7wQ+qoOrArJYEMzezXUfwj4Q5L2GwCTJbUGjChByPeSma0LsS4F9gSKSmI2AU+F4x7ANDNbH679N3AoUNok5lkz2whslLQaaAocQnT/fwJ+kvSfEtq4C7g2jOlaYBxRYrcFM5sITARo0bKVjVsc3392F3bIIz/+7IEZrF+/ns2bN1O/fn3Wr1/PZZddxlVXXUWzZs1o3bo1ALfddhtdu3YlIyODL7/8kqZNmyKJefPmsdNOO/HHP/6x0vbMZGZmkpGRUSl9VQSPP/XiPoa4x19YfP83jeSx5ZJYrfD9aKAncAxwuaQORMlHPzNbltiApAOA9WXoszPwfjHnqxHNsvxUqJ/Eh7cB/zSzpyVlAKMTzlmh9izMcvQkGtckSf8E1hAlXwMK9ZNe6pH8YmPoaLOkXDPLj2Ez0e+IgPfM7KBCfTUsZfvXAi+b2fFhpiuzcN/BJor/nfypLPtgSlCWfpMys6/yjyXdC+xwL7n56quvOP744wHIy8vjT3/6E7///e/p168fy5Yto1q1auy5554Fr0x68sknueuuu6hRowa1a9fmscce802/zrltFvck5iugiaTfAjlAX2AWsIeZvSzpNeAUoB7Rss45ks4Jyzidy7D0AICkjsCVwJBiqs0CzgHGhmvSw16aRA2AleH4jELnjpV0A9HSSQYwStKewOdmdq+knYmWv64D7pDUysw+llQXaEa0dJMmaW8z+4RoKerXWgbsKukgM3sjzAjtY2bvSVorqYeZvQYMLOL6xPEOKod4AOYQJXRjiJKs44HTfmWbc4F7wv2vQfT7NLGoypJ2M7NV4eHxwJKi6m6vWrZsyaJFi7Yqf+qpp5LUhuHDhzN8eJFbypxzrkxincSEJZBrgHlET5IfEC11PCypAdGT2wQzWyvpWuBW4F1J1YiWcErzculDJS0E6gCrgRElvDJpBFFy8S7R/X0VGFaozmjgCUlrgNnAXgnn3gVeBhoD15rZF5LOAC6WlEuUrJ0e9qYMAqaExAbgCjP7MOzBeFbSj0RP9vVLMc4imdnPkk4EJoT7WoPoXr5HtC/lfklGlMAlcxPRctIVQLl8+qGZLZA0iehnD/CvsialSdp8W9LTRD+Dr4iW09YVc8lNYebLgGzg7F/Tv3POubLRLysHLtUkjQZydvSX7KaSpHpmliOpDlECOtTMFpRX+y1atrJqJ48vr+Yq3RZ7YmL4KdZx3w/g8ade3McQ1/glzTezboXLYz0T41wFmChpP6L9VZPLM4FxzjlXvjyJ2UaSLgdOKlT8hJldt61tmtnoXxVUMSSdCZxbqHiumf0tWf1UkvQWsHOh4tPMbHFF921mf0oSzx1Er1xKNN7MHqjoeJxzzhXNk5htFJKVbU5YKlt4wo3Fk66ZHZDqGBKVZ6JXu2Z1lsVwGSZfZmYm2QMzUh2Gc84BMX/HXuecc87tuDyJcc4551wseRLjnHPOuVjyPTHOVaINuZtIG1Uub5VTruL4cmnnnPOZGOecc87FkicxzjnnnIslT2Kcc845F0uexDjnAPjpp5/o3r07nTp1ol27dlx99dUAzJ49my5dutC+fXtuuOEG8vLytrju7bffpkaNGjz55JOpCNs5twPzJMY5B8DOO+/M7NmzWbRoEVlZWcycOZPXX3+dM844g8cee4wlS5bQtGlTJk+eXHDNpk2bGDlyJH369Elh5M65HZUnMa5SSBokafeEx5mSuoXjy8qxnwxJzyT0+bWkhZI+kvS8pIPLq6/tjSTq1asHQG5uLrm5uVSvXp2ddtqJffbZB4Bu3brx1FNPFVxz22230a9fP5o0aZKSmJ1zOzZPYlxlGQTsXsS5cktikphqZp3NrDUwBvi3pLYV2F+sbdq0ifT0dJo0aULv3r3p3r07eXl5vPPOOwC88sorrFixAoCVK1cybdo0/vKXv6QyZOfcDsyTmCpG0nRJ8yW9J2mopOqSJklaImmxpPNDvRGSlkp6V9JjoayupPslzQuzD8eG8nahLCvUbx3qPitpUWi7f6ibLemGUPcdSV3CDMYnkoYlxHmxpLdDe38PZWmS3pd0b4h/lqTakk4EugGPhHZrJ7QzBqgdyh8JZacmxHuPpOqhPEfS2ND2i5K6hxmd/0n6Y0n31sxeBiYCQ4u5/38O41ok6SlJdUJ5U0nTQvmi/BkdSaeHe7BI0kNl+FFXSdWrVycrK4vPP/+cefPm8d577/HYY49x/vnn0717d+rUqUP16tUBOO+887jxxhupVs3/G3HOpYbMLNUxuASSGpnZd+GJ/m3gDGCMmfUO5xua2VpJXwB7mdnGhLLrgaVm9rCkhsA8oDPRDMSbZvaIpJ2A6sBRwO/N7M+h3QZmtk5SNnCjmd0l6RbgcKJPcK4FLDGzppL6ACcCZwMCngZuAj4DPga6mVmWpMeBp0M8mcBFZvZO6K/gsaQcM6sXytuGtk4ws1xJd4bYH5RkwFFm9l9J04C6wNHAfsBkM0uXlBHa7StpUIhleML9PQ4428z+UMT9/62ZfRuO/wF8ZWa3SZoKvGFmt4akqh7QHJgGHGxm3+T/7JK0OZSQODVuvGvXq269t+hfgBTp0KzBVmWTJ0+mVq1a9O/fv6Ds1VdfZfbs2YwePZoBAwaQ///HunXrqFWrFhdeeCE9evSotLjLKicnp2DJLI48/tSL+xjiGn+vXr3mm1m3wuX+jr1VzwhJx4fjPYCdgJaSbgOeBWaFc+8SzWxMB6aHsj7AHyVdFB7XAloAbwCXS2oO/NvMPpK0GBgn6UbgGTObkxDD0+H7YqCemf0A/CBpY0iO+oSvhaFePaA1URKz3MyyQvl8IK2M4z8c6Aq8LQmgNrA6nPsZmJkQ28aQ6CwuQz8q4Xz7kLw0JBrX86H8d8DpAGa2CVgn6XTgCTP7JpRvlcCE8olEM0C0aNnKxi2uev/ssgdm8PXXX1OzZk0aNmzIhg0buPLKKxk5ciT77bcfTZo0YePGjVxwwQXcfPPNZGRksGrVqoLrBw0aRN++fTnxxBNTOIqSZWZmkpGRkeowtpnHn3pxH0Pc4y+s6v1vugMLswhHAAeZ2Y9htmJnoBNwJDAMOBkYTDQD0RM4hihB6UD0BN3PzJYVavp9SW+Fa56TdLaZzZbUhWhG5h+SXjKza0L9jeH75oTj/Mc1Qj83mNk9heJPK1R/E1ESUqbbQDSrcmmSc7n2y9RhQWxmtllSaX+XOwPvF3N+EnCcmS0KMzkZpWw39latWsUZZ5zBpk2b2Lx5MyeffDJ9+/bl4osv5plnnmHz5s307t2b3/3ud6kO1TnnAE9iqpoGwJqQwLQBDgQaA9XM7ClJy4CHJVUD9jCzlyW9BpzCL7MG50g6x8xMUmczWyipJfA/M5sgqQXQUdIHwHdhqWctMKQMcT4PXCvpETPLkdQMyC3hmh+A+kWcy5VU08xygZeAGZJuMbPVkhoB9c3s0zLEl5Skw4iWdXoVU60+sEpSTWAgsDKUvwT8BUhcTpoNTJP0TzP7tqjlpLjo2LEjCxcu3Kp87NixjB07Foj+iktm0qRJFRiZc84l50lM1TITGCbpfWAZ8CbQDMgMiQvApUR7Wh6W1IBo5mJC2BNzLXAr8G6ovxzoSzR7c5qkXOBL4Hpgf2CspM1ECUipX2JiZrPC3pU3wpJPDnAq0cxLUSYBd0vaABxU6NzEEPMCMxso6QpgVhhDLvA3YFuTmP6SegB1iO5HPzMrbibmSuAt4OvwPT/xOheYKOksonH+xczekHQd8IqkTUTLa4O2MU7nnHNl5Bt7natELVq2smonj091GFsp7adYx3093eNPrbjHD/EfQ1zjl5R0Y6+/NtI555xzseTLSW6HJOkOopeOJxpvZg+kIh7nnHNl50mM2yGZ2d9S0W/tmtVZVsqlG+ecc8Xz5STnnHPOxZInMc4555yLJU9inHPOORdLvifGuUq0IXcTaaOerZS+SvuyaeeciyufiXHOOedcLHkS45xzzrlY8iTGOeecc7HkSYxzzjnnYsmTGOe2YytWrKBXr17st99+tGvXjvHjo89t6t+/P+np6aSnp5OWlkZ6ejoAP//8M2eeeSYdOnSgU6dORX5qtXPOVQX+6qQUkjQImGVmX4THmcBFZvaOpMvM7Ppy6icjtNs39DkW+ByoB/wP+LuZvV4efZW3wvfIlU2NGjUYN24cXbp04YcffqBr16707t2bqVOnFtS58MILadCgAQD33nsvAIsXL2b16tX84Q9/4O2336ZaNf97xzlX9fj/TKk1CNi9iHOXVWC/U82ss5m1BsYA/5bUtrw7kVSjuMelNIii71GVI6l6qmNItNtuu9GlSxcA6tevT9u2bVm5cmXBeTPj8ccfZ8CAAQAsXbqU3/3udwA0adKEhg0b8s4771R+4M45VwqxSGIkTZc0X9J7koZKqi5pkqQlkhZLOj/UGyFpqaR3JT0WyupKul/SPEkLJR0bytuFsqxQv3Wo+6ykRaHt/qFutqQbQt13JHWR9LykTyQNS4jzYklvh/b+HsrSJL0v6d4Q/yxJtSWdCHQDHgnt1k5oZwxQO5Q/EspOTYj3nvwnS0k5ksaGtl+U1F1SpqT/SfpjSffWzF4GJgJDi7n/rULbiyQtkLS3ImMTfgb59ypD0hxJTwNLkzyuHq7Lv09nJ/QzMrS1SNKY4u5RofiuCu0tkTRRkkJ5pqQbw337UNKhxfzsL5Y0Ipy/RdLscPy7hJ9BH0lvhHvwhKR6Cb8fN0paAJxU0j1PlezsbBYuXMgBBxxQUDZnzhyaNm1K69atAejUqRNPP/00eXl5LF++nPnz57NixYpUheycc8WKy3LSYDP7LjyJvQ3MB5qZWXsASQ1DvVHAXma2MaHscmC2mQ0OZfMkvQgMI/rU4kck7QRUB44CvjCzo0O7DRJi+MzM0iXdAkwi+gTkWsAS4G5JfYDWQHdAwNOSegKfhfIBZvZnSY8D/czsYUnDCctHoT8AzGyUpOFmlh7K2wL9gUPMLFfSncBA4EGgbhjfxZKmAf8AegP7AZOBp0txfxcAZxdz/hFgjJlNk1SLKPk9AUgHOgGNgbclvRrqdwHam9lyRUtZiY+HAuvMbH9JOwNzJc0C2gDHAgeY2Y+SGoWf+Rb3qAi3m9k14V49BPQF/hPO1TCz7pKOAq4GjiD5z34OcCEwgShx2llSTeBQ4FVJjYErgCPMbL2kkcAFwDWhn2/NrEuy4MKYhwI0brwrV3XIK2Yo5SdxP8uGDRs499xzGTJkCAsWLCgov+WWW+jevXtB3b333psXXniBNm3a0LRpU9q0acP7779fcD4nJyfW+2Q8/tSKe/wQ/zHEPf7C4pLEjJB0fDjeA9gJaCnpNuBZYFY49y7RX+3TgemhrA/wR0kXhce1gBbAG8DlkpoD/zazjyQtBsZJuhF4xszmJMSQnwwsBuqZ2Q/AD5LyE6Y+4WthqFePKHn5DFhuZlmhfD6QVsbxHw50JUoUAGoDq8O5n4GZCbFtDInO4jL0oyJPSPWJEsZpAGb2UyjvAUwxs03AV5JeAfYHvgfmmdnyhGYSH/cBOoZZFoAGRPfpCOABM/sx9PNdKWMH6CXpEqAO0Ah4j1+SmH+H74n3PdnPfj7QVdIuwEaixK4bURIzAjiQKDGcG34GO4V28k2lCGY2kWi2ixYtW9m4xZXzzy57YAYAubm59O3bl2HDhnHBBRcUnM/Ly6N///7Mnz+f5s2bF5QffvjhBccHH3wwJ5xwAvvttx8QJUYZGRmVEn9F8PhTK+7xQ/zHEPf4C6vySUz4S/4I4KDwF3omsDPRDMCRRH9VnwwMBo4GegLHED1JdSB6gu5nZssKNf2+pLfCNc9JOtvMZkvqQjQj8w9JL+X/hU/0xAawOeE4/3GN0M8NZnZPofjTCtXfRJSElOk2AJPN7NIk53LNzArHZmabVfo9KJ2B98sYU3HWF/NYwDlm9nxiBUlHbktHYWboTqCbma2QNJooUc2Xf+83EX7fzezRIn72y4n24LxOlBD3AloR3Zu9gRfMbEARoRQec5VgZpx11lm0bdt2iwQG4MUXX6RNmzZbJDA//vgjZkbdunV54YUXqFGjRkEC45xzVU0c9sQ0ANaEBKYN0V/EjYFqZvYU0RR/F0nVgD3CHo+R4bp6wPPAOQn7JDqH7y2B/5nZBGAG0ezA7sCPZvYw0St4ki4PFOF5YHDCPolmkpqUcM0PQP0izuWG5QyAl4AT89uT1EjSnmWIrUiSDiNa6rg32fkw4/S5pONC/Z0l1SFafumvaI/LrkTJ47xSdPk88Jf8sUnaR1Jd4AXgzNA2khqF+sXdI/glYfkm3PsTi6lLaHurn304NQe4CHg1HA8DFoYk8U3gEEmtQht1Je1TivGm1Ny5c3nooYeYPXt2wUuqn3vuOQAee+yxgg29+VavXk2XLl1o27YtN954Iw899FAqwnbOuVKp8jMxREslwyS9DywjejJpBmSGxAXgUqJ9DQ+HfSwCJpjZWknXArcC74b6y4n2TJwMnCYpF/gSuJ5oOWSspM1ALvCX0gZpZrPC3pU3Qr6UA5xKNANQlElE+2k2AAcVOjcxxLzAzAZKugKYFcaQC/wN+LS08RXSPywH1SG6H/3MrLiZmNOAeyRdE/o+CZgWYl4EGHCJmX0ZEs3i/ItoWWdBSCy/Bo4zs5mS0oF3JP0MPEf0Cq1JJNwjM9uQ2Fj4Gd9LtDfpS6I9UyVJ9rOHKHG5HHgj7Hv5KZRhZl8rern3lLCXB6IE+sNS9JcyPXr04JeJui1NmjRpq7K0tDSWLSs8aemcc1WTivoPzjlX/lq0bGXVTh5fKX1VxKdYx3093eNPrbjHD/EfQ1zjlzTfzLoVLo/DcpJzzjnn3FbisJzkKomkO4heOp5ovJk9kIp4CgsvId+rUPHIwpuEnXPO7Rg8iXEFzOxvqY6hOGZ2fMm1qrbaNauzrAKWeZxzbkfky0nOOeeciyVPYpxzzjkXS57EOOeccy6WfE+Mc5VoQ+4m0kY9W2HtV8TLqp1zrqrymRjnnHPOxZInMc4555yLJU9inHPOORdLnsQ455xzLpY8iXFuO7NixQp69erFfvvtR7t27Rg/PvqsptGjR9OsWbOtPs163rx5BWWdOnVi2rRpqQzfOedKzV+dFAOS0oBnzKy9pAxgBvA/ok+h/gq4ycyeKeb60UCOmd0sqRbwH2CumY2ugFgL+irDNTlmVm8b+sr/mISdiD6OIP/jl/9hZk+Wtb1CbWcCF5nZO4XKBwHdzGz4r2m/ItWoUYNx48bRpUsXfvjhB7p27Urv3r0BOP/887nooou2qN++fXveeecdatSowapVq+jUqRPHHHMMNWr4fw/OuarN/5eq4iQl+xnNMbO+4Xw6MF3SBjN7qYS2dgKeAuZXRAJT2fI/JiEhyUtPaUBVxG677cZuu+0GQP369Wnbti0rV64ssn6dOnUKjn/66SckVXiMzjlXHnw5qZxJSpO0JOHxRZJGSxohaamkdyU9Fs7VlXS/pHmSFko6NpQPkvS0pNlAsYmJmWUB1wAlzQzUAKYCH5nZqIRY35d0r6T3JM2SVDucS5f0Zoh3mqTfSGoiaX4430mSSWoRHn8iqU5ih5L2ljRT0nxJcyS1CeV7SXpD0mJJ/0ioX03SnZI+kPSCpOcknRjOdZX0SmjreUm7FXP/50haEL4OLqntkkg6U9KHkuaR8AGZko6R9Fb42b0oqWlp2qtM2dnZLFy4kAMOOACA22+/nY4dOzJ48GDWrFlTUO+tt96iXbt2dOjQgbvvvttnYZxzseD/U1WeUcBeZrZRUsNQdjkw28wGh7J5kl4M57oAHc3suzDTUJwFwMUl1LkEeMHMzitU3hoYYGZ/lvQ40A94GHgQOMfMXpF0DXC1mZ0nqZakXYBDgXeAQyW9Bqw2sx8L/RU/ERhmZh9JOgC4E/gdMB64y8welJT4oZMnAGnAfkAT4H3gfkk1gduAY83sa0n9geuAwUnGuRrobWY/SWoNTAG6FdV2CfeMkCz9HegKrANeBhaG068BB5qZSRoS7vGFSdoYCgwFaNx4V67qkFdSt9ssMzOz4HjDhg2ce+65DBkyhAULFtCxY0fuu+8+JHH//ffzpz/9iZEjRxbUv+OOO/j000+57LLLqFu3LjvttNNW7efk5GzRR9x4/KkV9/gh/mOIe/yFeRJTed4FHpE0HZgeyvoAf5SUv0mhFtAiHL9gZt+Vsu3SzP+/BhwsaR8z+zChfHmYzQGYD6RJagA0NLNXQvlk4Ilw/DrRbERP4Hrg96H/OVsEJNUDDgaeSEhsdg7fDyFKlgAeAm4Mxz2AJ8xsM/ClpJdD+b5Ae+CF0FZ1YFUR46wJ3B6W2TYB+5TQdkkOADLN7OswrqkJbTYHpoZEZydgebIGzGwiUUJHi5atbNziivtnlz0wA4Dc3Fz69u3LsGHDuOCCC7aq17JlS/r27UtGRsZW5yZPnkyjRo3o1q3bVucyMzOTXhMXHn9qxT1+iP8Y4h5/Yb6cVP7y2PK+1grfjwbuIJpheTvsdRHQz8zSw1cLM3s/1F9fhj47E80sFOdV4Dzgv4WWYjYmHG+i5MT2VaJZmD2JNhh3IkoQ5hSqVw1YmzC2dDNrm3DeSugnkYD3EtrpYGZ9iqh7PtFm505EMzBbTyeUn9uA282sA3A2v/ysU8rMOOuss2jbtu0WCcyqVb/kfdOmTaN9+/YALF++nLy8aHbo008/5YMPPiAtLa1SY3bOuW3hSUz5+wpoIum3knYG+hLd5z3M7GVgJNAAqAc8D5yjML0gqXNZO5PUEbiSKEEqlpk9BdwMzExY0kpWbx2wRtKhoeg0IH9WZg5wKtHems3Ad8BRRDM9iW18DyyXdFKIU5I6hdNzgVPC8cCEy+YC/cL+laZARihfBuwq6aDQVk1J7YoIvwGwKsR2GtGsTXFtl+Qt4LDw86wJnFSor/wds2eUsr0KN3fuXB566CFmz569xcupL7nkEjp06EDHjh15+eWXueWWWwB47bXX6NSpE+np6Rx//PHceeedNG7cOMWjcM65kvlyUjkzs9ywh2Qe0RPcB0RPpA+HZRoBE8xsraRrgVuBdyVVI1qO6FuKbg6VtJDoJdargRElvTIpIb67wpP404R9GkU4A7g7bNb9H3BmuD47JF2vhnqvAc3NbE2SNgYCd0m6gmiZ5zFgEXAu8KikkUSzOfmeAg4HlgIriPb6rDOzn8Mm3AnhHtYgum/vJenzTuApSacDM/llRitp28WMnzDeVYpeNv4GsBbISjg9mmi5bA0wm+hl3inXo0cPzLae6DrqqKOS1j/ttNM47bTTKjos55wrd0r2n51zqSKpnpnlSPotUSJ4iJl9WdXbLq0WLVtZtZPHV1j7Ff0p1nFfT/f4Uyvu8UP8xxDX+CXNN7OtNur5TIyrap4JS107AdeWc5JRkW0755yrZJ7EbEckXc6WezYgekXOdamIZ1uYWUZlti1pGlsvA400s+crKg7nnHPlw5OY7UhIVmKTsFQFZnZ8ZfZXu2Z1llXwko9zzu0o/NVJzjnnnIslT2Kcc845F0uexDjnnHMulnxPjHOVaEPuJtJGPVsubVX0y6mdc66q85kY55xzzsWSJzHOOeeciyVPYpxzzjkXS57EOOeccy6WPIlxLsZWrFhBr1692G+//WjXrh3jx0efy/Tdd9/Ru3dvWrduTe/evVmzJvp8zhkzZtCxY0fS09Pp1q0br732WnHNO+dcleZJjHMxVqNGDcaNG8fSpUt58803ueOOO1i6dCljxozh8MMP56OPPuLwww9nzJgxABx++OEsWrSIrKws7r//foYMGZLiETjn3LbbrpMYSWmSloTjDEnrJC2UtEzSq5L6pji+DEkHJzweJun0Sug3U9JWnwZaju2nSzqqFPW2GH8x9QZJur18otu+7LbbbnTp0gWA+vXr07ZtW1auXMmMGTM444wzADjjjDOYPn06APXq1UMSAOvXry84ds65ONpu3ydGUrKxzTGzvuF8OjBd0gYze6lSg/tFBpADvA5gZnenKI7ylg50A54roV4GCePfHkiqYWZ5qeg7OzubhQsXcsABB/DVV1+x2267AfB///d/fPXVVwX1pk2bxqWXXsrq1at59tnyec8a55xLhSqTxEhKA54xs/bh8UVAPeA7YBiQByw1s1Mk1QVuA9oDNYHRZjZD0iDghHBddeCMovozsyxJ1wDDgaRJjKS9gEdDezOA88ysnqQM4KKEhOh24B0zmySpK/DPcM03wCAzWyVpROI4gFHh8SZJpwLnAIcDOWZ2c0iy7gbqAJ8Ag81sjaRM4C2gF9AQOMvM5hQRf3XgRuD3wGbgXjO7rVCdu4D9gdrAk2Z2dSgfA/wxxDvLzC6SdBJwNbAJWGdmPZP0uRNwDVBbUg/gBuAF4H6gJfAjMBT4Psn4GwJXADsB3wIDzeyrwn0k6fOYZNdJGg20CP22AG41swnh9+dxoDnR78m1wP+AS83sBEnHAo8BDYhmK5eaWUtJewN3ALuGcfzZzD6QNAn4CegMzAUuKBTf0DBmGjfelas6lE+Ok5mZWXC8YcMGzj33XIYMGcKCBQvIy8vb4vymTZsKHv/mN7/h7rvvZtGiRQwfPpxx48aVus+cnJwt2o0bjz+14h4/xH8McY+/sCqTxBRjFLCXmW2U1DCUXQ7MNrPBoWyepBfDuS5ARzP7LiRGxVkAXFzM+fHAXWb2oKS/lRSopJpEydWxZva1pP5Enyo9uPA4zGytpLsJSUu4/vCE5h4EzjGzV0KydTVwXjhXw8y6hyWbq4EjighpKJAGpJtZnqRGSepcHu5VdeAlSR2BlcDxQBszs4T7fhVwpJmtTCjbgpn9LOkqoJuZDQ/jug1YaGbHSfod8KCZpScZ/2+AA0OfQ4BLgAuLGFui14q5rg1RwlcfWBaStt8DX5jZ0aHfBsB6ohkkgEOBJUTJXQ2ipBFgIjDMzD6SdABwJ/C7cK45cLCZbUpyTyaGa2nRspWNW1w+/+yyB2YAkJubS9++fRk2bBgXXBDlT82aNWPfffdlt912Y9WqVey+++5kZGRscX1GRgbjx4+nffv2NG7cuFR9ZmZmbtVOnHj8qRX3+CH+Y4h7/IXFYU/Mu8Aj4a/1/D9h+wCjJGUBmUAtor+0AV4ws+9K2XZJGwIOAaaE44dK0d6+RLNDL4TYriB6coPk40geVPSk2tDMXglFk4HEWY9/h+/ziZKUohwB3JO/vFHEfTlZ0gJgIdAO2A9YRzSzcJ+kE4hmHSCaZZgk6c9EMxil1YNw/8xsNvBbSbskqdcceF7SYqLksl0p2y/uumfNbKOZfQOsBpoCi4Hekm6UdKiZrQv36BNJbYHuRLNpPYkSmjmS6gEHA0+En+09wG4J/TyRLIGpaGbGWWedRdu2bQsSGIA//vGPTJ48GYDJkydz7LHHAvDxxx9jZgAsWLCAjRs38tvf/rayw3bOuXJRlWZi8tgyqaoVvh9N9GRyDHC5pA5EyUc/M1uW2ED463h9GfrsDLxfQh0rQ6wC3jOzg5Jck2wc22pj+L6JX/EzDMtlFwH7h6WqSUCtMGvTnWh560SiJbffmdmwcI+PBuZL6mpm3/6KcRR2G/BPM3s6LNmNLofrNiYcbyKaxfpQUhfgKOAfkl4ys2uAV4E/ALnAi8AkomTtYqKf91ozSy8ihrL83pWbuXPn8tBDD9GhQwfS09MBuP766xk1ahQnn3wy9913H3vuuSePP/44AE899RQPPvggNWvWpHbt2kydOtU39zrnYqsqJTFfAU0k/ZZos2dfYBawh5m9LOk14BSivSbPA+dIOicsIXQ2s4Vl6Swsm1wJFPca07mhz4eBgQnlnwL7SdqZaC/J4URLGsuAXSUdZGZvhOWlfYgSpWTj+AHYakbCzNZJWhNmCeYApwGvFK5XCi8AZ0t6OX85qdBszC5ET77rJDUlegLPDLMOdczsOUlzifaLIGlvM3sLeEvSH4A9iPagFPYD0fJNvjlE9+/akGR8Y2bfSyo8/gZES1lQzH6mJMp0naTdge/M7GFJa/nld2AO0TLeg2E58LdEMzdLwu/ZckknmdkTip75O5rZojLEWe569OhRMLNS2Esvbb3Va+TIkYwcObKiw3LOuUpRZZIYM8sNez/mET0hfUD0V/DDYXlFwISwl+Ra4FbgXUnVgOVESU9JDpW0kGiz7GpgRAmvTDoXeFTSSKKNvfmxrpD0ONG+ieVESzH5+0FOBCaEmGuEOD8sYhz/AZ4MG0nPKdT3GcDdkuoQJRFnlmJ8hf2LKIl6V1IucC9Q8FJlM1sU7scHwAqipA2iBGSGpFoh3vx1irGSWoeyl4CinsBf5pflvhuIZkbul/Qu0dJUfqJRePyjiZZr1gCzgb1KOc6yXtchjGUz0azLX0L5W0RJy6vh8bvA/9kvWcJA4C5JVxBtKH+Mou+Bc865Cqai/opzW5OUY2b1Uh2Hi68WLVtZtZPHl0tb2WOOLpd2yiLumwI9/tSKe/wQ/zHENX5J881sq/c3i8PGXuecc865rVSZ5aRUknQ5cFKh4ifM7LrEgqo6CyPpSKL3g0m03MyO3576Le3PqSqrXbM6y1Iwg+Kcc9sjT2KA8CQYmyfCwszseaLNztt1v3H/OTnnnCtfvpzknHPOuVjyJMY555xzseRJjHPOOediyffEOFeJNuRuIm3Ur//k6FS8vNo556oan4lxzjnnXCx5EuOcc865WPIkxjnnnHOx5EmMczE2ePBgmjRpQvv27QvKFi1axEEHHUSHDh045phj+P777wF44YUX6Nq1Kx06dKBr167Mnj07VWE751y58CTGuRgbNGgQM2fO3KJsyJAhjBkzhsWLF3P88cczduxYABo3bsx//vMfFi9ezOTJkznttNNSEbJzzpUbT2KqKElpkpaE4wxJ6yQtlLRM0quSSvOp3RUZX4akgxMeD5N0eiX0mylpqw8B21H17NmTRo0abVH24Ycf0rNnTwB69+7NU089BUDnzp3ZfffdAWjXrh0bNmxg48aNlRuwc86VI09iqiBJyV76PsfMOpvZvsAI4HZJh1dyaIkygIIkxszuNrMHUxeOy9euXTtmzJgBwBNPPMGKFSu2qvPUU0/RpUsXdt5558oOzznnyo3MLNUxxJ6kNOAZM2sfHl8E1AO+A4YBecBSMztFUl3gNqA9UBMYbWYzJA0CTgjXVQfOyG9TUgZwkZn1TehzMHBMUR+2KGkv4NHQ3gzgPDOrV7gtSbcD75jZJEldgX+Ga74BBpnZKkkjEscBjALeBDYBXwPnAIcDOWZ2s6R04G6gDvAJMNjM1kjKBN4CegENgbPMbE4R8Vcn+nDJ3wObgXvN7LbQxkVm9o6ku4D9gdrAk2Z2dbh2DPDHEO8sM7tI0knA1SHmdWbWs4h+04CHgLqhaLiZvR7OjQRODfH818xGSWoVxrpraPskM/ukUJtDgaEAjRvv2vWqW+9N1nWZdGjWoOD4yy+/5NJLL+WBBx4A4LPPPuO2225j3bp1HHLIIfz73/8uSGoAli9fzhVXXMFNN91Es2bNytRvTk4O9epVyc9BLRWPP7XiHj/Efwxxjb9Xr17zzWyrWXh/s7uKNQrYy8w2SmoYyi4HZpvZ4FA2T9KL4VwXoKOZfReeTIuzALi4mPPjgbvM7EFJfyspUEk1iZKrY83sa0n9iT5scXDhcZjZWkl3E5KWcH3irNCDwDlm9oqka4iSh/PCuRpm1l3SUaH8iCJCGgqkAelmliepUZI6l4d7VR14SVJHYCVwPNDGzCzhvl8FHGlmKxPKklkN9DaznyS1BqYA3ST9ATgWOMDMfkyI5xFgjJlNk1SLJLObZjYRmAjQomUrG7f41/+zyx6Y8ctxdjZ169YlI+OXstNPj1b2PvzwQ957772Cc59//jlDhw7l8ccf55BDDilzv5mZmVv0Ezcef2rFPX6I/xjiHn9hvpxUsd4FHpF0KtGsAEAfYJSkLCATqAW0COdeMLPvStm2Sjh/CNETMEQzCyXZl2h26IUQ2xVA83Au2TiSByU1ABqa2SuhaDKQOOvx7/B9PlGSUpQjgHvMLA+giPtysqQFwEKgHbAfsA74CbhP0gnAj6HuXGCSpD8TzXQVpSZwr6TFwBOhzfx4HjCzH/PjkVQfaGZm00LZT/nnU2n16tUAbN68mX/84x8MGzYMgLVr13L00UczZsyYbUpgnHOuqvEkpnzkseW9rBW+Hw3cQTTD8nbY6yKgn5mlh68WZvZ+qL++DH12Bt4voU6ytcKiYhXwXkJcHcysTzHj2Fb5O0k38StmAsNy2UXA4WbWEXgWqBWSnu7Ak0BfYCaAmQ0jSsz2AOZL+m0RTZ8PfAV0AroBO21rjJVhwIABHHTQQSxbtozmzZtz3333MWXKFPbZZx/atGnD7rvvzplnngnA7bffzscff8w111xDeno66enpBQmPc87FkS8nlY+vgCbhiTGH6MlzFrCHmb0s6TXgFKK9Js8D50g6Jyx3dDazhWXpLCybXAkMKaba3NDnw8DAhPJPgf0k7Uy0l+Rw4DVgGbCrpIPM7I2wvLQPUaKUbBw/ALsU7tTM1klaI+nQsN/lNOCVwvVK4QXgbEkv5y8nFZqN2YUo6VsnqSnwByBTUj2gjpk9J2ku8D8ASXub2VvAW2FpaA/g2yT9NgA+N7PNks7gl1mbF4CrJD2Sv5wUZmM+l3ScmU0P97R6Zc7GTJkyJWn5ueeeu1XZFVdcwRVXXFHRITnnXKXxJKYcmFlu2Psxj2hPxgdET34Ph+UVARPCXpJrgVuBdyVVA5YTJT0lOVTSQqLNsquBEWb2UjH1zwUeDZtRC3Z1mtkKSY8DS0LfC0P5z5JOBCaEmGuEOD8sYhz/AZ6UdCzRxt5EZwB3S6pDlEScWYrxFfYvoiTqXUm5wL3A7QnjWBTuxwfACqKkDaA+MCPsTxFwQSgfG/a4CHgJWFREv3cCT4WXi88kzI6Z2cywYfkdST8DzwGXESVp94Sffy5wUhizc865CuZJTDkxswnAhFLU2wCcnaR8EjAp4XE20R4VzCyTaIagLPEsBw7KfyzpvIRzlwCXJLkmiy33r+TrkaTuh0DHhKI5CeeygAOTXJORcPwNxeyJCctCF/BLEpKsjUFFXN49SXsnFNVXoXofseW4RiacGwOMSVL/d6Vp2znnXPnyPTHOOeeciyWfiYk5SZcTLWEkesLMrkssMLMq+cYAko4kej+YRMuLev+buPdbu2Z1lo05uiK7cM65HYYnMTEXkpXrSqxYRZnZ80SbnXeIfp1zzpUfX05yzjnnXCx5EuOcc865WPIkxjnnnHOx5HtinKtEG3I3kTbq2W2+Pts3BTvnXAGfiXHOOedcLHkS45xzzrlY8iTGOeecc7HkSYxzMTR48GCaNGlC+/btC8r69+9f8OnUaWlppKenA/Dtt9/Sq1cv6tWrx/Dhw1MUsXPOlT/f2OtcDA0aNIjhw4dz+umnF5RNnTq14PjCCy+kQYPo47Zq1arFtddey5IlS1iyZEmlx+qccxXFZ2JcpZA0SNLuCY8zJXULx5eVYz8Zkp5J6PNrSQslfSTpeUkHl1dfSfrOqai2C+vZsyeNGjVKes7MePzxxxkwYAAAdevWpUePHtSqVauywnPOuUrhSYyrLIOA3Ys4V25JTBJTzayzmbUm+gTqf0tqW4H9pdycOXNo2rQprVu3TnUozjlXoXw5qYqRNB3YA6gFjAfuC1/dAAPuN7NbJI0AhgF5wFIzO0VSXeA2oD1QExhtZjMktQMeAHYiSlz7AV8AjwPNgerAtWY2VVI2MAX4Q2h7KHAD0AoYa2Z3hzgvBk4GdgammdnVktKA/wKvAQcDK4FjgaND/I9I2gAclDDeMUBtSVnAe2Y2UNKpwIgQ71vAX81sU5jpuAs4ClhFlPzcBLQAzjOzp4u7t2b2sqSJYUznF3H/t7ivwJ+A/wHpZrY21PkI6AHUAR4F6gEziupX0tDQJ40b78pVHfKKC7NYmZmZBcdffvkl69ev36IM4JZbbqF79+5blX/wwQesXLlyq/KyyMnJ+VXXp5rHn1pxjx/iP4a4x1+YJzFVz2Az+05SbeBtYD7QzMzaA0hqGOqNAvYys40JZZcDs81scCibJ+lFoifl8Wb2iKSdiJKWo4AvzOzo0G6DhBg+M7N0SbcAk4BDiJKqJcDdkvoArYHugICnJfUEPgvlA8zsz5IeB/qZ2cOShgMXmdk7oT8AzGyUpOFmlh7K2wL9gUPMLFfSncBA4EGgbhjfxZKmAf8AegP7AZOBYpOYYAFwdjHnt7ivZrZZ0gzgeOABSQcAn5rZV5KeBu4yswcl/a2oBs1sIjARoEXLVjZu8bb/s8semPHLcXY2devWJSPjl7K8vDz69+/P/Pnzad68+ZbXZmeTk5OzRf2yyszM/FXXp5rHn1pxjx/iP4a4x1+YLydVPSMkLQLeJJqR2QloKek2Sb8Hvg/13iWa2TiVaNYAoA8wKsxqZBIlHi2AN4DLJI0E9jSzDcBioLekGyUdambrEmLITwYWA2+Z2Q9m9jWQnzD1CV8LiZKCNkTJC8ByM8sKx/OBtDKO/3CgK/B2GMfhQMtw7mdgZkJsr5hZbjgubT8q4Xyy+zqVKLECOCU8hii5mxKOHypl/xXqxRdfpE2bNlslMM45tz3yJKYKkZQBHAEcZGadiJKEnYFOREnJMOBfofrRwB1AF6In/BpET9D9zCw9fLUws/fN7FHgj8AG4DlJvzOzD8O1i4F/SLoqIZSN4fvmhOP8x/n93JDQTyszu6/QtQCbKPtsn4DJCW3va2ajw7lcM7PCsZlZflyl0Rl4v5jzye7rG0ArSbsCxwH/TqhvW7VQCQYMGMBBBx3EsmXLaN68OffdF93+xx57rGBDb6K0tDQuuOACJk2aRPPmzVm6dGllh+ycc+XOl5OqlgbAGjP7UVIb4ECgMVDNzJ6StAx4WFI1YI+wx+M1otmBesDzwDmSzjEzk9TZzBZKagn8z8wmSGoBdJT0AfBdWOpZCwwpQ5zPA9dKesTMciQ1A3JLuOYHoH4R53Il1QyzKi8BMyTdYmarJTUC6pvZp2WILylJhxHtTelVxPmk99XM1oblq38C75vZt+GSuaHOw0RLXpVmypQpScsnTZqUtDw7O7vignHOuRTxJKZqmQkMk/Q+sIxoSakZkBmeYAEuJdrT8nDYxyJgQniivRa4FXg31F8O9CXagHuapFzgS+B6YH9grKTNRAnIX0obpJnNCntX3gh7W3KAU4lmXooyiWg/zRYbe4OJIeYFYWPvFcCsMIZc4G/AtiYx/SXlb8JdTjRTVdRMTNL7Gs5NJdqjNCih/rnAo2GZrsiNvc455yqGJzFViJltJHpVUGHjk5T1SHL9BpJsWjWzMUQvL070fPgqXDct4XgSUfKR7Nz4IuJqn1Dn5oTjp4CnEuplJJwbCYxMeDyVX/adJMZWL+F4dLJzZpZJtPS2VfwlCTNBW93XcO4dCu2nMbPlbJmQXVHavpxzzv16vifGOeecc7HkMzFuhyTpDqJXFyUab2YPVGS/tWtWZ9mYoyuyC+ec22F4EuN2SGZW5Pu6OOeciwdfTnLOOedcLHkS45xzzrlY8iTGOeecc7Hke2Kcq0QbcjeRNurZbbo22zcEO+fcFnwmxjnnnHOx5EmMc84552LJkxjnnHPOxZInMc7FyODBg2nSpAnt2xd8ugOjR4+mWbNmpKenk56eznPPPbfFNZ999hn16tXj5ptvLtycc87FmicxzsXIoEGDmDlz5lbl559/PllZWWRlZXHUUUdtce6CCy7gD39I9pFczjkXb57EVGGS0iQtCccZktZJWihpmaRXJfUt4frRki4Kx7UkvSBpdAXFWtBXGa7J2ca+7pCUJWmppA3hOEvSidvSXnlI/FlVpJ49e9KoUaNS158+fTp77bUX7dq1q8ConHMuNTyJqaIkJXv5+xwz62xm+wIjgNslHV6KtnYi+gTp+YU//TmOzOxvZpYOHAV8Ymbp4evJFIeWMrfffjsdO3Zk8ODBrFmzBoCcnBxuvPFGrr766hRH55xzFcPfJ6acSEoDnjGz9uHxRUA94DtgGJAHLDWzUyTVBW4D2gM1gdFmNkPSIOCEcF114Iyi+jOzLEnXAMOBl4oJrQYwFfjIzEYlxPpf4DXgYGAlcKyZbZCUDtwN1AE+AQaHGP9rZl0ldQKygD3N7DNJnwAdCt2LvYE7gF2BH4E/m9kHkvYCHg3jm5FQvxpwO/A7YAWQC9xvZk9K6gr8M1zzDTDIzFYVHmQY00NA3VA03MxeL67tZDdL0hjgj0Q/r1nAtcC7wF5mtjn87D4AWgIdgfvDpbOStRfaHAoMBWjceFeu6pBXVNViZWZmAvDll1+yfv36gscdO3bkvvvuQxL3338/f/rTnxg5ciR33XUXffr04Z133iE7O5vatWsXXLOtcnJyfnUbqeTxp1bc44f4jyHu8RfmSUzFG0X0BLhRUsNQdjkw28wGh7J5kl4M57oAHc3su/DEXJwFwMUl1LkEeMHMzitU3hoYYGZ/lvQ40A94GHgQOMfMXglJ0tVmdl5YjtoFOBR4BzhU0mvAajP7UVJi2xOBYWb2kaQDgDuJkojxwF1m9qCkxA9gPAFIA/YDmgDvA/dLqkmU7B1rZl9L6g9cR5RYFbYa6G1mP0lqDUwBuhXVdrIbJem3wPFAGzMzSQ3NbJ2kLOAw4GWgL/C8meVKeoAoWXpV0thkbQKY2cRwT2jRspWNW7xt/+yyB2ZE37OzqVu3LhkZGVvVadmyJX379iUjI4Mrr7ySt956i8mTJ7N27VqqVatGu3btGD58+Db1D1EilazfuPD4Uyvu8UP8xxD3+AvzJKbivQs8Imk6MD2U9QH+mLCHpBbQIhy/YGbflbJtlVwlmm2RtI+ZfZhQvtzMssLxfCBNUgOgoZm9EsonA0+E49eBQ4CewPXA70P/c7YISKpHNLvzREJis3P4fghRsgTRrMmN4bgH8ISZbQa+lPRyKN+XaLbqhdBWdWCrWZigJtHyWjqwCdinhLaTWQf8BNwn6RngmVA+FehPlMScAtwZks+GZvZqwnhSsnt21apV7LbbbgBMmzat4JVLc+b88qMZPXo09erV+1UJjHPOVTWexJSfPLbcY1QrfD+a6In/GOBySR2Invz7mdmyxAbCrMX6MvTZmWhmoTivEiUj/5XUI2EpZmNCnU1A7VK0cyiwJ9FS0EjAgMLvoV8NWBv2rCRjJfSTSMB7ZnZQKeqeD3wFdAox/FSGfqLAzPIkdQcOB04kWqr7HfA0cL2kRkBXYDZQv6ztl4cBAwaQmZnJN998Q/Pmzfn73/9OZmYmWVlZSCItLY177rknFaE551yl8ySm/HwFNAlLEjlEyw6zgD3M7OWw9HIK0d6O54FzJJ0Tli06m9nCsnQmqSNwJTCkpLpm9pSkJsBMSYcVU2+dpDWSDjWzOcBpQP6szByipZxXw96Q74g21l5aqI3vJS2XdJKZPaFoCqWjmS0C5oZ78DAwMOGyucAZkiYT7aPJINo7swzYVdJBZvZGWF7ax8zeSxJ+A+DzENsZRLM2xbW9lTCLVMfMnpM0F/hfGFOOpLeJlsOeMbNNwFpJa0Ni+Fqh8VSYKVOmbFV21llnlXjd6NGjKyAa55xLLU9iyknYI3ENMI9oo+wHRE+kD4dlGgETzGytpGuBW4F3w8bT5URJT0kOlbSQaNPtamCEmRW3qTcxvrskNSWaVRhaTNUzgLsl1SF6Ej8zXJ8dEpL85ZPXgOZmtiZJGwOBuyRdQbTM8xiwCDgXeFTSSBI29hK9cupwYCnR5tsFwDoz+zm8bHpCuIc1iO5bsiTmTuApSacDM/llRitp20WMvT4wQ1Itop/XBQnnphItrWUklJ1JtHfHKGZjr3POuYohs7LM7jtXMSTVCzMevyVKBA8xsy+rettl1aJlK6t28vhturYqfIp13DcFevypFff4If5jiGv8kuabWbfC5T4T46qKZ8Jm2Z2Aa8s5yajItp1zzqWIJzHbAUmXAycVKn7CzK5LRTzbwswyKrNtSdOAvQoVjzSz5ysqDoDaNauzrArMqDjn3PbAk5jtQEhWYpOwVAVmdnyqY3DOOffr+McOOOeccy6WPIlxzjnnXCx5EuOcc865WPI9Mc5Vog25m0gbVfhNjkunKrzE2jnnqhKfiXHOOedcLHkS45xzzrlY8iTGOeecc7HkSYxzMTJ48GCaNGlC+/btC8pGjx5Ns2bNSE9PJz09neeeew6AefPmFZR16tSJadOmpSps55yrEJ7EOBcjgwYNYubMmVuVn3/++WRlZZGVlcVRRx0FQPv27XnnnXfIyspi5syZnH322eTl5VV2yM45V2E8iXGVStIgSbsnPM6U1C0cX1aO/WRIeiahz68lLZT0kaTnJR1cwvWTwidoF9luKvTs2ZNGjRqVqm6dOnWoUSN6AeJPP/1E9CHkzjm3/fAkxlW2QcDuRZwrtyQmialm1tnMWgNjgH9LaluB/VWq22+/nY4dOzJ48GDWrFlTUP7WW2/Rrl07OnTowN13312Q1Djn3PbA/0eroiRNB/YAagHjgfvCVzfAgPvN7BZJI4BhQB6w1MxOkVQXuA1oD9QERpvZDEntgAeIPs25GtAP+AJ4HGgOVCf6lOepkrKBKcAfQttDgRuAVsBYM7s7xHkxcDKwMzDNzK6WlAb8F3gNOBhYCRwLHB3if0TSBuCghPGOAWpLygLeM7OBkk4FRoR43wL+amabJOUAdwFHAauIkp+bgBbAeWb2dHH31sxeljQxjOn8Uvwsfg/cCvwYxpRf3p3oZ1ML2ACcaWbLklw/NPRF48a7clWHbVvSyczMBODLL79k/fr1BY87duzIfffdhyTuv/9+/vSnPzFy5MiC6+644w4+/fRTLrvsMurWrctOO+20Tf0D5OTkFPQbRx5/asU9foj/GOIef2GexFRdg83sO0m1gbeB+UAzM2sPIKlhqDcK2MvMNiaUXQ7MNrPBoWyepBeJkp3xZvaIpJ2IkpajgC/M7OjQboOEGD4zs3RJtwCTgEOInrCXAHdL6gO0BroDAp6W1BP4LJQPMLM/S3oc6GdmD0saDlxkZu+E/gAws1GShptZeihvC/QHDjGzXEl3AgOBB4G6YXwXh0+j/gfQG9gPmAwUm8QEC4CzS6okqRZwL/A74GNgasLpD4BDzSxP0hHA9USJ4RbMbCIwEaBFy1Y2bvG2/bPLHpgRfc/Opm7dumRkZGxVp2XLlvTt2zfpucmTJ9OoUSO6deu2Tf1DlEglazsuPP7Uinv8EP8xxD3+wnw5qeoaIWkR8CbRjMxOQEtJt4WZge9DvXeJZjZOJZoxAegDjAqzGplEiUcL4A3gMkkjgT3NbAOwGOgt6UZJh5rZuoQY8pOBxcBbZvaDmX0N5CdMfcLXQqKkoA1R8gKw3MyywvF8IK2M4z8c6Aq8HcZxONAynPsZyN/duhh4xcxyw3Fp+yntBpE2RGP5yMwMeDjhXAPgCUlLgFuAdqVss1ytWrWq4HjatGkFr1xavnx5wUbeTz/9lA8++IC0tLRUhOiccxXCZ2KqIEkZwBHAQWb2o6RMouWaTsCRRDMqJwODiZZoegLHAJdL6kD0BN0vydLG+5LeCtc8J+lsM5stqQvRjMw/JL1kZteE+hvD980Jx/mPa4R+bjCzewrFn1ao/iagdllvAzDZzC5Nci43JBRbxGZmmyWV9ne6M/B+GWMq7FrgZTM7Pow581e2V6IBAwaQmZnJN998Q/Pmzfn73/9OZmYmWVlZSCItLY177ol+HK+99hpjxoyhZs2aVKtWjTvvvJPGjRtXdIjOOVdpPImpmhoAa0IC0wY4EGgMVDOzpyQtAx6WVA3YI+zxeA04BagHPA+cI+kcMzNJnc1soaSWwP/MbIKkFkBHSR8A34WlnrXAkDLE+TxwraRHzCxHUjMgt4RrfgDqF3EuV1LNMKvyEjBD0i1mtlpSI6C+mX1ahviSknQY0R6VXqWo/gGQJmlvM/sEGJBwrgHRfh+INixXuClTpmxVdtZZZyWte9ppp3HaaadVdEjOOZcynsRUTTOBYZLeB5YRLSk1AzJD4gJwKdGelofDPhYBE8xsraRriTaivhvqLwf6Es3enCYpF/iSaA/H/sBYSZuJEpC/lDZIM5sV9q68Efa25ACnEs28FGUS0X6aLTb2BhNDzAvCxt4rgFlhDLnA34BtTWL6S+oB1CG6H/3MrMSZGDP7KWzMfVbSj8AcfknCbgImhzi37VMdnXPObTP9MivvnKtoLVq2smonj9+ma6vCp1jHfVOgx59acY8f4j+GuMYvab6ZbfWqBN/Y65xzzrlY8uUkt0OTdAfRS8cTjTezByqiv9o1q7OsCsyoOOfc9sCTGLdDM7O/pToG55xz28aXk5xzzjkXS57EOOeccy6WPIlxzjnnXCyVak+MpL2Bz8Pn82QAHYEHzWxtxYXm3PZnQ+4m0kaV/S1lqsLLq51zrqop7UzMU8AmSa2I3pBsD+DRCovKOeecc64EpU1iNptZHnA8cJuZXQzsVnFhOeecc84Vr7RJTK6kAcAZwDOhrGbFhOScc845V7LSJjFnEn3OzXVmtlzSXsBDFReWc66wwYMH06RJE9q3b7/VuXHjxiGJb775BoCxY8eSnp5Oeno67du3p3r16nz33XeVHbJzzlWoUiUxZrYUGAksCI+Xm9mNFRmYc25LgwYNYubMmVuVr1ixglmzZtGiRYuCsosvvpisrCyysrK44YYbOOyww2jUqFFlhuuccxWuVEmMpGOALKJPV0ZSuqSnKzCuHZ6kNElLwnGGpHWSFkpaJulVSX1THF+GpIMTHg+TdHol9JspaasPAasKJE2SdGJFtd+zZ8+kicj555/PTTfdRPgk8a1MmTKFAQMGVFRYzjmXMqX92IHRQHcgE8DMsiS1rKCYdniSkv1c5phZ33A+HZguaYOZvVSpwf0iA8gBXgcws7tTFMcObcaMGTRr1oxOnTolPf/jjz8yc+ZMbr/99kqOzDnnKl5pk5hcM1tX6C+9zRUQTyxJSgOeMbP24fFFQD3gO2AYkAcsNbNTJNUFbgPaE22OHm1mMyQNAk4I11Un2kSdVEgirwGGA0mTmLBv6dHQ3gzgPDOrF97n56KEhOh24B0zmySpK/DPcM03wCAzWyVpROI4gFHh8SZJpwLnAIcDOWZ2c0iy7gbqAJ8Ag81sjaRM4C2gF9AQOMvM5hQRf3XgRuD3RL9r95rZbYXq3AXsD9QGnjSzq0P5GOCPId5ZZnaRpJOAq4FNwDoz61lEv+2AB4CdiGYq+wFnASvM7I5QZzRRAjeO6GfZG1gB/FxEm0OBoQCNG+/KVR3yklUrVmZmJgBffvkl69evJzMzk59++olRo0YxduzYgsdz586lQYMGBdfNnj2bNm3a8O6775a5z2RycnIKYokjjz+14h4/xH8McY+/sNImMe9J+hNQXVJrYAThL3BXrFHAXuFNAhuGssuB2WY2OJTNk/RiONcF6Ghm34XEqDgLgIuLOT8euMvMHpRU4occSqpJ9IR8rJl9Lak/cB0wuPA4zGytpLsJSUu4/vCE5h4EzjGzV0KydTVwXjhXw8y6SzoqlB9RREhDgTQg3czyJCXb0HF5uFfVgZckdQRWEr0VQBszs4T7fhVwpJmtTChLZhjRp1g/ImknooRyKnArcEeoczJwZOhnX2A/oClRgnd/4QbNbCLR+yvRomUrG7e47J+7mj0wI/qenU3dunXJyMhg8eLFfPvttwwfPhyAb775hnPOOYd58+bxf//3fwCMHz+e4cOHk5GRUeY+k8nMzCy3tlLB40+tuMcP8R9D3OMvrLSvTjoHaAdsJPrrfh2/PCm5or0LPBJmK/L//O4DjJKURbQ8VwvI35H5gpmV9iUkyTdA/OIQYEo4Ls0ryfYlmh16IcR2BdA8nEs2juRBSQ2Ahmb2SiiaDCTOevw7fJ9PlKQU5QjgnvD+RBRxX06WtABYSPT7uR/R7+ZPwH2STgB+DHXnApMk/ZkoMSnKG8BlkkYCe5rZBjNbCDSRtLukTsAaM1sRxjXFzDaZ2RfA7GLaLXcdOnRg9erVZGdnk52dTfPmzVmwYEFBArNu3TpeeeUVjj322MoMyznnKk2JSUz4K/dZM7vczPYPX1eY2U+VEF9c5LHlvawVvh9N9Nd7F+DtsNdFQD8zSw9fLczs/VB/fRn67Ay8X0IdK0OsAt5LiKuDmfUpZhzbamP4vonSzwRuJSyXXQQcbmYdgWeBWiHp6Q48CfQlbEY3s2FEidkewHxJv03Wrpk9SrQUtQF4TtLvwqkngBOB/kQzM5VuwIABHHTQQSxbtozmzZtz3333FVt/2rRp9OnTh7p161ZShM45V7lKTGLMbBOwOfyF7ZL7iugv9d9K2pnoybMasIeZvUz08vQGRHtNngfOUdhgJKlzWTsLyyZX8svyRjJzgVPC8cCE8k+B/STtHJZV8peBlgG7Sjoo9FFTUjtJRY3jB6B+4U7NbB2wRtKhoeg04JXC9UrhBeDs/IQpyXLSLkRJ3zpJTYE/hHr1gAZm9hxwPtAplO9tZm+Z2VXA10TJzFbChvX/mdkEor1EHcOpqUT380SihAbgVaC/pOqSdiPa61NhpkyZwqpVq8jNzeXzzz/nrLPO2uJ8dnY2jRs3Lng8aNAgHnvssYoMyTnnUqq0fwnnAIslvUDCbIGZjaiQqGLGzHLD3o95RHsyPiBasng4JH8CJoS9JNcS7a94NyQIy4mSnpIcKmkh0WbZ1cCIEl6ZdC7waFgWmZEQ6wpJjwNLQt8LQ/nP4eXBE0LMNUKcHxYxjv8AT0o6lmi5MdEZwN2S6gD/I3qzxLL6F7AP0X3KBe4FCl5iY2aLwv34gGhT7dxwqj4wQ1KtEO8FoXxs2M8los3Qi4ro92TgtNDnl8D1ob/3JNUHVprZqlB3GvA7or0wnxEtRTnnnKskpU1i/s0vexlcEuEv9wmlqLcBODtJ+SRgUsLjbKI9KphZJtEMSFniWU70LssASDov4dwlwCVJrsliy/0r+Xokqfshv8xSAMxJOJcFHJjkmoyE428oZk9MWBa6gF+SkGRtDCri8u5J2juhqL4K1RsDjCniXIdCj43oFWLOOedSoFRJjJlNruhAnHPOOefKolRJjKTlJNkkamb+hncpJuly4KRCxU+Y2XWJBWZWr/KiKj1JRxK9H0yi5WZ2/PbYr3POufJT2uWkxLd5r0X0pOkfxFIFhGTluhIrVlFm9jzRZucdot/aNauzbMzRld2tc85tl0r7AZDfJnytNLNbiV5265xzzjmXEqVdTuqS8LAa0czMr3mvEOecc865X6W0ici4hOM8opfmnlz+4TjnnHPOlU5pk5izzOx/iQXhHVOdc2WwIXcTaaOeLdM12b6HxjnnkirtZyc9Wcoy55xzzrlKUexMjKQ2RB+s1yB8mF6+XfjlM3ecc8455ypdSctJ+xK9JX5D4JiE8h+AP1dQTM4555xzJSp2OcnMZpjZmUBfMzsz4WuEmb1eSTE654DBgwfTpEkT2rdvv9W5cePGIYlvvvkGADNjxIgRtGrVio4dO7JgwYLKDtc55ypcaffELJT0N0l3Sro//6tCI3PObWHQoEHMnDlzq/IVK1Ywa9YsWrRoUVD23//+l48++oiPPvqIiRMn8pe//KUyQ3XOuUpR2iTmIeD/gCOBV4DmREtKrhJJGiRp94THmZK6hePLyrGfDEnPJPT5taSFkj6S9Lykg8sSZzH1zgufdF1SvYJx7sh69uxJo0Zbv1H2+eefz0033YSkgrIZM2Zw+umnI4kDDzyQtWvXsmrVqq2udc65OCttEtPKzK4E1ocPgzwaOKDiwnJFGAQUlRyUWxKTxFQz62xmrYk+4fnfktoWU38QRceZ6DygxCQmTiRVr8z+ZsyYQbNmzejUqdMW5StXrmSPPfYoeNy8eXNWrlxZmaE551yFK+37xOSG72sltQe+BJpUTEi/nqTpwB5Er6AaD9wXvroRfZDl/WZ2i6QRwDCiN/BbamanSKoL3Aa0B2oCo81shqR2wAPATkTJXz/gC+Bxopmp6sC1ZjZVUjYwBfhDaHsocAPQChhrZneHOC8metPAnYFpZna1pDTgv8BrwMHASuBYosSxG/CIpA3AQQnjHQPUlpQFvGdmAyWdCowI8b4F/NXMNknKAe4CjgJWESU/NwEtgPPM7Oni7q2ZvSxpYhjT+Unu/YlJ4jwYuJno9+1t4C/A2USJzsuSvjGzXpLuAvYHagNPmtnVxcWS0GfS68LPYTLRpvSawElm9oGkw4h+LyD6fehJlJw9b2ZPS5oGrDGzwZIGA3ub2eUl3NN7gCOAvxH97BLjGxruF40b78pVHfJKM6wCmZmZBcdffvkl69evJzMzk59++olRo0YxduzYgsdz586lQYMGfPvttyxcuJC8vKivNWvWMH/+fHJycsrUd2E5OTlbxBM3Hn9qxT1+iP8Y4h7/VsysxC9gCPAb4DDgf8BqYFhprk3FF9AofK8NLAG6Ai8knG8Yvn8B7Fyo7Hrg1Pwy4EMgP7EZGMp3Cm33A+5NaLdB+J4N/CUc3wK8C9QHdgW+CuV9gImAiJKiZ4ieTNOIEp/0UO/xhHgygW4J/RU8BnISytsC/wFqhsd3AqeHYwP+EI6nAbOInuA7AVmhPAN4JhwPAm4vdH+PA/5bzP1PjKsWsALYJzx+kChZyr9PjZP83KqHNjomG3cxP+/C12UD54TjvwL/+v/27jxMquLq4/j3J7KjIAEMgjjiAgriKLhFwTEqLuACIkhMIqIh+romQTFqFDUmLjHuSQQXjAviAmjQiAQYQQRZh01FjRD3XdABxAHO+0fVYNP0bDAzPRfO53n6mXvr1q0693ZrH6qqu+P2v4DD43YjQnJ1BiHBBJgJzIjbDxGmUcu6p33L89rcdfc9bLch4yr0SLV06VLr0KGDmZktWLDAmjdvbrvttpvttttuVqtWLdt1113t448/tkGDBtnjjz++4by9997bPvroI9tSkydP3uI2ssnjz66kx2+W/GtIavzAbMvw/9Ty/gDk/Wb2tZm9bGZtzayFxdGEGupiSfOBGYQRmTpAW0l3Szoe+CbWW0AYMfg5IXGAkFxcEUc18glvwm2A6cCVkoYAu5nZamAhcKykmyV1NbMVKTEUj2gsBF4zs2/N7HNgjaQmsZ/uwDxgLtAe2Cues9TMCuL2HEJiUxFHExK3WfE6jgbaxmPfA8WrQxcCL5tZUdwubz8qu8oG7QjX81bcf5iQrGXSV9Jcwj3pAOxbzj5KO290/Jt6H6cBf40jcU3MbC0wFegqaV/gdeBTSS0JI0mvUvo9XQc8U85YK81+++3HZ599xrJly1i2bBmtW7dm7ty5/PjHP+bkk0/mn//8J2bGjBkzaNy4MS1btqzuEJ1zrkqVK4mRtLOkByT9O+7vK+mcqg1t80jKIwzrH2Zm+xPe2OoSRhryCdNH98fqPYB7gQMJb07bE96gTzOz3PhoY2ZvmNnjwMnAauAFST+Nb8wHEhKAP0q6JiWUNfHv+pTt4v3ifv6c0s+eZvZA2rkQ3iAr+mObAh5OabudmQ2Nx4piVrtRbGZWHFd5HAC8UcGYSg84/IzFYOBoM+sEPE85vlCxHOcV38sN99HMbiKMLtYHpklqb2YfEkbejgemEJKavoQRrm8p/Z5+Z2brNvviy6l///4cdthhLFmyhNatW/PAAw+UWPfEE0+kbdu27LnnnvzqV7/ib3/7W1WH55xz1a68b1ojCMPqV8X9t4BRhHUmNU1jwnqGVfEbhw8FmgHbmdkzkpYAj0raDtjVwhqPVwjTCY2A8cBFki4yM5N0gJnNk9QWeNfM7pLUBugk6U3gKzN7VNJywhtjeY0HbpD0mJkVSmrFD2uPSvItYVoqkyJJteOoykTgWUm3m9lnkpoCO5jZ/yoQX0ZxPckg4KhyxrkEyJG0p5m9A/yC8Am31HpfEL4FeiWwQtLOhPVE+eUIqcLnSdrDzBYCCyUdRBgFe5Mwcncp8FPgR4Sf1ij+eY0qu6flNXLkyFKPL1u2bMO2JO69994qjsg557KrvElMMzN7UtLvAcxsraQq/5fnZnoROE/SG4Q30BlAKyA/Ji4Avyesn3hUUmPCv7LvMrPlkm4A7gAWxPpLCd9a3Bf4haQiwsLmPxEWk94qaT0hASn3l3GY2UvxEz7T40djC4GfE0YMSjIC+Ef6wt5oWIx5roWFvVcDL8VrKCIsON3cN9x+ko4gfJJoKWGkqrSRmPQ4zwaeiiNds4DiqchhwIuSPrKwsHceIZl4nzDlUyYzm78Z510q6SjCSNRiwkJqCKMv3c3sHUn/A5rGMszs9Uq+p84557aQfphZKKWSlE9YxDrBzA6UdChws5kdWcXxObdVadN2T9uu751lV0xRk37FOj8/n7y8vGyHsdk8/uxKevyQ/GtIavyS5pjZJt8XVt6RmN8SFqruIWka4VM2fSoxPuecc865CinrV6zbmNl7ZjY3roVoR5h6WRLXXrhtmKR7gcPTiu80s4eqqL/XCIu0U/0irm9xzjm3jSlrJGYs4dM3EL619bSqDccliZldUM39Jf5bouvXrsWSGjQ95JxzSVbWR6xTvw+kbYm1nHPOOeeqWVlJjJWw7ZxzzjmXVWVNJ+0v6RvCiEz9uE3cNzPbsUqjc84555wrQalJjJlV6y/yOre1W120jpwrni+1Tk36SLVzztVk5frZAeecc865msaTGOecc84lkicxzjnnnEskT2Kcq6HuvPNOOnbsSIcOHbjjjjsAGDp0KK1atSI3N5fc3FxeeOGF7AbpnHNZVN6fHXDOVaNFixYxfPhwZs6cSZ06dTj++OPp2bMnAL/5zW8YPHhwliN0zrns85EYt1kkXZm2/2o19JknaVxV91MTvPHGGxxyyCE0aNCA7bffniOPPJLRo0dnOyznnKtRPIlxm2ujJMbMfpKtQLZGHTt2ZOrUqXz55ZesWrWKF154gffffx+Ae+65h06dOjFw4EC+/vrrLEfqnHPZIzP/It6tlaSrgLOAz4D3gTlAT2Cwmc2W1AyYbWY5kmoBNwF5hB9ZvNfM7pPUEhgF7EiYfjwf6AFcBiwEFpvZmZIKzayRJAG3ACcQvuX5j2Y2SlIeMBT4AugYY/m5lfAClHQQcCfQEFgDHA10jrH3lHRwPF4PWA2cbWZLJHUAHgLqEJL004CPgCeB1kAt4AYzG1VCv9cAJwH1gVeBX5uZSdoT+AfhF9zXAaeb2X8lDQF+DqwH/m1mV2RocxAwCKBZs+adr7ljeKauN9ivVWMAnn/+eZ599lnq169PTk4OtWvX5mc/+xmNGzdGEg8++CBffvklQ4YMKbW9ylRYWEijRo2qrb/K5vFnV9Ljh+RfQ1LjP+qoo+aYWZf0ck9itlKSOgMjgEMIycdcwptwSUnMIKCFmf1RUl1gGnA60BuoZ2Y3xkSngZl9W5y0pPRXnMScBpwHHA80A2bFGNoBzwIdCEnFNOAyM3slQ+x1gDeBfmY2S9KOwCrgCH5IYnYEVpnZWknHAOeb2WmS7gZmmNljsZ1awInA8Wb2q9h+YzNbUcJ9a2pmX8XtR4Anzexf8Re0bzKzMZLqERKkI4E/AMeY2arUc0vSpu2etl3fO0urkvHL7q688kpat27N//3f//1Qb9kyevbsyaJFi0ptrzLl5+eTl5dXbf1VNo8/u5IePyT/GpIav6SMSYwv7N16dQXGmNkqAEnPlVG/O9BJUp+43xjYi5CEPCipNjDWzArKaOcIYKSZrQM+lfQycBDwDTDTzD6I8RQAOcAmSQwh4fnYzGYBmNk38ZzUOo2BhyXtRRjxqR3LpwNXSWoNjDaztyUtBG6TdDMwzsymlhL/UZIuBxoATYHFkvKBVmY2JsbzXYznGOCh4ntcVgJTUZ999hktWrTgvffeY/To0cyYMYOPP/6Yli1bAjBmzBg6duxYmV0651yieBKz7VnLD2uh6qWUC7jIzMannyCpG2EKaYSkv5rZPzez7zUp2+vYstffDcBkM+slKQfIBzCzx+OoSQ/gBUm/NrNJkg4kjMj8UdJEM7s+vcE4wvI3oIuZvS9pKBvfo2p12mmn8eWXX1K7dm3uvfdemjRpwkUXXURBQQGSyMnJ4b777stWeM45l3WexGy9phCSjj8TnueTgPuAZYS1JTOBPin1xwPnS5pkZkWS9gY+JEwJfWBmw+M004HAP4EiSbXNrCit36nAryU9TBjJ6EZYP9O+ArEvAVpKOihOJ+1AWPeSqnGMD2BAcaGktsC7ZnaXpDaE0aU3ga/M7FFJy4FzS+i3OGH5QlIjwv15Ok6ffSDpVDMbG+9DLWACcI2kx8o7nVQRU6duOmD0yCOPVFbzzjmXeJ7EbKXMbK6kUcB8wsLeWfHQX4An4xqY1F8ivJ8wvTM3Ls79HDiVsND3MklFQCHwy1h/GLBA0lwzOzOlnTHAYbFfAy43s08klTuJMbPvJfUD7pZUn5DAHJNW7RbCdNLVadfRF/hFjPcT4E+E6axbJa0HigiLkzP1u1zScGBRPHdWyuFfAPdJuj62cbqZvSgpF5gt6XvgBdI+teWcc67q+MLebUScGik0s79kO5Zt2eYu7K0pkroosJjHn11Jjx+Sfw1Jjb+khb3+PTHOOeecSySfTtpGmNnQbMeQiaQxwO5pxUMyLTDeGvp1zjlXeTyJcVllZr22pX7r167Fkho8XeScc0ni00nOOeecSyRPYpxzzjmXSJ7EOOeccy6RfE2Mc9VoddE6cq54vtQ6Nfkj1s45V5P4SIxzzjnnEsmTGOecc84lkicxzjnnnEskT2Kcq6HuvPNOOnbsSIcOHbjjjjs2OnbbbbchiS+++CI7wTnnXA3gSYxzNdCiRYsYPnw4M2fOZP78+YwbN4533nkHgPfff5+XXnqJNm3aZDlK55zLLk9i3BaRdGXa/qvV0GeepHFV3Ee+pE1+bEzSAEn3VGXfAG+88QaHHHIIDRo0YPvtt+fII49k9OjRAPzmN7/hlltuIfzYuHPObbs8iXFbaqMkxsx+kq1AtiYdO3Zk6tSpfPnll6xatYoXXniB999/n2effZZWrVqx//77ZztE55zLOv+emG2ApKuAs4DPgPeBOUBPYLCZzZbUDJhtZjmSagE3AXlAXeBeM7tPUktgFLAj4XVzPtADqC+pAFhsZmdKKjSzRgrDBLcAJwAG/NHMRknKA4YCXwAdYyw/NzMrIfaDgDuBhsAa4Oi04wfH4/WA1cDZZrZEUgfgIaAOIVk/DfgIeBJoDdQCbjCzUeW4f2cDvweWA/NjHEg6Cbg69vElcKaZfZrh/EHAIIBmzZpzzX5rS+0vPz8fgFNOOYXDDjuM+vXrk5OTw9KlS7niiiu49dZbyc/P57vvvmPatGk0bty4rEuoNIWFhRviSyKPP7uSHj8k/xqSHn86lfDe4bYSkjoDI4BDCMnHXOAflJzEDAJamNkfJdUFpgGnA72BemZ2Y0x0GpjZt8VJS0p/xUnMacB5wPFAM2BWjKEd8CzQgZBUTAMuM7NXMsReB3gT6GdmsyTtCKwCjoix9ywuM7O1ko4Bzjez0yTdDcwws8diO7WAE4HjzexXsf3GZraihPuWDwwGPgReAzoDK4DJwDwzu1DSTsByMzNJ5wL7mNnvSns+2rTd07bre2dpVTJ+2d2VV17JzjvvzI033kiDBg0A+OCDD9hll12YOXMmP/7xj0tts7Lk5+eTl5dXLX1VBY8/u5IePyT/GpIav6Q5ZrbJFL+PxGz9ugJjzGwVgKTnyqjfHegkqU/cbwzsRUhCHpRUGxhrZgVltHMEMNLM1gGfSnoZOAj4BphpZh/EeAqAHGCTJIaQ8HxsZrMAzOybeE5qncbAw5L2Ioz41I7l04GrJLUGRpvZ25IWArdJuhkYZ2ZTy7gGCIlXvpl9HvseBewdj7UGRsVRqjrA0nK0V26fffYZLVq04L333mP06NHMmDGDSy65ZMPxnJwcZs+eTbNmzSqzW+ecSwxfE7PtWssPz3+9lHIBF5lZbnzsbmYvmdkUoBthZGKEpF9uQd9rUrbXsWXJ9A3AZDPrCJxEvBYzexw4mTDF9IKkn5rZW8CBwELgj5Ku2YJ+Ae4G7jGz/YBfs/F93GKnnXYa++67LyeddBL33nsvTZo0qczmnXMu8TyJ2fpNAU6VVF/SDoQ3eoBlhCkSgD4p9ccD58cRFyTtLamhpN2AT81sOHA/IRkAKCqum2Yq0E9SLUnNCQnQzArGvgRoGdfFIGkHSekJT2NCYgUwoLhQUlvgXTO7izB91UnSLoSpp0eBW1OuoTSvAUdK+lG8ztNL6PusCl1ZOUydOpXXX3+d+fPnc/TRR29yfNmyZT4K45zbpvl00lbOzObGKZD5hIW9s+KhvwBPxjUwqb9IeD9hemduXJz7OXAqYaHvZZKKgEKgeCRmGLBA0lwzOzOlnTHAYbFfAy43s08kta9A7N9L6gfcLak+YVTlmLRqtxCmk65Ou46+wC9ivJ8AfyJMZ90qaT1QRFicXFYMH0saSpieWg4UpBweCjwl6WtgErB7ea/NOefclvMkZhtgZjcCNwLEN2TM7E2gU0q1q2P5esLHpq/cuBUejo/0tocAQ1L2G8W/BlwWH6n184H8lP0Ly4h9FnBoWvGGNsxsOj+sUUm9jpsIn7JKNT4+ymRmeSnbDxE+6ZRe51nCKI9zzrks8Okk55xzziWSj8RsY8xsaLZjyETSGDadjhliZuUaOUlav84557acJzGuRjCzXttCv/Vr12JJhu+Bcc45V3E+neScc865RPIkxjnnnHOJ5EmMc8455xLJ18Q4V41WF60j54rnSzye6XeTnHPOZeYjMc4555xLJE9inHPOOZdInsQ455xzLpE8iXGuBrr99tvp0KEDHTt2pH///nz33Xd07dqV3NxccnNz2WWXXTj11FOzHaZzzmWVL+x1rob58MMPueuuu3j99depX78+ffv25YknnmDq1Kkb6px22mmccsopWYzSOeeyL1EjMZJyJC2K23mSVkiaJ2mJpCmSemY5vjxJP0nZP0/SL0s7p5L6zZfUpar7yQZJuZJOzHYc1W3t2rWsXr2atWvXsmrVKnbZZZcNx7755hsmTZrkIzHOuW1eYpIYSZlGjaaa2QFm1g64GLhH0tHVHFqqPGBDEmNm/zCzf2YvnOyTVKu0/XLIBRKTxJTwOq2QVq1aMXjwYNq0aUPLli1p3Lgx3bt333B87NixHH300ey4445b2pVzziWazKxqGpZygHFm1jHuDwYaAV8B5wFrgdfN7AxJDYG7gY5AbWComT0raQDQO55XCziruE1JecBgM+uZ0udA4KSSfg9H0u7A47G9Z4FLzaxReluS7gFmm9kISZ2Bv8ZzvgAGmNnHki5OvQ7gCmAGsA74HLgIOBooNLO/SMoF/gE0AP4LDDSzryXlA68BRwFNgHPM7Id5g43jrwXcDBwPrAeGm9ndsY3BZjZb0t+Bg4D6wNNmdm089ybg5BjvS2Y2WNLpwLUx5hVm1q2C/R4N/IUwLTkLON/M1khaBowCjgVuAW5K2/8KuA6oG+/F2WZWKOkg4E6gIbAm1l8Yr+VD4M9mNipDfAfH8+oBq2N7S+Lr5+R4z/cAxpjZ5fF6HgC6AAY8CDwG/NvMOkvaHygAdjOz9yT9F9gvxvUPoE3s+lIzmyZpaGy/LfCemfVPi28QMAigWbPmna+5Y3im2wzAfq0a8+2333LttddyzTXX0KhRI4YOHcqRRx7JscceC8CQIUM48cQTOfLII0tsp6oUFhbSqFGjau+3snj82ZX0+CH515DU+I866qg5ZrbpjIOZVckDyAEWpewPBoYCHwF1Y1mT+PdPwM+Ly4C3CG8YA4APgKbpbRJGPcal9ZkLvFFKTM8Bv4zbFxASjE3aAu6JfdcGXgWax/J+wINxO9N1DCUkE6TvAwuAI+P29cAdcTsfuC1unwj8p5T4zweeBraP+01T2uiSVlYrlncCfgQs4YektTjehUCr1LLy9ktIGN4H9o5l/yS8qQMsAy5POX/DPtAMmAI0jPtDgGuAOsC7wEGxfEdCcjQAuKeM19qOKbEdAzwTtwfENhvHeP8H7Ap0BiaknF98PxbHti4kJGVnArsB0+Pxx4Ej4nYb4mstPs9zgPpl/Xex6+572G5DxpX4MDN78sknbeDAgVbs4YcftvPPP9/MzD7//HNr2rSprV692rJh8uTJWem3snj82ZX0+M2Sfw1JjZ8wsLDJ/1OzsbB3AfCYpLHA2FjWHTg5jtZAeMMp/tfuBDP7qpxtq4zjhwOnxe1HCKMLpWlHGB2aIAlCYvBxPJbpOjIHJTUmvFG+HIseBp5KqTI6/p1DSNRKcgzwDzNbC1DCfekb/+W/PdAS2JcwUvQd8ICkccC4WHcaMELSkykxlKvfOFqx1MzeSrmmC4A74n76iEnx/qExpmnxntYBphPu9cdmNiv28Q1ArFOWxsDDkvYijKzUTjk20cxWxLZeJyQli4G2ku4GngdeinVfJbxGuhES6+MJr6nikbFjgH1TYtpRUvE/aZ4zs9XlCbYsbdq0YcaMGaxatYr69eszceJEunQJ/wB5+umn6dmzJ/Xq1auMrpxzLtGqck3M2rT2i/+v2wO4FzgQmBXXEAg4zcxy46ONmb0R66+sQJ8HAG+UUSfT/FlJsQpYnBLXfmZWvDgh03VsrjXx7zq24BNjcbpsMHC0mXUivEHXi8nHwYTRlJ7AiwBmdh5wNWF0Yo6kH232FWwq/Xkr3hchMS2+p/ua2Tlb2NcNwGQLU5cn8cPzBz/cW4j318y+BvYnjFSdB9wfj08BuhISnWdjnSP4IYnZDjg0JfZWZlaYdn1b7JBDDqFPnz4ceOCB7Lfffqxfv55BgwYB8MQTT9C/f/8yWnDOuW1DVSYxnwItJP1IUl3Cm+d2wK5mNpkwjdCYsNZkPHCR4j9xJR1Q0c4kdQL+QEgsSjINOCNun5lS/j/Cv7DrSmpCWMsCYQqmuaTDYh+1JXWQVNJ1fAvskN5pHAn4WlLXWPQL4OX0euUwAfh1ccIkqWna8R0Jb6YrJO0MnBDrNQIam9kLwG8Ib85I2sPMXjOzawjreHatQL9LgBxJe1bwmmYAhxefJ6mhpL1jey3juhgk7RD7y3hP0zQmrJmBMIVUKknNgO3M7BlCEndgPDQV+DnwtpmtJ6zdORF4JR5/ibDWqbid3LL62lzXXXcdb775JosWLeKRRx6hbt26AOTn53P88cdXVbfOOZcoVZbEmFkRYe3HTMKb4JuE6ZhHJS0E5gF3mdlywr+kawMLJC2O++XRtfgj1oTk5WIzm1hK/UuAC2L/rVJifR94ElgU/86L5d8DfYCbJc0nLPb8SSnX8S+gl6SClISl2FnArZIWENbuXF/Oa0x1P/Ae4T7NB36WetDM5sd43iSs35gWD+0AjIt9vwL8NpbfKmlh/Nj6q8D88vZrZt8BZwNPxfuwnrDotVRm9jkh0RgZ45kOtI/3uh9wd+xjAmFEZTIhwSyQ1K+EZm8B/ixpHuUbyWoF5EsqAB4Ffh9jW0YYKZoS670CLI8jNxA+AddF0oI4NXVeOfpyzjlXRars00lJIKnQzJK3TNslVpu2e9p2fe8s8XhN/xXr/Px88vLysh3GZvP4syvp8UPyryGp8UvK+OmkxHxPjHPOOedcqq3yZwckXQWcnlb8lJndmFpQU0dhJB3Hpp+cWmolfP9N0vstL0lnE6YEU00zswuyEY9zzrns2iqTmJis3FhmxRrKzMYTFjtvE/2Wl5k9BDyU7Ti2RP3atVhSw6eMnHMuKXw6yTnnnHOJ5EmMc8455xLJkxjnnHPOJdJWuSbGuZpqddE6cq54PuOxmv7xauecq2l8JMY555xzieRJjHPOOecSyZMY55xzziWSJzHO1SBLliwhNzd3w2PHHXfkjjvuoKCggEMPPZTc3Fy6dOnCzJkzsx2qc85lnS/sda4GadeuHQUFBQCsW7eOVq1a0atXL371q19x7bXXcsIJJ/DCCy9w+eWXk5+fn9VYnXMu27bZkRhJOfHXm5GUJ2lF8S9iS5oiqedmtjtA0j2VG225+s2TNK6a+kq9d10k3VUd/WaIo7A64pF0uqTFktZL6pJ27PeS3omvm+Mqs9+JEyeyxx57sNtuuyGJb775BoAVK1awyy67VGZXzjmXSNvkSIykTNc91cx6xuO5wFhJq81sYrUGlzBmNhuYXVXtS6plZuuyHM8ioDdwX1ps+wJnAB2AXYD/SNq7IvGW5oknnqB///4A3HHHHRx33HEMHjyY9evX8+qrr1ZGF845l2iJSGIk5QDjzKxj3B8MNAK+As4D1gKvm9kZkhoCdwMdgdrAUDN7VtIAwhtRI6AWcFZJ/ZlZgaTrgQuBjEmMpObAP4A2sehSM5uWVuck4GqgDvAlcKaZfSppKLAHsCfQDLjFzIZLagmMAnYkPDfnm9lUSd2B64C6wH+Bs82sUNLxwB3AKuCVMu7hUGB3oG2M+TfAocAJwIfASWZWJKkz8Nd4n74ABpjZx7H8wdjcSynt5gGDzaynpIOBO4F6wOoY55J4708GGsTrHmNml5cSayEhYTgGuCC2OzAevt/M7ijl3NR4hsZrLb7mO8zsrljvD8DPgc+B94E5ZvaXTG2a2RvxnPRDpwBPmNkaYKmkd4CDgelpMQ0CBgE0a9aca/ZbmzH21OmhoqIinnnmGXr27El+fj533XUX55xzDkceeSSTJ0+md+/e3HbbbSXdhipTWFiY6Gksjz+7kh4/JP8akh7/Jsysxj+AHGBRyv5gYCjwEVA3ljWJf/8E/Ly4DHgLaAgMAD4Amqa3CeQRkqTUPnOBN0qJ6XHgiLjdprhu7OeeuL0ToLh9LnBb3B4KzAfqE5KY9wn/kv8dcFWsUwvYIR6fAjSM5UOAawiJwvvAXoCAJ9OvIS3eoYREpzawPyHxOSEeGwOcGo+9CjSP5f2AB+P2AqBb3L41070jJl9x+xjgmZR78i7QOMb9P2DXUmI1oG/c7gwsjM9hI2AxcEA8Vljacxmv+VVC8teMkEjWBg4CCmIsOwBvExKfsl6H+UCXlP17iK+1uP8A0Ke0NnbdfQ/bbci4jI9UY8eOtWOPPXbD/o477mjr1683M7P169fbDjvsYNkwefLkrPRbWTz+7Ep6/GbJv4akxg/Mtgz/T03ESEwpFgCPSRoLjI1l3YGT42gNhDeq4tGSCWb2VTnb3uSf3WmOAfZN+df5jpIapdVpDYyKIyx1gKUpx541s9XAakmTCf+CnwU8KKk2MNbCiNCRwL7AtNhXHcK/9NsDS83sbQBJjxL/tV+Kf1sYbVlISJJejOULCYlAO8II1oTYVy3gY0lNCEnilFj/EcIITrrGwMOS9iIkIrVTjk00sxUx1teB3QhJWCbrgGfi9hGEkZuV8dzRQFdgXhnXWux5CyMlayR9BuwMHE64/98B30n6VznbqjYjR47cMJUEsMsuu/Dyyy+Tl5fHpEmT2GuvvbIYnXPO1QxJSWLWsvEi5Hrxbw+gG3AScJWk/QjJx2lmtiS1AUmHACsr0OcBwBulHN8OODS+Eab2k7p7N/BXM3suTnMMTTlmae2ZmU2R1I1wXSMk/RX4mpB89U+tHNftVNSa2NF6SUUxuwVYT3gtCFhsZoel9dWknO3fAEw2s15xCjA/ve9oHaW/9r6zSlpXUsF+K+pDYNeU/daxbIusXLmSCRMmcN99PyzBGT58OJdccglr166lXr16DBs2bEu7cc65xEvKp5M+BVpI+pGkukBPQuy7mtlkwhRLY8J0w3jgIsVsQtIBFe1MUifgD8C9pVR7Cbgo5ZzcDHUa88ObWvoanFMk1ZP0I8IUyCxJuwGfmtlw4H7gQGAGcLikPWM/DSXtDbwJ5EjaI7bXny23BGgu6bDYV21JHcxsObBc0hGx3pklnJ96vQMqIR6AqcCpkhrE9U69YtmWmAacFO9/I8LraXM8B5whqa6k3QlTe1v8BS4NGzbkyy+/pHHjxhvKjjjiCObMmcP8+fN57bXX6Ny585Z245xziZeIkZg4BXI94Q3iQ8IbeC3gUUmNCSMId5nZckk3EBa7LpC0HWEKpzxvUl0lzSMsPv0MuNhK/2TSxcC9khYQ7uMUwiLjVEOBpyR9DUwiLKwttgCYTFircYOZfSTpLOAySUVAIfBLM/s8LowdGRM4gKvN7K24YPR5SasIb+w7lOM6S2Rm30vqA9wV7+v2hHu5GDibMNVlpCzsTXMLYTrpaiDzrxxWPKa5kkbwQ3Jwv5mVdyqppDZnSXqO8Bx8SphOW1FSfUm9CKNqzQn3u8DMjjOzxZKeBF4njBZeUIkjSM4558qgH2YUXHWJn5optBI+DeOqnqRGFj7h1YCQgA4ys7lV3W+btnvadn3vzHgsCb9inZ+fT15eXrbD2Gwef3YlPX5I/jUkNX5Jc8ysS3p5IkZinKsCw+L3vNQDHq6OBMY551zl8iSmDJKuAk5PK37KzG7c3DbNbOgWBVUKSWcDl6QVTzOzC6qqz80l6TXCx59T/cLMFlZ132b2swzx3Ev45FKqO83soaqOxznnXMV5ElOGmKxsdsJS3eIbbiLedM3skGzHkKo6Er36tWuxJAHTRs45lwRJ+XSSc84559xGPIlxzjnnXCJ5EuOcc865RPI1Mc5Vo9VF68i5IvNX6CThI9bOOVeT+EiMc8455xLJkxjnnHPOJZInMc4555xLJF8T41wNsmTJEvr167dh/9133+X6669n+vTpLFkSfph9+fLlNGnShIKCgixF6ZxzNYMnMc7VIO3atduQnKxbt45WrVrRq1cvLr300g11fve73230C9fOObet8ukkV6NIujJt/9Vq6DNP0riq7qeiJk6cyB577MFuu+22oczMePLJJ+nfv38WI3POuZrBkxhX02yUxJjZT7IVSLY98cQTmyQrU6dOZeedd2avvfbKUlTOOVdzyMyyHYNLmPijmGcBnwHvA3OAnsBgM5stqRkw28xyJNUCbgLyCD/2eK+Z3SepJTAK2JEwrXk+0AO4DFgILDazMyUVmlkjSQJuAU4ADPijmY2SlAcMBb4AOsZYfm4lvLAlHQTcCTQE1gBHA51j7D0lHRyP1wNWA2eb2RJJHQi/SVWHkPyfBnwEPAm0BmoBN5jZqAx9DgIGATRr1rzzNXcMz3hf92v1wxRRUVERffr04aGHHqJp06Ybym+//XZatWpF3759M7ZR1QoLC2nUqFFW+q4MHn92JT1+SP41JDX+o446ao6ZdUkv9zUxrkIkdQbOAHIJr5+5hMShJOcAK8zsIEl1gWmSXgJ6A+PN7MaY6DQws6mSLjSz3Azt9I597g80A2ZJmhKPHQB0ICQV0wi/RP1KhtjrEBKnfmY2S9KOhEQl1ZtAVzNbK+kY4E+EhOU8wi9aPxbbqQWcCHxkZj1i+xkXqpjZMGAYQJu2e9ptCzP/Z7fszLwN288++yyHHHIIvXv33lC2du1a+vXrx5w5c2jdunXGNqpafn4+eXl5Wem7Mnj82ZX0+CH515D0+NN5EuMqqiswxsxWAUh6roz63YFOkvrE/cbAXsAs4EFJtYGxZlZQRjtHACPNbB3wqaSXgYOAb4CZZvZBjKcAyCFDEgO0Az42s1kAZvZNPCe1TmPgYUl7EUZ8asfy6cBVkloDo83sbUkLgdsk3QyMM7OpZVxDuY0cOXKTqaT//Oc/tG/fPmsJjHPO1TS+JsZVlrX88Hqql1Iu4CIzy42P3c3sJTObAnQDPgRGSPrlFvS9JmV7HVuWnN8ATDazjsBJxGsxs8eBkwkjNy9I+qmZvQUcSJj++qOka7ag3w1WrlzJhAkTNhqFgcxrZJxzblvmSYyrqCnAqZLqS9qB8EYPsIywtgSgT0r98cD5ccQFSXtLaihpN+BTMxsO3E9IBgCKiuummQr0k1RLUnNCAjSzgrEvAVrGdTFI2kFSesLTmJBYAQwoLpTUFnjXzO4CniWMLu0CrDKzR4FbU65hizRs2JAvv/xyk49RjxgxgvPOO68yunDOua2CTye5CjGzuZJGAfMJC3tnxUN/AZ6Mi1hTf+HwfsL0zty4OPdz4FTCQt/LJBUBhUDxSMwwYIGkuWZ2Zko7Y4DDYr8GXG5mn0hqX4HYv5fUD7hbUn3CqMoxadVuIUwnXZ12HX2BX8R4PyGslTkIuFXSeqCIsDjZOedcNfEkxlWYmd0I3AggaWgsexPolFLt6li+nvCx6Ss3boWH4yO97SHAkJT9RvGvET65dFla/XwgP2X/wjJinwUcmla8oQ0zmw7sneE6biJ8yirV+PhwzjmXBT6d5JxzzrlE8pEYt0XMbGi2Y8hE0hhg97TiIWbmIyfOObeV8CTGbZXMrFe2Y8ikfu1aLLmpR7bDcM65rYJPJznnnHMukTyJcc4551wieRLjnHPOuUTyNTHOVaPVRevIueL5jcqW+RoZ55zbLD4S45xzzrlE8iTGOeecc4nkSYxzzjnnEsmTGOdqiJycHPbbbz9yc3Pp0qULAEOHDqVVq1bk5uaSm5vLCy+8kOUonXOu5vCFvc7VIJMnT6ZZs2Yblf3mN79h8ODBWYrIOedqLh+JqeEk5UhaFLfzJK2QNE/SEklTJPXMcnx5kn6Ssn+epF+Wdk4l9ZsvqUsVtp8naVwJx5ZJapbpmHPOuerjSUwNJinTSNlUMzvAzNoBFwP3SDq6mkNLlQdsSGLM7B9m9s/shZNckujevTudO3dm2LBhG8rvueceOnXqxMCBA/n666+zGKFzztUsMrNsx7DVkJQDjDOzjnF/MNAI+Ao4D1gLvG5mZ0hqCNwNdARqA0PN7FlJA4De8bxawFnFbUrKAwabWc+UPgcCJ5X0W0GSdgcej+09C1xqZo3S25J0DzDbzEZI6gz8NZ7zBTDAzD6WdHHqdQBXADOAdcDnwEXA0UChmf1FUi7wD6AB8F9goJl9LSkfeA04CmgCnGNmU0uIvxZwM3A8sB4YbmZ3xzYGm9lsSX8HDgLqA0+b2bXx3JuAk2O8L5nZYEmnA9fGmFeYWbcS+t1wfyT9CBgJtAKmA8cCnc3sC0ljgV2BesCdZjYsQ1uDgEEAzZo173zNHcM3Or5fq8YAfP755zRv3pyvv/6awYMHc/HFF7PrrrvSuHFjJPHggw/y5ZdfMmTIkEwhV4vCwkIaNWqUtf63lMefXUmPH5J/DUmN/6ijjppjZpuMvvuamOpxBbC7ma2R1CSWXQVMMrOBsWympP/EYwcCnczsq5gYlWYucFkpx+8E/m5m/5R0QVmBSqpNSK5OMbPPJfUDbgQGpl+HmS2X9A9i0hLPTx0V+idwkZm9LOl6QvJwaTy2vZkdLOnEWH5MCSENAnKAXDNbK6lphjpXxXtVC5goqRPwIdALaG9mlnLfrwGOM7MPU8rKci3wipldL6kHcE7KsYGx7/rALEnPmNmXqSfHxGYYQJu2e9ptCzf+z27ZmXmbdDh//nyKioro3bv3hrK2bdvSs2dP8vI2rV9d8vPzs9r/lvL4syvp8UPyryHp8afz6aTqsQB4TNLPCaMCAN2BKyQVAPmEf8m3iccmmNlX5WxbZRw/nDCKAPBIOdprRxgdmhBjuxpoHY9luo7MQUmNgSZm9nIsehhIHfUYHf/OISQpJTkGuM/M1gKUcF/6SpoLzAM6APsCK4DvgAck9QZWxbrTgBGSfkUY6SqPbsCjsf/ngdQ5nYslzSeMSO0K7FXONjeycuVKvv322w3bL730Eh07duTjjz/eUGfMmDF07Nhxc5p3zrmtko/EVK61bJwY1ot/exDeCE8CrpK0HyH5OM3MlqQ2IOkQYGUF+jwAeKOMOpnmDEuKVcBiMzsswzmZrmNzrYl/17EFr8M4XTYYOChOVY0A6sVRm4MJ01t9gAuBn5rZefEe9wDmSOqcPnJSgb7zCEnWYWa2Kk5x1SvtnJJ8+umn9OoVZgTXrl3Lz372M44//nh+8YtfUFBQgCRycnK47777Nqd555zbKnkSU7k+BVrENRSFQE/gJWBXM5ss6RXgDMJak/HARZIuitMdB5jZvIp0FqdN/gCcW0q1abHPR4EzU8r/B+wrqS5hLcnRwCvAEqC5pMPMbHqcXtqbkChluo5vgR3TOzWzFZK+ltQ1rnf5BfByer1ymAD8WtLk4umktNGYHQlJ3wpJOwMnAPmSGgENzOwFSdOAdwEk7WFmrwGvSTqBMHpSVhIzBfgZ8Md4zk6xvDHwdUxg2gOHbsb1AWGqaP78+ZuUP/JIeQbPnHNu2+RJTCUys6K49mMmYU3Gm4Qpi0fj9IqAu+JakhuAO4AFkrYDlhKSnrJ0lTSPsFj2M+BiM5tYSv1LgMclDSEs7C2O9X1JTwKLYt/zYvn3kvoAd8WYt49xvlXCdfwLeFrSKYSFvanOAv4hqQEhiTi7HNeX7n5CErVAUhEwHLgn5Trmx/vxJvA+IWkD2AF4VlK9GO9vY/mtkvaKZROBTTOHTV0HjJS0GHgVeC+WvwicJ+kNQvI3YzOuzznn3GbyJKaSmdldwF3lqLca+HWG8hHAiJT9ZYQ1KphZPuFf/xWJZymwYWpI0qUpxy4HLs9wTgEbr18pdkSGum8BnVKKpqYcKyDD6ISZ5aVsf0Epa2LiWpjf8kMSkqmNASWcfnCG9npnqpihXj5hrRJxuql7CVVPKE97zjnnKp8v7HXOOedcIvlIzFZC0lXA6WnFT5nZjakFZlYjvyBA0nGE74NJtbSk779Jer/OOee2nCcxW4mYrNxYZsUayszGExY7b9X91q9diyU39aiu7pxzbqvm00nOOeecSyRPYpxzzjmXSJ7EOOeccy6RfE2Mc9VoddE6cq54fsP+Ml8f45xzm81HYpxzzjmXSJ7EOOeccy6RPIlxzjnnXCJ5EuNcln333XccfPDB7L///nTo0IFrr70WgEmTJnHggQfSsWNHzjrrLNauXZvlSJ1zrmbxJMa5LKtbty6TJk1i/vz5FBQU8OKLL/Lqq69y1lln8cQTT7Bo0SJ22203Hn744WyH6pxzNYonMQkkKUfSoridJ2mFpHmSlkiaIqnUX8OWdKqkfTez71xJJ6bsnyzpihLqFm5OH1VF0v2be91VSRKNGoVfgygqKqKoqIhatWpRp04d9t57bwCOPfZYnnnmmWyG6ZxzNY4nMQkjKdPH4qea2QFm1g64GLhH0tGlNHMqsLlv5rnAhiTGzJ4zs5s2s61qI6mWmZ1rZq9nO5ZM1q1bR25uLi1atODYY4/l4IMPZu3atcyePRuAp59+mvfffz/LUTrnXM0iM8t2DFs1STnAODPrGPcHA42Ar4DzgLXA62Z2hqSGwN1AR6A2MNTMnpU0AOgdz6sFnFXcpqQ8YLCZ9UzpcyBwUqYfMZT0E2AcsCI+TouH7gWaA6uAX5nZm5JOB64F1sW6xwDvAPWBD4E/x+0uZnahpN2Bx2OczwKXlvaDk5IuA/oCdYExZnatpF7AhbGvHwMvA92A44FeQGOgFfComV0X2/k5IXmrA7wG/J+ZrYsjQffFti4A/hjv1WxJ3YHrYt//Bc42s0JJy4CHgZPic3B6vBeN4nPTBTDgOjN7pqR20q5zEDAIoFmz5p2vuWP4hmP7tWq80T0pLCzkD3/4AxdffDGrVq3ivvvuo6ioiC5dujB9+nTuv//+km5ntSgsLNwwapREHn92JT1+SP41JDX+o446ao6ZddnkgJn5owofQA6wKGV/MDAU+AioG8uaxL9/An5eXAa8BTQEBgAfAE3T2wTyCAlNap+5wBulxDQC6JOyPxHYK24fAkyK2wuBVmkxDgDuSTl3wz7wHPDLuH0BUFhKDN2BYYAII4LjgG7x2KOERGYc0D+ln4+BHxESp0WEhGIf4F9A7VjvbykxGNA3pc/8eE4zYArQMJYPAa6J28uAi+L2/wH3x+2bgTtS2tqptHZKeuy6+x6225BxGx6ZXHfddXbrrbduVDZ+/Hg7/fTTM9avTpMnT852CFvE48+upMdvlvxrSGr8wGzL8P9Un07KngXAY3EUofhjJ92BKyQVEN5w6wFt4rEJZvZVOdtWeYOIIww/AZ6K/d4HtIyHpwEjJP2KMAJUlsOBkXH7kTLqdo+PecBcoD2wVzx2EfB7YI2ZjUw5Z4KZfWlmq4HRwBHA0UBnYFaM/2igbay/Dsi0kORQwnTatHjOWcBuKcdHx79zCAkjhNGce4srmNnX5WinXD7//HOWL18OwOrVq5kwYQLt27fns88+A2DNmjXcfPPNnHfeeRVt2jnntmr+swNVby0brz2qF//2IEyTnARcJWk/QvJxmpktSW1A0iHAygr0eQDwRjnrbgcsN7Pc9ANmdl7suwcwR1LncrRX3vlJAX82s/syHGsNrAd2lrSdma0voW2L7TxsZr/P0M53ZrauhL4nmFn/EmJbE/+uo/T/Rspqp1w+/vhjzjrrLNatW8f69evp27cvPXv25LLLLmPcuHGsX7+e888/n5/+9Kdb0o1zzm11fCSm6n0KtJD0I0l1gZ6E+76rmU0mTEE0JqwjGQ9cJEkAkg6oaGeSOgF/IGXUIINvgR0AzOwbYGlc/4KC/eP2Hmb2mpldA3wO7Jp6bgbTgDPi9pllhDoeGBhHgpDUSlKLuHD5QaA/IRH7bco5x0pqKqk+YXHyNMJUWB9JLWI7TSWVNRoyAzhc0p7xnIaS9i7jnAmEKTLiOTttZjub6NSpE/PmzWPBggUsWrSIa665BoBbb72VN954gyVLlnDppZdWtFnnnNvqeRJTxcysCLgemEl4I3yTMDXzqKSFhOmUu8xsOXADYTHpAkmL4355dC3+iDUhebnYzCaWUv8J4LJ4zh6EhOMcSfOBxcApsd6tkhbGj3O/CswHJgP7SiqQ1C+t3UuAC+J1tSotYDN7ibAIeHqs/zQhObqS8GmrVwgJzLmS9omnzSRMDy0AnjGz2RY+bXQ18JKkBYR73JJSmNnnhDU2I+M50wnTWaX5I7CTpEXxPh21me0455yrJD6dVA3M7C7grnLUWw38OkP5CMJi3OL9ZYRPMGFm+YSRnIrEM41NP2J9fIZ6vTOc/hVwUFrZiFh/KXBYSvnVZcRxJ3BnWvH1Kce/JSYFcVrrAzM7NUM7o4BRGcobpe3npWxPynAdmFlOyvZswsJpLHzi6KwM9TO245xzrur5SIxzzjnnEslHYrZikq4CTk8rfsrMbqzGGPZj008qrTGzQyrSTvpolHPOOedJzFYsJivVlrCUEMNCwvfWOKB+7VosualHtsNwzrmtgk8nOeeccy6RPIlxzjnnXCJ5EuOcc865RPIkxrlqtLpoHTlXPE/OFc9nOxTnnEs8T2Kcc845l0iexDjnnHMukTyJcc4551wieRLjXBZ99913HHzwwey///506NCBa6+9FoBzzjmH/fffn06dOtGnTx8KCwuzHKlzztU8nsQ4l0V169Zl0qRJzJ8/n4KCAl588UVmzJjB7bffzvz581mwYAFt2rThnnvuyXaozjlX4yQyiZGUE39ZGUl5klYU/4qzpCmSemY7xmyJ9+ZnKft5ksalbP+kEvvKl9Qlbi+Lv3i9UNLrkv4oqV5l9VUZJN0vKf2HL7NKEo0ahd+pLCoqoqioCEnsuOOOAJgZq1evRlI2w3TOuRopcUmMpEw/lTDVzA4ws3bAxcA9ko6u5tBqihzgZyUcywMqLYnJ4Cgz2w84GGgL3FeFfVWIpFpmdq6ZvZ7tWNKtW7eO3NxcWrRowbHHHsshh4SflTr77LP58Y9/zJtvvslFF12U5Sidc67mqfIkJnXUJO4PljRU0sXxX+wLJD0RjzWU9KCkmXFk5ZRYPkDSc5ImARNL68/MCoDrgQtLiekkSa/FPv4jaedYfqSkgviYJ2kHSS3j6E6BpEWSusa63SVNlzRX0lOSGsXym1Ku6y+x7PR47nxJU1KuaaykCXEU40JJv439zpDUNNbbQ9KLkuZImiqpfSwfIekuSa9KeldSn3h5NwFdY7y/SX0egPOA38RjXSU1l/SMpFnxcXisO1TSw7G//0nqLemWOMryoqTaZTwHhbGvU4uvo4Tn4bLY7wJJ18WyXpImKmgp6S1JP47369k4+vO2pGtT2vl5fM0USLpPUq1YXijpNknzgcO08chRSc/fMknXxfKFKfe7kaSHYtkCSaeV1k5F1KpVi4KCAj744ANmzpzJokXhP5eHHnqIjz76iH322YdRo0ZVtFnnnNvqZfMHIK8AdjezNZKaxLKrgElmNjCWzZT0n3jsQKCTmX0V35BLMxe4rJTjrwCHmplJOhe4HPgdMBi4wMymxTej74BBwHgzuzG+OTaQ1Ay4GjjGzFZKGgL8VtK9QC+gfWy7+LquAY4zsw9TygA6AgcA9YB3gCFmdoCk24FfAncAw4DzzOxtSYcAfwN+Gs9vCRwBtAeeA56O93WwmfWEMIUEYGbLJP0DKDSz4uTqceB2M3tFUhtgPLBPbHsP4ChgX2A6cJqZXS5pDNADGFvK/cXMvpG0FNgLeC39uKTu8djBgIDnJHUzszExQbgAOB641sw+UZhOOTjes1XALEnPAyuBfsDhZlYk6W/AmcA/gYbAa2b2u9hncd8Znz9C8gvwhZkdKOn/CK+Jc4E/ACviSBOSdipHO8X9DSK8jmjWrDnX7LcWgPz8/E3uW05ODvfeey/9+vXbUNauXTuGDRvG7rvvXtotrxaFhYUZ404Kjz+7kh4/JP8akh5/umwmMQuAxySN5Yc3xO7AyZIGx/16QJu4PcHMvipn22UtIGgNjJLUEqgDLI3l04C/SnoMGG1mH0iaBTwYRx/GmlmBpCMJb+7T4htjHcIb/QpC4vOAwjqUcSntjpD0JDA6JY7JZvYt8K2kFcC/YvlCoFNMpH4CPKUf1kTUTTl/rJmtB15XHE2qoGOAfVPa3jFlJOHfMSlYCNQCXkyJLaec7Zf2PHSPj3lxvxEhqZkCXAQsAmaY2ciUcyaY2ZcAkkYTEri1QGdCUgNQH/gs1l8HPJOh70PJ/PwVK36O5gC94/YxwBnFFczsa4W1V6W1U1x3GCEZpU3bPe22heE/u2Vn5vH5559Tu3ZtmjRpwurVq/nDH/7A5ZdfTuvWrdlzzz0xM8aNG8fhhx9OXl5ehkupXvn5+TUijs3l8WdX0uOH5F9D0uNPVx1JzFo2nrYqXuzZA+gGnARcJWk/wpveaWa2JLWBOAKxsgJ9HgC8Ucrxu4G/mtlzcaRiKICZ3RT/dX8i4Y3pODObIqlbjHeEpL8CXxPeUPunNyzpYOBooA9hSuunZnZevIYewBxJnWP1NSmnrk/ZX094brYDlptZbgnXkXr+5qz83I4wIvVd2jVsaNvM1ksqMjNLi61UknYgJDtvlVQF+LOZZVo30zr2s7Ok7WKiBmBp9Sy287CZ/T5DO9+Z2boS+s74/EXF93UdpV9rWe2U6eOPP+ass85i3bp1rF+/nr59+9KjRw+6du3KN998g5mx//778/e//31zu3DOua1WdSQxnwItJP0IKAR6Ai8Bu5rZZEmvEP6F24gwnXGRpIvidMwBZjavxJYzkNSJMPR/binVGgMfxu2zUs7dw8wWAgslHQS0l7Qa+MDMhkuqS5jWuhG4V9KeZvaOpIZAK+AjoIGZvSBpGvBuSruvAa9JOgHYtTzXUjwlI+l0M3tKIbvoZGbzSzntW2CHUo7tmLL/EmHU49YYZ25cU7RF4mjO3wgjRV+XUG08cIOkx8ysUFIroAj4CngQ6E94bn4L/CWec6zCGpvVwKnAQMLU0rOSbjezz+LxHczsf6WEOIMMz5+ZlZRwAUwgTHFdGq9xp81sZyOdOnVi3rxNX+LTpk0rbxPOObfNqvIkJk5JXA/MJCQObxKmJx6V1Jjwr9m7zGy5pBsI60AWSNqOMM1Tno9Ld5U0D2hAmEq42MxKWwA8lDBF8zUwCShebHCppKMIowCLgX8TEqzLJBURkrBfmtnnkgYAI2NiA2FtxLeEN9R68bp+G4/dKmmvWDYRmA/kluO6IKzv+Lukq4HawBPx/JIsANYpLGYdwQ/TNRCmq55WWDB9EeGTXPdKWkB4LUwhLMjdXJNjorUdMAa4oaSKZvaSpH2A6XHkpxD4eex/alynM58f1r5AeA09QxipedTMZgPEe/NSfM0UEZKNEpOYUp6/0pKPPxLu1SLCCM11ZjZ6M9pxzjlXSfTDLIFzNVdMFrqYWYmfOkuCNm33tO363gnAspt6ZDmaikv6fLrHn11Jjx+Sfw1JjV/SHDPrkl6euO+Jcc4555yD7H46qcpJugo4Pa34KTO7MRvxbIvigu1H0orXmNkhFWnHzEYQpsecc845YCtPYmKy4glLFsWF0rnZjqOmqF+7FksSOI3knHM1kU8nOeeccy6RPIlxzjnnXCJ5EuOcc865RPIkxrlqtLpoHTlXPF92Reecc2XyJMY555xzieRJjHPOOecSyZMY55xzziWSJzHOZcnAgQNp0aIFHTt23FBWUFDAoYceSm5uLl26dGHmzJlZjNA552o2T2Kcy5IBAwbw4osvblR2+eWXc+2111JQUMD111/P5ZdfnqXonHOu5vMkxtVYkq5M2381W7FUhW7dutG0adONyiTxzTffALBixQp22WWXbITmnHOJsFX/7IBLvCuBPxXvmNlPshhLtbjjjjs47rjjGDx4MOvXr+fVV7eqvM055yqVj8S4LSLpKklvSXpF0khJgyXlS+oSjzeTtCxu15J0q6RZkhZI+nUsbylpiqQCSYskdZV0E1A/lj0W6xXGv4rtLJK0UFK/WJ4X+35a0puSHpOkUmJfJuk6SXNjO+1j+VBJg1PqLZKUEx9vShoRr/kxScdImibpbUkHb+n9/Pvf/87tt9/O+++/z+23384555yzpU0659xWy0di3GaT1Bk4g/ADj9sDc4E5pZxyDrDCzA6SVBeYJukloDcw3sxulFQLaGBmUyVdaGa5GdrpHfvcH2gGzJI0JR47AOgAfARMAw4HXiklpi/M7EBJ/wcMBs4t47L3JPwy+kBgFvAz4AjgZMLI0anpJ0gaBAwCaNasOdfst5b8/HwAPvnkE1auXLlh/8EHH6RXr17k5+fTvHlzpk+fvuFYTVBYWFij4qkojz+7kh4/JP8akh5/Ok9i3JboCowxs1UAkp4ro353oJOkPnG/MbAXIRl4UFJtYKyZFZTRzhHASDNbB3wq6WXgIOAbYKaZfRDjKQByKD2JGR3/ziEkR2VZGn+ZG0mLgYlmZpIWxr42YWbDgGEAbdruabct3J5lZ+YBsGzZMho2bEheXtjfddddkUReXh4TJ06kffv2G47VBPn5+TUqnory+LMr6fFD8q8h6fGn8yTGVYW1/DBVWS+lXMBFZjY+/QRJ3YAewAhJfzWzf25m32tSttdR9mu8uH5q3dT4YeNrSG1/fcr++nL0tZH+/fuTn5/PF198QevWrbnuuusYPnw4l1xyCWvXrqVevXoMGzasIk0659w2xZMYtyWmEJKOPxNeSycB9wHLgM7ATKBPSv3xwPmSJplZkaS9gQ8JU0IfmNnwOM10IPBPoEhSbTMrSut3KvBrSQ8DTYFuwGVA+0q6rmVATwBJBwK7V1K7Gxk5cmTG8jlzSpuRc845V8yTGLfZzGyupFHAfOAzwrQQwF+AJ+NakNRfO7yfMOUyNy64/ZywhiQPuExSEVAI/DLWHwYskDTXzM5MaWcMcFjs14DLzeyT4oW5leAZ4Jdxuug14K1Katc551wl8iTGbREzuxG4EcKnemLZm0CnlGpXx/L1hMWvV27cCg/HR3rbQ4AhKfuN4l8jjLxcllY/H8hP2b+wjNhzUrZnE5IpzGw1Yf1OJh1TzhmQsr0s9Zhzzrmq5x+xds4551wi+UiMqzRmNjTbMWQiaQybrmsZkmmBsXPOueTwJMZt9cysV7ZjKFa/di2W3NQj22E459xWwaeTnHPOOZdInsQ455xzLpE8iXHOOedcInkS41w1Wl20LtshOOfcVsOTGOecc84lkicxzjnnnEskT2Kcc845l0iexDiXJQMHDqRFixZ07LjxrxXcfffdtG/fng4dOnD55ZdnKTrnnKv5PIlxLksGDBjAiy++uFHZ5MmTefbZZ5k/fz6LFy9m8ODBWYrOOedqPk9iXKWQdGXa/qvZimVLSRog6Z4SjhVWVj/dunWjadOmG5X9/e9/54orrqBu3boAtGjRorK6c865rY4nMa6ybJTEmNlPshVIkr311ltMnTqVQw45hCOPPJJZs2ZlOyTnnKuxPInZhki6StJbkl6RNFLSYEn5krrE480kLYvbtSTdKmmWpAWSfh3LW0qaIqlA0iJJXSXdBNSPZY/FeoXxr2I7iyQtlNQvlufFvp+W9KakxySplNiXSbpO0tzYTvtYPlTS4JR6iyTlxMebkkbEa35M0jGSpkl6W9LB5bxnu0uaHvv8Y0p5I0kTU+I5pWLPRmZr167lq6++YsaMGdx666307dsXM6uMpp1zbqvjPwC5jZDUGTgDyCU873OBOaWccg6wwswOklQXmCbpJaA3MN7MbpRUC2hgZlMlXWhmuRna6R373B9oBsySNCUeOwDoAHwETAMOB14pJaYvzOxASf8HDAbOLeOy9wROBwYCs4CfAUcAJxNGjk4t43yAO4G/m9k/JV2QUv4d0MvMvpHUDJgh6TnLkHFIGgQMAmjWrDn5+fkbjn3yySesXLlyQ1mDBg1o27YtL7/8MgDff/89zz77LE2aNClHqFWvsLBwo/iTxuPPrqTHD8m/hqTHn86TmG1HV2CMma0CkPRcGfW7A50k9Yn7jYG9CMnAg5JqA2PNrKCMdo4ARprZOuBTSS8DBwHfADPN7IMYTwGQQ+lJzOj4dw4hOSrLUjNbGNtfDEw0M5O0MPZVHocDp8XtR4Cb47aAP0nqBqwHWgE7A5+kN2Bmw4BhAG3a7ml5eXkbji1btoyGDRtSXDZw4EA++ugj8vLyeOutt9huu+045ZRTKGWQqlrl5+eTGn/SePzZlfT4IfnXkPT403kS49byw7RivZRyAReZ2fj0E+Ibdw9ghKS/mtk/N7PvNSnb6yj79VhcP7Vuavyw8TWktr8+ZX99OfpKlWk+50ygOdDZzIriNFy9DPVK1L9/f/Lz8/niiy9o3bo11113HQMHDmTgwIF07NiROnXq8PDDD9eYBMY552oaT2K2HVMIScefCc/7ScB9wDKgMzAT6JNSfzxwvqRJ8U16b+BDwpTQB2Y2PE4zHQj8EyiSVNvMitL6nQr8WtLDQFOgG3AZ0L6SrmsZ0BNA0oHA7pXUbrFphGm4RwmJS7HGwGfx3hwF7FbRhkeOHJmx/NFHH92MMJ1zbtvjC3u3EWY2FxgFzAf+TZgWAvgLIVmZR0hQit0PvA7MlbSIkPBsD+QB82P9foQ1IxCmSxYUL+xNMQZYEPudBFxuZptMuWyBZ4CmcbroQuCtSmwb4BLggjgF1Sql/DGgSyz/JfBmJffrnHOuDD4Ssw0xsxuBGyF8qieWvQl0Sql2dSxfT1j8euXGrfBwfKS3PQQYkrLfKP41wsjLZWn184H8lP0Ly4g9J2V7NiGZwsxWE9bvZNIx5ZwBKdvLUo9l6GsEMCJuLwUOSzlcfH++SCt3zjlXzXwkxjnnnHOJ5CMx2ygzG5rtGDKRNIZN17UMybTAuBL6OpswXZRqmpldkKm+c865msWTGFejmFmvauzrIeCh6uoPoH7tWtXZnXPObdV8Osk555xzieRJjHPOOecSyZMY55xzziWSJzHOVaPVReuyHYJzzm01PIlxzjnnXCJ5EuOcc865RPIkxjnnnHOJ5EmMc1kwcOBAWrRoQceOP/z6wVNPPUWHDh3YbrvtmD17dhajc865ZPAkxrksGDBgAC+++OJGZR07dmT06NF069YtS1E551yybLNJjKSc+OvMSMqTtELSPElLJE2R1HMz2x0g6Z7KjbZc/eZJGldNfaXeuy6S7qqOfjPEUVgd8Ug6XdJiSesldUkpz5G0WlJBfPyjvG1269aNpk2bblS2zz770K5du0qM3Dnntm7b5M8OSMp03VPNrGc8nguMlbTazCZWa3AJE39RusrmPiTVMrNyfy65iuJZBPQG7stw7L9mllvJ/TnnnCuHRIzEpP5LO+4PljRU0sWSXpe0QNIT8VhDSQ9KmhlHVk6J5QMkPSdpElBqYmJmBcD1wIWlxNRc0jOSZsXH4RnqnCTptRjHfyTtHMuHSnpE0nRJb0v6VSxvGUeBCiQtktQ1lnePdedKekpSo1h+vKQ3Jc0lvMmWdg+HSnpY0lRJ/5PUW9ItkhZKelFS7Vivs6SXJc2RNF5Sy5Ty+ZLmAxektLthBEjSwTHOeZJeldQu5d6Pjv28LemWMmItlHRb7OswSb+N92ORpEvLODc1nqHxtZAv6V1JF6fU+0McdXtF0khJg0tq08zeMLMlpfXrnHOu+iV9JOYKYHczWyOpSSy7CphkZgNj2UxJ/4nHDgQ6mdlXknLKaHsucFkpx+8EbjezVyS1AcYD+6TVeQU41MxM0rnA5cDv4rFOwKFAQ2CepOeB/sB4M7tRUi2ggaRmwNXAMWa2UtIQ4LcxERgO/BR4BxhVxvUA7AEcBewLTAdOM7PLFX45ukeM4W7gFDP7XFI/4EZgIOGHEi80symSbi2h/TeBrma2VtIxwJ+A0+KxXOAAYA2wRNLdZvZ+Ce00BF4zs99J6gycDRwCCHhN0stmNq8c1wvQPl7zDrHfv8dYTgP2B2oTnus55Wwv3e6S5gHfAFeb2dT0CpIGAYMAmjVrTn5+PgCffPIJK1eu3LBfbPny5cyZM4fCwsLNDKnqFBYWbhJvknj82ZX0+CH515D0+NMlPYlZADwmaSwwNpZ1B05O+Zd1PaBN3J5gZl+Vs22VcfwYYF9pQ7Udi0dIUrQGRsXRjDrA0pRjz5rZamC1pMnAwcAs4ME4KjLWzAokHUlIOqbFvuoQEpD2wFIzextA0qPEN8pS/NvMiiQtBGoBxStLFwI5QDugIzAh9lUL+Dgmg03MbEqs/whwQob2GwMPS9oLMEKCUGyima2Isb4O7AaUlMSsA56J20cAY8xsZTx3NNAVKG8S87yZrQHWSPoM2Bk4nHD/vwO+k/SvcraV7mOgjZl9GZOtsZI6mNk3qZXMbBgwDKBN2z0tLy8PgGXLltGwYUOK94s1adKEzp0706VLF2qa/Pz8TeJNEo8/u5IePyT/GpIef7pETCcBa9k41nrxbw/gXsIIyyyFtS4ijDDkxkcbM3sj1l9ZgT4PAN4o5fh2hFGW4n5amVn6P53vBu4xs/2AX6fEDeFNPpXFJKEb8CEwQtIv4/VMSOlnXzM7pwLXkWpN7Gg9UGRmxTGsJyS0Ahan9LWfmXWvQPs3AJPNrCNwEhtf75qU7XWUnkB/V5F1MGWoSL8VYmZrzOzLuD0H+C+wd3nO7d+/P4cddhhLliyhdevWPPDAA4wZM4bWrVszffp0evTowXHHHVdZoTrn3FYpKUnMp0ALST+SVBfoSYh9VzObDAwhjAI0IkzrXKQ4lCDpgIp2JqkT8AdCglSSl4CLUs7JzVCnMSEhATgr7dgpkupJ+hGQR0jCdgM+NbPhwP2E5GwGcLikPWM/DSXtTZi6yZG0R2yvf/mvsERLgOaSDot91Y4jC8uB5ZKOiPXOLOH81OsdUAnxAEwFTpXUQFJDoFcs2xLTgJPi/W9EeD1VmMK6qFpxuy2wF/Buec4dOXIkH3/8MUVFRXzwwQecc8459OrViw8++IA1a9bw6aefMn78+M0JyznnthmJSGLMrIiw0HYmMIHwBl4LeDROjcwD7opvtjcQpjEWSFoc98uja1yQuoSQvFxcxieTLga6KCwqfh04L0OdocBTkuYAX6QdWwBMJiQpN5jZR4RkZn5cY9EPuNPMPickBCMlLSBOJcWpkEHA8woLez8r53WWyMy+B/oAN8dFtQXAT+Lhs4F7JRVQ8lTbLcCfY/yVMuJhZnOBEYTn/jXg/gqshympzVnAc4Tn4N+E6bQVJdWX1EvSB8BhhPtdnF10I7zOCoCngfMqMF3pnHNuC+mHGQVXXSQNBQrN7C/ZjmVbJamRmRVKagBMAQbFhKlKtWm7p7337jtV3U2VSfp8usefXUmPH5J/DUmNX9IcM9tkoWDSF/Y6t7mGSdqXsG7n4epIYJxzzlUuT2LKIOkq4PS04qfM7MbNbdPMhm5RUKWQdDZwSVrxNDO7IFP9bJL0GlA3rfgXZrawqvs2s59liOdewieXUt1pZg9VdTzOOecqzpOYMsRkZbMTluoW33AT8aZrZodkO4ZU1ZHo1a9dq6q7cM65bUYiFvY655xzzqXzJMY555xzieRJjHPOOecSyZMY56rR6qLK+iJi55xznsQ455xzLpE8iXHOOedcInkS45xzzrlE8iTGuSwYOHAgLVq0oGPHjhvKnnrqKTp06MB2223H7Nmzsxidc84lgycxzmXBgAEDePHFFzcq69ixI6NHj6Zbt25Ziso555LFkxi3VZKUI2lR3M6TtKL4V8olTZHUs4zzh0oaXFq7W6Jbt240bdp0o7J99tmHdu3abWnTzjm3zfCfHXBbHUmZXtdTzaxnPJ4LjJW02swmVmtwzjnnKo2PxLisSx/dkDQ4joRcLOl1SQskPRGPNZT0oKSZcWTllFg+QNJzkiYBpSYmZlYAXA9cWM74OkuaL2k+cEFKeY6kqZLmxsdPKnzxzjnnNpuPxLia7ApgdzNbI6lJLLsKmGRmA2PZTEn/iccOBDqZ2VeScspoey5wWTnjeAi40MymSLo1pfwz4Fgz+07SXsBIoEv6yZIGAYMAmjVrTn5+PgCffPIJK1eu3LBfbPny5cyZM4fCwsJyhld9CgsLN4k3STz+7Ep6/JD8a0h6/Ok8iXE12QLgMUljgbGxrDtwcsp6lXpAm7g9wcy+KmfbKlelkCg1MbMpsegR4IS4XRu4J05PrQP2ztSGmQ0DhgG0abun5eXlAbBs2TIaNmxI8X6xJk2a0LlzZ7p02SQfyrr8/PxN4k0Sjz+7kh4/JP8akh5/Op9OcjXBWjZ+LdaLf3sA9xJGWGbFtS4CTjOz3PhoY2ZvxPorK9DnAcAbZdYq3W+AT4H9CSMwdcp7Yv/+/TnssMNYsmQJrVu35oEHHmDMmDG0bt2a6dOn06NHD4477rgtDM8557ZuPhLjaoJPgRaSfgQUAj2Bl4BdzWyypFeAM4BGwHjgIkkXmZlJOsDM5lWkM0mdgD8A55ZV18yWS1ou6QgzewU4M+VwY+ADM1sv6SygVnljGDlyZMbyXr16lbcJ55zb5nkS47LOzIokXQ/MBD4E3iQkBI9KakwYfbkrJhQ3AHcACyRtBywlJD1l6SppHtCAsJbl4gp8Muls4EFJRkiuiv0NeEbSL4EXqdhIkHPOuS3kSYyrEczsLuCuctRbDfw6Q/kIYETK/jKgY9zOJ4yaVCSeoSnbcwhTRsUuj+VvA51SyodUpA/nnHNbxtfEOOeccy6RfCTGbdMkXQWcnlb8lJndmI14nHPOlZ8nMW6bFpOVaktY6tcu99pf55xzZfDpJOecc84lkicxzjnnnEskT2Kcc845l0iexDhXjVYXrct2CM45t9XwJMY555xzieRJjHPOOecSyZMY55xzziWSJzHOZcHy5cvp06cP7du3Z5999mH69OnZDsk55xLHv+zOuSy45JJLOP7443n66af5/vvvWbVqVbZDcs65xPGRmK2cpMKU7RMlvSVpt6ruq5z1h0oavBn97CepID6+krQ0bv+nom1VJkkjJPUpq96KFSuYMmUK55xzDgB16tShSZMmVR2ec85tdTyJ2UZIOprwK9EnmNn/sh3PljCzhWaWa2a5wHPAZXH/mCyHVi5Lly6lefPmnH322RxwwAGce+65rFy5MtthOedc4ngSsw2Q1A0YDvQ0s//GshGS7pL0qqR3i0cQFNwqaZGkhZL6xfJ7JZ0ct8dIejBuD5S0yW8PSbpM0ixJCyRdl1J+VRwNegVol1J+UKxbUNx/LK8V94vb+nUp13lNrLdI0jBJKq3tEtroIGlmrLtA0l6SbpJ0QUqdoZIGx3t1j6QlcRSoRXmej7Vr1zJ37lzOP/985s2bR8OGDbnpppvKc6pzzrkUviZm61cXGAvkmdmbacdaAkcA7QkjGk8DvYFcYH+gGTBL0hRgKtA11msVzyWWPZHaqKTuwF7AwYCA52IitRI4I7a/PTAXmBNPewj4lZlNl5T6jn4OsMLMDpJUF5gm6SUzW5rhWu8xs+tjDI8APYF/ldJ2JucBd5rZY5LqALWAUcAdwL2xTl/gOKAXIRHbF9gZeB14ML1BSYOAQQDNmjXnvffeo1mzZqxevZr8/Hz22GMPHn/8cY4++ugyQsu+wsJC8vPzsx3GZvP4syvp8UPyryHp8afzJGbrVwS8SkgGLkk7NtbM1gOvS9o5lh0BjDSzdcCnkl4GDiIkMZdK2pfwZr2TpJbAYcDFae12j495cb8RIanZARhjZqsAJD0X/zYBdjCz4o/oPE5IQIrb6pSy1qRxbCtTEnOUpMuBBkBTYLGkqaW0ncl04CpJrYHRZvY2ME9SC0m7AM2Br83sfUm/S7lXH0malKlBMxsGDANo03ZP6927N7fffjstW7akXbt25Ofn07VrV/Ly8koJq2bIz89PRJwl8fizK+nxQ/KvIenxp/MkZuu3njByMFHSlWb2p5Rja1K2VVojZvZhTDaOB6YQkoS+QKGZfZtWXcCfzey+jQqlSzcjfgEXmdn4UitJ9YC/AV1igjEUqFfRzszscUmvAT2AFyT92swmAU8BfYAfE0Zmtsjdd9/NmWeeyffff0/btm156KGHtrRJ55zb5viamG1AHPnoAZwp6Zwyqk8F+sW1KM2BbsDMeGwGcCkhiZkKDI5/040HBkpqBCCplaQW8bxTJdWXtANwUoxvOfCtpEPi+WektXW+pNqxrb0lNczQZ3HC8kXst0852t6EpLbAu2Z2F/As0CkeGhXP7UNIaIjXU3yvWgJHldZ2qtzcXGbPns2CBQsYO3YsO+20U3lPdc45F/lIzDbCzL6SdDwwRdLnpVQdQ5gimg8YcLmZfRKPTQW6m9k7kv5HGI3ZJIkxs5ck7QNMj2trC4Gfm9lcSaNi258Bs1JOOwcYLmk98DKwIpbfD+QAc+NC3c+BUzP0uVzScGAR8Ek5286kL/ALSUWxnT/F9hfHxOtDM/s45V79lDC99h5hKso551w18SRmK2dmjVK23wd2j7vPZapnZgZcFh/pbT0APBC3i4CGmdqI23cCd2Zo40Zgk08zAYvNrBOApCuA2bH+euDK+Mh0fQNStq8Gri5v2yW0dxOQcfGvme2Xtm/AhSW15Zxzrmp5EuNqih6Sfk94Tf4PGJCQtp1zzmWJJzGuRjCzUVTCgtnyti3pOODmtKpLzaxXVcTgnHOu8nkS47ZJ8dNOpX7iqSrUr12rurt0zrmtln86yTnnnHOJ5EmMc8455xLJkxjnnHPOJZInMc5Vo9VF67IdgnPObTU8iXHOOedcInkS45xzzrlE8iTGOeecc4nkSYxzWbB8+XL69OlD+/bt2WeffZg+3X92yTnnKsq/7M65LLjkkks4/vjjefrpp/n+++9ZtWpVtkNyzrnE8ZGYbYSkwpTtEyW9JWm3qu6rnPWHShq8Gf3sJ6kgPr6StDRu/6eibWVoO0/SuBKOLZPUbHPbXrFiBVOmTOGcc84BoE6dOjRp0mRzm3POuW2WJzHbGElHA3cBJ5jZ/7Idz5Yws4VmlmtmuYRf5b4s7h+T5dBKtXTpUpo3b87ZZ5/NAQccwLnnnsvKlSuzHZZzziWOJzHbEEndgOFATzP7bywbIekuSa9KeldSn1guSbdKWiRpoaR+sfxeSSfH7TGSHozbAyXdmKHPyyTNkrRA0nUp5VfF0aBXgHYp5QfFugXF/cfyWnG/uK1fl3Kd18R6iyQNk6TS2i7HffuRpJckLZZ0P6CUY2MlzYnHBpWnvbVr1zJ37lzOP/985s2bR8OGDbnpppvKc6pzzrkUviZm21EXGAvkmdmbacdaAkcA7QkjGk8DvYFcYH+gGTBL0hRgKtA11msVzyWWPZHaqKTuwF7AwYQ3/udiIrUSOCO2vz0wF5gTT3sI+JWZTZeU+s5+DrDCzA6SVBeYJuklM1ua4VrvMbPrYwyPAD2Bf5XSdlmuBV4xs+sl9YixFBtoZl9Jqh/v0TNm9mXafRgEDAJo1qw57733Hs2aNWP16tXk5+ezxx578Pjjj3P00UdXIKTsKCwsJD8/P9thbDaPP7uSHj8k/xqSHn86T2K2HUXAq4Q34EvSjo01s/XA65J2jmVHACPNbB3wqaSXgYMIScylkvYFXgd2ktQSOAy4OK3d7vExL+43IiQ1OwBjzGwVgKTn4t8mwA5mVvxRnccJCUhxW52KR4qAxrGtTEnMUZIuBxoATYHFkqaW0nZZuhGSOszseUlfpxy7WFKvuL1rjGmjJMbMhgHDANq03dN69+7N7bffTsuWLWnXrh35+fl07dqVvLy8coaTPfn5+YmIsyQef3YlPX5I/jUkPf50nsRsO9YDfYGJkq40sz+lHFuTsi1KYWYfxmTjeGAKIUnoCxSa2bdp1QX82czu26hQunQz4hdwkZmNL7WSVA/4G9DFzN6XNBSotxn9lR2QlAccAxxmZqsk5Ze3r7vvvpszzzyT77//nrZt2/LQQw9VRYjOObdV8zUx25A48tEDOFPSOWVUnwr0i2tRmhNGI2bGYzOASwlJzFRgcPybbjwwUFIjAEmtJLWI550qqb6kHYCTYnzLgW8lHRLPPyOtrfMl1Y5t7S2pYYY+i5OIL2K/fcrRdlmmAD+L/Z4A7BTLGwNfxwSmPXBoeRvMzc1l9uzZLFiwgLFjx7LTTjuVfZJzzrmN+EjMNiau3zgemCLp81KqjiFMEc0HDLjczD6Jx6YC3c3sHUn/I4zGbJLEmNlLkvYBpse1tYXAz81srqRRse3PgFkpp50DDJe0HngZWBHL7wdygLlxoe7nwKkZ+lwuaTiwCPiknG2X5TpgpKTFhCm592L5i8B5kt4AlhCSO+ecc9XEk5hthJk1Stl+H9g97j6XqZ6ZGXBZfKS39QDwQNwuAhpmaiNu3wncmaGNG4FNPs0ELDazTgCSrgBmx/rrgSvjI9P1DUjZvhq4urxtl9BePpAft78krMnJ5ISS2nDOOVe1PIlxNU0PSb8nvDb/BwxISNvOOeeqmScxrkYxs1HAqOpqW9JxwM1pVZeaWS+cc87VaJ7EuG1a/LRTqZ94qkz1a9eqrq6cc26r559Ocs4551wieRLjnHPOuUTyJMY555xzieRJjHPOOecSyZMY55xzziWSJzHOOeecSyRPYpxzzjmXSJ7EOOeccy6RPIlxzjnnXCJ5EuOcc865RPIkxjnnnHOJ5EmMc8455xJJZpbtGJzbZkj6FliS7Ti2QDPgi2wHsQU8/uxKevyQ/GtIavy7mVnz9EL/FWvnqtcSM+uS7SA2l6TZHn/2ePzZl/RrSHr86Xw6yTnnnHOJ5EmMc8455xLJkxjnqtewbAewhTz+7PL4sy/p15D0+DfiC3udc845l0g+EuOcc865RPIkxjnnnHOJ5EmMc9VA0vGSlkh6R9IV2Y6nJJKWSVooqUDS7FjWVNIESW/HvzvFckm6K17TAkkHZinmByV9JmlRSlmFY5Z0Vqz/tqSzshz/UEkfxuehQNKJKcd+H+NfIum4lPKsvMYk7SppsqTXJS2WdEksT8RzUEr8iXgOJNWTNFPS/Bj/dbF8d0mvxVhGSaoTy+vG/Xfi8ZyyrqtGMzN/+MMfVfgAagH/BdoCdYD5wL7ZjquEWJcBzdLKbgGuiNtXADfH7ROBfwMCDgVey1LM3YADgUWbGzPQFHg3/t0pbu+UxfiHAoMz1N03vn7qArvH11WtbL7GgJbAgXF7B+CtGGcinoNS4k/EcxDvY6O4XRt4Ld7XJ4EzYvk/gPPj9v8B/4jbZwCjSruu6ngNbcnDR2Kcq3oHA++Y2btm9j3wBHBKlmOqiFOAh+P2w8CpKeX/tGAG0ERSy+oOzsymAF+lFVc05uOACWb2lZl9DUwAjq/y4Ckx/pKcAjxhZmvMbCnwDuH1lbXXmJl9bGZz4/a3wBtAKxLyHJQSf0lq1HMQ72Nh3K0dHwb8FHg6lqff/+Ln5WngaEmi5Ouq0TyJca7qtQLeT9n/gNL/J5lNBrwkaY6kQbFsZzP7OG5/Auwct2vydVU05pp4LRfG6ZYHi6diqOHxx6mJAwijAYl7DtLih4Q8B5JqSSoAPiMkf/8FlpvZ2gyxbIgzHl8B/IgacP83hycxzrlUR5jZgcAJwAWSuqUetDDunKjvZUhizMDfgT2AXOBj4LasRlMOkhoBzwCXmtk3qceS8BxkiD8xz4GZrTOzXKA1YfSkfXYjqj6exDhX9T4Edk3Zbx3Lahwz+zD+/QwYQ/gf4qfF00Tx72exek2+rorGXKOuxcw+jW9M64Hh/DCsXyPjl1SbkAA8ZmajY3FinoNM8SftOQAws+XAZOAwwjRd8e8jpsayIc54vDHwJTUg/s3hSYxzVW8WsFf8tEAdwmK657Ic0yYkNZS0Q/E20B1YRIi1+JMiZwHPxu3ngF/GT5scCqxImT7ItorGPB7oLmmnOG3QPZZlRdraol6E5wFC/GfET5jsDuwFzCSLr7G4nuIB4A0z+2vKoUQ8ByXFn5TnQFJzSU3idn3gWMK6nslAn1gt/f4XPy99gElxpKyk66rZsr2y2B/+2BYehE9kvEWYq74q2/GUEGNbwqcT5gOLi+MkzJdPBN4G/gM0jeUC7o3XtBDokqW4RxKG+4sI8/jnbE7MwEDCYsZ3gLOzHP8jMb4FhDeXlin1r4rxLwFOyPZrDDiCMFW0ACiIjxOT8hyUEn8ingOgEzAvxrkIuCaWtyUkIe8ATwF1Y3m9uP9OPN62rOuqyQ//2QHnnHPOJZJPJznnnHMukTyJcc4551wieRLjnHPOuUTyJMY555xzieRJjHPOOecSyZMY55yrBJLWpfzicUHqrwNXoI1TJe1bBeEhaRdJT5dds1L7zE399WfnKtv2ZVdxzjlXDqstfPX7ljgVGAe8Xt4TJG1vP/xGTonM7CN++PKzKhe/DTYX6AK8UF39um2Lj8Q451wVkdRZ0svxBzXHp3wN/68kzZI0X9IzkhpI+glwMnBrHMnZQ1K+pC7xnGaSlsXtAZKekzQJmBi/bflBSTMlzZO0ya8nS8qRtCjl/LGSJkhaJulCSb+N586Q1DTWy5d0Z4xnkaSDY3nTeP6CWL9TLB8q6RFJ0whfFnc90C+e30/SwZKmx35eldQuJZ7Rkl6U9LakW1LiPl7S3HivJsayMq/XbRt8JMY55ypHfYVfEgZYCvQF7gZOMbPPJfUDbiR8K+1oMxsOIOmPwDlmdrek54BxZvZ0PFZafwcCnczsK0l/Inx9/MD4FfQzJf3HzFaWcn5Hwi821yN8e+sQMztA0u3AL4E7Yr0GZpar8GOgD8bzrgPmmdmpkn4K/JMw6gKwL+GHRFdLGkD4Rt4L4/XsCHQ1s7WSjgH+BJwWz8uN8awBlki6G/iO8LtF3cxsaXFyRfhm2Yper9sKeRLjnHOVY6PpJEkdCW/4E2IyUovw8wIAHWPy0gRoxOb9RtAEM/sqbncHTpY0OO7XA9oQfkOnJJPN7FvgW0krgH/F8oWEr7IvNhLAzKZI2jEmDUcQkw8zmyTpRzFBAXjOzFaX0Gdj4GFJexG+6r92yrGJZrYCQNLrwG7ATsAUM1sa+9qS63VbIU9inHOuaghYbGaHZTg2AjjVzObH0Yq8EtpYyw/T/vXSjqWOOgg4zcyWVCC+NSnb61P217Pxe0P6b9OU9Vs1pY2G3EBInnrFhc/5JcSzjtLfnzbnet1WyNfEOOdc1VgCNJd0GICk2pI6xGM7AB9Lqg2cmXLOt/FYsWVA57hd2qLc8cBFikM+kg7Y8vA36BfbPILwi9MrgKnEuCXlAV+Y2TcZzk2/nsbAh3F7QDn6ngF0U/hVZVKmk6ryel2CeBLjnHNVwMy+JyQeN0uaT/h15J/Ew38AXgOmAW+mnPYEcFlcrLoH8BfgfEnzgGaldHcDYWpmgaTFcb+yfBf7/wfhF7YBhgKdJS0AbgLOKuHcycC+xQt7gVuAP8f2ypwJMLPPgUHA6HgPR8VDVXm9LkH8V6ydc85lJCkfGGxms7Mdi3OZ+EiMc8455xLJR2Kcc845l0g+EuOcc865RPIkxjnnnHOJ5EmMc8455xLJkxjnnHPOJZInMc4555xLpP8HzcFw1BRKScIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "0 번째 fold\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "==============================\n",
+      "train, test shape\n",
+      "(2524490, 135) (1466, 135)\n",
+      "==============================\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Finishing last run (ID:sfd7mank) before initializing another..."
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<br/>Waiting for W&B process to finish, PID 18269<br/>Program ended successfully."
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find user logs for this run at: <code>/opt/ml/code/wandb/run-20210619_172446-sfd7mank/logs/debug.log</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find internal logs for this run at: <code>/opt/ml/code/wandb/run-20210619_172446-sfd7mank/logs/debug-internal.log</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h3>Run summary:</h3><br/><style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
+       "    </style><table class=\"wandb\">\n",
+       "<tr><td>training_auc</td><td>0.84845</td></tr><tr><td>valid_1_auc</td><td>0.80498</td></tr><tr><td>_runtime</td><td>100</td></tr><tr><td>_timestamp</td><td>1624123590</td></tr><tr><td>_step</td><td>99</td></tr></table>"
+      ],
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+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h3>Run history:</h3><br/><style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
+       "    </style><table class=\"wandb\">\n",
+       "<tr><td>training_auc</td><td>▁▂▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇█████████</td></tr><tr><td>valid_1_auc</td><td>▁▂▃▃▄▄▄▄▅▅▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇▇███████████</td></tr><tr><td>_runtime</td><td>▁▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇▇▇██</td></tr><tr><td>_timestamp</td><td>▁▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇▇▇██</td></tr><tr><td>_step</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr></table><br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                    <br/>Synced <strong style=\"color:#cdcd00\">solar-meadow-2577</strong>: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/sfd7mank\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/sfd7mank</a><br/>\n",
+       "                "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "...Successfully finished last run (ID:sfd7mank). Initializing new run:<br/><br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.10.32 is available!  To upgrade, please run:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                Tracking run with wandb version 0.10.30<br/>\n",
+       "                Syncing run <strong style=\"color:#cdcd00\">olive-bush-2578</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
+       "                Project page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT</a><br/>\n",
+       "                Run page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/txgdytuh\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/txgdytuh</a><br/>\n",
+       "                Run data is saved locally in <code>/opt/ml/code/wandb/run-20210619_172637-txgdytuh</code><br/><br/>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Info] Number of positive: 1652874, number of negative: 871616\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.543932 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 25000\n",
+      "[LightGBM] [Info] Number of data points in the train set: 2524490, number of used features: 37\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654736 -> initscore=0.639922\n",
+      "[LightGBM] [Info] Start training from score 0.639922\n",
+      "[100]\ttraining's auc: 0.848478\tvalid_1's auc: 0.813116\n",
+      "VALID AUC : 0.8131155387686989 ACC : 0.732605729877217\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "0 번째 fold\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "==============================\n",
+      "train, test shape\n",
+      "(2524448, 135) (1508, 135)\n",
+      "==============================\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Finishing last run (ID:txgdytuh) before initializing another..."
+      ],
+      "text/plain": [
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<br/>Waiting for W&B process to finish, PID 18302<br/>Program ended successfully."
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+     "data": {
+      "text/html": [
+       "Find user logs for this run at: <code>/opt/ml/code/wandb/run-20210619_172637-txgdytuh/logs/debug.log</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find internal logs for this run at: <code>/opt/ml/code/wandb/run-20210619_172637-txgdytuh/logs/debug-internal.log</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h3>Run summary:</h3><br/><style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
+       "    </style><table class=\"wandb\">\n",
+       "<tr><td>training_auc</td><td>0.84848</td></tr><tr><td>valid_1_auc</td><td>0.81312</td></tr><tr><td>_runtime</td><td>104</td></tr><tr><td>_timestamp</td><td>1624123704</td></tr><tr><td>_step</td><td>99</td></tr></table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h3>Run history:</h3><br/><style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
+       "    </style><table class=\"wandb\">\n",
+       "<tr><td>training_auc</td><td>▁▂▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇█████████</td></tr><tr><td>valid_1_auc</td><td>▁▂▃▄▄▄▅▅▅▆▆▆▆▇▆▇▇▇▇▇▇▇██████████████████</td></tr><tr><td>_runtime</td><td>▁▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇██</td></tr><tr><td>_timestamp</td><td>▁▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇██</td></tr><tr><td>_step</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr></table><br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                    <br/>Synced <strong style=\"color:#cdcd00\">olive-bush-2578</strong>: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/txgdytuh\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/txgdytuh</a><br/>\n",
+       "                "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "...Successfully finished last run (ID:txgdytuh). Initializing new run:<br/><br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.10.32 is available!  To upgrade, please run:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                Tracking run with wandb version 0.10.30<br/>\n",
+       "                Syncing run <strong style=\"color:#cdcd00\">leafy-disco-2579</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
+       "                Project page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT</a><br/>\n",
+       "                Run page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/ow74wc8y\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/ow74wc8y</a><br/>\n",
+       "                Run data is saved locally in <code>/opt/ml/code/wandb/run-20210619_172831-ow74wc8y</code><br/><br/>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Info] Number of positive: 1652870, number of negative: 871578\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.425213 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 25008\n",
+      "[LightGBM] [Info] Number of data points in the train set: 2524448, number of used features: 37\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654745 -> initscore=0.639963\n",
+      "[LightGBM] [Info] Start training from score 0.639963\n",
+      "[100]\ttraining's auc: 0.848424\tvalid_1's auc: 0.837722\n",
+      "VALID AUC : 0.8377216952857799 ACC : 0.7672413793103449\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "0 번째 fold\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "==============================\n",
+      "train, test shape\n",
+      "(2524466, 135) (1490, 135)\n",
+      "==============================\n",
+      "\n"
+     ]
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find user logs for this run at: <code>/opt/ml/code/wandb/run-20210619_172831-ow74wc8y/logs/debug.log</code>"
+      ],
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+       "<IPython.core.display.HTML object>"
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+     "metadata": {},
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+    },
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+       "Find internal logs for this run at: <code>/opt/ml/code/wandb/run-20210619_172831-ow74wc8y/logs/debug-internal.log</code>"
+      ],
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+       "<IPython.core.display.HTML object>"
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+       "<h3>Run summary:</h3><br/><style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
+       "    </style><table class=\"wandb\">\n",
+       "<tr><td>training_auc</td><td>0.84842</td></tr><tr><td>valid_1_auc</td><td>0.83772</td></tr><tr><td>_runtime</td><td>101</td></tr><tr><td>_timestamp</td><td>1624123815</td></tr><tr><td>_step</td><td>99</td></tr></table>"
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+       "<h3>Run history:</h3><br/><style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
+       "    </style><table class=\"wandb\">\n",
+       "<tr><td>training_auc</td><td>▁▂▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇█████████</td></tr><tr><td>valid_1_auc</td><td>▁▄▄▅▄▄▆▅▆▆▇▆▆▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇████████████</td></tr><tr><td>_runtime</td><td>▁▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇██</td></tr><tr><td>_timestamp</td><td>▁▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇██</td></tr><tr><td>_step</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr></table><br/>"
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+     "data": {
+      "text/html": [
+       "\n",
+       "                    <br/>Synced <strong style=\"color:#cdcd00\">leafy-disco-2579</strong>: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/ow74wc8y\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/ow74wc8y</a><br/>\n",
+       "                "
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+       "<IPython.core.display.HTML object>"
+      ]
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "...Successfully finished last run (ID:ow74wc8y). Initializing new run:<br/><br/>"
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.10.32 is available!  To upgrade, please run:\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                Tracking run with wandb version 0.10.30<br/>\n",
+       "                Syncing run <strong style=\"color:#cdcd00\">serene-sun-2580</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
+       "                Project page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT</a><br/>\n",
+       "                Run page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/48bggk8q\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/48bggk8q</a><br/>\n",
+       "                Run data is saved locally in <code>/opt/ml/code/wandb/run-20210619_173022-48bggk8q</code><br/><br/>\n",
+       "            "
+      ],
+      "text/plain": [
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Info] Number of positive: 1652841, number of negative: 871625\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.599690 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 24994\n",
+      "[LightGBM] [Info] Number of data points in the train set: 2524466, number of used features: 37\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[100]\ttraining's auc: 0.848487\tvalid_1's auc: 0.809136\n",
+      "VALID AUC : 0.8091360507079913 ACC : 0.723489932885906\n",
+      "\n"
+     ]
+    },
+    {
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\n",
+      "text/plain": [
+       "<Figure size 432x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# # Best features\n",
+    "# FEATS = ['assessmentItemID_acc', 'assessmentItemID_correct_answer', 'assessmentItemID_total_answer',\n",
+    "#          'userID_question_class_acc', 'userID_question_class_correct_answer', 'userID_question_class_total_answer',\n",
+    "#          'question_class', 'userID', 'assessmentItemID']\n",
+    "\n",
+    "# def learning_rate_decay(current_iter):\n",
+    "#     lr = 1e-1 * (.999 ** (current_iter % 50))\n",
+    "#     return lr\n",
+    "\n",
+    "# set parameters\n",
+    "params = set_params()\n",
+    "\n",
+    "for fold_num, (train_df, test_df) in enumerate(zip(train_lst, test_lst)):\n",
+    "    fold_num = 0\n",
+    "    print(\"@\"*50)\n",
+    "    print(fold_num, \"번째 fold\")\n",
+    "    print(\"@\"*50)\n",
+    "\n",
+    "    # X, y 값 분리\n",
+    "    y_train = train_df[\"answerCode\"]\n",
+    "    train = train_df.drop([\"answerCode\"], axis=1)\n",
+    "\n",
+    "    y_test = test_df[\"answerCode\"]\n",
+    "    test = test_df.drop([\"answerCode\"], axis=1)\n",
+    "\n",
+    "    print(\"=\"*30)\n",
+    "    print(\"train, test shape\")\n",
+    "    print(train.shape, test.shape)\n",
+    "    print(\"=\"*30)\n",
+    "    print()\n",
+    "\n",
+    "    lgb_train = lgb.Dataset(train[FEATS], y_train)\n",
+    "    lgb_test = lgb.Dataset(test[FEATS], y_test)\n",
+    "\n",
+    "    now = datetime.now()\n",
+    "    wandb.init(project='P4-DKT', config=params, entity=\"team-ikyo\")\n",
+    "    wandb.run.name = \"sun-lgbm-fold\" + str(fold_num) + \" time: \" + \" \".join(map(str, [now.month, now.day, now.hour, now.minute]))\n",
+    "\n",
+    "    # train\n",
+    "    model = lgb.train(params,\n",
+    "                      lgb_train,\n",
+    "                      valid_sets = [lgb_train, lgb_test],\n",
+    "                      verbose_eval = 100,\n",
+    "                      callbacks=[wandb_callback()])\n",
+    "                     # lgb.reset_parameter(learning_rate = learning_rate_decay)])\n",
+    "\n",
+    "    preds = model.predict(test[FEATS])\n",
+    "    acc = accuracy_score(y_test, np.where(preds >= 0.5, 1, 0))\n",
+    "    auc = roc_auc_score(y_test, preds)\n",
+    "\n",
+    "    print(f'VALID AUC : {auc} ACC : {acc}\\n')\n",
+    "\n",
+    "    # show feature importance\n",
+    "    fig, ax = plt.subplots(figsize=(6,12))\n",
+    "    lgb.plot_importance(model, max_num_features=100, height=0.8, ax=ax)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Permutation importance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def permutation_importance(model, X_val, y_val, metric, threshold=0.001, minimize=True, verbose=True):\n",
+    "    results = {}\n",
+    "    \n",
+    "    y_pred = model.predict(X_val)\n",
+    "    \n",
+    "    results['base_score'] = metric(y_val, y_pred)\n",
+    "    if verbose:\n",
+    "        print(f'Base score {results[\"base_score\"]:.5}')\n",
+    "\n",
+    "    for col in X_val.columns:\n",
+    "        if col in ['assessmentItemID', 'testId', 'question_class', 'question_num', 'userID', 'KnowledgeTag']:\n",
+    "            continue\n",
+    "        \n",
+    "        freezed_col = X_val[col].copy()\n",
+    "\n",
+    "        X_val[col] = np.random.permutation(X_val[col])\n",
+    "        \n",
+    "        preds = model.predict(X_val)\n",
+    "        results[col] = metric(y_val, preds)\n",
+    "\n",
+    "        X_val[col] = freezed_col\n",
+    "        \n",
+    "        if verbose:\n",
+    "            print(f'column: {col} - {results[col]:.5}')\n",
+    "    \n",
+    "    if minimize:\n",
+    "        bad_features = [k for k in results if results[k] < results['base_score'] + threshold]\n",
+    "    else:\n",
+    "        bad_features = [k for k in results if results[k] > results['base_score'] - threshold]\n",
+    "    bad_features.remove('base_score')\n",
+    "    \n",
+    "    return results, bad_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Base score 0.80914\n",
+      "column: assessmentItemID_acc - 0.80782\n",
+      "column: assessmentItemID_correct_answer - 0.80947\n",
+      "column: assessmentItemID_total_answer - 0.80899\n",
+      "column: userID_question_class_acc - 0.80577\n",
+      "column: userID_question_class_correct_answer - 0.8078\n",
+      "column: userID_question_class_total_answer - 0.80901\n",
+      "column: question_class_acc - 0.80777\n",
+      "column: question_class_correct_answer - 0.80914\n",
+      "column: question_class_total_answer - 0.80914\n",
+      "column: userID_testid_experience - 0.80914\n",
+      "column: userID_assessmentItemID_experience - 0.8089\n",
+      "column: assessmentItemID_lda - 0.80734\n",
+      "column: userID_question_class_lda - 0.80877\n",
+      "column: question_class_lda - 0.80767\n",
+      "column: question_num_lda - 0.80914\n",
+      "column: userID_lda - 0.80905\n",
+      "column: KnowledgeTag_lda - 0.80907\n",
+      "column: userID_KnowledgeTag_lda - 0.80394\n",
+      "column: all_data_lda - 0.62124\n",
+      "column: assessmentItemID_svd - 0.80932\n",
+      "column: userID_question_class_svd - 0.8087\n",
+      "column: question_class_svd - 0.80914\n",
+      "column: question_num_svd - 0.80914\n",
+      "column: userID_svd - 0.80914\n",
+      "column: KnowledgeTag_svd - 0.80913\n",
+      "column: userID_KnowledgeTag_svd - 0.8079\n",
+      "column: all_data_svd - 0.80914\n",
+      "column: userID_elapsed_median_rolling_5 - 0.78766\n",
+      "column: userID_elapsed_median_rolling_10 - 0.80861\n",
+      "column: userID_elapsed_median_rolling_15 - 0.80908\n",
+      "column: userID_elapsed_median_rolling_30 - 0.80914\n"
+     ]
+    }
+   ],
+   "source": [
+    "results, bad_features = permutation_importance(model, test[FEATS], y_test, roc_auc_score, minimize=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['assessmentItemID_correct_answer',\n",
+       " 'assessmentItemID_total_answer',\n",
+       " 'userID_question_class_total_answer',\n",
+       " 'question_class_correct_answer',\n",
+       " 'question_class_total_answer',\n",
+       " 'userID_testid_experience',\n",
+       " 'userID_assessmentItemID_experience',\n",
+       " 'userID_question_class_lda',\n",
+       " 'question_num_lda',\n",
+       " 'userID_lda',\n",
+       " 'KnowledgeTag_lda',\n",
+       " 'assessmentItemID_svd',\n",
+       " 'userID_question_class_svd',\n",
+       " 'question_class_svd',\n",
+       " 'question_num_svd',\n",
+       " 'userID_svd',\n",
+       " 'KnowledgeTag_svd',\n",
+       " 'all_data_svd',\n",
+       " 'userID_elapsed_median_rolling_10',\n",
+       " 'userID_elapsed_median_rolling_15',\n",
+       " 'userID_elapsed_median_rolling_30']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bad_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['userID_question_class_correct_answer',\n",
+       " 'userID_elapsed_median_rolling_5',\n",
+       " 'question_class',\n",
+       " 'assessmentItemID_lda',\n",
+       " 'userID_question_class_acc',\n",
+       " 'question_class_acc',\n",
+       " 'question_class_lda',\n",
+       " 'testId',\n",
+       " 'all_data_lda',\n",
+       " 'assessmentItemID_acc',\n",
+       " 'assessmentItemID',\n",
+       " 'userID_KnowledgeTag_lda',\n",
+       " 'KnowledgeTag',\n",
+       " 'userID',\n",
+       " 'question_num',\n",
+       " 'userID_KnowledgeTag_svd']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_FEATS = list(set(FEATS) - set(bad_features))\n",
+    "\n",
+    "new_FEATS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### AutoML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_optuna(num_leaves, min_data_in_leaf, max_bin, bagging_fraction, feature_fraction, lambda_l1, lambda_l2,\n",
+    "                 train_df=train_df):\n",
+    "    skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=2021)\n",
+    "    \n",
+    "    params = {}\n",
+    "    params[\"boosting_type\"] = \"dart\" # gbdt, dart, goss\n",
+    "    params[\"learning_rate\"] = 5e-2 # 1e-1, 5e-2, 1e-2, 5e-3, 1e-3\n",
+    "    params[\"objective\"] = \"binary\"\n",
+    "    params[\"metric\"] = \"auc\" # binary_logloss, rmse, huber, auc\n",
+    "    params[\"num_iterations\"] = 10 # 100\n",
+    "    params[\"max_depth\"] = -1 # -1\n",
+    "    params[\"num_leaves\"] = int(num_leaves) # 31 이상적으로 num_leaves값은 2 ^ (max_depth) 값보다 적거나 같아야 합니다.\n",
+    "    params[\"min_data_in_leaf\"] = int(min_data_in_leaf) # 20 100 ~ 1000 수백 또는 수천 개로 정하는 것\n",
+    "    params[\"max_bin\"] = int(max_bin) # 256\n",
+    "    params[\"bagging_fraction\"] = bagging_fraction # 1.0\n",
+    "    params[\"feature_fraction\"] = feature_fraction # 1.0\n",
+    "    params[\"lambda_l1\"] = lambda_l1 # 0.0\n",
+    "    params[\"lambda_l2\"] = lambda_l2 # 0.0\n",
+    "    params[\"random_state\"] = 2021\n",
+    "\n",
+    "    auc_score = 0\n",
+    "    \n",
+    "    print(\"@\"*50)\n",
+    "    print(\"start\")\n",
+    "    print(\"@\"*50)\n",
+    "\n",
+    "    for fold, (train_index, test_index) in enumerate(skf.split(train_df, train_df[\"answerCode\"])):\n",
+    "        temp_train = train_df.iloc[train_index,:]\n",
+    "        temp_valid = train_df.iloc[test_index,:]\n",
+    "        \n",
+    "        y_train = train_df[\"answerCode\"].iloc[train_index]\n",
+    "        y_test = train_df[\"answerCode\"].iloc[test_index]\n",
+    "        \n",
+    "        lgb_train = lgb.Dataset(temp_train[new_FEATS], y_train)\n",
+    "        lgb_test = lgb.Dataset(temp_valid[new_FEATS], y_test)\n",
+    "\n",
+    "        # train\n",
+    "        model = lgb.train(params,\n",
+    "                          lgb_train,\n",
+    "                          valid_sets = [lgb_train, lgb_test],\n",
+    "                          verbose_eval = 100,\n",
+    "                          callbacks=[wandb_callback()])\n",
+    "        \n",
+    "        \n",
+    "        preds = model.predict(temp_valid[new_FEATS])\n",
+    "        acc = accuracy_score(y_test, np.where(preds >= 0.5, 1, 0))\n",
+    "        auc = roc_auc_score(y_test, preds)\n",
+    "\n",
+    "        auc_score += auc\n",
+    "\n",
+    "    print(auc_score / 5)\n",
+    "    return auc_score / 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lgbm_bo = BayesianOptimization(train_optuna, {'num_leaves': (16, 512),\n",
+    "                                              'min_data_in_leaf': (20, 1000),\n",
+    "                                              'max_bin': (10, 256),\n",
+    "                                              'bagging_fraction': (0.5, 1),\n",
+    "                                              'feature_fraction': (0.5, 1),\n",
+    "                                              'lambda_l1' : (0, 10),\n",
+    "                                              'lambda_l2' : (0, 10)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   | baggin... | featur... | lambda_l1 | lambda_l2 |  max_bin  | min_da... | num_le... |\n",
+      "-------------------------------------------------------------------------------------------------------------\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032849 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19315\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.262100 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 19297\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035968 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19309\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.4991880736397416\n",
+      "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.4992  \u001b[0m | \u001b[0m 0.6942  \u001b[0m | \u001b[0m 0.8675  \u001b[0m | \u001b[0m 7.646   \u001b[0m | \u001b[0m 6.016   \u001b[0m | \u001b[0m 176.7   \u001b[0m | \u001b[0m 620.6   \u001b[0m | \u001b[0m 256.4   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.150602 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 19485\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.226611 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 19467\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107780 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 19479\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.4990461546350316\n",
+      "| \u001b[0m 2       \u001b[0m | \u001b[0m 0.499   \u001b[0m | \u001b[0m 0.5351  \u001b[0m | \u001b[0m 0.6165  \u001b[0m | \u001b[0m 4.278   \u001b[0m | \u001b[0m 7.662   \u001b[0m | \u001b[0m 193.4   \u001b[0m | \u001b[0m 945.2   \u001b[0m | \u001b[0m 183.7   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034947 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19255\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.271151 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 19237\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032964 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19249\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.49917224286305384\n",
+      "| \u001b[0m 3       \u001b[0m | \u001b[0m 0.4992  \u001b[0m | \u001b[0m 0.9048  \u001b[0m | \u001b[0m 0.9592  \u001b[0m | \u001b[0m 5.62    \u001b[0m | \u001b[0m 7.988   \u001b[0m | \u001b[0m 170.4   \u001b[0m | \u001b[0m 618.5   \u001b[0m | \u001b[0m 262.8   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.023693 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19305\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.021775 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19287\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.023475 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19299\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.49937466830404\n",
+      "| \u001b[95m 4       \u001b[0m | \u001b[95m 0.4994  \u001b[0m | \u001b[95m 0.9172  \u001b[0m | \u001b[95m 0.5705  \u001b[0m | \u001b[95m 7.658   \u001b[0m | \u001b[95m 3.864   \u001b[0m | \u001b[95m 175.3   \u001b[0m | \u001b[95m 627.9   \u001b[0m | \u001b[95m 259.7   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035023 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19015\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033781 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 18997\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031771 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19009\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.49824766561616196\n",
+      "| \u001b[0m 5       \u001b[0m | \u001b[0m 0.4982  \u001b[0m | \u001b[0m 0.6968  \u001b[0m | \u001b[0m 0.9719  \u001b[0m | \u001b[0m 5.153   \u001b[0m | \u001b[0m 1.699   \u001b[0m | \u001b[0m 146.9   \u001b[0m | \u001b[0m 46.22   \u001b[0m | \u001b[0m 243.6   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.022291 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19315\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.021259 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19297\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.023667 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19309\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.4993547741350028\n",
+      "| \u001b[0m 6       \u001b[0m | \u001b[0m 0.4994  \u001b[0m | \u001b[0m 0.7699  \u001b[0m | \u001b[0m 0.5432  \u001b[0m | \u001b[0m 5.242   \u001b[0m | \u001b[0m 8.487   \u001b[0m | \u001b[0m 176.8   \u001b[0m | \u001b[0m 650.2   \u001b[0m | \u001b[0m 256.2   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.087377 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 19385\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.090262 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 19367\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.029710 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19379\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.49966067721580476\n",
+      "| \u001b[95m 7       \u001b[0m | \u001b[95m 0.4997  \u001b[0m | \u001b[95m 0.7796  \u001b[0m | \u001b[95m 0.6905  \u001b[0m | \u001b[95m 5.743   \u001b[0m | \u001b[95m 9.019   \u001b[0m | \u001b[95m 183.7   \u001b[0m | \u001b[95m 646.5   \u001b[0m | \u001b[95m 282.9   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.043650 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19665\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032418 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19647\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033105 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19659\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.4986258538546828\n",
+      "| \u001b[0m 8       \u001b[0m | \u001b[0m 0.4986  \u001b[0m | \u001b[0m 0.7325  \u001b[0m | \u001b[0m 0.9752  \u001b[0m | \u001b[0m 5.997   \u001b[0m | \u001b[0m 3.319   \u001b[0m | \u001b[0m 211.3   \u001b[0m | \u001b[0m 648.8   \u001b[0m | \u001b[0m 294.8   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033679 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19335\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033990 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19317\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035500 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19329\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.49920670655196353\n",
+      "| \u001b[0m 9       \u001b[0m | \u001b[0m 0.4992  \u001b[0m | \u001b[0m 0.9761  \u001b[0m | \u001b[0m 0.8682  \u001b[0m | \u001b[0m 5.045   \u001b[0m | \u001b[0m 4.903   \u001b[0m | \u001b[0m 178.1   \u001b[0m | \u001b[0m 643.5   \u001b[0m | \u001b[0m 247.0   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.022415 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19325\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.021907 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19307\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.023632 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19319\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.499337872789756\n",
+      "| \u001b[0m 10      \u001b[0m | \u001b[0m 0.4993  \u001b[0m | \u001b[0m 0.9536  \u001b[0m | \u001b[0m 0.5449  \u001b[0m | \u001b[0m 6.762   \u001b[0m | \u001b[0m 1.704   \u001b[0m | \u001b[0m 177.4   \u001b[0m | \u001b[0m 640.2   \u001b[0m | \u001b[0m 246.4   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034827 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19265\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033483 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19247\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032816 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19259\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.49926421899546386\n",
+      "| \u001b[0m 11      \u001b[0m | \u001b[0m 0.4993  \u001b[0m | \u001b[0m 0.6679  \u001b[0m | \u001b[0m 0.8229  \u001b[0m | \u001b[0m 7.179   \u001b[0m | \u001b[0m 9.145   \u001b[0m | \u001b[0m 171.1   \u001b[0m | \u001b[0m 651.4   \u001b[0m | \u001b[0m 271.2   \u001b[0m |\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "start\n",
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031930 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19335\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581083\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.033664 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19317\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682977, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639892\n",
+      "[LightGBM] [Info] Start training from score 0.639892\n",
+      "[LightGBM] [Info] Number of positive: 1101894, number of negative: 581084\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.035002 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 19329\n",
+      "[LightGBM] [Info] Number of data points in the train set: 1682978, number of used features: 16\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.654729 -> initscore=0.639890\n",
+      "[LightGBM] [Info] Start training from score 0.639890\n",
+      "0.499341976800152\n",
+      "| \u001b[0m 12      \u001b[0m | \u001b[0m 0.4993  \u001b[0m | \u001b[0m 0.5206  \u001b[0m | \u001b[0m 0.9012  \u001b[0m | \u001b[0m 0.6585  \u001b[0m | \u001b[0m 7.539   \u001b[0m | \u001b[0m 178.8   \u001b[0m | \u001b[0m 635.5   \u001b[0m | \u001b[0m 294.3   \u001b[0m |\n",
+      "=============================================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "lgbm_bo.maximize(init_points=2, n_iter=10, acq='ei')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "writing prediction : output/output6_19_17_30.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_preds = model.predict(test[FEATS])\n",
+    "\n",
+    "# SAVE OUTPUT\n",
+    "output_dir = 'output/'\n",
+    "write_path = os.path.join(output_dir, f\"output\" + \"_\".join(map(str, [now.month, now.day, now.hour, now.minute])) +  \".csv\")\n",
+    "if not os.path.exists(output_dir):\n",
+    "    os.makedirs(output_dir)    \n",
+    "with open(write_path, 'w', encoding='utf8') as w:\n",
+    "    print(\"writing prediction : {}\".format(write_path))\n",
+    "    w.write(\"id,prediction\\n\")\n",
+    "    for id, p in enumerate(total_preds):\n",
+    "        w.write('{},{}\\n'.format(id,p))"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/code/baseline/lgbm_function.py b/code/LGBM/lgbm_function.py
similarity index 66%
rename from code/baseline/lgbm_function.py
rename to code/LGBM/lgbm_function.py
index c9647d3..b37936e 100644
--- a/code/baseline/lgbm_function.py
+++ b/code/LGBM/lgbm_function.py
@@ -5,7 +5,6 @@
 import lightgbm as lgb
 from sklearn.metrics import roc_auc_score
 from sklearn.metrics import accuracy_score
-from feature_engineering import feature_engineering
 import numpy as np
 import random
 from matplotlib import pylab as plt
@@ -18,22 +17,21 @@ def set_params():
     # 3순위 min_data_in_leaf
     params = {}
     params["boosting_type"] = "dart" # gbdt, dart, goss
-    params["learning_rate"] = 1e-1 # 1e-1, 5e-2, 1e-2, 5e-3, 1e-3
+    params["learning_rate"] = 5e-2 # 1e-1, 5e-2, 1e-2, 5e-3, 1e-3
     params["objective"] = "binary"
     params["metric"] = "auc" # binary_logloss, rmse, huber, auc
-    params["num_iterations"] = 300 # 100
-    params["max_depth"] = 6 # -1
-    params["num_leaves"] = 30 # 31 이상적으로 num_leaves값은 2 ^ (max_depth) 값보다 적거나 같아야 합니다.
-    params["min_data_in_leaf"] = 5000 # 20 100 ~ 1000 수백 또는 수천 개로 정하는 것
-    params["max_bin"] = 32 # 256
-    params["scale_pos_weight"] = 1.1 # 1.1~1.5 data 불균형
-    params["tree_learner"] = "serial" # serial, feature, data, voting
-    params["early_stopping_rounds"] = 100
-    params["bagging_fraction"] = 0.8 # 1.0
-    params["feature_fraction"] = 0.5 # 1.0
-    params["lambda_l1"] = 1e-1 # 0.0
-    params["lambda_l2"] = 1e-1 # 0.0
-    
+    params["num_iterations"] = 100 # 100
+    params["max_depth"] = -1 # -1
+    params["num_leaves"] = 127 # 31 이상적으로 num_leaves값은 2 ^ (max_depth) 값보다 적거나 같아야 합니다.
+    params["min_data_in_leaf"] = 100 # 20 100 ~ 1000 수백 또는 수천 개로 정하는 것
+    params["max_bin"] = 256 # 256
+#     params["scale_pos_weight"] = 0.9 # 1.1~1.5 data 불균형
+#     params["tree_learner"] = "serial" # serial, feature, data, voting
+#     params["early_stopping_rounds"] = 50
+    params["bagging_fraction"] = 0.7 # 1.0
+    params["feature_fraction"] = 0.7 # 1.0
+    params["lambda_l1"] = 0.1 # 0.0
+    params["lambda_l2"] = 0.1 # 0.0   
     
     print("="*30)
     print(params)
@@ -52,7 +50,7 @@ def custom_train_test_split(df, ratio=0.2):
     train_lst = []
     test_lst = []
 
-    max_train_data_len = 0.2*len(df)
+    max_train_data_len = int(ratio*len(df))
     sum_of_train_data = 0
 
     user_ids_lst = []
@@ -69,15 +67,16 @@ def custom_train_test_split(df, ratio=0.2):
         user_ids_lst.append(user_ids)
         
     for user_ids in user_ids_lst:
-        train_lst.append(df[df['userID'].isin(user_ids) == False])
+        train = df[df['userID'].isin(user_ids) == False]        
         test = df[df['userID'].isin(user_ids)]
-        #test데이터셋은 각 유저의 마지막 interaction만 추출
+        train = pd.concat([train, test[test['userID'] == test['userID'].shift(-1)]])
+        train_lst.append(train)
         test_lst.append(test[test['userID'] != test['userID'].shift(-1)])
         
     return train_lst, test_lst
 
 
-def inference(FEATS, model, auc, acc, time):
+def inference(FEATS, model, auc, acc, time, test=False):
     print("="*30)
     print("Start inference")
     print("="*30)
@@ -100,11 +99,9 @@ def inference(FEATS, model, auc, acc, time):
     test_df = pd.concat([df, test_df])
 
     not_test_df = test_df[test_df["answerCode"] != -1]
-    not_test_df = feature_engineering(not_test_df)
     not_test_df["is_test"] = False
 
     test_df = test_df[test_df["answerCode"] == -1]
-    test_df["question_class"] = test_df["assessmentItemID"].apply(lambda x: x[2])
     test_df["is_test"] = True
     
     print("="*30)
@@ -113,19 +110,31 @@ def inference(FEATS, model, auc, acc, time):
     print("="*30)
     print()
     
-    test_df = pd.merge(test_df, not_test_df[["userID", "user_mean"]].drop_duplicates(), on=["userID"], how="inner")
-    test_df = pd.merge(test_df, not_test_df[["question_class", "question_class_mean"]].drop_duplicates(), on=["question_class"], how="inner")
+    not_test_df.sort_values(by=["userID", "Timestamp"], inplace=True)
     
-    def random_answering(data):
-        return 1 if random.random() < data["user_mean"] * data["question_class_mean"] else 0
-
-    test_df["answerCode"] = test_df[["user_mean", "question_class_mean"]].apply(random_answering, axis=1)
-    test_df.drop(["question_class", "user_mean", "question_class_mean"], axis=1, inplace=True)
+    user_mean = not_test_df.groupby(["userID"])["answerCode"].agg(["mean"])
+    user_mean.columns = ["user_mean"]
     
     data = pd.concat([not_test_df, test_df], join="inner")
+    
+    df = pd.merge(data, user_mean, on=["userID"], how="left")
+    
+    df["next_userID"] = df["userID"].shift(-1)
+    
+    def random_answering(data):
+        if data["is_test"]:
+            return 1 if random.random() < 0.5 else 0
+        else:
+            return data["answerCode"]
+
+    df["answercode"] = df.apply(random_answering, axis=1)
 
     # FEATURE ENGINEERING
-    data = feature_engineering(data)
+    df = feature_engineering(df)
+    
+    new_df = df.shift(1)
+    new_df.columns = ["pred_" + i for i in new_df.columns]
+    data = pd.concat([df, new_df], axis=1)
 
     # TEST DATA
     test_df = data[data["is_test"]]
diff --git a/code/Machine Learning/none.txt b/code/Machine Learning/none.txt
deleted file mode 100644
index 8b13789..0000000
--- a/code/Machine Learning/none.txt	
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/code/baseline/args.py b/code/baseline/args.py
deleted file mode 100644
index ac9404f..0000000
--- a/code/baseline/args.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import os
-import argparse
-
-
-def parse_args(mode='train'):
-    parser = argparse.ArgumentParser()
-
-    
-    parser.add_argument('--seed', default=42, type=int, help='seed')
-    parser.add_argument('--run_name', default='teamikyo', type=str, help='wandb run name')
-    
-    parser.add_argument('--device', default='cpu', type=str, help='cpu or gpu')
-
-    parser.add_argument('--data_dir', default='../data/', type=str, help='data directory')
-    parser.add_argument('--asset_dir', default='asset/', type=str, help='data directory')
-    
-    parser.add_argument('--file_name', default='train_data.csv', type=str, help='train file name')
-    
-    parser.add_argument('--model_dir', default='models/', type=str, help='model directory')
-    parser.add_argument('--model_name', default='model.pt', type=str, help='model file name')
-
-    parser.add_argument('--output_dir', default='output/', type=str, help='output directory')
-    parser.add_argument('--test_file_name', default='test_data.csv', type=str, help='test file name')
-    
-    parser.add_argument('--max_seq_len', default=20, type=int, help='max sequence length')
-    parser.add_argument('--num_workers', default=1, type=int, help='number of workers')
-
-    # 모델
-    parser.add_argument('--hidden_dim', default=64, type=int, help='hidden dimension size')
-    parser.add_argument('--n_layers', default=2, type=int, help='number of layers')
-    parser.add_argument('--n_heads', default=2, type=int, help='number of heads')
-    parser.add_argument('--drop_out', default=0.2, type=float, help='drop out rate')
-    
-    # 훈련
-    parser.add_argument('--n_epochs', default=20, type=int, help='number of epochs')
-    parser.add_argument('--batch_size', default=64, type=int, help='batch size')
-    parser.add_argument('--lr', default=0.0001, type=float, help='learning rate')
-    parser.add_argument('--clip_grad', default=10, type=int, help='clip grad')
-    parser.add_argument('--patience', default=5, type=int, help='for early stopping')
-    
-
-    parser.add_argument('--log_steps', default=50, type=int, help='print log per n steps')
-    
-
-    ### 중요 ###
-    parser.add_argument('--model', default='lstm', type=str, help='model type')
-    parser.add_argument('--optimizer', default='adam', type=str, help='optimizer type')
-    parser.add_argument('--scheduler', default='plateau', type=str, help='scheduler type')
-    
-    args = parser.parse_args()
-
-    return args
diff --git a/code/baseline/baseline.ipynb b/code/baseline/baseline.ipynb
deleted file mode 100644
index 55c7789..0000000
--- a/code/baseline/baseline.ipynb
+++ /dev/null
@@ -1,1950 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Nv5EvIVPnz0y"
-   },
-   "source": [
-    "# LSTM 활용한 베이스라인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: easydict in /opt/conda/lib/python3.7/site-packages (1.9)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install easydict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "id": "wtJhitPznz06"
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "import torch\n",
-    "import easydict\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "import time\n",
-    "import datetime\n",
-    "from datetime import datetime\n",
-    "import random\n",
-    "import wandb"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6w3E-ACunz07"
-   },
-   "source": [
-    "## 1. 데이터 로드 및 전처리 컴포넌트"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "id": "od9O-ttAnz08"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from datetime import datetime\n",
-    "import time\n",
-    "import tqdm\n",
-    "import pandas as pd\n",
-    "import random\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "\n",
-    "class Preprocess:\n",
-    "    def __init__(self,args):\n",
-    "        self.args = args\n",
-    "        self.train_data = None\n",
-    "        self.test_data = None\n",
-    "        \n",
-    "\n",
-    "    def get_train_data(self):\n",
-    "        return self.train_data\n",
-    "\n",
-    "    def get_test_data(self):\n",
-    "        return self.test_data\n",
-    "\n",
-    "    def split_data(self, data, ratio=0.9, shuffle=True, seed=0):\n",
-    "        \"\"\"\n",
-    "        split data into two parts with a given ratio.\n",
-    "        \"\"\"\n",
-    "        if shuffle:\n",
-    "            random.seed(seed) # fix to default seed 0\n",
-    "            random.shuffle(data)\n",
-    "\n",
-    "        size = int(len(data) * ratio)\n",
-    "        data_1 = data[:size]\n",
-    "        data_2 = data[size:]\n",
-    "\n",
-    "        return data_1, data_2\n",
-    "\n",
-    "    def __save_labels(self, encoder, name):\n",
-    "        le_path = os.path.join(self.args.asset_dir, name + '_classes.npy')\n",
-    "        np.save(le_path, encoder.classes_)\n",
-    "\n",
-    "    def __preprocessing(self, df, is_train = True):\n",
-    "        cate_cols = ['assessmentItemID', 'testId', 'KnowledgeTag']\n",
-    "\n",
-    "        if not os.path.exists(self.args.asset_dir):\n",
-    "            os.makedirs(self.args.asset_dir)\n",
-    "            \n",
-    "        for col in cate_cols:\n",
-    "            \n",
-    "            \n",
-    "            le = LabelEncoder()\n",
-    "            if is_train:\n",
-    "                #For UNKNOWN class\n",
-    "                a = df[col].unique().tolist() + ['unknown']\n",
-    "                le.fit(a)\n",
-    "                self.__save_labels(le, col)\n",
-    "            else:\n",
-    "                label_path = os.path.join(self.args.asset_dir,col+'_classes.npy')\n",
-    "                le.classes_ = np.load(label_path)\n",
-    "                \n",
-    "                df[col] = df[col].apply(lambda x: x if x in le.classes_ else 'unknown')\n",
-    "\n",
-    "            #모든 컬럼이 범주형이라고 가정\n",
-    "            df[col]= df[col].astype(str)\n",
-    "            test = le.transform(df[col])\n",
-    "            df[col] = test\n",
-    "            \n",
-    "\n",
-    "        def convert_time(s):\n",
-    "            timestamp = time.mktime(datetime.strptime(s, '%Y-%m-%d %H:%M:%S').timetuple())\n",
-    "            return int(timestamp)\n",
-    "\n",
-    "        df['Timestamp'] = df['Timestamp'].apply(convert_time)\n",
-    "        \n",
-    "        return df\n",
-    "\n",
-    "    def __feature_engineering(self, df):\n",
-    "        #TODO\n",
-    "        return df\n",
-    "\n",
-    "    def load_data_from_file(self, file_name, is_train=True):\n",
-    "        csv_file_path = os.path.join(self.args.data_dir, file_name)\n",
-    "        df = pd.read_csv(csv_file_path)#, nrows=100000)\n",
-    "        df = self.__feature_engineering(df)\n",
-    "        df = self.__preprocessing(df, is_train)\n",
-    "\n",
-    "        # 추후 feature를 embedding할 시에 embedding_layer의 input 크기를 결정할때 사용\n",
-    "\n",
-    "                \n",
-    "        self.args.n_questions = len(np.load(os.path.join(self.args.asset_dir,'assessmentItemID_classes.npy')))\n",
-    "        self.args.n_test = len(np.load(os.path.join(self.args.asset_dir,'testId_classes.npy')))\n",
-    "        self.args.n_tag = len(np.load(os.path.join(self.args.asset_dir,'KnowledgeTag_classes.npy')))\n",
-    "        \n",
-    "        df = df.sort_values(by=['userID','Timestamp'], axis=0)\n",
-    "        columns = ['userID', 'assessmentItemID', 'testId', 'answerCode', 'KnowledgeTag']\n",
-    "        group = df[columns].groupby('userID').apply(\n",
-    "                lambda r: (\n",
-    "                    r['testId'].values, \n",
-    "                    r['assessmentItemID'].values,\n",
-    "                    r['KnowledgeTag'].values,\n",
-    "                    r['answerCode'].values\n",
-    "                )\n",
-    "            )\n",
-    "\n",
-    "        return group.values\n",
-    "\n",
-    "    def load_train_data(self, file_name):\n",
-    "        self.train_data = self.load_data_from_file(file_name)\n",
-    "\n",
-    "    def load_test_data(self, file_name):\n",
-    "        self.test_data = self.load_data_from_file(file_name, is_train= False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "E-MQhPevnz08"
-   },
-   "source": [
-    "## 2. 데이터 셋 / 데이터 로더"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "id": "h29rn8YNnz09"
-   },
-   "outputs": [],
-   "source": [
-    "class DKTDataset(torch.utils.data.Dataset):\n",
-    "    def __init__(self, data, args):\n",
-    "        self.data = data\n",
-    "        self.args = args\n",
-    "\n",
-    "    def __getitem__(self, index):\n",
-    "        row = self.data[index]\n",
-    "\n",
-    "        # 각 data의 sequence length\n",
-    "        seq_len = len(row[0])\n",
-    "\n",
-    "        test, question, tag, correct = row[0], row[1], row[2], row[3]\n",
-    "        \n",
-    "\n",
-    "        cate_cols = [test, question, tag, correct]\n",
-    "\n",
-    "        # max seq len을 고려하여서 이보다 길면 자르고 아닐 경우 그대로 냅둔다\n",
-    "        if seq_len > self.args.max_seq_len:\n",
-    "            for i, col in enumerate(cate_cols):\n",
-    "                cate_cols[i] = col[-self.args.max_seq_len:]\n",
-    "            mask = np.ones(self.args.max_seq_len, dtype=np.int16)\n",
-    "        else:\n",
-    "            mask = np.zeros(self.args.max_seq_len, dtype=np.int16)\n",
-    "            mask[-seq_len:] = 1\n",
-    "\n",
-    "        # mask도 columns 목록에 포함시킴\n",
-    "        cate_cols.append(mask)\n",
-    "\n",
-    "        # np.array -> torch.tensor 형변환\n",
-    "        for i, col in enumerate(cate_cols):\n",
-    "            cate_cols[i] = torch.tensor(col)\n",
-    "\n",
-    "        return cate_cols\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.data)\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "def collate(batch):\n",
-    "    col_n = len(batch[0])\n",
-    "    col_list = [[] for _ in range(col_n)]\n",
-    "    max_seq_len = len(batch[0][-1])\n",
-    "\n",
-    "        \n",
-    "    # batch의 값들을 각 column끼리 그룹화\n",
-    "    for row in batch:\n",
-    "        for i, col in enumerate(row):\n",
-    "            pre_padded = torch.zeros(max_seq_len)\n",
-    "            pre_padded[-len(col):] = col\n",
-    "            col_list[i].append(pre_padded)\n",
-    "\n",
-    "\n",
-    "    for i, _ in enumerate(col_list):\n",
-    "        col_list[i] =torch.stack(col_list[i])\n",
-    "    \n",
-    "    return tuple(col_list)\n",
-    "\n",
-    "\n",
-    "def get_loaders(args, train, valid):\n",
-    "\n",
-    "    pin_memory = False\n",
-    "    train_loader, valid_loader = None, None\n",
-    "    \n",
-    "    if train is not None:\n",
-    "        trainset = DKTDataset(train, args)\n",
-    "        train_loader = torch.utils.data.DataLoader(trainset, num_workers=args.num_workers, shuffle=True,\n",
-    "                            batch_size=args.batch_size, pin_memory=pin_memory, collate_fn=collate)\n",
-    "    if valid is not None:\n",
-    "        valset = DKTDataset(valid, args)\n",
-    "        valid_loader = torch.utils.data.DataLoader(valset, num_workers=args.num_workers, shuffle=False,\n",
-    "                            batch_size=args.batch_size, pin_memory=pin_memory, collate_fn=collate)\n",
-    "\n",
-    "    return train_loader, valid_loader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "QyiplxY6nz0-"
-   },
-   "source": [
-    "## 3. LSTM 기반의 모델"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "id": "aO72oKAgnz0-"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F \n",
-    "import numpy as np\n",
-    "import copy\n",
-    "import math\n",
-    "\n",
-    "try:\n",
-    "    from transformers.modeling_bert import BertConfig, BertEncoder, BertModel    \n",
-    "except:\n",
-    "    from transformers.models.bert.modeling_bert import BertConfig, BertEncoder, BertModel    \n",
-    "\n",
-    "class LSTM(nn.Module):\n",
-    "    def __init__(self, args):\n",
-    "        super(LSTM, self).__init__()\n",
-    "        self.args = args\n",
-    "        self.device = args.device\n",
-    "\n",
-    "        self.hidden_dim = self.args.hidden_dim\n",
-    "        self.n_layers = self.args.n_layers\n",
-    "\n",
-    "        # Embedding \n",
-    "        # interaction은 현재 correct로 구성되어있다. correct(1, 2) + padding(0)\n",
-    "        self.embedding_interaction = nn.Embedding(3, self.hidden_dim//3)\n",
-    "        self.embedding_test = nn.Embedding(self.args.n_test + 1, self.hidden_dim//3)\n",
-    "        self.embedding_question = nn.Embedding(self.args.n_questions + 1, self.hidden_dim//3)\n",
-    "        self.embedding_tag = nn.Embedding(self.args.n_tag + 1, self.hidden_dim//3)\n",
-    "\n",
-    "        # embedding combination projection\n",
-    "        self.comb_proj = nn.Linear((self.hidden_dim//3)*4, self.hidden_dim)\n",
-    "\n",
-    "        self.lstm = nn.LSTM(self.hidden_dim,\n",
-    "                            self.hidden_dim,\n",
-    "                            self.n_layers,\n",
-    "                            batch_first=True)\n",
-    "        \n",
-    "        # Fully connected layer\n",
-    "        self.fc = nn.Linear(self.hidden_dim, 1)\n",
-    "\n",
-    "        self.activation = nn.Sigmoid()\n",
-    "\n",
-    "    def init_hidden(self, batch_size):\n",
-    "        h = torch.zeros(\n",
-    "            self.n_layers,\n",
-    "            batch_size,\n",
-    "            self.hidden_dim)\n",
-    "        h = h.to(self.device)\n",
-    "\n",
-    "        c = torch.zeros(\n",
-    "            self.n_layers,\n",
-    "            batch_size,\n",
-    "            self.hidden_dim)\n",
-    "        c = c.to(self.device)\n",
-    "\n",
-    "        return (h, c)\n",
-    "\n",
-    "    def forward(self, input):\n",
-    "        test, question, tag, _, mask, interaction, _ = input\n",
-    "        batch_size = interaction.size(0)\n",
-    "\n",
-    "        # Embedding\n",
-    "        embed_interaction = self.embedding_interaction(interaction)\n",
-    "        embed_test = self.embedding_test(test)\n",
-    "        embed_question = self.embedding_question(question)\n",
-    "        embed_tag = self.embedding_tag(tag)\n",
-    "\n",
-    "        embed = torch.cat([embed_interaction,\n",
-    "                           embed_test,\n",
-    "                           embed_question,\n",
-    "                           embed_tag,], 2)\n",
-    "\n",
-    "        X = self.comb_proj(embed)\n",
-    "\n",
-    "        hidden = self.init_hidden(batch_size)\n",
-    "        out, hidden = self.lstm(X, hidden)\n",
-    "        out = out.contiguous().view(batch_size, -1, self.hidden_dim)\n",
-    "\n",
-    "        out = self.fc(out)\n",
-    "        preds = self.activation(out).view(batch_size, -1)\n",
-    "\n",
-    "        return preds\n",
-    "    \n",
-    "class LSTMATTN(nn.Module):\n",
-    "\n",
-    "    def __init__(self, args):\n",
-    "        super(LSTMATTN, self).__init__()\n",
-    "        self.args = args\n",
-    "        self.device = args.device\n",
-    "\n",
-    "        self.hidden_dim = self.args.hidden_dim\n",
-    "        self.n_layers = self.args.n_layers\n",
-    "        self.n_heads = self.args.n_heads\n",
-    "        self.drop_out = self.args.drop_out\n",
-    "\n",
-    "        # Embedding \n",
-    "        # interaction은 현재 correct로 구성되어있다. correct(1, 2) + padding(0)\n",
-    "        self.embedding_interaction = nn.Embedding(3, self.hidden_dim//3)\n",
-    "        self.embedding_test = nn.Embedding(self.args.n_test + 1, self.hidden_dim//3)\n",
-    "        self.embedding_question = nn.Embedding(self.args.n_questions + 1, self.hidden_dim//3)\n",
-    "        self.embedding_tag = nn.Embedding(self.args.n_tag + 1, self.hidden_dim//3)\n",
-    "\n",
-    "        # embedding combination projection\n",
-    "        self.comb_proj = nn.Linear((self.hidden_dim//3)*4, self.hidden_dim)\n",
-    "\n",
-    "        self.lstm = nn.LSTM(self.hidden_dim,\n",
-    "                            self.hidden_dim,\n",
-    "                            self.n_layers,\n",
-    "                            batch_first=True)\n",
-    "        \n",
-    "        self.config = BertConfig( \n",
-    "            3, # not used\n",
-    "            hidden_size=self.hidden_dim,\n",
-    "            num_hidden_layers=1,\n",
-    "            num_attention_heads=self.n_heads,\n",
-    "            intermediate_size=self.hidden_dim,\n",
-    "            hidden_dropout_prob=self.drop_out,\n",
-    "            attention_probs_dropout_prob=self.drop_out,\n",
-    "        )\n",
-    "        self.attn = BertEncoder(self.config)            \n",
-    "    \n",
-    "        # Fully connected layer\n",
-    "        self.fc = nn.Linear(self.hidden_dim, 1)\n",
-    "\n",
-    "        self.activation = nn.Sigmoid()\n",
-    "\n",
-    "    def init_hidden(self, batch_size):\n",
-    "        h = torch.zeros(\n",
-    "            self.n_layers,\n",
-    "            batch_size,\n",
-    "            self.hidden_dim)\n",
-    "        h = h.to(self.device)\n",
-    "\n",
-    "        c = torch.zeros(\n",
-    "            self.n_layers,\n",
-    "            batch_size,\n",
-    "            self.hidden_dim)\n",
-    "        c = c.to(self.device)\n",
-    "\n",
-    "        return (h, c)\n",
-    "\n",
-    "    def forward(self, input):\n",
-    "\n",
-    "        test, question, tag, _, mask, interaction, _ = input\n",
-    "\n",
-    "        batch_size = interaction.size(0)\n",
-    "\n",
-    "        # Embedding\n",
-    "\n",
-    "        embed_interaction = self.embedding_interaction(interaction)\n",
-    "        embed_test = self.embedding_test(test)\n",
-    "        embed_question = self.embedding_question(question)\n",
-    "        embed_tag = self.embedding_tag(tag)\n",
-    "        \n",
-    "\n",
-    "        embed = torch.cat([embed_interaction,\n",
-    "                           embed_test,\n",
-    "                           embed_question,\n",
-    "                           embed_tag,], 2)\n",
-    "\n",
-    "        X = self.comb_proj(embed)\n",
-    "\n",
-    "        hidden = self.init_hidden(batch_size)\n",
-    "        out, hidden = self.lstm(X, hidden)\n",
-    "        out = out.contiguous().view(batch_size, -1, self.hidden_dim)\n",
-    "                \n",
-    "        extended_attention_mask = mask.unsqueeze(1).unsqueeze(2)\n",
-    "        extended_attention_mask = extended_attention_mask.to(dtype=torch.float32)\n",
-    "        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0\n",
-    "        head_mask = [None] * self.n_layers\n",
-    "        \n",
-    "        encoded_layers = self.attn(out, extended_attention_mask, head_mask=head_mask)        \n",
-    "        sequence_output = encoded_layers[-1]\n",
-    "        \n",
-    "        out = self.fc(sequence_output)\n",
-    "\n",
-    "        preds = self.activation(out).view(batch_size, -1)\n",
-    "\n",
-    "        return preds\n",
-    "\n",
-    "\n",
-    "class Bert(nn.Module):\n",
-    "\n",
-    "    def __init__(self, args):\n",
-    "        super(Bert, self).__init__()\n",
-    "        self.args = args\n",
-    "        self.device = args.device\n",
-    "\n",
-    "        # Defining some parameters\n",
-    "        self.hidden_dim = self.args.hidden_dim\n",
-    "        self.n_layers = self.args.n_layers\n",
-    "\n",
-    "        # Embedding \n",
-    "        # interaction은 현재 correct으로 구성되어있다. correct(1, 2) + padding(0)\n",
-    "        self.embedding_interaction = nn.Embedding(3, self.hidden_dim//3)\n",
-    "        self.embedding_test = nn.Embedding(self.args.n_test + 1, self.hidden_dim//3)\n",
-    "        self.embedding_question = nn.Embedding(self.args.n_questions + 1, self.hidden_dim//3)\n",
-    "        self.embedding_tag = nn.Embedding(self.args.n_tag + 1, self.hidden_dim//3)\n",
-    "\n",
-    "        # embedding combination projection\n",
-    "        self.comb_proj = nn.Linear((self.hidden_dim//3)*4, self.hidden_dim)\n",
-    "\n",
-    "        # Bert config\n",
-    "        self.config = BertConfig( \n",
-    "            3, # not used\n",
-    "            hidden_size=self.hidden_dim,\n",
-    "            num_hidden_layers=self.args.n_layers,\n",
-    "            num_attention_heads=self.args.n_heads,\n",
-    "            max_position_embeddings=self.args.max_seq_len          \n",
-    "        )\n",
-    "\n",
-    "        # Defining the layers\n",
-    "        # Bert Layer\n",
-    "        self.encoder = BertModel(self.config)  \n",
-    "\n",
-    "        # Fully connected layer\n",
-    "        self.fc = nn.Linear(self.args.hidden_dim, 1)\n",
-    "       \n",
-    "        self.activation = nn.Sigmoid()\n",
-    "\n",
-    "\n",
-    "    def forward(self, input):\n",
-    "        test, question, tag, _, mask, interaction, _ = input\n",
-    "        batch_size = interaction.size(0)\n",
-    "\n",
-    "        # 신나는 embedding\n",
-    "        \n",
-    "        embed_interaction = self.embedding_interaction(interaction)\n",
-    "        embed_test = self.embedding_test(test)\n",
-    "        embed_question = self.embedding_question(question)\n",
-    "        embed_tag = self.embedding_tag(tag)\n",
-    "\n",
-    "        embed = torch.cat([embed_interaction,\n",
-    "        \n",
-    "                           embed_test,\n",
-    "                           embed_question,\n",
-    "        \n",
-    "                           embed_tag,], 2)\n",
-    "\n",
-    "        X = self.comb_proj(embed)\n",
-    "\n",
-    "        # Bert\n",
-    "        encoded_layers = self.encoder(inputs_embeds=X, attention_mask=mask)\n",
-    "        out = encoded_layers[0]\n",
-    "        out = out.contiguous().view(batch_size, -1, self.hidden_dim)\n",
-    "        out = self.fc(out)\n",
-    "        preds = self.activation(out).view(batch_size, -1)\n",
-    "\n",
-    "        return preds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "NEaAa6Prnz0_"
-   },
-   "source": [
-    "## 4. 모델 훈련을 위한 함수들"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "id": "r_wU37QGnz0_"
-   },
-   "outputs": [],
-   "source": [
-    "import os, sys\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "import tarfile\n",
-    "import torch\n",
-    "from torch import nn\n",
-    "import torch.nn.functional as F\n",
-    "from torch.optim import Adam, AdamW\n",
-    "\n",
-    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
-    "\n",
-    "from transformers import get_linear_schedule_with_warmup\n",
-    "from transformers import get_cosine_with_hard_restarts_schedule_with_warmup\n",
-    "\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import scipy.stats\n",
-    "\n",
-    "\n",
-    "# 훈련을 하기 위한 세팅\n",
-    "def get_optimizer(model, args):\n",
-    "    if args.optimizer == 'adam':\n",
-    "        optimizer = Adam(model.parameters(), lr=args.lr, weight_decay=0.01)\n",
-    "    if args.optimizer == 'adamW':\n",
-    "        optimizer = AdamW(model.parameters(), lr=args.lr, weight_decay=0.01)\n",
-    "    \n",
-    "    # 모든 parameter들의 grad값을 0으로 초기화\n",
-    "    optimizer.zero_grad()\n",
-    "    \n",
-    "    return optimizer\n",
-    "\n",
-    "def get_scheduler(optimizer, args):\n",
-    "    if args.scheduler == 'plateau':\n",
-    "        scheduler = ReduceLROnPlateau(optimizer, patience=10, factor=0.5, mode='max', verbose=True)\n",
-    "    elif args.scheduler == 'linear_warmup':\n",
-    "        scheduler = get_linear_schedule_with_warmup(optimizer,\n",
-    "                                                    num_warmup_steps=args.warmup_steps,\n",
-    "                                                    num_training_steps=args.total_steps)\n",
-    "    elif args.scheduler == 'cosine_warmup':\n",
-    "        scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(optimizer,\n",
-    "                                                    num_warmup_steps=args.warmup_steps,\n",
-    "                                                    num_training_steps=args.total_steps)\n",
-    "    return scheduler\n",
-    "\n",
-    "def get_criterion(pred, target):\n",
-    "    loss = nn.BCELoss(reduction=\"none\")\n",
-    "    return loss(pred, target)\n",
-    "\n",
-    "def get_metric(targets, preds):\n",
-    "    auc = roc_auc_score(targets, preds)\n",
-    "    acc = accuracy_score(targets, np.where(preds >= 0.5, 1, 0))\n",
-    "    return auc, acc\n",
-    "\n",
-    "def get_model(args):\n",
-    "    \"\"\"\n",
-    "    Load model and move tensors to a given devices.\n",
-    "    \"\"\"\n",
-    "    if args.model == 'lstm': model = LSTM(args)\n",
-    "    model.to(args.device)\n",
-    "\n",
-    "    return model\n",
-    "\n",
-    "\n",
-    "# 배치 전처리\n",
-    "def process_batch(batch, args):\n",
-    "    test, question, tag, correct, mask = batch\n",
-    "    \n",
-    "    # change to float\n",
-    "    mask = mask.type(torch.FloatTensor)\n",
-    "    correct = correct.type(torch.FloatTensor)\n",
-    "\n",
-    "    #  interaction을 임시적으로 correct를 한칸 우측으로 이동한 것으로 사용\n",
-    "    #    saint의 경우 decoder에 들어가는 input이다\n",
-    "    interaction = correct + 1 # 패딩을 위해 correct값에 1을 더해준다.\n",
-    "    interaction = interaction.roll(shifts=1, dims=1)\n",
-    "    interaction[:, 0] = 0 # set padding index to the first sequence\n",
-    "    interaction = (interaction * mask).to(torch.int64)\n",
-    "    # print(interaction)\n",
-    "    # exit()\n",
-    "    #  test_id, question_id, tag\n",
-    "    test = ((test + 1) * mask).to(torch.int64)\n",
-    "    question = ((question + 1) * mask).to(torch.int64)\n",
-    "    tag = ((tag + 1) * mask).to(torch.int64)\n",
-    "\n",
-    "    # gather index\n",
-    "    # 마지막 sequence만 사용하기 위한 index\n",
-    "    gather_index = torch.tensor(np.count_nonzero(mask, axis=1))\n",
-    "    gather_index = gather_index.view(-1, 1) - 1\n",
-    "\n",
-    "\n",
-    "    # device memory로 이동\n",
-    "\n",
-    "    test = test.to(args.device)\n",
-    "    question = question.to(args.device)\n",
-    "\n",
-    "\n",
-    "    tag = tag.to(args.device)\n",
-    "    correct = correct.to(args.device)\n",
-    "    mask = mask.to(args.device)\n",
-    "\n",
-    "    interaction = interaction.to(args.device)\n",
-    "    gather_index = gather_index.to(args.device)\n",
-    "\n",
-    "    return (test, question,\n",
-    "            tag, correct, mask,\n",
-    "            interaction, gather_index)\n",
-    "\n",
-    "\n",
-    "# loss계산하고 parameter update!\n",
-    "def compute_loss(preds, targets):\n",
-    "    \"\"\"\n",
-    "    Args :\n",
-    "        preds   : (batch_size, max_seq_len)\n",
-    "        targets : (batch_size, max_seq_len)\n",
-    "\n",
-    "    \"\"\"\n",
-    "    loss = get_criterion(preds, targets)\n",
-    "    #마지막 시퀀드에 대한 값만 loss 계산\n",
-    "    loss = loss[:,-1]\n",
-    "    loss = torch.mean(loss)\n",
-    "    return loss\n",
-    "\n",
-    "def update_params(loss, model, optimizer, args):\n",
-    "    loss.backward()\n",
-    "    torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad)\n",
-    "    optimizer.step()\n",
-    "    optimizer.zero_grad()\n",
-    "\n",
-    "def save_checkpoint(state, model_dir, model_filename):\n",
-    "    print('saving model ...')\n",
-    "    if not os.path.exists(model_dir):\n",
-    "        os.makedirs(model_dir)    \n",
-    "    torch.save(state, os.path.join(model_dir, model_filename))\n",
-    "\n",
-    "def load_model(args):\n",
-    "    model_path = os.path.join(args.model_dir, args.model_name)\n",
-    "    print(\"Loading Model from:\", model_path)\n",
-    "    load_state = torch.load(model_path)\n",
-    "    model = get_model(args)\n",
-    "\n",
-    "    # 1. load model state\n",
-    "    model.load_state_dict(load_state['state_dict'], strict=True)\n",
-    "    print(\"Loading Model from:\", model_path, \"...Finished.\")\n",
-    "    \n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "YO_xFaJYnz1B"
-   },
-   "source": [
-    "## 5. 전체 프로세스를 담당하는 함수들"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "id": "BMiIOHgJnz1D"
-   },
-   "outputs": [],
-   "source": [
-    "def run(args, train_data, valid_data):\n",
-    "    train_loader, valid_loader = get_loaders(args, train_data, valid_data)\n",
-    "    \n",
-    "    # only when using warmup scheduler\n",
-    "    args.total_steps = int(len(train_loader.dataset) / args.batch_size) * (args.n_epochs)\n",
-    "    args.warmup_steps = args.total_steps // 10\n",
-    "            \n",
-    "    model = get_model(args)\n",
-    "    optimizer = get_optimizer(model, args)\n",
-    "    scheduler = get_scheduler(optimizer, args)\n",
-    "\n",
-    "    best_auc = -1\n",
-    "    early_stopping_counter = 0\n",
-    "    for epoch in range(args.n_epochs):\n",
-    "\n",
-    "        print(f\"Start Training: Epoch {epoch + 1}\")\n",
-    "        \n",
-    "        ### TRAIN\n",
-    "        train_auc, train_acc, train_loss = train(train_loader, model, optimizer, args)\n",
-    "        \n",
-    "        ### VALID\n",
-    "        auc, acc, _, _ = validate(valid_loader, model, args)\n",
-    "\n",
-    "        ### TODO: model save or early stopping\n",
-    "        wandb.log({\"epoch\": epoch, \"train_loss\": train_loss, \"train_auc\": train_auc, \"train_acc\":train_acc,\n",
-    "                  \"valid_auc\":auc, \"valid_acc\":acc})\n",
-    "        if auc >= best_auc:\n",
-    "            best_auc = auc\n",
-    "            # torch.nn.DataParallel로 감싸진 경우 원래의 model을 가져옵니다.\n",
-    "            model_to_save = model.module if hasattr(model, 'module') else model\n",
-    "            save_checkpoint({\n",
-    "                'epoch': epoch + 1,\n",
-    "                'state_dict': model_to_save.state_dict(),\n",
-    "                },\n",
-    "                args.model_dir, 'model.pt',\n",
-    "            )\n",
-    "            early_stopping_counter = 0\n",
-    "        else:\n",
-    "            early_stopping_counter += 1\n",
-    "            if early_stopping_counter >= args.patience:\n",
-    "                print(f'EarlyStopping counter: {early_stopping_counter} out of {args.patience}')\n",
-    "                break\n",
-    "\n",
-    "        # scheduler\n",
-    "        if args.scheduler == 'plateau':\n",
-    "            scheduler.step(best_auc)\n",
-    "        else:\n",
-    "            scheduler.step()\n",
-    "\n",
-    "\n",
-    "def train(train_loader, model, optimizer, args):\n",
-    "    model.train()\n",
-    "\n",
-    "    total_preds = []\n",
-    "    total_targets = []\n",
-    "    losses = []\n",
-    "    for step, batch in enumerate(train_loader):\n",
-    "        input = process_batch(batch, args)\n",
-    "        preds = model(input)\n",
-    "        targets = input[3] # correct\n",
-    "\n",
-    "\n",
-    "        loss = compute_loss(preds, targets)\n",
-    "        update_params(loss, model, optimizer, args)\n",
-    "\n",
-    "        if step % args.log_steps == 0:\n",
-    "            print(f\"Training steps: {step} Loss: {str(loss.item())}\")\n",
-    "        \n",
-    "        # predictions\n",
-    "        preds = preds[:,-1]\n",
-    "        targets = targets[:,-1]\n",
-    "\n",
-    "        if args.device == 'cuda':\n",
-    "            preds = preds.to('cpu').detach().numpy()\n",
-    "            targets = targets.to('cpu').detach().numpy()\n",
-    "        else: # cpu\n",
-    "            preds = preds.detach().numpy()\n",
-    "            targets = targets.detach().numpy()\n",
-    "        \n",
-    "        total_preds.append(preds)\n",
-    "        total_targets.append(targets)\n",
-    "        losses.append(loss)\n",
-    "      \n",
-    "\n",
-    "    total_preds = np.concatenate(total_preds)\n",
-    "    total_targets = np.concatenate(total_targets)\n",
-    "\n",
-    "    # Train AUC / ACC\n",
-    "    auc, acc = get_metric(total_targets, total_preds)\n",
-    "    loss_avg = sum(losses)/len(losses)\n",
-    "    print(f'TRAIN AUC : {auc} ACC : {acc}')\n",
-    "    return auc, acc, loss_avg\n",
-    "    \n",
-    "\n",
-    "def validate(valid_loader, model, args):\n",
-    "    model.eval()\n",
-    "\n",
-    "    total_preds = []\n",
-    "    total_targets = []\n",
-    "    for step, batch in enumerate(valid_loader):\n",
-    "        input = process_batch(batch, args)\n",
-    "\n",
-    "        preds = model(input)\n",
-    "        targets = input[3] # correct\n",
-    "\n",
-    "\n",
-    "        # predictions\n",
-    "        preds = preds[:,-1]\n",
-    "        targets = targets[:,-1]\n",
-    "    \n",
-    "        if args.device == 'cuda':\n",
-    "            preds = preds.to('cpu').detach().numpy()\n",
-    "            targets = targets.to('cpu').detach().numpy()\n",
-    "        else: # cpu\n",
-    "            preds = preds.detach().numpy()\n",
-    "            targets = targets.detach().numpy()\n",
-    "\n",
-    "        total_preds.append(preds)\n",
-    "        total_targets.append(targets)\n",
-    "\n",
-    "    total_preds = np.concatenate(total_preds)\n",
-    "    total_targets = np.concatenate(total_targets)\n",
-    "\n",
-    "    # Train AUC / ACC\n",
-    "    auc, acc = get_metric(total_targets, total_preds)\n",
-    "    \n",
-    "    print(f'VALID AUC : {auc} ACC : {acc}\\n')\n",
-    "\n",
-    "    return auc, acc, total_preds, total_targets\n",
-    "\n",
-    "\n",
-    "\n",
-    "def inference(args, test_data):\n",
-    "    \n",
-    "    model = load_model(args)\n",
-    "    model.eval()\n",
-    "    _, test_loader = get_loaders(args, None, test_data)\n",
-    "    \n",
-    "    \n",
-    "    total_preds = []\n",
-    "    \n",
-    "    for step, batch in enumerate(test_loader):\n",
-    "        input = process_batch(batch, args)\n",
-    "\n",
-    "        preds = model(input)\n",
-    "        \n",
-    "\n",
-    "        # predictions\n",
-    "        preds = preds[:,-1]\n",
-    "        \n",
-    "\n",
-    "        if args.device == 'cuda':\n",
-    "            preds = preds.to('cpu').detach().numpy()\n",
-    "        else: # cpu\n",
-    "            preds = preds.detach().numpy()\n",
-    "            \n",
-    "        total_preds+=list(preds)\n",
-    "\n",
-    "    write_path = os.path.join(args.output_dir, \"output.csv\")\n",
-    "    if not os.path.exists(args.output_dir):\n",
-    "        os.makedirs(args.output_dir)    \n",
-    "    with open(write_path, 'w', encoding='utf8') as w:\n",
-    "        print(\"writing prediction : {}\".format(write_path))\n",
-    "        w.write(\"id,prediction\\n\")\n",
-    "        for id, p in enumerate(total_preds):\n",
-    "            w.write('{},{}\\n'.format(id,p))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "gPEE00qUnz1E"
-   },
-   "source": [
-    "## 6.실행부분"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "id": "qZmwQenqnz1E"
-   },
-   "outputs": [],
-   "source": [
-    "data_dir = '/opt/ml/input/data/train_dataset'\n",
-    "file_name = 'train_data.csv'\n",
-    "test_file_name = 'test_data.csv'\n",
-    "\n",
-    "config = {}\n",
-    "\n",
-    "# 설정\n",
-    "config['seed'] = 42\n",
-    "config['device'] = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "config['data_dir'] = data_dir\n",
-    "config['asset_dir'] = 'asset'\n",
-    "config['model_dir'] = 'models'\n",
-    "config['model_name'] = 'model.pt'\n",
-    "config['output_dir'] = 'output'\n",
-    "\n",
-    "# 데이터\n",
-    "config['max_seq_len'] = 30\n",
-    "config['num_workers'] = 1\n",
-    "\n",
-    "# 모델\n",
-    "config['hidden_dim'] = 64\n",
-    "config['n_layers'] = 2\n",
-    "config['dropout'] = 0.2\n",
-    "\n",
-    "# 훈련\n",
-    "config['n_epochs'] = 100\n",
-    "config['batch_size'] = 64\n",
-    "config['lr'] = 5e-5\n",
-    "config['clip_grad'] = 10\n",
-    "config['log_steps'] = 50\n",
-    "config['patience'] = 30\n",
-    "\n",
-    "\n",
-    "### 중요 ###\n",
-    "config['model'] = 'lstm'\n",
-    "config['optimizer'] = 'adam'\n",
-    "config['scheduler'] = 'cosine_warmup'\n",
-    "\n",
-    "args = easydict.EasyDict(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def setSeeds(seed = 42):\n",
-    "    # 랜덤 시드를 설정하여 매 코드를 실행할 때마다 동일한 결과를 얻게 합니다.\n",
-    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
-    "    random.seed(seed)\n",
-    "    np.random.seed(seed)\n",
-    "    torch.manual_seed(seed)    \n",
-    "    torch.cuda.manual_seed(seed)\n",
-    "    torch.backends.cudnn.deterministic = True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "setSeeds(42)\n",
-    "\n",
-    "preprocess = Preprocess(args)\n",
-    "preprocess.load_train_data(file_name)\n",
-    "\n",
-    "train_data = preprocess.get_train_data()\n",
-    "train_data, valid_data = preprocess.split_data(train_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Calling wandb.login() after wandb.init() has no effect.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wandb.login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "Finishing last run (ID:2y20u2i4) before initializing another..."
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<br/>Waiting for W&B process to finish, PID 3232<br/>Program ended successfully."
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Find user logs for this run at: <code>/opt/ml/code/wandb/run-20210524_172457-2y20u2i4/logs/debug.log</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Find internal logs for this run at: <code>/opt/ml/code/wandb/run-20210524_172457-2y20u2i4/logs/debug-internal.log</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<h3>Run summary:</h3><br/><style>\n",
-       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
-       "    </style><table class=\"wandb\">\n",
-       "<tr><td>epoch</td><td>49</td></tr><tr><td>train_loss</td><td>0.63742</td></tr><tr><td>train_auc</td><td>0.74035</td></tr><tr><td>train_acc</td><td>0.68082</td></tr><tr><td>valid_auc</td><td>0.72291</td></tr><tr><td>valid_acc</td><td>0.67164</td></tr><tr><td>_runtime</td><td>86</td></tr><tr><td>_timestamp</td><td>1621877183</td></tr><tr><td>_step</td><td>49</td></tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<h3>Run history:</h3><br/><style>\n",
-       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
-       "    </style><table class=\"wandb\">\n",
-       "<tr><td>epoch</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇███</td></tr><tr><td>train_loss</td><td>███████████████████▇▇▇▇▇▇▇▇▇▆▆▆▆▅▅▄▄▃▃▂▁</td></tr><tr><td>train_auc</td><td>▁▁▁▁▁▂▂▂▃▃▃▄▄▅▅▅▆▆▆▆▇▇▇▇▇▇▇▇████████████</td></tr><tr><td>train_acc</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂▄▅▆▆▇▇██████</td></tr><tr><td>valid_auc</td><td>▁▁▁▁▂▂▂▂▃▃▄▄▅▅▅▆▆▆▇▇▇▇▇▇████████████████</td></tr><tr><td>valid_acc</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▃▄▄▅▆▇▇▇▇████</td></tr><tr><td>_runtime</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>_timestamp</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>_step</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇███</td></tr></table><br/>"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
-      ],
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-      ]
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-     "output_type": "display_data"
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-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "                    <br/>Synced <strong style=\"color:#cdcd00\">young-hill-35</strong>: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/2y20u2i4\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/2y20u2i4</a><br/>\n",
-       "                "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "...Successfully finished last run (ID:2y20u2i4). Initializing new run:<br/><br/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "                Tracking run with wandb version 0.10.30<br/>\n",
-       "                Syncing run <strong style=\"color:#cdcd00\">super-shadow-36</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
-       "                Project page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT</a><br/>\n",
-       "                Run page: <a href=\"https://wandb.ai/team-ikyo/P4-DKT/runs/gmpr78jx\" target=\"_blank\">https://wandb.ai/team-ikyo/P4-DKT/runs/gmpr78jx</a><br/>\n",
-       "                Run data is saved locally in <code>/opt/ml/code/wandb/run-20210524_173510-gmpr78jx</code><br/><br/>\n",
-       "            "
-      ],
-      "text/plain": [
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-     "output_type": "display_data"
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-       "<h1>Run(gmpr78jx)</h1><iframe src=\"https://wandb.ai/team-ikyo/P4-DKT/runs/gmpr78jx\" style=\"border:none;width:100%;height:400px\"></iframe>"
-      ],
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-       "<wandb.sdk.wandb_run.Run at 0x7ff858ca58d0>"
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-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wandb.init(project='P4-DKT', config=config, entity=\"team-ikyo\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "id": "v9qV6aXonz1E",
-    "outputId": "0d36ac2e-7ca2-4fc0-cf4c-ea296bc40ce4"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Start Training: Epoch 1\n",
-      "Training steps: 0 Loss: 0.692417562007904\n",
-      "Training steps: 50 Loss: 0.6840407848358154\n",
-      "TRAIN AUC : 0.5706521217682164 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5666883771558069 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 2\n",
-      "Training steps: 0 Loss: 0.6862214207649231\n",
-      "Training steps: 50 Loss: 0.6924540996551514\n",
-      "TRAIN AUC : 0.5707625153638439 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.566964962839375 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 3\n",
-      "Training steps: 0 Loss: 0.7004860639572144\n",
-      "Training steps: 50 Loss: 0.692416787147522\n",
-      "TRAIN AUC : 0.5710979420582505 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5674422961965007 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 4\n",
-      "Training steps: 0 Loss: 0.6815810799598694\n",
-      "Training steps: 50 Loss: 0.6838415265083313\n",
-      "TRAIN AUC : 0.571644671579053 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5681025329895343 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 5\n",
-      "Training steps: 0 Loss: 0.6949415802955627\n",
-      "Training steps: 50 Loss: 0.6862828135490417\n",
-      "TRAIN AUC : 0.5723872642625272 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.568981361693775 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 6\n",
-      "Training steps: 0 Loss: 0.6926754713058472\n",
-      "Training steps: 50 Loss: 0.7078460454940796\n",
-      "TRAIN AUC : 0.5733982865281766 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5700743212498104 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 7\n",
-      "Training steps: 0 Loss: 0.6875584125518799\n",
-      "Training steps: 50 Loss: 0.7003850340843201\n",
-      "TRAIN AUC : 0.5746439916824325 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5711940471623201 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 8\n",
-      "Training steps: 0 Loss: 0.6917406916618347\n",
-      "Training steps: 50 Loss: 0.6930617094039917\n",
-      "TRAIN AUC : 0.5760360427921309 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5725591313425112 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 9\n",
-      "Training steps: 0 Loss: 0.7017080783843994\n",
-      "Training steps: 50 Loss: 0.7089024782180786\n",
-      "TRAIN AUC : 0.5776695592141432 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.574240950740982 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 10\n",
-      "Training steps: 0 Loss: 0.6867542266845703\n",
-      "Training steps: 50 Loss: 0.6907169818878174\n",
-      "TRAIN AUC : 0.5795056109392223 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5763064212489182 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 11\n",
-      "Training steps: 0 Loss: 0.6878455877304077\n",
-      "Training steps: 50 Loss: 0.6867403388023376\n",
-      "TRAIN AUC : 0.5815521383658551 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5782157546774208 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 12\n",
-      "Training steps: 0 Loss: 0.7022501826286316\n",
-      "Training steps: 50 Loss: 0.6819038391113281\n",
-      "TRAIN AUC : 0.5837682815268284 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5805176613342136 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 13\n",
-      "Training steps: 0 Loss: 0.6965742707252502\n",
-      "Training steps: 50 Loss: 0.698516845703125\n",
-      "TRAIN AUC : 0.5862256694334912 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5830916926151622 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 14\n",
-      "Training steps: 0 Loss: 0.6859776973724365\n",
-      "Training steps: 50 Loss: 0.6959233283996582\n",
-      "TRAIN AUC : 0.5888533899160249 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5857237176684719 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 15\n",
-      "Training steps: 0 Loss: 0.6952184438705444\n",
-      "Training steps: 50 Loss: 0.6844170093536377\n",
-      "TRAIN AUC : 0.5916244235629119 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.588440502850617 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 16\n",
-      "Training steps: 0 Loss: 0.696398138999939\n",
-      "Training steps: 50 Loss: 0.6886947751045227\n",
-      "TRAIN AUC : 0.5946340000143369 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5916167771522381 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 17\n",
-      "Training steps: 0 Loss: 0.6868777275085449\n",
-      "Training steps: 50 Loss: 0.6872795224189758\n",
-      "TRAIN AUC : 0.5977235870088464 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5945566153050027 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 18\n",
-      "Training steps: 0 Loss: 0.682781994342804\n",
-      "Training steps: 50 Loss: 0.6983034610748291\n",
-      "TRAIN AUC : 0.6009908404194957 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.5975856746460149 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 19\n",
-      "Training steps: 0 Loss: 0.6877943277359009\n",
-      "Training steps: 50 Loss: 0.6941701173782349\n",
-      "TRAIN AUC : 0.6044240702151793 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6010965284035652 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 20\n",
-      "Training steps: 0 Loss: 0.6820396184921265\n",
-      "Training steps: 50 Loss: 0.6988538503646851\n",
-      "TRAIN AUC : 0.6079523642260786 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6043218743587228 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 21\n",
-      "Training steps: 0 Loss: 0.6819553971290588\n",
-      "Training steps: 50 Loss: 0.686287522315979\n",
-      "TRAIN AUC : 0.6115711456947325 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6076185972644783 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 22\n",
-      "Training steps: 0 Loss: 0.6871113181114197\n",
-      "Training steps: 50 Loss: 0.6858992576599121\n",
-      "TRAIN AUC : 0.6151929048128189 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.611397114586772 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 23\n",
-      "Training steps: 0 Loss: 0.6912109851837158\n",
-      "Training steps: 50 Loss: 0.6891967058181763\n",
-      "TRAIN AUC : 0.6189247266765958 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6152782362755507 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 24\n",
-      "Training steps: 0 Loss: 0.691832423210144\n",
-      "Training steps: 50 Loss: 0.6918518543243408\n",
-      "TRAIN AUC : 0.6227046320645422 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6191325916078549 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 25\n",
-      "Training steps: 0 Loss: 0.7003605365753174\n",
-      "Training steps: 50 Loss: 0.6976995468139648\n",
-      "TRAIN AUC : 0.6264152243906985 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6231252397819435 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 26\n",
-      "Training steps: 0 Loss: 0.681491494178772\n",
-      "Training steps: 50 Loss: 0.7046642899513245\n",
-      "TRAIN AUC : 0.6302583221166015 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6273186356295893 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 27\n",
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-      "Training steps: 50 Loss: 0.686962366104126\n",
-      "TRAIN AUC : 0.6341556792321634 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6315744863090086 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 28\n",
-      "Training steps: 0 Loss: 0.6937505006790161\n",
-      "Training steps: 50 Loss: 0.6912407875061035\n",
-      "TRAIN AUC : 0.6380334059181334 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6353886921066015 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 29\n",
-      "Training steps: 0 Loss: 0.6831233501434326\n",
-      "Training steps: 50 Loss: 0.6911698579788208\n",
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-      "VALID AUC : 0.6393055022706793 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 30\n",
-      "Training steps: 0 Loss: 0.6827176809310913\n",
-      "Training steps: 50 Loss: 0.6942216157913208\n",
-      "TRAIN AUC : 0.6456827116019698 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6427940507311676 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 31\n",
-      "Training steps: 0 Loss: 0.6906517744064331\n",
-      "Training steps: 50 Loss: 0.6942943334579468\n",
-      "TRAIN AUC : 0.6495278495691507 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6470855898858863 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 32\n",
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-      "Training steps: 50 Loss: 0.687474250793457\n",
-      "TRAIN AUC : 0.6533110083148103 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.6517518580312454 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 33\n",
-      "Training steps: 0 Loss: 0.6927112936973572\n",
-      "Training steps: 50 Loss: 0.672516942024231\n",
-      "TRAIN AUC : 0.6570383085627823 ACC : 0.5214001327140013\n",
-      "VALID AUC : 0.655543758531776 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 34\n",
-      "Training steps: 0 Loss: 0.6943120956420898\n",
-      "Training steps: 50 Loss: 0.6772613525390625\n",
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-      "VALID AUC : 0.6591126060616875 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 35\n",
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-      "Training steps: 50 Loss: 0.6883639097213745\n",
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-      "VALID AUC : 0.6629045065622184 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 36\n",
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-      "Training steps: 50 Loss: 0.6896501183509827\n",
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-      "VALID AUC : 0.6659558712002926 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 37\n",
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-      "Training steps: 50 Loss: 0.6821715235710144\n",
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-      "VALID AUC : 0.6697031611066996 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 38\n",
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-      "Training steps: 50 Loss: 0.679345965385437\n",
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-      "VALID AUC : 0.6728526690518464 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 39\n",
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-      "Training steps: 50 Loss: 0.6989715099334717\n",
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-      "VALID AUC : 0.6759575664028693 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 40\n",
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-      "Training steps: 50 Loss: 0.6754833459854126\n",
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-      "VALID AUC : 0.6789286319715206 ACC : 0.517910447761194\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 41\n",
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-      "Training steps: 50 Loss: 0.6812664270401001\n",
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-      "VALID AUC : 0.681475896895995 ACC : 0.517910447761194\n",
-      "\n",
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-      "Start Training: Epoch 42\n",
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-      "Training steps: 50 Loss: 0.6852411031723022\n",
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-      "\n",
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-      "Start Training: Epoch 43\n",
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-      "Training steps: 50 Loss: 0.6838175058364868\n",
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-      "\n",
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-      "Start Training: Epoch 44\n",
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-      "Training steps: 50 Loss: 0.6900765895843506\n",
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-      "VALID AUC : 0.6894478100659345 ACC : 0.517910447761194\n",
-      "\n",
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-      "Start Training: Epoch 45\n",
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-      "Training steps: 50 Loss: 0.6832900047302246\n",
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-      "VALID AUC : 0.6917318724850777 ACC : 0.517910447761194\n",
-      "\n",
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-      "Start Training: Epoch 46\n",
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-      "Training steps: 50 Loss: 0.6838119626045227\n",
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-      "VALID AUC : 0.6937661155771273 ACC : 0.517910447761194\n",
-      "\n",
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-      "Start Training: Epoch 47\n",
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-      "Training steps: 50 Loss: 0.6807537078857422\n",
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-      "VALID AUC : 0.6957289817185784 ACC : 0.517910447761194\n",
-      "\n",
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-      "Start Training: Epoch 48\n",
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-      "TRAIN AUC : 0.7004263552854104 ACC : 0.5215660252156602\n",
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-      "\n",
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-      "Start Training: Epoch 49\n",
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-      "Training steps: 50 Loss: 0.6872391700744629\n",
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-      "VALID AUC : 0.6993781283179129 ACC : 0.517910447761194\n",
-      "\n",
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-      "Start Training: Epoch 50\n",
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-      "Training steps: 50 Loss: 0.6915051937103271\n",
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-      "\n",
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-      "Start Training: Epoch 51\n",
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-      "\n",
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-      "Start Training: Epoch 52\n",
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-      "Training steps: 50 Loss: 0.6869640350341797\n",
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-      "\n",
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-      "Start Training: Epoch 53\n",
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-      "VALID AUC : 0.7050704401281216 ACC : 0.517910447761194\n",
-      "\n",
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-      "Start Training: Epoch 54\n",
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-      "Training steps: 50 Loss: 0.6901024580001831\n",
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-      "\n",
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-      "Start Training: Epoch 55\n",
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-      "\n",
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-      "Start Training: Epoch 56\n",
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-      "\n",
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-      "Start Training: Epoch 57\n",
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-      "VALID AUC : 0.7092638359757675 ACC : 0.517910447761194\n",
-      "\n",
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-      "Start Training: Epoch 58\n",
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-      "Training steps: 50 Loss: 0.6832144260406494\n",
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-      "VALID AUC : 0.7100043718382242 ACC : 0.517910447761194\n",
-      "\n",
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-      "Start Training: Epoch 59\n",
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-      "VALID AUC : 0.7110259544436612 ACC : 0.5194029850746269\n",
-      "\n",
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-      "Start Training: Epoch 60\n",
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-      "\n",
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-      "Start Training: Epoch 61\n",
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-      "VALID AUC : 0.7120832255243975 ACC : 0.517910447761194\n",
-      "\n",
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-      "Start Training: Epoch 62\n",
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-      "\n",
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-      "Start Training: Epoch 63\n",
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-      "Start Training: Epoch 64\n",
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-      "Start Training: Epoch 65\n",
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-      "\n",
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-      "Start Training: Epoch 66\n",
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-      "\n",
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-      "Start Training: Epoch 67\n",
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-      "Start Training: Epoch 68\n",
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-      "\n",
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-      "Start Training: Epoch 69\n",
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-      "\n",
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-      "Start Training: Epoch 70\n",
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-      "Training steps: 50 Loss: 0.6751073598861694\n",
-      "TRAIN AUC : 0.7285517430001802 ACC : 0.5668546781685467\n",
-      "VALID AUC : 0.7171955996109955 ACC : 0.564179104477612\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 71\n",
-      "Training steps: 0 Loss: 0.6840928792953491\n",
-      "Training steps: 50 Loss: 0.6745134592056274\n",
-      "TRAIN AUC : 0.7293068528396023 ACC : 0.579628400796284\n",
-      "VALID AUC : 0.7177220046216576 ACC : 0.5746268656716418\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 72\n",
-      "Training steps: 0 Loss: 0.6894361972808838\n",
-      "Training steps: 50 Loss: 0.680274486541748\n",
-      "TRAIN AUC : 0.7300070967311475 ACC : 0.5925680159256802\n",
-      "VALID AUC : 0.7180030513646382 ACC : 0.5835820895522388\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 73\n",
-      "Training steps: 0 Loss: 0.6819484233856201\n",
-      "Training steps: 50 Loss: 0.6783482432365417\n",
-      "TRAIN AUC : 0.730717266120801 ACC : 0.5990378234903783\n",
-      "VALID AUC : 0.7185249953158876 ACC : 0.582089552238806\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 74\n",
-      "Training steps: 0 Loss: 0.6840368509292603\n",
-      "Training steps: 50 Loss: 0.6785203814506531\n",
-      "TRAIN AUC : 0.7314435368740526 ACC : 0.6068347710683477\n",
-      "VALID AUC : 0.7190781666830239 ACC : 0.5850746268656717\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 75\n",
-      "Training steps: 0 Loss: 0.6774231791496277\n",
-      "Training steps: 50 Loss: 0.6709319353103638\n",
-      "TRAIN AUC : 0.7320755153952747 ACC : 0.6157929661579297\n",
-      "VALID AUC : 0.7193012196536434 ACC : 0.5880597014925373\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 76\n",
-      "Training steps: 0 Loss: 0.6719912886619568\n",
-      "Training steps: 50 Loss: 0.6652551889419556\n",
-      "TRAIN AUC : 0.732552931854287 ACC : 0.6189449236894492\n",
-      "VALID AUC : 0.7199793006843265 ACC : 0.6\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 77\n",
-      "Training steps: 0 Loss: 0.6745172739028931\n",
-      "Training steps: 50 Loss: 0.6809003353118896\n",
-      "TRAIN AUC : 0.7329577818937961 ACC : 0.6279031187790312\n",
-      "VALID AUC : 0.7203495686155548 ACC : 0.6059701492537314\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 78\n",
-      "Training steps: 0 Loss: 0.6692463159561157\n",
-      "Training steps: 50 Loss: 0.6750683784484863\n",
-      "TRAIN AUC : 0.7332547748538608 ACC : 0.635036496350365\n",
-      "VALID AUC : 0.7207733692597318 ACC : 0.6238805970149254\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 79\n",
-      "Training steps: 0 Loss: 0.6872453689575195\n",
-      "Training steps: 50 Loss: 0.6738052368164062\n",
-      "TRAIN AUC : 0.7340794183216975 ACC : 0.6420039814200398\n",
-      "VALID AUC : 0.7209428895174026 ACC : 0.6358208955223881\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 80\n",
-      "Training steps: 0 Loss: 0.6732134222984314\n",
-      "Training steps: 50 Loss: 0.676743745803833\n",
-      "TRAIN AUC : 0.7344128047748262 ACC : 0.6473125414731254\n",
-      "VALID AUC : 0.721290852151569 ACC : 0.6432835820895523\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 81\n",
-      "Training steps: 0 Loss: 0.6713000535964966\n",
-      "Training steps: 50 Loss: 0.6630432605743408\n",
-      "TRAIN AUC : 0.7350863601047912 ACC : 0.6531187790311878\n",
-      "VALID AUC : 0.7215674378351371 ACC : 0.6477611940298508\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 82\n",
-      "Training steps: 0 Loss: 0.6677560210227966\n",
-      "Training steps: 50 Loss: 0.668387234210968\n",
-      "TRAIN AUC : 0.7352542664477911 ACC : 0.6566025215660252\n",
-      "VALID AUC : 0.721817257162231 ACC : 0.6388059701492538\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 83\n",
-      "Training steps: 0 Loss: 0.6648356914520264\n",
-      "Training steps: 50 Loss: 0.6758044958114624\n",
-      "TRAIN AUC : 0.7357784430312251 ACC : 0.6595885865958858\n",
-      "VALID AUC : 0.7219600110634274 ACC : 0.6477611940298508\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 84\n",
-      "Training steps: 0 Loss: 0.6734097599983215\n",
-      "Training steps: 50 Loss: 0.661230742931366\n",
-      "TRAIN AUC : 0.736174525547405 ACC : 0.6624087591240876\n",
-      "VALID AUC : 0.7219867774199018 ACC : 0.655223880597015\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 85\n",
-      "Training steps: 0 Loss: 0.6649569272994995\n",
-      "Training steps: 50 Loss: 0.671761691570282\n",
-      "TRAIN AUC : 0.7365350968370193 ACC : 0.6662242866622429\n",
-      "VALID AUC : 0.722080459667562 ACC : 0.6447761194029851\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 86\n",
-      "Training steps: 0 Loss: 0.6692578792572021\n",
-      "Training steps: 50 Loss: 0.6655640602111816\n",
-      "TRAIN AUC : 0.7370357279332742 ACC : 0.6713669542136695\n",
-      "VALID AUC : 0.7222633631034698 ACC : 0.6522388059701493\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 87\n",
-      "Training steps: 0 Loss: 0.6766065359115601\n",
-      "Training steps: 50 Loss: 0.6757673025131226\n",
-      "TRAIN AUC : 0.7372363884200317 ACC : 0.6746848042468481\n",
-      "VALID AUC : 0.7224730328958522 ACC : 0.6567164179104478\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 88\n",
-      "Training steps: 0 Loss: 0.6472039818763733\n",
-      "Training steps: 50 Loss: 0.6517682671546936\n",
-      "TRAIN AUC : 0.7374914185797604 ACC : 0.6743530192435302\n",
-      "VALID AUC : 0.7224507275987903 ACC : 0.6597014925373135\n",
-      "\n",
-      "Start Training: Epoch 89\n",
-      "Training steps: 0 Loss: 0.6545861959457397\n",
-      "Training steps: 50 Loss: 0.6705600023269653\n",
-      "TRAIN AUC : 0.7378045680450795 ACC : 0.6756801592568016\n",
-      "VALID AUC : 0.7225131824305637 ACC : 0.6597014925373135\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 90\n",
-      "Training steps: 0 Loss: 0.654556155204773\n",
-      "Training steps: 50 Loss: 0.6534612774848938\n",
-      "TRAIN AUC : 0.7380298217104831 ACC : 0.6775049767750497\n",
-      "VALID AUC : 0.7225845593811618 ACC : 0.6656716417910448\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 91\n",
-      "Training steps: 0 Loss: 0.6601536273956299\n",
-      "Training steps: 50 Loss: 0.6620394587516785\n",
-      "TRAIN AUC : 0.7385535020190117 ACC : 0.6803251493032515\n",
-      "VALID AUC : 0.7225221045493884 ACC : 0.6626865671641791\n",
-      "\n",
-      "Start Training: Epoch 92\n",
-      "Training steps: 0 Loss: 0.6564822196960449\n",
-      "Training steps: 50 Loss: 0.6467278003692627\n",
-      "TRAIN AUC : 0.7387894531657101 ACC : 0.679163901791639\n",
-      "VALID AUC : 0.7226024036188115 ACC : 0.6671641791044776\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 93\n",
-      "Training steps: 0 Loss: 0.6503366231918335\n",
-      "Training steps: 50 Loss: 0.6717678904533386\n",
-      "TRAIN AUC : 0.7389809601375454 ACC : 0.681320504313205\n",
-      "VALID AUC : 0.7227719238764821 ACC : 0.6716417910447762\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 94\n",
-      "Training steps: 0 Loss: 0.6541410684585571\n",
-      "Training steps: 50 Loss: 0.6537812948226929\n",
-      "TRAIN AUC : 0.7395030413380455 ACC : 0.681320504313205\n",
-      "VALID AUC : 0.7229057556588538 ACC : 0.673134328358209\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 95\n",
-      "Training steps: 0 Loss: 0.6492453813552856\n",
-      "Training steps: 50 Loss: 0.6344279050827026\n",
-      "TRAIN AUC : 0.7396138760669221 ACC : 0.6823158593231586\n",
-      "VALID AUC : 0.7229503662529777 ACC : 0.6761194029850747\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 96\n",
-      "Training steps: 0 Loss: 0.6675565242767334\n",
-      "Training steps: 50 Loss: 0.6522634029388428\n",
-      "TRAIN AUC : 0.7398092980963447 ACC : 0.6838088918380889\n",
-      "VALID AUC : 0.722905755658854 ACC : 0.6761194029850747\n",
-      "\n",
-      "Start Training: Epoch 97\n",
-      "Training steps: 0 Loss: 0.6629478931427002\n",
-      "Training steps: 50 Loss: 0.6766805648803711\n",
-      "TRAIN AUC : 0.7398948779467012 ACC : 0.6821499668214996\n",
-      "VALID AUC : 0.7230128210847512 ACC : 0.6761194029850747\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 98\n",
-      "Training steps: 0 Loss: 0.6685456037521362\n",
-      "Training steps: 50 Loss: 0.6543511152267456\n",
-      "TRAIN AUC : 0.739927356382178 ACC : 0.6821499668214996\n",
-      "VALID AUC : 0.7230306653224008 ACC : 0.6746268656716418\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 99\n",
-      "Training steps: 0 Loss: 0.6607905626296997\n",
-      "Training steps: 50 Loss: 0.6353657841682434\n",
-      "TRAIN AUC : 0.7402753002325324 ACC : 0.681486396814864\n",
-      "VALID AUC : 0.7231377307482981 ACC : 0.673134328358209\n",
-      "\n",
-      "saving model ...\n",
-      "Start Training: Epoch 100\n",
-      "Training steps: 0 Loss: 0.648962676525116\n",
-      "Training steps: 50 Loss: 0.6430833339691162\n",
-      "TRAIN AUC : 0.7404201022216021 ACC : 0.681320504313205\n",
-      "VALID AUC : 0.7230128210847511 ACC : 0.6761194029850747\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "run(args, train_data, valid_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "QFY0zXGFnz1F"
-   },
-   "source": [
-    "## Inference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "PcTCBhrZnz1G"
-   },
-   "outputs": [],
-   "source": [
-    "preprocess = Preprocess(args)\n",
-    "preprocess.load_test_data(test_file_name)\n",
-    "test_data = preprocess.get_test_data()\n",
-    "inference(args, test_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "3강_lstm_baseline.ipynb",
-   "provenance": []
-  },
-  "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": "3.7.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/code/baseline/dkt/criterion.py b/code/baseline/dkt/criterion.py
deleted file mode 100644
index 3d46a7f..0000000
--- a/code/baseline/dkt/criterion.py
+++ /dev/null
@@ -1,7 +0,0 @@
-
-import torch.nn as nn
-
-
-def get_criterion(pred, target):
-    loss = nn.BCELoss(reduction="none")
-    return loss(pred, target)
\ No newline at end of file
diff --git a/code/baseline/dkt/dataloader.py b/code/baseline/dkt/dataloader.py
deleted file mode 100644
index c542aa9..0000000
--- a/code/baseline/dkt/dataloader.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import os
-from datetime import datetime
-import time
-import tqdm
-import pandas as pd
-import random
-from sklearn.preprocessing import LabelEncoder
-import numpy as np
-import torch
-
-class Preprocess:
-    def __init__(self,args):
-        self.args = args
-        self.train_data = None
-        self.test_data = None
-        
-
-    def get_train_data(self):
-        return self.train_data
-
-    def get_test_data(self):
-        return self.test_data
-
-    def split_data(self, data, ratio=0.7, shuffle=True, seed=0):
-        """
-        split data into two parts with a given ratio.
-        """
-        if shuffle:
-            random.seed(seed) # fix to default seed 0
-            random.shuffle(data)
-
-        size = int(len(data) * ratio)
-        data_1 = data[:size]
-        data_2 = data[size:]
-
-        return data_1, data_2
-
-    def __save_labels(self, encoder, name):
-        le_path = os.path.join(self.args.data_dir, name + '_classes.npy')
-        np.save(le_path, encoder.classes_)
-
-    def __preprocessing(self, df, is_train = True):
-        cate_cols = ['assessmentItemID', 'testId', 'KnowledgeTag']
-
-        if not os.path.exists(self.args.asset_dir):
-            os.makedirs(self.args.asset_dir)
-            
-        for col in cate_cols:
-            
-            
-            le = LabelEncoder()
-            if is_train:
-                #For UNKNOWN class
-                a = df[col].unique().tolist() + ['unknown']
-                le.fit(a)
-                self.__save_labels(le, col)
-            else:
-                label_path = os.path.join(self.args.asset_dir,col+'_classes.npy')
-                le.classes_ = np.load(label_path)
-                
-                df[col] = df[col].apply(lambda x: x if x in le.classes_ else 'unknown')
-
-            #모든 컬럼이 범주형이라고 가정
-            df[col]= df[col].astype(str)
-            test = le.transform(df[col])
-            df[col] = test
-            
-
-        def convert_time(s):
-            timestamp = time.mktime(datetime.strptime(s, '%Y-%m-%d %H:%M:%S').timetuple())
-            return int(timestamp)
-
-        df['Timestamp'] = df['Timestamp'].apply(convert_time)
-        
-        return df
-
-    def __feature_engineering(self, df):
-        #TODO
-        return df
-
-    def load_data_from_file(self, file_name, is_train=True):
-        csv_file_path = os.path.join(self.args.data_dir, file_name)
-        df = pd.read_csv(csv_file_path)#, nrows=100000)
-        df = self.__feature_engineering(df)
-        df = self.__preprocessing(df, is_train)
-
-        # 추후 feature를 embedding할 시에 embedding_layer의 input 크기를 결정할때 사용
-
-                
-        self.args.n_questions = len(np.load(os.path.join(self.args.data_dir,'assessmentItemID_classes.npy')))
-        self.args.n_test = len(np.load(os.path.join(self.args.data_dir,'testId_classes.npy')))
-        self.args.n_tag = len(np.load(os.path.join(self.args.data_dir,'KnowledgeTag_classes.npy')))
-        
-
-
-        df = df.sort_values(by=['userID','Timestamp'], axis=0)
-        columns = ['userID', 'assessmentItemID', 'testId', 'answerCode', 'KnowledgeTag']
-        group = df[columns].groupby('userID').apply(
-                lambda r: (
-                    r['testId'].values, 
-                    r['assessmentItemID'].values,
-                    r['KnowledgeTag'].values,
-                    r['answerCode'].values
-                )
-            )
-
-        return group.values
-
-    def load_train_data(self, file_name):
-        self.train_data = self.load_data_from_file(file_name)
-
-    def load_test_data(self, file_name):
-        self.test_data = self.load_data_from_file(file_name, is_train= False)
-
-
-class DKTDataset(torch.utils.data.Dataset):
-    def __init__(self, data, args):
-        self.data = data
-        self.args = args
-
-    def __getitem__(self, index):
-        row = self.data[index]
-
-        # 각 data의 sequence length
-        seq_len = len(row[0])
-
-        test, question, tag, correct = row[0], row[1], row[2], row[3]
-        
-
-        cate_cols = [test, question, tag, correct]
-
-        # max seq len을 고려하여서 이보다 길면 자르고 아닐 경우 그대로 냅둔다
-        if seq_len > self.args.max_seq_len:
-            for i, col in enumerate(cate_cols):
-                cate_cols[i] = col[-self.args.max_seq_len:]
-            mask = np.ones(self.args.max_seq_len, dtype=np.int16)
-        else:
-            mask = np.zeros(self.args.max_seq_len, dtype=np.int16)
-            mask[-seq_len:] = 1
-
-        # mask도 columns 목록에 포함시킴
-        cate_cols.append(mask)
-
-        # np.array -> torch.tensor 형변환
-        for i, col in enumerate(cate_cols):
-            cate_cols[i] = torch.tensor(col)
-
-        return cate_cols
-
-    def __len__(self):
-        return len(self.data)
-
-
-from torch.nn.utils.rnn import pad_sequence
-
-def collate(batch):
-    col_n = len(batch[0])
-    col_list = [[] for _ in range(col_n)]
-    max_seq_len = len(batch[0][-1])
-
-        
-    # batch의 값들을 각 column끼리 그룹화
-    for row in batch:
-        for i, col in enumerate(row):
-            pre_padded = torch.zeros(max_seq_len)
-            pre_padded[-len(col):] = col
-            col_list[i].append(pre_padded)
-
-
-    for i, _ in enumerate(col_list):
-        col_list[i] =torch.stack(col_list[i])
-    
-    return tuple(col_list)
-
-
-def get_loaders(args, train, valid):
-
-    pin_memory = False
-    train_loader, valid_loader = None, None
-    
-    if train is not None:
-        trainset = DKTDataset(train, args)
-        train_loader = torch.utils.data.DataLoader(trainset, num_workers=args.num_workers, shuffle=True,
-                            batch_size=args.batch_size, pin_memory=pin_memory, collate_fn=collate)
-    if valid is not None:
-        valset = DKTDataset(valid, args)
-        valid_loader = torch.utils.data.DataLoader(valset, num_workers=args.num_workers, shuffle=False,
-                            batch_size=args.batch_size, pin_memory=pin_memory, collate_fn=collate)
-
-    return train_loader, valid_loader
\ No newline at end of file
diff --git a/code/baseline/dkt/metric.py b/code/baseline/dkt/metric.py
deleted file mode 100644
index 9ffe2f2..0000000
--- a/code/baseline/dkt/metric.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from sklearn.metrics import roc_auc_score, accuracy_score
-import numpy as np
-
-def get_metric(targets, preds):
-    auc = roc_auc_score(targets, preds)
-    acc = accuracy_score(targets, np.where(preds >= 0.5, 1, 0))
-
-    return auc, acc
\ No newline at end of file
diff --git a/code/baseline/dkt/model.py b/code/baseline/dkt/model.py
deleted file mode 100644
index 267256c..0000000
--- a/code/baseline/dkt/model.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F 
-import numpy as np
-import copy
-import math
-
-try:
-    from transformers.modeling_bert import BertConfig, BertEncoder, BertModel    
-except:
-    from transformers.models.bert.modeling_bert import BertConfig, BertEncoder, BertModel    
-
-
-
-
-class LSTM(nn.Module):
-
-    def __init__(self, args):
-        super(LSTM, self).__init__()
-        self.args = args
-        self.device = args.device
-
-        self.hidden_dim = self.args.hidden_dim
-        self.n_layers = self.args.n_layers
-
-        # Embedding 
-        # interaction은 현재 correct로 구성되어있다. correct(1, 2) + padding(0)
-        self.embedding_interaction = nn.Embedding(3, self.hidden_dim//3)
-        self.embedding_test = nn.Embedding(self.args.n_test + 1, self.hidden_dim//3)
-        self.embedding_question = nn.Embedding(self.args.n_questions + 1, self.hidden_dim//3)
-        self.embedding_tag = nn.Embedding(self.args.n_tag + 1, self.hidden_dim//3)
-
-        # embedding combination projection
-        self.comb_proj = nn.Linear((self.hidden_dim//3)*4, self.hidden_dim)
-
-        self.lstm = nn.LSTM(self.hidden_dim,
-                            self.hidden_dim,
-                            self.n_layers,
-                            batch_first=True)
-        
-        # Fully connected layer
-        self.fc = nn.Linear(self.hidden_dim, 1)
-
-        self.activation = nn.Sigmoid()
-
-    def init_hidden(self, batch_size):
-        h = torch.zeros(
-            self.n_layers,
-            batch_size,
-            self.hidden_dim)
-        h = h.to(self.device)
-
-        c = torch.zeros(
-            self.n_layers,
-            batch_size,
-            self.hidden_dim)
-        c = c.to(self.device)
-
-        return (h, c)
-
-    def forward(self, input):
-
-        test, question, tag, _, mask, interaction, _ = input
-
-        batch_size = interaction.size(0)
-
-        # Embedding
-
-        embed_interaction = self.embedding_interaction(interaction)
-        embed_test = self.embedding_test(test)
-        embed_question = self.embedding_question(question)
-        embed_tag = self.embedding_tag(tag)
-        
-
-        embed = torch.cat([embed_interaction,
-                           embed_test,
-                           embed_question,
-                           embed_tag,], 2)
-
-        X = self.comb_proj(embed)
-
-        hidden = self.init_hidden(batch_size)
-        out, hidden = self.lstm(X, hidden)
-        out = out.contiguous().view(batch_size, -1, self.hidden_dim)
-
-        out = self.fc(out)
-        preds = self.activation(out).view(batch_size, -1)
-
-        return preds
-
diff --git a/code/baseline/dkt/optimizer.py b/code/baseline/dkt/optimizer.py
deleted file mode 100644
index 1548373..0000000
--- a/code/baseline/dkt/optimizer.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from torch.optim import Adam, AdamW
-
-def get_optimizer(model, args):
-    if args.optimizer == 'adam':
-        optimizer = Adam(model.parameters(), lr=args.lr, weight_decay=0.01)
-    if args.optimizer == 'adamW':
-        optimizer = AdamW(model.parameters(), lr=args.lr, weight_decay=0.01)
-    
-    # 모든 parameter들의 grad값을 0으로 초기화
-    optimizer.zero_grad()
-    
-    return optimizer
\ No newline at end of file
diff --git a/code/baseline/dkt/scheduler.py b/code/baseline/dkt/scheduler.py
deleted file mode 100644
index 0823313..0000000
--- a/code/baseline/dkt/scheduler.py
+++ /dev/null
@@ -1,14 +0,0 @@
-
-from torch.optim.lr_scheduler import ReduceLROnPlateau
-
-from transformers import get_linear_schedule_with_warmup
-
-
-def get_scheduler(optimizer, args):
-    if args.scheduler == 'plateau':
-        scheduler = ReduceLROnPlateau(optimizer, patience=10, factor=0.5, mode='max', verbose=True)
-    elif args.scheduler == 'linear_warmup':
-        scheduler = get_linear_schedule_with_warmup(optimizer,
-                                                    num_warmup_steps=args.warmup_steps,
-                                                    num_training_steps=args.total_steps)
-    return scheduler
\ No newline at end of file
diff --git a/code/baseline/dkt/trainer.py b/code/baseline/dkt/trainer.py
deleted file mode 100644
index 9b12650..0000000
--- a/code/baseline/dkt/trainer.py
+++ /dev/null
@@ -1,288 +0,0 @@
-import os
-import torch
-import numpy as np
-
-
-from .dataloader import get_loaders
-from .optimizer import get_optimizer
-from .scheduler import get_scheduler
-from .criterion import get_criterion
-from .metric import get_metric
-from .model import LSTM
-
-import wandb
-
-def run(args, train_data, valid_data):
-    train_loader, valid_loader = get_loaders(args, train_data, valid_data)
-    
-    # only when using warmup scheduler
-    args.total_steps = int(len(train_loader.dataset) / args.batch_size) * (args.n_epochs)
-    args.warmup_steps = args.total_steps // 10
-            
-    model = get_model(args)
-    optimizer = get_optimizer(model, args)
-    scheduler = get_scheduler(optimizer, args)
-
-    best_auc = -1
-    early_stopping_counter = 0
-    for epoch in range(args.n_epochs):
-
-        print(f"Start Training: Epoch {epoch + 1}")
-        
-        ### TRAIN
-        train_auc, train_acc, train_loss = train(train_loader, model, optimizer, args)
-        
-        ### VALID
-        auc, acc = validate(valid_loader, model, args)
-
-        ### TODO: model save or early stopping
-        wandb.log({"epoch": epoch, "train_loss": train_loss, "train_auc": train_auc, "train_acc":train_acc,
-                  "valid_auc":auc, "valid_acc":acc})
-        if auc > best_auc:
-            best_auc = auc
-            # torch.nn.DataParallel로 감싸진 경우 원래의 model을 가져옵니다.
-            model_to_save = model.module if hasattr(model, 'module') else model
-            save_checkpoint({
-                'epoch': epoch + 1,
-                'state_dict': model_to_save.state_dict(),
-                },
-                args.model_dir, 'model.pt',
-            )
-            early_stopping_counter = 0
-        else:
-            early_stopping_counter += 1
-            if early_stopping_counter >= args.patience:
-                print(f'EarlyStopping counter: {early_stopping_counter} out of {args.patience}')
-                break
-
-        # scheduler
-        if args.scheduler == 'plateau':
-            scheduler.step(best_auc)
-        else:
-            scheduler.step()
-
-
-def train(train_loader, model, optimizer, args):
-    model.train()
-
-    total_preds = []
-    total_targets = []
-    losses = []
-    for step, batch in enumerate(train_loader):
-        input = process_batch(batch, args)
-        preds = model(input)
-        targets = input[3] # correct
-
-
-        loss = compute_loss(preds, targets)
-        update_params(loss, model, optimizer, args)
-
-        if step % args.log_steps == 0:
-            print(f"Training steps: {step} Loss: {str(loss.item())}")
-        
-        # predictions
-        preds = preds[:,-1]
-        targets = targets[:,-1]
-
-        if args.device == 'cuda':
-            preds = preds.to('cpu').detach().numpy()
-            targets = targets.to('cpu').detach().numpy()
-        else: # cpu
-            preds = preds.detach().numpy()
-            targets = targets.detach().numpy()
-        
-        total_preds.append(preds)
-        total_targets.append(targets)
-        losses.append(loss)
-      
-
-    total_preds = np.concatenate(total_preds)
-    total_targets = np.concatenate(total_targets)
-
-    # Train AUC / ACC
-    auc, acc = get_metric(total_targets, total_preds)
-    loss_avg = sum(losses)/len(losses)
-    print(f'TRAIN AUC : {auc} ACC : {acc}')
-    return auc, acc, loss_avg
-    
-
-def validate(valid_loader, model, args):
-    model.eval()
-
-    total_preds = []
-    total_targets = []
-    for step, batch in enumerate(valid_loader):
-        input = process_batch(batch, args)
-
-        preds = model(input)
-        targets = input[3] # correct
-
-
-        # predictions
-        preds = preds[:,-1]
-        targets = targets[:,-1]
-    
-        if args.device == 'cuda':
-            preds = preds.to('cpu').detach().numpy()
-            targets = targets.to('cpu').detach().numpy()
-        else: # cpu
-            preds = preds.detach().numpy()
-            targets = targets.detach().numpy()
-
-        total_preds.append(preds)
-        total_targets.append(targets)
-
-    total_preds = np.concatenate(total_preds)
-    total_targets = np.concatenate(total_targets)
-
-    # Train AUC / ACC
-    auc, acc = get_metric(total_targets, total_preds)
-    
-    print(f'VALID AUC : {auc} ACC : {acc}\n')
-
-    return auc, acc
-
-
-
-def inference(args, test_data):
-    
-    model = load_model(args)
-    model.eval()
-    _, test_loader = get_loaders(args, None, test_data)
-    
-    
-    total_preds = []
-    
-    for step, batch in enumerate(test_loader):
-        input = process_batch(batch, args)
-
-        preds = model(input)
-        
-
-        # predictions
-        preds = preds[:,-1]
-        
-
-        if args.device == 'cuda':
-            preds = preds.to('cpu').detach().numpy()
-        else: # cpu
-            preds = preds.detach().numpy()
-            
-        total_preds+=list(preds)
-
-    write_path = os.path.join(args.output_dir, "output.csv")
-    if not os.path.exists(args.output_dir):
-        os.makedirs(args.output_dir)    
-    with open(write_path, 'w', encoding='utf8') as w:
-        w.write("id,prediction\n")
-        for id, p in enumerate(total_preds):
-            w.write('{},{}\n'.format(id,p))
-
-
-
-
-def get_model(args):
-    """
-    Load model and move tensors to a given devices.
-    """
-    if args.model == 'lstm': model = LSTM(args)
-    if args.model == 'lstmattn': model = LSTMATTN(args)
-    if args.model == 'bert': model = Bert(args)
-    
-
-    model.to(args.device)
-
-    return model
-
-
-# 배치 전처리
-def process_batch(batch, args):
-
-    test, question, tag, correct, mask = batch
-    
-    
-    # change to float
-    mask = mask.type(torch.FloatTensor)
-    correct = correct.type(torch.FloatTensor)
-
-    #  interaction을 임시적으로 correct를 한칸 우측으로 이동한 것으로 사용
-    #    saint의 경우 decoder에 들어가는 input이다
-    interaction = correct + 1 # 패딩을 위해 correct값에 1을 더해준다.
-    interaction = interaction.roll(shifts=1, dims=1)
-    interaction[:, 0] = 0 # set padding index to the first sequence
-    interaction = (interaction * mask).to(torch.int64)
-    # print(interaction)
-    # exit()
-    #  test_id, question_id, tag
-    test = ((test + 1) * mask).to(torch.int64)
-    question = ((question + 1) * mask).to(torch.int64)
-    tag = ((tag + 1) * mask).to(torch.int64)
-
-    # gather index
-    # 마지막 sequence만 사용하기 위한 index
-    gather_index = torch.tensor(np.count_nonzero(mask, axis=1))
-    gather_index = gather_index.view(-1, 1) - 1
-
-
-    # device memory로 이동
-
-    test = test.to(args.device)
-    question = question.to(args.device)
-
-
-    tag = tag.to(args.device)
-    correct = correct.to(args.device)
-    mask = mask.to(args.device)
-
-    interaction = interaction.to(args.device)
-    gather_index = gather_index.to(args.device)
-
-    return (test, question,
-            tag, correct, mask,
-            interaction, gather_index)
-
-
-# loss계산하고 parameter update!
-def compute_loss(preds, targets):
-    """
-    Args :
-        preds   : (batch_size, max_seq_len)
-        targets : (batch_size, max_seq_len)
-
-    """
-    loss = get_criterion(preds, targets)
-    #마지막 시퀀드에 대한 값만 loss 계산
-    loss = loss[:,-1]
-    loss = torch.mean(loss)
-    return loss
-
-def update_params(loss, model, optimizer, args):
-    loss.backward()
-    torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad)
-    optimizer.step()
-    optimizer.zero_grad()
-
-
-
-def save_checkpoint(state, model_dir, model_filename):
-    print('saving model ...')
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)    
-    torch.save(state, os.path.join(model_dir, model_filename))
-
-
-
-def load_model(args):
-    
-    
-    model_path = os.path.join(args.model_dir, args.model_name)
-    print("Loading Model from:", model_path)
-    load_state = torch.load(model_path)
-    model = get_model(args)
-
-    # 1. load model state
-    model.load_state_dict(load_state['state_dict'], strict=True)
-   
-    
-    print("Loading Model from:", model_path, "...Finished.")
-    return model
\ No newline at end of file
diff --git a/code/baseline/dkt/utils.py b/code/baseline/dkt/utils.py
deleted file mode 100644
index ca8a411..0000000
--- a/code/baseline/dkt/utils.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import os, random, torch
-import numpy as np
-def setSeeds(seed = 42):
-    # 랜덤 시드를 설정하여 매 코드를 실행할 때마다 동일한 결과를 얻게 합니다.
-    os.environ['PYTHONHASHSEED'] = str(seed)
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)    
-    torch.cuda.manual_seed(seed)
-    torch.backends.cudnn.deterministic = True
\ No newline at end of file
diff --git a/code/baseline/evaluation.py b/code/baseline/evaluation.py
deleted file mode 100644
index 1088905..0000000
--- a/code/baseline/evaluation.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import pandas as pd
-from sklearn.metrics import roc_auc_score, accuracy_score
-import json
-import numpy as np
-
-def evaluation(gt_path, pred_path):
-    """
-    Args:
-        gt_path (string) : root directory of ground truth file
-        pred_path (string) : root directory of prediction file (output of inference.py)
-    """
-    #어떤 gt를 사용하느냐에 따라 달라짐, 
-    #제출 ID에서 gt에 있는 ID 값만 채점
-    
-    gt = pd.read_csv(gt_path, index_col='id')
-    total_targets = gt['answerCode'].values
-
-    pred = pd.read_csv(pred_path,index_col='id')
-    #ground truth에 있는 id 값만 골라내기
-    total_preds = pred.loc[list(gt.index),'prediction']
-
-    # AUROC
-    auroc = roc_auc_score(total_targets, total_preds)
-    acc = accuracy_score(total_targets, np.where(total_preds >= 0.5, 1, 0))
-    results={}
-    results['accuracy'] = {
-        'value': f'{acc:.4f}',
-        'rank': False,
-        'decs': True,
-    }
-    results['auroc'] = {
-        'value': f'{auroc:.4f}',
-        'rank': True,
-        'decs': True,
-    }
-
-    return json.dumps(results)
diff --git a/code/baseline/feature_engineering.py b/code/baseline/feature_engineering.py
deleted file mode 100644
index cd79ae2..0000000
--- a/code/baseline/feature_engineering.py
+++ /dev/null
@@ -1,686 +0,0 @@
-import time
-import random
-from datetime import datetime
-import pandas as pd
-import numpy as np
-from tqdm import tqdm
-from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
-tqdm.pandas()
-
-def IK_question_acc(df):
-    assessmentItemID_groupby = df.groupby('assessmentItemID').agg({
-        'answercode': 'mean'
-    })
-    
-    df["IK_question_acc"] = assessmentItemID_groupby["answercode"][df["assessmentItemID"]].values
-    return df
-
-def IK_KnowledgeTag_acc(df):
-    KnowledgeTag_groupby = df.groupby('KnowledgeTag').agg({
-        'answercode': 'mean'
-    })
-    
-    df["IK_KnowledgeTag_acc"] = KnowledgeTag_groupby["answercode"][df["KnowledgeTag"]].values
-    
-    return df
-
-def solved_question(df):
-    df["solved_question"] = df.groupby(["userID"]).cumcount()
-    
-    return df
-
-def user_question_class_solved(df):
-    if "question_class" not in df.columns:
-        df = question_class(df)
-        
-    df["user_question_class_solved"] = df.groupby(["userID", "question_class"]).cumcount()
-    return df
-
-def userID_elapsed_cate(df, max_time=600):
-    df.sort_values(by=["userID", "Timestamp"], inplace=True)
-
-    # sample별 elapsed time 
-    diff = df.loc[:, ['userID', 'Timestamp']].groupby('userID').diff().shift(-1)
-    elapsed = diff['Timestamp'].apply(lambda x: int(x.total_seconds() // 10 * 10) if max_time > x.total_seconds() else 1)
-    df['userID_elapsed_cate'] = elapsed
-    
-    return df
-
-def userID_testid_experience(df):
-    # userID별 시간 순으로 정렬
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    
-    # userID 별로 testid를 풀어본 적 있는지
-    df["userID_testid_experience"] = df.groupby(["userID", "testId"])['testId'].cumcount()
-    df['userID_testid_experience'] = df['userID_testid_experience'].apply(lambda x : 1 if x > 0 else 0)
-    return df
-
-def userID_assessmentItemID_experience(df):
-    # userID별 시간 순으로 정렬
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    
-    # userID 별로 assessmentItemID를 풀어본 적 있는지
-    df["userID_assessmentItemID_experience"] = df.groupby(["userID", "assessmentItemID"])['assessmentItemID'].cumcount()
-    df['userID_assessmentItemID_experience'] = df['userID_assessmentItemID_experience'].apply(lambda x : 1 if x > 0 else 0)
-    return df
-
-def userID_time_diff_from_last(df):
-    
-    def convert_time(s):
-        timestamp = time.mktime(datetime.strptime(s, '%Y-%m-%d %H:%M:%S').timetuple())
-        return int(timestamp)
-    
-    # 초 단위 시간
-    df['sec'] = df['Timestamp'].apply(convert_time)
-    
-    # userID별 시간 순으로 정렬 + index column 생성
-    df = df.sort_values(by=['userID', 'sec']).reset_index(drop=False)
-    
-    # userID별 마지막 index 값
-    last_idx_group = df.groupby(['userID'])['index'].agg(["max"])
-    last_idx_group = last_idx_group.reset_index()
-    last_idx_group.columns = ['userID', 'last_index']
-    df = pd.merge(df, last_idx_group, on=["userID"], how="left")
-    
-    def changed_time(x):
-        last_time = df['sec'][x['last_index']]
-        period = last_time-x['sec']
-        return period
-
-    # userID별 마지막 index의 시간과의 차이 계산
-    df["userID_time_diff_from_last"] = df.apply(changed_time, axis=1)
-    
-    df.drop('sec', axis=1, inplace=True)
-    df.drop('index', axis=1, inplace=True)
-    return df
-
-def userID_KnowledgeTag_relative(df):
-    # userID별 시간 순으로 정렬
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    # userID, KnowledgeTag 키값 생성(temp)
-    df["tmp"] = df[["userID", "KnowledgeTag"]].apply(lambda data: str(data["userID"]) + "_" + str(data["KnowledgeTag"]), axis=1)
-    # userID, KnowledgeTag별 누적 풀이 수, 정답 수, 정답률
-    df["userID_KnowledgeTag_total_answer"] = df.groupby("tmp")["answercode"].cumcount()
-    df["userID_KnowledgeTag_correct_answer"] = df.groupby("tmp")["answercode"].transform(lambda x: x.cumsum().shift(1))
-    df['userID_KnowledgeTag_correct_answer'].fillna(0, inplace=True)
-    df["userID_KnowledgeTag_acc"] = df["userID_KnowledgeTag_correct_answer"] / df["userID_KnowledgeTag_total_answer"]
-    df['userID_KnowledgeTag_acc'].fillna(0, inplace=True)
-    df.drop('tmp', axis=1, inplace=True)
-    return df
-
-def userID_question_num_relative(df):
-    # question_num이 있어야 계산 가능
-    if 'question_num' not in df.columns:
-        df = question_num(df)
-        
-    # userID별 시간 순으로 정렬
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    # userID_question_class 키값 생성(temp)
-    df["tmp"] = df[["userID", "question_num"]].apply(lambda data: str(data["userID"]) + "_" + data["question_num"], axis=1)
-    # userID, question_num별 누적 풀이 수, 정답 수, 정답률
-    df["userID_question_num_total_answer"] = df.groupby("tmp")["answercode"].cumcount()
-    df["userID_question_num_correct_answer"] = df.groupby("tmp")["answercode"].transform(lambda x: x.cumsum().shift(1))
-    df['userID_question_num_correct_answer'].fillna(0, inplace=True)
-    df["userID_question_num_acc"] = df["userID_question_num_correct_answer"] / df["userID_question_num_total_answer"]
-    df['userID_question_num_acc'].fillna(0, inplace=True)
-    df.drop('tmp', axis=1, inplace=True)
-    return df
-
-def userID_elapsed_median(df, max_time=600):
-    # 약 1m 50s 소요(Progress bar 2개 생김)
-    # userID별 시간 순으로 정렬
-    df.sort_values(by=["userID", "Timestamp"], inplace=True)
-
-    # sample별 elapsed time 
-    diff = df.loc[:, ['userID', 'Timestamp']].groupby('userID').diff().shift(-1)
-    elapsed = diff['Timestamp'].progress_apply(lambda x: x.total_seconds() if max_time > x.total_seconds() else None)
-    df['userID_elapsed_median'] = elapsed
-    
-    # userID별 마지막 문제의 풀이 시간(데이터에서 알 수 없는)을
-    # userID별 문제 풀이 시간의 "중앙값"으로 반환하기 위한 Aggregation
-    user_median = df.groupby('userID')['userID_elapsed_median'].median()
-    df = pd.merge(df, user_median, on=["userID"], how="left")
-    
-    # 결측치 중앙값 변환 및 임시 열 삭제
-    df["userID_elapsed_median_x"] = df["userID_elapsed_median_x"].fillna('missing')
-    def changed_elapsed(data):
-        return data["userID_elapsed_median_x"] if data["userID_elapsed_median_x"] != 'missing' else data["userID_elapsed_median_y"]
-    df['userID_elapsed_median'] = df.progress_apply(changed_elapsed, axis=1)
-    df.drop('userID_elapsed_median_x', axis=1, inplace=True)
-    df.drop('userID_elapsed_median_y', axis=1, inplace=True)
-    return df
-
-
-def userID_elapsed_median_rolling(df, window=5):
-    # userID_elapsed_median이 있어야 이동평균 계산 가능
-    if 'userID_elapsed_median' not in df.columns:
-        df = userID_elapsed_median(df)
-    # userID별 시간 순으로 정렬
-    df.sort_values(by=["userID", "Timestamp"], inplace=True)
-    
-    # userID별 문제 풀이 시간의 이동평균
-    df['userID_elapsed_median_rolling'] = df.groupby(['userID'])['userID_elapsed_median'].rolling(window).mean().values
-    # 유저별 window-1만큼 N/A data가 생김(rolling의 특성상 앞데이터에 생김)
-    # 유저별 userID_elapsed_median_rolling의 중앙값으로 대체
-    def changed_mean_time(data):
-        return data["userID_elapsed_median_rolling_x"] if data["userID_elapsed_median_rolling_x"] != 'missing' else data["userID_elapsed_median_rolling_y"]
-    user_median = df.groupby('userID')['userID_elapsed_median_rolling'].median()
-    df = pd.merge(df, user_median, on=["userID"], how="left")
-    
-    # 결측치 중앙값 변환 및 임시 열 삭제
-    df['userID_elapsed_median_rolling_x'] = df['userID_elapsed_median_rolling_x'].fillna('missing')
-    df['userID_elapsed_median_rolling'] = df.progress_apply(changed_mean_time, axis=1)
-    df.drop('userID_elapsed_median_rolling_x', axis=1, inplace=True)
-    df.drop('userID_elapsed_median_rolling_y', axis=1, inplace=True)
-    return df
-
-
-def question_num(df):
-    # 문제지 안 문제 번호
-    df["question_num"] = df["assessmentItemID"].apply(lambda x: x[-3:])
-    return df
-
-
-def question_class(df):
-    # 문제지 안 문제 번호
-    df["question_class"] = df["assessmentItemID"].apply(lambda x: x[2])
-    return df
-
-
-def KnowledgeTag_relative(df):
-    df.reset_index(drop=True, inplace=True)
-    # KnowledgeTag별 누적 풀이 수, 정답 수, 정답률
-    df_KnowledgeTag = df.sort_values(by=["KnowledgeTag", "Timestamp"])
-    df['KnowledgeTag_total_answer'] = df_KnowledgeTag.groupby("KnowledgeTag")["answerCode"].cumcount()
-    df["KnowledgeTag_correct_answer"] = df_KnowledgeTag.groupby("KnowledgeTag")["answerCode"].transform(lambda x: x.cumsum().shift(1)).fillna(0)
-    df["KnowledgeTag_acc"] = (df["KnowledgeTag_correct_answer"] / df["KnowledgeTag_total_answer"]).fillna(0)
-    return df
-
-
-def assessmentItemID_relative(df):
-    df.reset_index(drop=True, inplace=True)
-    # assessmentItemID별 누적 풀이 수, 정답 수, 정답률
-    df_assessmentItemID = df.sort_values(by=["assessmentItemID", "Timestamp"])
-    df['assessmentItemID_total_answer'] = df_assessmentItemID.groupby("assessmentItemID")["answerCode"].cumcount()
-    df["assessmentItemID_correct_answer"] = df_assessmentItemID.groupby("assessmentItemID")["answerCode"].transform(lambda x: x.cumsum().shift(1)).fillna(0)
-    df["assessmentItemID_acc"] = (df["assessmentItemID_correct_answer"] / df["assessmentItemID_total_answer"]).fillna(0)
-    return df
-
-
-def question_class_relative(df):
-    # userID_elapsed_median이 있어야 이동평균 계산 가능
-    if 'question_class' not in df.columns:
-        df = question_class(df)
-    # Question Class 별 누적 풀이 수, 정답 수, 정답률
-    df.sort_values(by=["question_class", "Timestamp"], inplace=True)
-    df["question_class_correct_answer"] = df.groupby("question_class")["answerCode"].transform(lambda x: x.cumsum().shift(1)).fillna(0)
-    df["question_class_total_answer"] = df.groupby("question_class")["answerCode"].cumcount()
-    df["question_class_acc"] = (df["question_class_correct_answer"] / df["question_class_total_answer"]).fillna(0)
-    return df
-
-
-def userID_question_class_relative(df):
-    # question_class 있어야 계산 가능
-    if 'question_class' not in df.columns:
-        df = question_class(df)
-    
-    # userID별 시간 순으로 정렬
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    # userID_question_class 키값 생성(temp)
-    df["tmp"] = df[["userID", "question_class"]].apply(lambda data: str(data["userID"]) + "_" + data["question_class"], axis=1)
-    # userID_question_class 별 누적 풀이 수, 정답 수, 정답률
-    df["userID_question_class_total_answer"] = df.groupby("tmp")["answercode"].cumcount()
-    df["userID_question_class_correct_answer"] = df.groupby("tmp")["answercode"].transform(lambda x: x.cumsum().shift(1))
-    df['userID_question_class_correct_answer'].fillna(0, inplace=True)
-    df["userID_question_class_acc"] = df["userID_question_class_correct_answer"] / df["userID_question_class_total_answer"]
-    df['userID_question_class_acc'].fillna(0, inplace=True)
-    df.drop('tmp', axis=1, inplace=True)
-    return df
-
-
-def userID_relative(df):
-    # userID별 시간 순으로 정렬
-    df.sort_values(by=["userID", "Timestamp"], inplace=True)
-    #user 별 누적 풀이 수, 정답 수, 정답률
-    df["userID_correct_answer"] = df.groupby("userID")["answerCode"].transform(lambda x: x.cumsum().shift(1)).fillna(0)
-    df["userID_total_answer"] = df.groupby("userID")["answerCode"].cumcount()
-    df["userID_acc"] = (df["userID_correct_answer"] / df["userID_total_answer"]).fillna(0)
-    return df
-
-
-def userID_acc_rolling(df, window=5):
-    # user_acc 있어야 이동평균 계산 가능
-    if 'userID_acc' not in df.columns:
-        df = userID_relative(df)
-    # userID별 시간 순으로 정렬
-    df.sort_values(by=["userID", "Timestamp"], inplace=True)
-    
-    # userID별 정답률(user_acc)의 이동 평균
-    df['userID_acc_rolling'] = df.groupby(['userID'])['userID_acc'].rolling(window).mean().values
-    # userID별 window-1만큼 N/A data가 생김(rolling의 특성상 앞데이터에 생김)
-    # userID별 user_acc_rolling의 중앙값으로 대체
-    def changed_user_acc_rolling(data):
-        return data["userID_acc_rolling_x"] if data["userID_acc_rolling_x"] != 'missing' else data["userID_acc_rolling_y"]
-    user_median = df.groupby('userID')['userID_acc_rolling'].median()
-    df = pd.merge(df, user_median, on=["userID"], how="left")
-    # 결측치 중앙값 변환 및 임시 열 삭제
-    df['userID_acc_rolling_x'] = df['userID_acc_rolling_x'].fillna('missing')
-    df['userID_acc_rolling'] = df.progress_apply(changed_user_acc_rolling, axis=1)
-    df.drop('userID_acc_rolling_x', axis=1, inplace=True)
-    df.drop('userID_acc_rolling_y', axis=1, inplace=True)
-    return df
-
-
-def userID_elapsed_median_rolling(df, window=5):
-    # userID_elapsed_median이 있어야 이동평균 계산 가능
-    if 'userID_elapsed_median' not in df.columns:
-        df = userID_elapsed_median(df)
-    # userID별 시간 순으로 정렬
-    df.sort_values(by=["userID", "Timestamp"], inplace=True)
-    
-    # userID별 문제 풀이 시간의 이동평균
-    df['userID_elapsed_median_rolling'] = df.groupby(['userID'])['userID_elapsed_median'].rolling(window).mean().values
-    # 유저별 window-1만큼 N/A data가 생김(rolling의 특성상 앞데이터에 생김)
-    # 유저별 userID_elapsed_median_rolling의 중앙값으로 대체
-    def changed_mean_time(data):
-        return data["userID_elapsed_median_rolling_x"] if data["userID_elapsed_median_rolling_x"] != 'missing' else data["userID_elapsed_median_rolling_y"]
-    user_median = df.groupby('userID')['userID_elapsed_median_rolling'].median()
-    df = pd.merge(df, user_median, on=["userID"], how="left")
-    
-    # 결측치 중앙값 변환 및 임시 열 삭제
-    df['userID_elapsed_median_rolling_x'] = df['userID_elapsed_median_rolling_x'].fillna('missing')
-    df['userID_elapsed_median_rolling'] = df.progress_apply(changed_mean_time, axis=1)
-    df.drop('userID_elapsed_median_rolling_x', axis=1, inplace=True)
-    df.drop('userID_elapsed_median_rolling_y', axis=1, inplace=True)
-    return df
-
-
-def assessmentItemID_time_relative(df):
-    # 문제별 풀이 시간의 중앙값&평균값
-    # userID_elapsed_median 있어야 assessmentItemID_time 계산 가능
-    if 'userID_elapsed_median' not in df.columns:
-        df = userID_elapsed_median(df)
-    # assessmentItemID별 풀이 시간의 중앙값&평균값
-    df_total_agg = df.copy()
-    agg_df = df_total_agg.groupby('assessmentItemID')['userID_elapsed_median'].agg(['median', 'mean'])
-    # mapping을 위해 pandas DataFrame을 dictionary형태로 변환
-    agg_dict = agg_df.to_dict()
-    # 구한 통계량을 각 사용자에게 mapping
-    df['assessmentItemID_time_median'] = df_total_agg['assessmentItemID'].map(agg_dict['median'])
-    df['assessmentItemID_time_mean'] = df_total_agg['assessmentItemID'].map(agg_dict['mean'])
-    return df
-
-
-def userID_time_relative(df):
-    # 유저별 풀이 시간의 중앙값&평균값
-    # userID_elapsed_median 있어야 userID_time_relative 계산 가능
-    if 'userID_elapsed_median' not in df.columns:
-        df = userID_elapsed_median(df)
-    # assessmentItemID별 풀이 시간의 중앙값&평균값
-    df_total_agg = df.copy()
-    agg_df = df_total_agg.groupby('userID')['userID_elapsed_median'].agg(['median', 'mean'])
-    # mapping을 위해 pandas DataFrame을 dictionary형태로 변환
-    agg_dict = agg_df.to_dict()
-    # 구한 통계량을 각 사용자에게 mapping
-    df['userID_time_median'] = df_total_agg['userID'].map(agg_dict['median'])
-    df['userID_time_mean'] = df_total_agg['userID'].map(agg_dict['mean'])
-    return df
-
-
-def userID_elapsed_normalize(df):
-    # userID_elapsed_normalize 있어야 userID_elapsed_normalize 계산 가능
-    if 'userID_elapsed_normalize' not in df.columns:
-        df = userID_elapsed_median(df)
-    df_total_norm = df.copy()
-    df['userID_elapsed_normalize'] = df_total_norm.groupby('userID')['userID_elapsed_median'].transform(lambda x: (x - x.mean())/x.std())
-    return df
-
-
-def lda_feature(df):
-    df.reset_index(drop=True, inplace=True)
-    if 'assessmentItemID_total_answer' not in df.columns:
-        df = assessmentItemID_relative(df)
-    if 'KnowledgeTag_total_answer' not in df.columns:
-        df = KnowledgeTag_relative(df)
-    if 'question_class_correct_answer' not in df.columns:
-        df = question_class_relative(df)
-    if 'userID_question_class_correct_answer' not in df.columns:
-        df = userID_question_class_relative(df)
-    # lda_latent_factor 변수
-    lda = LDA(n_components=1)
-    y = df['answerCode']
-    
-    #assessmentItemID_lda
-    X = df[['assessmentItemID_total_answer', 'assessmentItemID_correct_answer','assessmentItemID_acc']]
-    df['assessmentItemID_lda'] = lda.fit_transform(X, y)
-    # KnowledgeTag_lda
-    X = df[['KnowledgeTag_total_answer', 'KnowledgeTag_correct_answer','KnowledgeTag_acc']]
-    df['KnowledgeTag_lda'] = lda.fit_transform(X, y)
-    # question_class_lda
-    X = df[['question_class_correct_answer', 'question_class_total_answer','question_class_acc']]
-    df['question_class_lda'] = lda.fit_transform(X, y)
-    # user_question_class_lda
-    X = df[['userID_question_class_correct_answer', 'userID_question_class_total_answer','userID_question_class_acc']]
-    df['userID_question_class_lda'] = lda.fit_transform(X, y)
-    return df
-
-
-def find_time_difference(data):
-    if data["userID"] == data["userID_shift"]:
-        temp_time_difference = int(((data["Timestamp"] - data["next_timestamp"]) / pd.to_timedelta(1, unit='D')) * (60 * 60 * 24))
-        if temp_time_difference > 600: # 10분 넘는 경우 # 변경 가능
-            return 600
-        elif temp_time_difference > 3600: # 1시간 넘는 경우 # 변경 가능:
-            return 0
-        return temp_time_difference
-    else:
-        return 0
-
-    
-def feature_engineering_sun(df):
-    # assessmentItemID, timestamp 기준 정렬
-    df.sort_values(by=["KnowledgeTag", "Timestamp"], inplace=True)
-    
-    # KnowledgeTag 풀이 수, 정답 수, 정답률을 시간순으로 누적해서 계산
-    df["KnowledgeTag_correct_answer"] = df.groupby("KnowledgeTag")["answercode"].transform(lambda x: x.cumsum().shift(1))
-    df["KnowledgeTag_total_answer"] = df.groupby("KnowledgeTag")["answercode"].cumcount()
-    df["KnowledgeTag_acc"] = df["KnowledgeTag_correct_answer"] / df["KnowledgeTag_total_answer"]
-    
-    # assessmentItemID, timestamp 기준 정렬
-    df.sort_values(by=["assessmentItemID", "Timestamp"], inplace=True)
-    
-    # assessmentItemID 풀이 수, 정답 수, 정답률을 시간순으로 누적해서 계산
-    df["question_correct_answer"] = df.groupby("assessmentItemID")["answercode"].transform(lambda x: x.cumsum().shift(1))
-    df["question_total_answer"] = df.groupby("assessmentItemID")["answercode"].cumcount()
-    df["question_acc"] = df["question_correct_answer"] / df["question_total_answer"]
-    
-    # question class
-    df["question_class"] = df["assessmentItemID"].apply(lambda x: x[2])
-    # user_question_class
-    df["userID_question_class"] = df[["userID", "question_class"]].apply(lambda data: str(data["userID"]) + "_" + data["question_class"], axis=1)
-    
-    # question_class, timestamp 기준 정렬
-    df.sort_values(by=["question_class", "Timestamp"], inplace=True)
-    
-     # question_class 정답 수, 풀이 수, 정답률을 시간순으로 누적해서 계산
-    df["question_class_correct_answer"] = df.groupby("question_class")["answercode"].transform(lambda x: x.cumsum().shift(1))
-    df["question_class_total_answer"] = df.groupby("question_class")["answercode"].cumcount()
-    df["question_class_acc"] = df["question_class_correct_answer"] / df["question_class_total_answer"]
-    
-    # assessmentItemID, timestamp 기준 정렬
-    df.sort_values(by=["userID_question_class", "Timestamp"], inplace=True)
-    
-    # userID_question_class 정답 수, 풀이 수, 정답률을 시간순으로 누적해서 계산
-    df["user_question_class_correct_answer"] = df.groupby("userID_question_class")["answercode"].transform(lambda x: x.cumsum().shift(1))
-    df["user_question_class_total_answer"] = df.groupby("userID_question_class")["answercode"].cumcount()
-    df["user_question_class_acc"] = df["user_question_class_correct_answer"] / df["user_question_class_total_answer"]
-    
-    # user별 timestamp 기준 정렬
-    df.sort_values(by=["userID", "Timestamp"], inplace=True)
-    
-    # user 문제 푼 시간 측정
-    df["next_timestamp"] = df["Timestamp"].shift(-1)
-    df["userID_shift"] = df["userID"].shift(-1)
-    # 3min 25s 소요..
-    df["time_difference"] = df[["userID", "userID_shift", "Timestamp", "next_timestamp"]].apply(find_time_difference, axis=1)
-    
-    # question class
-    df["question_class"] = df["assessmentItemID"].apply(lambda x: x[2])
-    
-    # user의 문제 풀이 수, 정답 수, 정답률을 시간순으로 누적해서 계산
-    df["user_correct_answer"] = df.groupby("userID")["answercode"].transform(lambda x: x.cumsum().shift(1))
-    df["user_total_answer"] = df.groupby("userID")["answercode"].cumcount()
-    df["user_acc"] = df["user_correct_answer"] / df["user_total_answer"]
-    
-    # testId 기준 mean, sumanswercode
-    group_test = df.groupby(["testId"])["answercode"].agg(["mean", "sum"])
-    group_test.columns = ["test_mean", "test_sum"]
-    # knowledge_tag 기준 mean, sum
-    group_tag = df.groupby(["KnowledgeTag"])["answercode"].agg(["mean", "sum"])
-    group_tag.columns = ["tag_mean", "tag_sum"]
-    # userID 기준 mean, sum
-    group_user = df.groupby(["userID"])["answercode"].agg(["sum"])
-    group_user.columns = ["user_count"]
-    # question 기준 mean, sum
-    group_question = df.groupby(["assessmentItemID"])["answercode"].agg(["mean", "sum"])
-    group_question.columns = ["question_mean", "question_count"]
-    # question class(assessmentItemID 두 번째 숫자) 기준 mean, sum
-    group_question_class = df.groupby(["question_class"])["answercode"].agg(["mean", "sum"])
-    group_question_class.columns = ["question_class_mean", "question_class_count"]
-    # time_difference 기준 mean, median
-    group_time_difference = df.groupby(["userID"])["time_difference"].agg(["mean", "median"])
-    group_time_difference.columns = ["time_difference_mean", "time_difference_median"]
-    # userID_question_class 기준 mean, sum
-    group_user_question_class = df.groupby(["userID_question_class"])["answercode"].agg(["mean", "sum"])
-    group_user_question_class.columns = ["user_question_class_mean", "user_question_class_count"]
-    
-    # merge
-    df = pd.merge(df, group_test, on=["testId"], how="left")
-    df = pd.merge(df, group_tag, on=["KnowledgeTag"], how="left")
-    df = pd.merge(df, group_user, on=["userID"], how="left")
-    df = pd.merge(df, group_question, on=["assessmentItemID"], how="left")
-    df = pd.merge(df, group_question_class, on=["question_class"], how="left")
-    df = pd.merge(df, group_time_difference, on=["userID"], how="left")
-    df = pd.merge(df, group_user_question_class, on=["userID_question_class"], how="left")
-    
-    return df
-
-# ======================================================================================================= #
-
-# ACC 같은 민감한 정보를 Categorical로 바꾸는 함수
-def make_grade(data) :
-    if data < 0.05 : return 0
-    elif data < 0.10 : return 1
-    elif data < 0.15 : return 2
-    elif data < 0.20 : return 3
-    elif data < 0.25 : return 4
-    elif data < 0.30 : return 5
-    elif data < 0.35 : return 6
-    elif data < 0.40 : return 7
-    elif data < 0.45 : return 8
-    elif data < 0.50 : return 9
-    elif data < 0.55 : return 10
-    elif data < 0.60 : return 11
-    elif data < 0.65 : return 12
-    elif data < 0.70 : return 13
-    elif data < 0.75 : return 14
-    elif data < 0.80 : return 15
-    elif data < 0.85 : return 16
-    elif data < 0.90 : return 17
-    elif data < 0.95 : return 18
-    else : return 19
-
-    
-# dataframe을 만들고 기본 세팅하는 함수 (1분 소요)
-def get_df() :
-    dtype = {
-        'userID': 'int16',
-        'answerCode': 'int8',
-        'KnowledgeTag': 'int16'
-    }
-
-    DATA_PATH = '/opt/ml/input/data/train_dataset/train_data.csv'
-    TEST_DATA_PATH = '/opt/ml/input/data/train_dataset/test_data.csv'
-
-    train_df = pd.read_csv(DATA_PATH, dtype=dtype, parse_dates=['Timestamp'])
-    train_df = train_df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    test_df = pd.read_csv(TEST_DATA_PATH, dtype=dtype, parse_dates=['Timestamp'])
-    test_df = test_df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    
-    train_df['is_test'] = False
-    test_df['is_test'] = True
-    
-    df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
-    df['next_userID'] = df['userID'].shift(-1).fillna(9999)
-    
-    def answer_masking(df):
-        if df['userID'] != df['next_userID']:
-            return 1 if random.random() < 0.5 else 0
-        else:
-            return df['answerCode']
-    df['masked_answer'] = df.apply(answer_masking, axis=1)
-    
-    return df
-
-
-# 문제의 난이도 Feature
-def get_question_grade(df): 
-    tmp_df = df.groupby('assessmentItemID')['masked_answer'].mean().reset_index()
-    tmp_df.columns = ['assessmentItemID', 'question_grade']
-    tmp_df['question_grade'] = tmp_df['question_grade'].apply(make_grade)
-    df = pd.merge(left=df, right=tmp_df, on=['assessmentItemID'], how='left')
-    return df
-
-
-# 문제의 번호 Feature
-def get_question_order(df) :
-    df['question_order'] = df['assessmentItemID'].apply(lambda x : int(x[-3:]))
-    return df
-
-
-# 문제의 대분류 Feature
-def get_question_large_cate(df) :
-    df['question_large_cate'] = df.apply(lambda x : int(x['testId'][2]), axis=1)
-    return df
-
-
-# User가 해당 대분류의 문제를 몇번 풀었는지 Feature
-def get_userID_cnt_item_in_largeCate(df) :
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    if 'question_large_cate' not in df.columns :
-        df = get_question_large_cate(df)
-    tmp_df = df.copy()
-    tmp_df['tmp'] = tmp_df[["userID", "question_large_cate"]].apply(lambda data: str(data["userID"]) + "_" + str(data["question_large_cate"]), axis=1)
-    df['userID_cnt_item_in_largeCate'] = tmp_df.groupby('tmp')['assessmentItemID'].cumcount()
-    return df
-
-
-# User의 해당 대분류에 대한 정답률 Grade Feature
-def get_userID_answerRate_in_largeCate(df) :
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    if 'question_large_cate' not in df.columns :
-        df = get_question_large_cate(df)
-    tmp_df = df.copy()
-    tmp_df['tmp'] = tmp_df.apply(lambda data: str(data["userID"]) + "_" + str(data["question_large_cate"]), axis=1)
-    tmp_df['answers'] = tmp_df.groupby("tmp")["masked_answer"].transform(lambda x: x.cumsum().shift(1)).fillna(0)
-    df['userID_answerRate_in_largeCate'] = (tmp_df['answers']/tmp_df['userID_cnt_item_in_largeCate']).fillna(1)
-    df['userID_answerRate_in_largeCate'] = df['userID_answerRate_in_largeCate'].apply(make_grade)
-    return df
-
-
-# User의 해당 Tag에 대한 정답률 Grade Feature
-def get_userID_answerRate_in_tag(df) :
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    tmp_df = df.copy()
-    tmp_df['tmp'] = tmp_df.apply(lambda data: str(data["userID"]) + "_" + str(data["KnowledgeTag"]), axis=1)
-    tmp_df['answers'] = tmp_df.groupby("tmp")["masked_answer"].transform(lambda x: x.cumsum().shift(1)).fillna(0)
-    tmp_df['userID_cnt_item_in_tag'] = tmp_df.groupby('tmp')['assessmentItemID'].cumcount()
-    df['userID_answerRate_in_tag'] = (tmp_df['answers']/tmp_df['userID_cnt_item_in_tag']).fillna(1)
-    df['userID_answerRate_in_tag'] = df['userID_answerRate_in_tag'].apply(make_grade)
-    return df
-
-
-# User가 해당 문제를 풀어본 경험 Feature
-def get_userID_question_experience(df):
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    df['userID_question_experience'] = df.groupby(["userID", "assessmentItemID"])['assessmentItemID'].cumcount()
-    return df
-
-
-# User가 문제를 순서대로 접근하는지 Feature
-def get_question_solve_order(df) :
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    if 'question_order' not in df.columns :
-        df = get_question_order(df)
-    tmp_df = df.loc[:, ['userID', 'testId', 'question_order']].groupby(['userID', 'testId']).diff().fillna(1)
-    df['question_solve_order'] = tmp_df['question_order'].apply(lambda x : 1 if x == 1 else 0)
-    return df
-
-
-# 문제를 푸는데 걸리는 시간 Feature (UserID와 TestID 기준으로 구하고, Max 및 nan 값은 125로 사용)
-def get_userID_elapsed_by_test_125(df) :
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    diff = df.loc[:, ['userID', 'testId', 'Timestamp']].groupby(['userID','testId']).diff().shift(-1).fillna(pd.Timedelta(seconds=0))
-    diff = diff.fillna(pd.Timedelta(seconds=0))
-    diff = diff['Timestamp'].apply(lambda x: x.total_seconds())
-    df['elapsed_solving'] = diff
-    df['elapsed_solving'] = df['elapsed_solving'].apply(lambda x : 125 if x == 0 or x > 125 else x)
-    return df
-
-
-# User가 문제를 풀때 걸리는 시간의 중앙값 Feature
-def get_userID_elapsed_median(df) :
-    if 'elapsed_solving' not in df.columns :
-        df = get_userID_elapsed_by_test_125(df)
-    tmp_df = df.groupby('userID')['elapsed_solving'].median()
-    tmp_df.name = 'userID_elapsed_median'
-    tmp_df = tmp_df.reset_index()
-    df = pd.merge(left=df, right=tmp_df, on='userID', how='left')
-    return df
-
-
-# 문제를 맞춘 사람과 못맞춘 사람이 걸리는 시간의 중앙값 Feature
-def get_question_elapsed_median(df) :
-    tmp_df = df.groupby(['assessmentItemID','masked_answer']).agg({'elapsed_solving':'median'})
-    tmp_df = tmp_df.reset_index()
-    tmp_df_correct = tmp_df[tmp_df['masked_answer']==1]
-    tmp_df_incorrect = tmp_df[tmp_df['masked_answer']==0]
-    tmp_df_correct.columns = ['assessmentItemID', 'masked_answer', 'question_correct_elapsed_median']
-    tmp_df_incorrect.columns = ['assessmentItemID', 'masked_answer', 'question_incorrect_elapsed_median']
-    tmp_df_correct = tmp_df_correct.drop('masked_answer', axis=1)
-    tmp_df_incorrect = tmp_df_incorrect.drop('masked_answer', axis=1)
-    df = pd.merge(left=df, right=tmp_df_correct, on=['assessmentItemID'], how='left')
-    df = pd.merge(left=df, right=tmp_df_incorrect, on=['assessmentItemID'], how='left')
-    return df
-
-
-# 문제를 접근한 날짜 Feature (월단위)
-def get_question_solve_month(df) :
-    df["question_solve_month"] = df["Timestamp"].apply(lambda x: x.month)
-    return df
-
-
-# User가 이전에 몇 문제를 풀었는지 Feature
-def get_userID_cnt_items(df) :
-    df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)
-    df['userID_cnt_items'] = df.groupby("userID")["assessmentItemID"].cumcount()
-    return df
-
-
-# User가 이전에 몇개의 시험지를 풀었는지 Feature
-def get_userID_cnt_tests(df) :
-    tmp_df = df[['userID','testId']]
-    tmp_df = tmp_df.drop_duplicates()
-    tmp_df['userID_cnt_tests'] = tmp_df.groupby('userID')['testId'].cumcount()
-    df = pd.merge(left=df, right=tmp_df, on=['userID','testId'], how='left')
-    return df
-
-
-# User가 이전에 몇개의 Tag를 풀었는지 Feature
-def get_userID_cnt_tags(df) :
-    tmp_df = df[['userID','KnowledgeTag']]
-    tmp_df = tmp_df.drop_duplicates()
-    tmp_df['userID_cnt_tags'] = tmp_df.groupby('userID')['KnowledgeTag'].cumcount()
-    df = pd.merge(left=df, right=tmp_df, on=['userID','KnowledgeTag'], how='left')
-    return df
-
-
-# Dataframe부터, 전체 Feature Engineering까지 수행되는 Code
-def get_all() :
-	df = get_df()
-	df = get_question_grade(df)
-	df = get_question_order(df)
-	df = get_question_large_cate(df)
-	df = get_userID_cnt_item_in_largeCate(df)
-	df = get_userID_answerRate_in_largeCate(df)
-	df = get_userID_answerRate_in_tag(df)
-	df = get_userID_question_experience(df)
-	df = get_question_solve_order(df)
-	df = get_userID_elapsed_by_test_125(df)
-	df = get_userID_elapsed_median(df)
-	df = get_question_elapsed_median(df)
-	df = get_question_solve_month(df)
-	df = get_userID_cnt_items(df)
-	df = get_userID_cnt_tests(df)
-	df = get_userID_cnt_tags(df)
-	return df
diff --git a/code/baseline/inference.py b/code/baseline/inference.py
deleted file mode 100644
index 246f906..0000000
--- a/code/baseline/inference.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import os
-from args import parse_args
-from dkt.dataloader import Preprocess
-from dkt import trainer
-import torch
-def main(args):
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    args.device = device
-
-    args.data_dir = os.environ.get('SM_CHANNEL_EVAL', args.data_dir)
-    args.model_dir = os.environ.get('SM_CHANNEL_MODEL', args.model_dir)
-    args.output_dir = os.environ.get('SM_OUTPUT_DATA_DIR ', args.output_dir)
-    preprocess = Preprocess(args)
-    preprocess.load_test_data(args.test_file_name)
-    test_data = preprocess.get_test_data()
-    
-
-    trainer.inference(args, test_data)
-    
-
-if __name__ == "__main__":
-    args = parse_args(mode='train')
-    os.makedirs(args.model_dir, exist_ok=True)
-    main(args)
\ No newline at end of file
diff --git a/code/baseline/lgbm_baseline.ipynb b/code/baseline/lgbm_baseline.ipynb
deleted file mode 100644
index 514f51d..0000000
--- a/code/baseline/lgbm_baseline.ipynb
+++ /dev/null
@@ -1,582 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LGBM Baseline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-05-24T09:49:29.375544Z",
-     "start_time": "2021-05-24T09:49:28.999092Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "import random\n",
-    "import warnings\n",
-    "import lightgbm as lgb\n",
-    "from wandb.lightgbm import wandb_callback\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from matplotlib import pylab as plt\n",
-    "from lgbm_function import inference, set_params, custom_train_test_split\n",
-    "from feature_engineering import feature_engineering\n",
-    "from datetime import datetime\n",
-    "import wandb\n",
-    "\n",
-    "%matplotlib inline\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. 데이터 로딩"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-05-24T09:49:29.678737Z",
-     "start_time": "2021-05-24T09:49:29.376581Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2266586, 6)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>userID</th>\n",
-       "      <th>assessmentItemID</th>\n",
-       "      <th>testId</th>\n",
-       "      <th>answerCode</th>\n",
-       "      <th>Timestamp</th>\n",
-       "      <th>KnowledgeTag</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>A060001001</td>\n",
-       "      <td>A060000001</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2020-03-24 00:17:11</td>\n",
-       "      <td>7224</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>A060001002</td>\n",
-       "      <td>A060000001</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2020-03-24 00:17:14</td>\n",
-       "      <td>7225</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>A060001003</td>\n",
-       "      <td>A060000001</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2020-03-24 00:17:22</td>\n",
-       "      <td>7225</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>A060001004</td>\n",
-       "      <td>A060000001</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2020-03-24 00:17:29</td>\n",
-       "      <td>7225</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>A060001005</td>\n",
-       "      <td>A060000001</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2020-03-24 00:17:36</td>\n",
-       "      <td>7225</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   userID assessmentItemID      testId  answerCode           Timestamp  \\\n",
-       "0       0       A060001001  A060000001           1 2020-03-24 00:17:11   \n",
-       "1       0       A060001002  A060000001           1 2020-03-24 00:17:14   \n",
-       "2       0       A060001003  A060000001           1 2020-03-24 00:17:22   \n",
-       "3       0       A060001004  A060000001           1 2020-03-24 00:17:29   \n",
-       "4       0       A060001005  A060000001           1 2020-03-24 00:17:36   \n",
-       "\n",
-       "   KnowledgeTag  \n",
-       "0          7224  \n",
-       "1          7225  \n",
-       "2          7225  \n",
-       "3          7225  \n",
-       "4          7225  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_dir = '/opt/ml/input/data/train_dataset'\n",
-    "csv_file_path = os.path.join(data_dir, 'train_data.csv')\n",
-    "df = pd.read_csv(csv_file_path, parse_dates=['Timestamp'])\n",
-    "print(df.shape)\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Feature Engineering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-05-24T09:49:29.683739Z",
-     "start_time": "2021-05-24T09:49:28.981Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "df = feature_engineering(df)\n",
-    "df.head(2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Cross Validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 유저별 분리\n",
-    "train_lst, test_lst = custom_train_test_split(df)\n",
-    "\n",
-    "# 사용할 Feature 설정\n",
-    "FEATS = [\"user_acc\", \"user_mean\", \"user_count\", \"user_correct_answer\", \"question_mean\", \"question_class_mean\"]\n",
-    "\n",
-    "# set parameters\n",
-    "params = set_params()\n",
-    "\n",
-    "# \"test_sum\", \"question_class_count\", \"tag_sum\", \"question_count\", \"tag_mean\", \"test_mean\",\n",
-    "\n",
-    "for fold_num, (train, test) in enumerate(zip(train_lst, test_lst)):\n",
-    "    print(\"@\"*50)\n",
-    "    print(fold_num, \"번째 fold\")\n",
-    "    print(\"@\"*50)\n",
-    "    \n",
-    "    # X, y 값 분리\n",
-    "    y_train = train[\"answerCode\"]\n",
-    "    train = train.drop([\"answerCode\"], axis=1)\n",
-    "\n",
-    "    y_test = test[\"answerCode\"]\n",
-    "    test = test.drop([\"answerCode\"], axis=1)\n",
-    "    \n",
-    "    print(\"=\"*30)\n",
-    "    print(\"train, test shape\")\n",
-    "    print(train.shape, test.shape)\n",
-    "    print(\"=\"*30)\n",
-    "    print()\n",
-    "    \n",
-    "    lgb_train = lgb.Dataset(train[FEATS], y_train)\n",
-    "    lgb_test = lgb.Dataset(test[FEATS], y_test)\n",
-    "    \n",
-    "    now = datetime.now()\n",
-    "    wandb.init(project='P4-DKT', config=params, entity=\"team-ikyo\")\n",
-    "    wandb.run.name = \"sun-lgbm-fold\" + str(fold_num) + \" time: \" + \" \".join(map(str, [now.month, now.day, now.hour, now.minute]))\n",
-    "    \n",
-    "    # train\n",
-    "    model = lgb.train(params,\n",
-    "                      lgb_train,\n",
-    "                      valid_sets = [lgb_train, lgb_test],\n",
-    "                      verbose_eval = 100,\n",
-    "                      callbacks=[wandb_callback()])\n",
-    "\n",
-    "    preds = model.predict(test[FEATS])\n",
-    "    acc = accuracy_score(y_test, np.where(preds >= 0.5, 1, 0))\n",
-    "    auc = roc_auc_score(y_test, preds)\n",
-    "\n",
-    "    print(f'VALID AUC : {auc} ACC : {acc}\\n')\n",
-    "    \n",
-    "    # show feature importance\n",
-    "    fig, ax = plt.subplots(figsize=(6,12))\n",
-    "    lgb.plot_importance(model, max_num_features=100, height=0.8, ax=ax)\n",
-    "    plt.show()\n",
-    "    \n",
-    "    # inference\n",
-    "    inference(FEATS, model, auc, acc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Result to csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception in thread Thread-7:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/opt/conda/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
-      "    self.run()\n",
-      "  File \"/opt/conda/lib/python3.7/threading.py\", line 870, in run\n",
-      "    self._target(*self._args, **self._kwargs)\n",
-      "  File \"/opt/conda/lib/python3.7/site-packages/wandb/sdk/wandb_run.py\", line 180, in check_network_status\n",
-      "    status_response = self._interface.communicate_network_status()\n",
-      "  File \"/opt/conda/lib/python3.7/site-packages/wandb/sdk/interface/interface.py\", line 747, in communicate_network_status\n",
-      "    resp = self._communicate(req, timeout=timeout, local=True)\n",
-      "  File \"/opt/conda/lib/python3.7/site-packages/wandb/sdk/interface/interface.py\", line 537, in _communicate\n",
-      "    return self._communicate_async(rec, local=local).get(timeout=timeout)\n",
-      "  File \"/opt/conda/lib/python3.7/site-packages/wandb/sdk/interface/interface.py\", line 542, in _communicate_async\n",
-      "    raise Exception(\"The wandb backend process has shutdown\")\n",
-      "Exception: The wandb backend process has shutdown\n",
-      "\n",
-      "Exception in thread Thread-6:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/opt/conda/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
-      "    self.run()\n",
-      "  File \"/opt/conda/lib/python3.7/threading.py\", line 870, in run\n",
-      "    self._target(*self._args, **self._kwargs)\n",
-      "  File \"/opt/conda/lib/python3.7/site-packages/wandb/sdk/wandb_run.py\", line 198, in check_status\n",
-      "    status_response = self._interface.communicate_stop_status()\n",
-      "  File \"/opt/conda/lib/python3.7/site-packages/wandb/sdk/interface/interface.py\", line 735, in communicate_stop_status\n",
-      "    resp = self._communicate(req, timeout=timeout, local=True)\n",
-      "  File \"/opt/conda/lib/python3.7/site-packages/wandb/sdk/interface/interface.py\", line 537, in _communicate\n",
-      "    return self._communicate_async(rec, local=local).get(timeout=timeout)\n",
-      "  File \"/opt/conda/lib/python3.7/site-packages/wandb/sdk/interface/interface.py\", line 542, in _communicate_async\n",
-      "    raise Exception(\"The wandb backend process has shutdown\")\n",
-      "Exception: The wandb backend process has shutdown\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from glob import glob\n",
-    "import pandas as pd\n",
-    "\n",
-    "output_path = \"/opt/ml/code/output/cross_validation/output.csv\"\n",
-    "csv_file_path_list = glob(\"/opt/ml/code/output/*.csv\")\n",
-    "print(csv_file_path_list)\n",
-    "\n",
-    "# concat result dataframe\n",
-    "result = pd.read_csv(csv_file_path_list[0])[\"prediction\"]\n",
-    "for csv_file_path in csv_file_path_list[1:]:\n",
-    "    result = pd.concat([result, pd.read_csv(csv_file_path)[\"prediction\"]], axis=1)\n",
-    "\n",
-    "# mean result dataframe\n",
-    "result = pd.DataFrame(result.mean(axis=1)).reset_index().rename(columns = {0:\"prediction\", \"index\":\"id\"})\n",
-    "result.to_csv(output_path, index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Grid Search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 485,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "FEATS = [\"user_correct_answer\", \"time_difference\",\n",
-    "             \"user_acc\", \"test_mean\", \"test_sum\", \n",
-    "             \"tag_mean\", \"tag_sum\", \"user_mean\", \"user_count\",\n",
-    "             \"question_mean\", \"question_count\", \"question_class_mean\", \"question_class_count\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 488,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid_FEATS = [[\"user_correct_answer\", \"time_difference\",\n",
-    "             \"user_acc\", \"test_mean\", \"test_sum\", \n",
-    "             \"tag_mean\", \"tag_sum\", \"user_mean\", \"user_count\",\n",
-    "             \"question_mean\", \"question_count\", \"question_class_mean\", \"question_class_count\"]]\n",
-    "\n",
-    "for comb_num in range(6, 13, 2):\n",
-    "    for features in list(combinations(FEATS, comb_num)):\n",
-    "        grid_FEATS.append(list(features))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 367,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for FEATS in grid_FEATS:\n",
-    "#     # 유저별 분리\n",
-    "#     train, test = custom_train_test_split(df)\n",
-    "\n",
-    "#     # X, y 값 분리\n",
-    "#     y_train = train['answerCode']\n",
-    "#     train = train.drop(['answerCode'], axis=1)\n",
-    "\n",
-    "#     y_test = test['answerCode']\n",
-    "#     test = test.drop(['answerCode'], axis=1)\n",
-    "    \n",
-    "#     params = {}\n",
-    "#     params[\"boosting_type\"] = \"gbdt\" # gbdt, dart, goss\n",
-    "#     params[\"learning_rate\"] = 1e-1 # 1e-1, 5e-2, 1e-2, 5e-3, 1e-3\n",
-    "#     params[\"objective\"] = \"binary\"\n",
-    "#     params[\"metric\"] = \"auc\" # binary_logloss, rmse, huber, auc\n",
-    "#     params[\"num_iterations\"] = 1000 # 100\n",
-    "#     params[\"max_depth\"] = 5 # -1\n",
-    "#     params[\"num_leaves\"] = 10 # 31 이상적으로 num_leaves값은 2 ^ (max_depth) 값보다 적거나 같아야 합니다.\n",
-    "#     params[\"min_data_in_leaf\"] = 10000 # 20 100 ~ 1000 수백 또는 수천 개로 정하는 것\n",
-    "#     params[\"max_bin\"] = 16 # 256\n",
-    "#     params[\"min_split_gain\"] = 1e-2 # ?\n",
-    "#     params[\"scale_pos_weight\"] = 1.1 # 1.1~1.5\n",
-    "#     params[\"tree_learner\"] = \"serial\" # serial, feature, data, voting\n",
-    "#     params[\"early_stopping_rounds\"] = 50\n",
-    "#     params[\"bagging_fraction\"] = 0.8 # 1.0\n",
-    "#     params[\"lambda_l1\"] = 1e-1 # 0.0\n",
-    "#     params[\"lambda_l2\"] = 1e-1 # 0.0\n",
-    "\n",
-    "#     print(\"=\"*30)\n",
-    "#     print(\"=\"*30)\n",
-    "#     print(FEATS)\n",
-    "#     print(\"|\"*30)\n",
-    "#     print(params)\n",
-    "#     print(\"|\"*30)\n",
-    "#     lgb_train = lgb.Dataset(train[FEATS], y_train)\n",
-    "#     lgb_test = lgb.Dataset(test[FEATS], y_test)\n",
-    "\n",
-    "#     model = lgb.train(params,\n",
-    "#                       lgb_train,\n",
-    "#                       valid_sets = [lgb_train, lgb_test],\n",
-    "#                       verbose_eval = 500)\n",
-    "\n",
-    "#     preds = model.predict(test[FEATS])\n",
-    "#     acc = accuracy_score(y_test, np.where(preds >= 0.5, 1, 0))\n",
-    "#     auc = roc_auc_score(y_test, preds)\n",
-    "\n",
-    "#     print(f'VALID AUC : {auc} ACC : {acc}\\n')\n",
-    "\n",
-    "#     # LOAD TESTDATA\n",
-    "#     test_csv_file_path = os.path.join(data_dir, 'test_data.csv')\n",
-    "#     test_df = pd.read_csv(test_csv_file_path, parse_dates=['Timestamp'])\n",
-    "\n",
-    "#     # FEATURE ENGINEERING\n",
-    "#     test_df = feature_engineering(test_df)\n",
-    "\n",
-    "#     # LEAVE LAST INTERACTION ONLY\n",
-    "#     test_df = test_df[test_df['userID'] != test_df['userID'].shift(-1)]\n",
-    "\n",
-    "#     # DROP ANSWERCODE\n",
-    "#     test_df = test_df.drop(['answerCode'], axis=1)\n",
-    "\n",
-    "#     # MAKE PREDICTION\n",
-    "#     total_preds = model.predict(test_df[FEATS])\n",
-    "\n",
-    "#     # SAVE OUTPUT\n",
-    "#     output_dir = 'output/'\n",
-    "#     write_path = os.path.join(output_dir, f\"lgbm/output_VALID_AUC_{round(auc, 4)}_ACC_{round(acc, 4)}.csv\")\n",
-    "#     if not os.path.exists(output_dir):\n",
-    "#         os.makedirs(output_dir)    \n",
-    "#     with open(write_path, 'w', encoding='utf8') as w:\n",
-    "#         print(\"writing prediction : {}\".format(write_path))\n",
-    "#         w.write(\"id,prediction\\n\")\n",
-    "#         for id, p in enumerate(total_preds):\n",
-    "#             w.write('{},{}\\n'.format(id,p))\n",
-    "#     print(\"=\"*30)\n",
-    "#     print(\"=\"*30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.7.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/code/baseline/new_model.py b/code/baseline/new_model.py
deleted file mode 100644
index 70f201a..0000000
--- a/code/baseline/new_model.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from transformers.modeling_bert import BertConfig, BertEncoder, BertModel    
-except:
-    from transformers.models.bert.modeling_bert import BertConfig, BertEncoder, BertModel    
-
-
-class LSTMATTN(nn.Module):
-
-    def __init__(self, args):
-        super(LSTMATTN, self).__init__()
-        self.args = args
-        self.device = args.device
-
-        self.hidden_dim = self.args.hidden_dim
-        self.n_layers = self.args.n_layers
-        self.n_heads = self.args.n_heads
-        self.drop_out = self.args.drop_out
-
-        # Embedding 
-        # interaction은 현재 correct로 구성되어있다. correct(1, 2) + padding(0)
-        self.embedding_interaction = nn.Embedding(3, self.hidden_dim//3)
-        self.embedding_test = nn.Embedding(self.args.n_test + 1, self.hidden_dim//3)
-        self.embedding_question = nn.Embedding(self.args.n_questions + 1, self.hidden_dim//3)
-        self.embedding_tag = nn.Embedding(self.args.n_tag + 1, self.hidden_dim//3)
-
-        # embedding combination projection
-        self.comb_proj = nn.Linear((self.hidden_dim//3)*4, self.hidden_dim)
-
-        self.lstm = nn.LSTM(self.hidden_dim,
-                            self.hidden_dim,
-                            self.n_layers,
-                            batch_first=True)
-        
-        self.config = BertConfig( 
-            3, # not used
-            hidden_size=self.hidden_dim,
-            num_hidden_layers=1,
-            num_attention_heads=self.n_heads,
-            intermediate_size=self.hidden_dim,
-            hidden_dropout_prob=self.drop_out,
-            attention_probs_dropout_prob=self.drop_out,
-        )
-        self.attn = BertEncoder(self.config)            
-    
-        # Fully connected layer
-        self.fc = nn.Linear(self.hidden_dim, 1)
-
-        self.activation = nn.Sigmoid()
-
-    def init_hidden(self, batch_size):
-        h = torch.zeros(
-            self.n_layers,
-            batch_size,
-            self.hidden_dim)
-        h = h.to(self.device)
-
-        c = torch.zeros(
-            self.n_layers,
-            batch_size,
-            self.hidden_dim)
-        c = c.to(self.device)
-
-        return (h, c)
-
-    def forward(self, input):
-
-        test, question, tag, _, mask, interaction, _ = input
-
-        batch_size = interaction.size(0)
-
-        # Embedding
-
-        embed_interaction = self.embedding_interaction(interaction)
-        embed_test = self.embedding_test(test)
-        embed_question = self.embedding_question(question)
-        embed_tag = self.embedding_tag(tag)
-        
-
-        embed = torch.cat([embed_interaction,
-                           embed_test,
-                           embed_question,
-                           embed_tag,], 2)
-
-        X = self.comb_proj(embed)
-
-        hidden = self.init_hidden(batch_size)
-        out, hidden = self.lstm(X, hidden)
-        out = out.contiguous().view(batch_size, -1, self.hidden_dim)
-                
-        extended_attention_mask = mask.unsqueeze(1).unsqueeze(2)
-        extended_attention_mask = extended_attention_mask.to(dtype=torch.float32)
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-        head_mask = [None] * self.n_layers
-        
-        encoded_layers = self.attn(out, extended_attention_mask, head_mask=head_mask)        
-        sequence_output = encoded_layers[-1]
-        
-        out = self.fc(sequence_output)
-
-        preds = self.activation(out).view(batch_size, -1)
-
-        return preds
-
-
-class Bert(nn.Module):
-
-    def __init__(self, args):
-        super(Bert, self).__init__()
-        self.args = args
-        self.device = args.device
-
-        # Defining some parameters
-        self.hidden_dim = self.args.hidden_dim
-        self.n_layers = self.args.n_layers
-
-        # Embedding 
-        # interaction은 현재 correct으로 구성되어있다. correct(1, 2) + padding(0)
-        self.embedding_interaction = nn.Embedding(3, self.hidden_dim//3)
-        self.embedding_test = nn.Embedding(self.args.n_test + 1, self.hidden_dim//3)
-        self.embedding_question = nn.Embedding(self.args.n_questions + 1, self.hidden_dim//3)
-        self.embedding_tag = nn.Embedding(self.args.n_tag + 1, self.hidden_dim//3)
-
-        # embedding combination projection
-        self.comb_proj = nn.Linear((self.hidden_dim//3)*4, self.hidden_dim)
-
-        # Bert config
-        self.config = BertConfig( 
-            3, # not used
-            hidden_size=self.hidden_dim,
-            num_hidden_layers=self.args.n_layers,
-            num_attention_heads=self.args.n_heads,
-            max_position_embeddings=self.args.max_seq_len          
-        )
-
-        # Defining the layers
-        # Bert Layer
-        self.encoder = BertModel(self.config)  
-
-        # Fully connected layer
-        self.fc = nn.Linear(self.args.hidden_dim, 1)
-       
-        self.activation = nn.Sigmoid()
-
-
-    def forward(self, input):
-        test, question, tag, _, mask, interaction, _ = input
-        batch_size = interaction.size(0)
-
-        # 신나는 embedding
-        
-        embed_interaction = self.embedding_interaction(interaction)
-        embed_test = self.embedding_test(test)
-        embed_question = self.embedding_question(question)
-        embed_tag = self.embedding_tag(tag)
-
-        embed = torch.cat([embed_interaction,
-        
-                           embed_test,
-                           embed_question,
-        
-                           embed_tag,], 2)
-
-        X = self.comb_proj(embed)
-
-        # Bert
-        encoded_layers = self.encoder(inputs_embeds=X, attention_mask=mask)
-        out = encoded_layers[0]
-        out = out.contiguous().view(batch_size, -1, self.hidden_dim)
-        out = self.fc(out)
-        preds = self.activation(out).view(batch_size, -1)
-
-        return preds
\ No newline at end of file
diff --git a/code/baseline/requirements.txt b/code/baseline/requirements.txt
deleted file mode 100644
index c2bd6f9..0000000
--- a/code/baseline/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-torch
-pandas
-sklearn
-tqdm
-wandb
diff --git a/code/baseline/train.py b/code/baseline/train.py
deleted file mode 100644
index 1cc3bfc..0000000
--- a/code/baseline/train.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import os
-from args import parse_args
-from dkt.dataloader import Preprocess
-from dkt import trainer
-import torch
-from dkt.utils import setSeeds
-import wandb
-def main(args):
-    wandb.login()
-    
-    setSeeds(args.seed)
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    args.device = device
-
-    args.data_dir = os.environ.get('SM_CHANNEL_TRAIN', args.data_dir)
-    args.model_dir = os.environ.get('SM_MODEL_DIR', args.model_dir)
-
-
-    preprocess = Preprocess(args)
-    preprocess.load_train_data(args.file_name)
-    train_data = preprocess.get_train_data()
-    
-    train_data, valid_data = preprocess.split_data(train_data)
-    
-    wandb.init(project='P4-DKT', entity='team-ikyo', name=args.run_name, config=vars(args))
-    trainer.run(args, train_data, valid_data)
-    
-
-if __name__ == "__main__":
-    args = parse_args(mode='train')
-    os.makedirs(args.model_dir, exist_ok=True)
-    main(args)