Merge pull request #33 from boostcampaitech6/feat/32-VASP_baseline

Feat:Implement VASP_baseline #32

notebooks/ksj/EASE.ipynb

@@ -0,0 +1,224 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from scipy.sparse import csr_matrix\n",
+    "from datetime import datetime"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 실험용"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 데이터 로드\n",
+    "data_file = \"/opt/ml/input/data/train/train_ratings.csv\"\n",
+    "rating_df = pd.read_csv(data_file)\n",
+    "\n",
+    "# user와 item에 대한 고유한 인덱스 생성\n",
+    "user_list = rating_df['user'].unique()\n",
+    "item_list = rating_df['item'].unique()\n",
+    "\n",
+    "user_to_index = {user: index for index, user in enumerate(user_list)}\n",
+    "item_to_index = {item: index for index, item in enumerate(item_list)}\n",
+    "\n",
+    "# 데이터셋에서 user, item을 인덱스로 변환\n",
+    "rating_df['user'] = rating_df['user'].map(user_to_index)\n",
+    "rating_df['item'] = rating_df['item'].map(item_to_index)\n",
+    "\n",
+    "num_users = len(user_to_index)\n",
+    "num_items = len(item_to_index)\n",
+    "\n",
+    "# csr_matrix 생성\n",
+    "rows = rating_df['user'].values\n",
+    "cols = rating_df['item'].values\n",
+    "\n",
+    "rating_score = 0.9\n",
+    "data = np.full(len(rating_df), rating_score)\n",
+    "rating_matrix = csr_matrix((data, (rows, cols)), shape=(num_users, num_items))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class EASE:\n",
+    "    def __init__(self, _lambda):\n",
+    "        self.B = None\n",
+    "        self._lambda = _lambda\n",
+    "\n",
+    "    def train(self, X):\n",
+    "        try:\n",
+    "            # csr_matrix --> dense\n",
+    "            X_dense = X.toarray()\n",
+    "            print(\"X shape:\", X_dense.shape)\n",
+    "            \n",
+    "            G = X_dense.T @ X_dense\n",
+    "            print(\"G shape:\", G.shape)\n",
+    "            \n",
+    "            diag_indices = np.diag_indices_from(G)\n",
+    "            G[diag_indices] += self._lambda\n",
+    "            print(\"G after adding lambda on diag:\", G.shape)\n",
+    "            \n",
+    "            # dense --> pseudo-inverse 계산\n",
+    "            P = np.linalg.pinv(G)\n",
+    "            print(\"P shape:\", P.shape)\n",
+    "            \n",
+    "            self.B = P / -np.diag(P)\n",
+    "            self.B[diag_indices] = 0\n",
+    "            print(\"Final B shape:\", self.B.shape)\n",
+    "        except Exception as e:\n",
+    "            print(\"Error occurred:\", e)\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        # csr_matrix --> dense\n",
+    "        X_dense = X.toarray() if isinstance(X, csr_matrix) else X\n",
+    "        return X_dense @ self.B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape: (31360, 6807)\n",
+      "G shape: (6807, 6807)\n",
+      "G after adding lambda on diag: (6807, 6807)\n",
+      "P shape: (6807, 6807)\n",
+      "Final B shape: (6807, 6807)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EASE 모델 학습\n",
+    "_lambda = 750\n",
+    "ease = EASE(_lambda)\n",
+    "ease.train(rating_matrix)\n",
+    "\n",
+    "# 예측\n",
+    "predictions = ease.predict(rating_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 상위 N개 아이템 추출\n",
+    "\n",
+    "N = 10\n",
+    "top_n_items_per_user = []\n",
+    "\n",
+    "# 이미 평가한 아이템은 제외\n",
+    "predictions[rating_matrix.nonzero()] = -np.inf\n",
+    "\n",
+    "for user_idx in range(predictions.shape[0]):\n",
+    "    user_predictions = predictions[user_idx, :]\n",
+    "    top_n_indices = np.argpartition(user_predictions, -N)[-N:]\n",
+    "    top_n_indices_sorted = top_n_indices[np.argsort(user_predictions[top_n_indices])[::-1]]\n",
+    "    top_n_items_per_user.append(top_n_indices_sorted)\n",
+    "\n",
+    "# 인덱스를 실제 아이템 ID로 변환\n",
+    "index_to_item = {index: item for item, index in item_to_index.items()}\n",
+    "top_n_items_per_user_ids = [[index_to_item[idx] for idx in user_items] for user_items in top_n_items_per_user]\n",
+    "\n",
+    "# 제출 파일 생성\n",
+    "result = []\n",
+    "for user_id, items in zip(user_list, top_n_items_per_user_ids):\n",
+    "    for item_id in items:\n",
+    "        result.append((user_id, item_id))\n",
+    "\n",
+    "# 저장\n",
+    "current_time = datetime.now().strftime('%Y%m%d-%H%M%S')\n",
+    "args_str = f\"submission_EASE-lam{_lambda}_tm{current_time}\"\n",
+    "submission_df = pd.DataFrame(result, columns=['user', 'item'])\n",
+    "submission_df.to_csv(f\"{args_str}.csv\", index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 하위 N개 아이템 추출\n",
+    "\n",
+    "N = 80\n",
+    "bottom_n_items_per_user = []  # 각 사용자별로 하위 N개 아이템의 인덱스를 저장할 리스트\n",
+    "\n",
+    "for user_idx in range(predictions.shape[0]):\n",
+    "    user_predictions = predictions[user_idx, :]\n",
+    "    bottom_n_indices = np.argpartition(user_predictions, N-1)[:N]\n",
+    "    bottom_n_indices_sorted = bottom_n_indices[np.argsort(user_predictions[bottom_n_indices])]\n",
+    "    bottom_n_items_per_user.append(bottom_n_indices_sorted)\n",
+    "\n",
+    "\n",
+    "bottom_n_items_per_user = np.array(bottom_n_items_per_user)\n",
+    "\n",
+    "# 인덱스를 실제 아이템 ID로 변환\n",
+    "index_to_item = {index: item for item, index in item_to_index.items()}\n",
+    "bottom_n_items_per_user_ids = [[index_to_item[idx] for idx in user_items] for user_items in bottom_n_items_per_user]\n",
+    "\n",
+    "# 제출 파일 생성\n",
+    "result = []\n",
+    "for user_id, items in zip(user_list, bottom_n_items_per_user_ids):\n",
+    "    for item_id in items:\n",
+    "        result.append((user_id, item_id))\n",
+    "\n",
+    "# 저장\n",
+    "current_time = datetime.now().strftime('%Y%m%d-%H%M%S')\n",
+    "args_str = f\"Negative_EASE-lam{_lambda}_tm{current_time}\"\n",
+    "submission_df = pd.DataFrame(result, columns=['user', 'item'])\n",
+    "submission_df.to_csv(f\"{args_str}.csv\", index=False)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.18 ('EASE': conda)",
+   "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": "3.8.18"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "78251029becf62da8b58a8ef92d6c9c1752dac293e5a0befc81af987f28f6887"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}