From 67aa634436c1f3d121719812ece8ea42e2492966 Mon Sep 17 00:00:00 2001 From: Namwoo Kim Date: Thu, 2 Jan 2025 23:54:38 +0900 Subject: [PATCH 1/2] =?UTF-8?q?16=EC=A3=BC=EC=B0=A8=20=EC=98=88=EC=8A=B5?= =?UTF-8?q?=EA=B3=BC=EC=A0=9C=20=EC=A0=9C=EC=B6=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...352\271\200\353\202\250\354\232\260.ipynb" | 1155 +++++++++++++++++ ..._\352\271\200\353\202\250\354\232\260.pdf" | Bin 0 -> 7360373 bytes 2 files changed, 1155 insertions(+) create mode 100644 "Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\353\202\250\354\232\260.ipynb" create mode 100644 "Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\353\202\250\354\232\260.pdf" diff --git "a/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\353\202\250\354\232\260.ipynb" "b/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\353\202\250\354\232\260.ipynb" new file mode 100644 index 0000000..944f534 --- /dev/null +++ "b/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\353\202\250\354\232\260.ipynb" @@ -0,0 +1,1155 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Chapter 9. 추천 시스템" + ], + "metadata": { + "id": "c9iIx4Pl3-kJ" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 04. 잠재 요인 협업 필터링" + ], + "metadata": { + "id": "aTSEyfoy4JDp" + } + }, + { + "cell_type": "markdown", + "source": [ + "### 확률적 경사 하강법을 이용한 행렬 분해" + ], + "metadata": { + "id": "UeNQmVXU4Td4" + } + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "Ot_FAfJo33Kc" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# 원본 행렬 R 생성, 분해 행렬 P와 Q 초기화, 잠재 요인 차원 K는 3으로 결정\n", + "R = np.array([[4, np.NaN, np.NaN, 2, np.NaN],\n", + " [np.NaN, 5, np.NaN, 3, 1],\n", + " [np.NaN, np.NaN, 3, 4, 4],\n", + " [5, 2, 1, 2, np.NaN]])\n", + "num_users, num_items = R.shape\n", + "K = 3\n", + "\n", + "# P와 Q 행렬의 크기를 지정하고 정규 분포를 가진 임의의 값으로 입력\n", + "np.random.seed(1)\n", + "P = np.random.normal(scale=1./K, size=(num_users, K))\n", + "Q = np.random.normal(scale=1./K, size=(num_items, K))" + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "\n", + "def get_rmse(R, P, Q, non_zeros):\n", + " error = 0\n", + " # 두 개의 분해된 행렬 P와 Q.T의 내적으로 예측 R 행렬 생성\n", + " full_pred_matrix = np.dot(P, Q.T)\n", + "\n", + " # 실제 R 행렬에서 널이 아닌 값의 위치 인덱스 추출해 실제 R 행렬과 예측 행렬의 RMSE 추출\n", + " x_non_zero_ind = [non_zero[0] for non_zero in non_zeros]\n", + " y_non_zero_ind = [non_zero[1] for non_zero in non_zeros]\n", + " R_non_zeros = R[x_non_zero_ind, y_non_zero_ind]\n", + " full_pred_matrix_non_zeros = full_pred_matrix[x_non_zero_ind, y_non_zero_ind]\n", + " mse = mean_squared_error(R_non_zeros, full_pred_matrix_non_zeros)\n", + " rmse = np.sqrt(mse)\n", + "\n", + " return rmse" + ], + "metadata": { + "id": "Zjb63dcX493C" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# R > 0인 행 위치, 열 위치, 값을 non_zeros 리스트에 저장\n", + "non_zeros = [ (i, j, R[i,j]) for i in range(num_users) for j in range(num_items) if R[i, j] > 0]\n", + "\n", + "steps = 1000\n", + "learning_rate = 0.01\n", + "r_lambda = 0.01\n", + "\n", + "# SGD 기법으로 P와 Q 매트릭스 계속 업데이트\n", + "for step in range(steps):\n", + " for i, j, r in non_zeros:\n", + " # 실제 값과 예측 값의 차이인 오류 값 구함\n", + " eij = r - np.dot(P[i, :], Q[j, :].T)\n", + " # Regularization을 반영한 SGD 업데이트 공식 적용\n", + " P[i, :] = P[i, :] + learning_rate*(eij * Q[j, :] - r_lambda*P[i, :])\n", + " Q[j, :] = Q[j, :] + learning_rate*(eij * P[i, :] - r_lambda*Q[j, :])\n", + "\n", + " rmse = get_rmse(R, P, Q, non_zeros)\n", + " if (step % 50) == 0:\n", + " print(\"### iteration step : \", step,\" rmse : \", rmse)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YL95MRpw5law", + "outputId": "beb72939-d51f-44ae-ac91-656b90652c98" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "### iteration step : 0 rmse : 3.261355059488935\n", + "### iteration step : 0 rmse : 3.26040057174686\n", + "### iteration step : 0 rmse : 3.253984404542389\n", + "### iteration step : 0 rmse : 3.2521583839863624\n", + "### iteration step : 0 rmse : 3.252335303789125\n", + "### iteration step : 0 rmse : 3.251072196430487\n", + "### iteration step : 0 rmse : 3.2492449982564864\n", + "### iteration step : 0 rmse : 3.247416477570409\n", + "### iteration step : 0 rmse : 3.241926055455223\n", + "### iteration step : 0 rmse : 3.2400454107613084\n", + "### iteration step : 0 rmse : 3.240166740749792\n", + "### iteration step : 0 rmse : 3.2388050277987723\n", + "### iteration step : 50 rmse : 0.5003190892212748\n", + "### iteration step : 50 rmse : 0.5001616291326989\n", + "### iteration step : 50 rmse : 0.49899601202578087\n", + "### iteration step : 50 rmse : 0.4988483450145831\n", + "### iteration step : 50 rmse : 0.49895189256631756\n", + "### iteration step : 50 rmse : 0.49833236830090993\n", + "### iteration step : 50 rmse : 0.4984148489378701\n", + "### iteration step : 50 rmse : 0.49792599580240876\n", + "### iteration step : 50 rmse : 0.4900605568692785\n", + "### iteration step : 50 rmse : 0.4890370238665435\n", + "### iteration step : 50 rmse : 0.48869176023997846\n", + "### iteration step : 50 rmse : 0.4876723101369648\n", + "### iteration step : 100 rmse : 0.15911521988578564\n", + "### iteration step : 100 rmse : 0.1588091617801093\n", + "### iteration step : 100 rmse : 0.1587409221708901\n", + "### iteration step : 100 rmse : 0.1582856952842508\n", + "### iteration step : 100 rmse : 0.1583080948216876\n", + "### iteration step : 100 rmse : 0.15828832993767403\n", + "### iteration step : 100 rmse : 0.15787486893092847\n", + "### iteration step : 100 rmse : 0.15792073606567072\n", + "### iteration step : 100 rmse : 0.15725245215457084\n", + "### iteration step : 100 rmse : 0.15710664164665206\n", + "### iteration step : 100 rmse : 0.15690252144190003\n", + "### iteration step : 100 rmse : 0.1564340384819247\n", + "### iteration step : 150 rmse : 0.07546004875264435\n", + "### iteration step : 150 rmse : 0.07544589133447106\n", + "### iteration step : 150 rmse : 0.07543234329653023\n", + "### iteration step : 150 rmse : 0.07514800672233914\n", + "### iteration step : 150 rmse : 0.07518867696418177\n", + "### iteration step : 150 rmse : 0.0752288950993841\n", + "### iteration step : 150 rmse : 0.07489318864469259\n", + "### iteration step : 150 rmse : 0.07493400425933257\n", + "### iteration step : 150 rmse : 0.07462695506527872\n", + "### iteration step : 150 rmse : 0.07464332131959663\n", + "### iteration step : 150 rmse : 0.0746444164156341\n", + "### iteration step : 150 rmse : 0.07455141311978046\n", + "### iteration step : 200 rmse : 0.04361016579439073\n", + "### iteration step : 200 rmse : 0.04370913068953006\n", + "### iteration step : 200 rmse : 0.04369072102767977\n", + "### iteration step : 200 rmse : 0.043475549832271414\n", + "### iteration step : 200 rmse : 0.0435313092537358\n", + "### iteration step : 200 rmse : 0.04359240037575283\n", + "### iteration step : 200 rmse : 0.04329647906053838\n", + "### iteration step : 200 rmse : 0.04332057192123618\n", + "### iteration step : 200 rmse : 0.04310448294502512\n", + "### iteration step : 200 rmse : 0.04313550286658552\n", + "### iteration step : 200 rmse : 0.04313786864806258\n", + "### iteration step : 200 rmse : 0.04325226798579314\n", + "### iteration step : 250 rmse : 0.029395183185609734\n", + "### iteration step : 250 rmse : 0.02954402948437167\n", + "### iteration step : 250 rmse : 0.02950187436758184\n", + "### iteration step : 250 rmse : 0.029329609713572593\n", + "### iteration step : 250 rmse : 0.02940211807327667\n", + "### iteration step : 250 rmse : 0.02946720568417511\n", + "### iteration step : 250 rmse : 0.029189294191791375\n", + "### iteration step : 250 rmse : 0.029198757426747605\n", + "### iteration step : 250 rmse : 0.028995742260002243\n", + "### iteration step : 250 rmse : 0.02904415445054541\n", + "### iteration step : 250 rmse : 0.029049587101179365\n", + "### iteration step : 250 rmse : 0.029248328780878973\n", + "### iteration step : 300 rmse : 0.022678715233749362\n", + "### iteration step : 300 rmse : 0.022844873864300484\n", + "### iteration step : 300 rmse : 0.022773566650325074\n", + "### iteration step : 300 rmse : 0.02263234507322516\n", + "### iteration step : 300 rmse : 0.02272006255153119\n", + "### iteration step : 300 rmse : 0.022778917442558434\n", + "### iteration step : 300 rmse : 0.022516243062381223\n", + "### iteration step : 300 rmse : 0.022515508246519694\n", + "### iteration step : 300 rmse : 0.02229491665298542\n", + "### iteration step : 300 rmse : 0.022367287171783136\n", + "### iteration step : 300 rmse : 0.022392303480653113\n", + "### iteration step : 300 rmse : 0.022621116143829466\n", + "### iteration step : 350 rmse : 0.019516973680183715\n", + "### iteration step : 350 rmse : 0.019681605297160464\n", + "### iteration step : 350 rmse : 0.019585635379668415\n", + "### iteration step : 350 rmse : 0.01946716545524988\n", + "### iteration step : 350 rmse : 0.01956568678979253\n", + "### iteration step : 350 rmse : 0.019614020075870497\n", + "### iteration step : 350 rmse : 0.019368393329296258\n", + "### iteration step : 350 rmse : 0.019361014872334943\n", + "### iteration step : 350 rmse : 0.019116038405167533\n", + "### iteration step : 350 rmse : 0.01920981547997513\n", + "### iteration step : 350 rmse : 0.019255623979392192\n", + "### iteration step : 350 rmse : 0.019493636196525135\n", + "### iteration step : 400 rmse : 0.01803666559195465\n", + "### iteration step : 400 rmse : 0.01819133106334419\n", + "### iteration step : 400 rmse : 0.018078504374883574\n", + "### iteration step : 400 rmse : 0.01797554592952707\n", + "### iteration step : 400 rmse : 0.018080509676855847\n", + "### iteration step : 400 rmse : 0.018118882879536648\n", + "### iteration step : 400 rmse : 0.017889686482489363\n", + "### iteration step : 400 rmse : 0.017878066671070433\n", + "### iteration step : 400 rmse : 0.01761224433968553\n", + "### iteration step : 400 rmse : 0.01772096734904666\n", + "### iteration step : 400 rmse : 0.01778179645659777\n", + "### iteration step : 400 rmse : 0.018022719092132704\n", + "### iteration step : 450 rmse : 0.017334045429542092\n", + "### iteration step : 450 rmse : 0.01747683493759156\n", + "### iteration step : 450 rmse : 0.01735361907510825\n", + "### iteration step : 450 rmse : 0.017260553985290646\n", + "### iteration step : 450 rmse : 0.01736909385010645\n", + "### iteration step : 450 rmse : 0.017399933857257726\n", + "### iteration step : 450 rmse : 0.01718431757863743\n", + "### iteration step : 450 rmse : 0.01716990649625117\n", + "### iteration step : 450 rmse : 0.01688861579579296\n", + "### iteration step : 450 rmse : 0.017006638154083088\n", + "### iteration step : 450 rmse : 0.01707679250866153\n", + "### iteration step : 450 rmse : 0.01731968595344266\n", + "### iteration step : 500 rmse : 0.016991609248052833\n", + "### iteration step : 500 rmse : 0.01712340891578616\n", + "### iteration step : 500 rmse : 0.01699398405641037\n", + "### iteration step : 500 rmse : 0.01690707049203008\n", + "### iteration step : 500 rmse : 0.01701760577221745\n", + "### iteration step : 500 rmse : 0.017043277556700362\n", + "### iteration step : 500 rmse : 0.01683803145900356\n", + "### iteration step : 500 rmse : 0.016821674312725313\n", + "### iteration step : 500 rmse : 0.016529281264429145\n", + "### iteration step : 500 rmse : 0.0166528887951985\n", + "### iteration step : 500 rmse : 0.016728541275490984\n", + "### iteration step : 500 rmse : 0.016973657887570753\n", + "### iteration step : 550 rmse : 0.016818969716266233\n", + "### iteration step : 550 rmse : 0.016941445597444732\n", + "### iteration step : 550 rmse : 0.0168082592988841\n", + "### iteration step : 550 rmse : 0.016725234339747562\n", + "### iteration step : 550 rmse : 0.01683693849143515\n", + "### iteration step : 550 rmse : 0.016859187050621206\n", + "### iteration step : 550 rmse : 0.016661644526141564\n", + "### iteration step : 550 rmse : 0.01664385102006508\n", + "### iteration step : 550 rmse : 0.016343446075494233\n", + "### iteration step : 550 rmse : 0.01647044082182643\n", + "### iteration step : 550 rmse : 0.01654932331426952\n", + "### iteration step : 550 rmse : 0.016796804595895633\n", + "### iteration step : 600 rmse : 0.016727439717439115\n", + "### iteration step : 600 rmse : 0.016842259158977232\n", + "### iteration step : 600 rmse : 0.016706687924467476\n", + "### iteration step : 600 rmse : 0.016626255644609397\n", + "### iteration step : 600 rmse : 0.016738696939262717\n", + "### iteration step : 600 rmse : 0.016758682415985614\n", + "### iteration step : 600 rmse : 0.0165668572000528\n", + "### iteration step : 600 rmse : 0.016547954461110684\n", + "### iteration step : 600 rmse : 0.016241668760761063\n", + "### iteration step : 600 rmse : 0.016370800056137867\n", + "### iteration step : 600 rmse : 0.016451627209257007\n", + "### iteration step : 600 rmse : 0.01670132290188466\n", + "### iteration step : 650 rmse : 0.016674291334806343\n", + "### iteration step : 650 rmse : 0.016782895588885082\n", + "### iteration step : 650 rmse : 0.016645698091647773\n", + "### iteration step : 650 rmse : 0.01656714079916223\n", + "### iteration step : 650 rmse : 0.016680091021598568\n", + "### iteration step : 650 rmse : 0.016698554271430792\n", + "### iteration step : 650 rmse : 0.016511017732427972\n", + "### iteration step : 650 rmse : 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750 rmse : 0.01660906046895522\n", + "### iteration step : 750 rmse : 0.016708562969098305\n", + "### iteration step : 750 rmse : 0.016569153528341783\n", + "### iteration step : 750 rmse : 0.016493367054249922\n", + "### iteration step : 750 rmse : 0.016607027966870924\n", + "### iteration step : 750 rmse : 0.01662368102752549\n", + "### iteration step : 750 rmse : 0.016441927271724666\n", + "### iteration step : 750 rmse : 0.0164208024653437\n", + "### iteration step : 750 rmse : 0.016104179990850755\n", + "### iteration step : 750 rmse : 0.016236628551952913\n", + "### iteration step : 750 rmse : 0.016320141009292095\n", + "### iteration step : 750 rmse : 0.016574200475705\n", + "### iteration step : 800 rmse : 0.016581161561119846\n", + "### iteration step : 800 rmse : 0.016677363428436936\n", + "### iteration step : 800 rmse : 0.016537069269613652\n", + "### iteration step : 800 rmse : 0.0164624613777787\n", + "### iteration step : 800 rmse : 0.016576412350487568\n", + "### iteration step : 800 rmse : 0.01659250180024954\n", + "### iteration step : 800 rmse : 0.01641271740942833\n", + "### iteration step : 800 rmse : 0.016391072859801518\n", + "### iteration step : 800 rmse : 0.01607242307736876\n", + "### iteration step : 800 rmse : 0.016205589842521878\n", + "### iteration step : 800 rmse : 0.016289609430091494\n", + "### iteration step : 800 rmse : 0.01654431582921597\n", + "### iteration step : 850 rmse : 0.01655222898431553\n", + "### iteration step : 850 rmse : 0.01664575121547569\n", + "### iteration step : 850 rmse : 0.016504627328190514\n", + "### iteration step : 850 rmse : 0.016431145801748863\n", + "### iteration step : 850 rmse : 0.016545370571042432\n", + "### iteration step : 850 rmse : 0.016561024020105147\n", + "### iteration step : 850 rmse : 0.016382795627019747\n", + "### iteration step : 850 rmse : 0.016360700076085824\n", + "### iteration step : 850 rmse : 0.016040446344395578\n", + "### iteration step : 850 rmse : 0.016174269580681345\n", + "### iteration step : 850 rmse : 0.016258737354641353\n", + "### iteration step : 850 rmse : 0.01651375177473524\n", + "### iteration step : 900 rmse : 0.016521280433777957\n", + "### iteration step : 900 rmse : 0.016612624200841405\n", + "### iteration step : 900 rmse : 0.016470695682261876\n", + "### iteration step : 900 rmse : 0.016398314989165292\n", + "### iteration step : 900 rmse : 0.016512806333073466\n", + "### iteration step : 900 rmse : 0.01652811087350182\n", + "### iteration step : 900 rmse : 0.016351122754892394\n", + "### iteration step : 900 rmse : 0.016328629783842166\n", + "### iteration step : 900 rmse : 0.016007096878603234\n", + "### iteration step : 900 rmse : 0.016141544071514122\n", + "### iteration step : 900 rmse : 0.016226430843994055\n", + "### iteration step : 900 rmse : 0.01648146573819501\n", + "### iteration step : 950 rmse : 0.016488081335748316\n", + "### iteration step : 950 rmse : 0.016577652134974717\n", + "### iteration step : 950 rmse : 0.01643492933498176\n", + "### iteration step : 950 rmse : 0.0163636366204062\n", + "### iteration step : 950 rmse : 0.01647839195954869\n", + "### iteration step : 950 rmse : 0.01649340903060659\n", + "### iteration step : 950 rmse : 0.016317416842511007\n", + "### iteration step : 950 rmse : 0.016294568571753248\n", + "### iteration step : 950 rmse : 0.015972009545965248\n", + "### iteration step : 950 rmse : 0.0161070634587959\n", + "### iteration step : 950 rmse : 0.016192355609214733\n", + "### iteration step : 950 rmse : 0.016447171683479155\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "pred_matrix = np.dot(P, Q.T)\n", + "print('예측 행렬:\\n', np.round(pred_matrix, 3))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "akqJmyk96eFu", + "outputId": "b7b10fa4-7629-47f3-91f0-8d1025b1a9ff" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "예측 행렬:\n", + " [[3.991 0.897 1.306 2.002 1.663]\n", + " [6.696 4.978 0.979 2.981 1.003]\n", + " [6.677 0.391 2.987 3.977 3.986]\n", + " [4.968 2.005 1.006 2.017 1.14 ]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 08. 파이썬 추천 시스템 패키지 - Surprise" + ], + "metadata": { + "id": "7q4HsMK26mju" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Surprise 패키지 소개" + ], + "metadata": { + "id": "pO3iI_vJ6qDE" + } + }, + { + "cell_type": "code", + "source": [ + "pip install scikit-surprise" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vRz5zocD6hyT", + "outputId": "5c8b9f50-9b52-46c0-a11a-cc2ef3fdf373" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting scikit-surprise\n", + " Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/154.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.4.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.26.4)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.13.1)\n", + "Building wheels for collected packages: scikit-surprise\n", + " Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357293 sha256=ce25d7540adccadb35affe9c1c47b77d3b31999468f4bfd36d13a413042da125\n", + " Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n", + "Successfully built scikit-surprise\n", + "Installing collected packages: scikit-surprise\n", + "Successfully installed scikit-surprise-1.1.4\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Surprise를 이용한 추천 시스템 구축" + ], + "metadata": { + "id": "h8FEfOK16xM9" + } + }, + { + "cell_type": "code", + "source": [ + "from surprise import SVD\n", + "from surprise import Dataset\n", + "from surprise import accuracy\n", + "from surprise.model_selection import train_test_split" + ], + "metadata": { + "id": "cPYE0Jxz6u7s" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "data = Dataset.load_builtin('ml-100k')\n", + "# 수행 시마다 동일하게 데이터를 분할하기 위해 random_state 값 부여\n", + "trainset, testset = train_test_split(data, test_size=.25, random_state=0)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "P-MTyHHk7A9w", + "outputId": "32018132-60a7-4d57-83de-829a2f8b87b9" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Dataset ml-100k could not be found. Do you want to download it? [Y/n] Y\n", + "Trying to download dataset from https://files.grouplens.org/datasets/movielens/ml-100k.zip...\n", + "Done! Dataset ml-100k has been saved to /root/.surprise_data/ml-100k\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "algo = SVD()\n", + "algo.fit(trainset)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NHPDcyh27UgZ", + "outputId": "bb843f91-70e8-4e91-f468-bb5fc26270f0" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "source": [ + "predictions = algo.test(testset)\n", + "print('prediction type:', type(predictions), 'size:', len(predictions))\n", + "print('prediction 결과의 최초 5개 추출')\n", + "predictions[:5]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e1dIMLMU7YGT", + "outputId": "1c3b233a-e036-436c-bc11-d30639cdaa0b" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "prediction type: size: 25000\n", + "prediction 결과의 최초 5개 추출\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[Prediction(uid='120', iid='282', r_ui=4.0, est=3.5420490217434244, details={'was_impossible': False}),\n", + " Prediction(uid='882', iid='291', r_ui=4.0, est=4.064088825693579, details={'was_impossible': False}),\n", + " Prediction(uid='535', iid='507', r_ui=5.0, est=3.906510091281124, details={'was_impossible': False}),\n", + " Prediction(uid='697', iid='244', r_ui=5.0, est=3.567075899332699, details={'was_impossible': False}),\n", + " Prediction(uid='751', iid='385', r_ui=4.0, est=3.257356138581865, details={'was_impossible': False})]" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "[(pred.uid, pred.iid, pred.est) for pred in predictions[:3]]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "flsX6hIy7ed8", + "outputId": "8c7940ce-8eaa-4fd2-f048-1a975bb2ffe5" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[('120', '282', 3.5420490217434244),\n", + " ('882', '291', 4.064088825693579),\n", + " ('535', '507', 3.906510091281124)]" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 사용자 아이디, 아이템 아이디는 문자열로 입력해야 함\n", + "uid = str(196)\n", + "iid = str(302)\n", + "pred = algo.predict(uid, iid)\n", + "print(pred)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Lj0Y2C167m15", + "outputId": "a7c2c095-5d21-46db-a19b-56bd4dc62217" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "user: 196 item: 302 r_ui = None est = 3.99 {'was_impossible': False}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "accuracy.rmse(predictions)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "g9TmvX2c7w1-", + "outputId": "057c6530-206a-4431-d1f6-c0a4df49bc3b" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 0.9509\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.9508991861025147" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Surprise 주요 모듈 소개" + ], + "metadata": { + "id": "qRO_Axqm74eL" + } + }, + { + "cell_type": "markdown", + "source": [ + "**OS 파일 데이터를 Surprise 데이터 세트로 로딩**" + ], + "metadata": { + "id": "icEAiYOC78iM" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "\n", + "ratings = pd.read_csv(\"/content/ratings.csv\")\n", + "# ratings_noh.csv 파일로 언로드 시 인덱스와 헤더를 모두 제거한 새로운 파일 생성\n", + "ratings.to_csv(\"/content/ratings_noh.csv\", index=False, header=False)" + ], + "metadata": { + "id": "xcqkqRwT70U3" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from surprise import Reader\n", + "\n", + "reader = Reader(line_format='user item rating timestamp', sep=',', rating_scale=(0.5, 5))\n", + "data = Dataset.load_from_file('/content/ratings_noh.csv', reader=reader)" + ], + "metadata": { + "id": "5i124qZUBMmH" + }, + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "trainset, testset = train_test_split(data, test_size=.25, random_state=0)\n", + "\n", + "# 수행 시마다 동일한 결과를 도출하기 위해 random_state 설정\n", + "algo = SVD(n_factors=50, random_state=0)\n", + "\n", + "# 학습 데이터 세트로 학습하고 나서 테스트 데이터 세트로 평점 예측 후 RMSE 평가\n", + "algo.fit(trainset)\n", + "predictions = algo.test(testset)\n", + "accuracy.rmse(predictions)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DBKT0RhUBbDA", + "outputId": "8fa6d9cc-82c7-4673-d4b9-59e320678831" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 0.8682\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.8681952927143516" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**판다스 DataFrame에서 Surprise 데이터 세트로 로딩**" + ], + "metadata": { + "id": "NLICD93RCsWJ" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "from surprise import Reader, Dataset\n", + "\n", + "ratings = pd.read_csv('/content/ratings.csv')\n", + "reader = Reader(rating_scale=(0.5, 5.0))\n", + "\n", + "# ratings DataFrame에서 컬럼은 사용자 아이디, 아이템 아이디, 평점 순서를 지켜야 함\n", + "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n", + "trainset, testset = train_test_split(data, test_size=.25, random_state=0)\n", + "\n", + "algo = SVD(n_factors=50, random_state=0)\n", + "algo.fit(trainset)\n", + "predictions = algo.test(testset)\n", + "accuracy.rmse(predictions)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XOglAbsdCoQf", + "outputId": "d4fb8081-8011-406f-b404-e61288c24b29" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 0.8682\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.8681952927143516" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 교차 검증과 하이퍼 파라미터 튜닝" + ], + "metadata": { + "id": "FrO9ghSqDCuO" + } + }, + { + "cell_type": "code", + "source": [ + "from surprise.model_selection import cross_validate\n", + "\n", + "# 판다스 DataFrame에서 Surprise 데이터 세트로 데이터 로딩\n", + "ratings = pd.read_csv('/content/ratings.csv')\n", + "reader = Reader(rating_scale=(0.5, 5.0))\n", + "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n", + "\n", + "algo = SVD(random_state=0)\n", + "cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QSa48celC_HC", + "outputId": "59542bb2-3507-4203-9dd2-5a718f17d7bc" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n", + "\n", + " Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std \n", + "RMSE (testset) 0.8707 0.8845 0.8686 0.8645 0.8736 0.8724 0.0067 \n", + "MAE (testset) 0.6698 0.6809 0.6662 0.6643 0.6727 0.6708 0.0058 \n", + "Fit time 2.67 1.80 3.39 1.79 1.81 2.29 0.64 \n", + "Test time 0.14 1.49 0.13 0.33 0.26 0.47 0.52 \n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'test_rmse': array([0.87067389, 0.88447785, 0.8686039 , 0.86453146, 0.87360213]),\n", + " 'test_mae': array([0.66975423, 0.68087162, 0.66615616, 0.66429301, 0.67269149]),\n", + " 'fit_time': (2.6746022701263428,\n", + " 1.801090955734253,\n", + " 3.3930437564849854,\n", + " 1.790968656539917,\n", + " 1.8149125576019287),\n", + " 'test_time': (0.13999199867248535,\n", + " 1.4949455261230469,\n", + " 0.12560439109802246,\n", + " 0.3288872241973877,\n", + " 0.261688232421875)}" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from surprise.model_selection import GridSearchCV\n", + "\n", + "# 최적화할 파라미터를 딕셔너리 형태로 지정\n", + "param_grid = {'n_epochs': [20, 40, 60], 'n_factors':[50, 100, 200]}\n", + "\n", + "# CV를 3개 폴드 세트로 지정, 성능 평가는 rmse, mse로 수행하도록 GridSearchCV 구성\n", + "gs = GridSearchCV(SVD, param_grid, measures=['rmse', 'mae'], cv=3)\n", + "gs.fit(data)\n", + "\n", + "# 최고 RMSE Evaluation 점수와 그때의 하이퍼 파라미터\n", + "print(gs.best_score['rmse'])\n", + "print(gs.best_params['rmse'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QDbBdbA3DVd7", + "outputId": "d76d7298-27ba-4bda-9bad-0c8dc7a3685d" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.8766257301024302\n", + "{'n_epochs': 20, 'n_factors': 50}\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Surprise를 이용한 개인화 영화 추천 시스템 구축" + ], + "metadata": { + "id": "2PtAUsEHD7CH" + } + }, + { + "cell_type": "code", + "source": [ + "# 다음 코드는 train_test_split()으로 분리되지 않은 데이터 세트에 fit()을 호출하여 오류가 발생합니다\n", + "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n", + "algo = SVD(n_factors=50, random_state=0)\n", + "algo.fit(data)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "id": "5krW-WAlD5Y6", + "outputId": "64ea0732-bcec-4fa8-bdb2-53e0c7db2f62" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "error", + "ename": "AttributeError", + "evalue": "'DatasetAutoFolds' object has no attribute 'n_users'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_from_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mratings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'userId'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'movieId'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rating'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0malgo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_factors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0malgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/surprise/prediction_algorithms/matrix_factorization.pyx\u001b[0m in \u001b[0;36msurprise.prediction_algorithms.matrix_factorization.SVD.fit\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/surprise/prediction_algorithms/matrix_factorization.pyx\u001b[0m in \u001b[0;36msurprise.prediction_algorithms.matrix_factorization.SVD.sgd\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'DatasetAutoFolds' object has no attribute 'n_users'" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from surprise.dataset import DatasetAutoFolds\n", + "\n", + "reader = Reader(line_format='user item rating timestamp', sep=',', rating_scale=(0.5, 5))\n", + "# DatasetAutoFolds 클래스르 ratings_noh.csv 파일 기반으로 생성\n", + "data_folds = DatasetAutoFolds(ratings_file='/content/ratings_noh.csv', reader=reader)\n", + "\n", + "# 전체 데이터를 학습 데이터로 생성\n", + "trainset = data_folds.build_full_trainset()" + ], + "metadata": { + "id": "Cs_JF7TGEQ8-" + }, + "execution_count": 27, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "algo = SVD(n_epochs=20, n_factors=50, random_state=0)\n", + "algo.fit(trainset)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uWVYI3AVEkPV", + "outputId": "cc206d1d-4cf8-4750-a94c-3843f702ab93" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 28 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 영화에 대한 상세 속성 정보 DataFrame 로딩\n", + "movies = pd.read_csv('/content/movies.csv')\n", + "\n", + "# userId=9의 movieId 데이터를 추출해 movieId=42 데이터가 있는지 확인\n", + "movieIds = ratings[ratings['userId']==9]['movieId']\n", + "if movieIds[movieIds==42].count() == 0:\n", + " print('사용자 아이디 9는 영화 아이디 42의 평점 없음')\n", + "print(movies[movies['movieId']==42])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EYl8IWHGEniU", + "outputId": "935c7d4f-89cf-411d-a969-6a30fb81c621" + }, + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "사용자 아이디 9는 영화 아이디 42의 평점 없음\n", + " movieId title genres\n", + "38 42 Dead Presidents (1995) Action|Crime|Drama\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "uid = str(9)\n", + "iid = str(42)\n", + "\n", + "pred = algo.predict(uid, iid, verbose=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qXf3pkLjFA15", + "outputId": "fbfa1d9f-8192-4486-b3a1-902d3f7c6354" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "user: 9 item: 42 r_ui = None est = 3.13 {'was_impossible': False}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def get_unseen_surprise(ratings, movies, userId):\n", + " # 입력값으로 들어온 userId에 해당하는 사용자가 평점을 매긴 모든 영화를 리스트로 생성\n", + " seen_movies = ratings[ratings['userId'] == userId]['movieId'].tolist()\n", + "\n", + " # 모든 영화의 movieId를 리스트로 생성\n", + " total_movies = movies['movieId'].tolist()\n", + "\n", + " # 모든 영화의 movieId 중 이미 평점을 매긴 영화의 movieId를 제외한 후 리스트로 생성\n", + " unseen_movies = [movie for movie in total_movies if movie not in seen_movies]\n", + " print('평점 매긴 영화 수:', len(seen_movies), '추천 대상 영화 수:', len(unseen_movies), '전체 영화 수:', len(total_movies))\n", + "\n", + " return unseen_movies\n", + "\n", + "unseen_movies = get_unseen_surprise(ratings, movies, 9)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9YJJBSczFEEi", + "outputId": "5ece7579-b8dd-4848-8695-cedaf1f51bc1" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "평점 매긴 영화 수: 46 추천 대상 영화 수: 9696 전체 영화 수: 9742\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def recomm_movie_by_surprise(algo, userId, unseen_movies, top_n=10):\n", + " # 알고리즘 객체의 predict() 메서드를 평점이 없는 영화에 반복 수행한 후 결과를 list 객체로 저장\n", + " predictions = [algo.predict(str(userId), str(movieId)) for movieId in unseen_movies]\n", + "\n", + " # predictions list 객체는 surprise의 Predictions 객체를 원소로 가지고 있음\n", + " # [Prediction(uid='9', iid='1', est=3.69), Prediction(uid='9', iid='2', est=2.98),,,,]\n", + "\n", + " # 이를 est 값으로 정렬하기 위해서 아래의 sortkey_est 함수를 정의\n", + " # sortkey_est 함수는 list 객체의 sort() 함수의 키 값으로 사용되어 정렬 수행\n", + " def sortkey_est(pred):\n", + " return pred.est\n", + "\n", + " # sortkey_est() 반환값의 내림 차순으로 정렬 수행하고 top_n개의 최상위 값 추출\n", + " predictions.sort(key=sortkey_est, reverse=True)\n", + " top_predictions = predictions[:top_n]\n", + "\n", + " # top_n으로 추출된 영화의 정보 추출. 영화 아이디, 추천 예상 평점, 제목 추출\n", + " top_movie_ids = [int(pred.iid) for pred in top_predictions]\n", + " top_movie_rating = [pred.est for pred in top_predictions]\n", + " top_movie_titles = movies[movies.movieId.isin(top_movie_ids)]['title']\n", + "\n", + " top_movie_preds = [(id, title, rating) for id, title, rating in zip(top_movie_ids, top_movie_titles, top_movie_rating)]\n", + "\n", + " return top_movie_preds\n", + "\n", + "unseen_movies = get_unseen_surprise(ratings, movies, 9)\n", + "top_movie_preds = recomm_movie_by_surprise(algo, 9, unseen_movies, top_n=10)\n", + "\n", + "print('##### Top-10 추천 영화 리스트 #####')\n", + "for top_movie in top_movie_preds:\n", + " print(top_movie[1], \":\", top_movie[2])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "m8EiNaujFW_i", + "outputId": "190dfaf2-dca1-4bae-d40a-e4b78d3c2590" + }, + "execution_count": 32, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "평점 매긴 영화 수: 46 추천 대상 영화 수: 9696 전체 영화 수: 9742\n", + "##### Top-10 추천 영화 리스트 #####\n", + "Usual Suspects, The (1995) : 4.306302135700814\n", + "Star Wars: Episode IV - A New Hope (1977) : 4.281663842987387\n", + "Pulp Fiction (1994) : 4.278152632122759\n", + "Silence of the Lambs, The (1991) : 4.226073566460876\n", + "Godfather, The (1972) : 4.1918097904381995\n", + "Streetcar Named Desire, A (1951) : 4.154746591122657\n", + "Star Wars: Episode V - The Empire Strikes Back (1980) : 4.122016128534504\n", + "Star Wars: Episode VI - Return of the Jedi (1983) : 4.108009609093436\n", + "Goodfellas (1990) : 4.083464936588478\n", + "Glory (1989) : 4.07887165526957\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git "a/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\353\202\250\354\232\260.pdf" 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Captured For Eternity.\",\n \"May the schwartz be with you\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4800,\n \"samples\": [\n \"I Spy\",\n \"Who's Your Caddy?\",\n \"Sleepover\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vote_average\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.1946121628478925,\n \"min\": 0.0,\n \"max\": 10.0,\n \"num_unique_values\": 71,\n \"samples\": [\n 5.1,\n 7.2,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vote_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1234,\n \"min\": 0,\n \"max\": 13752,\n \"num_unique_values\": 1609,\n \"samples\": [\n 7604,\n 3428,\n 225\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 1 + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import warnings; warnings.filterwarnings('ignore')\n", + "\n", + "movies = pd.read_csv('/content/tmdb_5000_movies.csv')\n", + "print(movies.shape)\n", + "movies.head(1)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "주요 칼럼 추출" + ], + "metadata": { + "id": "qNsmjGPCIRJC" + } + }, + { + "cell_type": "code", + "source": [ + "movies_df = movies[['id', 'title', 'genres', 'vote_average', 'vote_count', 'popularity',\n", + " 'keywords', 'overview']]" + ], + "metadata": { + "id": "4TNKxIviIDUp" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "칼럼 형태 확인" + ], + "metadata": { + "id": "M5pid7eXIaHa" + } + }, + { + "cell_type": "code", + "source": [ + "pd.set_option('max_colwidth', 100)\n", + "movies_df[['genres', 'keywords']][:1]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 115 + }, + "id": "bJO6hkmsISoc", + "outputId": "5a65afd5-0bab-469a-cb53-ce9173ab257c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " genres \\\n", + "0 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {... \n", + "\n", + " keywords \n", + "0 [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp... 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    genreskeywords
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    \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"movies_df[['genres', 'keywords']][:1]\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"genres\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"keywords\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 장르 콘텐츠 유사도 측정" + ], + "metadata": { + "id": "wF-EwUNhI2DP" + } + }, + { + "cell_type": "markdown", + "source": [ + "CountVectorizer를 이용해 피처 벡터 행렬로 만들기" + ], + "metadata": { + "id": "p0JoUt53I8fo" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "# CountVectorizer를 적용하기 위해 공백문자로 word 단위가 구분되는 문자열로 변환\n", + "movies_df['genres_literal'] = movies_df['genres'].apply(lambda x : (' ').join(x))\n", + "count_vect = CountVectorizer(min_df=0.0, ngram_range=(1, 2))\n", + "genre_mat = count_vect.fit_transform(movies_df['genres_literal'])\n", + "print(genre_mat.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qN-9lN7WI4U3", + "outputId": "6874ceca-e0f0-4bde-f96a-baf600826cc3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4803, 276)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "cosine_similarities() 적용" + ], + "metadata": { + "id": "nWCIyAimJRiC" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics.pairwise import cosine_similarity\n", + "\n", + "genre_sim = cosine_similarity(genre_mat, genre_mat)\n", + "print(genre_sim.shape)\n", + "print(genre_sim[:2])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JcMPpFv2JUCv", + "outputId": "d8bb07d8-8654-4fb1-cf67-4e3bc4f137d2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4803, 4803)\n", + "[[1. 0.59628479 0.4472136 ... 0. 0. 0. ]\n", + " [0.59628479 1. 0.4 ... 0. 0. 0. ]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "genre_sim 객체의 기준 행별로 비교 대상이 되는 행의 유사도 값이 높은 순으로 정렬된 행렬의 위치 추출" + ], + "metadata": { + "id": "58I4OZflJhZo" + } + }, + { + "cell_type": "code", + "source": [ + "genre_sim_sorted_ind = genre_sim.argsort()[:, ::-1]\n", + "print(genre_sim_sorted_ind[:1])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IsN4DsWjJnAk", + "outputId": "ebd6cff7-98e0-4743-bd85-8b0069877fb3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[ 0 3494 813 ... 3038 3037 2401]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "자신인 0번 레코드를 제외하고 3494 레코드가 가장 유사도가 높음" + ], + "metadata": { + "id": "Brb9N06UJwQq" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 장르 콘텐츠 필터링을 이용한 영화 추천" + ], + "metadata": { + "id": "cp_tE6UeJ0r4" + } + }, + { + "cell_type": "code", + "source": [ + "def find_sim_movie(df, sorted_ind, title_name, top_n=10):\n", + " # 인자로 입력된 movies_df DataFrame에서 'title' 칼럼이 입력된 title_name 값인 DataFrame 추출\n", + " title_movie = df[df['title'] == title_name]\n", + "\n", + " # title_named을 가진 DataFrame의 index 객체를 ndarray로 반환하고\n", + " # sorted_ind 인자로 입력된 genre_sim_sorted_ind 객체에서 유사도 순으로 top_n개의 index 추출\n", + " title_index = title_movie.index.values\n", + " similar_indexes = sorted_ind[title_index, :(top_n)]\n", + "\n", + " # 추출된 top_n index 출력. top_n index는 2차원 데이터임\n", + " # dataframe에서 index로 사용하기 위해서 1차원 array로 변경\n", + " print(similar_indexes)\n", + " similar_indexes = similar_indexes.reshape(-1)\n", + "\n", + " return df.iloc[similar_indexes]" + ], + "metadata": { + "id": "HybFKVhtJ4i6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "similar_movies = find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather', 10)\n", + "similar_movies[['title', 'vote_average']]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 381 + }, + "id": "l3DRcCWcKgzZ", + "outputId": "4b134712-3869-40a0-ed76-66cdb0c1f391" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[2731 1243 3636 1946 2640 4065 1847 4217 883 3866]]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " title vote_average\n", + "2731 The Godfather: Part II 8.3\n", + "1243 Mean Streets 7.2\n", + "3636 Light Sleeper 5.7\n", + "1946 The Bad Lieutenant: Port of Call - New Orleans 6.0\n", + "2640 Things to Do in Denver When You're Dead 6.7\n", + "4065 Mi America 0.0\n", + "1847 GoodFellas 8.2\n", + "4217 Kids 6.8\n", + "883 Catch Me If You Can 7.7\n", + "3866 City of God 8.1" + ], + "text/html": [ + "\n", + "
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    \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "ratings_matrix" + } + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# title 칼럼을 얻기 위해 movies와 조인\n", + "rating_movies = pd.merge(ratings, movies, on='movieId')\n", + "\n", + "# columns='title'로 title 칼럼으로 pivot 수행\n", + "ratings_matrix = rating_movies.pivot_table('rating', index='userId', columns='title')\n", + "\n", + "# NaN 값을 모두 0으로 변환\n", + "ratings_matrix = ratings_matrix.fillna(0)\n", + "ratings_matrix.head(3)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 345 + }, + "id": "JUexDpmCNEpa", + "outputId": "e6ebc80a-c4a0-4e4e-c326-f7c27be7dede" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "title '71 (2014) 'Hellboy': The Seeds of Creation (2004) \\\n", + "userId \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "\n", + "title 'Round Midnight (1986) 'Salem's Lot (2004) \\\n", + "userId \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "\n", + "title 'Til There Was You (1997) 'Tis the Season for Love (2015) \\\n", + "userId \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "\n", + "title 'burbs, The (1989) 'night Mother (1986) (500) Days of Summer (2009) \\\n", + "userId \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "\n", + "title *batteries not included (1987) ... Zulu (2013) [REC] (2007) \\\n", + "userId ... \n", + "1 0.0 ... 0.0 0.0 \n", + "2 0.0 ... 0.0 0.0 \n", + "3 0.0 ... 0.0 0.0 \n", + "\n", + "title [REC]² (2009) [REC]³ 3 Génesis (2012) \\\n", + "userId \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "\n", + "title anohana: The Flower We Saw That Day - The Movie (2013) \\\n", + "userId \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "\n", + "title eXistenZ (1999) xXx (2002) xXx: State of the Union (2005) \\\n", + "userId \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "\n", + "title ¡Three Amigos! (1986) À nous la liberté (Freedom for Us) (1931) \n", + "userId \n", + "1 4.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "\n", + "[3 rows x 9719 columns]" + ], + "text/html": [ + "\n", + "
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    " + ] + }, + "metadata": {}, + "execution_count": 25 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 아이템 기반 최근접 이웃 협업 필터링으로 개인화된 영화 추천" + ], + "metadata": { + "id": "jzuqLSeCN_cw" + } + }, + { + "cell_type": "markdown", + "source": [ + "아이템 기반 협업 필터링에서 개인화된 예측 평점\n", + "\n", + "![image.png](data:image/png;base64,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"userId \n", + "1 0.070345 0.577855 \n", + "2 0.018260 0.042744 \n", + "3 0.011884 0.030279 \n", + "\n", + "title 'Round Midnight (1986) 'Salem's Lot (2004) \\\n", + "userId \n", + "1 0.321696 0.227055 \n", + "2 0.018861 0.000000 \n", + "3 0.064437 0.003762 \n", + "\n", + "title 'Til There Was You (1997) 'Tis the Season for Love (2015) \\\n", + "userId \n", + "1 0.206958 0.194615 \n", + "2 0.000000 0.035995 \n", + "3 0.003749 0.002722 \n", + "\n", + "title 'burbs, The (1989) 'night Mother (1986) (500) Days of Summer (2009) \\\n", + "userId \n", + "1 0.249883 0.102542 0.157084 \n", + "2 0.013413 0.002314 0.032213 \n", + "3 0.014625 0.002085 0.005666 \n", + "\n", + "title *batteries not included (1987) ... Zulu (2013) [REC] (2007) \\\n", + "userId ... \n", + "1 0.178197 ... 0.113608 0.181738 \n", + "2 0.014863 ... 0.015640 0.020855 \n", + "3 0.006272 ... 0.006923 0.011665 \n", + "\n", + "title [REC]² (2009) [REC]³ 3 Génesis (2012) \\\n", + "userId \n", + "1 0.133962 0.128574 \n", + "2 0.020119 0.015745 \n", + "3 0.011800 0.012225 \n", + "\n", + "title anohana: The Flower We Saw That Day - The Movie (2013) \\\n", + "userId \n", + "1 0.006179 \n", + "2 0.049983 \n", + "3 0.000000 \n", + "\n", + "title eXistenZ (1999) xXx (2002) xXx: State of the Union (2005) \\\n", + "userId \n", + "1 0.212070 0.192921 0.136024 \n", + "2 0.014876 0.021616 0.024528 \n", + "3 0.008194 0.007017 0.009229 \n", + "\n", + "title ¡Three Amigos! (1986) À nous la liberté (Freedom for Us) (1931) \n", + "userId \n", + "1 0.292955 0.720347 \n", + "2 0.017563 0.000000 \n", + "3 0.010420 0.084501 \n", + "\n", + "[3 rows x 9719 columns]" + ], + "text/html": [ + "\n", + "
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    title'71 (2014)'Hellboy': The Seeds of Creation (2004)'Round Midnight (1986)'Salem's Lot (2004)'Til There Was You (1997)'Tis the Season for Love (2015)'burbs, The (1989)'night Mother (1986)(500) Days of Summer (2009)*batteries not included (1987)...Zulu (2013)[REC] (2007)[REC]² (2009)[REC]³ 3 Génesis (2012)anohana: The Flower We Saw That Day - The Movie (2013)eXistenZ (1999)xXx (2002)xXx: State of the Union (2005)¡Three Amigos! (1986)À nous la liberté (Freedom for Us) (1931)
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    \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "ratings_pred_matrix" + } + }, + "metadata": {}, + "execution_count": 30 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "\n", + "# 사용자가 평점을 부여한 영화에 대해서만 예측 성능 평가 MSE를 구함\n", + "def get_mse(pred, actual):\n", + " # 평점이 있는 실제 영화만 추출\n", + " pred = pred[actual.nonzero()].flatten()\n", + " actual = actual[actual.nonzero()].flatten()\n", + " return mean_squared_error(pred, actual)\n", + "\n", + "print('아이템 기반 모든 최근접 이웃 MSE: ', get_mse(ratings_pred, ratings_matrix.values))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AldMMHQEOxDA", + "outputId": "6a28df5d-ccb1-4f81-b895-ab405c63a8a0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "아이템 기반 모든 최근접 이웃 MSE: 9.895354759094706\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def predict_rating_topsim(ratings_arr, item_sim_arr, n=20):\n", + " # 사용자-아이템 평점 행렬 크기만큼 0으로 채운 예측 행렬 초기화\n", + " pred = np.zeros(ratings_arr.shape)\n", + "\n", + " # 사용자-아이템 평점 행렬의 열 크기만큼 루프 수행\n", + " for col in range(ratings_arr.shape[1]):\n", + " # 유사도 행렬에서 유사도가 큰 순으로 n개 데이터 행렬의 인덱스 반환\n", + " top_n_items = [np.argsort(item_sim_arr[:, col])[:-n-1:-1]]\n", + " # 개인화된 예측 평점 계산\n", + " for row in range(ratings_arr.shape[0]):\n", + " pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n", + " pred[row, col] /= np.sum(np.abs(item_sim_arr[col, :][top_n_items]))\n", + "\n", + " return pred" + ], + "metadata": { + "id": "ZYvF3IgHPFSZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "ratings_pred = predict_rating_topsim(ratings_matrix.values, item_sim_df.values, n=20)\n", + "print('아이템 기반 최근접 TOP-20 이웃 MSE: ', get_mse(ratings_pred, ratings_matrix.values))\n", + "\n", + "# 계싼된 예측 평점 데이터는 DataFrame으로 재생성\n", + "ratings_pred_matrix = pd.DataFrame(data=ratings_pred, index=ratings_matrix.index, columns=ratings_matrix.columns)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l4T0Xp4YPcwf", + "outputId": "be5e59bb-59bb-4bc9-906b-90ac5757dc42" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "아이템 기반 최근접 TOP-20 이웃 MSE: 3.6949827608772314\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "평점 높은 순으로 나열" + ], + "metadata": { + "id": "g7nZtGuiPmAb" + } + }, + { + "cell_type": "code", + "source": [ + "user_rating_id = ratings_matrix.loc[9, :]\n", + "user_rating_id[user_rating_id > 0].sort_values(ascending=False)[:10]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 429 + }, + "id": "YJEMepG9Pnva", + "outputId": "3cf6288f-4afa-4d4f-927a-910ea94a0ed3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "title\n", + "Adaptation (2002) 5.0\n", + "Citizen Kane (1941) 5.0\n", + "Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981) 5.0\n", + "Producers, The (1968) 5.0\n", + "Lord of the Rings: The Two Towers, The (2002) 5.0\n", + "Lord of the Rings: The Fellowship of the Ring, The (2001) 5.0\n", + "Back to the Future (1985) 5.0\n", + "Austin Powers in Goldmember (2002) 5.0\n", + "Minority Report (2002) 4.0\n", + "Witness (1985) 4.0\n", + "Name: 9, dtype: float64" + ], + "text/html": [ + "
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Harry Potter and the Philosopher's Stone) (2001)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pred_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.06614432811511851,\n \"min\": 0.6767108283499336,\n \"max\": 0.8662018746933645,\n \"num_unique_values\": 10,\n \"samples\": [\n 0.6895905595608812,\n 0.8578535950426878,\n 0.7651586070058114\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 37 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# 9.7 행렬 분해를 이용한 잠재 요인 협업 필터링 실습" + ], + "metadata": { + "id": "jtXsL2NfGyBE" + } + }, + { + "cell_type": "markdown", + "source": [ + "SGD 기반 행렬 분해 적용" + ], + "metadata": { + "id": "7omzYA9EQ1xD" + } + }, + { + "cell_type": "code", + "source": [ + "def get_rmse(R, P, Q, non_zeros):\n", + " error = 0\n", + " # 두 개의 분해된 행렬 P와 Q.T의 내적으로 예측 R 행렬 생성\n", + " full_pred_matrix = np.dot(P, Q.T)\n", + "\n", + " # 실제 R 행렬에서 널이 아닌 값의 위치 인덱스 추출해 실제 R 행렬과 예측 행렬의 RMSE 추출\n", + " x_non_zero_ind = [non_zero[0] for non_zero in non_zeros]\n", + " y_non_zero_ind = [non_zero[1] for non_zero in non_zeros]\n", + " R_non_zeros = R[x_non_zero_ind, y_non_zero_ind]\n", + " full_pred_matrix_non_zeros = full_pred_matrix[x_non_zero_ind, y_non_zero_ind]\n", + " mse = mean_squared_error(R_non_zeros, full_pred_matrix_non_zeros)\n", + " rmse = np.sqrt(mse)\n", + "\n", + " return rmse" + ], + "metadata": { + "id": "olUfWfeTSRkB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def matrix_factorization(R, K, steps=200, learning_rate=0.01, r_lambda=0.01):\n", + " num_users, num_items = R.shape\n", + " # P와 Q 매트릭스의 크기를 지정하고 정규 분포를 가진 랜덤한 값으로 입력\n", + " np.random.seed(1)\n", + " P = np.random.normal(scale=1./K, size=(num_users, K))\n", + " Q = np.random.normal(scale=1./K, size=(num_items, K))\n", + "\n", + " prev_rmse = 10000\n", + " break_count = 0\n", + "\n", + " # R > 0 인 행 위치, 열 위치, 값을 non_zeros 리스트 객체에 저장\n", + " non_zeros = [(i, j, R[i, j]) for i in range(num_users) for j in range(num_items) if R[i, j] > 0]\n", + "\n", + " # SGD 기법으로 P와 Q 매트릭스를 계속 업데이트\n", + " for step in range(steps):\n", + " for i, j, r in non_zeros:\n", + " # 실제 값과 예측 값의 차이인 오류 값 구함\n", + " eij = r - np.dot(P[i, :], Q[j, :].T)\n", + " # Regularization을 반영한 SGD 업데이트 공식 적용\n", + " P[i, :] = P[i, :] + learning_rate*(eij * Q[j, :] - r_lambda*P[i, :])\n", + " Q[j, :] = Q[j, :] + learning_rate*(eij * P[i, :] - r_lambda*Q[j, :])\n", + "\n", + " rmse = get_rmse(R, P, Q, non_zeros)\n", + " if (step % 10) == 0:\n", + " print(\"### iteration step : \", step, \"rmse : \", rmse)\n", + "\n", + " return P, Q" + ], + "metadata": { + "id": "GEJgAICnG1Fl" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "movies = pd.read_csv('/content/movies.csv')\n", + "ratings = pd.read_csv('/content/ratings.csv')\n", + "ratings = ratings[['userId', 'movieId', 'rating']]\n", + "ratings_matrix = ratings.pivot_table('rating', index='userId', columns='movieId')\n", + "\n", + "# title 칼럼을 얻기 위해 moveis와 조인 수행\n", + "rating_movies = pd.merge(ratings, movies, on='movieId')\n", + "\n", + "# columns='title'로 title 칼럼으로 pivot 수행\n", + "ratings_matrix = rating_movies.pivot_table('rating', index='userId', columns='title')" + ], + "metadata": { + "id": "PvoTA5cjSVD2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "P, Q = matrix_factorization(ratings_matrix.values, K=50, steps=200, learning_rate=0.01,\n", + " r_lambda=0.01)\n", + "pred_matrix = np.dot(P, Q.T)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BW8JsPrKSuT6", + "outputId": "6e98e37b-1cd7-457a-e947-cd88b39c8b9e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "### iteration step : 0 rmse : 2.9023619751336867\n", + "### iteration step : 10 rmse : 0.7335768591017927\n", + "### iteration step : 20 rmse : 0.5115539026853442\n", + "### iteration step : 30 rmse : 0.37261628282537446\n", + "### iteration step : 40 rmse : 0.2960818299181014\n", + "### iteration step : 50 rmse : 0.2520353192341642\n", + "### iteration step : 60 rmse : 0.22487503275269854\n", + "### iteration step : 70 rmse : 0.2068545530233154\n", + "### iteration step : 80 rmse : 0.19413418783028685\n", + "### iteration step : 90 rmse : 0.18470082002720406\n", + "### iteration step : 100 rmse : 0.17742927527209104\n", + "### iteration step : 110 rmse : 0.1716522696470749\n", + "### iteration step : 120 rmse : 0.16695181946871726\n", + "### iteration step : 130 rmse : 0.16305292191997542\n", + "### iteration step : 140 rmse : 0.15976691929679646\n", + "### iteration step : 150 rmse : 0.1569598699945732\n", + "### iteration step : 160 rmse : 0.15453398186715425\n", + "### iteration step : 170 rmse : 0.15241618551077643\n", + "### iteration step : 180 rmse : 0.1505508073962831\n", + "### iteration step : 190 rmse : 0.1488947091323209\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "ratings_pred_matrix = pd.DataFrame(data=pred_matrix, index=ratings_matrix.index,\n", + " columns=ratings_matrix.columns)\n", + "ratings_pred_matrix.head(3)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 345 + }, + "id": "57iTHcvGS7p0", + "outputId": "aaf0c9c1-f4d5-469f-8917-f5ea56a01472" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "title '71 (2014) 'Hellboy': The Seeds of Creation (2004) \\\n", + "userId \n", + "1 3.055084 4.092018 \n", + "2 3.170119 3.657992 \n", + "3 2.307073 1.658853 \n", + "\n", + "title 'Round Midnight (1986) 'Salem's Lot (2004) \\\n", + "userId \n", + "1 3.564130 4.502167 \n", + "2 3.308707 4.166521 \n", + "3 1.443538 2.208859 \n", + "\n", + "title 'Til There Was You (1997) 'Tis the Season for Love (2015) \\\n", + "userId \n", + "1 3.981215 1.271694 \n", + "2 4.311890 1.275469 \n", + "3 2.229486 0.780760 \n", + "\n", + "title 'burbs, The (1989) 'night Mother (1986) (500) Days of Summer (2009) \\\n", + "userId \n", + "1 3.603274 2.333266 5.091749 \n", + "2 4.237972 1.900366 3.392859 \n", + "3 1.997043 0.924908 2.970700 \n", + "\n", + "title *batteries not included (1987) ... Zulu (2013) [REC] (2007) \\\n", + "userId ... \n", + "1 3.972454 ... 1.402608 4.208382 \n", + "2 3.647421 ... 0.973811 3.528264 \n", + "3 2.551446 ... 0.520354 1.709494 \n", + "\n", + "title [REC]² (2009) [REC]³ 3 Génesis (2012) \\\n", + "userId \n", + "1 3.705957 2.720514 \n", + "2 3.361532 2.672535 \n", + "3 2.281596 1.782833 \n", + "\n", + "title anohana: The Flower We Saw That Day - The Movie (2013) \\\n", + "userId \n", + "1 2.787331 \n", + "2 2.404456 \n", + "3 1.635173 \n", + "\n", + "title eXistenZ (1999) xXx (2002) xXx: State of the Union (2005) \\\n", + "userId \n", + "1 3.475076 3.253458 2.161087 \n", + "2 4.232789 2.911602 1.634576 \n", + "3 1.323276 2.887580 1.042618 \n", + "\n", + "title ¡Three Amigos! (1986) À nous la liberté (Freedom for Us) (1931) \n", + "userId \n", + "1 4.010495 0.859474 \n", + "2 4.135735 0.725684 \n", + "3 2.293890 0.396941 \n", + "\n", + "[3 rows x 9719 columns]" + ], + "text/html": [ + "\n", + "
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    title'71 (2014)'Hellboy': The Seeds of Creation (2004)'Round Midnight (1986)'Salem's Lot (2004)'Til There Was You (1997)'Tis the Season for Love (2015)'burbs, The (1989)'night Mother (1986)(500) Days of Summer (2009)*batteries not included (1987)...Zulu (2013)[REC] (2007)[REC]² (2009)[REC]³ 3 Génesis (2012)anohana: The Flower We Saw That Day - The Movie (2013)eXistenZ (1999)xXx (2002)xXx: State of the Union (2005)¡Three Amigos! (1986)À nous la liberté (Freedom for Us) (1931)
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    pred_score
    title
    Rear Window (1954)5.704612
    South Park: Bigger, Longer and Uncut (1999)5.451100
    Rounders (1998)5.298393
    Blade Runner (1982)5.244951
    Roger & Me (1989)5.191962
    Gattaca (1997)5.183179
    Ben-Hur (1959)5.130463
    Rosencrantz and Guildenstern Are Dead (1990)5.087375
    Big Lebowski, The (1998)5.038690
    Star Wars: Episode V - The Empire Strikes Back (1980)4.989601
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