diff --git "a/Week16_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\256\341\204\214\341\205\245\341\206\274\341\204\213\341\205\247\341\206\253.ipynb" "b/Week16_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\256\341\204\214\341\205\245\341\206\274\341\204\213\341\205\247\341\206\253.ipynb" new file mode 100644 index 0000000..fca2bee --- /dev/null +++ "b/Week16_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\256\341\204\214\341\205\245\341\206\274\341\204\213\341\205\247\341\206\253.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNSdPTMgWUAkFdV3YQ2PMEM"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["#**Week16_복습과제_우정연**"],"metadata":{"id":"ej0mfnFBYpaU"}},{"cell_type":"markdown","source":["##**9.5 콘텐츠 기반 필터링 실습 - TMDB 5000 영화 데이터 세트**"],"metadata":{"id":"nLnGpqXSYtmW"}},{"cell_type":"markdown","source":["- 유명한 영화 데이터 정보 사이트인 IMDB의 영화 중 주요 5000개 영화에 대한 메타 정보를 새롭게 가공해 캐글에서 제공하는 데이터 세트\n","- 콘텐츠 기반 필터링 수행"],"metadata":{"id":"0U9t21badHyZ"}},{"cell_type":"markdown","source":["###**[장르 속성을 이용한 영화 콘텐츠 기반 필터링]**\n","- 콘텐츠 기반 필터링: 사용자가 특정 영화를 감상하고 그 영화를 좋아했다면 그 영화와 비슷한 특성/속성, 구성 요소 등을 가진 다른 영화를 추천하는 것\n"," - 영화(또는 상품/서비스) 간의 유사성을 판단하는 기준이 영화를 구성하는 다양한 콘텐츠(장르, 감독, 배우, 평점, 키워드, 영화 설명)를 기반으로 하는 방식\n","- 영화 장르 속성을 기반으로 한 콘텐츠 기반 필터링 추천 시스템\n"," - 장르 칼럼 값의 유사도를 비교한 뒤 그중 높은 평점을 가지는 영화를 추천하는 방식"],"metadata":{"id":"FGxtkxqbdjmi"}},{"cell_type":"markdown","source":["###**[데이터 로딩 및 가공]**"],"metadata":{"id":"w0_J-gTDe3Jp"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":223},"id":"LrIHoW2AYhVT","executionInfo":{"status":"ok","timestamp":1736647966759,"user_tz":-540,"elapsed":30076,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"89fc81c4-a7ac-4785-da22-9806ae7374fa"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","(4803, 20)\n"]},{"output_type":"execute_result","data":{"text/plain":[" budget genres \\\n","0 237000000 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam... \n","\n"," homepage id \\\n","0 http://www.avatarmovie.com/ 19995 \n","\n"," keywords original_language \\\n","0 [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":... en \n","\n"," original_title overview \\\n","0 Avatar In the 22nd century, a paraplegic Marine is di... \n","\n"," popularity production_companies \\\n","0 150.437577 [{\"name\": \"Ingenious Film Partners\", \"id\": 289... \n","\n"," production_countries release_date revenue \\\n","0 [{\"iso_3166_1\": \"US\", \"name\": \"United States o... 2009-12-10 2787965087 \n","\n"," runtime spoken_languages status \\\n","0 162.0 [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso... Released \n","\n"," tagline title vote_average vote_count \n","0 Enter the World of Pandora. Avatar 7.2 11800 "],"text/html":["\n","
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budgetgenreshomepageidkeywordsoriginal_languageoriginal_titleoverviewpopularityproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_count
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What he doesn't expect is to get teamed up with a cocky civilian, World Class Boxing Champion Kelly Robinson, on a dangerous top secret espionage mission. Their assignment: using equal parts skill and humor, catch Arnold Gundars, one of the world's most successful arms dealers.\",\n \"When \\\"street smart\\\" rapper Christopher \\\"C-Note\\\" Hawkins (Big Boi) applies for a membership to all-white Carolina Pines Country Club, the establishment's proprietors are hardly ready to oblige him.\",\n \"As their first year of high school looms ahead, best friends Julie, Hannah, Yancy and Farrah have one last summer sleepover. Little do they know they're about to embark on the adventure of a lifetime. Desperate to shed their nerdy status, they take part in a night-long scavenger hunt that pits them against their popular archrivals. Everything under the sun goes on -- from taking Yancy's father's car to sneaking into nightclubs!\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"popularity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 31.816649749537806,\n \"min\": 0.0,\n \"max\": 875.581305,\n \"num_unique_values\": 4802,\n \"samples\": [\n 13.267631,\n 0.010909,\n 5.842299\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"production_companies\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3697,\n \"samples\": [\n \"[{\\\"name\\\": \\\"Paramount Pictures\\\", \\\"id\\\": 4}, {\\\"name\\\": \\\"Cherry Alley Productions\\\", \\\"id\\\": 2232}]\",\n \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}, {\\\"name\\\": \\\"Dune Entertainment\\\", \\\"id\\\": 444}, {\\\"name\\\": \\\"Regency Enterprises\\\", \\\"id\\\": 508}, {\\\"name\\\": \\\"Guy Walks into a Bar Productions\\\", \\\"id\\\": 2645}, {\\\"name\\\": \\\"Deep River Productions\\\", \\\"id\\\": 2646}, {\\\"name\\\": \\\"Friendly Films (II)\\\", \\\"id\\\": 81136}]\",\n \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}]\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"production_countries\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 469,\n \"samples\": [\n \"[{\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"FR\\\", \\\"name\\\": \\\"France\\\"}]\",\n \"[{\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"CA\\\", \\\"name\\\": \\\"Canada\\\"}, {\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}]\",\n \"[{\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}, {\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}]\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 3280,\n \"samples\": [\n \"1966-10-16\",\n \"1987-07-31\",\n \"1993-09-23\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"revenue\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 162857100,\n \"min\": 0,\n \"max\": 2787965087,\n \"num_unique_values\": 3297,\n \"samples\": [\n 11833696,\n 10462500,\n 17807569\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"runtime\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 22.611934588844207,\n \"min\": 0.0,\n \"max\": 338.0,\n \"num_unique_values\": 156,\n \"samples\": [\n 74.0,\n 85.0,\n 170.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"spoken_languages\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 544,\n \"samples\": [\n \"[{\\\"iso_639_1\\\": \\\"es\\\", \\\"name\\\": \\\"Espa\\\\u00f1ol\\\"}, {\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"fr\\\", \\\"name\\\": \\\"Fran\\\\u00e7ais\\\"}, {\\\"iso_639_1\\\": \\\"hu\\\", \\\"name\\\": \\\"Magyar\\\"}]\",\n \"[{\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"pt\\\", \\\"name\\\": \\\"Portugu\\\\u00eas\\\"}]\",\n \"[{\\\"iso_639_1\\\": \\\"de\\\", \\\"name\\\": \\\"Deutsch\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"la\\\", \\\"name\\\": \\\"Latin\\\"}, {\\\"iso_639_1\\\": \\\"pl\\\", \\\"name\\\": \\\"Polski\\\"}]\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"status\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Released\",\n \"Post Production\",\n \"Rumored\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tagline\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3944,\n \"samples\": [\n \"When you're 17, every day is war.\",\n \"An Unspeakable Horror. A Creative Genius. 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","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","\n","movies = pd.read_csv('drive/My Drive/Colab Notebooks/data/archive/tmdb_5000_movies.csv')\n","print(movies.shape)\n","movies.head(1)"]},{"cell_type":"code","source":["movies_df = movies[['id', 'title', 'genres', 'vote_average', 'vote_count', 'popularity',\n"," 'keywords', 'overview']]\n","\n","pd.set_option('max_colwidth', 100)\n","movies_df[['genres', 'keywords']][:1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":98},"id":"MMFPmu9ZhIs0","executionInfo":{"status":"ok","timestamp":1736647966760,"user_tz":-540,"elapsed":6,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"58c48423-0264-4e3d-9f0e-d73076a61206"},"execution_count":2,"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... "],"text/html":["\n","
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genreskeywords
0[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp...
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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\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"[{\\\"id\\\": 28, \\\"name\\\": \\\"Action\\\"}, {\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 878, \\\"name\\\": \\\"Science Fiction\\\"}]\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"keywords\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"[{\\\"id\\\": 1463, \\\"name\\\": \\\"culture clash\\\"}, {\\\"id\\\": 2964, \\\"name\\\": \\\"future\\\"}, {\\\"id\\\": 3386, \\\"name\\\": \\\"space war\\\"}, {\\\"id\\\": 3388, \\\"name\\\": \\\"space colony\\\"}, {\\\"id\\\": 3679, \\\"name\\\": \\\"society\\\"}, {\\\"id\\\": 3801, \\\"name\\\": \\\"space travel\\\"}, {\\\"id\\\": 9685, \\\"name\\\": \\\"futuristic\\\"}, {\\\"id\\\": 9840, \\\"name\\\": \\\"romance\\\"}, {\\\"id\\\": 9882, \\\"name\\\": \\\"space\\\"}, {\\\"id\\\": 9951, \\\"name\\\": \\\"alien\\\"}, {\\\"id\\\": 10148, \\\"name\\\": \\\"tribe\\\"}, {\\\"id\\\": 10158, \\\"name\\\": \\\"alien planet\\\"}, {\\\"id\\\": 10987, \\\"name\\\": \\\"cgi\\\"}, {\\\"id\\\": 11399, \\\"name\\\": \\\"marine\\\"}, {\\\"id\\\": 13065, \\\"name\\\": \\\"soldier\\\"}, {\\\"id\\\": 14643, \\\"name\\\": \\\"battle\\\"}, {\\\"id\\\": 14720, \\\"name\\\": \\\"love affair\\\"}, {\\\"id\\\": 165431, \\\"name\\\": \\\"anti war\\\"}, {\\\"id\\\": 193554, \\\"name\\\": \\\"power relations\\\"}, {\\\"id\\\": 206690, \\\"name\\\": \\\"mind and soul\\\"}, {\\\"id\\\": 209714, \\\"name\\\": \\\"3d\\\"}]\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":2}]},{"cell_type":"code","source":["from ast import literal_eval\n","movies_df['genres'] = movies_df['genres'].apply(literal_eval)\n","movies_df['keywords'] = movies_df['keywords'].apply(literal_eval)\n","\n","movies_df['genres'] = movies_df['genres'].apply(lambda x : [y['name'] for y in x])\n","movies_df['keywords'] = movies_df['keywords'].apply(lambda x : [ y['name'] for y in x])\n","movies_df[['genres', 'keywords']][:1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":98},"id":"Q40yqG43hoK-","executionInfo":{"status":"ok","timestamp":1736647967273,"user_tz":-540,"elapsed":518,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"05a38269-b39d-4ab5-a253-a6a07b06d6c6"},"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" genres \\\n","0 [Action, Adventure, Fantasy, Science Fiction] \n","\n"," keywords \n","0 [culture clash, future, space war, space colony, society, space travel, futuristic, romance, spa... "],"text/html":["\n","
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genreskeywords
0[Action, Adventure, Fantasy, Science Fiction][culture clash, future, space war, space colony, society, space travel, futuristic, romance, spa...
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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":3}]},{"cell_type":"markdown","source":["###**[장르 콘텐츠 유사도 측정]**\n","- genres 칼럼은 여러 개의 개별 장르가 리스트로 구성돼 있음\n"," - genres를 문자열로 변경한 뒤 이를 CountVectorizer로 피처 벡터화한 행렬 데이터 값을 코사인 유사도로 비교하는 방법\n"," 1. 문자열로 변환된 genres 칼럼을 Count 기반으로 피처 벡터화 변환\n"," 2. genres 문자열을 피처 벡터화 행렬로 변환한 데이터 세트를 코사인 유사도를 통해 비교. 이를 위해 데이터 세트의 레코드 별로 타 레코드와 장르에서 코사인 유사도 값을 가지는 객체를 생성\n"," 3. 장르 유사도가 높은 영화 중 평점이 높은 순으로 영화 추천\n"," "],"metadata":{"id":"yLpt_SVriOEN"}},{"cell_type":"code","source":["from sklearn.feature_extraction.text import CountVectorizer\n","\n","# CountVectorizer를 적용하기 위해 공백 문자로 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":"lAjsawNHi3M8","executionInfo":{"status":"ok","timestamp":1736647970173,"user_tz":-540,"elapsed":2903,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"29fba238-f159-4a94-efef-132219b5ee7d"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["(4803, 276)\n"]}]},{"cell_type":"markdown","source":["- cosine_similarity(): 코사인 유사도 계산\n"," - 기준 행과 비교 행의 코사인 유사도를 행렬 형태로 반환하는 함수"],"metadata":{"id":"-i29huO1jk44"}},{"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[:1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RGEaZVQzjuoy","executionInfo":{"status":"ok","timestamp":1736647970806,"user_tz":-540,"elapsed":636,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"e4c04263-6b88-429e-faab-8264570ab7e3"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["(4803, 4803)\n","[[1. 0.59628479 0.4472136 ... 0. 0. 0. ]]\n"]}]},{"cell_type":"markdown","source":["- genre_sim 객체: movies_df의 genre_literal 칼럼을 피처 벡터화한 행렬(genre_mat) 데이터의 행 별 유사도 정보를 가지고 있음.\n"," - movies_df DataFrame의 행별 장르 유사도 값을 가지고 있음\n","- movies_df의 개별 레코드에 대해 가장 장르 유사도가 높은 순으로 다른 레코드 추출\n"," - genre_sim 객체 이용"],"metadata":{"id":"1ZVgMW8GkEok"}},{"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":"FRYv5LSnkmIZ","executionInfo":{"status":"ok","timestamp":1736647972147,"user_tz":-540,"elapsed":1342,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"62750ba2-26c7-401c-fa87-7f7bda484850"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 0 3494 813 ... 3038 3037 2401]]\n"]}]},{"cell_type":"markdown","source":["- 반환 값이 의미하는 것\n"," - 0번 레코드의 경우 자신인 0번 레코드를 제외하면 3494번 레코드가 가장 유사도가 높고, 그 다음이 813번, 가장 유사도가 낮은 레코드는 2401번 레코드"],"metadata":{"id":"BVYJe4gSku_T"}},{"cell_type":"markdown","source":["###**[장르 콘텐츠 필터링을 이용한 영화 추천]**\n","- find_sim_movie()\n"," - 장르 유사도에 따라 영화를 추천하는 함수"],"metadata":{"id":"1dXB02mqk7iT"}},{"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_named를 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 위해 1차원 array로 변경\n"," print(similar_indexes)\n"," similar_indexes = similar_indexes.reshape(-1)\n","\n"," return df.iloc[similar_indexes]"],"metadata":{"id":"76RQEG91lEyg","executionInfo":{"status":"ok","timestamp":1736647972147,"user_tz":-540,"elapsed":16,"user":{"displayName":"우정연","userId":"02785637225882896926"}}},"execution_count":7,"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":"Qfeo_9a2mNRc","executionInfo":{"status":"ok","timestamp":1736647972147,"user_tz":-540,"elapsed":15,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"a3e547fb-c81a-4556-857c-72ccfad1a7db"},"execution_count":8,"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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titlevote_average
2731The Godfather: Part II8.3
1243Mean Streets7.2
3636Light Sleeper5.7
1946The Bad Lieutenant: Port of Call - New Orleans6.0
2640Things to Do in Denver When You're Dead6.7
4065Mi America0.0
1847GoodFellas8.2
4217Kids6.8
883Catch Me If You Can7.7
3866City of God8.1
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titlevote_averagevote_count
3519Stiff Upper Lips10.01
4247Me You and Five Bucks10.02
4045Dancer, Texas Pop. 8110.01
4662Little Big Top10.01
3992Sardaarji9.52
2386One Man's Hero9.32
2970There Goes My Baby8.52
1881The Shawshank Redemption8.58205
2796The Prisoner of Zenda8.411
3337The Godfather8.45893
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \" ascending = False)[:10]\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"The Prisoner of Zenda\",\n \"Me You and Five Bucks\",\n \"One Man's Hero\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vote_average\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7366591251499343,\n \"min\": 8.4,\n \"max\": 10.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 9.5,\n 8.4,\n 9.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vote_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3020,\n \"min\": 1,\n \"max\": 8205,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 5893,\n 8205\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["- 왜곡된 평점 데이터를 회피할 수 있도록 평점에 평가 횟수를 반영할 수 있는 새로운 평가 방식이 필요\n","- 유명한 영화 평점 사이트인 IMDB - 평가 횟수에 대한 가중치가 부여된 평점 방식을 사용\n"," - 가중 평점 공식\n"," - Weighted Rating = (v/(v+m)) * R + (m/(v+m)) * C\n"," - v: 개별 영화에 평점을 투표한 횟수\n"," - m: 평점을 부여하기 위한 최소 투표 횟수\n"," - R: 개별 영화에 대한 평균 평점\n"," - C: 전체 영화에 대한 평균 평점"],"metadata":{"id":"j6tjIONynnYF"}},{"cell_type":"code","source":["C = movies_df['vote_average'].mean()\n","m = movies_df['vote_count'].quantile(0.6)\n","print('C:', round(C, 3), 'm:', round(m,3))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i5k5uijNoTc3","executionInfo":{"status":"ok","timestamp":1736647972147,"user_tz":-540,"elapsed":12,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"c6509aa3-3e58-4cf9-c281-7199bb81018c"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["C: 6.092 m: 370.2\n"]}]},{"cell_type":"code","source":["percentile = 0.6\n","m = movies['vote_count'].quantile(percentile)\n","C = movies['vote_average'].mean()\n","\n","def weighted_vote_average(record):\n"," v = record['vote_count']\n"," R = record['vote_average']\n","\n"," return ( (v/(v+m)) * R ) + ( (m/(m+v)) * C )\n","\n","movies['weighted_vote'] = movies.apply(weighted_vote_average, axis = 1)\n","movies_df = movies.copy()\n","\n","movies_df[['title', 'vote_average', 'weighted_vote', 'vote_count']].sort_values('weighted_vote', ascending = False)[:10]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"yKB-NPLEokG8","executionInfo":{"status":"ok","timestamp":1736647972148,"user_tz":-540,"elapsed":11,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"17200a13-480e-4770-8f3a-a8a943dd2fee"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" title vote_average weighted_vote vote_count\n","1881 The Shawshank Redemption 8.5 8.396052 8205\n","3337 The Godfather 8.4 8.263591 5893\n","662 Fight Club 8.3 8.216455 9413\n","3232 Pulp Fiction 8.3 8.207102 8428\n","65 The Dark Knight 8.2 8.136930 12002\n","1818 Schindler's List 8.3 8.126069 4329\n","3865 Whiplash 8.3 8.123248 4254\n","809 Forrest Gump 8.2 8.105954 7927\n","2294 Spirited Away 8.3 8.105867 3840\n","2731 The Godfather: Part II 8.3 8.079586 3338"],"text/html":["\n","
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titlevote_averageweighted_votevote_count
1881The Shawshank Redemption8.58.3960528205
3337The Godfather8.48.2635915893
662Fight Club8.38.2164559413
3232Pulp Fiction8.38.2071028428
65The Dark Knight8.28.13693012002
1818Schindler's List8.38.1260694329
3865Whiplash8.38.1232484254
809Forrest Gump8.28.1059547927
2294Spirited Away8.38.1058673840
2731The Godfather: Part II8.38.0795863338
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titlevote_averageweighted_vote
2731The Godfather: Part II8.38.079586
1847GoodFellas8.27.976937
3866City of God8.17.759693
1663Once Upon a Time in America8.27.657811
883Catch Me If You Can7.77.557097
281American Gangster7.47.141396
4041This Is England7.46.739664
1149American Hustle6.86.717525
1243Mean Streets7.26.626569
2839Rounders6.96.530427
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"ratings_matrix"}},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","source":["- 사용자가 평점을 매기지 않은 영화가 칼럼으로 변환 -> NaN 값으로 할당\n","- NaN -> 0\n","- 칼럼명을 movieId가 아닌 영화명(title)으로 변경"],"metadata":{"id":"cSpLgTTvujOf"}},{"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 피벗 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":346},"id":"8W4oYaHuuy1F","executionInfo":{"status":"ok","timestamp":1736647973587,"user_tz":-540,"elapsed":7,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"845f6fbb-3de7-4aa6-8c65-3635b51f3cca"},"execution_count":15,"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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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)
userId
10.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.04.00.0
20.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
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title
'71 (2014)0.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.04.0
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title
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'Hellboy': The Seeds of Creation (2004)0.01.0000000.7071070.00.00.00.0000000.00.0000000.0...0.00.0000000.0000000.0000000.00.00.0000000.0000000.00.0
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Godfather, The (1972)
title
Godfather, The (1972)1.000000
Godfather: Part II, The (1974)0.821773
Goodfellas (1990)0.664841
One Flew Over the Cuckoo's Nest (1975)0.620536
Star Wars: Episode IV - A New Hope (1977)0.595317
Fargo (1996)0.588614
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"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["item_sim_df[\"Inception (2010)\"].sort_values(ascending = False)[1:6]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":272},"id":"iRYPnz-E14Sf","executionInfo":{"status":"ok","timestamp":1736647978546,"user_tz":-540,"elapsed":6,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"1181a045-0627-442b-b199-d400352f6932"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["title\n","Dark Knight, The (2008) 0.727263\n","Inglourious Basterds (2009) 0.646103\n","Shutter Island (2010) 0.617736\n","Dark Knight Rises, The (2012) 0.617504\n","Fight Club (1999) 0.615417\n","Name: Inception (2010), dtype: float64"],"text/html":["
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Inception (2010)
title
Dark Knight, The (2008)0.727263
Inglourious Basterds (2009)0.646103
Shutter Island (2010)0.617736
Dark Knight Rises, The (2012)0.617504
Fight Club (1999)0.615417
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"]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","source":["###**[아이템 기반 최근접 이웃 협업 필터링으로 개인화된 영화 추천]**\n","- 앞 예제: 사용자 평점을 기준으로 유사도 생성 -> 개인적인 취향을 반영하지 않고 영화 간 유사도만을 기준으로 추천한 것임\n","- 영화 유사도 데이터를 이용한 최근접 이웃 협업 필터링으로 개인에게 최적화된 영화 추천 구현\n"," - 가장 큰 특징: 개인이 아직 관람하지 않은 영화를 추천\n"," - 아직 관람하지 않은 영화에 대해 아이템 유사도와 기존 관람한 영화의 평점 데이터를 기반으로 새롭게 모든 영화의 예측 평점을 계산한 후 높은 예측 평점을 가진 영화를 추천하는 방식\n"," ![스크린샷 2025-01-11 오후 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)\n"," ![스크린샷 2025-01-11 오후 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Si,N와 Ru,N에 나오는 N의 값은 아이템의 최근접 이웃 범위 계수(item neighbor)를 의미\n"," - 특정 아이템과 유사도가 가장 높은 Top-N개의 다른 아이템을 추출하는 데 사용됨"],"metadata":{"id":"e-Rv3wVm3VLL"}},{"cell_type":"code","source":["def predict_rating(ratings_arr, item_sim_arr):\n"," ratings_pred = ratings_arr.dot(item_sim_arr)/np.array([np.abs(item_sim_arr).sum(axis = 1)])\n"," return ratings_pred\n","ratings_pred = predict_rating(ratings_matrix.values, item_sim_df.values)\n","ratings_pred_matrix = pd.DataFrame(data=ratings_pred, index = ratings_matrix.index,\n"," columns = ratings_matrix.columns)\n","ratings_pred_matrix.head(3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":346},"id":"QW_-X3E02GLZ","executionInfo":{"status":"ok","timestamp":1736647984800,"user_tz":-540,"elapsed":6259,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"213fd092-936f-4f97-9537-64df0656ace6"},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["title '71 (2014) 'Hellboy': The Seeds of Creation (2004) \\\n","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":20}]},{"cell_type":"markdown","source":["- 예측 평점이 사용자별 영화의 실제 평점과 영화의 코사인 유사도를 내적한 값이기에 기존에 영황를 관람하지 않아 0에 해당했던 실제 영화 평점이 예측에서는 값이 부여되는 경우가 많이 발생\n","- 예측 평점이 실제 평점에 비해 작을 수 있음\n"," - 내적 결과를 코사인 유사도 벡터 합으로 나누었기 때문에 생기는 현상\n","- 원래의 실제 평점과 얼마나 차이가 있는지 확인\n"," - 예측 평가 지표 MSE 적용\n"," - 사용자가 영화의 평점을 주지 않은 경우: 앞에서는 평점을 0으로 부과 / but 개인화된 예측 점수는 평점을 주지 않은 영화에 대해서도 아이템 유사도에 기반해 평점을 예측\n"," - 실제와 예측 평점의 차이는 기존에 평점이 부여된 데이터에 대해서만 오차 정도를 측정"],"metadata":{"id":"qbBQF7dn4i74"}},{"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":"XzE1US9e6xJC","executionInfo":{"status":"ok","timestamp":1736647984801,"user_tz":-540,"elapsed":4,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"11154690-56f5-4b44-d3c6-a852306d3d9d"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["아이템 기반 모든 최근접 이웃 MSE: 9.895354759094706\n"]}]},{"cell_type":"markdown","source":["- 실제 값과 예측 값은 서로 스케일이 다르므로 MSE가 클 수도 있음\n","- 중요한 것은 MSE를 감소시키는 방향으로 개선하는 것\n","- predict_rating() 함수: 해당 영화와 다른 모든 영화 간의 유사도 벡터를 적용한 것\n"," - 특정 영화와 가장 비슷한 유사도를 가지는 영화에 대해서만 유사도 벡터를 적용하는 함수로 변경 predict_rating_topsim(rating_arr, item_sim_arr, n = 20)"],"metadata":{"id":"EYn8lfJi7NWW"}},{"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","\n"," # 개인화된 예측 평점을 계산\n"," for row in range(ratings_arr.shape[0]):\n"," # top_n_items의 유사도와 평점을 곱하여 예측 평점 계산\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\n","\n","# 예측 평점 행렬 계산\n","ratings_pred = predict_rating_topsim(ratings_matrix.values, item_sim_df.values, n=20)\n","\n","# MSE 계산 (get_mse 함수는 별도로 정의되어 있어야 합니다)\n","print('아이템 기반 최근접 TOP-20 이웃 MSE: ', get_mse(ratings_pred, ratings_matrix.values))\n","\n","# 계산된 예측 평점 데이터를 DataFrame으로 변환\n","ratings_pred_matrix = pd.DataFrame(data=ratings_pred, index=ratings_matrix.index, columns=ratings_matrix.columns)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7oJzkC-JDk6A","executionInfo":{"status":"ok","timestamp":1736648074636,"user_tz":-540,"elapsed":89838,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"df45620a-a2ca-452f-e11b-2bbb6ae4c1f0"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["아이템 기반 최근접 TOP-20 이웃 MSE: 3.6949827608772314\n"]}]},{"cell_type":"markdown","source":["- MSE 향상됨\n","- 특정 사용자에 대해 영화 추천"],"metadata":{"id":"-UgHsrFe9IXu"}},{"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":"rtY_PCAx9Nvv","executionInfo":{"status":"ok","timestamp":1736648074637,"user_tz":-540,"elapsed":9,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"21bb7ef6-9757-4ac3-ab96-4fa3a3fdc842"},"execution_count":23,"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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9
title
Adaptation (2002)5.0
Citizen Kane (1941)5.0
Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)5.0
Producers, The (1968)5.0
Lord of the Rings: The Two Towers, The (2002)5.0
Lord of the Rings: The Fellowship of the Ring, The (2001)5.0
Back to the Future (1985)5.0
Austin Powers in Goldmember (2002)5.0
Minority Report (2002)4.0
Witness (1985)4.0
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"]},"metadata":{},"execution_count":23}]},{"cell_type":"markdown","source":["- 사용자에게 아이템 기반 협업 필터링을 통해 영화 추천\n"," - get_unseen_movies(): 먼저 사용자가 이미 평점을 준 영화를 제외하고 추천할 수 있도록 평점을 주지 않은 영화를 리스트 객체로 반환하는 함수"],"metadata":{"id":"f_-7wITv9akU"}},{"cell_type":"code","source":["def get_unseen_movies(ratings_matrix, userId):\n"," # userId로 입력받은 사용자의 모든 영화 정보를 추출해 Series로 반환함\n"," # 반환된 user_rating은 영화명(title)을 인덱스로 가지는 Series 객체임\n"," user_rating = ratings_matrix.loc[userId, :]\n","\n"," # user_rating이 0보다 크면 기존에 관람한 영화임. 대상 인덱스를 추출해 list 객체로 만듬\n"," already_seen = user_rating[user_rating > 0].index.tolist()\n","\n"," # 모든 영화명을 list 객체로 만듬\n"," movies_list = ratings_matrix.columns.tolist()\n","\n"," # list comprehension으로 already_seen에 해당하는 영화는 movies_list에서 제외함\n"," unseen_list = [movie for movie in movies_list if movie not in already_seen]\n","\n"," return unseen_list"],"metadata":{"id":"meJG1BiP9oSn","executionInfo":{"status":"ok","timestamp":1736648074637,"user_tz":-540,"elapsed":8,"user":{"displayName":"우정연","userId":"02785637225882896926"}}},"execution_count":24,"outputs":[]},{"cell_type":"code","source":["def recomm_movie_by_userid(pred_df, userId, unseen_list, top_n = 10):\n"," # 예측 평점 DataFrame에서 사용자 id 인덱스와 unseen_list로 들어온 영화명 칼럼을 추출해\n"," # 가장 예측 평점이 높은 순으로 정렬함\n"," recomm_movies = pred_df.loc[userId, unseen_list].sort_values(ascending = False)[:top_n]\n"," return recomm_movies\n","\n","# 사용자가 관람하지 않는 영화명 추출\n","unseen_list = get_unseen_movies(ratings_matrix, 9)\n","\n","# 아이템 기반의 최근접 이웃 협업 필터링으로 영화 추천\n","recomm_movies = recomm_movie_by_userid(ratings_pred_matrix, 9, unseen_list, top_n=10)\n","\n","# 평점 데이터를 DataFrame으로 생성\n","recomm_movies = pd.DataFrame(data = recomm_movies.values, index = recomm_movies.index,\n"," columns = ['pred_score'])\n","recomm_movies"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"Nzga-mfaCnmv","executionInfo":{"status":"ok","timestamp":1736648074637,"user_tz":-540,"elapsed":8,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"531a77a3-84ab-42d5-e424-bdb30d6dafca"},"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" pred_score\n","title \n","Shrek (2001) 0.866202\n","Spider-Man (2002) 0.857854\n","Last Samurai, The (2003) 0.817473\n","Indiana Jones and the Temple of Doom (1984) 0.816626\n","Matrix Reloaded, The (2003) 0.800990\n","Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001) 0.765159\n","Gladiator (2000) 0.740956\n","Matrix, The (1999) 0.732693\n","Pirates of the Caribbean: The Curse of the Black Pearl (2003) 0.689591\n","Lord of the Rings: The Return of the King, The (2003) 0.676711"],"text/html":["\n","
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pred_score
title
Shrek (2001)0.866202
Spider-Man (2002)0.857854
Last Samurai, The (2003)0.817473
Indiana Jones and the Temple of Doom (1984)0.816626
Matrix Reloaded, The (2003)0.800990
Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)0.765159
Gladiator (2000)0.740956
Matrix, The (1999)0.732693
Pirates of the Caribbean: The Curse of the Black Pearl (2003)0.689591
Lord of the Rings: The Return of the King, The (2003)0.676711
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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":25}]},{"cell_type":"markdown","source":["##**9.7 행렬 분해를 이용한 잠재 요인 협업 필터링 실습**\n","- 행렬 분해를 이용한 잠재 요인 협업 필터링 구현\n","- 행렬 분해 잠재 요인 협업 필터링\n"," - SVD 나 NMF 등을 적용 가능\n"," - 일반적으로 행렬 분해에는 SVD가 자주 사용되지만 사용자-아이템 평점 행렬에는 사용자가 평점을 매기지 않은 null 데이터가 많아 주로 SGD나 ALS 기반 행렬분해를 이용\n"," - 본 예제: SGD 기반의 행렬 분해를 구현하고 이를 기반으로 사용자에게 영화 추천\n"," - 확률적 경사 하강법을 이용"],"metadata":{"id":"qJhqKu-KE0nq"}},{"cell_type":"code","source":["def matrix_factorization(R, K, steps = 100, learning_rate = 0.01, r_lambda = 0.01):\n"," num_users, num_items = R.shape\n"," # P와 Q 매트릭스의 크기를 지정하고 정규 분포를 가진 랜덤 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 > 0인 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 오류값 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":"P8tyPhykFWYI","executionInfo":{"status":"ok","timestamp":1736648074637,"user_tz":-540,"elapsed":7,"user":{"displayName":"우정연","userId":"02785637225882896926"}}},"execution_count":26,"outputs":[]},{"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":"w_bNHw9DIkML","executionInfo":{"status":"ok","timestamp":1736648074637,"user_tz":-540,"elapsed":6,"user":{"displayName":"우정연","userId":"02785637225882896926"}}},"execution_count":27,"outputs":[]},{"cell_type":"markdown","source":["- matrix_factorization() 함수를 이용해 행렬 분해"],"metadata":{"id":"SU5-i_mwItkD"}},{"cell_type":"code","source":["movies = pd.read_csv('drive/My Drive/Colab Notebooks/data/ml-latest-small/movies.csv')\n","ratings = pd.read_csv('drive/My Drive/Colab Notebooks/data/ml-latest-small/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 movies와 조인 수행\n","rating_movies = pd.merge(ratings, movies, on='movieId')\n","# columns = 'title' 로 title 칼럼으로 pivot 수행\n","ratings_matrix = rating_movies.pivot_table('rating', index = 'userId', columns = 'title')\n","\n","P, Q = matrix_factorization(ratings_matrix.values, K = 50, steps = 100, learning_rate = 0.01,\n"," r_lambda = 0.01)\n","pred_matrix = np.dot(P, Q.T)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3myjUQbAG6qQ","outputId":"41f086af-95e0-4a98-8656-f53f7e66cfd1","executionInfo":{"status":"error","timestamp":1736662940715,"user_tz":-540,"elapsed":444030,"user":{"displayName":"우정연","userId":"02785637225882896926"}}},"execution_count":28,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","### iteration step: 0 rmse: 2.9871688379972077\n","### iteration step: 0 rmse: 2.9871760812162016\n","### iteration step: 0 rmse: 2.9871787637561815\n","### iteration step: 0 rmse: 2.987172927868749\n","### iteration step: 0 rmse: 2.9871638181056\n","### iteration step: 0 rmse: 2.9871533877060092\n","### iteration step: 0 rmse: 2.9871364458206915\n","### iteration step: 0 rmse: 2.9871276085318113\n","### iteration step: 0 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rmse: 2.9023619751336867\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\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 9\u001b[0m \u001b[0mratings_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrating_movies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'rating'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'userId'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'title'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m P, Q = matrix_factorization(ratings_matrix.values, K = 50, steps = 100, learning_rate = 0.01,\n\u001b[0m\u001b[1;32m 12\u001b[0m r_lambda = 0.01)\n\u001b[1;32m 13\u001b[0m \u001b[0mpred_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m\u001b[0m in \u001b[0;36mmatrix_factorization\u001b[0;34m(R, K, steps, learning_rate, r_lambda)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlearning_rate\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0meij\u001b[0m \u001b[0;34m*\u001b[0m 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\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[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'### iteration step: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" rmse: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrmse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m\u001b[0m in \u001b[0;36mget_rmse\u001b[0;34m(R, P, Q, non_zeros)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# 두 개의 분해된 행렬 P와 Q.T의 내적으로 예측 R 행렬 생성\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mfull_pred_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# 실제 R 행렬에서 널이 아닌 값의 위치 인덱스 추출해 실제 R 행렬과 예측 행렬의 RMSE 추출\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"markdown","source":["- 시간이 너무 오래 걸려서 포기했어요..\n"],"metadata":{"id":"rFBNtYL7LeRe"}},{"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":180},"id":"KDdacpouHPu6","executionInfo":{"status":"error","timestamp":1736662962848,"user_tz":-540,"elapsed":409,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"c06be56b-14e1-476a-8464-94136b08907e"},"execution_count":31,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'pred_matrix' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m ratings_pred_matrix = pd.DataFrame(data = pred_matrix, index = ratings_matrix.index,\n\u001b[0m\u001b[1;32m 2\u001b[0m columns = ratings_matrix.columns)\n\u001b[1;32m 3\u001b[0m \u001b[0mratings_pred_matrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'pred_matrix' is not defined"]}]},{"cell_type":"markdown","source":["- 예측 사용자-아이템 평점 행렬 정보를 이용해 개인화된 영화 추천"],"metadata":{"id":"EO_gf8MZVTGj"}},{"cell_type":"code","source":["def get_unseen_movies(ratings_matrix, userId):\n"," # userId로 입력받은 사용자의 모든 영화 정보를 추출해 Series로 반환함\n"," # 반환된 user_rating은 영화명(title)을 인덱스로 가지는 Series 객체임\n"," user_rating = ratings_matrix.loc[userId, :]\n","\n"," # user_rating이 0보다 크면 기존에 관람한 영화임. 대상 인덱스를 추출해 list 객체로 만듬\n"," already_seen = user_rating[user_rating > 0].index.tolist()\n","\n"," # 모든 영화명을 list 객체로 만듬\n"," movies_list = ratings_matrix.columns.tolist()\n","\n"," # list comprehension으로 already_seen에 해당하는 영화는 movies_list에서 제외함\n"," unseen_list = [movie for movie in movies_list if movie not in already_seen]\n","\n"," return unseen_list\n","\n","def recomm_movie_by_userid(pred_df, userId, unseen_list, top_n = 10):\n"," # 예측 평점 DataFrame에서 사용자 id 인덱스와 unseen_list로 들어온 영화명 칼럼을 추출해\n"," # 가장 예측 평점이 높은 순으로 정렬함\n"," recomm_movies = pred_df.loc[userId, unseen_list].sort_values(ascending = False)[:top_n]\n"," return recomm_movies\n"],"metadata":{"id":"UhAqnyHeVdjL","executionInfo":{"status":"ok","timestamp":1736662957091,"user_tz":-540,"elapsed":932,"user":{"displayName":"우정연","userId":"02785637225882896926"}}},"execution_count":30,"outputs":[]},{"cell_type":"code","source":["# 사용자가 관람하지 않은 영화명 추출\n","unseen_list = get_unseen_movies(ratings_matrix, 9)\n","\n","# 잠재 요인 협업 필터링으로 영화 추천\n","recomm_movies = recomm_movie_by_userid(ratings_pred_matrix, 9, unseen_list, top_n = 10)\n","\n","# 평점 데이터를 DataFrame으로 생성\n","recomm_movies = pd.DataFrame(data=recomm_movies.values, index = recomm_movies.index,\n"," columns = ['pred_score'])\n","recomm_movies"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"Hl5YnubAVpg8","executionInfo":{"status":"ok","timestamp":1736662953475,"user_tz":-540,"elapsed":1352,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"4f614b49-e1ca-4434-8669-a1bfa17fbedf"},"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" pred_score\n","title \n","Shrek (2001) 0.866202\n","Spider-Man (2002) 0.857854\n","Last Samurai, The (2003) 0.817473\n","Indiana Jones and the Temple of Doom (1984) 0.816626\n","Matrix Reloaded, The (2003) 0.800990\n","Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001) 0.765159\n","Gladiator (2000) 0.740956\n","Matrix, The (1999) 0.732693\n","Pirates of the Caribbean: The Curse of the Black Pearl (2003) 0.689591\n","Lord of the Rings: The Return of the King, The (2003) 0.676711"],"text/html":["\n","
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pred_score
title
Shrek (2001)0.866202
Spider-Man (2002)0.857854
Last Samurai, The (2003)0.817473
Indiana Jones and the Temple of Doom (1984)0.816626
Matrix Reloaded, The (2003)0.800990
Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)0.765159
Gladiator (2000)0.740956
Matrix, The (1999)0.732693
Pirates of the Caribbean: The Curse of the Black Pearl (2003)0.689591
Lord of the Rings: The Return of the King, The (2003)0.676711
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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":29}]},{"cell_type":"markdown","source":["- 앞 절의 아이템 기반 협업 필터링 결과와는 추천 영화가 많이 다름"],"metadata":{"id":"vWkEsroJWEmj"}}]} \ No newline at end of file diff --git "a/Week16_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\256\341\204\214\341\205\245\341\206\274\341\204\213\341\205\247\341\206\253.ipynb" "b/Week16_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\256\341\204\214\341\205\245\341\206\274\341\204\213\341\205\247\341\206\253.ipynb" new file mode 100644 index 0000000..173b386 --- /dev/null +++ "b/Week16_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\256\341\204\214\341\205\245\341\206\274\341\204\213\341\205\247\341\206\253.ipynb" @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMKuHMW/+aMi4gBu3KjvcJu"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["#**Week16_예습과제_우정연**"],"metadata":{"id":"lwkE7mnYcVdY"}},{"cell_type":"markdown","source":["##**9.4 잠재 요인 협업 필터링**"],"metadata":{"id":"etIbojzBcY77"}},{"cell_type":"markdown","source":["- SGD(Stochastic Gradient Descent: 확률적 경사 하강법)을 이용해 행렬 분해를 수행하는 예제\n"," - 분해하려는 원본 행렬 R을 P와 Q로 분해한 뒤에 다시 P와 Q.T의 내적으로 예측 행렬을 만드는 예제"],"metadata":{"id":"irGqmJ7Ncfa-"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"l4XSdBp7cSn1","executionInfo":{"status":"ok","timestamp":1736080475992,"user_tz":-540,"elapsed":369,"user":{"displayName":"우정연","userId":"02785637225882896926"}}},"outputs":[],"source":["import numpy as np\n","\n","# 원본 행렬 R 생성, 분해 행렬 P와 Q 초기화, 잠재 요인 차원 K는 3으로 설정\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","\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))\n"]},{"cell_type":"markdown","source":["- 실제 R 행렬과 예측 행렬의 오차를 구하는 `get_rmse()` 함수\n"," - `get_rmse()` 함수: 실제 R 행렬의 널이 아닌 행렬 값의 위치 인덱스를 추출해 이 인덱스에 있는 실제 R 행렬 값과 분해된 P, Q를 이용해 다시 조합된 예측 행렬 값의 RMSE 값을 반환"],"metadata":{"id":"3ljxS6GedY0i"}},{"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":"S4CDSSm1d5Yr","executionInfo":{"status":"ok","timestamp":1736080814017,"user_tz":-540,"elapsed":323,"user":{"displayName":"우정연","userId":"02785637225882896926"}}},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":["- SGD 기반으로 행렬 분석 수행\n"," - R에서 널 값을 제외한 데이터의 행렬 인덱스 추출\n"," - steps는 SGD를 반복해서 업데이트할 횟수, learning_rate은 SGD의 학습률, r_lambda는 L2 Regularization 계수\n"," - steps = 1000 동안 반복하면서 새로운 p,q값으로 업데이트\n"," - get_rmse() 함수를 통해 50회 반복시마다 오류 값을 출력"],"metadata":{"id":"j87zwMHXevVG"}},{"cell_type":"code","source":["# R > 0 인 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 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"," eji = r - np.dot(P[i, :], Q[j, :].T)\n"," # Regularization을 반영한 SGD 업데이트 공식 적용\n"," P[i, :] = P[i, :] + learning_rate * (eji * Q[j, :] - r_lambda * P[i, :])\n"," Q[j, :] = Q[j, :] + learning_rate * (eji * 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":"0XQTC1WMfKeE","executionInfo":{"status":"ok","timestamp":1736081218967,"user_tz":-540,"elapsed":8352,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"58a6a071-9ef1-44ff-bbca-651418a63a62"},"execution_count":3,"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 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3))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yMsVpaeQgcKc","executionInfo":{"status":"ok","timestamp":1736081295748,"user_tz":-540,"elapsed":314,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"0b5774d9-ba9a-48bf-f588-a0f2b4057ddd"},"execution_count":4,"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":["##**9.8 파이썬 추천 시스템 패키지 - Surprise**"],"metadata":{"id":"4V1-lwidhJ2v"}},{"cell_type":"markdown","source":["###**[Surprise 패키지 소개]**"],"metadata":{"id":"Mu-EJSfGhOnS"}},{"cell_type":"code","source":["pip install scikit-surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KR_dEjj7hI0I","executionInfo":{"status":"ok","timestamp":1736081587740,"user_tz":-540,"elapsed":80838,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"c625fb85-7f5f-4b07-ccee-55d26880f942"},"execution_count":6,"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[31m5.2 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=2357279 sha256=20c86d0cd1e9477a52ad50850d0e6d2594ea0637e4df88a29ac567a8f1598dbb\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":"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\n","\n","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)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t7GVxHrJh0qZ","executionInfo":{"status":"ok","timestamp":1736082327942,"user_tz":-540,"elapsed":5118,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"9d97fe05-f3ce-4d9b-e152-5fc0a11942d5"},"execution_count":7,"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":"markdown","source":["- SVD로 잠재 요인 협업 필터링 수행\n","- 학습된 추천 알고리즘을 기반으로 테스트 데이터 세트에 대해 추천 수행 - test()와 predict(): Surprise에서 추천을 예측하는 메서드\n"," - test(): 사용자-아이템 평점 데이터 세트 전체에 대해 추천을 예측하는 메서드, 입력된 데이터 세트에 대해 추천 데이터 세트를 만들어 줌\n"," - predict(): 개별 사용자와 영화에 대한 추천 평점을 반환해 줌"],"metadata":{"id":"LZVSXycKqNl1"}},{"cell_type":"code","source":["algo = SVD()\n","algo.fit(trainset)\n","\n","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":"EbfhieJZqNAa","executionInfo":{"status":"ok","timestamp":1736083912276,"user_tz":-540,"elapsed":1809,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"76f9f38b-67bc-4eb9-e9b0-93bb348b50e0"},"execution_count":9,"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=3.386932093447826, details={'was_impossible': False}),\n"," Prediction(uid='882', iid='291', r_ui=4.0, est=3.8147246721394614, details={'was_impossible': False}),\n"," Prediction(uid='535', iid='507', r_ui=5.0, est=3.9161230361701462, details={'was_impossible': False}),\n"," Prediction(uid='697', iid='244', r_ui=5.0, est=3.7186230640100977, details={'was_impossible': False}),\n"," Prediction(uid='751', iid='385', r_ui=4.0, est=3.3514339985975967, details={'was_impossible': False})]"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["- Prediction 객체에서 uid, iid, est 속성을 추출한 예제"],"metadata":{"id":"lOfdBNKit-K8"}},{"cell_type":"code","source":["[ (pred.uid, pred.iid, pred.est) for pred in predictions[:3] ]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HCQPxkGvrNqD","executionInfo":{"status":"ok","timestamp":1736084851807,"user_tz":-540,"elapsed":368,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"6db835c7-e9c9-4119-d9c4-c180af5ffca2"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('120', '282', 3.386932093447826),\n"," ('882', '291', 3.8147246721394614),\n"," ('535', '507', 3.9161230361701462)]"]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","source":["- Surprise 패키지의 다른 추천 예측 메서드인 predict()를 이용해 추천 예측\n"],"metadata":{"id":"fAi4wgmRuJ8_"}},{"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":"Ye7NodtDusG0","executionInfo":{"status":"ok","timestamp":1736085032932,"user_tz":-540,"elapsed":339,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"ad8d701b-6ad4-49e1-f3d2-efbc0259d46a"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["user: 196 item: 302 r_ui = None est = 4.17 {'was_impossible': False}\n"]}]},{"cell_type":"markdown","source":["- Surprise의 accuracy 모듈의 rmse()를 이용해 RMSE 평가 결과를 확인"],"metadata":{"id":"VlILRDoQu5nZ"}},{"cell_type":"code","source":["accuracy.rmse(predictions)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PF10RhvHvygr","executionInfo":{"status":"ok","timestamp":1736085295478,"user_tz":-540,"elapsed":388,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"16ba2e11-02a8-41ba-f6f6-ec1a08288612"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["RMSE: 0.9482\n"]},{"output_type":"execute_result","data":{"text/plain":["0.9481869292906402"]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","source":["###**[Surprise 주요 모듈 소개]**\n","OS 파일 데이터를 Surprise 데이터 세트로 로딩"],"metadata":{"id":"y2bGlb_Hv2xT"}},{"cell_type":"code","source":["import pandas as pd\n","\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","ratings = pd.read_csv('drive/My Drive/Colab Notebooks/data/ml-latest-small/ratings.csv')\n","# ratings_noh.csv 파일로 업로드 시 인덱스와 헤더를 모두 제거한 새로운 파일 생성\n","ratings.to_csv('drive/My Drive/Colab Notebooks/data/ml-latest-small/ratings_noh.csv', index = False, header = False)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jl3okyqgy_fb","executionInfo":{"status":"ok","timestamp":1736086745150,"user_tz":-540,"elapsed":20449,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"802df31e-93d4-4a7a-e38c-621f23b99a9d"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","source":["- ratings_noh.csv 파일은 ratings.csv 파일에서 헤더가 삭제된 파일\n","- ratings_noh.csv를 Dataset 모듈의 load_from_file()을 이용해 Dataset로 로드"],"metadata":{"id":"Jp2ZZm0c1c7N"}},{"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('drive/My Drive/Colab Notebooks/data/ml-latest-small/ratings_noh.csv', reader = reader)\n"],"metadata":{"id":"JLGCdsjl1cFT","executionInfo":{"status":"ok","timestamp":1736087400651,"user_tz":-540,"elapsed":701,"user":{"displayName":"우정연","userId":"02785637225882896926"}}},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":["- SVD 행렬 분해 기법을 이용한 추천 예측\n"," - 잠재 요인 크기 K 값을 나타내는 파라미터인 n_factors를 50으로 설정해 데이터를 학습한 뒤 테스트 데이터 세트를 적용해 예측 평점 구하기\n"," - 예측 평점과 실제 평점 데이터를 RMSE로 평가"],"metadata":{"id":"CP4ZFbnG5_GF"}},{"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)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OZgs_QCW6C4k","executionInfo":{"status":"ok","timestamp":1736088076157,"user_tz":-540,"elapsed":3024,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"6df21012-9329-45b4-8b46-20eaf1f9e875"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["RMSE: 0.8682\n"]},{"output_type":"execute_result","data":{"text/plain":["0.8681952927143516"]},"metadata":{},"execution_count":17}]},{"cell_type":"markdown","source":["- 판다스 DataFrame에서 Surprise 데이터 세트로 로딩"],"metadata":{"id":"a78PT_cg6dxr"}},{"cell_type":"code","source":["import pandas as pd\n","from surprise import Reader, Dataset\n","\n","ratings = pd.read_csv('drive/My Drive/Colab Notebooks/data/ml-latest-small/ratings.csv')\n","reader = Reader(rating_scale = (0.5, 5.0))\n","\n","# ratings DataFrame에서 칼럼은 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":"pe3CebYI7mqe","executionInfo":{"status":"ok","timestamp":1736088549978,"user_tz":-540,"elapsed":2340,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"58869a32-e718-41de-e8d3-29ebab35d4ed"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["RMSE: 0.8682\n"]},{"output_type":"execute_result","data":{"text/plain":["0.8681952927143516"]},"metadata":{},"execution_count":18}]},{"cell_type":"markdown","source":["###**[교차 검증과 하이퍼 파라미터 튜닝]**"],"metadata":{"id":"Qs0ae5Gm_Bs8"}},{"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('drive/My Drive/Colab Notebooks/data/ml-latest-small/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', 'MSE'], cv = 5, verbose = True)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iD_1pXpr8RBU","executionInfo":{"status":"ok","timestamp":1736089429107,"user_tz":-540,"elapsed":12536,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"e08ffe55-0ef6-4c72-c09c-76956900d87b"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Evaluating RMSE, MSE of algorithm SVD on 5 split(s).\n","\n"," Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std \n","RMSE (testset) 0.8703 0.8748 0.8732 0.8734 0.8720 0.8727 0.0015 \n","MSE (testset) 0.7574 0.7653 0.7625 0.7628 0.7603 0.7617 0.0026 \n","Fit time 1.65 2.59 2.00 1.68 1.67 1.92 0.36 \n","Test time 0.23 0.55 0.13 0.12 0.12 0.23 0.16 \n"]},{"output_type":"execute_result","data":{"text/plain":["{'test_rmse': array([0.87030739, 0.87482049, 0.87321225, 0.87341209, 0.87196986]),\n"," 'test_mse': array([0.75743495, 0.7653109 , 0.76249963, 0.76284869, 0.76033143]),\n"," 'fit_time': (1.647949457168579,\n"," 2.5882577896118164,\n"," 1.9982025623321533,\n"," 1.6811463832855225,\n"," 1.673377513885498),\n"," 'test_time': (0.23110675811767578,\n"," 0.5467901229858398,\n"," 0.13285279273986816,\n"," 0.12348580360412598,\n"," 0.12383365631103516)}"]},"metadata":{},"execution_count":19}]},{"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":"FqKfgdg1A5hV","executionInfo":{"status":"ok","timestamp":1736090040994,"user_tz":-540,"elapsed":129644,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"c8a73c09-8c5d-4e31-b304-851c1306f839"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["0.8774511791810724\n","{'n_epochs': 20, 'n_factors': 50}\n"]}]},{"cell_type":"markdown","source":["###**[Surprise를 이용한 개인화 영화 추천 시스템 구축]**"],"metadata":{"id":"S9Rv_hUYBeO7"}},{"cell_type":"code","source":["# 다음 코드는 train_test_split()으로 분리되지 않는 데이터 세트에 fit()을 호출해 오류 발생\n","data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']])\n","algo = SVD(n_factors = 50, random_state = 0)\n","algo.fit(data)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":198},"id":"xHYlxgmhBj7g","executionInfo":{"status":"error","timestamp":1736090312408,"user_tz":-540,"elapsed":3,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"5865214b-f1ec-49cf-ce37-f84ab28159ad"},"execution_count":22,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"Dataset.load_from_df() missing 1 required positional argument: 'reader'","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\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 1\u001b[0m \u001b[0;31m# 다음 코드는 train_test_split()으로 분리되지 않는 데이터 세트에 fit()을 호출해 오류 발생\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\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[1;32m 4\u001b[0m \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[0;31mTypeError\u001b[0m: Dataset.load_from_df() missing 1 required positional argument: 'reader'"]}]},{"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","# DatasestAutoFolds 클래스를 ratings_noh.csv 파일 기반으로 생성\n","data_folds = DatasetAutoFolds(ratings_file = 'drive/My Drive/Colab Notebooks/data/ml-latest-small/ratings_noh.csv', reader = reader)\n","\n","# 전체 데이터를 학습 데이터로 생성함\n","trainset = data_folds.build_full_trainset()\n","\n","algo = SVD(n_epochs = 20, n_factors = 50, random_state = 0)\n","algo.fit(trainset)\n","\n","# 영화에 대한 상세 속성 정보 DataFrame 로딩\n","movies = pd.read_csv('drive/My Drive/Colab Notebooks/data/ml-latest-small/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":"TN6sjkZpB4Ie","executionInfo":{"status":"ok","timestamp":1736090329362,"user_tz":-540,"elapsed":2432,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"52214e6a-c8f4-46af-e6f3-61e0ad65af6e"},"execution_count":24,"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":"8cXYfwFOFdNe","executionInfo":{"status":"ok","timestamp":1736090991720,"user_tz":-540,"elapsed":356,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"977c25a2-324b-43ff-8ce8-9c438d5044f2"},"execution_count":25,"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),\n"," '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":"Z6HH-tdRFliU","executionInfo":{"status":"ok","timestamp":1736091692397,"user_tz":-540,"elapsed":333,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"c462d128-835e-47c5-818e-e43176c1c460"},"execution_count":27,"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","\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 Prediction 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 위해 아래의 sortkey_est 함수를 정의함\n"," # sortkey_est 함수는 list 객체의 sort() 함수의 키 값으로 사용되어 정렬 수행\n","\n"," def sortkey_est(pred):\n"," return pred.est\n","\n"," # sortkey_est() 반환값의 내림 차순으로 정렬 수행하고 top_n 개의 최상위 값 추출\n"," predictions.sort(key = sortkey_est, reverse = True)\n"," top_predictions = predictions[:top_n]\n","\n"," # top_n 으로 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,\n"," top_movie_titles,\n"," top_movie_rating)]\n","\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":"pH4q6QK9IKOG","executionInfo":{"status":"ok","timestamp":1736092187225,"user_tz":-540,"elapsed":325,"user":{"displayName":"우정연","userId":"02785637225882896926"}},"outputId":"fed9b73a-8608-463b-872d-dfff3519ba31"},"execution_count":30,"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_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\256\341\204\214\341\205\245\341\206\274\341\204\213\341\205\247\341\206\253.pdf" "b/Week16_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\256\341\204\214\341\205\245\341\206\274\341\204\213\341\205\247\341\206\253.pdf" new file mode 100644 index 0000000..dd9c63c Binary files /dev/null and "b/Week16_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\256\341\204\214\341\205\245\341\206\274\341\204\213\341\205\247\341\206\253.pdf" differ