diff --git "a/Week16_\353\263\265\354\212\265\352\263\274\354\240\234_\352\271\200\353\221\220\355\230\204.ipynb" "b/Week16_\353\263\265\354\212\265\352\263\274\354\240\234_\352\271\200\353\221\220\355\230\204.ipynb" new file mode 100644 index 0000000..4b08f39 --- /dev/null +++ "b/Week16_\353\263\265\354\212\265\352\263\274\354\240\234_\352\271\200\353\221\220\355\230\204.ipynb" @@ -0,0 +1,2804 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6ab46f3c", + "metadata": {}, + "source": [ + "# 9.6" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "01a725ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4803, 20)\n" + ] + }, + { + "data": { + "text/html": [ + "
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budgetgenreshomepageidkeywordsoriginal_languageoriginal_titleoverviewpopularityproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_count
0237000000[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...http://www.avatarmovie.com/19995[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...enAvatarIn the 22nd century, a paraplegic Marine is di...150.437577[{\"name\": \"Ingenious Film Partners\", \"id\": 289...[{\"iso_3166_1\": \"US\", \"name\": \"United States o...2009-12-102787965087162.0[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...ReleasedEnter the World of Pandora.Avatar7.211800
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" + ], + "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 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import warnings; warnings.filterwarnings('ignore')\n", + "movies = pd.read_csv('tmdb_5000_movies.csv') \n", + "print(movies.shape)\n", + "movies.head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "99b6d242", + "metadata": {}, + "outputs": [], + "source": [ + "movies_df = movies[['id', 'title', 'genres', 'vote_average', 'vote_count', 'popularity', 'keywords', 'overview']]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "143d171e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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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" + ], + "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... " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.set_option('max_colwidth', 100)\n", + "movies_df[['genres', 'keywords' ]][: 1 ]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "951af716", + "metadata": {}, + "outputs": [], + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "78a232f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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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" + ], + "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... " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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 ]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "324090f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4803, 276)\n" + ] + } + ], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\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=1, ngram_range=(1, 2)) \n", + "genre_mat = count_vect.fit_transform(movies_df['genres_literal']) \n", + "print(genre_mat.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5cfbf743", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4803, 4803)\n", + "[[1. 0.59628479 0.4472136 ... 0. 0. 0. ]\n", + " [0.59628479 1. 0.4 ... 0. 0. 0. ]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics.pairwise import cosine_similarity\n", + "genre_sim = cosine_similarity(genre_mat, genre_mat)\n", + "print(genre_sim.shape)\n", + "print(genre_sim[:2])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3cb140d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 3494 813 ... 3038 3037 2401]]\n" + ] + } + ], + "source": [ + "genre_sim_sorted_ind = genre_sim.argsort()[:, ::-1] \n", + "print(genre_sim_sorted_ind[:1])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f62c19cd", + "metadata": {}, + "outputs": [], + "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", + " # title_name을 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]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "af7ccf2a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2731 1243 3636 1946 2640 4065 1847 4217 883 3866]]\n" + ] + }, + { + "data": { + "text/html": [ + "
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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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" + ], + "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" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "similar_movies = find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather', 10) \n", + "similar_movies[['title', 'vote_average']]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "11b3f9a7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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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" + ], + "text/plain": [ + " title vote_average vote_count\n", + "3519 Stiff Upper Lips 10.0 1\n", + "4247 Me You and Five Bucks 10.0 2\n", + "4045 Dancer, Texas Pop. 81 10.0 1\n", + "4662 Little Big Top 10.0 1\n", + "3992 Sardaarji 9.5 2\n", + "2386 One Man's Hero 9.3 2\n", + "2970 There Goes My Baby 8.5 2\n", + "1881 The Shawshank Redemption 8.5 8205\n", + "2796 The Prisoner of Zenda 8.4 11\n", + "3337 The Godfather 8.4 5893" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "movies_df[['title', 'vote_average', 'vote_count']].sort_values('vote_average', ascending=False)[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e946ddfe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C: 6.092 m: 370.2\n" + ] + } + ], + "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))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4dab9573", + "metadata": {}, + "outputs": [], + "source": [ + "percentile = 0.6\n", + "m = movies_df['vote_count'].quantile(percentile)\n", + "C = movies_df['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_df['weighted_vote'] = movies.apply(weighted_vote_average, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1c7eff8c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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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" + ], + "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" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "movies_df[['title', 'vote_average', 'weighted_vote', 'vote_count']].sort_values( \n", + " 'weighted_vote', ascending=False)[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "49b69752", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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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" + ], + "text/plain": [ + " title vote_average weighted_vote\n", + "2731 The Godfather: Part II 8.3 8.079586\n", + "1847 GoodFellas 8.2 7.976937\n", + "3866 City of God 8.1 7.759693\n", + "1663 Once Upon a Time in America 8.2 7.657811\n", + "883 Catch Me If You Can 7.7 7.557097\n", + "281 American Gangster 7.4 7.141396\n", + "4041 This Is England 7.4 6.739664\n", + "1149 American Hustle 6.8 6.717525\n", + "1243 Mean Streets 7.2 6.626569\n", + "2839 Rounders 6.9 6.530427" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def find_sim_movie(df, sorted_ind, title_name, top_n=10):\n", + " title_movie = df[df['title'] == title_name] \n", + " title_index = title_movie.index.values\n", + " # top_n의 2배에 해당하는 장르 유사성이 높은 인덱스 추출\n", + " similar_indexes = sorted_ind[title_index, :(top_n*2)]\n", + " similar_indexes = similar_indexes.reshape(-1)\n", + " # 기준 영화 인덱스는 제외\n", + " similar_indexes = similar_indexes[similar_indexes != title_index]\n", + " # top_n의 2배에 해당하는 후보군에서 weighted_vote가 높은 순으로 top_n만큼 추출\n", + " return df.iloc[similar_indexes].sort_values('weighted_vote', ascending=False)[:top_n]\n", + "similar_movies = find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather', 10) \n", + "similar_movies[['title', 'vote_average', 'weighted_vote']]" + ] + }, + { + "cell_type": "markdown", + "id": "2bdb1519", + "metadata": {}, + "source": [ + "# 9.7" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7339ee27", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(9742, 3)\n", + "(100836, 4)\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "movies = pd.read_csv('movies.csv')\n", + "ratings = pd.read_csv('ratings.csv')\n", + "print(movies.shape)\n", + "print(ratings.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "9e2778c5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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3 rows × 9724 columns

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" + ], + "text/plain": [ + "movieId 1 2 3 4 5 6 7 8 \\\n", + "userId \n", + "1 4.0 NaN 4.0 NaN NaN 4.0 NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + "movieId 9 10 ... 193565 193567 193571 193573 193579 193581 \\\n", + "userId ... \n", + "1 NaN NaN ... NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN ... NaN NaN NaN NaN NaN NaN \n", + "3 NaN NaN ... NaN NaN NaN NaN NaN NaN \n", + "\n", + "movieId 193583 193585 193587 193609 \n", + "userId \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "\n", + "[3 rows x 9724 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ratings = ratings[['userId', 'movieId', 'rating']]\n", + "ratings_matrix = ratings.pivot_table('rating', index='userId', columns='movieId') \n", + "ratings_matrix.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "1c8f5213", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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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" + ], + "text/plain": [ + "title '71 (2014) \\\n", + "title \n", + "'71 (2014) 1.0 \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 \n", + "'Round Midnight (1986) 0.0 \n", + "\n", + "title 'Hellboy': The Seeds of Creation (2004) \\\n", + "title \n", + "'71 (2014) 0.000000 \n", + "'Hellboy': The Seeds of Creation (2004) 1.000000 \n", + "'Round Midnight (1986) 0.707107 \n", + "\n", + "title 'Round Midnight (1986) \\\n", + "title \n", + "'71 (2014) 0.000000 \n", + "'Hellboy': The Seeds of Creation (2004) 0.707107 \n", + "'Round Midnight (1986) 1.000000 \n", + "\n", + "title 'Salem's Lot (2004) \\\n", + "title \n", + "'71 (2014) 0.0 \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 \n", + "'Round Midnight (1986) 0.0 \n", + "\n", + "title 'Til There Was You (1997) \\\n", + "title \n", + "'71 (2014) 0.0 \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 \n", + "'Round Midnight (1986) 0.0 \n", + "\n", + "title 'Tis the Season for Love (2015) \\\n", + "title \n", + "'71 (2014) 0.0 \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 \n", + "'Round Midnight (1986) 0.0 \n", + "\n", + "title 'burbs, The (1989) \\\n", + "title \n", + "'71 (2014) 0.000000 \n", + "'Hellboy': The Seeds of Creation (2004) 0.000000 \n", + "'Round Midnight (1986) 0.176777 \n", + "\n", + "title 'night Mother (1986) \\\n", + "title \n", + "'71 (2014) 0.0 \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 \n", + "'Round Midnight (1986) 0.0 \n", + "\n", + "title (500) Days of Summer (2009) \\\n", + "title \n", + "'71 (2014) 0.141653 \n", + "'Hellboy': The Seeds of Creation (2004) 0.000000 \n", + "'Round Midnight (1986) 0.000000 \n", + "\n", + "title *batteries not included (1987) ... \\\n", + "title ... \n", + "'71 (2014) 0.0 ... \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 ... \n", + "'Round Midnight (1986) 0.0 ... \n", + "\n", + "title Zulu (2013) [REC] (2007) \\\n", + "title \n", + "'71 (2014) 0.0 0.342055 \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 0.000000 \n", + "'Round Midnight (1986) 0.0 0.000000 \n", + "\n", + "title [REC]² (2009) \\\n", + "title \n", + "'71 (2014) 0.543305 \n", + "'Hellboy': The Seeds of Creation (2004) 0.000000 \n", + "'Round Midnight (1986) 0.000000 \n", + "\n", + "title [REC]³ 3 Génesis (2012) \\\n", + "title \n", + "'71 (2014) 0.707107 \n", + "'Hellboy': The Seeds of Creation (2004) 0.000000 \n", + "'Round Midnight (1986) 0.000000 \n", + "\n", + "title anohana: The Flower We Saw That Day - The Movie (2013) \\\n", + "title \n", + "'71 (2014) 0.0 \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 \n", + "'Round Midnight (1986) 0.0 \n", + "\n", + "title eXistenZ (1999) xXx (2002) \\\n", + "title \n", + "'71 (2014) 0.0 0.139431 \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 0.000000 \n", + "'Round Midnight (1986) 0.0 0.000000 \n", + "\n", + "title xXx: State of the Union (2005) \\\n", + "title \n", + "'71 (2014) 0.327327 \n", + "'Hellboy': The Seeds of Creation (2004) 0.000000 \n", + "'Round Midnight (1986) 0.000000 \n", + "\n", + "title ¡Three Amigos! (1986) \\\n", + "title \n", + "'71 (2014) 0.0 \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 \n", + "'Round Midnight (1986) 0.0 \n", + "\n", + "title À nous la liberté (Freedom for Us) (1931) \n", + "title \n", + "'71 (2014) 0.0 \n", + "'Hellboy': The Seeds of Creation (2004) 0.0 \n", + "'Round Midnight (1986) 0.0 \n", + "\n", + "[3 rows x 9719 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.pairwise import cosine_similarity\n", + "\n", + "item_sim = cosine_similarity(ratings_matrix_T, ratings_matrix_T)\n", + "# cosine_similarity()로 반환된 넘파이 행렬을 영화명을 매핑해 DataFrame으로 변환 \n", + "item_sim_df = pd.DataFrame(data=item_sim, index=ratings_matrix.columns,\n", + "columns=ratings_matrix.columns) \n", + "print(item_sim_df.shape) \n", + "item_sim_df.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2fbf92a0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "title\n", + "Godfather, The (1972) 1.000000\n", + "Godfather: Part II, The (1974) 0.821773\n", + "Goodfellas (1990) 0.664841\n", + "One Flew Over the Cuckoo's Nest (1975) 0.620536\n", + "Star Wars: Episode IV - A New Hope (1977) 0.595317\n", + "Fargo (1996) 0.588614\n", + "Name: Godfather, The (1972), dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "item_sim_df[\"Godfather, The (1972)\"].sort_values(ascending=False)[:6]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "bf72aba8", + "metadata": {}, + "outputs": [ + { + "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" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "item_sim_df[\"Inception (2010)\"].sort_values(ascending=False)[1:6]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "00d09a25", + "metadata": {}, + "outputs": [], + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "fb3d2eca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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
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" + ], + "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]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f9272b79", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "아이템 기반 모든 최근접 이웃 MSE: 9.895354759094706\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "# 사용자가 평점을 부여한 영화에 대해서만 예측 성능 평가 이으드를 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", + "print('아이템 기반 모든 최근접 이웃 MSE:', get_mse(ratings_pred, ratings_matrix.values))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0186744", + "metadata": {}, + "outputs": [], + "source": [ + "def predict_rating_topsim(ratings_arr, item_sim_arr, n=20):\n", + " pred = np.zeros(ratings_arr.shape)\n", + "\n", + " for col in range(ratings_arr.shape[1]):\n", + " # col 아이템과 가장 유사한 top-n 아이템 인덱스\n", + " top_n_items = np.argsort(item_sim_arr[col, :])[:-n-1:-1]\n", + "\n", + " for row in range(ratings_arr.shape[0]):\n", + " sim_scores = item_sim_arr[col, top_n_items]\n", + " ratings = ratings_arr[row, top_n_items]\n", + "\n", + " pred[row, col] = sim_scores.dot(ratings)\n", + " pred[row, col] /= np.sum(np.abs(sim_scores)) + 1e-8 \n", + " return pred\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "3539a1b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "아이템 기반 최근접 Top-20 이웃 MSE: 3.694999366583795\n" + ] + } + ], + "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", + "# 계산된 예측 평점 데이터는 DataFrame으로 재생성\n", + "ratings_pred_matrix = pd.DataFrame(data=ratings_pred, index= ratings_matrix.index, columns = ratings_matrix.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "d1417cd6", + "metadata": {}, + "outputs": [ + { + "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" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_rating_id = ratings_matrix.loc[9, :]\n", + "user_rating_id[ user_rating_id > 0].sort_values(ascending=False)[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "190c4c1c", + "metadata": {}, + "outputs": [], + "source": [ + "def get_unseen_movies(ratingsjnatrix, 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 = ratingsjnatrix.loc[userId, :]\n", + " # user_rating이 0보다 크면 기존에 관람한 영화임. 대상 인덱스를 추출해 list 객체로 만듦. \n", + " already_seen = user_rating[user_rating > 0].index.tolist()\n", + " # 모든 영화명을 list 객체로 만듦. \n", + " movies_list = ratings_matrix.columns.tolist()\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", + " return unseen_list" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "dc3339cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pred_score
title
Venom (1982)0.303278
Dr. Goldfoot and the Bikini Machine (1965)0.258705
Frankie and Johnny (1966)0.234754
English Vinglish (2012)0.214774
Harmonists, The (1997)0.169338
Passenger, The (Professione: reporter) (1975)0.163884
Marriage of Maria Braun, The (Ehe der Maria Braun, Die) (1979)0.163884
Child, The (L'enfant) (2005)0.163884
3:10 to Yuma (1957)0.163884
Story of Women (Affaire de femmes, Une) (1988)0.163884
\n", + "
" + ], + "text/plain": [ + " pred_score\n", + "title \n", + "Venom (1982) 0.303278\n", + "Dr. Goldfoot and the Bikini Machine (1965) 0.258705\n", + "Frankie and Johnny (1966) 0.234754\n", + "English Vinglish (2012) 0.214774\n", + "Harmonists, The (1997) 0.169338\n", + "Passenger, The (Professione: reporter) (1975) 0.163884\n", + "Marriage of Maria Braun, The (Ehe der Maria Braun, Die) (1979) 0.163884\n", + "Child, The (L'enfant) (2005) 0.163884\n", + "3:10 to Yuma (1957) 0.163884\n", + "Story of Women (Affaire de femmes, Une) (1988) 0.163884" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def recomm_movie_by_userid(pred_df, userId, unseen_list, top_n=10):\n", + " # 예측 평점 DataFrame에서 사용자id 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", + "# 사용자가 관람하지 않은 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, columns=['pred_score'])\n", + "recomm_movies" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "1fa765c9", + "metadata": {}, + "outputs": [], + "source": [ + "def get_rmse(R, P, Q, non_zeros):\n", + " error = 0\n", + "\n", + " # non_zeros : (user, item, 실제평점) 튜플 리스트\n", + " for i, j, r in non_zeros:\n", + " pred_r = np.dot(P[i, :], Q[j, :].T)\n", + " error += (r - pred_r) ** 2\n", + "\n", + " rmse = np.sqrt(error / len(non_zeros))\n", + " return rmse\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "3b9466be", + "metadata": {}, + "outputs": [], + "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 입력합니다.\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", + " # 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", + " # SGD기법으로 우와 Q 매트릭스를 계속 업데이트.\n", + " for step in range(steps):\n", + " for i, j, r in non_zeros:\n", + " \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", + " rmse = get_rmse(R, P, Q, non_zeros)\n", + " if (step % 10) == 0 :\n", + " print(\"### iteration step : \", step, \" rmse : \", rmse)\n", + " return P, Q" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "9da3593e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "movies = pd.read_csv('movies.csv')\n", + "ratings = pd.read_csv('ratings.csv')\n", + "ratings = ratings[['userId', 'movieId', 'rating']]\n", + "ratings_matrix = ratings.pivot_table('rating', index='userId', columns='movieId')\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')" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "44094e6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### iteration step : 0 rmse : 2.9023619751337115\n", + "### iteration step : 10 rmse : 0.7335768591017939\n", + "### iteration step : 20 rmse : 0.5115539026853437\n", + "### iteration step : 30 rmse : 0.37261628282537723\n", + "### iteration step : 40 rmse : 0.29608182991810134\n", + "### iteration step : 50 rmse : 0.252035319234162\n", + "### iteration step : 60 rmse : 0.22487503275269882\n", + "### iteration step : 70 rmse : 0.2068545530233151\n", + "### iteration step : 80 rmse : 0.19413418783028688\n", + "### iteration step : 90 rmse : 0.18470082002720317\n", + "### iteration step : 100 rmse : 0.1774292752720908\n", + "### iteration step : 110 rmse : 0.1716522696470752\n", + "### iteration step : 120 rmse : 0.1669518194687151\n", + "### iteration step : 130 rmse : 0.16305292191997445\n", + "### iteration step : 140 rmse : 0.15976691929679607\n", + "### iteration step : 150 rmse : 0.15695986999457326\n", + "### iteration step : 160 rmse : 0.1545339818671543\n", + "### iteration step : 170 rmse : 0.15241618551077693\n", + "### iteration step : 180 rmse : 0.1505508073962834\n", + "### iteration step : 190 rmse : 0.14889470913232067\n" + ] + } + ], + "source": [ + "P, Q = matrix_factorization(ratings_matrix.values, K=50, steps=200, learning_rate=0.01, r_lambda = 0.01) \n", + "pred_matrix = np.dot(P, Q.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "dcb5d502", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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
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23.1701193.6579923.3087074.1665214.3118901.2754694.2379721.9003663.3928593.647421...0.9738113.5282643.3615322.6725352.4044564.2327892.9116021.6345764.1357350.725684
32.3070731.6588531.4435382.2088592.2294860.7807601.9970430.9249082.9707002.551446...0.5203541.7094942.2815961.7828331.6351731.3232762.8875801.0426182.2938900.396941
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3 rows × 9719 columns

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" + ], + "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]" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ratings_pred_matrix = pd.DataFrame(data=pred_matrix, index= ratings_matrix.index, \n", + "columns = ratings_matrix.columns)\n", + "ratings_pred_matrix.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "8901f3b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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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" + ], + "text/plain": [ + " pred_score\n", + "title \n", + "Rear Window (1954) 5.704612\n", + "South Park: Bigger, Longer and Uncut (1999) 5.451100\n", + "Rounders (1998) 5.298393\n", + "Blade Runner (1982) 5.244951\n", + "Roger & Me (1989) 5.191962\n", + "Gattaca (1997) 5.183179\n", + "Ben-Hur (1959) 5.130463\n", + "Rosencrantz and Guildenstern Are Dead (1990) 5.087375\n", + "Big Lebowski, The (1998) 5.038690\n", + "Star Wars: Episode V - The Empire Strikes Back (1980) 4.989601" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 사용자가 관람하지 않은 영화명 추출\n", + "unseen_list = get_unseen_movies(ratings_matrix, 9)\n", + "# 잠재 요인 협업 필터링으로 영화 추천\n", + "recomm_movies = recomm_movie_by_userid(ratings_pred_matrix, 9, unseen_list, top_n=10)\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": { + "kernelspec": { + "display_name": "mlenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}