diff --git "a/notebooks/final_ARS.ipynb" "b/notebooks/final_ARS.ipynb" --- "a/notebooks/final_ARS.ipynb" +++ "b/notebooks/final_ARS.ipynb" @@ -1,11561 +1,11561 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "YG4fX89xbPeQ" - }, - "source": [ - "### EDA" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "I0-UozYqPKzD" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 678 - }, - "id": "likh-JLqO50o", - "outputId": "82a49b50-f756-4cbf-c440-700d20b384dd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape of the Dataset: (12194, 18)\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"df_anime\",\n \"rows\": 12194,\n \"fields\": [\n {\n \"column\": \"anime_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11437,\n \"min\": 1,\n \"max\": 34527,\n \"num_unique_values\": 12194,\n \"samples\": [\n 3834,\n 32936,\n 8792\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genres\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3256,\n \"samples\": [\n \"Mystery, Sci-Fi, Space\",\n \"Music, Sci-Fi\",\n \"Magic, Romance\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12194,\n \"samples\": [\n \"Hoshi no Ko Chobin\",\n \"Gin no Guardian\",\n \"Madobe Nanami no Windows 7 de PC Jisaku Ouen Commercial!!\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"average_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 552,\n \"samples\": [\n \"4.06\",\n \"8.03\",\n \"7.32\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"overview\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11689,\n \"samples\": [\n \"Serebii, a Legendary Pok\\u00e9mon known for its ability to traverse time, is hunted by an unnamed Pok\\u00e9mon poacher seeking to capture it. Yukinari, a young Pok\\u00e9mon trainer who enjoys drawing portraits of Pok\\u00e9mon, tries to protect Serebii after it stumbles upon him; but in the middle of its escape, both vanish without a trace.\\n\\nForty years later, ambitious Pok\\u00e9mon trainer Satoshi hopes to sight rare Pok\\u00e9mon around his local area. White, a boat driver, takes Satoshi to his village. Satoshi is accompanied by his three closest friends: Takeshi, a former gym leader training to be a great Pok\\u00e9mon breeder; Kasumi, a young girl wanting to become a skilled Water-type trainer; and Pikachu, Satoshi's Pok\\u00e9mon partner and first comrade.\\n\\nMeanwhile, Yukinari and Serebii reappear in Satoshi's present time and run into his group. Masked Lord Vicious, the strongest executive staff member of Team Rocket, desires to capture Serebii. Using the Dark Ball, a variant of Monster Ball that corrupts the Pok\\u00e9mon caught within and draws out their maximum power, Vicious can transform innocent Pok\\u00e9mon into powerful and frightening obstacles\\u2014including Serebii itself! Placed in a tough position, Satoshi, Yukinari, and their friends must work together to defeat Vicious and save Serebii and themselves.\",\n \"East Force meets West Force and all Hell breaks loose. The Solonoids, that lovable race of female warriors, are at it again, fighting amongst themselves. During a heated battle, however, it looks like the leaders of the two factions have hung their warriors out to dry. In the middle of all this fighting and chaos, East Force detects a transmission from an unidentified planet. The Gall Force gals leave their posts to go. Lufy, the West Force's Ace Pilot, who is after Rabby, follows them to the planet.\\n\\n(Source: AnimeNfo)\",\n \"Netto's father Yuuichirou Hikari has made a scientific breakthrough by introducing the \\\"synchro chips\\\". If an operator and his or her navi are in a special enviroment known as a \\\"dimensional area\\\", they can fuse together in the real world via a technique called \\\"cross fusion\\\"! Yuuichirou's first test subject, Misaki Gorou, attempts the process and sadly fails. Netto offers to try with Rockman, but his father forbids it. Cross Fusion puts enormous strain on the operator's health, and battling in the real world could mean death. \\n\\n(Source: Official Site)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"TV\",\n \"Movie\",\n \"ONA\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"episodes\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 200,\n \"samples\": [\n \"93\",\n \"27\",\n \"110\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"producers\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3070,\n \"samples\": [\n \"AIC, Lantis, Media Factory, Pony Canyon, Rakuonsha, AT-X, KlockWorx, Ryukyu Asahi Broadcasting\",\n \"Tama Production, Tokyo MX\",\n \"KAGAYA Studio\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"licensors\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 253,\n \"samples\": [\n \"ADV Films, Media Blasters\",\n \"Funimation\",\n \"Central Park Media, Maiden Japan\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"studios\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 941,\n \"samples\": [\n \"Group TAC, Ginga Ya\",\n \"J.C.Staff, Production I.G\",\n \"drop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"source\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 16,\n \"samples\": [\n \"Visual novel\",\n \"Manga\",\n \"Novel\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"anime_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"PG-13 - Teens 13 or older\",\n \"R - 17+ (violence & profanity)\",\n \"UNKNOWN\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rank\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 8846,\n \"samples\": [\n \"12549\",\n \"7778\",\n \"7761\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"popularity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5211,\n \"min\": 1,\n \"max\": 19844,\n \"num_unique_values\": 10304,\n \"samples\": [\n 5880,\n 7617,\n 17889\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"favorites\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5821,\n \"min\": 0,\n \"max\": 217606,\n \"num_unique_values\": 1357,\n \"samples\": [\n 46556,\n 1136,\n 1570\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"scored by\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6497,\n \"samples\": [\n \"19239\",\n \"1926\",\n \"300\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"members\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 191364,\n \"min\": 142,\n \"max\": 3744541,\n \"num_unique_values\": 8192,\n \"samples\": [\n 9390,\n 8376,\n 1998\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"image url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12135,\n \"samples\": [\n \"https://cdn.myanimelist.net/images/anime/1195/111544.jpg\",\n \"https://cdn.myanimelist.net/images/anime/6/26448.jpg\",\n \"https://cdn.myanimelist.net/images/anime/1405/112400.jpg\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "df_anime" - }, - "text/html": [ - "\n", - "
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anime_idgenresnameaverage_ratingoverviewtypeepisodesproducerslicensorsstudiossourceanime_ratingrankpopularityfavoritesscored bymembersimage url
04181Drama, Fantasy, Romance, Slice of Life, Supern...Clannad: After Story8.93Clannad: After Story, the sequel to the critic...TV24Pony Canyon, TBS, Rakuonsha, Animation DoSentai FilmworksKyoto AnimationVisual novelPG-13 - Teens 13 or older19114689496397291149886https://cdn.myanimelist.net/images/anime/1299/...
128735Drama, Historical, JoseiShouwa Genroku Rakugo Shinjuu8.57Yotarou is a former yakuza member fresh out of...TV13Starchild Records, Mainichi Broadcasting Syste...UNKNOWNStudio DeenMangaPG-13 - Teens 13 or older93804571191359281445https://cdn.myanimelist.net/images/anime/1354/...
25205Action, Mystery, Romance, Supernatural, ThrillerKara no Kyoukai Movie 7: Satsujin Kousatsu (Go)8.39In February 1999, a string of murders has Shik...Movie1NotesAniplex of AmericaufotableLight novelR - 17+ (violence & profanity)18211152261108703200492https://cdn.myanimelist.net/images/anime/9/566...
3170Comedy, Drama, School, Shounen, SportsSlam Dunk8.54Hanamichi Sakuragi, infamous for his temper, m...TV101TV Asahi, AnimaxFlatiron Film Company, Geneon Entertainment USAToei AnimationMangaPG-13 - Teens 13 or older1087976879128920283226https://cdn.myanimelist.net/images/anime/12/86...
410162Josei, Slice of LifeUsagi Drop8.36Daikichi Kawachi is a 30-year-old bachelor wor...TV11Dentsu, Fuji TV, Toho, Tohokushinsha Film Corp...NIS America, Inc.Production I.GMangaPG-13 - Teens 13 or older2024255975237156479967https://cdn.myanimelist.net/images/anime/2/296...
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\n" - ], - "text/plain": [ - " anime_id genres \\\n", - "0 4181 Drama, Fantasy, Romance, Slice of Life, Supern... \n", - "1 28735 Drama, Historical, Josei \n", - "2 5205 Action, Mystery, Romance, Supernatural, Thriller \n", - "3 170 Comedy, Drama, School, Shounen, Sports \n", - "4 10162 Josei, Slice of Life \n", - "\n", - " name average_rating \\\n", - "0 Clannad: After Story 8.93 \n", - "1 Shouwa Genroku Rakugo Shinjuu 8.57 \n", - "2 Kara no Kyoukai Movie 7: Satsujin Kousatsu (Go) 8.39 \n", - "3 Slam Dunk 8.54 \n", - "4 Usagi Drop 8.36 \n", - "\n", - " overview type episodes \\\n", - "0 Clannad: After Story, the sequel to the critic... TV 24 \n", - "1 Yotarou is a former yakuza member fresh out of... TV 13 \n", - "2 In February 1999, a string of murders has Shik... Movie 1 \n", - "3 Hanamichi Sakuragi, infamous for his temper, m... TV 101 \n", - "4 Daikichi Kawachi is a 30-year-old bachelor wor... TV 11 \n", - "\n", - " producers \\\n", - "0 Pony Canyon, TBS, Rakuonsha, Animation Do \n", - "1 Starchild Records, Mainichi Broadcasting Syste... \n", - "2 Notes \n", - "3 TV Asahi, Animax \n", - "4 Dentsu, Fuji TV, Toho, Tohokushinsha Film Corp... \n", - "\n", - " licensors studios \\\n", - "0 Sentai Filmworks Kyoto Animation \n", - "1 UNKNOWN Studio Deen \n", - "2 Aniplex of America ufotable \n", - "3 Flatiron Film Company, Geneon Entertainment USA Toei Animation \n", - "4 NIS America, Inc. Production I.G \n", - "\n", - " source anime_rating rank popularity favorites \\\n", - "0 Visual novel PG-13 - Teens 13 or older 19 114 68949 \n", - "1 Manga PG-13 - Teens 13 or older 93 804 5711 \n", - "2 Light novel R - 17+ (violence & profanity) 182 1115 2261 \n", - "3 Manga PG-13 - Teens 13 or older 108 797 6879 \n", - "4 Manga PG-13 - Teens 13 or older 202 425 5975 \n", - "\n", - " scored by members image url \n", - "0 639729 1149886 https://cdn.myanimelist.net/images/anime/1299/... \n", - "1 91359 281445 https://cdn.myanimelist.net/images/anime/1354/... \n", - "2 108703 200492 https://cdn.myanimelist.net/images/anime/9/566... \n", - "3 128920 283226 https://cdn.myanimelist.net/images/anime/12/86... \n", - "4 237156 479967 https://cdn.myanimelist.net/images/anime/2/296... " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_anime=pd.read_csv(\"/content/Animes.csv\")\n", - "print(\"Shape of the Dataset:\", df_anime.shape)\n", - "df_anime.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 488 - }, - "id": "Wuxs0dgVI1UL", - "outputId": "fc2c7b6e-8214-4ff5-8ded-54451869e89b" - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "anime_name = 'One Piece' # Replace with the desired anime name\n", - "anime_row = df_anime[df_anime['name'] == anime_name].iloc[0]\n", - "\n", - "image_url = anime_row['image url']\n", - "display(Image(url=image_url, width=300))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 224 - }, - "id": "shOuDaGzNfIl", - "outputId": "c2d140e4-955f-42d0-8c48-c1d361ed7abf" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape of the dataset: (1112830, 4)\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "df_rating" - }, - "text/html": [ - "\n", - "
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user_idusernameanime_idrating
0357zhambi329499
1357zhambi117597
2357zhambi63476
3357zhambi16828
4357zhambi364567
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count
genres
Hentai821
Comedy517
Music298
Kids197
Comedy, Slice of Life176
......
Action, Comedy, Fantasy, Hentai, Horror, Mecha, Romance, Supernatural1
Action, Hentai, Martial Arts1
Action, Hentai, Historical, Mecha, Military1
Drama, Music, Psychological, Romance, Slice of Life, Yaoi1
Adventure, Fantasy, Hentai1
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" - ], - "text/plain": [ - "genres\n", - "Hentai 821\n", - "Comedy 517\n", - "Music 298\n", - "Kids 197\n", - "Comedy, Slice of Life 176\n", - " ... \n", - "Action, Comedy, Fantasy, Hentai, Horror, Mecha, Romance, Supernatural 1\n", - "Action, Hentai, Martial Arts 1\n", - "Action, Hentai, Historical, Mecha, Military 1\n", - "Drama, Music, Psychological, Romance, Slice of Life, Yaoi 1\n", - "Adventure, Fantasy, Hentai 1\n", - "Name: count, Length: 3256, dtype: int64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_anime['genres'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 335 - }, - "id": "VMPKGQMgj9K_", - "outputId": "f88e6945-b1d5-41cf-f0d3-9dd2f07bf6b6" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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count
anime_rating
PG-13 - Teens 13 or older4520
G - All Ages3406
Rx - Hentai1122
PG - Children1105
R+ - Mild Nudity952
R - 17+ (violence & profanity)896
UNKNOWN193
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count
producers
UNKNOWN4621
NHK260
Pink Pineapple189
Sanrio142
Bandai Visual118
......
Lantis, AT-X, Barnum Studio, Magic Capsule, Warner Bros. Japan, KlockWorx, Shueisha, Bulls Eye1
Dentsu, Sotsu Music Publishing1
TV Tokyo, Kadokawa Shoten, Kadokawa Pictures Japan, KlockWorx, NTT Docomo, Dwango1
Kadokawa Shoten, Rondo Robe1
Frontier Works, Media Factory, Mainichi Broadcasting System, Movic, Gentosha Comics1
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767229750ComedyAAA de Ikou!!: Yuuna & AkikoUNKNOWNNo description available for this anime.OVA1Opera HouseUNKNOWNUNKNOWNUnknownG - All Ages14448159140UNKNOWN397https://cdn.myanimelist.net/images/anime/5/715...
767625063Comedy, Drama, HistoricalAnime Roukyoku Kikou Shimizu no JirochoudenUNKNOWNA story based on Shimizu no Jirochou, an early...TV30UNKNOWNUNKNOWNUNKNOWNUnknownG - All Ages14562164280UNKNOWN359https://cdn.myanimelist.net/images/anime/4/641...
776230914Kids, SportsAnimax TaisouUNKNOWNNo description available for this anime.Special30UNKNOWNUNKNOWNUNKNOWNUnknownG - All Ages14557179130UNKNOWN254https://cdn.myanimelist.net/images/anime/6/743...
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1103534488Comedy, Kids, Sci-FiGan Gan Ganko-chanUNKNOWNThe anime follows a boy named Gen, who time tr...TV10UNKNOWNUNKNOWN10GaugeOtherG - All Ages16001159320UNKNOWN397https://cdn.myanimelist.net/images/anime/8/831...
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1357zhambi117597Action, Game, Romance, School, Sci-FiAccel World7.23Haruyuki Arita is an overweight, bullied middl...TV24...VIZ MediaSunriseLight novelPG-13 - Teens 13 or older28552633882380124697894https://cdn.myanimelist.net/images/anime/1002/...
2357zhambi63476Comedy, Romance, School, Super PowerBaka to Test to Shoukanjuu7.52Fumizuki Academy isn't a typical Japanese high...TV13...FunimationSILVER LINK.Light novelPG-13 - Teens 13 or older16453145586339916620729https://cdn.myanimelist.net/images/anime/3/503...
7357zhambi289996Drama, School, Super PowerCharlotte7.75If not for his ability to take over people's m...TV13...Aniplex of AmericaP.A. WorksOriginalPG-13 - Teens 13 or older102566221809390701536653https://cdn.myanimelist.net/images/anime/12/74...
9357zhambi18187Action, Adventure, Demons, Fantasy, Shounen, S...Claymore7.74When a shapeshifting demon with a thirst for h...TV26...FunimationMadhouseMangaR+ - Mild Nudity10362928252317263650639https://cdn.myanimelist.net/images/anime/3/218...
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"import tensorflow as tf\n", - "\n", - "## Import necessary modules for collaborative filtering\n", - "from tensorflow.keras.layers import Input, Embedding, Dot, Flatten, Dense\n", - "from tensorflow.keras.models import Model\n", - "from tensorflow.keras.optimizers import Adam\n", - "from collections import defaultdict\n", - "from collections import Counter\n", - "\n", - "## Import necessary modules for content-based filtering\n", - "from sklearn.feature_extraction.text import TfidfVectorizer\n", - "from sklearn.metrics.pairwise import linear_kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "E5W9cLbu-MvR", - "outputId": "b5f4c9d0-7acb-42ab-b3dd-b4a7ada88f0c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Score: 7.307548322744714\n" - ] - } - ], - "source": [ - "avg_score = np.mean(df['rating'])\n", - "print('Average Score:', avg_score)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "iVVi9_A9-Mrl" - }, - "outputs": [], - "source": [ - "scaler = MinMaxScaler(feature_range=(0, 1))\n", - "df['scaled_score'] = scaler.fit_transform(df[['rating']])" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xSfeiwl8-MoV", - "outputId": "3765d804-278e-4530-a50f-bfe52a72c098" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of unique users: 813, Number of unique anime: 14579\n", - "Minimum rating: 5, Maximum rating: 10\n" - ] - } - ], - "source": [ - "## Encoding user IDs\n", - "user_encoder = LabelEncoder()\n", - "df[\"user_encoded\"] = user_encoder.fit_transform(df[\"user_id\"])\n", - "num_users = len(user_encoder.classes_)\n", - "\n", - "## Encoding anime IDs\n", - "anime_encoder = LabelEncoder()\n", - "df[\"anime_encoded\"] = anime_encoder.fit_transform(df[\"anime_id\"])\n", - "num_animes = len(anime_encoder.classes_)\n", - "\n", - "# Printing dataset information\n", - "print(\"Number of unique users: {}, Number of unique anime: {}\".format(num_users, num_animes))\n", - "print(\"Minimum rating: {}, Maximum rating: {}\".format(min(df['rating']), max(df['rating'])))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qBu74dfr-Mkk", - "outputId": "62179123-5ea0-49f4-b618-8aad5977e25e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape of X: (1112830, 2)\n", - "Shape of y: (1112830,)\n" - ] - } - ], - "source": [ - "# Shuffle the dataset\n", - "df = shuffle(df, random_state=100)\n", - "\n", - "# Create feature matrix X and target variable y\n", - "X = df[['user_encoded', 'anime_encoded']].values\n", - "y = df[\"scaled_score\"].values\n", - "\n", - "# Printing dataset information\n", - "print(\"Shape of X:\", X.shape)\n", - "print(\"Shape of y:\", y.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iP2HuHv0-Mhd", - "outputId": "33ad6be8-7a74-4ec0-b8e5-e576f4946f63" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of samples in the training set: 1102830\n", - "Number of samples in the test set: 10000\n" - ] - } - ], - "source": [ - "test_set_size = 10000 # Number of samples to include in the test set\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_set_size, random_state=73)\n", - "\n", - "print(\"Number of samples in the training set:\", len(y_train))\n", - "print(\"Number of samples in the test set:\", len(y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "id": "xue2NDXI-Md8" - }, - "outputs": [], - "source": [ - "# Prepare input data for model training and evaluation\n", - "X_train_array = [X_train[:, 0], X_train[:, 1]]\n", - "X_test_array = [X_test[:, 0], X_test[:, 1]]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "id": "UsBsSr3s-May" - }, - "outputs": [], - "source": [ - "def RecommenderNet(num_users, num_animes, embedding_size=128):\n", - " # User input layer and embedding layer\n", - " user = Input(name='user_encoded', shape=[1])\n", - " user_embedding = Embedding(name='user_embedding', input_dim=num_users, output_dim=embedding_size)(user)\n", - "\n", - " # Anime input layer and embedding layer\n", - " anime = Input(name='anime_encoded', shape=[1])\n", - " anime_embedding = Embedding(name='anime_embedding', input_dim=num_animes, output_dim=embedding_size)(anime)\n", - "\n", - " # Dot product of user and anime embeddings\n", - " dot_product = Dot(name='dot_product', normalize=True, axes=2)([user_embedding, anime_embedding])\n", - " flattened = Flatten()(dot_product)\n", - "\n", - " # Dense layers for prediction\n", - " dense = Dense(64, activation='relu')(flattened)\n", - " output = Dense(1, activation='sigmoid')(dense)\n", - "\n", - " # Create and compile the model\n", - " model = Model(inputs=[user, anime], outputs=output)\n", - " model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.001), metrics=[\"mae\", \"mse\"])\n", - "\n", - " return model\n", - "model = RecommenderNet(num_users, num_animes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "cKr7GRrOjcyL" - }, - "outputs": [], - "source": [ - "!pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.13-cp310-cp310-linux_x86_64.whl\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 487 - }, - "id": "BBRvWziYjbls", - "outputId": "149d5bf9-20be-4624-eef8-bef7267f017d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All devices: [LogicalDevice(name='/device:TPU:0', device_type='TPU'), LogicalDevice(name='/device:TPU:1', device_type='TPU'), LogicalDevice(name='/device:TPU:2', device_type='TPU'), LogicalDevice(name='/device:TPU:3', device_type='TPU'), LogicalDevice(name='/device:TPU:4', device_type='TPU'), LogicalDevice(name='/device:TPU:5', device_type='TPU'), LogicalDevice(name='/device:TPU:6', device_type='TPU'), LogicalDevice(name='/device:TPU:7', device_type='TPU')]\n" - 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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-              "┃ Layer (type)              ┃ Output Shape           ┃        Param # ┃ Connected to           ┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-              "│ user_encoded (InputLayer) │ (None, 1)              │              0 │ -                      │\n",
-              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
-              "│ anime_encoded             │ (None, 1)              │              0 │ -                      │\n",
-              "│ (InputLayer)              │                        │                │                        │\n",
-              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
-              "│ user_embedding            │ (None, 1, 128)         │        104,064 │ user_encoded[0][0]     │\n",
-              "│ (Embedding)               │                        │                │                        │\n",
-              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
-              "│ anime_embedding           │ (None, 1, 128)         │      1,866,112 │ anime_encoded[0][0]    │\n",
-              "│ (Embedding)               │                        │                │                        │\n",
-              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
-              "│ dot_product (Dot)         │ (None, 1, 1)           │              0 │ user_embedding[0][0],  │\n",
-              "│                           │                        │                │ anime_embedding[0][0]  ��\n",
-              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
-              "│ flatten_1 (Flatten)       │ (None, 1)              │              0 │ dot_product[0][0]      │\n",
-              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
-              "│ dense_2 (Dense)           │ (None, 64)             │            128 │ flatten_1[0][0]        │\n",
-              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
-              "│ dense_3 (Dense)           │ (None, 1)              │             65 │ dense_2[0][0]          │\n",
-              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
-              "
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"model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "id": "tAW-DSmn-MXB" - }, - "outputs": [], - "source": [ - "## Import necessary callbacks\n", - "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping\n", - "\n", - "# Define the initial learning rate, minimum learning rate, maximum learning rate, and batch size\n", - "start_lr = 0.00001\n", - "min_lr = 0.00001\n", - "max_lr = 0.00005\n", - "batch_size = 10000\n", - "\n", - "\n", - "# Define the number of epochs for ramp-up, sustain, and exponential decay\n", - "rampup_epochs = 5\n", - "sustain_epochs = 0\n", - "exp_decay = .8\n", - "\n", - "# Learning rate schedule function\n", - "def lrfn(epoch):\n", - " if epoch < rampup_epochs:\n", - " return (max_lr - start_lr) / rampup_epochs * epoch + start_lr\n", - " elif epoch < rampup_epochs + sustain_epochs:\n", - " return max_lr\n", - " else:\n", - " return (max_lr - min_lr) * exp_decay**(epoch - rampup_epochs - sustain_epochs) + min_lr\n", - "\n", - "# Learning rate scheduler callback\n", - "lr_callback = LearningRateScheduler(lambda epoch: lrfn(epoch), verbose=0)\n", - "\n", - "# File path for saving the model weights\n", - "checkpoint_filepath = 'myanimeweights.weights.h5'\n", - "\n", - "# Model checkpoint callback to save the best weights\n", - "model_checkpoints = ModelCheckpoint(filepath=checkpoint_filepath,\n", - " save_weights_only=True,\n", - " monitor='val_loss',\n", - " mode='min',\n", - " save_best_only=True)\n", - "\n", - "# Early stopping callback to prevent overfitting\n", - "early_stopping = EarlyStopping(patience=3, monitor='val_loss', mode='min', restore_best_weights=True)\n", - "\n", - "# Define the list of callbacks\n", - "my_callbacks = [\n", - " model_checkpoints,\n", - " lr_callback,\n", - " early_stopping\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Hf1Hr03I-MTw", - "outputId": "b8a5ed33-50b6-458c-ebfe-76f65c67a41f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 46ms/step - loss: 0.6932 - mae: 0.2255 - mse: 0.0732 - val_loss: 0.6929 - val_mae: 0.2204 - val_mse: 0.0702 - learning_rate: 1.0000e-05\n", - "Epoch 2/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 29ms/step - loss: 0.6928 - mae: 0.2243 - mse: 0.0725 - val_loss: 0.6923 - val_mae: 0.2198 - val_mse: 0.0699 - learning_rate: 1.8000e-05\n", - "Epoch 3/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6921 - mae: 0.2236 - mse: 0.0723 - val_loss: 0.6916 - val_mae: 0.2190 - val_mse: 0.0695 - learning_rate: 2.6000e-05\n", - "Epoch 4/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6915 - mae: 0.2242 - mse: 0.0727 - val_loss: 0.6909 - val_mae: 0.2179 - val_mse: 0.0691 - learning_rate: 3.4000e-05\n", - "Epoch 5/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 29ms/step - loss: 0.6905 - mae: 0.2219 - mse: 0.0717 - val_loss: 0.6903 - val_mae: 0.2168 - val_mse: 0.0689 - learning_rate: 4.2000e-05\n", - "Epoch 6/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6898 - mae: 0.2205 - mse: 0.0713 - val_loss: 0.6900 - val_mae: 0.2157 - val_mse: 0.0687 - learning_rate: 5.0000e-05\n", - "Epoch 7/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6893 - mae: 0.2181 - mse: 0.0703 - val_loss: 0.6898 - val_mae: 0.2150 - val_mse: 0.0686 - learning_rate: 4.2000e-05\n", - "Epoch 8/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6878 - mae: 0.2181 - mse: 0.0705 - val_loss: 0.6897 - val_mae: 0.2145 - val_mse: 0.0685 - learning_rate: 3.5600e-05\n", - "Epoch 9/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6870 - mae: 0.2167 - mse: 0.0700 - val_loss: 0.6893 - val_mae: 0.2139 - val_mse: 0.0684 - learning_rate: 3.0480e-05\n", - "Epoch 10/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6852 - mae: 0.2154 - mse: 0.0693 - val_loss: 0.6888 - val_mae: 0.2131 - val_mse: 0.0681 - learning_rate: 2.6384e-05\n", - "Epoch 11/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6831 - mae: 0.2136 - mse: 0.0683 - val_loss: 0.6880 - val_mae: 0.2121 - val_mse: 0.0677 - learning_rate: 2.3107e-05\n", - "Epoch 12/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6815 - mae: 0.2118 - mse: 0.0674 - val_loss: 0.6871 - val_mae: 0.2110 - val_mse: 0.0673 - learning_rate: 2.0486e-05\n", - "Epoch 13/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6795 - mae: 0.2091 - mse: 0.0659 - val_loss: 0.6861 - val_mae: 0.2098 - val_mse: 0.0668 - learning_rate: 1.8389e-05\n", - "Epoch 14/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6778 - mae: 0.2073 - mse: 0.0651 - val_loss: 0.6850 - val_mae: 0.2086 - val_mse: 0.0662 - learning_rate: 1.6711e-05\n", - "Epoch 15/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6760 - mae: 0.2057 - mse: 0.0644 - val_loss: 0.6837 - val_mae: 0.2074 - val_mse: 0.0656 - learning_rate: 1.5369e-05\n", - "Epoch 16/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6740 - mae: 0.2035 - mse: 0.0633 - val_loss: 0.6825 - val_mae: 0.2062 - val_mse: 0.0650 - learning_rate: 1.4295e-05\n", - "Epoch 17/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6719 - mae: 0.2030 - mse: 0.0628 - val_loss: 0.6812 - val_mae: 0.2051 - val_mse: 0.0644 - learning_rate: 1.3436e-05\n", - "Epoch 18/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6703 - mae: 0.2011 - mse: 0.0618 - val_loss: 0.6799 - val_mae: 0.2040 - val_mse: 0.0637 - learning_rate: 1.2749e-05\n", - "Epoch 19/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6684 - mae: 0.1981 - mse: 0.0604 - val_loss: 0.6786 - val_mae: 0.2028 - val_mse: 0.0631 - learning_rate: 1.2199e-05\n", - "Epoch 20/20\n", - "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 30ms/step - loss: 0.6667 - mae: 0.1968 - mse: 0.0595 - val_loss: 0.6773 - val_mae: 0.2017 - val_mse: 0.0625 - learning_rate: 1.1759e-05\n" - ] - } - ], - "source": [ - "# Model training\n", - "batch_size = 1024 * tpu_strategy.num_replicas_in_sync\n", - "history = model.fit(\n", - " x=X_train_array,\n", - " y=y_train,\n", - " batch_size=batch_size,\n", - " epochs=20,\n", - " verbose=1,\n", - " validation_data=(X_test_array, y_test),\n", - " callbacks=my_callbacks\n", - ")\n", - "\n", - "model.load_weights(checkpoint_filepath)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "xibONJ2W-MQh", - "outputId": "2bb48a74-fd57-4298-9768-bb9fffdd308f" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Training results visualization\n", - "plt.plot(history.history[\"loss\"][0:-2])\n", - "plt.plot(history.history[\"val_loss\"][0:-2])\n", - "plt.title(\"Training Loss\")\n", - "plt.ylabel(\"Loss\")\n", - "plt.xlabel(\"Epoch\")\n", - "plt.legend([\"Train\", \"Validation\"], loc=\"upper left\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "s-p1X7eZRcRU", - "outputId": "8119c518-69ab-49e3-c78b-c553f06c212f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - loss: 0.6745 - mae: 0.2032 - mse: 0.0639\n", - "Test Loss: 0.6732999682426453, Test MAE: 0.2060622125864029, Test MSE: 0.06531194597482681\n" - ] - } - ], - "source": [ - "# Evaluate the model on the test set\n", - "evaluation = model.evaluate(X_test_array, y_test)\n", - "print(f\"Test Loss: {evaluation[0]}, Test MAE: {evaluation[1]}, Test MSE: {evaluation[2]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "id": "5eZltYvrnadX" - }, - "outputs": [], - "source": [ - "def get_unrated_anime(user_id, df, num_animes):\n", - " watched_anime_ids = df[df['user_id'] == user_id]['anime_id'].tolist()\n", - " all_anime_ids = list(range(num_animes))\n", - " unrated_anime_ids = [anime_id for anime_id in all_anime_ids if anime_id not in watched_anime_ids]\n", - " return unrated_anime_ids" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "id": "QS0Zg4DwnYIF" - }, - "outputs": [], - "source": [ - "def recommend_anime(user_id, model, num_animes, n_recommendations=10):\n", - " unrated_anime_ids = get_unrated_anime(user_id, df, num_animes)\n", - " user_array = np.array([user_id] * len(unrated_anime_ids))\n", - " anime_array = np.array(unrated_anime_ids)\n", - "\n", - " predictions = model.predict([user_array, anime_array])\n", - " predicted_ratings = predictions.flatten()\n", - "\n", - " top_indices = predicted_ratings.argsort()[-n_recommendations:][::-1]\n", - " recommended_anime_ids = [unrated_anime_ids[i] for i in top_indices]\n", - "\n", - " return recommended_anime_ids" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sJ4RJNCPnelV", - "outputId": "b66b14dd-a19b-4fef-cb8f-f4eb42679a0c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m456/456\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 43ms/step\n", - "Original Anime IDs for User 0: [37208 41339 3927 329 8142 36129 5091 16706 35843 28891]\n", - "Recommended Anime Names for User 0: ['Planetes' 'Haikyuu!! Second Season'\n", - " 'Kami nomi zo Shiru Sekai: Megami-hen'\n", - " 'Kidou Senshi Gundam 00 Second Season' 'Colorful (Movie)'\n", - " 'Manga Sarutobi Sasuke']\n" - ] - } - ], - "source": [ - "user_id = 10\n", - "recommended_anime_ids = recommend_anime(user_id, model, num_animes, n_recommendations=10)\n", - "\n", - "# Map the recommended anime IDs back to their names\n", - "original_anime_ids = anime_encoder.inverse_transform(recommended_anime_ids)\n", - "print(\"Original Anime IDs for User 0:\", original_anime_ids)\n", - "recommended_anime_names = df_anime[df_anime['anime_id'].isin(original_anime_ids)]['name'].values\n", - "print(\"Recommended Anime Names for User 0:\", recommended_anime_names)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3f4p_zFMDdXd" - }, - "source": [ - "#### 2) Singular Value Decomposition - SVD\n", - "\n", - "Singular Value Decomposition (SVD) is a powerful **matrix factorization technique** widely used in collaborative filtering for recommendation systems. It helps reduce the dimensionality of user-item interaction data while preserving essential patterns, allowing us to make accurate recommendations based on user preferences.\n", - " \n", - "SVD helps uncover **latent relationships** between users and items, making it effective for personalized recommendations. By approximating missing values (unrated items), we can predict user preferences and recommend anime based on their past interactions. \n", - "\n", - "In this notebook, we will use SVD to build a recommendation model and evaluate its performance in predicting user preferences." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "RBHd69NvDgyc", - "outputId": "5b274a78-e948-4ab8-977f-13210ce84813" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting surprise\n", - " Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n", - "Collecting scikit-surprise (from surprise)\n", - " Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m3.0 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/python3.11/dist-packages (from scikit-surprise->surprise) (1.4.2)\n", - "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-surprise->surprise) (1.26.4)\n", - "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-surprise->surprise) (1.13.1)\n", - "Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\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-cp311-cp311-linux_x86_64.whl size=2505176 sha256=6cd9480d9f79089b96685f62df64f3247d2e3c8c6e28a8ebb4ac50c35806bdaf\n", - " Stored in directory: /root/.cache/pip/wheels/2a/8f/6e/7e2899163e2d85d8266daab4aa1cdabec7a6c56f83c015b5af\n", - "Successfully built scikit-surprise\n", - "Installing collected packages: scikit-surprise, surprise\n", - "Successfully installed scikit-surprise-1.1.4 surprise-0.1\n" - ] - } - ], - "source": [ - "!pip install surprise" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "VbKyIBLnTUFL", - "outputId": "4dd5a2a4-4590-4d0d-ca0f-bac4ef8eb4ad" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from surprise import SVD, Dataset, Reader\n", - "from surprise.model_selection import cross_validate\n", - "\n", - "# Prepare the data for Surprise\n", - "reader = Reader(rating_scale=(1, 10))\n", - "data = Dataset.load_from_df(df_merged[['user_id', 'anime_id', 'rating']], reader)\n", - "\n", - "# Train the SVD model\n", - "svd = SVD()\n", - "cross_validate(svd, data, cv=5)\n", - "\n", - "# Fit the model on the entire dataset\n", - "trainset = data.build_full_trainset()\n", - "svd.fit(trainset)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "id": "c0qPhkSzKvdd" - }, - "outputs": [], - "source": [ - "# Function to get top recommendations for a user\n", - "def get_collab_recommendations(user_id, n=10):\n", - " anime_ids = df_merged['anime_id'].unique()\n", - " predictions = [(anime_id, svd.predict(user_id, anime_id).est) for anime_id in anime_ids]\n", - " predictions.sort(key=lambda x: x[1], reverse=True)\n", - " anime_ids = [pred[0] for pred in predictions[:n]]\n", - " anime_names = set(df_merged[df_merged['anime_id'].isin(anime_ids)]['name'].tolist())\n", - " return anime_names" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kQ4htwfYTUBH", - "outputId": "94964b78-3e39-469a-d909-e42e70fd5075" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Clannad: After Story',\n", - " 'Code Geass: Hangyaku no Lelouch',\n", - " 'Code Geass: Hangyaku no Lelouch R2',\n", - " 'Fullmetal Alchemist: Brotherhood',\n", - " 'Ginga Eiyuu Densetsu',\n", - " 'Gintama°',\n", - " 'Hajime no Ippo',\n", - " 'Kimi no Na wa.',\n", - " 'Steins;Gate',\n", - " 'Suzumiya Haruhi no Shoushitsu'}" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_collab_recommendations(34, n=10)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tIdBRcwnM0pc" - }, - "source": [ - "#### 3) Item-Based Collaborative Filtering using KNN- Anime recommendations based on user ratings\n", - "\n", - "Item-Based Collaborative Filtering (IBCF) is a recommendation technique that suggests items similar to those a user has already rated or interacted with. Unlike **user-based collaborative filtering**, which finds similar users, **item-based filtering** focuses on item-to-item similarities. \n", - "\n", - "- How It Works:\n", - " 1. **Compute Item Similarity**: Using **K-Nearest Neighbors (KNN)**, we measure similarity between anime based on user rating patterns. \n", - " 2. **Find Nearest Neighbors**: Identify anime that are most similar to the ones a user has rated. \n", - " 3. **Generate Recommendations**: Suggest anime that are highly rated by users who liked similar items. " - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "FDSs7u4aM0Ep", - "outputId": "8c976525-3338-4ea4-aa11-56a0eeda2fbb" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(597114, 21)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = df_merged.copy()\n", - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 637 - }, - "id": "YE9XO3V7fjmj", - "outputId": "772fd4dc-9fcc-42a7-9b2c-21b9d595e391" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "df" - }, - "text/html": [ - "\n", - "
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1357zhambi117597Action, Game, Romance, School, Sci-FiAccel World7.23Haruyuki Arita is an overweight, bullied middl...TV24...VIZ MediaSunriseLight novelPG-13 - Teens 13 or older28552633882380124697894https://cdn.myanimelist.net/images/anime/1002/...
2357zhambi63476Comedy, Romance, School, Super PowerBaka to Test to Shoukanjuu7.52Fumizuki Academy isn't a typical Japanese high...TV13...FunimationSILVER LINK.Light novelPG-13 - Teens 13 or older16453145586339916620729https://cdn.myanimelist.net/images/anime/3/503...
7357zhambi289996Drama, School, Super PowerCharlotte7.75If not for his ability to take over people's m...TV13...Aniplex of AmericaP.A. WorksOriginalPG-13 - Teens 13 or older102566221809390701536653https://cdn.myanimelist.net/images/anime/12/74...
9357zhambi18187Action, Adventure, Demons, Fantasy, Shounen, S...Claymore7.74When a shapeshifting demon with a thirst for h...TV26...FunimationMadhouseMangaR+ - Mild Nudity10362928252317263650639https://cdn.myanimelist.net/images/anime/3/218...
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" - ], - "text/plain": [ - "NearestNeighbors(algorithm='brute', metric='cosine')" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.sparse import csr_matrix\n", - "from sklearn.neighbors import NearestNeighbors\n", - "\n", - "item_user_matrix = csr_matrix(anime_pivot.values)\n", - "knn_item_based = NearestNeighbors(metric = 'cosine', algorithm = 'brute')\n", - "knn_item_based.fit(item_user_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "id": "dQkd4zIdXTxK" - }, - "outputs": [], - "source": [ - "def get_item_based_recommendations(anime_name, n_recommendations=5):\n", - " # Find the index of the anime title\n", - " if anime_name not in anime_pivot.index:\n", - " return f\"Anime title '{anime_name}' not found in the dataset.\"\n", - "\n", - " query_index = anime_pivot.index.get_loc(anime_name)\n", - "\n", - " # Use the KNN model to find the nearest neighbors\n", - " distances, indices = knn_item_based.kneighbors(anime_pivot.iloc[query_index,:].values.reshape(1, -1), n_neighbors=n_recommendations + 1)\n", - "\n", - " recommendations = []\n", - " for i in range(1, len(distances.flatten())):\n", - " anime_title = anime_pivot.index[indices.flatten()[i]]\n", - " distance = distances.flatten()[i]\n", - " recommendations.append((anime_title, distance))\n", - "\n", - " return recommendations" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "K7wvMvI0M3cw", - "outputId": "9e9604d5-a806-41f6-990e-9176f024b785" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Based on user rating, If you like 'One Piece' you will definetly like below recommendations....\n", - "1: Bleach, with distance of 0.2537\n", - "2: Naruto, with distance of 0.2768\n", - "3: One Piece Film: Strong World, with distance of 0.2836\n", - "4: Fairy Tail, with distance of 0.2905\n", - "5: Soul Eater, with distance of 0.2945\n", - "6: Death Note, with distance of 0.2964\n", - "7: Code Geass: Hangyaku no Lelouch, with distance of 0.3043\n", - "8: Naruto: Shippuuden, with distance of 0.3101\n", - "9: Code Geass: Hangyaku no Lelouch R2, with distance of 0.3106\n", - "10: Fullmetal Alchemist: Brotherhood, with distance of 0.3122\n" - ] - } - ], - "source": [ - "anime_name = \"One Piece\"\n", - "item_based_recommendations = get_item_based_recommendations(anime_name,10)\n", - "\n", - "if isinstance(item_based_recommendations, str):\n", - " print(item_based_recommendations)\n", - "else:\n", - " print(f\"Based on user rating, If you like '{anime_name}' you will definetly like below recommendations....\")\n", - " for i, (title, distance) in enumerate(item_based_recommendations, 1):\n", - " print(f\"{i}: {title}, with distance of {distance:.4f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XXKBq-x9qALF" - }, - "source": [ - "#### 4) User-Based Collaborative Filtering using KNN\n", - "\n", - "\n", - "User-Based Collaborative Filtering (UBCF) is a recommendation approach that suggests items to users based on the preferences of similar users. Unlike **item-based filtering**, which focuses on item similarities, **user-based filtering** identifies users with similar rating patterns and recommends items they have liked. \n", - "\n", - "- How It Works:\n", - " 1. **Compute User Similarity**: Using **K-Nearest Neighbors (KNN)**, we measure similarity between users based on their anime rating history. \n", - " 2. **Find Nearest Neighbors**: Identify users who have rated anime similarly. \n", - " 3. **Generate Recommendations**: Recommend anime that similar users have highly rated but the target user has not watched yet. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 391 - }, - "id": "btDeSD4ap384", - "outputId": "683cc21a-fc4e-4324-aa73-a37eb9430b5d" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "user_pivot" - }, - "text/html": [ - "\n", - "
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NearestNeighbors(algorithm='brute', metric='cosine')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" - ], - "text/plain": [ - "NearestNeighbors(algorithm='brute', metric='cosine')" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.sparse import csr_matrix\n", - "from sklearn.neighbors import NearestNeighbors\n", - "\n", - "user_item_matrix = csr_matrix(user_pivot.values)\n", - "knn_user_based = NearestNeighbors(metric = 'cosine', algorithm = 'brute')\n", - "knn_user_based.fit(user_item_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "id": "W_lzzcUr2Qoq" - }, - "outputs": [], - "source": [ - "def get_user_based_recommendations(user_id, n_recommendations=5):\n", - " # Convert user_id to the same type as user_pivot index\n", - " user_id = float(user_id)\n", - "\n", - " # Check if the user exists in the matrix\n", - " if user_id not in user_pivot.index:\n", - " return f\"User '{user_id}' not found in the dataset.\"\n", - "\n", - " # Find the user index\n", - " user_idx = user_pivot.index.get_loc(user_id)\n", - "\n", - " # Find the nearest neighbors (most similar users)\n", - " distances, indices = knn_user_based.kneighbors(user_pivot.iloc[user_idx, :].values.reshape(1, -1), n_neighbors=n_recommendations + 1)\n", - "\n", - " # Get the list of anime this user has already rated\n", - " user_rated_anime = set(user_pivot.columns[user_pivot.iloc[user_idx, :] > 100])\n", - "\n", - " # Gather all anime rated by nearest neighbors\n", - " all_neighbor_ratings = []\n", - " for i in range(1, len(distances.flatten())):\n", - " neighbor_idx = indices.flatten()[i]\n", - " neighbor_rated_anime = user_pivot.iloc[neighbor_idx, :]\n", - " neighbor_ratings = neighbor_rated_anime[neighbor_rated_anime > 0]\n", - " all_neighbor_ratings.extend(neighbor_ratings.index)\n", - "\n", - " # Count the frequency of each anime rated by the neighbors\n", - " from collections import Counter\n", - " anime_counter = Counter(all_neighbor_ratings)\n", - "\n", - " # Recommend the most common anime among the neighbors that the user hasn't rated yet\n", - " recommendations = [(anime, count) for anime, count in anime_counter.items() if anime not in user_rated_anime]\n", - " recommendations.sort(key=lambda x: x[1], reverse=True)\n", - "\n", - " # Return the top N recommendations\n", - " return recommendations[:n_recommendations]" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Pwenx_vZ2TSz", - "outputId": "b98596e5-cab9-4cdf-d59f-4be10d4f23c9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "User-based recommendations for user ID 817:\n", - "Accel World, Recommended by 10 similar users\n", - "Ai Yori Aoshi, Recommended by 10 similar users\n", - "Amagami SS, Recommended by 10 similar users\n", - "Angel Beats!, Recommended by 10 similar users\n", - "Ano Hi Mita Hana no Namae wo Bokutachi wa Mada Shiranai., Recommended by 10 similar users\n", - "Ano Natsu de Matteru, Recommended by 10 similar users\n", - "Another, Recommended by 10 similar users\n", - "Ao no Exorcist, Recommended by 10 similar users\n", - "Arakawa Under the Bridge, Recommended by 10 similar users\n", - "Asura Cryin' 2, Recommended by 10 similar users\n" - ] - } - ], - "source": [ - "user_id = 817\n", - "user_recommendations = get_user_based_recommendations(user_id,10)\n", - "\n", - "# Check if recommendations is a string (indicating an error)\n", - "if isinstance(user_recommendations, str):\n", - " print(user_recommendations)\n", - "else:\n", - " print(f\"User-based recommendations for user ID {user_id}:\")\n", - " for anime, count in user_recommendations:\n", - " print(f\"{anime}, Recommended by {count} similar users\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lTIbtd3uL4Tm" - }, - "source": [ - "## Content based Filtering" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Content-Based Filtering (CBF) also referred to as cognitive filtering, recommends anime based on their content attributes, such as **genre, synopsis, or other metadata**. Unlike collaborative filtering, which relies on user interactions, CBF focuses on **similarities between anime** using textual data and machine learning techniques. \n", - "\n", - "1. **TF-IDF with Sigmoid Kernel** \n", - " - **TF-IDF (Term Frequency-Inverse Document Frequency)** transforms text into numerical features. \n", - " - The **Sigmoid Kernel** measures similarity between anime based on their textual descriptions. \n", - "\n", - "2. **CountVectorizer with Linear Kernel** \n", - " - **CountVectorizer** converts text into a bag-of-words representation. \n", - " - The **Linear Kernel** computes the similarity between anime using these feature vectors. \n", - "\n", - "- Why Use These Methods?\n", - " - **TF-IDF + Sigmoid Kernel** captures the importance of words while smoothing similarity scores. \n", - " - **CountVectorizer + Linear Kernel** is efficient for comparing anime descriptions at a high level. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zzorVg94k4HF" - }, - "source": [ - "#### 1) Using TFIDF" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PShKOncdVfbJ" - }, - "source": [ - "##### a. Sigmoid Kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "id": "rVvjDyI9MKSr" - }, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 678 - }, - "id": "cO7tLpcxjLh0", - "outputId": "69147217-a12c-484f-9573-c63a3bf4175c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape of the Dataset: (12194, 18)\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"df\",\n \"rows\": 12194,\n \"fields\": [\n {\n \"column\": \"anime_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11437,\n \"min\": 1,\n \"max\": 34527,\n \"num_unique_values\": 12194,\n \"samples\": [\n 3834,\n 32936,\n 8792\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genres\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3256,\n \"samples\": [\n \"Mystery, Sci-Fi, Space\",\n \"Music, Sci-Fi\",\n \"Magic, Romance\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12194,\n \"samples\": [\n \"Hoshi no Ko Chobin\",\n \"Gin no Guardian\",\n \"Madobe Nanami no Windows 7 de PC Jisaku Ouen Commercial!!\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"average_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 552,\n \"samples\": [\n \"4.06\",\n \"8.03\",\n \"7.32\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"overview\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11689,\n \"samples\": [\n \"Serebii, a Legendary Pok\\u00e9mon known for its ability to traverse time, is hunted by an unnamed Pok\\u00e9mon poacher seeking to capture it. Yukinari, a young Pok\\u00e9mon trainer who enjoys drawing portraits of Pok\\u00e9mon, tries to protect Serebii after it stumbles upon him; but in the middle of its escape, both vanish without a trace.\\n\\nForty years later, ambitious Pok\\u00e9mon trainer Satoshi hopes to sight rare Pok\\u00e9mon around his local area. White, a boat driver, takes Satoshi to his village. Satoshi is accompanied by his three closest friends: Takeshi, a former gym leader training to be a great Pok\\u00e9mon breeder; Kasumi, a young girl wanting to become a skilled Water-type trainer; and Pikachu, Satoshi's Pok\\u00e9mon partner and first comrade.\\n\\nMeanwhile, Yukinari and Serebii reappear in Satoshi's present time and run into his group. Masked Lord Vicious, the strongest executive staff member of Team Rocket, desires to capture Serebii. Using the Dark Ball, a variant of Monster Ball that corrupts the Pok\\u00e9mon caught within and draws out their maximum power, Vicious can transform innocent Pok\\u00e9mon into powerful and frightening obstacles\\u2014including Serebii itself! Placed in a tough position, Satoshi, Yukinari, and their friends must work together to defeat Vicious and save Serebii and themselves.\",\n \"East Force meets West Force and all Hell breaks loose. The Solonoids, that lovable race of female warriors, are at it again, fighting amongst themselves. During a heated battle, however, it looks like the leaders of the two factions have hung their warriors out to dry. In the middle of all this fighting and chaos, East Force detects a transmission from an unidentified planet. The Gall Force gals leave their posts to go. Lufy, the West Force's Ace Pilot, who is after Rabby, follows them to the planet.\\n\\n(Source: AnimeNfo)\",\n \"Netto's father Yuuichirou Hikari has made a scientific breakthrough by introducing the \\\"synchro chips\\\". If an operator and his or her navi are in a special enviroment known as a \\\"dimensional area\\\", they can fuse together in the real world via a technique called \\\"cross fusion\\\"! Yuuichirou's first test subject, Misaki Gorou, attempts the process and sadly fails. Netto offers to try with Rockman, but his father forbids it. Cross Fusion puts enormous strain on the operator's health, and battling in the real world could mean death. \\n\\n(Source: Official Site)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"TV\",\n \"Movie\",\n \"ONA\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"episodes\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 200,\n \"samples\": [\n \"93\",\n \"27\",\n \"110\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"producers\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3070,\n \"samples\": [\n \"AIC, Lantis, Media Factory, Pony Canyon, Rakuonsha, AT-X, KlockWorx, Ryukyu Asahi Broadcasting\",\n \"Tama Production, Tokyo MX\",\n \"KAGAYA Studio\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"licensors\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 253,\n \"samples\": [\n \"ADV Films, Media Blasters\",\n \"Funimation\",\n \"Central Park Media, Maiden Japan\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"studios\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 941,\n \"samples\": [\n \"Group TAC, Ginga Ya\",\n \"J.C.Staff, Production I.G\",\n \"drop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"source\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 16,\n \"samples\": [\n \"Visual novel\",\n \"Manga\",\n \"Novel\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"anime_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"PG-13 - Teens 13 or older\",\n \"R - 17+ (violence & profanity)\",\n \"UNKNOWN\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rank\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 8846,\n \"samples\": [\n \"12549\",\n \"7778\",\n \"7761\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"popularity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5211,\n \"min\": 1,\n \"max\": 19844,\n \"num_unique_values\": 10304,\n \"samples\": [\n 5880,\n 7617,\n 17889\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"favorites\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5821,\n \"min\": 0,\n \"max\": 217606,\n \"num_unique_values\": 1357,\n \"samples\": [\n 46556,\n 1136,\n 1570\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"scored by\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6497,\n \"samples\": [\n \"19239\",\n \"1926\",\n \"300\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"members\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 191364,\n \"min\": 142,\n \"max\": 3744541,\n \"num_unique_values\": 8192,\n \"samples\": [\n 9390,\n 8376,\n 1998\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"image url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12135,\n \"samples\": [\n \"https://cdn.myanimelist.net/images/anime/1195/111544.jpg\",\n \"https://cdn.myanimelist.net/images/anime/6/26448.jpg\",\n \"https://cdn.myanimelist.net/images/anime/1405/112400.jpg\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "df" - }, - "text/html": [ - "\n", - "
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anime_idgenresnameaverage_ratingoverviewtypeepisodesproducerslicensorsstudiossourceanime_ratingrankpopularityfavoritesscored bymembersimage url
04181Drama, Fantasy, Romance, Slice of Life, Supern...Clannad: After Story8.93Clannad: After Story, the sequel to the critic...TV24Pony Canyon, TBS, Rakuonsha, Animation DoSentai FilmworksKyoto AnimationVisual novelPG-13 - Teens 13 or older19114689496397291149886https://cdn.myanimelist.net/images/anime/1299/...
128735Drama, Historical, JoseiShouwa Genroku Rakugo Shinjuu8.57Yotarou is a former yakuza member fresh out of...TV13Starchild Records, Mainichi Broadcasting Syste...UNKNOWNStudio DeenMangaPG-13 - Teens 13 or older93804571191359281445https://cdn.myanimelist.net/images/anime/1354/...
25205Action, Mystery, Romance, Supernatural, ThrillerKara no Kyoukai Movie 7: Satsujin Kousatsu (Go)8.39In February 1999, a string of murders has Shik...Movie1NotesAniplex of AmericaufotableLight novelR - 17+ (violence & profanity)18211152261108703200492https://cdn.myanimelist.net/images/anime/9/566...
3170Comedy, Drama, School, Shounen, SportsSlam Dunk8.54Hanamichi Sakuragi, infamous for his temper, m...TV101TV Asahi, AnimaxFlatiron Film Company, Geneon Entertainment USAToei AnimationMangaPG-13 - Teens 13 or older1087976879128920283226https://cdn.myanimelist.net/images/anime/12/86...
410162Josei, Slice of LifeUsagi Drop8.36Daikichi Kawachi is a 30-year-old bachelor wor...TV11Dentsu, Fuji TV, Toho, Tohokushinsha Film Corp...NIS America, Inc.Production I.GMangaPG-13 - Teens 13 or older2024255975237156479967https://cdn.myanimelist.net/images/anime/2/296...
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\n" - ], - "text/plain": [ - " anime_id genres \\\n", - "0 4181 Drama, Fantasy, Romance, Slice of Life, Supern... \n", - "1 28735 Drama, Historical, Josei \n", - "2 5205 Action, Mystery, Romance, Supernatural, Thriller \n", - "3 170 Comedy, Drama, School, Shounen, Sports \n", - "4 10162 Josei, Slice of Life \n", - "\n", - " name average_rating \\\n", - "0 Clannad: After Story 8.93 \n", - "1 Shouwa Genroku Rakugo Shinjuu 8.57 \n", - "2 Kara no Kyoukai Movie 7: Satsujin Kousatsu (Go) 8.39 \n", - "3 Slam Dunk 8.54 \n", - "4 Usagi Drop 8.36 \n", - "\n", - " overview type episodes \\\n", - "0 Clannad: After Story, the sequel to the critic... TV 24 \n", - "1 Yotarou is a former yakuza member fresh out of... TV 13 \n", - "2 In February 1999, a string of murders has Shik... Movie 1 \n", - "3 Hanamichi Sakuragi, infamous for his temper, m... TV 101 \n", - "4 Daikichi Kawachi is a 30-year-old bachelor wor... TV 11 \n", - "\n", - " producers \\\n", - "0 Pony Canyon, TBS, Rakuonsha, Animation Do \n", - "1 Starchild Records, Mainichi Broadcasting Syste... \n", - "2 Notes \n", - "3 TV Asahi, Animax \n", - "4 Dentsu, Fuji TV, Toho, Tohokushinsha Film Corp... \n", - "\n", - " licensors studios \\\n", - "0 Sentai Filmworks Kyoto Animation \n", - "1 UNKNOWN Studio Deen \n", - "2 Aniplex of America ufotable \n", - "3 Flatiron Film Company, Geneon Entertainment USA Toei Animation \n", - "4 NIS America, Inc. Production I.G \n", - "\n", - " source anime_rating rank popularity favorites \\\n", - "0 Visual novel PG-13 - Teens 13 or older 19 114 68949 \n", - "1 Manga PG-13 - Teens 13 or older 93 804 5711 \n", - "2 Light novel R - 17+ (violence & profanity) 182 1115 2261 \n", - "3 Manga PG-13 - Teens 13 or older 108 797 6879 \n", - "4 Manga PG-13 - Teens 13 or older 202 425 5975 \n", - "\n", - " scored by members image url \n", - "0 639729 1149886 https://cdn.myanimelist.net/images/anime/1299/... \n", - "1 91359 281445 https://cdn.myanimelist.net/images/anime/1354/... \n", - "2 108703 200492 https://cdn.myanimelist.net/images/anime/9/566... \n", - "3 128920 283226 https://cdn.myanimelist.net/images/anime/12/86... \n", - "4 237156 479967 https://cdn.myanimelist.net/images/anime/2/296... " - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv(\"/content/Animes.csv\")\n", - "print(\"Shape of the Dataset:\", df.shape)\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "id": "8-vUA38AMMCS" - }, - "outputs": [], - "source": [ - "df.dropna(inplace = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9GvI7884MMCS", - "outputId": "96c483b6-b506-489d-f0ef-f20cdf7fcfa2" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['anime_id', 'genres', 'name', 'average_rating', 'overview', 'type',\n", - " 'episodes', 'producers', 'licensors', 'studios', 'source',\n", - " 'anime_rating', 'rank', 'popularity', 'favorites', 'scored by',\n", - " 'members', 'image url'],\n", - " dtype='object')" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "id": "syXBYlBqL9O_" - }, - "outputs": [], - "source": [ - "from sklearn.feature_extraction.text import TfidfVectorizer\n", - "\n", - "tfv = TfidfVectorizer(min_df=3,\n", - " strip_accents='unicode', analyzer='word',token_pattern=r'\\w{1,}',\n", - " ngram_range=(1, 3),\n", - " stop_words = 'english')\n", - "\n", - "tfv_matrix = tfv.fit_transform(df['genres'])" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EDUM2vu7L99g", - "outputId": "69816148-37e1-48f4-b6af-d69337b8bfeb" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(12133, 1552)" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tfv_matrix.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "id": "0jVvemqYL95s" - }, - "outputs": [], - "source": [ - "from sklearn.metrics.pairwise import sigmoid_kernel\n", - "sig = sigmoid_kernel(tfv_matrix, tfv_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "id": "Qy4uip8AL92D" - }, - "outputs": [], - "source": [ - "indices = pd.Series(df.index, index=df['name']).drop_duplicates()" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "id": "ezJ6vl80L9xo" - }, - "outputs": [], - "source": [ - "def get_rec_sig(title, sig=sig,n_recommendations = 10):\n", - " if title not in indices.index:\n", - " print(f\"Anime title '{title}' not found in the dataset.\")\n", - " # Get the index corresponding to original_title\n", - " idx = indices[title]\n", - "\n", - " # Get the pairwsie similarity scores\n", - " sig_scores = list(enumerate(sig[idx]))\n", - "\n", - " # Sort the movies\n", - " sig_scores = sorted(sig_scores, key=lambda x: x[1], reverse=True)\n", - "\n", - " sig_scores = sig_scores[1:n_recommendations+1]\n", - "\n", - " # Movie indices\n", - " anime_indices = [i[0] for i in sig_scores]\n", - " return pd.DataFrame({'Anime name': df['name'].iloc[anime_indices].values,\n", - " 'Rating': df['average_rating'].iloc[anime_indices].values})" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 519 - }, - "id": "1sktZ0lWL9uO", - "outputId": "a09176a6-dc66-48ec-9b63-a46f2afdec48" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"get_rec_sig('Naruto', n_recommendations=15)\",\n \"rows\": 15,\n \"fields\": [\n {\n \"column\": \"Anime name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 15,\n \"samples\": [\n \"Kyutai Panic Adventure!\",\n \"Ben-To\",\n \"Boruto: Naruto the Movie\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rating\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 14,\n \"samples\": [\n \"7.49\",\n \"7.68\",\n \"7.4\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe" - }, - "text/html": [ - "\n", - "
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Anime nameRating
0Boruto: Naruto the Movie7.4
1Naruto7.99
2Naruto x UT7.37
3Boruto: Naruto the Movie - Naruto ga Hokage ni...7.33
4Naruto: Shippuuden Movie 4 - The Lost Tower7.42
5Naruto: Shippuuden Movie 3 - Hi no Ishi wo Tsu...7.33
6Naruto: Shippuuden - Sunny Side Battle7.56
7Naruto Soyokazeden Movie: Naruto to Mashin to ...6.96
8Battle Spirits: Ryuuko no Ken4.78
9Kyutai Panic Adventure!4.67
10Ranma ½: Akumu! Shunmin Kou7.49
11Ben-To7.2
12Naruto: Shippuuden Movie 6 - Road to Ninja7.68
13Rekka no Honoo7.34
14Naruto: Honoo no Chuunin Shiken! Naruto vs. Ko...7.17
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\n" - ], - "text/plain": [ - " Anime name Rating\n", - "0 Boruto: Naruto the Movie 7.4\n", - "1 Naruto 7.99\n", - "2 Naruto x UT 7.37\n", - "3 Boruto: Naruto the Movie - Naruto ga Hokage ni... 7.33\n", - "4 Naruto: Shippuuden Movie 4 - The Lost Tower 7.42\n", - "5 Naruto: Shippuuden Movie 3 - Hi no Ishi wo Tsu... 7.33\n", - "6 Naruto: Shippuuden - Sunny Side Battle 7.56\n", - "7 Naruto Soyokazeden Movie: Naruto to Mashin to ... 6.96\n", - "8 Battle Spirits: Ryuuko no Ken 4.78\n", - "9 Kyutai Panic Adventure! 4.67\n", - "10 Ranma ½: Akumu! Shunmin Kou 7.49\n", - "11 Ben-To 7.2\n", - "12 Naruto: Shippuuden Movie 6 - Road to Ninja 7.68\n", - "13 Rekka no Honoo 7.34\n", - "14 Naruto: Honoo no Chuunin Shiken! Naruto vs. Ko... 7.17" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_rec_sig('Naruto', n_recommendations=15)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9JTNMQdTWDQV" - }, - "source": [ - "##### b. Linear Kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "id": "6D9pKHkBWDQX" - }, - "outputs": [], - "source": [ - "from sklearn.metrics.pairwise import linear_kernel\n", - "\n", - "lin = linear_kernel(tfv_matrix, tfv_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "id": "xFlCrJOeWDQX" - }, - "outputs": [], - "source": [ - "indices = pd.Series(df.index, index=df['name']).drop_duplicates()" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "id": "nikXuR1fWDQY" - }, - "outputs": [], - "source": [ - "def get_rec_lin(title, lin=lin,n_recommendations = 10):\n", - " if title not in indices.index:\n", - " print(f\"Anime title '{title}' not found in the dataset.\")\n", - " # Get the index corresponding to original_title\n", - " idx = indices[title]\n", - "\n", - " # Get the pairwsie similarity scores\n", - " lin_scores = list(enumerate(lin[idx]))\n", - "\n", - " # Sort the movies\n", - " lin_scores = sorted(lin_scores, key=lambda x: x[1], reverse=True)\n", - "\n", - " lin_scores = lin_scores[1:n_recommendations+1]\n", - "\n", - " # Movie indices\n", - " anime_indices = [i[0] for i in lin_scores]\n", - " return pd.DataFrame({'Anime name': df['name'].iloc[anime_indices].values,\n", - " 'Rating': df['average_rating'].iloc[anime_indices].values})" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 519 - }, - "id": "sRJM0gdqWDQY", - "outputId": "8feb0c8b-9193-4be8-f731-6e81c346e9da" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"get_rec_lin('Naruto', n_recommendations=15)\",\n \"rows\": 15,\n \"fields\": [\n {\n \"column\": \"Anime name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 15,\n \"samples\": [\n \"Kyutai Panic Adventure!\",\n \"Ben-To\",\n \"Boruto: Naruto the Movie\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rating\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 14,\n \"samples\": [\n \"7.49\",\n \"7.68\",\n \"7.4\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe" - }, - "text/html": [ - "\n", - "
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Anime nameRating
0Boruto: Naruto the Movie7.4
1Naruto7.99
2Naruto x UT7.37
3Boruto: Naruto the Movie - Naruto ga Hokage ni...7.33
4Naruto: Shippuuden Movie 4 - The Lost Tower7.42
5Naruto: Shippuuden Movie 3 - Hi no Ishi wo Tsu...7.33
6Naruto: Shippuuden - Sunny Side Battle7.56
7Naruto Soyokazeden Movie: Naruto to Mashin to ...6.96
8Battle Spirits: Ryuuko no Ken4.78
9Kyutai Panic Adventure!4.67
10Ranma ½: Akumu! Shunmin Kou7.49
11Ben-To7.2
12Naruto: Shippuuden Movie 6 - Road to Ninja7.68
13Rekka no Honoo7.34
14Naruto: Honoo no Chuunin Shiken! Naruto vs. Ko...7.17
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\n" - ], - "text/plain": [ - " Anime name Rating\n", - "0 Boruto: Naruto the Movie 7.4\n", - "1 Naruto 7.99\n", - "2 Naruto x UT 7.37\n", - "3 Boruto: Naruto the Movie - Naruto ga Hokage ni... 7.33\n", - "4 Naruto: Shippuuden Movie 4 - The Lost Tower 7.42\n", - "5 Naruto: Shippuuden Movie 3 - Hi no Ishi wo Tsu... 7.33\n", - "6 Naruto: Shippuuden - Sunny Side Battle 7.56\n", - "7 Naruto Soyokazeden Movie: Naruto to Mashin to ... 6.96\n", - "8 Battle Spirits: Ryuuko no Ken 4.78\n", - "9 Kyutai Panic Adventure! 4.67\n", - "10 Ranma ½: Akumu! Shunmin Kou 7.49\n", - "11 Ben-To 7.2\n", - "12 Naruto: Shippuuden Movie 6 - Road to Ninja 7.68\n", - "13 Rekka no Honoo 7.34\n", - "14 Naruto: Honoo no Chuunin Shiken! Naruto vs. Ko... 7.17" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_rec_lin('Naruto', n_recommendations=15)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rtPCq8UMkyE-" - }, - "source": [ - "#### 2) Using count vectorizer" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "id": "DY4p2FYCk1PT" - }, - "outputs": [], - "source": [ - "from sklearn.feature_extraction.text import CountVectorizer\n", - "from sklearn.metrics.pairwise import cosine_similarity\n", - "# Initialize CountVectorizer\n", - "count_vectorizer = CountVectorizer(analyzer='word', token_pattern=r'\\w{1,}', ngram_range=(1, 3), stop_words='english',max_features=5000)\n", - "\n", - "# Fit and transform the genre data\n", - "count_matrix = count_vectorizer.fit_transform(df['genres'])\n", - "\n", - "# Compute the cosine similarity matrix\n", - "cosine_sim = cosine_similarity(count_matrix, count_matrix)\n", - "\n", - "# Create a reverse mapping of anime titles to indices\n", - "indices = pd.Series(df.index, index=df['name']).drop_duplicates()\n", - "\n", - "# Function to get recommendations\n", - "def get_rec(title, cosine_sim=cosine_sim, n_recommendations=5):\n", - " if title not in indices.index:\n", - " return f\"Anime title '{title}' not found in the dataset.\"\n", - "\n", - " # Get the index of the anime that matches the title\n", - " idx = indices[title]\n", - "\n", - " # Get the pairwise similarity scores of all anime with that anime\n", - " sim_scores = list(enumerate(cosine_sim[idx]))\n", - "\n", - " # Sort the anime based on the similarity scores\n", - " sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n", - "\n", - " # Get the scores of the most similar anime (excluding the input anime itself)\n", - " sim_scores = sim_scores[1:n_recommendations+1]\n", - "\n", - " # Get the anime indices\n", - " anime_indices = [i[0] for i in sim_scores]\n", - "\n", - " # Return the top n most similar anime\n", - " return pd.DataFrame({'Anime name': df['name'].iloc[anime_indices].values,\n", - " 'Rating': df['average_rating'].iloc[anime_indices].values})" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 519 - }, - "id": "PXxkJk2qleU0", - "outputId": "fab7b7ec-d72b-4d22-e714-b4e14be2ce00" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"get_rec( \\\"Naruto\\\", n_recommendations=15)\",\n \"rows\": 15,\n \"fields\": [\n {\n \"column\": \"Anime name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 15,\n \"samples\": [\n \"Kyutai Panic Adventure!\",\n \"Naruto: Shippuuden Movie 6 - Road to Ninja\",\n \"Boruto: Naruto the Movie\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rating\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 14,\n \"samples\": [\n \"7.49\",\n \"7.34\",\n \"7.4\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe" - }, - "text/html": [ - "\n", - "
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Anime nameRating
0Boruto: Naruto the Movie7.4
1Naruto7.99
2Naruto x UT7.37
3Boruto: Naruto the Movie - Naruto ga Hokage ni...7.33
4Naruto: Shippuuden Movie 4 - The Lost Tower7.42
5Naruto: Shippuuden Movie 3 - Hi no Ishi wo Tsu...7.33
6Naruto: Shippuuden - Sunny Side Battle7.56
7Naruto Soyokazeden Movie: Naruto to Mashin to ...6.96
8Battle Spirits: Ryuuko no Ken4.78
9Kyutai Panic Adventure!4.67
10Ranma ½: Akumu! Shunmin Kou7.49
11Naruto: Shippuuden Movie 6 - Road to Ninja7.68
12Rekka no Honoo7.34
13Naruto: Honoo no Chuunin Shiken! Naruto vs. Ko...7.17
14Street Fighter Zero The Animation6.51
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\n" - ], - "text/plain": [ - " Anime name Rating\n", - "0 Boruto: Naruto the Movie 7.4\n", - "1 Naruto 7.99\n", - "2 Naruto x UT 7.37\n", - "3 Boruto: Naruto the Movie - Naruto ga Hokage ni... 7.33\n", - "4 Naruto: Shippuuden Movie 4 - The Lost Tower 7.42\n", - "5 Naruto: Shippuuden Movie 3 - Hi no Ishi wo Tsu... 7.33\n", - "6 Naruto: Shippuuden - Sunny Side Battle 7.56\n", - "7 Naruto Soyokazeden Movie: Naruto to Mashin to ... 6.96\n", - "8 Battle Spirits: Ryuuko no Ken 4.78\n", - "9 Kyutai Panic Adventure! 4.67\n", - "10 Ranma ½: Akumu! Shunmin Kou 7.49\n", - "11 Naruto: Shippuuden Movie 6 - Road to Ninja 7.68\n", - "12 Rekka no Honoo 7.34\n", - "13 Naruto: Honoo no Chuunin Shiken! Naruto vs. Ko... 7.17\n", - "14 Street Fighter Zero The Animation 6.51" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_rec( \"Naruto\", n_recommendations=15)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ny0Ub9tJmZk6" - }, - "source": [ - "As you can see both Count Vectorizer and TF-IDF are giving same recommendations. We will lean towards TfidfVectorizer for more accurate and meaningful recommendations, especially in content-based systems that handle large or complex textual data. CountVectorizer might be used in scenarios where quick and simple solutions are sufficient or for smaller datasets." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QSPSoR1RH9ZE" - }, - "source": [ - "## Popularity-Based Filtering" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": { - "id": "5r8qPk9PzL_S" - }, - "outputs": [], - "source": [ - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": { - "id": "ppgmlYpA20D3" - }, - "outputs": [], - "source": [ - "df = df_anime.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 340 - }, - "id": "GK_FQCX53Hth", - "outputId": "9a55ecdc-302e-409f-d68d-f39ba7437943" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"df\",\n \"rows\": 12194,\n \"fields\": [\n {\n \"column\": \"anime_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11437,\n \"min\": 1,\n \"max\": 34527,\n \"num_unique_values\": 12194,\n \"samples\": [\n 3834,\n 32936,\n 8792\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genres\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3256,\n \"samples\": [\n \"Mystery, Sci-Fi, Space\",\n \"Music, Sci-Fi\",\n \"Magic, Romance\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12194,\n \"samples\": [\n \"Hoshi no Ko Chobin\",\n \"Gin no Guardian\",\n \"Madobe Nanami no Windows 7 de PC Jisaku Ouen Commercial!!\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"average_rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.9422757691869167,\n \"min\": 1.85,\n \"max\": 9.1,\n \"num_unique_values\": 551,\n \"samples\": [\n 4.06,\n 8.03,\n 7.32\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"overview\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11689,\n \"samples\": [\n \"Serebii, a Legendary Pok\\u00e9mon known for its ability to traverse time, is hunted by an unnamed Pok\\u00e9mon poacher seeking to capture it. Yukinari, a young Pok\\u00e9mon trainer who enjoys drawing portraits of Pok\\u00e9mon, tries to protect Serebii after it stumbles upon him; but in the middle of its escape, both vanish without a trace.\\n\\nForty years later, ambitious Pok\\u00e9mon trainer Satoshi hopes to sight rare Pok\\u00e9mon around his local area. White, a boat driver, takes Satoshi to his village. Satoshi is accompanied by his three closest friends: Takeshi, a former gym leader training to be a great Pok\\u00e9mon breeder; Kasumi, a young girl wanting to become a skilled Water-type trainer; and Pikachu, Satoshi's Pok\\u00e9mon partner and first comrade.\\n\\nMeanwhile, Yukinari and Serebii reappear in Satoshi's present time and run into his group. Masked Lord Vicious, the strongest executive staff member of Team Rocket, desires to capture Serebii. Using the Dark Ball, a variant of Monster Ball that corrupts the Pok\\u00e9mon caught within and draws out their maximum power, Vicious can transform innocent Pok\\u00e9mon into powerful and frightening obstacles\\u2014including Serebii itself! Placed in a tough position, Satoshi, Yukinari, and their friends must work together to defeat Vicious and save Serebii and themselves.\",\n \"East Force meets West Force and all Hell breaks loose. The Solonoids, that lovable race of female warriors, are at it again, fighting amongst themselves. During a heated battle, however, it looks like the leaders of the two factions have hung their warriors out to dry. In the middle of all this fighting and chaos, East Force detects a transmission from an unidentified planet. The Gall Force gals leave their posts to go. Lufy, the West Force's Ace Pilot, who is after Rabby, follows them to the planet.\\n\\n(Source: AnimeNfo)\",\n \"Netto's father Yuuichirou Hikari has made a scientific breakthrough by introducing the \\\"synchro chips\\\". If an operator and his or her navi are in a special enviroment known as a \\\"dimensional area\\\", they can fuse together in the real world via a technique called \\\"cross fusion\\\"! Yuuichirou's first test subject, Misaki Gorou, attempts the process and sadly fails. Netto offers to try with Rockman, but his father forbids it. Cross Fusion puts enormous strain on the operator's health, and battling in the real world could mean death. \\n\\n(Source: Official Site)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"TV\",\n \"Movie\",\n \"ONA\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"episodes\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 200,\n \"samples\": [\n \"93\",\n \"27\",\n \"110\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"producers\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3070,\n \"samples\": [\n \"AIC, Lantis, Media Factory, Pony Canyon, Rakuonsha, AT-X, KlockWorx, Ryukyu Asahi Broadcasting\",\n \"Tama Production, Tokyo MX\",\n \"KAGAYA Studio\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"licensors\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 253,\n \"samples\": [\n \"ADV Films, Media Blasters\",\n \"Funimation\",\n \"Central Park Media, Maiden Japan\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"studios\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 941,\n \"samples\": [\n \"Group TAC, Ginga Ya\",\n \"J.C.Staff, Production I.G\",\n \"drop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"source\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 16,\n \"samples\": [\n \"Visual novel\",\n \"Manga\",\n \"Novel\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"anime_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"PG-13 - Teens 13 or older\",\n \"R - 17+ (violence & profanity)\",\n \"UNKNOWN\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rank\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 8846,\n \"samples\": [\n \"12549\",\n \"7778\",\n \"7761\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"popularity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5211,\n \"min\": 1,\n \"max\": 19844,\n \"num_unique_values\": 10304,\n \"samples\": [\n 5880,\n 7617,\n 17889\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"favorites\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5821,\n \"min\": 0,\n \"max\": 217606,\n \"num_unique_values\": 1357,\n \"samples\": [\n 46556,\n 1136,\n 1570\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"scored by\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6497,\n \"samples\": [\n \"19239\",\n \"1926\",\n \"300\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"members\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 191364,\n \"min\": 142,\n \"max\": 3744541,\n \"num_unique_values\": 8192,\n \"samples\": [\n 9390,\n 8376,\n 1998\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"image url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12135,\n \"samples\": [\n \"https://cdn.myanimelist.net/images/anime/1195/111544.jpg\",\n \"https://cdn.myanimelist.net/images/anime/6/26448.jpg\",\n \"https://cdn.myanimelist.net/images/anime/1405/112400.jpg\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "df" - }, - "text/html": [ - "\n", - "
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anime_idgenresnameaverage_ratingoverviewtypeepisodesproducerslicensorsstudiossourceanime_ratingrankpopularityfavoritesscored bymembersimage url
04181Drama, Fantasy, Romance, Slice of Life, Supern...Clannad: After Story8.93Clannad: After Story, the sequel to the critic...TV24Pony Canyon, TBS, Rakuonsha, Animation DoSentai FilmworksKyoto AnimationVisual novelPG-13 - Teens 13 or older19114689496397291149886https://cdn.myanimelist.net/images/anime/1299/...
128735Drama, Historical, JoseiShouwa Genroku Rakugo Shinjuu8.57Yotarou is a former yakuza member fresh out of...TV13Starchild Records, Mainichi Broadcasting Syste...UNKNOWNStudio DeenMangaPG-13 - Teens 13 or older93804571191359281445https://cdn.myanimelist.net/images/anime/1354/...
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namepopularity
29Shingeki no Kyojin1
176Death Note2
66Fullmetal Alchemist: Brotherhood3
39One Punch Man4
508Sword Art Online5
57Boku no Hero Academia6
726Naruto8
855Tokyo Ghoul9
38Hunter x Hunter (2011)10
102Kimi no Na wa.11
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\n" - ], - "text/plain": [ - " name popularity\n", - "29 Shingeki no Kyojin 1\n", - "176 Death Note 2\n", - "66 Fullmetal Alchemist: Brotherhood 3\n", - "39 One Punch Man 4\n", - "508 Sword Art Online 5\n", - "57 Boku no Hero Academia 6\n", - "726 Naruto 8\n", - "855 Tokyo Ghoul 9\n", - "38 Hunter x Hunter (2011) 10\n", - "102 Kimi no Na wa. 11" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def popular_animes(n=10):\n", - " sorted_df = df.sort_values(by=['popularity'], ascending=[ True])\n", - " top_n_anime = sorted_df.head(n)\n", - " return top_n_anime[['name', 'popularity']]\n", - "\n", - "popular_animes(n=10)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OCh8zH5O3-SG" - }, - "source": [ - "#### 2) Top n animes based on Rank" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - }, - "id": "O1sVeB6R5oWY", - "outputId": "ab8c1927-073c-4ba9-f856-58fe805b69b4" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"top_ranked_animes(n=10)\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Koe no Katachi\",\n \"Gintama\\u00b0\",\n \"Ginga Eiyuu Densetsu\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rank\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.400617248673217,\n \"min\": 3.0,\n \"max\": 19.0,\n \"num_unique_values\": 10,\n \"samples\": [\n 17.0,\n 4.0,\n 12.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe" - }, - "text/html": [ - "\n", - "
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138Steins;Gate9.07
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38Hunter x Hunter (2011)9.04
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176Death Note3713315
66Fullmetal Alchemist: Brotherhood3176556
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508Sword Art Online2951821
57Boku no Hero Academia2882333
726Naruto2717330
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Shingeki no Kyojin\n", - "2. Death Note\n", - "3. Fullmetal Alchemist: Brotherhood\n", - "4. One Punch Man\n", - "5. Sword Art Online\n", - "6. Boku no Hero Academia\n", - "7. Naruto\n", - "8. Tokyo Ghoul\n", - "9. Hunter x Hunter (2011)\n", - "10. Kimi no Na wa.\n" - ] - } - ], - "source": [ - "def popular_anime_among_members(n=10):\n", - " popular_animes = df.sort_values(by=['members', 'average_rating'], ascending=[False, False]).drop_duplicates(subset='name')['name'].head(n).tolist()\n", - "\n", - " # Print the top 'n' unique popular animes\n", - " print(f\"Top {n} unique popular animes based on members:\")\n", - " for i, anime in enumerate(popular_animes, 1):\n", - " print(f\"{i}. {anime}\")\n", - " return None\n", - "\n", - "popular_anime_among_members(n=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "fKAM4gRTIcQ4", - "outputId": "f3f8c7de-e5b4-4856-84dc-5884f8cdd9d7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top 10 animes based on average rating:\n", - "1. Fullmetal Alchemist: Brotherhood - 9.1\n", - "2. Steins;Gate - 9.07\n", - "3. Gintama° - 9.06\n", - "4. Hunter x Hunter (2011) - 9.04\n", - "5. Gintama' - 9.04\n", - "6. Gintama': Enchousen - 9.03\n", - "7. Ginga Eiyuu Densetsu - 9.02\n", - "8. Gintama. - 8.98\n", - "9. Koe no Katachi - 8.94\n", - "10. Gintama - 8.94\n" - ] - } - ], - "source": [ - "def top_avg_rated(n=10):\n", - "\n", - " # Get the top 'n' animes based on average rating, ensuring unique names\n", - " top_animes = (\n", - " df_merged.drop_duplicates(subset='name')\n", - " .nlargest(n, 'average_rating')[['name', 'average_rating']]\n", - " )\n", - "\n", - " # Print the top 'n' animes\n", - " print(f\"Top {n} animes based on average rating:\")\n", - " for i, row in enumerate(top_animes.itertuples(index=False), 1):\n", - " print(f\"{i}. {row.name} - {row.average_rating}\")\n", - " return None\n", - "\n", - "# Example usage\n", - "top_avg_rated(n=10)\n" - ] - } - ], - "metadata": { - "accelerator": "TPU", - "colab": { - "gpuType": "V28", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "YG4fX89xbPeQ" + }, + "source": [ + "### EDA" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "I0-UozYqPKzD" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 678 + }, + "id": "likh-JLqO50o", + "outputId": "82a49b50-f756-4cbf-c440-700d20b384dd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the Dataset: (12194, 18)\n" + ] + }, + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"df_anime\",\n \"rows\": 12194,\n \"fields\": [\n {\n \"column\": \"anime_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11437,\n \"min\": 1,\n \"max\": 34527,\n \"num_unique_values\": 12194,\n \"samples\": [\n 3834,\n 32936,\n 8792\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genres\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3256,\n \"samples\": [\n \"Mystery, Sci-Fi, Space\",\n \"Music, Sci-Fi\",\n \"Magic, Romance\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12194,\n \"samples\": [\n \"Hoshi no Ko Chobin\",\n \"Gin no Guardian\",\n \"Madobe Nanami no Windows 7 de PC Jisaku Ouen Commercial!!\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"average_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 552,\n \"samples\": [\n \"4.06\",\n \"8.03\",\n \"7.32\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"overview\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11689,\n \"samples\": [\n \"Serebii, a Legendary Pok\\u00e9mon known for its ability to traverse time, is hunted by an unnamed Pok\\u00e9mon poacher seeking to capture it. Yukinari, a young Pok\\u00e9mon trainer who enjoys drawing portraits of Pok\\u00e9mon, tries to protect Serebii after it stumbles upon him; but in the middle of its escape, both vanish without a trace.\\n\\nForty years later, ambitious Pok\\u00e9mon trainer Satoshi hopes to sight rare Pok\\u00e9mon around his local area. White, a boat driver, takes Satoshi to his village. Satoshi is accompanied by his three closest friends: Takeshi, a former gym leader training to be a great Pok\\u00e9mon breeder; Kasumi, a young girl wanting to become a skilled Water-type trainer; and Pikachu, Satoshi's Pok\\u00e9mon partner and first comrade.\\n\\nMeanwhile, Yukinari and Serebii reappear in Satoshi's present time and run into his group. Masked Lord Vicious, the strongest executive staff member of Team Rocket, desires to capture Serebii. Using the Dark Ball, a variant of Monster Ball that corrupts the Pok\\u00e9mon caught within and draws out their maximum power, Vicious can transform innocent Pok\\u00e9mon into powerful and frightening obstacles\\u2014including Serebii itself! Placed in a tough position, Satoshi, Yukinari, and their friends must work together to defeat Vicious and save Serebii and themselves.\",\n \"East Force meets West Force and all Hell breaks loose. The Solonoids, that lovable race of female warriors, are at it again, fighting amongst themselves. During a heated battle, however, it looks like the leaders of the two factions have hung their warriors out to dry. In the middle of all this fighting and chaos, East Force detects a transmission from an unidentified planet. The Gall Force gals leave their posts to go. Lufy, the West Force's Ace Pilot, who is after Rabby, follows them to the planet.\\n\\n(Source: AnimeNfo)\",\n \"Netto's father Yuuichirou Hikari has made a scientific breakthrough by introducing the \\\"synchro chips\\\". If an operator and his or her navi are in a special enviroment known as a \\\"dimensional area\\\", they can fuse together in the real world via a technique called \\\"cross fusion\\\"! Yuuichirou's first test subject, Misaki Gorou, attempts the process and sadly fails. Netto offers to try with Rockman, but his father forbids it. Cross Fusion puts enormous strain on the operator's health, and battling in the real world could mean death. \\n\\n(Source: Official Site)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"TV\",\n \"Movie\",\n \"ONA\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"episodes\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 200,\n \"samples\": [\n \"93\",\n \"27\",\n \"110\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"producers\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3070,\n \"samples\": [\n \"AIC, Lantis, Media Factory, Pony Canyon, Rakuonsha, AT-X, KlockWorx, Ryukyu Asahi Broadcasting\",\n \"Tama Production, Tokyo MX\",\n \"KAGAYA Studio\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"licensors\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 253,\n \"samples\": [\n \"ADV Films, Media Blasters\",\n \"Funimation\",\n \"Central Park Media, Maiden Japan\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"studios\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 941,\n \"samples\": [\n \"Group TAC, Ginga Ya\",\n \"J.C.Staff, Production I.G\",\n \"drop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"source\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 16,\n \"samples\": [\n \"Visual novel\",\n \"Manga\",\n \"Novel\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"anime_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"PG-13 - Teens 13 or older\",\n \"R - 17+ (violence & profanity)\",\n \"UNKNOWN\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rank\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 8846,\n \"samples\": [\n \"12549\",\n \"7778\",\n \"7761\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"popularity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5211,\n \"min\": 1,\n \"max\": 19844,\n \"num_unique_values\": 10304,\n \"samples\": [\n 5880,\n 7617,\n 17889\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"favorites\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5821,\n \"min\": 0,\n \"max\": 217606,\n \"num_unique_values\": 1357,\n \"samples\": [\n 46556,\n 1136,\n 1570\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"scored by\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6497,\n \"samples\": [\n \"19239\",\n \"1926\",\n \"300\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"members\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 191364,\n \"min\": 142,\n \"max\": 3744541,\n \"num_unique_values\": 8192,\n \"samples\": [\n 9390,\n 8376,\n 1998\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"image url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12135,\n \"samples\": [\n \"https://cdn.myanimelist.net/images/anime/1195/111544.jpg\",\n \"https://cdn.myanimelist.net/images/anime/6/26448.jpg\",\n \"https://cdn.myanimelist.net/images/anime/1405/112400.jpg\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "df_anime" + }, + "text/html": [ + "\n", + "
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anime_idgenresnameaverage_ratingoverviewtypeepisodesproducerslicensorsstudiossourceanime_ratingrankpopularityfavoritesscored bymembersimage url
04181Drama, Fantasy, Romance, Slice of Life, Supern...Clannad: After Story8.93Clannad: After Story, the sequel to the critic...TV24Pony Canyon, TBS, Rakuonsha, Animation DoSentai FilmworksKyoto AnimationVisual novelPG-13 - Teens 13 or older19114689496397291149886https://cdn.myanimelist.net/images/anime/1299/...
128735Drama, Historical, JoseiShouwa Genroku Rakugo Shinjuu8.57Yotarou is a former yakuza member fresh out of...TV13Starchild Records, Mainichi Broadcasting Syste...UNKNOWNStudio DeenMangaPG-13 - Teens 13 or older93804571191359281445https://cdn.myanimelist.net/images/anime/1354/...
25205Action, Mystery, Romance, Supernatural, ThrillerKara no Kyoukai Movie 7: Satsujin Kousatsu (Go)8.39In February 1999, a string of murders has Shik...Movie1NotesAniplex of AmericaufotableLight novelR - 17+ (violence & profanity)18211152261108703200492https://cdn.myanimelist.net/images/anime/9/566...
3170Comedy, Drama, School, Shounen, SportsSlam Dunk8.54Hanamichi Sakuragi, infamous for his temper, m...TV101TV Asahi, AnimaxFlatiron Film Company, Geneon Entertainment USAToei AnimationMangaPG-13 - Teens 13 or older1087976879128920283226https://cdn.myanimelist.net/images/anime/12/86...
410162Josei, Slice of LifeUsagi Drop8.36Daikichi Kawachi is a 30-year-old bachelor wor...TV11Dentsu, Fuji TV, Toho, Tohokushinsha Film Corp...NIS America, Inc.Production I.GMangaPG-13 - Teens 13 or older2024255975237156479967https://cdn.myanimelist.net/images/anime/2/296...
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user_idusernameanime_idrating
0357zhambi329499
1357zhambi117597
2357zhambi63476
3357zhambi16828
4357zhambi364567
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count
genres
Hentai821
Comedy517
Music298
Kids197
Comedy, Slice of Life176
......
Action, Comedy, Fantasy, Hentai, Horror, Mecha, Romance, Supernatural1
Action, Hentai, Martial Arts1
Action, Hentai, Historical, Mecha, Military1
Drama, Music, Psychological, Romance, Slice of Life, Yaoi1
Adventure, Fantasy, Hentai1
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" + ], + "text/plain": [ + "genres\n", + "Hentai 821\n", + "Comedy 517\n", + "Music 298\n", + "Kids 197\n", + "Comedy, Slice of Life 176\n", + " ... \n", + "Action, Comedy, Fantasy, Hentai, Horror, Mecha, Romance, Supernatural 1\n", + "Action, Hentai, Martial Arts 1\n", + "Action, Hentai, Historical, Mecha, Military 1\n", + "Drama, Music, Psychological, Romance, Slice of Life, Yaoi 1\n", + "Adventure, Fantasy, Hentai 1\n", + "Name: count, Length: 3256, dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_anime['genres'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 335 + }, + "id": "VMPKGQMgj9K_", + "outputId": "f88e6945-b1d5-41cf-f0d3-9dd2f07bf6b6" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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count
anime_rating
PG-13 - Teens 13 or older4520
G - All Ages3406
Rx - Hentai1122
PG - Children1105
R+ - Mild Nudity952
R - 17+ (violence & profanity)896
UNKNOWN193
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count
producers
UNKNOWN4621
NHK260
Pink Pineapple189
Sanrio142
Bandai Visual118
......
Lantis, AT-X, Barnum Studio, Magic Capsule, Warner Bros. Japan, KlockWorx, Shueisha, Bulls Eye1
Dentsu, Sotsu Music Publishing1
TV Tokyo, Kadokawa Shoten, Kadokawa Pictures Japan, KlockWorx, NTT Docomo, Dwango1
Kadokawa Shoten, Rondo Robe1
Frontier Works, Media Factory, Mainichi Broadcasting System, Movic, Gentosha Comics1
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count
studios
UNKNOWN2977
Toei Animation726
Sunrise442
J.C.Staff296
Madhouse294
......
KKC Animation Production1
Echo1
Project Team Muu1
Studio March1
IKK Room1
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663928247Comedy, MusicPankis! 2-jigenUNKNOWNSt. Muse Academy: the place for the \"sound of ...TV24UNKNOWNUNKNOWNDLEOriginalG - All Ages18802138111UNKNOWN698https://cdn.myanimelist.net/images/anime/11/74...
767229750ComedyAAA de Ikou!!: Yuuna & AkikoUNKNOWNNo description available for this anime.OVA1Opera HouseUNKNOWNUNKNOWNUnknownG - All Ages14448159140UNKNOWN397https://cdn.myanimelist.net/images/anime/5/715...
767625063Comedy, Drama, HistoricalAnime Roukyoku Kikou Shimizu no JirochoudenUNKNOWNA story based on Shimizu no Jirochou, an early...TV30UNKNOWNUNKNOWNUNKNOWNUnknownG - All Ages14562164280UNKNOWN359https://cdn.myanimelist.net/images/anime/4/641...
776230914Kids, SportsAnimax TaisouUNKNOWNNo description available for this anime.Special30UNKNOWNUNKNOWNUNKNOWNUnknownG - All Ages14557179130UNKNOWN254https://cdn.myanimelist.net/images/anime/6/743...
.........................................................
1102932596Adventure, Comedy, Fantasy, KidsThe Snack World (TV)UNKNOWNIn \"a certain era\" on \"a certain continent,\" b...TV50TOHO animation, Level-5UNKNOWNOLM, OLM DigitalMixed mediaG - All Ages12922137521UNKNOWN713https://cdn.myanimelist.net/images/anime/3/833...
1103534488Comedy, Kids, Sci-FiGan Gan Ganko-chanUNKNOWNThe anime follows a boy named Gen, who time tr...TV10UNKNOWNUNKNOWN10GaugeOtherG - All Ages16001159320UNKNOWN397https://cdn.myanimelist.net/images/anime/8/831...
1104034456ComedySentai Hero Sukiyaki Force: Gunma no Heiwa wo ...UNKNOWNThe anime will be a \"surreal comedy\" that emph...TV24UNKNOWNUNKNOWNStudio 4°COriginalG - All Ages19473143260UNKNOWN593https://cdn.myanimelist.net/images/anime/10/83...
119999304HentaiHappy DayUNKNOWNA 2000 film originally banned in South Korea, ...OVA1UNKNOWNUNKNOWNUNKNOWNUnknownRx - HentaiUNKNOWN139031UNKNOWN675https://cdn.myanimelist.net/images/anime/1385/...
1206420007Action, HentaiHi Gekiga Ukiyoe Senya IchiyaUNKNOWNAn adult movie about a painter whose pictures ...Movie1Toei VideoUNKNOWNUNKNOWNUnknownRx - HentaiUNKNOWN137071UNKNOWN722https://cdn.myanimelist.net/images/anime/6/533...
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1357zhambi117597Action, Game, Romance, School, Sci-FiAccel World7.23Haruyuki Arita is an overweight, bullied middl...TV24...VIZ MediaSunriseLight novelPG-13 - Teens 13 or older28552633882380124697894https://cdn.myanimelist.net/images/anime/1002/...
2357zhambi63476Comedy, Romance, School, Super PowerBaka to Test to Shoukanjuu7.52Fumizuki Academy isn't a typical Japanese high...TV13...FunimationSILVER LINK.Light novelPG-13 - Teens 13 or older16453145586339916620729https://cdn.myanimelist.net/images/anime/3/503...
7357zhambi289996Drama, School, Super PowerCharlotte7.75If not for his ability to take over people's m...TV13...Aniplex of AmericaP.A. WorksOriginalPG-13 - Teens 13 or older102566221809390701536653https://cdn.myanimelist.net/images/anime/12/74...
9357zhambi18187Action, Adventure, Demons, Fantasy, Shounen, S...Claymore7.74When a shapeshifting demon with a thirst for h...TV26...FunimationMadhouseMangaR+ - Mild Nudity10362928252317263650639https://cdn.myanimelist.net/images/anime/3/218...
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Number of samples to include in the test set\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_set_size, random_state=73)\n", + "\n", + "print(\"Number of samples in the training set:\", len(y_train))\n", + "print(\"Number of samples in the test set:\", len(y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "xue2NDXI-Md8" + }, + "outputs": [], + "source": [ + "# Prepare input data for model training and evaluation\n", + "X_train_array = [X_train[:, 0], X_train[:, 1]]\n", + "X_test_array = [X_test[:, 0], X_test[:, 1]]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "UsBsSr3s-May" + }, + "outputs": [], + "source": [ + "def RecommenderNet(num_users, num_animes, embedding_size=128):\n", + " # User input layer and embedding layer\n", + " user = Input(name='user_encoded', shape=[1])\n", + " user_embedding = Embedding(name='user_embedding', input_dim=num_users, output_dim=embedding_size)(user)\n", + "\n", + " # Anime input layer and embedding layer\n", + " anime = Input(name='anime_encoded', shape=[1])\n", + " anime_embedding = Embedding(name='anime_embedding', input_dim=num_animes, output_dim=embedding_size)(anime)\n", + "\n", + " # Dot product of user and anime embeddings\n", + " dot_product = Dot(name='dot_product', normalize=True, axes=2)([user_embedding, anime_embedding])\n", + " flattened = Flatten()(dot_product)\n", + "\n", + " # Dense layers for prediction\n", + " dense = Dense(64, activation='relu')(flattened)\n", + " output = Dense(1, activation='sigmoid')(dense)\n", + "\n", + " # Create and compile the model\n", + " model = Model(inputs=[user, anime], outputs=output)\n", + " model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.001), metrics=[\"mae\", \"mse\"])\n", + "\n", + " return model\n", + "model = RecommenderNet(num_users, num_animes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cKr7GRrOjcyL" + }, + "outputs": [], + "source": [ + "!pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.13-cp310-cp310-linux_x86_64.whl\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "id": "BBRvWziYjbls", + "outputId": "149d5bf9-20be-4624-eef8-bef7267f017d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All devices: [LogicalDevice(name='/device:TPU:0', device_type='TPU'), LogicalDevice(name='/device:TPU:1', device_type='TPU'), LogicalDevice(name='/device:TPU:2', device_type='TPU'), LogicalDevice(name='/device:TPU:3', device_type='TPU'), LogicalDevice(name='/device:TPU:4', device_type='TPU'), LogicalDevice(name='/device:TPU:5', device_type='TPU'), LogicalDevice(name='/device:TPU:6', device_type='TPU'), LogicalDevice(name='/device:TPU:7', device_type='TPU')]\n" + ] + }, + { + "data": { + "text/html": [ + "
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+              "┃ Layer (type)              ┃ Output Shape           ┃        Param # ┃ Connected to           ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ user_encoded (InputLayer) │ (None, 1)              │              0 │ -                      │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ anime_encoded             │ (None, 1)              │              0 │ -                      │\n",
+              "│ (InputLayer)              │                        │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ user_embedding            │ (None, 1, 128)         │        104,064 │ user_encoded[0][0]     │\n",
+              "│ (Embedding)               │                        │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ anime_embedding           │ (None, 1, 128)         │      1,866,112 │ anime_encoded[0][0]    │\n",
+              "│ (Embedding)               │                        │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ dot_product (Dot)         │ (None, 1, 1)           │              0 │ user_embedding[0][0],  │\n",
+              "│                           │                        │                │ anime_embedding[0][0]  │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ flatten_1 (Flatten)       │ (None, 1)              │              0 │ dot_product[0][0]      │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ dense_2 (Dense)           │ (None, 64)             │            128 │ flatten_1[0][0]        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ dense_3 (Dense)           │ (None, 1)              │             65 │ dense_2[0][0]          │\n",
+              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
+              "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│ user_encoded (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", + "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", + "│ anime_encoded │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", + "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", + "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", + "│ user_embedding │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m104,064\u001b[0m │ user_encoded[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n", + "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", + "│ anime_embedding │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m1,866,112\u001b[0m │ anime_encoded[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n", + "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", + "│ dot_product (\u001b[38;5;33mDot\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m) �� \u001b[38;5;34m0\u001b[0m │ user_embedding[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ │ │ │ anime_embedding[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", + "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dot_product[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", + "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │ flatten_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", + "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m65\u001b[0m │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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"model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "tAW-DSmn-MXB" + }, + "outputs": [], + "source": [ + "## Import necessary callbacks\n", + "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping\n", + "\n", + "# Define the initial learning rate, minimum learning rate, maximum learning rate, and batch size\n", + "start_lr = 0.00001\n", + "min_lr = 0.00001\n", + "max_lr = 0.00005\n", + "batch_size = 10000\n", + "\n", + "\n", + "# Define the number of epochs for ramp-up, sustain, and exponential decay\n", + "rampup_epochs = 5\n", + "sustain_epochs = 0\n", + "exp_decay = .8\n", + "\n", + "# Learning rate schedule function\n", + "def lrfn(epoch):\n", + " if epoch < rampup_epochs:\n", + " return (max_lr - start_lr) / rampup_epochs * epoch + start_lr\n", + " elif epoch < rampup_epochs + sustain_epochs:\n", + " return max_lr\n", + " else:\n", + " return (max_lr - min_lr) * exp_decay**(epoch - rampup_epochs - sustain_epochs) + min_lr\n", + "\n", + "# Learning rate scheduler callback\n", + "lr_callback = LearningRateScheduler(lambda epoch: lrfn(epoch), verbose=0)\n", + "\n", + "# File path for saving the model weights\n", + "checkpoint_filepath = 'myanimeweights.weights.h5'\n", + "\n", + "# Model checkpoint callback to save the best weights\n", + "model_checkpoints = ModelCheckpoint(filepath=checkpoint_filepath,\n", + " save_weights_only=True,\n", + " monitor='val_loss',\n", + " mode='min',\n", + " save_best_only=True)\n", + "\n", + "# Early stopping callback to prevent overfitting\n", + "early_stopping = EarlyStopping(patience=3, monitor='val_loss', mode='min', restore_best_weights=True)\n", + "\n", + "# Define the list of callbacks\n", + "my_callbacks = [\n", + " model_checkpoints,\n", + " lr_callback,\n", + " early_stopping\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hf1Hr03I-MTw", + "outputId": "b8a5ed33-50b6-458c-ebfe-76f65c67a41f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 46ms/step - loss: 0.6932 - mae: 0.2255 - mse: 0.0732 - val_loss: 0.6929 - val_mae: 0.2204 - val_mse: 0.0702 - learning_rate: 1.0000e-05\n", + "Epoch 2/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 29ms/step - loss: 0.6928 - mae: 0.2243 - mse: 0.0725 - val_loss: 0.6923 - val_mae: 0.2198 - val_mse: 0.0699 - learning_rate: 1.8000e-05\n", + "Epoch 3/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6921 - mae: 0.2236 - mse: 0.0723 - val_loss: 0.6916 - val_mae: 0.2190 - val_mse: 0.0695 - learning_rate: 2.6000e-05\n", + "Epoch 4/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6915 - mae: 0.2242 - mse: 0.0727 - val_loss: 0.6909 - val_mae: 0.2179 - val_mse: 0.0691 - learning_rate: 3.4000e-05\n", + "Epoch 5/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 29ms/step - loss: 0.6905 - mae: 0.2219 - mse: 0.0717 - val_loss: 0.6903 - val_mae: 0.2168 - val_mse: 0.0689 - learning_rate: 4.2000e-05\n", + "Epoch 6/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6898 - mae: 0.2205 - mse: 0.0713 - val_loss: 0.6900 - val_mae: 0.2157 - val_mse: 0.0687 - learning_rate: 5.0000e-05\n", + "Epoch 7/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6893 - mae: 0.2181 - mse: 0.0703 - val_loss: 0.6898 - val_mae: 0.2150 - val_mse: 0.0686 - learning_rate: 4.2000e-05\n", + "Epoch 8/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6878 - mae: 0.2181 - mse: 0.0705 - val_loss: 0.6897 - val_mae: 0.2145 - val_mse: 0.0685 - learning_rate: 3.5600e-05\n", + "Epoch 9/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6870 - mae: 0.2167 - mse: 0.0700 - val_loss: 0.6893 - val_mae: 0.2139 - val_mse: 0.0684 - learning_rate: 3.0480e-05\n", + "Epoch 10/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6852 - mae: 0.2154 - mse: 0.0693 - val_loss: 0.6888 - val_mae: 0.2131 - val_mse: 0.0681 - learning_rate: 2.6384e-05\n", + "Epoch 11/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6831 - mae: 0.2136 - mse: 0.0683 - val_loss: 0.6880 - val_mae: 0.2121 - val_mse: 0.0677 - learning_rate: 2.3107e-05\n", + "Epoch 12/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6815 - mae: 0.2118 - mse: 0.0674 - val_loss: 0.6871 - val_mae: 0.2110 - val_mse: 0.0673 - learning_rate: 2.0486e-05\n", + "Epoch 13/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6795 - mae: 0.2091 - mse: 0.0659 - val_loss: 0.6861 - val_mae: 0.2098 - val_mse: 0.0668 - learning_rate: 1.8389e-05\n", + "Epoch 14/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6778 - mae: 0.2073 - mse: 0.0651 - val_loss: 0.6850 - val_mae: 0.2086 - val_mse: 0.0662 - learning_rate: 1.6711e-05\n", + "Epoch 15/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6760 - mae: 0.2057 - mse: 0.0644 - val_loss: 0.6837 - val_mae: 0.2074 - val_mse: 0.0656 - learning_rate: 1.5369e-05\n", + "Epoch 16/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6740 - mae: 0.2035 - mse: 0.0633 - val_loss: 0.6825 - val_mae: 0.2062 - val_mse: 0.0650 - learning_rate: 1.4295e-05\n", + "Epoch 17/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6719 - mae: 0.2030 - mse: 0.0628 - val_loss: 0.6812 - val_mae: 0.2051 - val_mse: 0.0644 - learning_rate: 1.3436e-05\n", + "Epoch 18/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6703 - mae: 0.2011 - mse: 0.0618 - val_loss: 0.6799 - val_mae: 0.2040 - val_mse: 0.0637 - learning_rate: 1.2749e-05\n", + "Epoch 19/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - loss: 0.6684 - mae: 0.1981 - mse: 0.0604 - val_loss: 0.6786 - val_mae: 0.2028 - val_mse: 0.0631 - learning_rate: 1.2199e-05\n", + "Epoch 20/20\n", + "\u001b[1m135/135\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 30ms/step - loss: 0.6667 - mae: 0.1968 - mse: 0.0595 - val_loss: 0.6773 - val_mae: 0.2017 - val_mse: 0.0625 - learning_rate: 1.1759e-05\n" + ] + } + ], + "source": [ + "# Model training\n", + "batch_size = 1024 * tpu_strategy.num_replicas_in_sync\n", + "history = model.fit(\n", + " x=X_train_array,\n", + " y=y_train,\n", + " batch_size=batch_size,\n", + " epochs=20,\n", + " verbose=1,\n", + " validation_data=(X_test_array, y_test),\n", + " callbacks=my_callbacks\n", + ")\n", + "\n", + "model.load_weights(checkpoint_filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "xibONJ2W-MQh", + "outputId": "2bb48a74-fd57-4298-9768-bb9fffdd308f" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Training results visualization\n", + "plt.plot(history.history[\"loss\"][0:-2])\n", + "plt.plot(history.history[\"val_loss\"][0:-2])\n", + "plt.title(\"Training Loss\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.legend([\"Train\", \"Validation\"], loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "s-p1X7eZRcRU", + "outputId": "8119c518-69ab-49e3-c78b-c553f06c212f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - loss: 0.6745 - mae: 0.2032 - mse: 0.0639\n", + "Test Loss: 0.6732999682426453, Test MAE: 0.2060622125864029, Test MSE: 0.06531194597482681\n" + ] + } + ], + "source": [ + "# Evaluate the model on the test set\n", + "evaluation = model.evaluate(X_test_array, y_test)\n", + "print(f\"Test Loss: {evaluation[0]}, Test MAE: {evaluation[1]}, Test MSE: {evaluation[2]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "5eZltYvrnadX" + }, + "outputs": [], + "source": [ + "def get_unrated_anime(user_id, df, num_animes):\n", + " watched_anime_ids = df[df['user_id'] == user_id]['anime_id'].tolist()\n", + " all_anime_ids = list(range(num_animes))\n", + " unrated_anime_ids = [anime_id for anime_id in all_anime_ids if anime_id not in watched_anime_ids]\n", + " return unrated_anime_ids" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "QS0Zg4DwnYIF" + }, + "outputs": [], + "source": [ + "def recommend_anime(user_id, model, num_animes, n_recommendations=10):\n", + " unrated_anime_ids = get_unrated_anime(user_id, df, num_animes)\n", + " user_array = np.array([user_id] * len(unrated_anime_ids))\n", + " anime_array = np.array(unrated_anime_ids)\n", + "\n", + " predictions = model.predict([user_array, anime_array])\n", + " predicted_ratings = predictions.flatten()\n", + "\n", + " top_indices = predicted_ratings.argsort()[-n_recommendations:][::-1]\n", + " recommended_anime_ids = [unrated_anime_ids[i] for i in top_indices]\n", + "\n", + " return recommended_anime_ids" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sJ4RJNCPnelV", + "outputId": "b66b14dd-a19b-4fef-cb8f-f4eb42679a0c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m456/456\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 43ms/step\n", + "Original Anime IDs for User 0: [37208 41339 3927 329 8142 36129 5091 16706 35843 28891]\n", + "Recommended Anime Names for User 0: ['Planetes' 'Haikyuu!! Second Season'\n", + " 'Kami nomi zo Shiru Sekai: Megami-hen'\n", + " 'Kidou Senshi Gundam 00 Second Season' 'Colorful (Movie)'\n", + " 'Manga Sarutobi Sasuke']\n" + ] + } + ], + "source": [ + "user_id = 10\n", + "recommended_anime_ids = recommend_anime(user_id, model, num_animes, n_recommendations=10)\n", + "\n", + "# Map the recommended anime IDs back to their names\n", + "original_anime_ids = anime_encoder.inverse_transform(recommended_anime_ids)\n", + "print(\"Original Anime IDs for User 0:\", original_anime_ids)\n", + "recommended_anime_names = df_anime[df_anime['anime_id'].isin(original_anime_ids)]['name'].values\n", + "print(\"Recommended Anime Names for User 0:\", recommended_anime_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3f4p_zFMDdXd" + }, + "source": [ + "#### 2) Singular Value Decomposition - SVD\n", + "\n", + "Singular Value Decomposition (SVD) is a powerful **matrix factorization technique** widely used in collaborative filtering for recommendation systems. It helps reduce the dimensionality of user-item interaction data while preserving essential patterns, allowing us to make accurate recommendations based on user preferences.\n", + " \n", + "SVD helps uncover **latent relationships** between users and items, making it effective for personalized recommendations. By approximating missing values (unrated items), we can predict user preferences and recommend anime based on their past interactions. \n", + "\n", + "In this notebook, we will use SVD to build a recommendation model and evaluate its performance in predicting user preferences." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RBHd69NvDgyc", + "outputId": "5b274a78-e948-4ab8-977f-13210ce84813" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting surprise\n", + " Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n", + "Collecting scikit-surprise (from surprise)\n", + " Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m3.0 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/python3.11/dist-packages (from scikit-surprise->surprise) (1.4.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-surprise->surprise) (1.26.4)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-surprise->surprise) (1.13.1)\n", + "Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\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-cp311-cp311-linux_x86_64.whl size=2505176 sha256=6cd9480d9f79089b96685f62df64f3247d2e3c8c6e28a8ebb4ac50c35806bdaf\n", + " Stored in directory: /root/.cache/pip/wheels/2a/8f/6e/7e2899163e2d85d8266daab4aa1cdabec7a6c56f83c015b5af\n", + "Successfully built scikit-surprise\n", + "Installing collected packages: scikit-surprise, surprise\n", + "Successfully installed scikit-surprise-1.1.4 surprise-0.1\n" + ] + } + ], + "source": [ + "!pip install surprise" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VbKyIBLnTUFL", + "outputId": "4dd5a2a4-4590-4d0d-ca0f-bac4ef8eb4ad" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from surprise import SVD, Dataset, Reader\n", + "from surprise.model_selection import cross_validate\n", + "\n", + "# Prepare the data for Surprise\n", + "reader = Reader(rating_scale=(1, 10))\n", + "data = Dataset.load_from_df(df_merged[['user_id', 'anime_id', 'rating']], reader)\n", + "\n", + "# Train the SVD model\n", + "svd = SVD()\n", + "cross_validate(svd, data, cv=5)\n", + "\n", + "# Fit the model on the entire dataset\n", + "trainset = data.build_full_trainset()\n", + "svd.fit(trainset)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "id": "c0qPhkSzKvdd" + }, + "outputs": [], + "source": [ + "# Function to get top recommendations for a user\n", + "def get_collab_recommendations(user_id, n=10):\n", + " anime_ids = df_merged['anime_id'].unique()\n", + " predictions = [(anime_id, svd.predict(user_id, anime_id).est) for anime_id in anime_ids]\n", + " predictions.sort(key=lambda x: x[1], reverse=True)\n", + " anime_ids = [pred[0] for pred in predictions[:n]]\n", + " anime_names = set(df_merged[df_merged['anime_id'].isin(anime_ids)]['name'].tolist())\n", + " return anime_names" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kQ4htwfYTUBH", + "outputId": "94964b78-3e39-469a-d909-e42e70fd5075" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Clannad: After Story',\n", + " 'Code Geass: Hangyaku no Lelouch',\n", + " 'Code Geass: Hangyaku no Lelouch R2',\n", + " 'Fullmetal Alchemist: Brotherhood',\n", + " 'Ginga Eiyuu Densetsu',\n", + " 'Gintama°',\n", + " 'Hajime no Ippo',\n", + " 'Kimi no Na wa.',\n", + " 'Steins;Gate',\n", + " 'Suzumiya Haruhi no Shoushitsu'}" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_collab_recommendations(34, n=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tIdBRcwnM0pc" + }, + "source": [ + "#### 3) Item-Based Collaborative Filtering using KNN- Anime recommendations based on user ratings\n", + "\n", + "Item-Based Collaborative Filtering (IBCF) is a recommendation technique that suggests items similar to those a user has already rated or interacted with. Unlike **user-based collaborative filtering**, which finds similar users, **item-based filtering** focuses on item-to-item similarities. \n", + "\n", + "- How It Works:\n", + " 1. **Compute Item Similarity**: Using **K-Nearest Neighbors (KNN)**, we measure similarity between anime based on user rating patterns. \n", + " 2. **Find Nearest Neighbors**: Identify anime that are most similar to the ones a user has rated. \n", + " 3. **Generate Recommendations**: Suggest anime that are highly rated by users who liked similar items. " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FDSs7u4aM0Ep", + "outputId": "8c976525-3338-4ea4-aa11-56a0eeda2fbb" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(597114, 21)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df_merged.copy()\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 637 + }, + "id": "YE9XO3V7fjmj", + "outputId": "772fd4dc-9fcc-42a7-9b2c-21b9d595e391" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df" + }, + "text/html": [ + "\n", + "
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1357zhambi117597Action, Game, Romance, School, Sci-FiAccel World7.23Haruyuki Arita is an overweight, bullied middl...TV24...VIZ MediaSunriseLight novelPG-13 - Teens 13 or older28552633882380124697894https://cdn.myanimelist.net/images/anime/1002/...
2357zhambi63476Comedy, Romance, School, Super PowerBaka to Test to Shoukanjuu7.52Fumizuki Academy isn't a typical Japanese high...TV13...FunimationSILVER LINK.Light novelPG-13 - Teens 13 or older16453145586339916620729https://cdn.myanimelist.net/images/anime/3/503...
7357zhambi289996Drama, School, Super PowerCharlotte7.75If not for his ability to take over people's m...TV13...Aniplex of AmericaP.A. WorksOriginalPG-13 - Teens 13 or older102566221809390701536653https://cdn.myanimelist.net/images/anime/12/74...
9357zhambi18187Action, Adventure, Demons, Fantasy, Shounen, S...Claymore7.74When a shapeshifting demon with a thirst for h...TV26...FunimationMadhouseMangaR+ - Mild Nudity10362928252317263650639https://cdn.myanimelist.net/images/anime/3/218...
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" + ], + "text/plain": [ + "NearestNeighbors(algorithm='brute', metric='cosine')" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.sparse import csr_matrix\n", + "from sklearn.neighbors import NearestNeighbors\n", + "\n", + "item_user_matrix = csr_matrix(anime_pivot.values)\n", + "knn_item_based = NearestNeighbors(metric = 'cosine', algorithm = 'brute')\n", + "knn_item_based.fit(item_user_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "dQkd4zIdXTxK" + }, + "outputs": [], + "source": [ + "def get_item_based_recommendations(anime_name, n_recommendations=5):\n", + " # Find the index of the anime title\n", + " if anime_name not in anime_pivot.index:\n", + " return f\"Anime title '{anime_name}' not found in the dataset.\"\n", + "\n", + " query_index = anime_pivot.index.get_loc(anime_name)\n", + "\n", + " # Use the KNN model to find the nearest neighbors\n", + " distances, indices = knn_item_based.kneighbors(anime_pivot.iloc[query_index,:].values.reshape(1, -1), n_neighbors=n_recommendations + 1)\n", + "\n", + " recommendations = []\n", + " for i in range(1, len(distances.flatten())):\n", + " anime_title = anime_pivot.index[indices.flatten()[i]]\n", + " distance = distances.flatten()[i]\n", + " recommendations.append((anime_title, distance))\n", + "\n", + " return recommendations" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K7wvMvI0M3cw", + "outputId": "9e9604d5-a806-41f6-990e-9176f024b785" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Based on user rating, If you like 'One Piece' you will definetly like below recommendations....\n", + "1: Bleach, with distance of 0.2537\n", + "2: Naruto, with distance of 0.2768\n", + "3: One Piece Film: Strong World, with distance of 0.2836\n", + "4: Fairy Tail, with distance of 0.2905\n", + "5: Soul Eater, with distance of 0.2945\n", + "6: Death Note, with distance of 0.2964\n", + "7: Code Geass: Hangyaku no Lelouch, with distance of 0.3043\n", + "8: Naruto: Shippuuden, with distance of 0.3101\n", + "9: Code Geass: Hangyaku no Lelouch R2, with distance of 0.3106\n", + "10: Fullmetal Alchemist: Brotherhood, with distance of 0.3122\n" + ] + } + ], + "source": [ + "anime_name = \"One Piece\"\n", + "item_based_recommendations = get_item_based_recommendations(anime_name,10)\n", + "\n", + "if isinstance(item_based_recommendations, str):\n", + " print(item_based_recommendations)\n", + "else:\n", + " print(f\"Based on user rating, If you like '{anime_name}' you will definetly like below recommendations....\")\n", + " for i, (title, distance) in enumerate(item_based_recommendations, 1):\n", + " print(f\"{i}: {title}, with distance of {distance:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XXKBq-x9qALF" + }, + "source": [ + "#### 4) User-Based Collaborative Filtering using KNN\n", + "\n", + "\n", + "User-Based Collaborative Filtering (UBCF) is a recommendation approach that suggests items to users based on the preferences of similar users. Unlike **item-based filtering**, which focuses on item similarities, **user-based filtering** identifies users with similar rating patterns and recommends items they have liked. \n", + "\n", + "- How It Works:\n", + " 1. **Compute User Similarity**: Using **K-Nearest Neighbors (KNN)**, we measure similarity between users based on their anime rating history. \n", + " 2. **Find Nearest Neighbors**: Identify users who have rated anime similarly. \n", + " 3. **Generate Recommendations**: Recommend anime that similar users have highly rated but the target user has not watched yet. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "id": "btDeSD4ap384", + "outputId": "683cc21a-fc4e-4324-aa73-a37eb9430b5d" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "user_pivot" + }, + "text/html": [ + "\n", + "
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NearestNeighbors(algorithm='brute', metric='cosine')
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" + ], + "text/plain": [ + "NearestNeighbors(algorithm='brute', metric='cosine')" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.sparse import csr_matrix\n", + "from sklearn.neighbors import NearestNeighbors\n", + "\n", + "user_item_matrix = csr_matrix(user_pivot.values)\n", + "knn_user_based = NearestNeighbors(metric = 'cosine', algorithm = 'brute')\n", + "knn_user_based.fit(user_item_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "W_lzzcUr2Qoq" + }, + "outputs": [], + "source": [ + "def get_user_based_recommendations(user_id, n_recommendations=5):\n", + " # Convert user_id to the same type as user_pivot index\n", + " user_id = float(user_id)\n", + "\n", + " # Check if the user exists in the matrix\n", + " if user_id not in user_pivot.index:\n", + " return f\"User '{user_id}' not found in the dataset.\"\n", + "\n", + " # Find the user index\n", + " user_idx = user_pivot.index.get_loc(user_id)\n", + "\n", + " # Find the nearest neighbors (most similar users)\n", + " distances, indices = knn_user_based.kneighbors(user_pivot.iloc[user_idx, :].values.reshape(1, -1), n_neighbors=n_recommendations + 1)\n", + "\n", + " # Get the list of anime this user has already rated\n", + " user_rated_anime = set(user_pivot.columns[user_pivot.iloc[user_idx, :] > 100])\n", + "\n", + " # Gather all anime rated by nearest neighbors\n", + " all_neighbor_ratings = []\n", + " for i in range(1, len(distances.flatten())):\n", + " neighbor_idx = indices.flatten()[i]\n", + " neighbor_rated_anime = user_pivot.iloc[neighbor_idx, :]\n", + " neighbor_ratings = neighbor_rated_anime[neighbor_rated_anime > 0]\n", + " all_neighbor_ratings.extend(neighbor_ratings.index)\n", + "\n", + " # Count the frequency of each anime rated by the neighbors\n", + " from collections import Counter\n", + " anime_counter = Counter(all_neighbor_ratings)\n", + "\n", + " # Recommend the most common anime among the neighbors that the user hasn't rated yet\n", + " recommendations = [(anime, count) for anime, count in anime_counter.items() if anime not in user_rated_anime]\n", + " recommendations.sort(key=lambda x: x[1], reverse=True)\n", + "\n", + " # Return the top N recommendations\n", + " return recommendations[:n_recommendations]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Pwenx_vZ2TSz", + "outputId": "b98596e5-cab9-4cdf-d59f-4be10d4f23c9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User-based recommendations for user ID 817:\n", + "Accel World, Recommended by 10 similar users\n", + "Ai Yori Aoshi, Recommended by 10 similar users\n", + "Amagami SS, Recommended by 10 similar users\n", + "Angel Beats!, Recommended by 10 similar users\n", + "Ano Hi Mita Hana no Namae wo Bokutachi wa Mada Shiranai., Recommended by 10 similar users\n", + "Ano Natsu de Matteru, Recommended by 10 similar users\n", + "Another, Recommended by 10 similar users\n", + "Ao no Exorcist, Recommended by 10 similar users\n", + "Arakawa Under the Bridge, Recommended by 10 similar users\n", + "Asura Cryin' 2, Recommended by 10 similar users\n" + ] + } + ], + "source": [ + "user_id = 817\n", + "user_recommendations = get_user_based_recommendations(user_id,10)\n", + "\n", + "# Check if recommendations is a string (indicating an error)\n", + "if isinstance(user_recommendations, str):\n", + " print(user_recommendations)\n", + "else:\n", + " print(f\"User-based recommendations for user ID {user_id}:\")\n", + " for anime, count in user_recommendations:\n", + " print(f\"{anime}, Recommended by {count} similar users\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lTIbtd3uL4Tm" + }, + "source": [ + "## Content based Filtering" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Content-Based Filtering (CBF) also referred to as cognitive filtering, recommends anime based on their content attributes, such as **genre, synopsis, or other metadata**. Unlike collaborative filtering, which relies on user interactions, CBF focuses on **similarities between anime** using textual data and machine learning techniques. \n", + "\n", + "1. **TF-IDF with Sigmoid Kernel** \n", + " - **TF-IDF (Term Frequency-Inverse Document Frequency)** transforms text into numerical features. \n", + " - The **Sigmoid Kernel** measures similarity between anime based on their textual descriptions. \n", + "\n", + "2. **CountVectorizer with Linear Kernel** \n", + " - **CountVectorizer** converts text into a bag-of-words representation. \n", + " - The **Linear Kernel** computes the similarity between anime using these feature vectors. \n", + "\n", + "- Why Use These Methods?\n", + " - **TF-IDF + Sigmoid Kernel** captures the importance of words while smoothing similarity scores. \n", + " - **CountVectorizer + Linear Kernel** is efficient for comparing anime descriptions at a high level. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zzorVg94k4HF" + }, + "source": [ + "#### 1) Using TFIDF" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PShKOncdVfbJ" + }, + "source": [ + "##### a. Sigmoid Kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "id": "rVvjDyI9MKSr" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 678 + }, + "id": "cO7tLpcxjLh0", + "outputId": "69147217-a12c-484f-9573-c63a3bf4175c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the Dataset: (12194, 18)\n" + ] + }, + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"df\",\n \"rows\": 12194,\n \"fields\": [\n {\n \"column\": \"anime_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11437,\n \"min\": 1,\n \"max\": 34527,\n \"num_unique_values\": 12194,\n \"samples\": [\n 3834,\n 32936,\n 8792\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genres\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3256,\n \"samples\": [\n \"Mystery, Sci-Fi, Space\",\n \"Music, Sci-Fi\",\n \"Magic, Romance\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12194,\n \"samples\": [\n \"Hoshi no Ko Chobin\",\n \"Gin no Guardian\",\n \"Madobe Nanami no Windows 7 de PC Jisaku Ouen Commercial!!\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"average_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 552,\n \"samples\": [\n \"4.06\",\n \"8.03\",\n \"7.32\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"overview\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11689,\n \"samples\": [\n \"Serebii, a Legendary Pok\\u00e9mon known for its ability to traverse time, is hunted by an unnamed Pok\\u00e9mon poacher seeking to capture it. Yukinari, a young Pok\\u00e9mon trainer who enjoys drawing portraits of Pok\\u00e9mon, tries to protect Serebii after it stumbles upon him; but in the middle of its escape, both vanish without a trace.\\n\\nForty years later, ambitious Pok\\u00e9mon trainer Satoshi hopes to sight rare Pok\\u00e9mon around his local area. White, a boat driver, takes Satoshi to his village. Satoshi is accompanied by his three closest friends: Takeshi, a former gym leader training to be a great Pok\\u00e9mon breeder; Kasumi, a young girl wanting to become a skilled Water-type trainer; and Pikachu, Satoshi's Pok\\u00e9mon partner and first comrade.\\n\\nMeanwhile, Yukinari and Serebii reappear in Satoshi's present time and run into his group. Masked Lord Vicious, the strongest executive staff member of Team Rocket, desires to capture Serebii. Using the Dark Ball, a variant of Monster Ball that corrupts the Pok\\u00e9mon caught within and draws out their maximum power, Vicious can transform innocent Pok\\u00e9mon into powerful and frightening obstacles\\u2014including Serebii itself! Placed in a tough position, Satoshi, Yukinari, and their friends must work together to defeat Vicious and save Serebii and themselves.\",\n \"East Force meets West Force and all Hell breaks loose. The Solonoids, that lovable race of female warriors, are at it again, fighting amongst themselves. During a heated battle, however, it looks like the leaders of the two factions have hung their warriors out to dry. In the middle of all this fighting and chaos, East Force detects a transmission from an unidentified planet. The Gall Force gals leave their posts to go. Lufy, the West Force's Ace Pilot, who is after Rabby, follows them to the planet.\\n\\n(Source: AnimeNfo)\",\n \"Netto's father Yuuichirou Hikari has made a scientific breakthrough by introducing the \\\"synchro chips\\\". If an operator and his or her navi are in a special enviroment known as a \\\"dimensional area\\\", they can fuse together in the real world via a technique called \\\"cross fusion\\\"! Yuuichirou's first test subject, Misaki Gorou, attempts the process and sadly fails. Netto offers to try with Rockman, but his father forbids it. Cross Fusion puts enormous strain on the operator's health, and battling in the real world could mean death. \\n\\n(Source: Official Site)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"TV\",\n \"Movie\",\n \"ONA\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"episodes\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 200,\n \"samples\": [\n \"93\",\n \"27\",\n \"110\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"producers\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3070,\n \"samples\": [\n \"AIC, Lantis, Media Factory, Pony Canyon, Rakuonsha, AT-X, KlockWorx, Ryukyu Asahi Broadcasting\",\n \"Tama Production, Tokyo MX\",\n \"KAGAYA Studio\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"licensors\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 253,\n \"samples\": [\n \"ADV Films, Media Blasters\",\n \"Funimation\",\n \"Central Park Media, Maiden Japan\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"studios\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 941,\n \"samples\": [\n \"Group TAC, Ginga Ya\",\n \"J.C.Staff, Production I.G\",\n \"drop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"source\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 16,\n \"samples\": [\n \"Visual novel\",\n \"Manga\",\n \"Novel\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"anime_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"PG-13 - Teens 13 or older\",\n \"R - 17+ (violence & profanity)\",\n \"UNKNOWN\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rank\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 8846,\n \"samples\": [\n \"12549\",\n \"7778\",\n \"7761\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"popularity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5211,\n \"min\": 1,\n \"max\": 19844,\n \"num_unique_values\": 10304,\n \"samples\": [\n 5880,\n 7617,\n 17889\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"favorites\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5821,\n \"min\": 0,\n \"max\": 217606,\n \"num_unique_values\": 1357,\n \"samples\": [\n 46556,\n 1136,\n 1570\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"scored by\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6497,\n \"samples\": [\n \"19239\",\n \"1926\",\n \"300\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"members\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 191364,\n \"min\": 142,\n \"max\": 3744541,\n \"num_unique_values\": 8192,\n \"samples\": [\n 9390,\n 8376,\n 1998\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"image url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12135,\n \"samples\": [\n \"https://cdn.myanimelist.net/images/anime/1195/111544.jpg\",\n \"https://cdn.myanimelist.net/images/anime/6/26448.jpg\",\n \"https://cdn.myanimelist.net/images/anime/1405/112400.jpg\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "df" + }, + "text/html": [ + "\n", + "
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anime_idgenresnameaverage_ratingoverviewtypeepisodesproducerslicensorsstudiossourceanime_ratingrankpopularityfavoritesscored bymembersimage url
04181Drama, Fantasy, Romance, Slice of Life, Supern...Clannad: After Story8.93Clannad: After Story, the sequel to the critic...TV24Pony Canyon, TBS, Rakuonsha, Animation DoSentai FilmworksKyoto AnimationVisual novelPG-13 - Teens 13 or older19114689496397291149886https://cdn.myanimelist.net/images/anime/1299/...
128735Drama, Historical, JoseiShouwa Genroku Rakugo Shinjuu8.57Yotarou is a former yakuza member fresh out of...TV13Starchild Records, Mainichi Broadcasting Syste...UNKNOWNStudio DeenMangaPG-13 - Teens 13 or older93804571191359281445https://cdn.myanimelist.net/images/anime/1354/...
25205Action, Mystery, Romance, Supernatural, ThrillerKara no Kyoukai Movie 7: Satsujin Kousatsu (Go)8.39In February 1999, a string of murders has Shik...Movie1NotesAniplex of AmericaufotableLight novelR - 17+ (violence & profanity)18211152261108703200492https://cdn.myanimelist.net/images/anime/9/566...
3170Comedy, Drama, School, Shounen, SportsSlam Dunk8.54Hanamichi Sakuragi, infamous for his temper, m...TV101TV Asahi, AnimaxFlatiron Film Company, Geneon Entertainment USAToei AnimationMangaPG-13 - Teens 13 or older1087976879128920283226https://cdn.myanimelist.net/images/anime/12/86...
410162Josei, Slice of LifeUsagi Drop8.36Daikichi Kawachi is a 30-year-old bachelor wor...TV11Dentsu, Fuji TV, Toho, Tohokushinsha Film Corp...NIS America, Inc.Production I.GMangaPG-13 - Teens 13 or older2024255975237156479967https://cdn.myanimelist.net/images/anime/2/296...
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\n" + ], + "text/plain": [ + " anime_id genres \\\n", + "0 4181 Drama, Fantasy, Romance, Slice of Life, Supern... \n", + "1 28735 Drama, Historical, Josei \n", + "2 5205 Action, Mystery, Romance, Supernatural, Thriller \n", + "3 170 Comedy, Drama, School, Shounen, Sports \n", + "4 10162 Josei, Slice of Life \n", + "\n", + " name average_rating \\\n", + "0 Clannad: After Story 8.93 \n", + "1 Shouwa Genroku Rakugo Shinjuu 8.57 \n", + "2 Kara no Kyoukai Movie 7: Satsujin Kousatsu (Go) 8.39 \n", + "3 Slam Dunk 8.54 \n", + "4 Usagi Drop 8.36 \n", + "\n", + " overview type episodes \\\n", + "0 Clannad: After Story, the sequel to the critic... TV 24 \n", + "1 Yotarou is a former yakuza member fresh out of... TV 13 \n", + "2 In February 1999, a string of murders has Shik... Movie 1 \n", + "3 Hanamichi Sakuragi, infamous for his temper, m... TV 101 \n", + "4 Daikichi Kawachi is a 30-year-old bachelor wor... TV 11 \n", + "\n", + " producers \\\n", + "0 Pony Canyon, TBS, Rakuonsha, Animation Do \n", + "1 Starchild Records, Mainichi Broadcasting Syste... \n", + "2 Notes \n", + "3 TV Asahi, Animax \n", + "4 Dentsu, Fuji TV, Toho, Tohokushinsha Film Corp... \n", + "\n", + " licensors studios \\\n", + "0 Sentai Filmworks Kyoto Animation \n", + "1 UNKNOWN Studio Deen \n", + "2 Aniplex of America ufotable \n", + "3 Flatiron Film Company, Geneon Entertainment USA Toei Animation \n", + "4 NIS America, Inc. Production I.G \n", + "\n", + " source anime_rating rank popularity favorites \\\n", + "0 Visual novel PG-13 - Teens 13 or older 19 114 68949 \n", + "1 Manga PG-13 - Teens 13 or older 93 804 5711 \n", + "2 Light novel R - 17+ (violence & profanity) 182 1115 2261 \n", + "3 Manga PG-13 - Teens 13 or older 108 797 6879 \n", + "4 Manga PG-13 - Teens 13 or older 202 425 5975 \n", + "\n", + " scored by members image url \n", + "0 639729 1149886 https://cdn.myanimelist.net/images/anime/1299/... \n", + "1 91359 281445 https://cdn.myanimelist.net/images/anime/1354/... \n", + "2 108703 200492 https://cdn.myanimelist.net/images/anime/9/566... \n", + "3 128920 283226 https://cdn.myanimelist.net/images/anime/12/86... \n", + "4 237156 479967 https://cdn.myanimelist.net/images/anime/2/296... " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"/content/Animes.csv\")\n", + "print(\"Shape of the Dataset:\", df.shape)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "id": "8-vUA38AMMCS" + }, + "outputs": [], + "source": [ + "df.dropna(inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9GvI7884MMCS", + "outputId": "96c483b6-b506-489d-f0ef-f20cdf7fcfa2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['anime_id', 'genres', 'name', 'average_rating', 'overview', 'type',\n", + " 'episodes', 'producers', 'licensors', 'studios', 'source',\n", + " 'anime_rating', 'rank', 'popularity', 'favorites', 'scored by',\n", + " 'members', 'image url'],\n", + " dtype='object')" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "id": "syXBYlBqL9O_" + }, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "\n", + "tfv = TfidfVectorizer(min_df=3,\n", + " strip_accents='unicode', analyzer='word',token_pattern=r'\\w{1,}',\n", + " ngram_range=(1, 3),\n", + " stop_words = 'english')\n", + "\n", + "tfv_matrix = tfv.fit_transform(df['genres'])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EDUM2vu7L99g", + "outputId": "69816148-37e1-48f4-b6af-d69337b8bfeb" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(12133, 1552)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tfv_matrix.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "id": "0jVvemqYL95s" + }, + "outputs": [], + "source": [ + "from sklearn.metrics.pairwise import sigmoid_kernel\n", + "sig = sigmoid_kernel(tfv_matrix, tfv_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "id": "Qy4uip8AL92D" + }, + "outputs": [], + "source": [ + "indices = pd.Series(df.index, index=df['name']).drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "id": "ezJ6vl80L9xo" + }, + "outputs": [], + "source": [ + "def get_rec_sig(title, sig=sig,n_recommendations = 10):\n", + " if title not in indices.index:\n", + " print(f\"Anime title '{title}' not found in the dataset.\")\n", + " # Get the index corresponding to original_title\n", + " idx = indices[title]\n", + "\n", + " # Get the pairwsie similarity scores\n", + " sig_scores = list(enumerate(sig[idx]))\n", + "\n", + " # Sort the movies\n", + " sig_scores = sorted(sig_scores, key=lambda x: x[1], reverse=True)\n", + "\n", + " sig_scores = sig_scores[1:n_recommendations+1]\n", + "\n", + " # Movie indices\n", + " anime_indices = [i[0] for i in sig_scores]\n", + " return pd.DataFrame({'Anime name': df['name'].iloc[anime_indices].values,\n", + " 'Rating': df['average_rating'].iloc[anime_indices].values})" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 519 + }, + "id": "1sktZ0lWL9uO", + "outputId": "a09176a6-dc66-48ec-9b63-a46f2afdec48" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"get_rec_sig('Naruto', n_recommendations=15)\",\n \"rows\": 15,\n \"fields\": [\n {\n \"column\": \"Anime name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 15,\n \"samples\": [\n \"Kyutai Panic Adventure!\",\n \"Ben-To\",\n \"Boruto: Naruto the Movie\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rating\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 14,\n \"samples\": [\n \"7.49\",\n \"7.68\",\n \"7.4\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, + "text/html": [ + "\n", + "
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Anime nameRating
0Boruto: Naruto the Movie7.4
1Naruto7.99
2Naruto x UT7.37
3Boruto: Naruto the Movie - Naruto ga Hokage ni...7.33
4Naruto: Shippuuden Movie 4 - The Lost Tower7.42
5Naruto: Shippuuden Movie 3 - Hi no Ishi wo Tsu...7.33
6Naruto: Shippuuden - Sunny Side Battle7.56
7Naruto Soyokazeden Movie: Naruto to Mashin to ...6.96
8Battle Spirits: Ryuuko no Ken4.78
9Kyutai Panic Adventure!4.67
10Ranma ½: Akumu! Shunmin Kou7.49
11Ben-To7.2
12Naruto: Shippuuden Movie 6 - Road to Ninja7.68
13Rekka no Honoo7.34
14Naruto: Honoo no Chuunin Shiken! Naruto vs. Ko...7.17
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Anime nameRating
0Boruto: Naruto the Movie7.4
1Naruto7.99
2Naruto x UT7.37
3Boruto: Naruto the Movie - Naruto ga Hokage ni...7.33
4Naruto: Shippuuden Movie 4 - The Lost Tower7.42
5Naruto: Shippuuden Movie 3 - Hi no Ishi wo Tsu...7.33
6Naruto: Shippuuden - Sunny Side Battle7.56
7Naruto Soyokazeden Movie: Naruto to Mashin to ...6.96
8Battle Spirits: Ryuuko no Ken4.78
9Kyutai Panic Adventure!4.67
10Ranma ½: Akumu! Shunmin Kou7.49
11Ben-To7.2
12Naruto: Shippuuden Movie 6 - Road to Ninja7.68
13Rekka no Honoo7.34
14Naruto: Honoo no Chuunin Shiken! Naruto vs. Ko...7.17
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Anime nameRating
0Boruto: Naruto the Movie7.4
1Naruto7.99
2Naruto x UT7.37
3Boruto: Naruto the Movie - Naruto ga Hokage ni...7.33
4Naruto: Shippuuden Movie 4 - The Lost Tower7.42
5Naruto: Shippuuden Movie 3 - Hi no Ishi wo Tsu...7.33
6Naruto: Shippuuden - Sunny Side Battle7.56
7Naruto Soyokazeden Movie: Naruto to Mashin to ...6.96
8Battle Spirits: Ryuuko no Ken4.78
9Kyutai Panic Adventure!4.67
10Ranma ½: Akumu! Shunmin Kou7.49
11Naruto: Shippuuden Movie 6 - Road to Ninja7.68
12Rekka no Honoo7.34
13Naruto: Honoo no Chuunin Shiken! Naruto vs. Ko...7.17
14Street Fighter Zero The Animation6.51
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Naruto vs. Ko... 7.17\n", + "14 Street Fighter Zero The Animation 6.51" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_rec( \"Naruto\", n_recommendations=15)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ny0Ub9tJmZk6" + }, + "source": [ + "As you can see both Count Vectorizer and TF-IDF are giving same recommendations. We will lean towards TfidfVectorizer for more accurate and meaningful recommendations, especially in content-based systems that handle large or complex textual data. CountVectorizer might be used in scenarios where quick and simple solutions are sufficient or for smaller datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QSPSoR1RH9ZE" + }, + "source": [ + "## Popularity-Based Filtering" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "id": "5r8qPk9PzL_S" + }, + "outputs": [], + "source": [ + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "id": "ppgmlYpA20D3" + }, + "outputs": [], + "source": [ + "df = df_anime.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 340 + }, + "id": "GK_FQCX53Hth", + "outputId": "9a55ecdc-302e-409f-d68d-f39ba7437943" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"df\",\n \"rows\": 12194,\n \"fields\": [\n {\n \"column\": \"anime_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11437,\n \"min\": 1,\n \"max\": 34527,\n \"num_unique_values\": 12194,\n \"samples\": [\n 3834,\n 32936,\n 8792\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genres\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3256,\n \"samples\": [\n \"Mystery, Sci-Fi, Space\",\n \"Music, Sci-Fi\",\n \"Magic, Romance\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12194,\n \"samples\": [\n \"Hoshi no Ko Chobin\",\n \"Gin no Guardian\",\n \"Madobe Nanami no Windows 7 de PC Jisaku Ouen Commercial!!\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"average_rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.9422757691869167,\n \"min\": 1.85,\n \"max\": 9.1,\n \"num_unique_values\": 551,\n \"samples\": [\n 4.06,\n 8.03,\n 7.32\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"overview\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11689,\n \"samples\": [\n \"Serebii, a Legendary Pok\\u00e9mon known for its ability to traverse time, is hunted by an unnamed Pok\\u00e9mon poacher seeking to capture it. Yukinari, a young Pok\\u00e9mon trainer who enjoys drawing portraits of Pok\\u00e9mon, tries to protect Serebii after it stumbles upon him; but in the middle of its escape, both vanish without a trace.\\n\\nForty years later, ambitious Pok\\u00e9mon trainer Satoshi hopes to sight rare Pok\\u00e9mon around his local area. White, a boat driver, takes Satoshi to his village. Satoshi is accompanied by his three closest friends: Takeshi, a former gym leader training to be a great Pok\\u00e9mon breeder; Kasumi, a young girl wanting to become a skilled Water-type trainer; and Pikachu, Satoshi's Pok\\u00e9mon partner and first comrade.\\n\\nMeanwhile, Yukinari and Serebii reappear in Satoshi's present time and run into his group. Masked Lord Vicious, the strongest executive staff member of Team Rocket, desires to capture Serebii. Using the Dark Ball, a variant of Monster Ball that corrupts the Pok\\u00e9mon caught within and draws out their maximum power, Vicious can transform innocent Pok\\u00e9mon into powerful and frightening obstacles\\u2014including Serebii itself! Placed in a tough position, Satoshi, Yukinari, and their friends must work together to defeat Vicious and save Serebii and themselves.\",\n \"East Force meets West Force and all Hell breaks loose. The Solonoids, that lovable race of female warriors, are at it again, fighting amongst themselves. During a heated battle, however, it looks like the leaders of the two factions have hung their warriors out to dry. In the middle of all this fighting and chaos, East Force detects a transmission from an unidentified planet. The Gall Force gals leave their posts to go. Lufy, the West Force's Ace Pilot, who is after Rabby, follows them to the planet.\\n\\n(Source: AnimeNfo)\",\n \"Netto's father Yuuichirou Hikari has made a scientific breakthrough by introducing the \\\"synchro chips\\\". If an operator and his or her navi are in a special enviroment known as a \\\"dimensional area\\\", they can fuse together in the real world via a technique called \\\"cross fusion\\\"! Yuuichirou's first test subject, Misaki Gorou, attempts the process and sadly fails. Netto offers to try with Rockman, but his father forbids it. Cross Fusion puts enormous strain on the operator's health, and battling in the real world could mean death. \\n\\n(Source: Official Site)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"TV\",\n \"Movie\",\n \"ONA\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"episodes\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 200,\n \"samples\": [\n \"93\",\n \"27\",\n \"110\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"producers\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3070,\n \"samples\": [\n \"AIC, Lantis, Media Factory, Pony Canyon, Rakuonsha, AT-X, KlockWorx, Ryukyu Asahi Broadcasting\",\n \"Tama Production, Tokyo MX\",\n \"KAGAYA Studio\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"licensors\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 253,\n \"samples\": [\n \"ADV Films, Media Blasters\",\n \"Funimation\",\n \"Central Park Media, Maiden Japan\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"studios\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 941,\n \"samples\": [\n \"Group TAC, Ginga Ya\",\n \"J.C.Staff, Production I.G\",\n \"drop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"source\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 16,\n \"samples\": [\n \"Visual novel\",\n \"Manga\",\n \"Novel\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"anime_rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"PG-13 - Teens 13 or older\",\n \"R - 17+ (violence & profanity)\",\n \"UNKNOWN\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rank\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 8846,\n \"samples\": [\n \"12549\",\n \"7778\",\n \"7761\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"popularity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5211,\n \"min\": 1,\n \"max\": 19844,\n \"num_unique_values\": 10304,\n \"samples\": [\n 5880,\n 7617,\n 17889\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"favorites\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5821,\n \"min\": 0,\n \"max\": 217606,\n \"num_unique_values\": 1357,\n \"samples\": [\n 46556,\n 1136,\n 1570\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"scored by\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6497,\n \"samples\": [\n \"19239\",\n \"1926\",\n \"300\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"members\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 191364,\n \"min\": 142,\n \"max\": 3744541,\n \"num_unique_values\": 8192,\n \"samples\": [\n 9390,\n 8376,\n 1998\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"image url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 12135,\n \"samples\": [\n \"https://cdn.myanimelist.net/images/anime/1195/111544.jpg\",\n \"https://cdn.myanimelist.net/images/anime/6/26448.jpg\",\n \"https://cdn.myanimelist.net/images/anime/1405/112400.jpg\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "df" + }, + "text/html": [ + "\n", + "
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anime_idgenresnameaverage_ratingoverviewtypeepisodesproducerslicensorsstudiossourceanime_ratingrankpopularityfavoritesscored bymembersimage url
04181Drama, Fantasy, Romance, Slice of Life, Supern...Clannad: After Story8.93Clannad: After Story, the sequel to the critic...TV24Pony Canyon, TBS, Rakuonsha, Animation DoSentai FilmworksKyoto AnimationVisual novelPG-13 - Teens 13 or older19114689496397291149886https://cdn.myanimelist.net/images/anime/1299/...
128735Drama, Historical, JoseiShouwa Genroku Rakugo Shinjuu8.57Yotarou is a former yakuza member fresh out of...TV13Starchild Records, Mainichi Broadcasting Syste...UNKNOWNStudio DeenMangaPG-13 - Teens 13 or older93804571191359281445https://cdn.myanimelist.net/images/anime/1354/...
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namepopularity
29Shingeki no Kyojin1
176Death Note2
66Fullmetal Alchemist: Brotherhood3
39One Punch Man4
508Sword Art Online5
57Boku no Hero Academia6
726Naruto8
855Tokyo Ghoul9
38Hunter x Hunter (2011)10
102Kimi no Na wa.11
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\n" + ], + "text/plain": [ + " name popularity\n", + "29 Shingeki no Kyojin 1\n", + "176 Death Note 2\n", + "66 Fullmetal Alchemist: Brotherhood 3\n", + "39 One Punch Man 4\n", + "508 Sword Art Online 5\n", + "57 Boku no Hero Academia 6\n", + "726 Naruto 8\n", + "855 Tokyo Ghoul 9\n", + "38 Hunter x Hunter (2011) 10\n", + "102 Kimi no Na wa. 11" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def popular_animes(n=10):\n", + " sorted_df = df.sort_values(by=['popularity'], ascending=[ True])\n", + " top_n_anime = sorted_df.head(n)\n", + " return top_n_anime[['name', 'popularity']]\n", + "\n", + "popular_animes(n=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OCh8zH5O3-SG" + }, + "source": [ + "#### 2) Top n animes based on Rank" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "O1sVeB6R5oWY", + "outputId": "ab8c1927-073c-4ba9-f856-58fe805b69b4" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"top_ranked_animes(n=10)\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Koe no Katachi\",\n \"Gintama\\u00b0\",\n \"Ginga Eiyuu Densetsu\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rank\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.400617248673217,\n \"min\": 3.0,\n \"max\": 19.0,\n \"num_unique_values\": 10,\n \"samples\": [\n 17.0,\n 4.0,\n 12.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, + "text/html": [ + "\n", + "
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66Fullmetal Alchemist: Brotherhood3176556
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508Sword Art Online2951821
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