{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"2022-01-14-rec-ml1m.ipynb","provenance":[{"file_id":"https://github.com/recohut/nbs/blob/main/raw/P799773%20%7C%20Recommenders%20on%20ML-1m.ipynb","timestamp":1644613230504},{"file_id":"1twXYeRKcQG-wGRCgN4zpwOEAm8MG1vMV","timestamp":1628668676656}],"collapsed_sections":[],"mount_file_id":"1Cy-U9kQbMTIs1BDwqYOA2DdU3xg1YpEv","authorship_tag":"ABX9TyM9iQ1yDn4Q8OR/oXBP6AgR"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Recommenders on ML-1m"],"metadata":{"id":"QBLgWidk3oiR"}},{"cell_type":"markdown","source":["## Setup"],"metadata":{"id":"VokPZEtG1Sil"}},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","from tqdm.notebook import tqdm\n","import math\n","\n","from tensorflow.keras.layers import Input, Dense\n","from tensorflow.keras.models import Model\n","\n","import matplotlib.pyplot as plt"],"metadata":{"id":"Qzecw902yzTt"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Data"],"metadata":{"id":"jyg_YwQv1VHA"}},{"cell_type":"code","source":["!wget -q --show-progress http://files.grouplens.org/datasets/movielens/ml-1m.zip\n","!unzip ml-1m.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SM8pU9WtyvVN","executionInfo":{"status":"ok","timestamp":1639029882296,"user_tz":-330,"elapsed":1458,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"4e376858-3639-43e9-dca0-871dcc759328"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\rml-1m.zip 0%[ ] 0 --.-KB/s \rml-1m.zip 85%[================> ] 4.81M 24.1MB/s \rml-1m.zip 100%[===================>] 5.64M 27.5MB/s in 0.2s \n","Archive: ml-1m.zip\n"," creating: ml-1m/\n"," inflating: ml-1m/movies.dat \n"," inflating: ml-1m/ratings.dat \n"," inflating: ml-1m/README \n"," inflating: ml-1m/users.dat \n"]}]},{"cell_type":"code","source":["df_ratings = pd.read_csv(\"./ml-1m/ratings.dat\",\n"," sep=\"::\",\n"," header=None,\n"," engine='python',\n"," names=[\"UserID\", \"MovieID\", \"Rating\", \"Timestamp\"])\n","df_ratings.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"C7a4g9bKy399","executionInfo":{"status":"ok","timestamp":1639030735099,"user_tz":-330,"elapsed":5966,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"a5b23854-9921-4589-dc90-b14538f7031c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
UserIDMovieIDRatingTimestamp
0111935978300760
116613978302109
219143978301968
3134084978300275
4123555978824291
\n","
"],"text/plain":[" UserID MovieID Rating Timestamp\n","0 1 1193 5 978300760\n","1 1 661 3 978302109\n","2 1 914 3 978301968\n","3 1 3408 4 978300275\n","4 1 2355 5 978824291"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["df_movies = pd.read_csv(\"./ml-1m/movies.dat\",\n"," sep=\"::\",\n"," header=None,\n"," engine='python',\n"," names=[\"MovieID\", \"Title\", \"Genres\"])\n","df_movies.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"QeDG1njUy6sx","executionInfo":{"status":"ok","timestamp":1639030735102,"user_tz":-330,"elapsed":45,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"24a9ba9c-e2a8-4070-d3ed-6cf7860f56cd"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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MovieIDTitleGenres
01Toy Story (1995)Animation|Children's|Comedy
12Jumanji (1995)Adventure|Children's|Fantasy
23Grumpier Old Men (1995)Comedy|Romance
34Waiting to Exhale (1995)Comedy|Drama
45Father of the Bride Part II (1995)Comedy
\n","
"],"text/plain":[" MovieID Title Genres\n","0 1 Toy Story (1995) Animation|Children's|Comedy\n","1 2 Jumanji (1995) Adventure|Children's|Fantasy\n","2 3 Grumpier Old Men (1995) Comedy|Romance\n","3 4 Waiting to Exhale (1995) Comedy|Drama\n","4 5 Father of the Bride Part II (1995) Comedy"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["df_users = pd.read_csv(\"./ml-1m/users.dat\",\n"," sep=\"::\",\n"," header=None,\n"," engine='python',\n"," names=[\"UserID\", \"Gender\", \"Age\", \"Occupation\", \"Zip-code\"])\n","df_users.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"TMgBHg7Ry-gk","executionInfo":{"status":"ok","timestamp":1639030735105,"user_tz":-330,"elapsed":44,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"d6fc221e-98a2-44ab-91e4-a4677154a8c9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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UserIDGenderAgeOccupationZip-code
01F11048067
12M561670072
23M251555117
34M45702460
45M252055455
\n","
"],"text/plain":[" UserID Gender Age Occupation Zip-code\n","0 1 F 1 10 48067\n","1 2 M 56 16 70072\n","2 3 M 25 15 55117\n","3 4 M 45 7 02460\n","4 5 M 25 20 55455"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["df_ratings.shape, df_movies.shape, df_users.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3VUpnZnazAsm","executionInfo":{"status":"ok","timestamp":1639030735108,"user_tz":-330,"elapsed":43,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"71e9d857-ac35-4382-891c-87cf2cbea1c6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((1000209, 4), (3883, 3), (6040, 5))"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["# merge\n","df_ratings_movies = pd.merge(df_ratings, df_movies, on='MovieID')\n","df = pd.merge(df_ratings_movies, df_users, on=\"UserID\")\n","df_raw = df.copy()\n","\n","# drop columns\n","df = df.drop(['Timestamp','Genres','Gender','Age','Occupation','Zip-code'], axis=1)"],"metadata":{"id":"8Z5Mu6QPzB3-"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sbEoj-MKxyla"},"source":["## Recommend Popular (most-watched) Movies"]},{"cell_type":"code","metadata":{"id":"5rpgzYkHx2YA"},"source":["# Recommend 5 most seen movies\n","def recommend_movie(n):\n"," movie_rank = df['MovieID'].value_counts()[:n]\n"," recommend_movies = df.loc[movie_rank.index]\n"," recommend = recommend_movies['Title']\n"," return recommend"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WpYPRsWtx6Tq","executionInfo":{"status":"ok","timestamp":1639030735978,"user_tz":-330,"elapsed":18,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"2e4efcd7-64b2-4165-aae6-6eff4c513bbf"},"source":["recommend_movie(5)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2858 Muppet Treasure Island (1996)\n","260 Dragonheart (1996)\n","1196 Close Encounters of the Third Kind (1977)\n","1210 Breakfast Club, The (1985)\n","480 Shanghai Noon (2000)\n","Name: Title, dtype: object"]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"YUg356Eqx7QW"},"source":["Evaluate"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TOUp5_Z7yBN_","executionInfo":{"status":"ok","timestamp":1639030739176,"user_tz":-330,"elapsed":606,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"d4e10476-63f3-463a-89ab-378090f41609"},"source":["# Split train, test set\n","x_train, x_test = train_test_split(df, test_size=0.05)\n","\n","# Recommend n most popular movies on a dataset\n","def popular_movie(dataset, n):\n"," movie_rank = dataset['MovieID'].value_counts()[:n]\n"," popular_movies = dataset.iloc[movie_rank.index]\n"," return popular_movies\n","\n","# Calculate hitrate@K\n","def hitrate(K):\n"," raw_ranking = popular_movie(df, K)\n"," pred_ranking = popular_movie(x_test, K)\n"," return raw_ranking['MovieID'].isin(pred_ranking['MovieID']).value_counts(normalize=True)[True]\n","\n","hitrate(100)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.07"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"2kWNgnmHyipn"},"source":["## Recommend Popular (high-rated) Movies"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hONKTtwyy6bN","executionInfo":{"status":"ok","timestamp":1639030054352,"user_tz":-330,"elapsed":8,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"d64742be-963b-4b1f-a761-0e081fa6555c"},"source":["# Recommend 5 movies with high ratings\n","def recommend_movie2(n):\n"," movie_sort = movie_mean.sort_values(ascending=False)[:n]\n"," recommend_movies = df.loc[movie_sort.index]\n"," recommendation = recommend_movies['Title']\n"," return recommendation\n","\n","movie_mean = df.groupby(['MovieID'])['Rating'].mean()\n","recommend_movie2(5)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["MovieID\n","3382 Sneakers (1992)\n","3172 Pi (1998)\n","3607 Candyman (1992)\n","3656 Junk Mail (1997)\n","3280 Teenage Mutant Ninja Turtles (1990)\n","Name: Title, dtype: object"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"SV33kdAGzAzC"},"source":["Evaluate"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yBKgYdWCzDSO","executionInfo":{"status":"ok","timestamp":1639030055490,"user_tz":-330,"elapsed":9,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"9d96f209-11bf-4b4c-c096-00ecf7b37039"},"source":["# Split train, test set\n","x_train, x_test = train_test_split(df, test_size=0.05)\n","\n","# dRecommend n most popular movies on a dataset\n","def popular_movie(dataset, n):\n"," movie_rank = dataset['MovieID'].value_counts()[:n]\n"," popular_movies = dataset.iloc[movie_rank.index]\n"," return popular_movies\n","\n","# Calculate hitrate@K\n","def hitrate(K):\n"," raw_ranking = popular_movie(df, K)\n"," pred_ranking = popular_movie(x_test, K)\n"," return raw_ranking['MovieID'].isin(pred_ranking['MovieID']).value_counts(normalize=True)[True]\n","\n","hitrate(100)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.1"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"i7LBzn1ozbTi"},"source":["# Accuracy calculation\n","def RMSE(y_true, y_pred):\n"," return np.sqrt(np.mean((np.array(y_true) - np.array(y_pred))**2))\n","\n","rmse = []\n","for user in set(df.index):\n"," y_true = df.loc[user]['Rating']\n"," y_pred = movie_mean[df.loc[user]['MovieID']]\n"," accuracy = RMSE(y_true, y_pred)\n"," rmse.append(accuracy)\n","print(np.mean(rmse))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Cscb1IJ1zhn3"},"source":["# Split train, test set\n","x = df.copy()\n","y = df['UserID']\n","x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.05,stratify=y)\n","\n","# Accuracy calculation\n","def RMSE(y_true, y_pred):\n"," return np.sqrt(np.mean((np.array(y_true) - np.array(y_pred))**2))\n","\n","# Calculate RMSE by model\n","def score(model):\n"," id_pairs = zip(x_test['UserID'], x_test['MovieID'])\n"," y_pred = np.array([model(user,movie) for (user,movie) in id_pairs])\n"," y_true = np.array(x_test['Rating'])\n"," return RMSE(y_true, y_pred)\n","\n","# Get full matrix with training df\n","rating_matrix = x_train.pivot(index='UserID', columns='MovieID', values='Rating')\n","\n","# The default model for calculating forecasts by the overall mean\n","def best_seller(user_id, movie_id):\n"," try:\n"," rating = train_mean[movie_id]\n"," except:\n"," rating = 3.0\n"," return rating\n","\n","train_mean = x_train.groupby(['MovieID'])['Rating'].mean()\n","score(best_seller)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"emC267Aw0-ZK"},"source":["## Recommend Genre-wise Popular Movies"]},{"cell_type":"code","metadata":{"id":"cP9S3NS-1mp8"},"source":["df = df_raw.copy()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"94ac4HRM1GHW"},"source":["Data preprocessing (extracting data only from users who have watched more than 50 movies)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":272},"id":"8iv3B9bs1CdO","executionInfo":{"status":"ok","timestamp":1630155524770,"user_tz":-330,"elapsed":758,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"0a0c9584-fd5f-476a-d7bc-07d354f1bb0a"},"source":["count = df['UserID'].value_counts()\n","count_index = count[count > 50]\n","data_pre = df[df['UserID'].isin(count_index)]\n","data_pre.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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UserIDMovieIDRatingTimestampTitleGenresGenderAgeOccupationZip-code
30255311935977946400One Flew Over the Cuckoo's Nest (1975)DramaM25096931
3026536615977979726James and the Giant Peach (1996)Animation|Children's|MusicalM25096931
3027539145977979589My Fair Lady (1964)Musical|RomanceM25096931
30285323555977948959Bug's Life, A (1998)Animation|Children's|ComedyM25096931
3029535945977979520Snow White and the Seven Dwarfs (1937)Animation|Children's|MusicalM25096931
\n","
"],"text/plain":[" UserID MovieID Rating Timestamp ... Gender Age Occupation Zip-code\n","3025 53 1193 5 977946400 ... M 25 0 96931\n","3026 53 661 5 977979726 ... M 25 0 96931\n","3027 53 914 5 977979589 ... M 25 0 96931\n","3028 53 2355 5 977948959 ... M 25 0 96931\n","3029 53 594 5 977979520 ... M 25 0 96931\n","\n","[5 rows x 10 columns]"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","metadata":{"id":"pavGGPUb1MYR"},"source":["x_train, x_test = train_test_split(data_pre, test_size=0.05)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vZ1ZezYU13yN"},"source":["Top-10 most-watched movies for the given genre"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":665},"id":"E7QZyPwN1VmK","executionInfo":{"status":"ok","timestamp":1630155532129,"user_tz":-330,"elapsed":1985,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"7a5d18e5-51be-4056-8189-2442a25b45f3"},"source":["def genre_pop(dataset, genre, n):\n"," dataset_genres = dataset['Genres'].str.get_dummies(\"|\")\n"," select_genre = dataset_genres[dataset_genres[genre]==1]\n"," genre_popmovie = dataset.loc[select_genre.index]\n"," genre_popmovie = genre_popmovie.reset_index()\n"," genre_popmovie_rank = genre_popmovie['MovieID'].value_counts()[:n]\n"," recomm_movie = genre_popmovie.loc[genre_popmovie_rank.index]\n"," return recomm_movie\n","\n","genre_pop(x_train, 'Comedy', 10)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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indexUserIDMovieIDRatingTimestampTitleGenresGenderAgeOccupationZip-code
28582792341123752978453240Money Pit, The (1986)ComedyF45143214
158088126687330763975438076Irma la Douce (1963)ComedyM56619027
1270144332354974977100643Much Ado About Nothing (1993)Comedy|RomanceM25098153
1197457671781562978570873Blue in the Face (1995)ComedyF45198029
2396657649343224975184082Swimming with Sharks (1995)Comedy|DramaF35797401
126591812740513044976388574Butch Cassidy and the Sundance Kid (1969)Action|Comedy|WesternM56113077
29972087531720004976460939Lethal Weapon (1987)Action|Comedy|Crime|DramaM35738555
35679282326327913976652161Airplane! (1980)ComedyF25722304
11927430113774976504613Batman Returns (1992)Action|Adventure|Comedy|CrimeM18461820
27918660911691532974861777Batman Forever (1995)Action|Adventure|Comedy|CrimeM252094402
\n","
"],"text/plain":[" index UserID MovieID Rating ... Gender Age Occupation Zip-code\n","2858 27923 411 2375 2 ... F 45 1 43214\n","1580 881266 873 3076 3 ... M 56 6 19027\n","1270 14433 235 497 4 ... M 25 0 98153\n","1197 457671 78 156 2 ... F 45 1 98029\n","2396 65764 934 322 4 ... F 35 7 97401\n","1265 918127 405 1304 4 ... M 56 1 13077\n","2997 20875 317 2000 4 ... M 35 7 38555\n","356 792823 263 2791 3 ... F 25 7 22304\n","1 19274 301 1377 4 ... M 18 4 61820\n","2791 86609 1169 153 2 ... M 25 20 94402\n","\n","[10 rows x 11 columns]"]},"metadata":{},"execution_count":35}]},{"cell_type":"markdown","metadata":{"id":"7beDLdyr1Wgy"},"source":["User's genre ranking in the training set"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gvPKFhkS2GFo","executionInfo":{"status":"ok","timestamp":1630155631792,"user_tz":-330,"elapsed":520,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"34c386ee-5dd5-45bb-f78a-ae483dbd7ba8"},"source":["def user_genre(dataset, userid):\n"," user_data = dataset[dataset['UserID']==userid]\n"," user_data_genres = user_data['Genres'].str.get_dummies(\"|\")\n"," user_genre_ranking = user_data_genres.sum().sort_values(ascending=False)\n"," return user_genre_ranking\n","\n","user_genre(x_train, 54)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Comedy 36\n","Drama 11\n","Western 4\n","Romance 4\n","Children's 3\n","Sci-Fi 2\n","Crime 2\n","Action 2\n","War 1\n","Thriller 1\n","Mystery 1\n","Musical 1\n","Horror 1\n","Fantasy 1\n","Animation 1\n","Adventure 1\n","dtype: int64"]},"metadata":{},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"yRSmOnzY2OZ6"},"source":["Recommend by genre"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":255},"id":"9jHEptFJ2RAW","executionInfo":{"status":"ok","timestamp":1630155677945,"user_tz":-330,"elapsed":2682,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"fcbf1901-5a8f-4ebb-9bf0-044d3cd3680c"},"source":["def user_genre_recommend(dataset, userid, n):\n"," genre_pref = user_genre(dataset, userid)\n"," recomm = genre_pop(dataset, genre_pref.index[0], n)\n"," return recomm\n","\n","user_genre_recommend(x_train, 54, 5)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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indexUserIDMovieIDRatingTimestampTitleGenresGenderAgeOccupationZip-code
28582792341123752978453240Money Pit, The (1986)ComedyF45143214
158088126687330763975438076Irma la Douce (1963)ComedyM56619027
1270144332354974977100643Much Ado About Nothing (1993)Comedy|RomanceM25098153
1197457671781562978570873Blue in the Face (1995)ComedyF45198029
2396657649343224975184082Swimming with Sharks (1995)Comedy|DramaF35797401
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"],"text/plain":[" index UserID MovieID Rating ... Gender Age Occupation Zip-code\n","2858 27923 411 2375 2 ... F 45 1 43214\n","1580 881266 873 3076 3 ... M 56 6 19027\n","1270 14433 235 497 4 ... M 25 0 98153\n","1197 457671 78 156 2 ... F 45 1 98029\n","2396 65764 934 322 4 ... F 35 7 97401\n","\n","[5 rows x 11 columns]"]},"metadata":{},"execution_count":37}]},{"cell_type":"markdown","metadata":{"id":"SuzCJPL42ZO_"},"source":["Evaluation"]},{"cell_type":"markdown","metadata":{"id":"WtlW0OJ62aas"},"source":["Ignoring genre"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I_VLJr462dMi","executionInfo":{"status":"ok","timestamp":1630157569004,"user_tz":-330,"elapsed":1149,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"7d6244f7-db91-452e-9e4d-77755d854659"},"source":["def popular_movie(dataset, n):\n"," movie_rank = dataset['MovieID'].value_counts()[:n]\n"," popular_movies = dataset.iloc[movie_rank.index]\n"," return popular_movies\n","\n","def hitrate1(K):\n"," raw_ranking = popular_movie(df, K)\n"," pred_ranking = popular_movie(x_test, K)\n"," return raw_ranking['MovieID'].isin(pred_ranking['MovieID']).value_counts(normalize=True)[True]\n","\n","s = 0\n","for i in range (100):\n"," s += hitrate1(100)\n","s /= 100\n","s"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.08000000000000006"]},"metadata":{},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"nMpUYkdQ2evo"},"source":["Considering genre"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B2xUHjRg2xWQ","executionInfo":{"status":"ok","timestamp":1630158240486,"user_tz":-330,"elapsed":377175,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"2ceeb35c-627e-4633-abc9-58e47ecc7f4e"},"source":["def user_genre_recommend(dataset, userid, n):\n"," genre_pref = user_genre(dataset, userid)\n"," recomm = genre_pop(dataset, genre_pref.index[0], n)\n"," return recomm\n","\n","def hitrate2(K):\n"," user = x_train.sample(n=1)['UserID'].values[0]\n"," raw_recomm = user_genre_recommend(data_pre, user, K)\n"," pred_recomm = user_genre_recommend(x_train, user, K)\n"," return raw_recomm['MovieID'].isin(pred_recomm['MovieID']).value_counts(normalize=True)[True]\n","\n","s = 0\n","count = 0\n","while count!=100:\n"," try:\n"," _s = hitrate2(100)\n"," count+=1\n"," except:\n"," pass\n"," if count%10==0:\n"," print(count)\n"," s += _s\n","s /= 100\n","s"],"execution_count":null,"outputs":[{"output_type":"stream","text":["10\n","20\n","30\n","40\n","50\n","60\n","70\n","80\n","90\n","100\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["0.2126000000000003"]},"metadata":{},"execution_count":45}]},{"cell_type":"markdown","metadata":{"id":"--pOiaTD_GOv"},"source":["## Recommend Popular movies by user occupation and age"]},{"cell_type":"code","source":["df = df_raw.copy()\n","\n","# drop columns\n","df = df.drop(['Timestamp','Gender','Zip-code'], axis=1)\n","\n","# leave only data with a rating of 3 or higher\n","data_pre = df[df['Rating'] > 2]"],"metadata":{"id":"jM4Z9wPE05Km"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["x_train, x_test = train_test_split(data_pre, test_size=0.05)"],"metadata":{"id":"X0rucNWx08KC"},"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"KCuNZY26_bAt","executionInfo":{"status":"ok","timestamp":1630158058729,"user_tz":-330,"elapsed":967,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"ca169ded-3f82-4d49-c9af-0af2b59cb012"},"source":["# Recommend n popular movies by occupation\n","def occu_pop(dataset, occu, n):\n"," data_occu = dataset[dataset['Occupation'] == occu]\n"," data_occu = data_occu.reset_index()\n"," occu_pop_rank = data_occu['MovieID'].value_counts()[:n]\n"," recommend_movies = data_occu.loc[occu_pop_rank.index]\n"," return recommend_movies\n","\n","# Recommend n movies depending on user's occupation\n","def user_occu_recommend(dataset, userid, n):\n"," user_occu = dataset[dataset['UserID'] == userid]['Occupation'].values[0]\n"," recomm = occu_pop(dataset, user_occu, n)\n"," return recomm\n"," \n","user_occu_recommend(x_train, 46, 5)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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indexUserIDMovieIDRatingTitleGenresAgeOccupation
285848847118515554True Romance (1993)Action|Crime|Romance2519
257195416928112474Heavenly Creatures (1994)Drama|Fantasy|Romance|Thriller2519
260917056208224323Stepmom (1998)Drama119
608488539185120113Back to the Future Part II (1989)Comedy|Sci-Fi2519
1210829307290611983Raiders of the Lost Ark (1981)Action|Adventure3519
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"],"text/plain":[" index UserID MovieID ... Genres Age Occupation\n","2858 488471 1851 555 ... Action|Crime|Romance 25 19\n","2571 954169 2811 247 ... Drama|Fantasy|Romance|Thriller 25 19\n","260 917056 2082 2432 ... Drama 1 19\n","608 488539 1851 2011 ... Comedy|Sci-Fi 25 19\n","1210 829307 2906 1198 ... Action|Adventure 35 19\n","\n","[5 rows x 8 columns]"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"wXpC7oQG_eyl","executionInfo":{"status":"ok","timestamp":1630158089448,"user_tz":-330,"elapsed":883,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"884637c3-af6d-4e0c-8c90-9e74b9e63654"},"source":["# Recommend n popular movies by age group\n","def age_pop(dataset, age, n):\n"," data_age = dataset[dataset['Age'] == age]\n"," data_age = data_age.reset_index()\n"," age_pop_rank = data_age['MovieID'].value_counts()[:n]\n"," recommend_movies = data_age.loc[age_pop_rank.index]\n"," return recommend_movies\n","\n","# Recommend n movies based on user's age\n","def user_age_recommend(dataset, userid, n):\n"," user_age = dataset[dataset['UserID'] == userid]['Age'].values[0]\n"," recomm = age_pop(dataset, user_age, n)\n"," return recomm\n","\n","user_age_recommend(x_train, 46, 5)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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indexUserIDMovieIDRatingTitleGenresAgeOccupation
2858917767544427244Runaway Bride (1999)Comedy|Romance184
1210273394096085Fargo (1996)Crime|Drama|Thriller1812
1196566939296822914Edward Scissorhands (1990)Drama|Romance180
260858428459226724Thirteenth Floor, The (1999)Drama|Sci-Fi|Thriller184
2571506553331238613Replacements, The (2000)Comedy184
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"],"text/plain":[" index UserID MovieID ... Genres Age Occupation\n","2858 917767 5444 2724 ... Comedy|Romance 18 4\n","1210 27339 409 608 ... Crime|Drama|Thriller 18 12\n","1196 566939 2968 2291 ... Drama|Romance 18 0\n","260 858428 4592 2672 ... Drama|Sci-Fi|Thriller 18 4\n","2571 506553 3312 3861 ... Comedy 18 4\n","\n","[5 rows x 8 columns]"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"KV94gx1P_mYO"},"source":["Evaluation\n","\n","Against Popularity baseline"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":83,"referenced_widgets":["2cd0a08aa8874ccba9ed7a620ff126f7","e47575e47f2d48beb18a526a9a29fd86","109bf2c4a72d45fd8b32c9c7054cd689","9291b2228d874ddbbef72052a322d3aa","8b28e746b5ca4cdbb3a804f4a450111f","48606b2370024685bf118fba84ca3a75","ebad54c8bbba40299fdd784588876c64","dbb95cb5ea5e45f08a81c6d81e703b94","c2c8be8f08e344959383284b48834b89","8fcb107f4f7e44c69493b2fb7b5fd0c1","36a37abe4b2145f5966a03f0d0c323b5"]},"id":"MkCy5CVT_n6w","executionInfo":{"status":"ok","timestamp":1630162834524,"user_tz":-330,"elapsed":848485,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"4eec2bc0-99f8-4711-93f8-e5e96fb1ccab"},"source":["def popular_movie(dataset, n):\n"," data_pop = dataset.copy()\n"," movie_rank = data_pop['MovieID'].value_counts()[:n]\n"," return movie_rank\n","\n","def hitrate1(K):\n"," raw_ranking = popular_movie(data_pre, K)\n"," pred_ranking = popular_movie(x_test, K)\n"," return pd.DataFrame(raw_ranking.index.isin(pred_ranking.index)).value_counts(normalize=True)[True]\n","\n","s = 0\n","for i in tqdm(range(len(x_test.index))):\n"," s += hitrate1(100)\n","s /= len(x_test.index)\n","s"],"execution_count":null,"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2cd0a08aa8874ccba9ed7a620ff126f7","version_major":2,"version_minor":0},"text/plain":[" 0%| | 0/41824 [00:00= 0.75):\n"," new_val = new_val + 1\n"," elif (frac >= 0.25):\n"," new_val = new_val + 0.5\n"," return max(min(new_val, 5.0), 1)\n","\n","def predict_column_rating(column_no):\n"," temp = input_df[input_df[column_no].notnull()][column_no]\n"," for index, null_rating in input_df[column_no].iteritems():\n"," num_sum = 0\n"," den_sum = 0\n"," if (np.isnan(null_rating)):\n"," for i,rating in temp.iteritems():\n"," num_sum = num_sum + sim[index-1][i-1] * (rating - mean_col[i])\n"," den_sum = den_sum + sim[index-1][i-1]\n"," if (den_sum == 0):\n"," input_df[column_no][index] = round_off_rating(mean_col[index])\n"," else:\n"," input_df[column_no][index] = round_off_rating(mean_col[index] + num_sum/den_sum)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"LmGagn0SDzYj"},"source":["for column_no in input_df:\n"," predict_column_rating(column_no)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":439},"id":"-e2PhfNzMtgX","executionInfo":{"status":"ok","timestamp":1630161527521,"user_tz":-330,"elapsed":653,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"6234f81e-fe15-4c7e-8c65-a44376984be0"},"source":["input_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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4811 rows × 64 columns

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4811 rows × 64 columns

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1s 11ms/step - loss: 0.0820 - val_loss: 0.0784\n","Epoch 2/100\n","39/39 [==============================] - 0s 2ms/step - loss: 0.0762 - val_loss: 0.0718\n","Epoch 3/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0681 - val_loss: 0.0555\n","Epoch 4/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0358 - val_loss: 0.0240\n","Epoch 5/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0219 - val_loss: 0.0203\n","Epoch 6/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0198 - val_loss: 0.0187\n","Epoch 7/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0182 - val_loss: 0.0170\n","Epoch 8/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0166 - val_loss: 0.0155\n","Epoch 9/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0152 - val_loss: 0.0141\n","Epoch 10/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0138 - val_loss: 0.0130\n","Epoch 11/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0127 - val_loss: 0.0120\n","Epoch 12/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0117 - val_loss: 0.0109\n","Epoch 13/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0105 - val_loss: 0.0096\n","Epoch 14/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0088 - val_loss: 0.0079\n","Epoch 15/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0075 - val_loss: 0.0072\n","Epoch 16/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0070 - val_loss: 0.0068\n","Epoch 17/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0067 - val_loss: 0.0067\n","Epoch 18/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0065 - val_loss: 0.0066\n","Epoch 19/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0063 - val_loss: 0.0063\n","Epoch 20/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0060 - val_loss: 0.0060\n","Epoch 21/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0058 - val_loss: 0.0057\n","Epoch 22/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0054 - val_loss: 0.0053\n","Epoch 23/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0050 - val_loss: 0.0049\n","Epoch 24/100\n","39/39 [==============================] - 0s 2ms/step - loss: 0.0046 - val_loss: 0.0045\n","Epoch 25/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0042 - val_loss: 0.0042\n","Epoch 26/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0040 - val_loss: 0.0041\n","Epoch 27/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0039 - val_loss: 0.0039\n","Epoch 28/100\n","39/39 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.0039\n","Epoch 29/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0038 - val_loss: 0.0038\n","Epoch 30/100\n","39/39 [==============================] - 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0s 3ms/step - loss: 0.0036 - val_loss: 0.0037\n","Epoch 50/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0036 - val_loss: 0.0036\n","Epoch 51/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 52/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 53/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 54/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 55/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 56/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 57/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 58/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 59/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 60/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 61/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 62/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 63/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0035 - val_loss: 0.0036\n","Epoch 64/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0034 - val_loss: 0.0036\n","Epoch 65/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0034 - val_loss: 0.0036\n","Epoch 66/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0034 - val_loss: 0.0036\n","Epoch 67/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0034 - val_loss: 0.0036\n","Epoch 68/100\n","39/39 [==============================] - 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val_loss: 0.0035\n","Epoch 78/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0033 - val_loss: 0.0034\n","Epoch 79/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0033 - val_loss: 0.0034\n","Epoch 80/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0033 - val_loss: 0.0034\n","Epoch 81/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0033 - val_loss: 0.0034\n","Epoch 82/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0032 - val_loss: 0.0034\n","Epoch 83/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0032 - val_loss: 0.0033\n","Epoch 84/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0032 - val_loss: 0.0033\n","Epoch 85/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0032 - val_loss: 0.0033\n","Epoch 86/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0032 - val_loss: 0.0033\n","Epoch 87/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0032 - val_loss: 0.0033\n","Epoch 88/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0032 - val_loss: 0.0033\n","Epoch 89/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0032 - val_loss: 0.0033\n","Epoch 90/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0032 - val_loss: 0.0033\n","Epoch 91/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0031 - val_loss: 0.0032\n","Epoch 92/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0031 - val_loss: 0.0032\n","Epoch 93/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0031 - val_loss: 0.0033\n","Epoch 94/100\n","39/39 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0032\n","Epoch 95/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0031 - val_loss: 0.0032\n","Epoch 96/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0031 - val_loss: 0.0032\n","Epoch 97/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0031 - val_loss: 0.0032\n","Epoch 98/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0031 - val_loss: 0.0032\n","Epoch 99/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0030 - val_loss: 0.0032\n","Epoch 100/100\n","39/39 [==============================] - 0s 3ms/step - loss: 0.0030 - val_loss: 0.0031\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":28}]},{"cell_type":"code","metadata":{"id":"uznUG75dGKpo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630161564735,"user_tz":-330,"elapsed":973,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"f1c32560-609a-405b-bdc5-433ebfa94a95"},"source":["encoded_output1 = encoder1.predict(input_df.astype(float))\n","encoded_output2 = encoder2.predict(encoded_output1)\n","decoded_output1 = decoder1.predict(encoded_output2)\n","decoded_output2 = decoder2.predict(decoded_output1)\n","encoded_output2"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.0000000e+00, 2.8876242e-01, 0.0000000e+00, 7.2445278e+00,\n"," 2.0862639e-01],\n"," [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 6.0118270e-01,\n"," 1.3619714e+00],\n"," [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 4.6480484e+00,\n"," 7.1844959e-01],\n"," ...,\n"," [0.0000000e+00, 2.2405386e-04, 0.0000000e+00, 4.7641506e+00,\n"," 6.6250229e-01],\n"," [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 3.9228556e+00,\n"," 7.8529972e-01],\n"," [0.0000000e+00, 7.5990593e-01, 0.0000000e+00, 4.4415331e+00,\n"," 1.1810944e+00]], dtype=float32)"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","metadata":{"id":"ZaBZlZdOGMh-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630161564736,"user_tz":-330,"elapsed":15,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"b46e88fd-4dc0-46b7-b2bf-7d68b77c70f6"},"source":["decoded_output2"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.95494324, 0.9596404 , 0.9512359 , ..., 0.958049 , 0.96002877,\n"," 0.95988166],\n"," [0.30084205, 0.21765906, 0.22469196, ..., 0.2167964 , 0.21715164,\n"," 0.2075283 ],\n"," [0.6712847 , 0.6752827 , 0.67567694, ..., 0.6842654 , 0.67781204,\n"," 0.6790005 ],\n"," ...,\n"," [0.6975429 , 0.69447374, 0.6965812 , ..., 0.70305765, 0.6982425 ,\n"," 0.6999866 ],\n"," [0.57714945, 0.59992576, 0.5955594 , ..., 0.6067458 , 0.6005735 ,\n"," 0.5995167 ],\n"," [0.8209668 , 0.65458286, 0.6323916 , ..., 0.73644984, 0.54032886,\n"," 0.48810017]], dtype=float32)"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"1nUwDmGpGOMb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630161567286,"user_tz":-330,"elapsed":2558,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"423d28c7-e7c4-4487-c8fe-a40db6018157"},"source":["ans = decoded_output2 * 5\n","for (x,y), value in np.ndenumerate(ans):\n"," ans[x][y] = round_off_rating(ans[x][y])\n","ans"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[5. , 5. , 5. , ..., 5. , 5. , 5. ],\n"," [1.5, 1. , 1. , ..., 1. , 1. , 1. ],\n"," [3.5, 3.5, 3.5, ..., 3.5, 3.5, 3.5],\n"," ...,\n"," [3.5, 3.5, 3.5, ..., 3.5, 3.5, 3.5],\n"," [3. , 3. , 3. , ..., 3. , 3. , 3. ],\n"," [4. , 3.5, 3. , ..., 3.5, 2.5, 2.5]], dtype=float32)"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"N7W-vwpHGPnQ","colab":{"base_uri":"https://localhost:8080/","height":439},"executionInfo":{"status":"ok","timestamp":1630161567289,"user_tz":-330,"elapsed":37,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"c3da9253-cbee-41fd-ff4a-e19949fc3a1e"},"source":["ans_df = pd.DataFrame(ans)\n","df = input_df.copy()\n","df = df * 5\n","ans_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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4811 rows × 64 columns

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"],"text/plain":[" 0 1 2 3 4 5 6 ... 57 58 59 60 61 62 63\n","0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 ... 5.0 5.0 5.0 5.0 5.0 5.0 5.0\n","1 1.5 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0\n","2 3.5 3.5 3.5 3.5 3.5 3.5 3.5 ... 3.5 3.5 3.5 3.5 3.5 3.5 3.5\n","3 4.5 4.0 4.0 3.5 3.5 4.5 4.0 ... 4.5 3.5 4.0 3.0 4.5 3.5 3.0\n","4 4.0 4.0 4.0 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0 4.0 4.0 4.0\n","... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n","4806 3.5 2.5 2.5 2.0 2.0 3.0 2.5 ... 3.0 2.0 2.5 1.5 2.5 2.0 1.5\n","4807 3.0 3.0 3.0 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0 3.0 3.0 3.0 3.0\n","4808 3.5 3.5 3.5 3.5 3.5 3.5 3.5 ... 3.5 3.5 3.5 3.5 3.5 3.5 3.5\n","4809 3.0 3.0 3.0 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0 3.0 3.0 3.0 3.0\n","4810 4.0 3.5 3.0 3.0 3.0 3.5 3.5 ... 4.0 3.0 3.0 2.5 3.5 2.5 2.5\n","\n","[4811 rows x 64 columns]"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","metadata":{"id":"CYngQcO9GR5I","colab":{"base_uri":"https://localhost:8080/","height":326},"executionInfo":{"status":"ok","timestamp":1630161567291,"user_tz":-330,"elapsed":34,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"9e483d95-74b8-4b63-f715-4920dcc5efb3"},"source":["plt.figure(figsize=(10,10))\n","plt.subplot(211)\n","line1, = plt.plot(range(1,65), df.iloc[6], 'b')\n","line2, = plt.plot(range(1,65), ans_df.iloc[3], 'k')\n","plt.ylabel('ratings')\n","plt.xlabel('movie ids')\n","plt.legend([line1, line2], ['Initial Filtered', 'Predicted AE'])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":33},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"C_L2Z7-PGT6-","colab":{"base_uri":"https://localhost:8080/","height":594},"executionInfo":{"status":"ok","timestamp":1630161568740,"user_tz":-330,"elapsed":1470,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"44784d2a-ed59-432e-b45a-266781733c30"},"source":["plt.figure(figsize=(50,50))\n","plt.subplot(212)\n","line1, = plt.plot(range(1,4812), df[1].tolist(), 'bo')\n","line2, = plt.plot(range(1,4812), ans_df[0].tolist(), 'ko')\n","plt.ylabel('ratings')\n","plt.xlabel('movie ids')\n","plt.legend([line1, line2], ['Initial Filtered', 'Predicted AE'])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":34},{"output_type":"display_data","data":{"image/png":"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"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["---"],"metadata":{"id":"okSRnAAa3C1h"}},{"cell_type":"code","source":["!pip install -q watermark\n","%reload_ext watermark\n","%watermark -a \"Sparsh A.\" -m -iv -u -t -d -p tensorflow"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e0yZVSfA3C1i","executionInfo":{"status":"ok","timestamp":1639031012561,"user_tz":-330,"elapsed":3342,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"e4bd5bc3-8eb5-40f2-933e-649be381b6a2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Author: Sparsh A.\n","\n","Last updated: 2021-12-09 06:23:40\n","\n","tensorflow: 2.7.0\n","\n","Compiler : GCC 7.5.0\n","OS : Linux\n","Release : 5.4.104+\n","Machine : x86_64\n","Processor : x86_64\n","CPU cores : 2\n","Architecture: 64bit\n","\n","pandas : 1.1.5\n","IPython : 5.5.0\n","numpy : 1.19.5\n","matplotlib: 3.2.2\n","\n"]}]},{"cell_type":"markdown","source":["---"],"metadata":{"id":"4fXtJXo_3C1j"}},{"cell_type":"markdown","source":["**END**"],"metadata":{"id":"N_GGXd7u3C1j"}}]}