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feat: final midterm report
This commit is contained in:
parent
c15e9d6618
commit
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@ -163,7 +163,7 @@ def preprocessing(rating_data, anime_contact_data, debug=False, user_threshold=5
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return pivot_table
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def predict(prediction_model, pivot_table, seed=42, anime="RANDOM"):
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def predict(prediction_model, pivot_table, seed=42, anime="RANDOM", recommendation_number=6):
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"""
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This will choose a random anime name and our prediction_model will predict similar anime.
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"""
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@ -178,7 +178,7 @@ def predict(prediction_model, pivot_table, seed=42, anime="RANDOM"):
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chosen_anime_name = anime
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distance, suggestions = prediction_model.kneighbors(
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query, n_neighbors=6)
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query, n_neighbors=recommendation_number)
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for i in range(0, len(distance.flatten())):
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if i == 0:
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print(f"Recommendations for {chosen_anime_name}:\n")
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@ -224,18 +224,21 @@ def handle_arguments():
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required=False, type=int, default=500)
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parser.add_argument('--anime_threshold', '-at', help='Specify minimal number of votes required for anime to be included in the data, set to -1 for no threshold',
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required=False, type=int, default=200)
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parser.add_argument('--recommendation_amount', '-ra', help='Specify how much anime should be recommended',
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required=False, type=int, default=5)
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# Parse the command-line arguments
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args = parser.parse_args()
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args.recommendation_amount = args.recommendation_amount + 1
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# Access the values of the arguments
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return args.seed, args.debug, args.data_limit, args.database, args.metric, args.algorithm, args.anime, args.neighbors, args.user_threshold, args.anime_threshold
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return args.seed, args.debug, args.data_limit, args.database, args.metric, args.algorithm, args.anime, args.neighbors, args.user_threshold, args.anime_threshold, args.recommendation_amount
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if __name__ == "__main__":
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seed, debug, data_limit, db, metric, algorithm, anime, neighbors, user_threshold, anime_threshold = handle_arguments()
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seed, debug, data_limit, db, metric, algorithm, anime, neighbors, user_threshold, anime_threshold, recommendation_amount = handle_arguments()
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RATING_DATA, ANIME_CONTACT_DATA = get_data(data_limit, db)
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PIVOT_TABLE = preprocessing(
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RATING_DATA, ANIME_CONTACT_DATA, debug, user_threshold, anime_threshold)
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MODEL = create_model(PIVOT_TABLE, metric, algorithm, neighbors)
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predict(MODEL, PIVOT_TABLE, seed, anime)
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predict(MODEL, PIVOT_TABLE, seed, anime, recommendation_amount)
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BIN
midterm/report/execution_time.png
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BIN
midterm/report/execution_time.png
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After Width: | Height: | Size: 38 KiB |
@ -15,14 +15,16 @@
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\documentclass[12pt]{article}
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\title{EARIN project Midterm report}
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\author{Krzysztof Rudnicki \\ Jakub Kliszko}
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\begin{document}
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in the data, set to -1 for no threshold
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\end{lstlisting}
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\section{Results}
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\subsection{Presentation}
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\subsubsection{Plots}
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\subsubsection{Tables}
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Currently recommendations are displayed in a following way:
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\begin{lstlisting}[language=bash]
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Recommendations for Kill la Kill:
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1: Shingeki no Kyojin, with distance of 0.11106648055176693:
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2: Steins;Gate, with distance of 0.12104265014640536:
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3: Toradora!, with distance of 0.12112848901274798:
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4: Sword Art Online, with distance of 0.13046005032340824:
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5: No Game No Life, with distance of 0.1306815843129835:
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6: One Punch Man, with distance of 0.14848484728234945:
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7: Angel Beats!, with distance of 0.15175709939974935:
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8: Hataraku Maou-sama!, with distance of 0.15244674042590045:
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9: Psycho-Pass, with distance of 0.15288022814590008:
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\end{lstlisting}
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Where we are given name of the anime for which we create recommendation and list of animes recommended with distance to original anime (lower is better)
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\subsection{Data size and execution time}
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\begin{figure}
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\caption{Chart showing how size of data taken impacts execution time }
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\includegraphics[width=\textwidth]{execution_time.png}
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\end{figure}
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This data was taken using default parameters execpt for increasing data size, each of three runs uses different seed
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\paragraph{Seed} We added seed in predict function for choosing random anime, using the same seed always returns same recommendations and choosing random anime is the only random part of our code \\
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User can specify their own seed by using -s or --seed flag by entering in command line:
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\begin{lstlisting}
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@ -58,9 +79,19 @@ Running it with CPU results in the model training for over 3 hours
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Suprisingly even though we based our preliminary report around different example code we managed to not make any corrections to preliminary report \\
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All of functionality that we want to implement is available in sklearn and scipy
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\subsection{Results and findings}
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||||
We can see that the rating is skewed towards higher values, users tend to give ratings of 7, 8 or 9 which inflates average rating to be well above 5
|
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\begin{figure}
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\caption{User rating count}
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\includegraphics[width=\textwidth]{user_rating.png}
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\end{figure}
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\section{Finishing project}
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\subsection{Embedding more data in user and anime}
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Currently we are only embedding pure rating values of users, we do not take into consideration, popularity, "controversy", studio which created the anime, length of anime (number of episodes and length of episodes), and when it was aired \\
|
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\subsection{Evaluating our model accuracy}
|
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We need to introduce some way to evaluate accuracy of our model, we will try to introduce at least some of the measures mentioned in preliminary report: precision, recall, F1 score and MAP
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||||
\subsection{More results representation}
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We still need to introduce more representation for our model results. Mainly how well it predicts similarity based on different parameter values (different modes, arguments and so on) \\
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||||
We already can modify those values easily from the code itself and as argument, we just need to run those values and collect results
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\end{document}
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Reference in New Issue
Block a user