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add result rounding

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Marysia 2024-12-07 03:17:45 +01:00
parent 18c03e5d02
commit aa534a578f
5 changed files with 102266 additions and 102266 deletions
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datasets/test_all.csv
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datasets/test_top.csv
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datasets/train_all.csv
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datasets/train_top.csv
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movies_data.ipynb
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@ -3,8 +3,8 @@
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@ -36,13 +36,13 @@
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@ -52,39 +52,39 @@
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"cell_type": "code", "cell_type": "code",
"source": "movies['genres'] = movies['genres'].str.split('|')", "source": "movies['genres'] = movies['genres'].str.split('|')",
"id": "2543aa8216425342", "id": "2543aa8216425342",
"outputs": [], "outputs": [],
"execution_count": 5 "execution_count": 21
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"cell_type": "code", "cell_type": "code",
"source": "data = pd.merge(ratings, movies, on=\"movieId\")", "source": "data = pd.merge(ratings, movies, on=\"movieId\")",
"id": "b3fa37255dccb066", "id": "b3fa37255dccb066",
"outputs": [], "outputs": [],
"execution_count": 6 "execution_count": 22
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@ -255,31 +255,31 @@
"</div>" "</div>"
] ]
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"execution_count": 7, "execution_count": 23,
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"cell_type": "code", "cell_type": "code",
"source": "train_data, test_data = split_by_user(data)", "source": "train_data, test_data = split_by_user(data)",
"id": "b66149513c0c6b0e", "id": "b66149513c0c6b0e",
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@ -450,18 +450,18 @@
"</div>" "</div>"
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@ -645,18 +645,18 @@
"</div>" "</div>"
] ]
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"cell_type": "code", "cell_type": "code",
@ -685,7 +685,7 @@
" genre_scores = user_pref[user_pref['genres'].isin(genres)]['avg_genre_rating']\n", " genre_scores = user_pref[user_pref['genres'].isin(genres)]['avg_genre_rating']\n",
"\n", "\n",
" if not genre_scores.empty:\n", " if not genre_scores.empty:\n",
" return genre_scores.mean()\n", " return round(genre_scores.mean(), 2)\n",
" else:\n", " else:\n",
" return 0\n", " return 0\n",
"\n", "\n",
@ -714,7 +714,7 @@
" genre_scores = user_pref[user_pref['genres'].isin(genres)]['avg_genre_rating']\n", " genre_scores = user_pref[user_pref['genres'].isin(genres)]['avg_genre_rating']\n",
"\n", "\n",
" if not genre_scores.empty:\n", " if not genre_scores.empty:\n",
" return genre_scores.mean()\n", " return round(genre_scores.mean(), 2)\n",
" else:\n", " else:\n",
" return 0\n", " return 0\n",
"\n", "\n",
@ -722,13 +722,13 @@
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"execution_count": 11 "execution_count": 27
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"cell_type": "code", "cell_type": "code",
@ -742,7 +742,7 @@
" similar_users = train_user_similarity_df[user_id].sort_values(ascending=False).index[1:top_n + 1]\n", " similar_users = train_user_similarity_df[user_id].sort_values(ascending=False).index[1:top_n + 1]\n",
" similar_ratings = train_user_movie.loc[similar_users, movie_id]\n", " similar_ratings = train_user_movie.loc[similar_users, movie_id]\n",
"\n", "\n",
" return similar_ratings.dropna().mean()\n", " return round(similar_ratings.dropna().mean(), 2)\n",
"\n", "\n",
"train_data['similarUsers'] = train_data.apply(\n", "train_data['similarUsers'] = train_data.apply(\n",
" lambda row: train_average(row['userId'], row['movieId']), axis=1\n", " lambda row: train_average(row['userId'], row['movieId']), axis=1\n",
@ -757,7 +757,7 @@
" similar_users = test_user_similarity_df[user_id].sort_values(ascending=False).index[1:top_n + 1]\n", " similar_users = test_user_similarity_df[user_id].sort_values(ascending=False).index[1:top_n + 1]\n",
" similar_ratings = test_user_movie.loc[similar_users, movie_id]\n", " similar_ratings = test_user_movie.loc[similar_users, movie_id]\n",
"\n", "\n",
" return similar_ratings.dropna().mean()\n", " return round(similar_ratings.dropna().mean(), 2)\n",
"\n", "\n",
"test_data['similarUsers'] = test_data.apply(\n", "test_data['similarUsers'] = test_data.apply(\n",
" lambda row: test_average(row['userId'], row['movieId']), axis=1\n", " lambda row: test_average(row['userId'], row['movieId']), axis=1\n",
@ -765,13 +765,13 @@
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@ -784,13 +784,13 @@
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@ -803,13 +803,13 @@
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@ -819,13 +819,13 @@
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@ -839,13 +839,13 @@
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@ -855,7 +855,7 @@
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