add result rounding
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Marysia
parent
18c03e5d02
commit
aa534a578f
54784
datasets/test_all.csv
54784
datasets/test_all.csv
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143754
datasets/train_all.csv
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datasets/train_all.csv
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@ -3,8 +3,8 @@
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@ -15,13 +15,13 @@
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@ -36,13 +36,13 @@
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@ -52,39 +52,39 @@
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],
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"id": "1bc78bafbae06c89",
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}
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},
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"cell_type": "code",
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"source": "movies['genres'] = movies['genres'].str.split('|')",
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"id": "2543aa8216425342",
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"outputs": [],
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"execution_count": 5
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"execution_count": 21
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}
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},
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"cell_type": "code",
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"source": "data = pd.merge(ratings, movies, on=\"movieId\")",
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"id": "b3fa37255dccb066",
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"outputs": [],
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"execution_count": 6
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"execution_count": 22
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},
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{
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"cell_type": "code",
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@ -255,31 +255,31 @@
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"</div>"
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]
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},
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"execution_count": 7,
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 7
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"execution_count": 23
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}
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},
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"cell_type": "code",
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"source": "train_data, test_data = split_by_user(data)",
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"id": "b66149513c0c6b0e",
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"outputs": [],
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"execution_count": 8
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"execution_count": 24
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},
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{
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"cell_type": "code",
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@ -450,18 +450,18 @@
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"</div>"
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]
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},
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"execution_count": 9,
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 9
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},
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{
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"cell_type": "code",
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@ -645,18 +645,18 @@
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"</div>"
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]
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},
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"execution_count": 10,
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 10
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}
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},
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"cell_type": "code",
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@ -685,7 +685,7 @@
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" genre_scores = user_pref[user_pref['genres'].isin(genres)]['avg_genre_rating']\n",
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"\n",
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" if not genre_scores.empty:\n",
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" return genre_scores.mean()\n",
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" return round(genre_scores.mean(), 2)\n",
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" else:\n",
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" return 0\n",
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"\n",
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@ -714,7 +714,7 @@
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" genre_scores = user_pref[user_pref['genres'].isin(genres)]['avg_genre_rating']\n",
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"\n",
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" if not genre_scores.empty:\n",
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" return genre_scores.mean()\n",
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" return round(genre_scores.mean(), 2)\n",
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" else:\n",
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" return 0\n",
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"\n",
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@ -722,13 +722,13 @@
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],
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"id": "88296c8c47cdbf60",
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"outputs": [],
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"execution_count": 11
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"execution_count": 27
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},
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{
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"metadata": {
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"ExecuteTime": {
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"start_time": "2024-12-07T02:11:35.300336Z"
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}
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},
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"cell_type": "code",
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@ -742,7 +742,7 @@
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" similar_users = train_user_similarity_df[user_id].sort_values(ascending=False).index[1:top_n + 1]\n",
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" similar_ratings = train_user_movie.loc[similar_users, movie_id]\n",
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"\n",
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" return similar_ratings.dropna().mean()\n",
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" return round(similar_ratings.dropna().mean(), 2)\n",
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"\n",
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"train_data['similarUsers'] = train_data.apply(\n",
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" lambda row: train_average(row['userId'], row['movieId']), axis=1\n",
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@ -757,7 +757,7 @@
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" similar_users = test_user_similarity_df[user_id].sort_values(ascending=False).index[1:top_n + 1]\n",
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" similar_ratings = test_user_movie.loc[similar_users, movie_id]\n",
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"\n",
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" return similar_ratings.dropna().mean()\n",
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" return round(similar_ratings.dropna().mean(), 2)\n",
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"\n",
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"test_data['similarUsers'] = test_data.apply(\n",
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" lambda row: test_average(row['userId'], row['movieId']), axis=1\n",
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@ -765,13 +765,13 @@
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],
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"id": "e931f4041a1802fb",
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"outputs": [],
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"execution_count": 12
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"execution_count": 28
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},
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"cell_type": "code",
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@ -784,13 +784,13 @@
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],
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"id": "ce65ce417e7f5207",
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"outputs": [],
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"execution_count": 13
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@ -803,13 +803,13 @@
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"id": "aa798201db531188",
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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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"id": "be9f6106c5e4b04a",
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"outputs": [],
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"execution_count": 17
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"execution_count": 33
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}
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],
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"metadata": {
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