diff --git a/user_based.ipynb b/user_based.ipynb
index 28f2623ab91332f09cc146fbc0d19d0b70196d28..d75d83b3db17809d0ebb23375bddcec2364abea0 100644
--- a/user_based.ipynb
+++ b/user_based.ipynb
@@ -11,19 +11,16 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 10,
"id": "00d1b249",
"metadata": {},
"outputs": [
{
- "ename": "ImportError",
- "evalue": "cannot import name 'Constant' from 'constants' (/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/constants.py)",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[1], line 14\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# -- add new imports here --\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# local imports\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mconstants\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Constant \u001b[38;5;28;01mas\u001b[39;00m C\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mloaders\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_ratings,load_items \n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msurprise\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KNNWithMeans, accuracy, AlgoBase, PredictionImpossible\n",
- "\u001b[0;31mImportError\u001b[0m: cannot import name 'Constant' from 'constants' (/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/constants.py)"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
]
}
],
@@ -59,7 +56,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "aafd1712",
"metadata": {},
"outputs": [
@@ -69,59 +66,18 @@
"text": [
"Computing the msd similarity matrix...\n",
"Done computing similarity matrix.\n",
- "user: 11 item: 364 r_ui = 4.00 est = 3.42 {'was_impossible': True, 'reason': 'User and/or item is unknown.'}\n"
+ "user: 11 item: 364 r_ui = None est = 2.49 {'actual_k': 2, 'was_impossible': False}\n"
]
}
],
"source": [
- "\n",
"# Create Surprise Dataset from the pandas DataFrame and Reader\n",
"surprise_data = load_ratings(surprise_format=True)\n",
"\n",
"trainset = surprise_data.build_full_trainset()\n",
"\n",
"\n",
- "testset = trainset.build_anti_testset()\n",
- "\n",
- "\n",
- "sim_options = {\n",
- " 'name': 'msd', # Mean Squared Difference (Mean Square Error)\n",
- " 'user_based': True, # User-based collaborative filtering\n",
- " 'min_support': 3 # Minimum number of common ratings required\n",
- "}\n",
- "\n",
- "\n",
- "# Build an algorithm, and train it.\n",
- "algo = KNNWithMeans(sim_options=sim_options, k=3, min_k=2)\n",
- "algo.fit(trainset)\n",
- "algo.test(testset)\n",
- "\n",
- "\n",
- "uid = str(11) # raw user id (as in the ratings file). They are **strings**!\n",
- "iid = str(364) \n",
- "\n",
- "pred = algo.predict(uid, iid, r_ui=4, verbose=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cf3ccdc0",
- "metadata": {},
- "outputs": [],
- "source": [
- "# -- load data, build trainset and anti testset --\n",
- "# it depends on the tiny dataset\n",
- "surprise_data = load_ratings(surprise_format=True)\n",
- "df_movies = load_items()\n",
- "\n",
- "# Assuming you have a pandas DataFrame named 'df' with columns ['user_id', 'item_id', 'rating']\n",
- "\n",
- "# Build train set with all available ratings\n",
- "trainset = surprise_data.build_full_trainset()\n",
- "\n",
- "# Build anti-test set\n",
- "testset = trainset.build_anti_testset()"
+ "testset = trainset.build_anti_testset()\n"
]
},
{
@@ -136,43 +92,34 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "e6fb78b7",
+ "id": "ce078b43",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Computing the msd similarity matrix...\n",
- "Done computing similarity matrix.\n",
- "3.4190898791540785\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "# -- using surprise's user-based algorithm, explore the impact of different parameters and displays predictions --\n",
+ "#User-based prediction for the user 11 and the item 364\n",
"\n",
- "# Define the similarity options\n",
"sim_options = {\n",
" 'name': 'msd', # Mean Squared Difference (Mean Square Error)\n",
" 'user_based': True, # User-based collaborative filtering\n",
" 'min_support': 3 # Minimum number of common ratings required\n",
"}\n",
"\n",
- "# Create an instance of KNNWithMeans with the specified options\n",
- "knn_model = KNNWithMeans(k=3, min_k=2, sim_options=sim_options)\n",
"\n",
- "# Train the algorithm on the trainset\n",
- "knn_model.fit(trainset).test(testset)\n",
+ "# Build an algorithm, and train it.\n",
+ "algo = KNNWithMeans(sim_options=sim_options, k=3, min_k=2)\n",
+ "algo.fit(trainset)\n",
+ "algo.test(testset)\n",
+ "\n",
+ "\n",
+ "uid = 11 # raw user id (as in the ratings file). They are **strings**!\n",
+ "iid = 364 \n",
"\n",
- "# Make an estimation for user 11 and item 364\n",
- "prediction = knn_model.predict('11', '364')\n",
- "print(prediction.est)"
+ "pred = algo.predict(uid, iid, verbose=True)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "ffe89c56",
"metadata": {},
"outputs": [
@@ -183,102 +130,102 @@
"Computing the msd similarity matrix...\n",
"Done computing similarity matrix.\n",
"Predictions with min_k = 1:\n",
- "User: 15, Item: 942, Rating: 3.7769516356699464\n",
- "User: 15, Item: 2117, Rating: 2.9340004894942537\n",
- "User: 15, Item: 2672, Rating: 2.371008709611413\n",
- "User: 15, Item: 5054, Rating: 3.010328638497653\n",
- "User: 15, Item: 6322, Rating: 1.711175832857413\n",
- "User: 15, Item: 6323, Rating: 1.7645762379992287\n",
- "User: 15, Item: 6757, Rating: 3.010328638497653\n",
- "User: 15, Item: 7700, Rating: 3.561484741491386\n",
- "User: 15, Item: 7981, Rating: 3.386000174210522\n",
- "User: 15, Item: 8600, Rating: 3.320743223639117\n",
- "User: 15, Item: 8620, Rating: 2.7538763809343654\n",
- "User: 15, Item: 31952, Rating: 3.7409900837647396\n",
- "User: 15, Item: 3, Rating: 2.222062601579949\n",
- "User: 15, Item: 64, Rating: 0.9224387353614938\n",
- "User: 15, Item: 206, Rating: 2.35668733389394\n",
- "User: 15, Item: 249, Rating: 3.1290259851652826\n",
- "User: 15, Item: 276, Rating: 2.1800017354806753\n",
- "User: 15, Item: 369, Rating: 2.3082373858282694\n",
- "User: 15, Item: 504, Rating: 2.2600496220227573\n",
- "User: 15, Item: 515, Rating: 3.6575674086958188\n",
- "User: 15, Item: 522, Rating: 2.4562020809509626\n",
- "User: 15, Item: 580, Rating: 1.9073310817298395\n",
- "User: 15, Item: 599, Rating: 2.780847470837928\n",
- "User: 15, Item: 915, Rating: 2.761094249104645\n",
- "User: 15, Item: 966, Rating: 3.0894953051643195\n",
- "User: 15, Item: 1274, Rating: 2.9873500196382845\n",
- "User: 15, Item: 1299, Rating: 3.0779327239728005\n",
- "User: 15, Item: 1345, Rating: 2.2037629856623138\n",
- "User: 15, Item: 1354, Rating: 2.001877412379849\n",
- "User: 15, Item: 532, Rating: 2.7123071345260277\n",
+ "User: 11, Item: 1214, Rating: 3.6041666666666665\n",
+ "User: 11, Item: 364, Rating: 2.49203431372549\n",
+ "User: 11, Item: 4308, Rating: 1.6041666666666667\n",
+ "User: 11, Item: 527, Rating: 3.898897058823529\n",
+ "User: 13, Item: 1997, Rating: 2.8\n",
+ "User: 13, Item: 4993, Rating: 3.2375\n",
+ "User: 13, Item: 2700, Rating: 2.8\n",
+ "User: 13, Item: 1721, Rating: 1.2374999999999998\n",
+ "User: 13, Item: 527, Rating: 3.2375\n",
+ "User: 17, Item: 2028, Rating: 3.8125\n",
+ "User: 17, Item: 4993, Rating: 4.128289473684211\n",
+ "User: 17, Item: 1214, Rating: 3.6875\n",
+ "User: 17, Item: 4308, Rating: 1.6875\n",
+ "User: 19, Item: 1997, Rating: 3.5\n",
+ "User: 19, Item: 2028, Rating: 3.5\n",
+ "User: 19, Item: 4993, Rating: 3.5\n",
+ "User: 19, Item: 5952, Rating: 3.5\n",
+ "User: 19, Item: 2700, Rating: 3.5\n",
+ "User: 19, Item: 1721, Rating: 3.5\n",
+ "User: 19, Item: 1214, Rating: 3.5\n",
+ "User: 19, Item: 364, Rating: 3.5\n",
+ "User: 23, Item: 1997, Rating: 2.782649253731343\n",
+ "User: 23, Item: 2700, Rating: 2.349813432835821\n",
+ "User: 27, Item: 1997, Rating: 4.666666666666667\n",
+ "User: 27, Item: 2028, Rating: 5.0\n",
+ "User: 27, Item: 5952, Rating: 5.0\n",
+ "User: 27, Item: 2700, Rating: 4.666666666666667\n",
+ "User: 27, Item: 1721, Rating: 3.104166666666667\n",
+ "User: 27, Item: 364, Rating: 4.604166666666667\n",
+ "User: 27, Item: 4308, Rating: 3.104166666666667\n",
"Computing the msd similarity matrix...\n",
"Done computing similarity matrix.\n",
"Predictions with min_k = 2:\n",
- "User: 15, Item: 942, Rating: 3.7769516356699464\n",
- "User: 15, Item: 2117, Rating: 2.9340004894942537\n",
- "User: 15, Item: 2672, Rating: 2.371008709611413\n",
- "User: 15, Item: 5054, Rating: 2.693661971830986\n",
- "User: 15, Item: 6322, Rating: 1.711175832857413\n",
- "User: 15, Item: 6323, Rating: 1.7645762379992287\n",
- "User: 15, Item: 6757, Rating: 2.693661971830986\n",
- "User: 15, Item: 7700, Rating: 3.561484741491386\n",
- "User: 15, Item: 7981, Rating: 3.386000174210522\n",
- "User: 15, Item: 8600, Rating: 3.320743223639117\n",
- "User: 15, Item: 8620, Rating: 2.7538763809343654\n",
- "User: 15, Item: 31952, Rating: 3.7409900837647396\n",
- "User: 15, Item: 3, Rating: 2.222062601579949\n",
- "User: 15, Item: 64, Rating: 0.9224387353614938\n",
- "User: 15, Item: 206, Rating: 2.35668733389394\n",
- "User: 15, Item: 249, Rating: 3.1290259851652826\n",
- "User: 15, Item: 276, Rating: 2.1800017354806753\n",
- "User: 15, Item: 369, Rating: 2.3082373858282694\n",
- "User: 15, Item: 504, Rating: 2.2600496220227573\n",
- "User: 15, Item: 515, Rating: 3.6575674086958188\n",
- "User: 15, Item: 522, Rating: 2.4562020809509626\n",
- "User: 15, Item: 580, Rating: 1.9073310817298395\n",
- "User: 15, Item: 599, Rating: 2.780847470837928\n",
- "User: 15, Item: 915, Rating: 2.761094249104645\n",
- "User: 15, Item: 966, Rating: 2.693661971830986\n",
- "User: 15, Item: 1274, Rating: 2.9873500196382845\n",
- "User: 15, Item: 1299, Rating: 3.0779327239728005\n",
- "User: 15, Item: 1345, Rating: 2.2037629856623138\n",
- "User: 15, Item: 1354, Rating: 2.001877412379849\n",
- "User: 15, Item: 532, Rating: 2.7123071345260277\n",
+ "User: 11, Item: 1214, Rating: 3.1666666666666665\n",
+ "User: 11, Item: 364, Rating: 2.49203431372549\n",
+ "User: 11, Item: 4308, Rating: 3.1666666666666665\n",
+ "User: 11, Item: 527, Rating: 3.898897058823529\n",
+ "User: 13, Item: 1997, Rating: 2.8\n",
+ "User: 13, Item: 4993, Rating: 2.8\n",
+ "User: 13, Item: 2700, Rating: 2.8\n",
+ "User: 13, Item: 1721, Rating: 2.8\n",
+ "User: 13, Item: 527, Rating: 2.8\n",
+ "User: 17, Item: 2028, Rating: 3.8125\n",
+ "User: 17, Item: 4993, Rating: 4.128289473684211\n",
+ "User: 17, Item: 1214, Rating: 3.25\n",
+ "User: 17, Item: 4308, Rating: 3.25\n",
+ "User: 19, Item: 1997, Rating: 3.5\n",
+ "User: 19, Item: 2028, Rating: 3.5\n",
+ "User: 19, Item: 4993, Rating: 3.5\n",
+ "User: 19, Item: 5952, Rating: 3.5\n",
+ "User: 19, Item: 2700, Rating: 3.5\n",
+ "User: 19, Item: 1721, Rating: 3.5\n",
+ "User: 19, Item: 1214, Rating: 3.5\n",
+ "User: 19, Item: 364, Rating: 3.5\n",
+ "User: 23, Item: 1997, Rating: 2.782649253731343\n",
+ "User: 23, Item: 2700, Rating: 2.349813432835821\n",
+ "User: 27, Item: 1997, Rating: 4.666666666666667\n",
+ "User: 27, Item: 2028, Rating: 4.666666666666667\n",
+ "User: 27, Item: 5952, Rating: 4.666666666666667\n",
+ "User: 27, Item: 2700, Rating: 4.666666666666667\n",
+ "User: 27, Item: 1721, Rating: 4.666666666666667\n",
+ "User: 27, Item: 364, Rating: 4.666666666666667\n",
+ "User: 27, Item: 4308, Rating: 4.666666666666667\n",
"Computing the msd similarity matrix...\n",
"Done computing similarity matrix.\n",
"Predictions with min_k = 3:\n",
- "User: 15, Item: 942, Rating: 3.7769516356699464\n",
- "User: 15, Item: 2117, Rating: 2.9340004894942537\n",
- "User: 15, Item: 2672, Rating: 2.371008709611413\n",
- "User: 15, Item: 5054, Rating: 2.693661971830986\n",
- "User: 15, Item: 6322, Rating: 2.693661971830986\n",
- "User: 15, Item: 6323, Rating: 1.7645762379992287\n",
- "User: 15, Item: 6757, Rating: 2.693661971830986\n",
- "User: 15, Item: 7700, Rating: 2.693661971830986\n",
- "User: 15, Item: 7981, Rating: 3.386000174210522\n",
- "User: 15, Item: 8600, Rating: 2.693661971830986\n",
- "User: 15, Item: 8620, Rating: 2.7538763809343654\n",
- "User: 15, Item: 31952, Rating: 2.693661971830986\n",
- "User: 15, Item: 3, Rating: 2.222062601579949\n",
- "User: 15, Item: 64, Rating: 0.9224387353614938\n",
- "User: 15, Item: 206, Rating: 2.35668733389394\n",
- "User: 15, Item: 249, Rating: 3.1290259851652826\n",
- "User: 15, Item: 276, Rating: 2.1800017354806753\n",
- "User: 15, Item: 369, Rating: 2.3082373858282694\n",
- "User: 15, Item: 504, Rating: 2.2600496220227573\n",
- "User: 15, Item: 515, Rating: 3.6575674086958188\n",
- "User: 15, Item: 522, Rating: 2.4562020809509626\n",
- "User: 15, Item: 580, Rating: 1.9073310817298395\n",
- "User: 15, Item: 599, Rating: 2.780847470837928\n",
- "User: 15, Item: 915, Rating: 2.761094249104645\n",
- "User: 15, Item: 966, Rating: 2.693661971830986\n",
- "User: 15, Item: 1274, Rating: 2.9873500196382845\n",
- "User: 15, Item: 1299, Rating: 3.0779327239728005\n",
- "User: 15, Item: 1345, Rating: 2.2037629856623138\n",
- "User: 15, Item: 1354, Rating: 2.001877412379849\n",
- "User: 15, Item: 532, Rating: 2.7123071345260277\n"
+ "User: 11, Item: 1214, Rating: 3.1666666666666665\n",
+ "User: 11, Item: 364, Rating: 3.1666666666666665\n",
+ "User: 11, Item: 4308, Rating: 3.1666666666666665\n",
+ "User: 11, Item: 527, Rating: 3.1666666666666665\n",
+ "User: 13, Item: 1997, Rating: 2.8\n",
+ "User: 13, Item: 4993, Rating: 2.8\n",
+ "User: 13, Item: 2700, Rating: 2.8\n",
+ "User: 13, Item: 1721, Rating: 2.8\n",
+ "User: 13, Item: 527, Rating: 2.8\n",
+ "User: 17, Item: 2028, Rating: 3.25\n",
+ "User: 17, Item: 4993, Rating: 3.25\n",
+ "User: 17, Item: 1214, Rating: 3.25\n",
+ "User: 17, Item: 4308, Rating: 3.25\n",
+ "User: 19, Item: 1997, Rating: 3.5\n",
+ "User: 19, Item: 2028, Rating: 3.5\n",
+ "User: 19, Item: 4993, Rating: 3.5\n",
+ "User: 19, Item: 5952, Rating: 3.5\n",
+ "User: 19, Item: 2700, Rating: 3.5\n",
+ "User: 19, Item: 1721, Rating: 3.5\n",
+ "User: 19, Item: 1214, Rating: 3.5\n",
+ "User: 19, Item: 364, Rating: 3.5\n",
+ "User: 23, Item: 1997, Rating: 2.5625\n",
+ "User: 23, Item: 2700, Rating: 2.5625\n",
+ "User: 27, Item: 1997, Rating: 4.666666666666667\n",
+ "User: 27, Item: 2028, Rating: 4.666666666666667\n",
+ "User: 27, Item: 5952, Rating: 4.666666666666667\n",
+ "User: 27, Item: 2700, Rating: 4.666666666666667\n",
+ "User: 27, Item: 1721, Rating: 4.666666666666667\n",
+ "User: 27, Item: 364, Rating: 4.666666666666667\n",
+ "User: 27, Item: 4308, Rating: 4.666666666666667\n"
]
}
],
@@ -333,7 +280,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "cc806424",
"metadata": {},
"outputs": [
@@ -343,109 +290,108 @@
"text": [
"\n",
"Prédictions avec min_support = 1:\n",
- "User: 15, Item: 942, Actual_k: 3\n",
- "User: 15, Item: 2117, Actual_k: 3\n",
- "User: 15, Item: 2672, Actual_k: 3\n",
- "User: 15, Item: 5054, Actual_k: 1\n",
- "User: 15, Item: 6322, Actual_k: 2\n",
- "User: 15, Item: 6323, Actual_k: 3\n",
- "User: 15, Item: 6757, Actual_k: 1\n",
- "User: 15, Item: 7700, Actual_k: 2\n",
- "User: 15, Item: 7981, Actual_k: 3\n",
- "User: 15, Item: 8600, Actual_k: 2\n",
- "User: 15, Item: 8620, Actual_k: 3\n",
- "User: 15, Item: 31952, Actual_k: 2\n",
- "User: 15, Item: 3, Actual_k: 3\n",
- "User: 15, Item: 64, Actual_k: 3\n",
- "User: 15, Item: 206, Actual_k: 3\n",
- "User: 15, Item: 249, Actual_k: 3\n",
- "User: 15, Item: 276, Actual_k: 3\n",
- "User: 15, Item: 369, Actual_k: 3\n",
- "User: 15, Item: 504, Actual_k: 3\n",
- "User: 15, Item: 515, Actual_k: 3\n",
- "User: 15, Item: 522, Actual_k: 3\n",
- "User: 15, Item: 580, Actual_k: 3\n",
- "User: 15, Item: 599, Actual_k: 3\n",
- "User: 15, Item: 915, Actual_k: 3\n",
- "User: 15, Item: 966, Actual_k: 1\n",
- "User: 15, Item: 1274, Actual_k: 3\n",
- "User: 15, Item: 1299, Actual_k: 3\n",
- "User: 15, Item: 1345, Actual_k: 3\n",
- "User: 15, Item: 1354, Actual_k: 3\n",
- "User: 15, Item: 532, Actual_k: 3\n",
+ "User: 11, Item: 1214, Actual_k: 1\n",
+ "User: 11, Item: 364, Actual_k: 2\n",
+ "User: 11, Item: 4308, Actual_k: 1\n",
+ "User: 11, Item: 527, Actual_k: 2\n",
+ "User: 13, Item: 1997, Actual_k: 0\n",
+ "User: 13, Item: 4993, Actual_k: 1\n",
+ "User: 13, Item: 2700, Actual_k: 0\n",
+ "User: 13, Item: 1721, Actual_k: 1\n",
+ "User: 13, Item: 527, Actual_k: 1\n",
+ "User: 17, Item: 2028, Actual_k: 2\n",
+ "User: 17, Item: 4993, Actual_k: 2\n",
+ "User: 17, Item: 1214, Actual_k: 1\n",
+ "User: 17, Item: 4308, Actual_k: 1\n",
+ "User: 19, Item: 1997, Actual_k: 0\n",
+ "User: 19, Item: 2028, Actual_k: 0\n",
+ "User: 19, Item: 4993, Actual_k: 0\n",
+ "User: 19, Item: 5952, Actual_k: 0\n",
+ "User: 19, Item: 2700, Actual_k: 0\n",
+ "User: 19, Item: 1721, Actual_k: 0\n",
+ "User: 19, Item: 1214, Actual_k: 0\n",
+ "User: 19, Item: 364, Actual_k: 0\n",
+ "User: 23, Item: 1997, Actual_k: 2\n",
+ "User: 23, Item: 2700, Actual_k: 2\n",
+ "User: 27, Item: 1997, Actual_k: 0\n",
+ "User: 27, Item: 2028, Actual_k: 1\n",
+ "User: 27, Item: 5952, Actual_k: 1\n",
+ "User: 27, Item: 2700, Actual_k: 0\n",
+ "User: 27, Item: 1721, Actual_k: 1\n",
+ "User: 27, Item: 364, Actual_k: 1\n",
+ "User: 27, Item: 4308, Actual_k: 1\n",
"\n",
"Prédictions avec min_support = 2:\n",
- "User: 15, Item: 942, Actual_k: 3\n",
- "User: 15, Item: 2117, Actual_k: 3\n",
- "User: 15, Item: 2672, Actual_k: 3\n",
- "User: 15, Item: 5054, Actual_k: 1\n",
- "User: 15, Item: 6322, Actual_k: 2\n",
- "User: 15, Item: 6323, Actual_k: 3\n",
- "User: 15, Item: 6757, Actual_k: 1\n",
- "User: 15, Item: 7700, Actual_k: 2\n",
- "User: 15, Item: 7981, Actual_k: 3\n",
- "User: 15, Item: 8600, Actual_k: 2\n",
- "User: 15, Item: 8620, Actual_k: 3\n",
- "User: 15, Item: 31952, Actual_k: 2\n",
- "User: 15, Item: 3, Actual_k: 3\n",
- "User: 15, Item: 64, Actual_k: 3\n",
- "User: 15, Item: 206, Actual_k: 3\n",
- "User: 15, Item: 249, Actual_k: 3\n",
- "User: 15, Item: 276, Actual_k: 3\n",
- "User: 15, Item: 369, Actual_k: 3\n",
- "User: 15, Item: 504, Actual_k: 3\n",
- "User: 15, Item: 515, Actual_k: 3\n",
- "User: 15, Item: 522, Actual_k: 3\n",
- "User: 15, Item: 580, Actual_k: 3\n",
- "User: 15, Item: 599, Actual_k: 3\n",
- "User: 15, Item: 915, Actual_k: 3\n",
- "User: 15, Item: 966, Actual_k: 1\n",
- "User: 15, Item: 1274, Actual_k: 3\n",
- "User: 15, Item: 1299, Actual_k: 3\n",
- "User: 15, Item: 1345, Actual_k: 3\n",
- "User: 15, Item: 1354, Actual_k: 3\n",
- "User: 15, Item: 532, Actual_k: 3\n",
+ "User: 11, Item: 1214, Actual_k: 1\n",
+ "User: 11, Item: 364, Actual_k: 2\n",
+ "User: 11, Item: 4308, Actual_k: 1\n",
+ "User: 11, Item: 527, Actual_k: 2\n",
+ "User: 13, Item: 1997, Actual_k: 0\n",
+ "User: 13, Item: 4993, Actual_k: 1\n",
+ "User: 13, Item: 2700, Actual_k: 0\n",
+ "User: 13, Item: 1721, Actual_k: 1\n",
+ "User: 13, Item: 527, Actual_k: 1\n",
+ "User: 17, Item: 2028, Actual_k: 2\n",
+ "User: 17, Item: 4993, Actual_k: 2\n",
+ "User: 17, Item: 1214, Actual_k: 1\n",
+ "User: 17, Item: 4308, Actual_k: 1\n",
+ "User: 19, Item: 1997, Actual_k: 0\n",
+ "User: 19, Item: 2028, Actual_k: 0\n",
+ "User: 19, Item: 4993, Actual_k: 0\n",
+ "User: 19, Item: 5952, Actual_k: 0\n",
+ "User: 19, Item: 2700, Actual_k: 0\n",
+ "User: 19, Item: 1721, Actual_k: 0\n",
+ "User: 19, Item: 1214, Actual_k: 0\n",
+ "User: 19, Item: 364, Actual_k: 0\n",
+ "User: 23, Item: 1997, Actual_k: 2\n",
+ "User: 23, Item: 2700, Actual_k: 2\n",
+ "User: 27, Item: 1997, Actual_k: 0\n",
+ "User: 27, Item: 2028, Actual_k: 1\n",
+ "User: 27, Item: 5952, Actual_k: 1\n",
+ "User: 27, Item: 2700, Actual_k: 0\n",
+ "User: 27, Item: 1721, Actual_k: 1\n",
+ "User: 27, Item: 364, Actual_k: 1\n",
+ "User: 27, Item: 4308, Actual_k: 1\n",
"\n",
"Prédictions avec min_support = 3:\n",
- "User: 15, Item: 942, Actual_k: 3\n",
- "User: 15, Item: 2117, Actual_k: 3\n",
- "User: 15, Item: 2672, Actual_k: 3\n",
- "User: 15, Item: 5054, Actual_k: 1\n",
- "User: 15, Item: 6322, Actual_k: 2\n",
- "User: 15, Item: 6323, Actual_k: 3\n",
- "User: 15, Item: 6757, Actual_k: 1\n",
- "User: 15, Item: 7700, Actual_k: 2\n",
- "User: 15, Item: 7981, Actual_k: 3\n",
- "User: 15, Item: 8600, Actual_k: 2\n",
- "User: 15, Item: 8620, Actual_k: 3\n",
- "User: 15, Item: 31952, Actual_k: 2\n",
- "User: 15, Item: 3, Actual_k: 3\n",
- "User: 15, Item: 64, Actual_k: 3\n",
- "User: 15, Item: 206, Actual_k: 3\n",
- "User: 15, Item: 249, Actual_k: 3\n",
- "User: 15, Item: 276, Actual_k: 3\n",
- "User: 15, Item: 369, Actual_k: 3\n",
- "User: 15, Item: 504, Actual_k: 3\n",
- "User: 15, Item: 515, Actual_k: 3\n",
- "User: 15, Item: 522, Actual_k: 3\n",
- "User: 15, Item: 580, Actual_k: 3\n",
- "User: 15, Item: 599, Actual_k: 3\n",
- "User: 15, Item: 915, Actual_k: 3\n",
- "User: 15, Item: 966, Actual_k: 1\n",
- "User: 15, Item: 1274, Actual_k: 3\n",
- "User: 15, Item: 1299, Actual_k: 3\n",
- "User: 15, Item: 1345, Actual_k: 3\n",
- "User: 15, Item: 1354, Actual_k: 3\n",
- "User: 15, Item: 532, Actual_k: 3\n",
+ "User: 11, Item: 1214, Actual_k: 1\n",
+ "User: 11, Item: 364, Actual_k: 2\n",
+ "User: 11, Item: 4308, Actual_k: 1\n",
+ "User: 11, Item: 527, Actual_k: 2\n",
+ "User: 13, Item: 1997, Actual_k: 0\n",
+ "User: 13, Item: 4993, Actual_k: 1\n",
+ "User: 13, Item: 2700, Actual_k: 0\n",
+ "User: 13, Item: 1721, Actual_k: 1\n",
+ "User: 13, Item: 527, Actual_k: 1\n",
+ "User: 17, Item: 2028, Actual_k: 2\n",
+ "User: 17, Item: 4993, Actual_k: 2\n",
+ "User: 17, Item: 1214, Actual_k: 1\n",
+ "User: 17, Item: 4308, Actual_k: 1\n",
+ "User: 19, Item: 1997, Actual_k: 0\n",
+ "User: 19, Item: 2028, Actual_k: 0\n",
+ "User: 19, Item: 4993, Actual_k: 0\n",
+ "User: 19, Item: 5952, Actual_k: 0\n",
+ "User: 19, Item: 2700, Actual_k: 0\n",
+ "User: 19, Item: 1721, Actual_k: 0\n",
+ "User: 19, Item: 1214, Actual_k: 0\n",
+ "User: 19, Item: 364, Actual_k: 0\n",
+ "User: 23, Item: 1997, Actual_k: 2\n",
+ "User: 23, Item: 2700, Actual_k: 2\n",
+ "User: 27, Item: 1997, Actual_k: 0\n",
+ "User: 27, Item: 2028, Actual_k: 1\n",
+ "User: 27, Item: 5952, Actual_k: 1\n",
+ "User: 27, Item: 2700, Actual_k: 0\n",
+ "User: 27, Item: 1721, Actual_k: 1\n",
+ "User: 27, Item: 364, Actual_k: 1\n",
+ "User: 27, Item: 4308, Actual_k: 1\n",
"\n",
"Matrice de similarité:\n",
- "[[1. 0.39130435 0.35942029 ... 0.24358974 0.28513238 0.21451104]\n",
- " [0.39130435 1. 0.32786885 ... 0.30967742 0.42424242 0.21621622]\n",
- " [0.35942029 0.32786885 1. ... 0.36666667 0.72727273 0.34375 ]\n",
- " ...\n",
- " [0.24358974 0.30967742 0.36666667 ... 1. 0.6779661 0.37569061]\n",
- " [0.28513238 0.42424242 0.72727273 ... 0.6779661 1. 0.83333333]\n",
- " [0.21451104 0.21621622 0.34375 ... 0.37569061 0.83333333 1. ]]\n",
+ "[[1. 0. 0.24615385 0. 0.43243243 0. ]\n",
+ " [0. 1. 0. 0. 0.17094017 0. ]\n",
+ " [0.24615385 0. 1. 0. 0.53333333 0. ]\n",
+ " [0. 0. 0. 1. 0. 0. ]\n",
+ " [0.43243243 0.17094017 0.53333333 0. 1. 0.25 ]\n",
+ " [0. 0. 0. 0. 0.25 1. ]]\n",
"None\n"
]
}
@@ -485,7 +431,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "d03ed9eb",
"metadata": {},
"outputs": [
@@ -493,13 +439,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[[3. 1.5 4. ... nan nan nan]\n",
- " [nan nan nan ... nan nan nan]\n",
- " [4. 3. 3. ... nan nan nan]\n",
- " ...\n",
- " [4.5 nan nan ... nan nan nan]\n",
- " [nan nan nan ... nan nan nan]\n",
- " [2. nan nan ... nan nan nan]]\n"
+ "[[1.5 4. 5. 4.5 3. 1. nan nan nan nan]\n",
+ " [nan 2. nan 2. nan nan 1. 5. 4. nan]\n",
+ " [5. nan nan 4.5 3. 1. nan 1.5 nan 4.5]\n",
+ " [nan nan nan nan nan nan nan nan 2. 5. ]\n",
+ " [nan 3. 3. 4. nan 1. 3. 2.5 1. 3. ]\n",
+ " [nan nan 5. nan nan nan 4. nan nan 5. ]]\n"
]
}
],
@@ -629,7 +574,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "be53ae27",
"metadata": {},
"outputs": [
@@ -637,10 +582,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "UserBased MAE: 1.5398252671298895\n",
- "UserBased RMSE: 1.5553141029705104\n",
- "KNNWithMeans MAE: 0.5419110316300769\n",
- "KNNWithMeans RMSE: 0.7019543155680094\n"
+ "UserBased MAE: 1.7175000000000002\n",
+ "UserBased RMSE: 1.7384170241918369\n",
+ "KNNWithMeans MAE: 0.661617428851614\n",
+ "KNNWithMeans RMSE: 0.8426896111887758\n"
]
}
],
@@ -686,7 +631,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "c20d8e19",
"metadata": {},
"outputs": [
@@ -698,10 +643,10 @@
"Done computing similarity matrix.\n",
"Computing the cosine similarity matrix...\n",
"Done computing similarity matrix.\n",
- "RMSE: 0.9799\n",
- "RMSE: 0.9871\n",
- "RMSE with MSD similarity: 0.9798533097556152\n",
- "RMSE with Jaccard similarity: 0.9870653791755158\n"
+ "RMSE: 1.0812\n",
+ "RMSE: 1.0910\n",
+ "RMSE with MSD similarity: 1.0811758629789194\n",
+ "RMSE with Jaccard similarity: 1.0910225374454734\n"
]
}
],