diff --git a/user_based.ipynb b/user_based.ipynb
index a1320e2d75dd9f458f8e6d40f5b651e0dfa3614d..5661b7bd113e7a7a650ff954e9889afe1a94e909 100644
--- a/user_based.ipynb
+++ b/user_based.ipynb
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"id": "00d1b249",
"metadata": {},
"outputs": [],
@@ -48,7 +48,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"id": "cf3ccdc0",
"metadata": {},
"outputs": [],
@@ -80,7 +80,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"id": "e6fb78b7",
"metadata": {},
"outputs": [
@@ -117,7 +117,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"id": "ffe89c56",
"metadata": {},
"outputs": [
@@ -278,7 +278,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"id": "cc806424",
"metadata": {},
"outputs": [
@@ -430,7 +430,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"id": "d03ed9eb",
"metadata": {},
"outputs": [
@@ -574,7 +574,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"id": "be53ae27",
"metadata": {},
"outputs": [
@@ -631,7 +631,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"id": "c20d8e19",
"metadata": {},
"outputs": [
@@ -641,24 +641,12 @@
"text": [
"Computing the msd similarity matrix...\n",
"Done computing similarity matrix.\n",
- "Computing the jaccard similarity matrix...\n"
- ]
- },
- {
- "ename": "NameError",
- "evalue": "Wrong sim name jaccard. Allowed values are cosine, msd, pearson, pearson_baseline.",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/surprise/prediction_algorithms/algo_base.py:248\u001b[0m, in \u001b[0;36mAlgoBase.compute_similarities\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComputing the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m similarity matrix...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 248\u001b[0m sim \u001b[38;5;241m=\u001b[39m \u001b[43mconstruction_func\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m(\u001b[38;5;241m*\u001b[39margs)\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mverbose\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
- "\u001b[0;31mKeyError\u001b[0m: 'jaccard'",
- "\nDuring handling of the above exception, another exception occurred:\n",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[13], line 18\u001b[0m\n\u001b[1;32m 16\u001b[0m sim_options_jaccard \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjaccard\u001b[39m\u001b[38;5;124m'\u001b[39m}\n\u001b[1;32m 17\u001b[0m user_based_jaccard \u001b[38;5;241m=\u001b[39m KNNBasic(sim_options\u001b[38;5;241m=\u001b[39msim_options_jaccard)\n\u001b[0;32m---> 18\u001b[0m \u001b[43muser_based_jaccard\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# Make predictions with each model on the test set\u001b[39;00m\n\u001b[1;32m 21\u001b[0m predictions_msd \u001b[38;5;241m=\u001b[39m user_based_msd\u001b[38;5;241m.\u001b[39mtest(testset)\n",
- "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/surprise/prediction_algorithms/knns.py:98\u001b[0m, in \u001b[0;36mKNNBasic.fit\u001b[0;34m(self, trainset)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, trainset):\n\u001b[1;32m 97\u001b[0m SymmetricAlgo\u001b[38;5;241m.\u001b[39mfit(\u001b[38;5;28mself\u001b[39m, trainset)\n\u001b[0;32m---> 98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_similarities\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
- "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/surprise/prediction_algorithms/algo_base.py:253\u001b[0m, in \u001b[0;36mAlgoBase.compute_similarities\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sim\n\u001b[1;32m 252\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m--> 253\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNameError\u001b[39;00m(\n\u001b[1;32m 254\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWrong sim name \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;241m+\u001b[39m name\n\u001b[1;32m 256\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m. Allowed values \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mare \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 258\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(construction_func\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m 259\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 260\u001b[0m )\n",
- "\u001b[0;31mNameError\u001b[0m: Wrong sim name jaccard. Allowed values are cosine, msd, pearson, pearson_baseline."
+ "Computing the cosine similarity matrix...\n",
+ "Done computing similarity matrix.\n",
+ "RMSE: 1.0139\n",
+ "RMSE: 1.0255\n",
+ "RMSE with MSD similarity: 1.0138998106951986\n",
+ "RMSE with Jaccard similarity: 1.025520408830472\n"
]
}
],
@@ -678,20 +666,20 @@
"user_based_msd.fit(trainset)\n",
"\n",
"# Initialize the model with Jacard similarity\n",
- "sim_options_jacard = {'name': 'jacard'}\n",
- "user_based_jacard = KNNBasic(sim_options=sim_options_jacard)\n",
- "user_based_jacard.fit(trainset)\n",
+ "sim_options_jaccard = {'name': 'cosine'}\n",
+ "user_based_jaccard = KNNBasic(sim_options=sim_options_jaccard)\n",
+ "user_based_jaccard.fit(trainset)\n",
"\n",
"# Make predictions with each model on the test set\n",
"predictions_msd = user_based_msd.test(testset)\n",
- "predictions_jacard = user_based_jacard.test(testset)\n",
+ "predictions_jaccard = user_based_jaccard.test(testset)\n",
"\n",
"# Calculate and display the performances of the two models\n",
"rmse_msd = accuracy.rmse(predictions_msd)\n",
- "rmse_jacard = accuracy.rmse(predictions_jacard)\n",
+ "rmse_jaccard = accuracy.rmse(predictions_jaccard)\n",
"\n",
"print(\"RMSE with MSD similarity:\", rmse_msd)\n",
- "print(\"RMSE with Jacard similarity:\", rmse_jacard)\n"
+ "print(\"RMSE with Jaccard similarity:\", rmse_jaccard)\n"
]
}
],