more notebook testing

notebooks/03_semantic_methods.ipynb

@@ -2,14 +2,112 @@
  "cells": [
   {
    "metadata": {},
+   "cell_type": "markdown",
+   "source": "Test word vectors",
+   "id": "b79941bf4553fd6"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-18T23:28:55.563335Z",
+     "start_time": "2025-11-18T23:28:53.763429Z"
+    }
+   },
    "cell_type": "code",
+   "source": [
+    "import import_ipynb\n",
+    "from notebooks.02_baseline_experiments.ipynb import *\n",
+    "\n",
+    "import spacy\n",
+    "nlp = spacy.load(\"en_core_web_md\")\n",
+    "\n",
+    "words = [\"cat\", \"dog\", \"feline\", \"vehicle\", \"car\"]\n",
+    "# Test work similarities\n",
+    "for word1 in words:\n",
+    "    for word2 in words:\n",
+    "        if word1 != word2:\n",
+    "            similarity = nlp.vocab[word1].similarity(nlp.vocab[word2])\n",
+    "            print(f\"{word1} - {word2}: {similarity:.3f}\")\n",
+    "\n",
+    "\n"
+   ],
+   "id": "8a3c4314a90086fe",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cat - dog: 1.000\n",
+      "cat - feline: 0.363\n",
+      "cat - vehicle: 0.078\n",
+      "cat - car: 0.193\n",
+      "dog - cat: 1.000\n",
+      "dog - feline: 0.363\n",
+      "dog - vehicle: 0.078\n",
+      "dog - car: 0.193\n",
+      "feline - cat: 0.363\n",
+      "feline - dog: 0.363\n",
+      "feline - vehicle: 0.180\n",
+      "feline - car: 0.050\n",
+      "vehicle - cat: 0.078\n",
+      "vehicle - dog: 0.078\n",
+      "vehicle - feline: 0.180\n",
+      "vehicle - car: 0.205\n",
+      "car - cat: 0.193\n",
+      "car - dog: 0.193\n",
+      "car - feline: 0.050\n",
+      "car - vehicle: 0.205\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Simple averaging",
+   "id": "8f32b5695f554268"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-18T23:45:03.085563Z",
+     "start_time": "2025-11-18T23:45:03.082190Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def sentence_similarity_ang(sent1, sent2):\n",
+    "    doc1 = nlp(sent1)\n",
+    "    doc2 = nlp(sent2)\n",
+    "\n",
+    "    # Vectors for each word, filter out words without vectors (medium model)\n",
+    "    vecs1 = [token.vector for token in doc1 if token.has_vector]\n",
+    "    vecs2 = [token.vector for token in doc2 if token.has_vector]\n",
+    "\n",
+    "    if not vecs1 or not vecs2:\n",
+    "        return 0.0\n",
+    "\n",
+    "    # Average vectors\n",
+    "    avg1 = sum(vecs1) / len(vecs1)\n",
+    "    avg2 = sum(vecs2) / len(vecs2)\n",
+    "\n",
+    "    #cosine similarity\n",
+    "    from sklearn.metrics.pairwise import cosine_similarity\n",
+    "    return cosine_similarity([avg1], [avg2])[0][0]\n"
+   ],
+   "id": "68a6757447e4a1c7",
    "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "8a3c4314a90086fe"
+   "execution_count": 3
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }