more notebook testing

notebooks/01_data_exploration.ipynb

@@ -9,12 +9,13 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-18T23:01:17.888318Z",
+     "end_time": "2025-11-19T00:00:02.012627Z",
-     "start_time": "2025-11-18T23:01:16.494987Z"
+     "start_time": "2025-11-19T00:00:00.160731Z"
     }
    },
    "cell_type": "code",
    "source": [
+    "import import_ipynb\n",
     "import spacy\n",
     "\n",
     "nlp = spacy.load(\"en_core_web_md\")  # Can swap for large model if required\n",
@@ -50,7 +51,7 @@
      ]
     }
    ],
-   "execution_count": 1
+   "execution_count": 2
   },
   {
    "metadata": {},

notebooks/02_baseline_experiments.ipynb

@@ -3,12 +3,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-18T23:15:35.056834Z",
+     "end_time": "2025-11-19T00:00:04.962487Z",
-     "start_time": "2025-11-18T23:15:35.051218Z"
+     "start_time": "2025-11-19T00:00:04.958995Z"
     }
    },
    "cell_type": "code",
    "source": [
+    "import import_ipynb\n",
+    "from notebooks.01_data_exploration import *\n",
+    "\n",
     "def jaccard_similarity(sent1, sent2):\n",
     "    # make lowercase and split into words\n",
     "    words1 = set(sent1.lower().split())\n",
@@ -30,16 +33,15 @@
    "id": "e60d024e969254a",
    "outputs": [
     {
-     "name": "stdout",
+     "ename": "SyntaxError",
-     "output_type": "stream",
+     "evalue": "invalid decimal literal (2501033926.py, line 2)",
-     "text": [
+     "output_type": "error",
-      "'The cat sat on the mat.' vs 'The cat sat on the mat.': 1.000\n",
+     "traceback": [
-      "'The cat sat on the mat.' vs 'On the mat sat the cat.': 0.429\n",
+      "  \u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[8]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[31m    \u001B[39m\u001B[31mfrom notebooks.01_data_exploration import *\u001B[39m\n                     ^\n\u001B[31mSyntaxError\u001B[39m\u001B[31m:\u001B[39m invalid decimal literal\n"
-      "'The cat sat on the mat.' vs 'The dog ran in the park': 0.111\n"
      ]
     }
    ],
-   "execution_count": 7
+   "execution_count": 8
   }
  ],
  "metadata": {

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,
+   "execution_count": 3
-   "source": "",
-   "id": "8a3c4314a90086fe"
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

notebooks/04_fusion_model.ipynb

@@ -0,0 +1,48 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "import import_ipynb\n",
+    "from notebooks.03_semantic_methods.ipynb import *\n",
+    "\n",
+    "def extract_all_features(sentence_pairs):\n",
+    "    features = []\n",
+    "    for sent1, sent2 in sentence_pairs:\n",
+    "        feature_vector = [\n",
+    "            jaccard_similarity(sent1, sent2),\n",
+    "            sentence_similarity_avg(sent1, sent2),\n",
+    "            sentence_similarity_sif(sent1, sent2),\n",
+    "            syntactic_similarity(sent1, sent2)\n",
+    "        ]"
+   ],
+   "id": "1c45d83192facfc6"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "labled_pairs = []\n",
+    "\n",
+    "X = extract_all_features(labled_pairs)\n",
+    "y = [0,1,0,1...] #Lables for pairs\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n"
+   ],
+   "id": "9665682bd5a7951e"
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}