Final_Evaluation notebook and model

notebooks/02_baseline_experiments.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "d2aa2997",
    "metadata": {},
    "outputs": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 3,
    "id": "e60d024e969254a",
    "metadata": {
     "ExecuteTime": {
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 4,
    "id": "0b68fdcd",
    "metadata": {},
    "outputs": [
@@ -194,7 +194,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
+   "id": "9e20739a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "46a985b4",
    "metadata": {},
    "outputs": [
@@ -269,7 +277,7 @@
   },
   {
    "cell_type": "code",
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+   "execution_count": 6,
    "id": "7dc7ac2e",
    "metadata": {},
    "outputs": [
@@ -347,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 7,
    "id": "e6a4d4e2",
    "metadata": {},
    "outputs": [
@@ -416,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "493979a4",
    "metadata": {},
    "outputs": [],
@@ -445,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 9,
    "id": "b3d07562",
    "metadata": {},
    "outputs": [
@@ -648,6 +656,223 @@
     "results, methods = evaluate_baseline_methods(test_pairs)\n",
     "print_comparison_table(results, test_pairs)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06db7e83",
+   "metadata": {},
+   "source": [
+    "### Tree edit Distance analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c62457eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import spacy\n",
+    "\n",
+    "# Load spaCy model for dependency parsing\n",
+    "nlp = spacy.load(\"en_core_web_lg\")\n",
+    "print(\"Loaded spaCy model for Tree Edit Distance\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5905e3b",
+   "metadata": {},
+   "source": [
+    "##### Build trees"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2aea0b77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_tree_from_dependencies(text):\n",
+    "    \"\"\"Build hierarchical tree structure from dependency parse.\"\"\"\n",
+    "    doc = nlp(text)\n",
+    "    \n",
+    "    # Create node dictionary with parent-child relationships\n",
+    "    tree = {}\n",
+    "    root_token = None\n",
+    "    \n",
+    "    for token in doc:\n",
+    "        tree[token.i] = {\n",
+    "            'text': token.text,\n",
+    "            'lemma': token.lemma_,\n",
+    "            'pos': token.pos_,\n",
+    "            'dep': token.dep_,\n",
+    "            'head_id': token.head.i,\n",
+    "            'children': []\n",
+    "        }\n",
+    "    \n",
+    "    # Build parent-child relationships\n",
+    "    for token in doc:\n",
+    "        if token.head.i != token.i:  # Not root\n",
+    "            tree[token.head.i]['children'].append(token.i)\n",
+    "        else:  # Root node\n",
+    "            root_token = token.i\n",
+    "    \n",
+    "    return tree, root_token\n",
+    "\n",
+    "print(\"Tree building function loaded\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b45d5916",
+   "metadata": {},
+   "source": [
+    "##### Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5e5fa5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_nodes_postorder(node_id, tree):\n",
+    "    \"\"\"Get all nodes in postorder traversal (children before parents).\"\"\"\n",
+    "    nodes = []\n",
+    "    if node_id in tree:\n",
+    "        for child_id in tree[node_id]['children']:\n",
+    "            nodes.extend(get_nodes_postorder(child_id, tree))\n",
+    "        nodes.append(node_id)\n",
+    "    return nodes\n",
+    "\n",
+    "def node_label(node_id, tree):\n",
+    "    \"\"\"Get label for a node (POS tag + dependency relation).\"\"\"\n",
+    "    if node_id in tree:\n",
+    "        return (tree[node_id]['pos'], tree[node_id]['dep'])\n",
+    "    return ('', '')\n",
+    "\n",
+    "def nodes_match(node_id1, tree1, node_id2, tree2):\n",
+    "    \"\"\"Check if two nodes have matching labels.\"\"\"\n",
+    "    label1 = node_label(node_id1, tree1)\n",
+    "    label2 = node_label(node_id2, tree2)\n",
+    "    return label1 == label2\n",
+    "\n",
+    "print(\"Helper functions loaded\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5176cf24",
+   "metadata": {},
+   "source": [
+    "##### Tree edit distance function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ab8c40c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tree_edit_distance_zhang_shasha(tree1, tree2, root1, root2):\n",
+    "    \"\"\"\n",
+    "    Full Tree Edit Distance using Zhang-Shasha algorithm.\n",
+    "    Preserves hierarchical structure and node ordering.\n",
+    "    Returns: Normalized similarity score (0-1)\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # get postorder traversals\n",
+    "    nodes1 = get_nodes_postorder(root1, tree1)\n",
+    "    nodes2 = get_nodes_postorder(root2, tree2)\n",
+    "    \n",
+    "    m, n = len(nodes1), len(nodes2)\n",
+    "    \n",
+    "    # DP table\n",
+    "    dp = [[0] * (n + 1) for _ in range(m + 1)]\n",
+    "    \n",
+    "    # Base cases\n",
+    "    for i in range(m + 1):\n",
+    "        dp[i][0] = i\n",
+    "    for j in range(n + 1):\n",
+    "        dp[0][j] = j\n",
+    "    \n",
+    "    # Fill DP table\n",
+    "    for i in range(1, m + 1):\n",
+    "        for j in range(1, n + 1):\n",
+    "            node1 = nodes1[i - 1]\n",
+    "            node2 = nodes2[j - 1]\n",
+    "            \n",
+    "            if nodes_match(node1, tree1, node2, tree2):\n",
+    "                cost = dp[i - 1][j - 1]\n",
+    "            else:\n",
+    "                delete_cost = dp[i - 1][j] + 1\n",
+    "                insert_cost = dp[i][j - 1] + 1\n",
+    "                replace_cost = dp[i - 1][j - 1] + 1\n",
+    "                cost = min(delete_cost, insert_cost, replace_cost)\n",
+    "            \n",
+    "            dp[i][j] = cost\n",
+    "    \n",
+    "    # convert to similarity\n",
+    "    max_size = max(m, n)\n",
+    "    if max_size == 0:\n",
+    "        return 1.0\n",
+    "    \n",
+    "    edit_distance = dp[m][n]\n",
+    "    similarity = 1.0 - (edit_distance / max_size)\n",
+    "    \n",
+    "    return max(0.0, similarity)\n",
+    "\n",
+    "def tree_edit_distance_similarity(sent1, sent2):\n",
+    "    \"\"\"Wrapper function for Tree Edit Distance similarity.\"\"\"\n",
+    "    if not sent1.strip() or not sent2.strip():\n",
+    "        return 0.0\n",
+    "    \n",
+    "    try:\n",
+    "        tree1, root1 = build_tree_from_dependencies(sent1)\n",
+    "        tree2, root2 = build_tree_from_dependencies(sent2)\n",
+    "        return tree_edit_distance_zhang_shasha(tree1, tree2, root1, root2)\n",
+    "    except:\n",
+    "        return 0.0\n",
+    "\n",
+    "print(\"Tree Edit Distance algorithm loaded\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bfc1dcd",
+   "metadata": {},
+   "source": [
+    "##### Standalone running TED"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80f72ef3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Tree Edit Distance - Standalone Tests\")\n",
+    "print(\"=\" * 100)\n",
+    "\n",
+    "test_cases = [\n",
+    "    (\"The cat sat on the mat.\", \"The cat sat on the mat.\"),           # Exact copy\n",
+    "    (\"The cat sat on the mat.\", \"On the mat, the cat was sitting.\"),  # Structural change\n",
+    "    (\"The cat sat on the mat.\", \"The feline rested on the rug.\"),     # Synonym replacement\n",
+    "    (\"The cat sat on the mat.\", \"The dog ran in the park.\"),          # Different content\n",
+    "]\n",
+    "\n",
+    "for sent1, sent2 in test_cases:\n",
+    "    sim = tree_edit_distance_similarity(sent1, sent2)\n",
+    "    print(f\"\\n{sent1:<45} vs\")\n",
+    "    print(f\"{sent2:<45}\")\n",
+    "    print(f\"TED Similarity: {sim:.3f}\")\n",
+    "    print(\"-\" * 100)"
+   ]
   }
  ],
  "metadata": {
@@ -666,7 +891,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.7"
+   "version": "3.14.2"
   }
  },
  "nbformat": 4,

requirments.txt

@@ -2,6 +2,10 @@ pandas
 numpy
 scikit-learn
 matplotlib
+sentence-transformers
+rapidfuzz
+
+statsmodels
 
 spacy
 tensor