more notebook testing

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
+}