diff --git a/McDonalds.ipynb b/McDonalds.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "b3c5c0d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import spacy\n",
+ "import matplotlib.pyplot as plt\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "import math\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn import svm\n",
+ "from sklearn.svm import SVC\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.metrics import classification_report\n",
+ "from transformers import DistilBertTokenizer, DistilBertModel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "0f1ada0b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load data and insert it to a dataframe\n",
+ "df = pd.read_csv('McDonald_s_Reviews.csv', encoding='ISO-8859-1')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "d07cf26a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>reviewer_id</th>\n",
+ " <th>store_name</th>\n",
+ " <th>category</th>\n",
+ " <th>store_address</th>\n",
+ " <th>latitude</th>\n",
+ " <th>longitude</th>\n",
+ " <th>rating_count</th>\n",
+ " <th>review_time</th>\n",
+ " <th>review</th>\n",
+ " <th>rating</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>McDonald's</td>\n",
+ " <td>Fast food restaurant</td>\n",
+ " <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
+ " <td>30.460718</td>\n",
+ " <td>-97.792874</td>\n",
+ " <td>1,240</td>\n",
+ " <td>3 months ago</td>\n",
+ " <td>Why does it look like someone spit on my food?...</td>\n",
+ " <td>1 star</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>McDonald's</td>\n",
+ " <td>Fast food restaurant</td>\n",
+ " <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
+ " <td>30.460718</td>\n",
+ " <td>-97.792874</td>\n",
+ " <td>1,240</td>\n",
+ " <td>5 days ago</td>\n",
+ " <td>It'd McDonalds. It is what it is as far as the...</td>\n",
+ " <td>4 stars</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>McDonald's</td>\n",
+ " <td>Fast food restaurant</td>\n",
+ " <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
+ " <td>30.460718</td>\n",
+ " <td>-97.792874</td>\n",
+ " <td>1,240</td>\n",
+ " <td>5 days ago</td>\n",
+ " <td>Made a mobile order got to the speaker and che...</td>\n",
+ " <td>1 star</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>McDonald's</td>\n",
+ " <td>Fast food restaurant</td>\n",
+ " <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
+ " <td>30.460718</td>\n",
+ " <td>-97.792874</td>\n",
+ " <td>1,240</td>\n",
+ " <td>a month ago</td>\n",
+ " <td>My mc. Crispy chicken sandwich was ���ï¿...</td>\n",
+ " <td>5 stars</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>McDonald's</td>\n",
+ " <td>Fast food restaurant</td>\n",
+ " <td>13749 US-183 Hwy, Austin, TX 78750, United States</td>\n",
+ " <td>30.460718</td>\n",
+ " <td>-97.792874</td>\n",
+ " <td>1,240</td>\n",
+ " <td>2 months ago</td>\n",
+ " <td>I repeat my order 3 times in the drive thru, a...</td>\n",
+ " <td>1 star</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " reviewer_id store_name category \\\n",
+ "0 1 McDonald's Fast food restaurant \n",
+ "1 2 McDonald's Fast food restaurant \n",
+ "2 3 McDonald's Fast food restaurant \n",
+ "3 4 McDonald's Fast food restaurant \n",
+ "4 5 McDonald's Fast food restaurant \n",
+ "\n",
+ " store_address latitude longitude \\\n",
+ "0 13749 US-183 Hwy, Austin, TX 78750, United States 30.460718 -97.792874 \n",
+ "1 13749 US-183 Hwy, Austin, TX 78750, United States 30.460718 -97.792874 \n",
+ "2 13749 US-183 Hwy, Austin, TX 78750, United States 30.460718 -97.792874 \n",
+ "3 13749 US-183 Hwy, Austin, TX 78750, United States 30.460718 -97.792874 \n",
+ "4 13749 US-183 Hwy, Austin, TX 78750, United States 30.460718 -97.792874 \n",
+ "\n",
+ " rating_count review_time \\\n",
+ "0 1,240 3 months ago \n",
+ "1 1,240 5 days ago \n",
+ "2 1,240 5 days ago \n",
+ "3 1,240 a month ago \n",
+ "4 1,240 2 months ago \n",
+ "\n",
+ " review rating \n",
+ "0 Why does it look like someone spit on my food?... 1 star \n",
+ "1 It'd McDonalds. It is what it is as far as the... 4 stars \n",
+ "2 Made a mobile order got to the speaker and che... 1 star \n",
+ "3 My mc. Crispy chicken sandwich was ���ï¿... 5 stars \n",
+ "4 I repeat my order 3 times in the drive thru, a... 1 star "
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# See the structure of the data\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "8d5b2e4e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Number of Reviews')"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# How many reviews exists of each rating?\n",
+ "\n",
+ "df['rating'].value_counts().plot(kind='bar', color=('yellow', 'red', 'darkblue', 'darkgreen','darkorange'))\n",
+ "\n",
+ "plt.title('Number of Reviews for each Rating')\n",
+ "plt.xlabel('Rating')\n",
+ "plt.ylabel('Number of Reviews')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "4d425a4a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Cluster 1 & 2 to negative, and 3, 4 & 5 to positive.\n",
+ "\n",
+ "df.loc[:, 'c_rating'] = df['rating'].replace({'1 star': 0, '2 stars': 0, '3 stars': 1, '4 stars': 1, '5 stars': 1})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "35cff577",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Number of Reviews')"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# How many reviews exists of each Cluster?\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "ax = df['c_rating'].value_counts().plot(kind='bar', color=('yellow', 'red'))\n",
+ "ax.set_xticklabels(['Positive', 'Negative'], rotation=0)\n",
+ "plt.title('Number of Reviews for each Cluster')\n",
+ "plt.xlabel('Cluster')\n",
+ "plt.ylabel('Number of Reviews')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "768e7ea7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "c_rating\n",
+ "0 12517\n",
+ "1 12517\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Creating a balanced dataset by using undersampling to remove excess observations\n",
+ "\n",
+ "ratings = df['c_rating'].unique()\n",
+ "n = df['c_rating'].value_counts().min()\n",
+ "\n",
+ "for i in ratings:\n",
+ " each_rating = df[df['c_rating'] == i]\n",
+ " length = len(each_rating)\n",
+ " \n",
+ " removed = each_rating.sample(length-n, random_state=1)\n",
+ " df = pd.concat([df, removed, removed]).drop_duplicates(keep=False)\n",
+ " \n",
+ "df['c_rating'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "d321e936",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[43], line 26\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[38;5;66;03m# Generate embeddings for the current batch\u001b[39;00m\n\u001b[0;32m 25\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[1;32m---> 26\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 28\u001b[0m \u001b[38;5;66;03m# Extract the last hidden state (embeddings) from DistilBERT outputs\u001b[39;00m\n\u001b[0;32m 29\u001b[0m batch_embeddings \u001b[38;5;241m=\u001b[39m outputs\u001b[38;5;241m.\u001b[39mlast_hidden_state\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\transformers\\models\\distilbert\\modeling_distilbert.py:599\u001b[0m, in \u001b[0;36mDistilBertModel.forward\u001b[1;34m(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[0;32m 595\u001b[0m head_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_head_mask(head_mask, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mnum_hidden_layers)\n\u001b[0;32m 597\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(input_ids, inputs_embeds) \u001b[38;5;66;03m# (bs, seq_length, dim)\u001b[39;00m\n\u001b[1;32m--> 599\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 600\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 601\u001b[0m \u001b[43m \u001b[49m\u001b[43mattn_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 602\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 603\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 604\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 605\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 606\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\transformers\\models\\distilbert\\modeling_distilbert.py:369\u001b[0m, in \u001b[0;36mTransformer.forward\u001b[1;34m(self, x, attn_mask, head_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[0;32m 361\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[0;32m 362\u001b[0m layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[0;32m 363\u001b[0m hidden_state,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 366\u001b[0m output_attentions,\n\u001b[0;32m 367\u001b[0m )\n\u001b[0;32m 368\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 369\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 370\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 371\u001b[0m \u001b[43m \u001b[49m\u001b[43mattn_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 372\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 373\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 374\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 376\u001b[0m hidden_state \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 378\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\transformers\\models\\distilbert\\modeling_distilbert.py:313\u001b[0m, in \u001b[0;36mTransformerBlock.forward\u001b[1;34m(self, x, attn_mask, head_mask, output_attentions)\u001b[0m\n\u001b[0;32m 310\u001b[0m sa_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msa_layer_norm(sa_output \u001b[38;5;241m+\u001b[39m x) \u001b[38;5;66;03m# (bs, seq_length, dim)\u001b[39;00m\n\u001b[0;32m 312\u001b[0m \u001b[38;5;66;03m# Feed Forward Network\u001b[39;00m\n\u001b[1;32m--> 313\u001b[0m ffn_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mffn\u001b[49m\u001b[43m(\u001b[49m\u001b[43msa_output\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# (bs, seq_length, dim)\u001b[39;00m\n\u001b[0;32m 314\u001b[0m ffn_output: torch\u001b[38;5;241m.\u001b[39mTensor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_layer_norm(ffn_output \u001b[38;5;241m+\u001b[39m sa_output) \u001b[38;5;66;03m# (bs, seq_length, dim)\u001b[39;00m\n\u001b[0;32m 316\u001b[0m output \u001b[38;5;241m=\u001b[39m (ffn_output,)\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\transformers\\models\\distilbert\\modeling_distilbert.py:254\u001b[0m, in \u001b[0;36mFFN.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 253\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[1;32m--> 254\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mapply_chunking_to_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mff_chunk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunk_size_feed_forward\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mseq_len_dim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\transformers\\pytorch_utils.py:241\u001b[0m, in \u001b[0;36mapply_chunking_to_forward\u001b[1;34m(forward_fn, chunk_size, chunk_dim, *input_tensors)\u001b[0m\n\u001b[0;32m 238\u001b[0m \u001b[38;5;66;03m# concatenate output at same dimension\u001b[39;00m\n\u001b[0;32m 239\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcat(output_chunks, dim\u001b[38;5;241m=\u001b[39mchunk_dim)\n\u001b[1;32m--> 241\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minput_tensors\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\transformers\\models\\distilbert\\modeling_distilbert.py:258\u001b[0m, in \u001b[0;36mFFN.ff_chunk\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 256\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mff_chunk\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[0;32m 257\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlin1(\u001b[38;5;28minput\u001b[39m)\n\u001b[1;32m--> 258\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mactivation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 259\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlin2(x)\n\u001b[0;32m 260\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout(x)\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[1;32m~\\Anaconda3\\envs\\liu-text-mining\\Lib\\site-packages\\transformers\\activations.py:78\u001b[0m, in \u001b[0;36mGELUActivation.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m---> 78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mact\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "# Create embeddings by leverageing the distilBERT, save in .CSV file\n",
+ "\n",
+ "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
+ "model = DistilBertModel.from_pretrained('distilbert-base-uncased')\n",
+ "\n",
+ "# Combine into tuples\n",
+ "review_rating_pairs = list(zip(df['review'].tolist(), df['c_rating'].tolist()))\n",
+ "\n",
+ "# Batch size\n",
+ "batch_size = 4\n",
+ "\n",
+ "all_average_embeddings = []\n",
+ "all_ratings = []\n",
+ "\n",
+ "# Process reviews in smaller batches\n",
+ "for i in range(0, len(review_rating_pairs), batch_size):\n",
+ " # Get review-rating pairs for the current batch\n",
+ " batch_review_ratings = review_rating_pairs[i:i+batch_size]\n",
+ " batch_reviews, batch_ratings = zip(*batch_review_ratings)\n",
+ " \n",
+ " # Tokenize reviews and generate inputs for DistilBERT\n",
+ " inputs = tokenizer(batch_reviews, return_tensors='pt', truncation=True, padding=True)\n",
+ " \n",
+ " # Generate embeddings for the current batch\n",
+ " with torch.no_grad():\n",
+ " outputs = model(**inputs)\n",
+ " \n",
+ " # Extract the last hidden state (embeddings) from DistilBERT outputs\n",
+ " batch_embeddings = outputs.last_hidden_state\n",
+ " \n",
+ " # Calculate average embeddings for the current batch\n",
+ " batch_average_embeddings = torch.mean(batch_embeddings, dim=1)\n",
+ " \n",
+ " # Append batch average embeddings to the list\n",
+ " all_average_embeddings.append(batch_average_embeddings)\n",
+ " \n",
+ " # Append batch ratings to the list\n",
+ " all_ratings.extend(batch_ratings)\n",
+ "\n",
+ "all_average_embeddings = torch.cat(all_average_embeddings, dim=0)\n",
+ "\n",
+ "embeddings_df = pd.DataFrame(all_average_embeddings.numpy())\n",
+ "embeddings_df['rating'] = all_ratings\n",
+ "\n",
+ "embeddings_df.to_csv('embeddings_and_ratings.csv', index=False)\n",
+ "\n",
+ "print(\"Saved\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "d63bc40a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Split embeddings into train and test 80/20\n",
+ "\n",
+ "df_embeddings = pd.read_csv('embeddings_and_ratings.csv')\n",
+ "\n",
+ "embeddings = df_embeddings.drop('rating', axis=1).values\n",
+ "ratings = df_embeddings['rating'].values\n",
+ "\n",
+ "# Convert ratings to tensor\n",
+ "y_tensor = torch.tensor(ratings, dtype=torch.float32)\n",
+ "\n",
+ "# Split into train and test\n",
+ "X_train, X_test, y_train, y_test = train_test_split(embeddings, y_tensor, test_size=0.2, random_state=42)\n",
+ "\n",
+ "# Convert data to PyTorch tensors\n",
+ "X_train_tensor = torch.tensor(X_train, dtype=torch.float32)\n",
+ "X_test_tensor = torch.tensor(X_test, dtype=torch.float32)\n",
+ "y_train_tensor = y_train\n",
+ "y_test_tensor = y_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "42168ebe",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'X_train' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[1], line 7\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m classification_report\n\u001b[0;32m 5\u001b[0m dc_classifier \u001b[38;5;241m=\u001b[39m DummyClassifier(strategy\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muniform\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 7\u001b[0m dc_classifier\u001b[38;5;241m.\u001b[39mfit(\u001b[43mX_train\u001b[49m, y_train)\n\u001b[0;32m 9\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m dc_classifier\u001b[38;5;241m.\u001b[39mpredict(X_test)\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(classification_report(y_test, y_pred))\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'X_train' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "# Baseline with Dummy Classifier\n",
+ "\n",
+ "from sklearn.dummy import DummyClassifier\n",
+ "from sklearn.metrics import classification_report\n",
+ "dc_classifier = DummyClassifier(strategy=\"uniform\")\n",
+ "\n",
+ "dc_classifier.fit(X_train, y_train)\n",
+ "\n",
+ "y_pred = dc_classifier.predict(X_test)\n",
+ "print(classification_report(y_test, y_pred))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a9cbf488",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# SVM on embeddings\n",
+ "\n",
+ "svm_classifier = SVC(kernel='linear')\n",
+ "svm_classifier.fit(X_train, y_train)\n",
+ "\n",
+ "y_pred = svm_classifier.predict(X_test)\n",
+ "\n",
+ "print(classification_report(y_test, y_pred))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "a18be5b7",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'LogisticRegression' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[2], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Logistic regression on embeddings\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m logistic_reg_classifier \u001b[38;5;241m=\u001b[39m \u001b[43mLogisticRegression\u001b[49m(max_iter\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m)\n\u001b[0;32m 4\u001b[0m logistic_reg_classifier\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[0;32m 6\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m logistic_reg_classifier\u001b[38;5;241m.\u001b[39mpredict(X_test)\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'LogisticRegression' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "# Logistic regression on embeddings\n",
+ "\n",
+ "logistic_reg_classifier = LogisticRegression(max_iter=1000)\n",
+ "logistic_reg_classifier.fit(X_train, y_train)\n",
+ "\n",
+ "y_pred = logistic_reg_classifier.predict(X_test)\n",
+ "\n",
+ "# Classification report\n",
+ "print(classification_report(y_test, y_pred))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "7d057ff7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch [1/10], Loss: 0.5344\n",
+ "Epoch [2/10], Loss: 0.3374\n",
+ "Epoch [3/10], Loss: 0.2444\n",
+ "Epoch [4/10], Loss: 0.2377\n",
+ "Epoch [5/10], Loss: 0.2513\n",
+ "Epoch [6/10], Loss: 0.2431\n",
+ "Epoch [7/10], Loss: 0.2191\n",
+ "Epoch [8/10], Loss: 0.1956\n",
+ "Epoch [9/10], Loss: 0.1812\n",
+ "Epoch [10/10], Loss: 0.1759\n",
+ "Mean Squared Error: 0.18214474618434906\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Define neural network model\n",
+ "class NeuralNetwork(nn.Module):\n",
+ " def __init__(self, input_size, hidden_size, output_size):\n",
+ " super(NeuralNetwork, self).__init__()\n",
+ " self.fc1 = nn.Linear(input_size, hidden_size)\n",
+ " self.fc2 = nn.Linear(hidden_size, output_size)\n",
+ " self.relu = nn.ReLU()\n",
+ "\n",
+ " def forward(self, x):\n",
+ " x = self.fc1(x)\n",
+ " x = self.relu(x)\n",
+ " x = self.fc2(x)\n",
+ " return x\n",
+ "\n",
+ "# Initialize model, loss function, and optimizer\n",
+ "input_size = X_train.shape[1]\n",
+ "hidden_size = 64\n",
+ "output_size = 1\n",
+ "model = NeuralNetwork(input_size, hidden_size, output_size)\n",
+ "criterion = nn.MSELoss()\n",
+ "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
+ "\n",
+ "# Train the model\n",
+ "num_epochs = 10\n",
+ "for epoch in range(num_epochs):\n",
+ " model.train()\n",
+ " optimizer.zero_grad()\n",
+ " outputs = model(X_train_tensor)\n",
+ " loss = criterion(outputs.squeeze(), y_train_tensor)\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}\")\n",
+ "\n",
+ "# Evaluate the model\n",
+ "model.eval()\n",
+ "with torch.no_grad():\n",
+ " y_pred = model(X_test_tensor)\n",
+ " mse = criterion(y_pred.squeeze(), y_test_tensor)\n",
+ " print(\"Mean Squared Error:\", mse.item())\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "c70e1b63",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Predictions vs Actual Ratings:\n",
+ "Prediction: 0.86, Actual: 1.00\n",
+ "Prediction: 0.15, Actual: 0.00\n",
+ "Prediction: 0.19, Actual: 0.00\n",
+ "Prediction: 0.19, Actual: 0.00\n",
+ "Prediction: 0.88, Actual: 1.00\n",
+ "Prediction: 0.55, Actual: 1.00\n",
+ "Prediction: 0.18, Actual: 0.00\n",
+ "Prediction: 0.41, Actual: 0.00\n",
+ "Prediction: 0.23, Actual: 0.00\n",
+ "Prediction: 0.08, Actual: 0.00\n",
+ "Prediction: 0.36, Actual: 0.00\n",
+ "Prediction: 0.39, Actual: 1.00\n",
+ "Prediction: 0.28, Actual: 0.00\n",
+ "Prediction: 0.75, Actual: 1.00\n",
+ "Prediction: 0.12, Actual: 0.00\n",
+ "Prediction: 0.40, Actual: 1.00\n",
+ "Prediction: 0.09, Actual: 0.00\n",
+ "Prediction: 0.69, Actual: 0.00\n",
+ "Prediction: 0.26, Actual: 0.00\n",
+ "Prediction: 0.25, Actual: 1.00\n",
+ "Prediction: 0.45, Actual: 1.00\n",
+ "Prediction: 0.31, Actual: 0.00\n",
+ "Prediction: 0.23, Actual: 1.00\n",
+ "Prediction: 1.01, Actual: 1.00\n",
+ "Prediction: 0.35, Actual: 1.00\n",
+ "Prediction: 0.80, Actual: 1.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 0.30, Actual: 0.00\n",
+ "Prediction: 0.86, Actual: 1.00\n",
+ "Prediction: 0.25, Actual: 0.00\n",
+ "Prediction: 0.19, Actual: 0.00\n",
+ "Prediction: 0.48, Actual: 1.00\n",
+ "Prediction: 0.13, Actual: 0.00\n",
+ "Prediction: 0.55, Actual: 0.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.46, Actual: 1.00\n",
+ "Prediction: 0.34, Actual: 0.00\n",
+ "Prediction: 0.18, Actual: 0.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.20, Actual: 0.00\n",
+ "Prediction: 0.86, Actual: 1.00\n",
+ "Prediction: 0.78, Actual: 1.00\n",
+ "Prediction: 0.15, Actual: 0.00\n",
+ "Prediction: 0.36, Actual: 0.00\n",
+ "Prediction: 0.15, Actual: 0.00\n",
+ "Prediction: 0.10, Actual: 0.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.86, Actual: 1.00\n",
+ "Prediction: 0.31, Actual: 0.00\n",
+ "Prediction: 0.81, Actual: 1.00\n",
+ "Prediction: 0.29, Actual: 0.00\n",
+ "Prediction: 0.36, Actual: 0.00\n",
+ "Prediction: 0.13, Actual: 0.00\n",
+ "Prediction: 0.41, Actual: 1.00\n",
+ "Prediction: 0.26, Actual: 0.00\n",
+ "Prediction: 0.19, Actual: 0.00\n",
+ "Prediction: 0.12, Actual: 0.00\n",
+ "Prediction: 0.15, Actual: 0.00\n",
+ "Prediction: 0.86, Actual: 1.00\n",
+ "Prediction: 0.86, Actual: 1.00\n",
+ "Prediction: 0.21, Actual: 1.00\n",
+ "Prediction: 0.39, Actual: 0.00\n",
+ "Prediction: 0.26, Actual: 0.00\n",
+ "Prediction: 0.15, Actual: 0.00\n",
+ "Prediction: 0.38, Actual: 1.00\n",
+ "Prediction: 0.11, Actual: 0.00\n",
+ "Prediction: 0.31, Actual: 1.00\n",
+ "Prediction: 0.34, Actual: 0.00\n",
+ "Prediction: 0.88, Actual: 1.00\n",
+ "Prediction: 0.87, Actual: 1.00\n",
+ "Prediction: 0.73, Actual: 0.00\n",
+ "Prediction: 0.31, Actual: 1.00\n",
+ "Prediction: 0.39, Actual: 0.00\n",
+ "Prediction: 0.46, Actual: 0.00\n",
+ "Prediction: 0.20, Actual: 0.00\n",
+ "Prediction: 0.41, Actual: 0.00\n",
+ "Prediction: 0.70, Actual: 1.00\n",
+ "Prediction: 0.96, Actual: 1.00\n",
+ "Prediction: 0.38, Actual: 0.00\n",
+ "Prediction: 0.72, Actual: 1.00\n",
+ "Prediction: 0.34, Actual: 0.00\n",
+ "Prediction: 0.11, Actual: 0.00\n",
+ "Prediction: 0.28, Actual: 0.00\n",
+ "Prediction: 0.52, Actual: 0.00\n",
+ "Prediction: 0.94, Actual: 1.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 0.26, Actual: 1.00\n",
+ "Prediction: 0.23, Actual: 0.00\n",
+ "Prediction: 0.25, Actual: 0.00\n",
+ "Prediction: 0.86, Actual: 1.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 0.75, Actual: 0.00\n",
+ "Prediction: 0.16, Actual: 0.00\n",
+ "Prediction: 0.34, Actual: 1.00\n",
+ "Prediction: 0.14, Actual: 0.00\n",
+ "Prediction: 0.42, Actual: 1.00\n",
+ "Prediction: 0.34, Actual: 0.00\n",
+ "Prediction: 0.45, Actual: 1.00\n",
+ "Prediction: 0.26, Actual: 0.00\n",
+ "Prediction: 0.64, Actual: 1.00\n",
+ "Prediction: 0.52, Actual: 1.00\n",
+ "Prediction: 0.18, Actual: 1.00\n",
+ "Prediction: 0.41, Actual: 0.00\n",
+ "Prediction: 0.37, Actual: 1.00\n",
+ "Prediction: 0.33, Actual: 0.00\n",
+ "Prediction: 0.13, Actual: 1.00\n",
+ "Prediction: 0.32, Actual: 0.00\n",
+ "Prediction: 0.16, Actual: 0.00\n",
+ "Prediction: 0.76, Actual: 0.00\n",
+ "Prediction: 0.18, Actual: 0.00\n",
+ "Prediction: 0.32, Actual: 1.00\n",
+ "Prediction: 0.08, Actual: 0.00\n",
+ "Prediction: 0.30, Actual: 1.00\n",
+ "Prediction: 0.68, Actual: 1.00\n",
+ "Prediction: 0.12, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.36, Actual: 0.00\n",
+ "Prediction: 0.09, Actual: 0.00\n",
+ "Prediction: 0.79, Actual: 0.00\n",
+ "Prediction: 0.44, Actual: 0.00\n",
+ "Prediction: 0.79, Actual: 0.00\n",
+ "Prediction: 0.23, Actual: 0.00\n",
+ "Prediction: 0.31, Actual: 1.00\n",
+ "Prediction: 0.12, Actual: 0.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.15, Actual: 0.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.19, Actual: 0.00\n",
+ "Prediction: 0.37, Actual: 1.00\n",
+ "Prediction: 0.73, Actual: 0.00\n",
+ "Prediction: 0.79, Actual: 1.00\n",
+ "Prediction: 0.49, Actual: 1.00\n",
+ "Prediction: 0.25, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.25, Actual: 0.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 0.60, Actual: 1.00\n",
+ "Prediction: 0.20, Actual: 0.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.26, Actual: 1.00\n",
+ "Prediction: 0.21, Actual: 1.00\n",
+ "Prediction: 0.43, Actual: 1.00\n",
+ "Prediction: 0.50, Actual: 0.00\n",
+ "Prediction: 0.48, Actual: 1.00\n",
+ "Prediction: 0.24, Actual: 0.00\n",
+ "Prediction: 0.81, Actual: 1.00\n",
+ "Prediction: 0.73, Actual: 0.00\n",
+ "Prediction: 0.54, Actual: 1.00\n",
+ "Prediction: 0.28, Actual: 0.00\n",
+ "Prediction: 0.73, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.34, Actual: 0.00\n",
+ "Prediction: 0.63, Actual: 0.00\n",
+ "Prediction: 0.28, Actual: 0.00\n",
+ "Prediction: 0.79, Actual: 0.00\n",
+ "Prediction: 0.42, Actual: 0.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 1.03, Actual: 1.00\n",
+ "Prediction: 0.33, Actual: 1.00\n",
+ "Prediction: 0.17, Actual: 0.00\n",
+ "Prediction: 0.29, Actual: 0.00\n",
+ "Prediction: 0.23, Actual: 0.00\n",
+ "Prediction: 0.79, Actual: 0.00\n",
+ "Prediction: 0.78, Actual: 1.00\n",
+ "Prediction: 0.17, Actual: 0.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 0.28, Actual: 0.00\n",
+ "Prediction: 0.54, Actual: 1.00\n",
+ "Prediction: 0.66, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.21, Actual: 1.00\n",
+ "Prediction: 0.31, Actual: 0.00\n",
+ "Prediction: 0.47, Actual: 0.00\n",
+ "Prediction: 0.39, Actual: 0.00\n",
+ "Prediction: 0.36, Actual: 1.00\n",
+ "Prediction: 0.23, Actual: 0.00\n",
+ "Prediction: 0.35, Actual: 1.00\n",
+ "Prediction: 0.09, Actual: 0.00\n",
+ "Prediction: 0.25, Actual: 0.00\n",
+ "Prediction: 0.31, Actual: 0.00\n",
+ "Prediction: 0.36, Actual: 1.00\n",
+ "Prediction: 0.36, Actual: 0.00\n",
+ "Prediction: 0.27, Actual: 0.00\n",
+ "Prediction: 0.26, Actual: 0.00\n",
+ "Prediction: 0.20, Actual: 0.00\n",
+ "Prediction: 0.69, Actual: 1.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 0.23, Actual: 0.00\n",
+ "Prediction: 0.49, Actual: 1.00\n",
+ "Prediction: 0.38, Actual: 0.00\n",
+ "Prediction: 0.36, Actual: 1.00\n",
+ "Prediction: 0.75, Actual: 1.00\n",
+ "Prediction: 0.57, Actual: 1.00\n",
+ "Prediction: 0.16, Actual: 0.00\n",
+ "Prediction: 0.15, Actual: 0.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 0.23, Actual: 1.00\n",
+ "Prediction: 0.59, Actual: 1.00\n",
+ "Prediction: 0.37, Actual: 1.00\n",
+ "Prediction: 0.73, Actual: 0.00\n",
+ "Prediction: 0.54, Actual: 1.00\n",
+ "Prediction: 0.21, Actual: 0.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.13, Actual: 0.00\n",
+ "Prediction: 0.21, Actual: 0.00\n",
+ "Prediction: 0.09, Actual: 1.00\n",
+ "Prediction: 0.59, Actual: 1.00\n",
+ "Prediction: 0.86, Actual: 1.00\n",
+ "Prediction: 0.54, Actual: 1.00\n",
+ "Prediction: 0.20, Actual: 0.00\n",
+ "Prediction: 0.33, Actual: 1.00\n",
+ "Prediction: 0.59, Actual: 1.00\n",
+ "Prediction: 0.24, Actual: 0.00\n",
+ "Prediction: 0.34, Actual: 1.00\n",
+ "Prediction: 0.21, Actual: 0.00\n",
+ "Prediction: 0.35, Actual: 0.00\n",
+ "Prediction: 0.62, Actual: 1.00\n",
+ "Prediction: 0.35, Actual: 0.00\n",
+ "Prediction: 0.11, Actual: 0.00\n",
+ "Prediction: 0.23, Actual: 0.00\n",
+ "Prediction: 0.21, Actual: 0.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.28, Actual: 0.00\n",
+ "Prediction: 0.21, Actual: 0.00\n",
+ "Prediction: 0.14, Actual: 0.00\n",
+ "Prediction: 0.19, Actual: 0.00\n",
+ "Prediction: 0.38, Actual: 1.00\n",
+ "Prediction: 0.18, Actual: 0.00\n",
+ "Prediction: 0.53, Actual: 1.00\n",
+ "Prediction: 0.21, Actual: 1.00\n",
+ "Prediction: 0.80, Actual: 1.00\n",
+ "Prediction: 0.15, Actual: 0.00\n",
+ "Prediction: 0.34, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.15, Actual: 0.00\n",
+ "Prediction: 0.30, Actual: 1.00\n",
+ "Prediction: 0.09, Actual: 0.00\n",
+ "Prediction: 0.19, Actual: 0.00\n",
+ "Prediction: 0.11, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.34, Actual: 0.00\n",
+ "Prediction: 0.36, Actual: 0.00\n",
+ "Prediction: 0.26, Actual: 0.00\n",
+ "Prediction: 0.35, Actual: 0.00\n",
+ "Prediction: 0.28, Actual: 0.00\n",
+ "Prediction: 0.21, Actual: 0.00\n",
+ "Prediction: 0.30, Actual: 0.00\n",
+ "Prediction: 0.90, Actual: 1.00\n",
+ "Prediction: 0.39, Actual: 1.00\n",
+ "Prediction: 0.33, Actual: 1.00\n",
+ "Prediction: 0.66, Actual: 1.00\n",
+ "Prediction: 0.38, Actual: 1.00\n",
+ "Prediction: 0.50, Actual: 1.00\n",
+ "Prediction: 0.33, Actual: 1.00\n",
+ "Prediction: 0.80, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.18, Actual: 1.00\n",
+ "Prediction: 0.67, Actual: 1.00\n",
+ "Prediction: 0.25, Actual: 1.00\n",
+ "Prediction: 0.34, Actual: 0.00\n",
+ "Prediction: 0.30, Actual: 1.00\n",
+ "Prediction: 0.57, Actual: 1.00\n",
+ "Prediction: 0.29, Actual: 0.00\n",
+ "Prediction: 0.69, Actual: 1.00\n",
+ "Prediction: 0.82, Actual: 1.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 0.31, Actual: 1.00\n",
+ "Prediction: 0.53, Actual: 1.00\n",
+ "Prediction: 0.61, Actual: 1.00\n",
+ "Prediction: 0.45, Actual: 1.00\n",
+ "Prediction: 0.09, Actual: 0.00\n",
+ "Prediction: 0.20, Actual: 0.00\n",
+ "Prediction: 0.30, Actual: 0.00\n",
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+ "Prediction: 0.18, Actual: 1.00\n",
+ "Prediction: 0.60, Actual: 1.00\n",
+ "Prediction: 0.79, Actual: 0.00\n",
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+ "Prediction: 0.86, Actual: 1.00\n",
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+ "Prediction: 0.34, Actual: 0.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
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+ "Prediction: 0.86, Actual: 0.00\n",
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+ "Prediction: 0.75, Actual: 1.00\n",
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+ "Prediction: 0.41, Actual: 0.00\n",
+ "Prediction: 0.07, Actual: 0.00\n",
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+ "Prediction: 0.15, Actual: 0.00\n",
+ "Prediction: 0.86, Actual: 1.00\n",
+ "Prediction: 0.86, Actual: 1.00\n",
+ "Prediction: 0.27, Actual: 0.00\n",
+ "Prediction: 0.16, Actual: 0.00\n",
+ "Prediction: 0.14, Actual: 0.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.56, Actual: 1.00\n",
+ "Prediction: 0.24, Actual: 0.00\n",
+ "Prediction: 0.33, Actual: 1.00\n",
+ "Prediction: 0.62, Actual: 1.00\n",
+ "Prediction: 0.27, Actual: 1.00\n",
+ "Prediction: 0.33, Actual: 1.00\n",
+ "Prediction: 0.23, Actual: 0.00\n",
+ "Prediction: 0.73, Actual: 1.00\n",
+ "Prediction: 0.39, Actual: 0.00\n",
+ "Prediction: 0.53, Actual: 1.00\n",
+ "Prediction: 0.34, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.47, Actual: 1.00\n",
+ "Prediction: 0.33, Actual: 1.00\n",
+ "Prediction: 0.23, Actual: 0.00\n",
+ "Prediction: 0.28, Actual: 0.00\n",
+ "Prediction: 0.97, Actual: 1.00\n",
+ "Prediction: 0.29, Actual: 0.00\n",
+ "Prediction: 0.29, Actual: 1.00\n",
+ "Prediction: 0.59, Actual: 1.00\n",
+ "Prediction: 0.63, Actual: 1.00\n",
+ "Prediction: 0.22, Actual: 0.00\n",
+ "Prediction: 0.55, Actual: 1.00\n",
+ "Prediction: 1.10, Actual: 1.00\n",
+ "Prediction: 0.34, Actual: 1.00\n",
+ "Prediction: 0.52, Actual: 0.00\n",
+ "Prediction: 0.65, Actual: 1.00\n",
+ "Prediction: 0.66, Actual: 1.00\n"
+ ]
+ }
+ ],
+ "source": [
+ "y_pred_np = y_pred.squeeze().numpy() # Ensure predictions are numpy arrays\n",
+ "y_test_np = y_test_tensor.numpy() # Ensure actual ratings are numpy arrays\n",
+ "\n",
+ "# Print predictions vs actual ratings\n",
+ "print(\"Predictions vs Actual Ratings:\")\n",
+ "for i in range(len(y_pred_np)):\n",
+ " print(f\"Prediction: {y_pred_np[i]:.2f}, Actual: {y_test_np[i]:.2f}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "7bb0055e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Precision for Ratings 0 and 1: [0.65592972 0.82223618]\n",
+ "Recall for Ratings 0 and 1: [0.88783195 0.52697262]\n",
+ "F1-score for Ratings 0 and 1: [0.75446278 0.64229637]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import precision_recall_fscore_support\n",
+ "\n",
+ "# Define a function to calculate precision, recall, and F1-score\n",
+ "def calculate_metrics(y_true, y_pred):\n",
+ " # Convert predictions and actual ratings to numpy arrays\n",
+ " y_true_np = y_true.numpy()\n",
+ " y_pred_np = y_pred.squeeze().detach().numpy()\n",
+ "\n",
+ " # Round predictions to 0 or 1\n",
+ " y_pred_rounded = (y_pred_np > 0.5).astype(int)\n",
+ "\n",
+ " # Calculate precision, recall, and F1-score for ratings 0 and 1\n",
+ " precision, recall, f1, _ = precision_recall_fscore_support(y_true_np, y_pred_rounded, labels=[0, 1], average=None)\n",
+ "\n",
+ " return precision, recall, f1\n",
+ "\n",
+ "# Calculate precision, recall, and F1-score for ratings 0 and 1\n",
+ "precision_01, recall_01, f1_01 = calculate_metrics(y_test_tensor, y_pred)\n",
+ "\n",
+ "# Print the results\n",
+ "print(\"Precision for Ratings 0 and 1:\", precision_01)\n",
+ "print(\"Recall for Ratings 0 and 1:\", recall_01)\n",
+ "print(\"F1-score for Ratings 0 and 1:\", f1_01)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "liu-text-mining",
+ "language": "python",
+ "name": "liu-text-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}