04_classification_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda install gensim\n",
    "!conda install fasttext"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fasttext效果测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-03-20 10:56:41--  https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz\n",
      "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 104.22.75.142, 172.67.9.4, 104.22.74.142\n",
      "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|104.22.75.142|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4503593528 (4.2G) [application/octet-stream]\n",
      "Saving to: ‘cc.en.300.bin.gz’\n",
      "\n",
      "cc.en.300.bin.gz     22%[===>                ] 978.62M  2.72MB/s    in 6m 50s  \n",
      "\n",
      "2023-03-20 11:03:33 (2.39 MB/s) - Read error at byte 1026161458/4503593528 (error:1408F119:SSL routines:ssl3_get_record:decryption failed or bad record mac). Retrying.\n",
      "\n",
      "--2023-03-20 11:03:34--  (try: 2)  https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz\n",
      "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|104.22.75.142|:443... connected.\n",
      "HTTP request sent, awaiting response... 206 Partial Content\n",
      "Length: 4503593528 (4.2G), 3477432070 (3.2G) remaining [application/octet-stream]\n",
      "Saving to: ‘cc.en.300.bin.gz’\n",
      "\n",
      "cc.en.300.bin.gz     56%[++++======>         ]   2.37G  5.96MB/s    in 8m 17s  \n",
      "\n",
      "2023-03-20 11:11:52 (2.92 MB/s) - Read error at byte 2547284735/4503593528 (error:1408F119:SSL routines:ssl3_get_record:decryption failed or bad record mac). Retrying.\n",
      "\n",
      "--2023-03-20 11:11:54--  (try: 3)  https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz\n",
      "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|104.22.75.142|:443... connected.\n",
      "HTTP request sent, awaiting response... 206 Partial Content\n",
      "Length: 4503593528 (4.2G), 1956308793 (1.8G) remaining [application/octet-stream]\n",
      "Saving to: ‘cc.en.300.bin.gz’\n",
      "\n",
      "cc.en.300.bin.gz     80%[+++++++++++====>    ]   3.37G  7.67MB/s    in 2m 41s  \n",
      "\n",
      "2023-03-20 11:14:35 (6.32 MB/s) - Read error at byte 3614428013/4503593528 (error:1408F119:SSL routines:ssl3_get_record:decryption failed or bad record mac). Retrying.\n",
      "\n",
      "--2023-03-20 11:14:38--  (try: 4)  https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz\n",
      "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|104.22.75.142|:443... connected.\n",
      "HTTP request sent, awaiting response... 206 Partial Content\n",
      "Length: 4503593528 (4.2G), 889165515 (848M) remaining [application/octet-stream]\n",
      "Saving to: ‘cc.en.300.bin.gz’\n",
      "\n",
      "cc.en.300.bin.gz    100%[++++++++++++++++===>]   4.19G  6.65MB/s    in 2m 7s   \n",
      "\n",
      "2023-03-20 11:16:46 (6.68 MB/s) - ‘cc.en.300.bin.gz’ saved [4503593528/4503593528]\n",
      "\n",
      "--2023-03-20 11:16:46--  http://./\n",
      "Resolving . (.)... 123.151.76.67, 61.151.174.112, 14.18.178.36, ...\n",
      "Connecting to . (.)|123.151.76.67|:80... failed: Operation timed out.\n",
      "Connecting to . (.)|61.151.174.112|:80... failed: Operation timed out.\n",
      "Connecting to . (.)|14.18.178.36|:80... ^C\n"
     ]
    }
   ],
   "source": [
    "!wget https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "!gunzip ./cc.en.300.bin.gz "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "import numpy as np\n",
    "# Load the FastText pre-trained model\n",
    "model = gensim.models.fasttext.load_facebook_model('cc.en.300.bin')\n",
    "\n",
    "def get_fasttext_vector(line):\n",
    "    vec = np.zeros(300) # Initialize an empty 300-dimensional vector\n",
    "    for word in line.split():\n",
    "        vec += model.wv[word]\n",
    "    vec /= len(line.split()) # Take the average over all words in the line\n",
    "    return vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_text = \"\"\"Wanted to save some to bring to my Chicago family but my North Carolina family ate all 4 boxes before I could pack. These are excellent...could serve to anyone\"\"\"\n",
    "negative_text = \"\"\"First, these should be called Mac - Coconut bars, as Coconut is the #2 ingredient and Mango is #3.  Second, lots of people don't like coconut.  I happen to be allergic to it.  Word to Amazon that if you want happy customers to make things like this more prominent.  Thanks.\"\"\"\n",
    "\n",
    "positive_example_in_fasttext = get_fasttext_vector(positive_text)\n",
    "negative_example_in_fasttext = get_fasttext_vector(negative_text)\n",
    "\n",
    "positive_review_in_fasttext = get_fasttext_vector(\"An Amazon review with a positive sentiment.\")\n",
    "negative_review_in_fasttext = get_fasttext_vector('An Amazon review with a negative sentiment.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fasttext好评例子的评分 : -0.000544\n",
      "Fasttext差评例子的评分 : 0.000369\n"
     ]
    }
   ],
   "source": [
    "from openai.embeddings_utils import cosine_similarity\n",
    "\n",
    "def get_fasttext_score(sample_embedding):\n",
    "  return cosine_similarity(sample_embedding, positive_review_in_fasttext) - cosine_similarity(sample_embedding, negative_review_in_fasttext)\n",
    "\n",
    "positive_score = get_fasttext_score(positive_example_in_fasttext)\n",
    "negative_score = get_fasttext_score(negative_example_in_fasttext)\n",
    "\n",
    "print(\"Fasttext好评例子的评分 : %f\" % (positive_score))\n",
    "print(\"Fasttext差评例子的评分 : %f\" % (negative_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T5效果测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install transformers if required\n",
    "#!conda install -y transformers -c conda-forge\n",
    "#!conda install -y pytorch torchvision -c pytorch\n",
    "#!conda install -y sentencepiece"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModel\n",
    "import torch\n",
    "\n",
    "# load the T5 tokenizer and model\n",
    "tokenizer = AutoTokenizer.from_pretrained('t5-small', model_max_length=512)\n",
    "model = AutoModel.from_pretrained('t5-small')\n",
    "\n",
    "# set the model to evaluation mode\n",
    "model.eval()\n",
    "\n",
    "# encode the input sentence\n",
    "def get_t5_vector(line):\n",
    "    input_ids = tokenizer.encode(line, return_tensors='pt', max_length=512, truncation=True)\n",
    "    # generate the vector representation\n",
    "    with torch.no_grad():\n",
    "        outputs = model.encoder(input_ids=input_ids)\n",
    "        vector = outputs.last_hidden_state.mean(dim=1)\n",
    "    return vector[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T5好评例子的评分 : -0.010294\n",
      "T5差评例子的评分 : -0.008990\n"
     ]
    }
   ],
   "source": [
    "positive_review_in_t5 = get_t5_vector(\"An Amazon review with a positive sentiment.\")\n",
    "negative_review_in_t5 = get_t5_vector('An Amazon review with a negative sentiment.')\n",
    "\n",
    "def test_t5():\n",
    "  positive_example_in_t5 = get_t5_vector(positive_text)\n",
    "  negative_example_in_t5 = get_t5_vector(negative_text)\n",
    "\n",
    "  def get_t5_score(sample_embedding):\n",
    "    return cosine_similarity(sample_embedding, positive_review_in_t5) - cosine_similarity(sample_embedding, negative_review_in_t5)\n",
    "\n",
    "  positive_score = get_t5_score(positive_example_in_t5)\n",
    "  negative_score = get_t5_score(negative_example_in_t5)\n",
    "\n",
    "  print(\"T5好评例子的评分 : %f\" % (positive_score))\n",
    "  print(\"T5差评例子的评分 : %f\" % (negative_score))\n",
    "\n",
    "test_t5()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
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       "model_id": "4cc8a70db87547678546dd9b433cf7fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T5好评例子的评分 : 0.010347\n",
      "T5差评例子的评分 : -0.023935\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained('t5-base', model_max_length=512)\n",
    "model = AutoModel.from_pretrained('t5-base')\n",
    "\n",
    "# set the model to evaluation mode\n",
    "model.eval()\n",
    "\n",
    "# encode the input sentence\n",
    "def get_t5_vector(line):\n",
    "    input_ids = tokenizer.encode(line, return_tensors='pt', max_length=512, truncation=True)\n",
    "    # generate the vector representation\n",
    "    with torch.no_grad():\n",
    "        outputs = model.encoder(input_ids=input_ids)\n",
    "        vector = outputs.last_hidden_state.mean(dim=1)\n",
    "    return vector[0]\n",
    "\n",
    "positive_review_in_t5 = get_t5_vector(\"An Amazon review with a positive sentiment.\")\n",
    "negative_review_in_t5 = get_t5_vector('An Amazon review with a negative sentiment.')\n",
    "\n",
    "test_t5()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "datafile_path = \"data/fine_food_reviews_with_embeddings_1k.csv\"\n",
    "\n",
    "df = pd.read_csv(datafile_path)\n",
    "\n",
    "\n",
    "df[\"t5_embedding\"] = df.Text.apply(get_t5_vector)\n",
    "# convert 5-star rating to binary sentiment\n",
    "df = df[df.Score != 3]\n",
    "df[\"sentiment\"] = df.Score.replace({1: \"negative\", 2: \"negative\", 4: \"positive\", 5: \"positive\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.60      0.90      0.72       136\n",
      "    positive       0.98      0.90      0.94       789\n",
      "\n",
      "    accuracy                           0.90       925\n",
      "   macro avg       0.79      0.90      0.83       925\n",
      "weighted avg       0.93      0.90      0.91       925\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import PrecisionRecallDisplay\n",
    "\n",
    "def evaluate_embeddings_approach():\n",
    "    def label_score(review_embedding):\n",
    "        return cosine_similarity(review_embedding, positive_review_in_t5) - cosine_similarity(review_embedding, negative_review_in_t5)\n",
    "\n",
    "    probas = df[\"t5_embedding\"].apply(lambda x: label_score(x))\n",
    "    preds = probas.apply(lambda x: 'positive' if x>0 else 'negative')\n",
    "\n",
    "    report = classification_report(df.sentiment, preds)\n",
    "    print(report)\n",
    "\n",
    "    display = PrecisionRecallDisplay.from_predictions(df.sentiment, probas, pos_label='positive')\n",
    "    _ = display.ax_.set_title(\"2-class Precision-Recall curve\")\n",
    "\n",
    "evaluate_embeddings_approach()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "8114e84f04cf14e493992e1b725447accf84073d5ec18e7063d492738bf032cb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}