Llama2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LLAMA 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Bottleneck unit testing available.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-28 11:20:30.769591: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-01-28 11:20:30.769634: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-01-28 11:20:30.770362: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-01-28 11:20:30.774961: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-01-28 11:20:31.576627: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import bitsandbytes as bnb\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "import transformers\n",
    "from datasets import Dataset\n",
    "from peft import LoraConfig, PeftConfig\n",
    "from trl import SFTTrainer\n",
    "from transformers import (AutoModelForCausalLM, \n",
    "                          AutoTokenizer, \n",
    "                          BitsAndBytesConfig, \n",
    "                          TrainingArguments, \n",
    "                          pipeline, \n",
    "                          logging)\n",
    "from sklearn.metrics import (accuracy_score, \n",
    "                             classification_report, \n",
    "                             confusion_matrix, \n",
    "                             ConfusionMatrixDisplay)\n",
    "from huggingface_hub import notebook_login\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorboard import notebook\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prompts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_prompt(data_point):\n",
    "    return f\"\"\"Analyze the sentiment of the sentence enclosed in square brackets, determine if it is happiness or fear or anger or sadness or disgust or shame or guilt or surprise, and return the answer by one word as the corresponding sentiment label 'happiness' or 'fear' or 'anger' or 'sadness' or 'disgust' or 'shame' or'guilt' or 'surprise'.\n",
    "            [{data_point[\"preprocessed_text\"]}] = {data_point[\"emotion\"]}\"\"\".strip()\n",
    "\n",
    "def generate_test_prompt(data_point):\n",
    "    return f\"\"\"Analyze the sentiment of the sentence enclosed in square brackets, determine if it is happiness or fear or anger or sadness or disgust or shame or guilt or surprise, and return the answer by one word as the corresponding sentiment label 'happiness' or 'fear' or 'anger' or 'sadness' or 'disgust' or 'shame' or'guilt' or 'surprise'.\n",
    "            [{data_point[\"preprocessed_text\"]}] = \"\"\".strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(test, model, tokenizer):\n",
    "    y_pred = []\n",
    "    for i in tqdm(range(len(test))):\n",
    "        prompt = test.iloc[i][\"text\"]\n",
    "        pipe = pipeline(task=\"text-generation\", \n",
    "                        model=model, \n",
    "                        tokenizer=tokenizer, \n",
    "                        max_new_tokens = 10,\n",
    "                        temperature = 0.1, \n",
    "                        return_full_text=False\n",
    "                       )\n",
    "        result = pipe(prompt)\n",
    "        generated_text = result[0]['generated_text']\n",
    "\n",
    "        emotion = generated_text.split()[0]\n",
    "        answer = ''.join([x for x in emotion if x.isalnum()])\n",
    "\n",
    "        if \"happiness\" in answer:\n",
    "            y_pred.append(\"happiness\")\n",
    "        elif \"fear\" in answer:\n",
    "            y_pred.append(\"fear\")\n",
    "        elif \"anger\" in answer:\n",
    "            y_pred.append(\"anger\")\n",
    "        elif \"sadness\" in answer:\n",
    "            y_pred.append(\"sadness\")\n",
    "        elif \"disgust\" in answer:\n",
    "            y_pred.append(\"disgust\")\n",
    "        elif \"shame\" in answer:\n",
    "            y_pred.append(\"shame\")\n",
    "        elif \"guilt\" in answer:\n",
    "            y_pred.append(\"guilt\")\n",
    "        elif \"surprise\" in answer:\n",
    "            y_pred.append(\"surprise\")\n",
    "        else:\n",
    "            y_pred.append(\"none\")\n",
    "    return y_pred\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(y_true, y_pred):\n",
    "    labels = ['happiness', 'fear', 'anger', 'sadness', 'disgust', 'shame', 'guilt', 'surprise']\n",
    "    \n",
    "    mapping = {\n",
    "        'happiness': 0,\n",
    "        'fear': 1,\n",
    "        'anger': 2,\n",
    "        'sadness': 3,\n",
    "        'disgust': 4,\n",
    "        'shame': 5,\n",
    "        'guilt': 6,\n",
    "        'surprise': 7,\n",
    "    }\n",
    "\n",
    "    reverse_mapping = {v: k for k, v in mapping.items()}  # Reverse mapping for converting numbers to labels\n",
    "\n",
    "    def map_func(x):\n",
    "        return mapping.get(x, 1)\n",
    "\n",
    "    y_true = np.vectorize(map_func)(y_true)\n",
    "    y_pred = np.vectorize(map_func)(y_pred)\n",
    "\n",
    "    # Calculate accuracy\n",
    "    accuracy = accuracy_score(y_true=y_true, y_pred=y_pred)\n",
    "    print(f'Accuracy: {accuracy:.3f}')\n",
    "\n",
    "    # Generate accuracy report\n",
    "    unique_labels = set(y_true)  # Get unique labels\n",
    "\n",
    "    for label_num in unique_labels:\n",
    "        label = reverse_mapping[label_num]\n",
    "        label_indices = [i for i in range(len(y_true)) if y_true[i] == label_num]\n",
    "        label_y_true = [y_true[i] for i in label_indices]\n",
    "        label_y_pred = [y_pred[i] for i in label_indices]\n",
    "        accuracy = accuracy_score(label_y_true, label_y_pred)\n",
    "        print(f'Accuracy for {label}: {accuracy:.3f}')\n",
    "\n",
    "    # Generate classification report\n",
    "    class_report = classification_report(y_true=[reverse_mapping[label_num] for label_num in y_true],\n",
    "                                         y_pred=[reverse_mapping[label_num] for label_num in y_pred])\n",
    "    print('\\nClassification Report:')\n",
    "    print(class_report)\n",
    "\n",
    "    # Generate confusion matrix\n",
    "    conf_matrix = confusion_matrix(y_true=y_true, y_pred=y_pred, labels=[0, 1, 2, 3, 4, 5, 6, 7])\n",
    "    print('\\nConfusion Matrix:')\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=labels)\n",
    "    disp.plot(cmap=plt.cm.Blues)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('full_dataset/full_dataset.csv')\n",
    "data = df[['emotion', 'preprocessed_text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=42)\n",
    "\n",
    "for train_index, test_valid_index in split.split(data, data['emotion']):\n",
    "    train_set = data.iloc[train_index]\n",
    "    test_valid_set = data.iloc[test_valid_index]\n",
    "\n",
    "split2 = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=42)\n",
    "for test_index, valid_index in split2.split(test_valid_set, test_valid_set['emotion']):\n",
    "    test_set = test_valid_set.iloc[test_index]\n",
    "    valid_set = test_valid_set.iloc[valid_index]\n",
    "    \n",
    "X_train = train_set.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.DataFrame(X_train.apply(generate_prompt, axis=1),\n",
    "                       columns=[\"text\"])\n",
    "X_eval = pd.DataFrame(valid_set.apply(generate_prompt, axis=1),\n",
    "                      columns=[\"text\"])\n",
    "                      \n",
    "y_true = test_set.emotion\n",
    "X_test = pd.DataFrame(test_set.apply(generate_test_prompt, axis=1), columns=[\"text\"])\n",
    "\n",
    "train_data = Dataset.from_pandas(X_train)\n",
    "eval_data = Dataset.from_pandas(X_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9993c771f1be45f0aed90a113286db2a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70a5b7ad54b64d989b3bd68b73b3e46f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_name = \"meta-llama/Llama-2-7b-hf\"\n",
    "\n",
    "compute_dtype = getattr(torch, \"float16\")\n",
    "\n",
    "quant_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=compute_dtype,\n",
    "    bnb_4bit_use_double_quant=False,\n",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    quantization_config=quant_config,\n",
    "    device_map=\"auto\"\n",
    ")\n",
    "\n",
    "model.config.use_cache = False\n",
    "model.config.pretraining_tp = 1\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, \n",
    "                                         use_auth_token=True,\n",
    "                                         )\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1961/1961 [15:23<00:00,  2.12it/s]\n"
     ]
    }
   ],
   "source": [
    "y_pred = predict(X_test, model, tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.420\n",
      "Accuracy for happiness: 0.881\n",
      "Accuracy for fear: 0.455\n",
      "Accuracy for anger: 0.448\n",
      "Accuracy for sadness: 0.356\n",
      "Accuracy for disgust: 0.255\n",
      "Accuracy for shame: 0.173\n",
      "Accuracy for guilt: 0.221\n",
      "Accuracy for surprise: 0.326\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.45      0.45      0.45       310\n",
      "     disgust       0.77      0.26      0.38       231\n",
      "        fear       0.42      0.45      0.44       299\n",
      "       guilt       0.44      0.22      0.29       208\n",
      "   happiness       0.30      0.88      0.45       310\n",
      "     sadness       0.75      0.36      0.48       323\n",
      "       shame       0.59      0.17      0.27       237\n",
      "    surprise       0.56      0.33      0.41        43\n",
      "\n",
      "    accuracy                           0.42      1961\n",
      "   macro avg       0.53      0.39      0.40      1961\n",
      "weighted avg       0.53      0.42      0.41      1961\n",
      "\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "peft_config = LoraConfig(\n",
    "    lora_alpha=16,\n",
    "    lora_dropout=0.1,\n",
    "    r=64,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "\n",
    "training_arguments = TrainingArguments(\n",
    "    output_dir=\"./resultsLlama\",\n",
    "    num_train_epochs=10,\n",
    "    per_device_train_batch_size=1,\n",
    "    gradient_accumulation_steps=8, # 4\n",
    "    optim=\"paged_adamw_32bit\",\n",
    "    save_steps=0,\n",
    "    logging_steps=25,\n",
    "    learning_rate=2e-4,\n",
    "    weight_decay=0.001,\n",
    "    fp16=True,\n",
    "    bf16=False,\n",
    "    max_grad_norm=0.3,\n",
    "    max_steps=-1,\n",
    "    warmup_ratio=0.03,\n",
    "    group_by_length=True,\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    report_to=\"tensorboard\",\n",
    "    evaluation_strategy=\"epoch\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1166d317e64143069b46d434474d7c85",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/5883 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "039403553c1246a0878aa03ecd3f400b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1962 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=train_data,\n",
    "    eval_dataset=eval_data,\n",
    "    peft_config=peft_config,\n",
    "    dataset_text_field=\"text\",\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
    "    packing=False,\n",
    "    max_seq_length=1024,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='7350' max='7350' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [7350/7350 2:45:27, Epoch 9/10]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.700400</td>\n",
       "      <td>0.584463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.548300</td>\n",
       "      <td>0.577476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.521000</td>\n",
       "      <td>0.577360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.556700</td>\n",
       "      <td>0.588153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.393400</td>\n",
       "      <td>0.603914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.511300</td>\n",
       "      <td>0.633528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.348700</td>\n",
       "      <td>0.669908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.331300</td>\n",
       "      <td>0.716971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.255400</td>\n",
       "      <td>0.726110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train model\n",
    "trainer.train()\n",
    "\n",
    "# Save trained model\n",
    "trainer.model.save_pretrained(\"trained-model-llama\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                                                                    | 0/1961 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1961/1961 [19:29<00:00,  1.68it/s]\n"
     ]
    }
   ],
   "source": [
    "y_pred = predict(X_test, model, tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.768\n",
      "Accuracy for happiness: 0.887\n",
      "Accuracy for fear: 0.873\n",
      "Accuracy for anger: 0.774\n",
      "Accuracy for sadness: 0.836\n",
      "Accuracy for disgust: 0.684\n",
      "Accuracy for shame: 0.641\n",
      "Accuracy for guilt: 0.543\n",
      "Accuracy for surprise: 0.860\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.71      0.77      0.74       310\n",
      "     disgust       0.74      0.68      0.71       231\n",
      "        fear       0.84      0.87      0.86       299\n",
      "       guilt       0.60      0.54      0.57       208\n",
      "   happiness       0.86      0.89      0.88       310\n",
      "     sadness       0.80      0.84      0.82       323\n",
      "       shame       0.70      0.64      0.67       237\n",
      "    surprise       0.95      0.86      0.90        43\n",
      "\n",
      "    accuracy                           0.77      1961\n",
      "   macro avg       0.78      0.76      0.77      1961\n",
      "weighted avg       0.77      0.77      0.77      1961\n",
      "\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation = pd.DataFrame({'text': X_test[\"text\"], \n",
    "                           'y_true':y_true, \n",
    "                           'y_pred': y_pred},\n",
    "                         )\n",
    "evaluation.to_csv(\"test_predictions.csv\", index=False)"
   ]
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}