personality_standard_kaggle.ipynb

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      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lAyamUwUIAVj",
        "outputId": "cb5ccdf1-70cb-4a6a-a05e-992549ebb3a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install langchain==0.0.147\n",
        "!pip install tiktoken==0.3.2\n",
        "!pip install openai"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from langchain.schema import Document\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.llms import OpenAI\n",
        "from langchain.chains import LLMChain\n",
        "import tiktoken\n",
        "from langchain import ConversationChain\n",
        "from langchain.memory import ConversationBufferMemory\n",
        "import sys\n",
        "sys.path.append('drive/MyDrive/Colab Notebooks')\n",
        "import pickle\n",
        "import os\n",
        "os.environ[\"OPENAI_API_KEY\"]= \"YOUR API KEY\""
      ],
      "metadata": {
        "id": "mVIX1dSlIxS5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load File"
      ],
      "metadata": {
        "id": "BtUiUaEpYGzj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "task = '3'\n",
        "rev_label = False\n",
        "shuffle = False\n",
        "file_name = \"mbti_test_zero_standard_{}.csv\".format(task)\n",
        "\n",
        "with open('Kaggle/test.pkl', 'rb') as f:\n",
        "  data = pickle.load(f)\n",
        "def process_data(data):\n",
        "  poster = data['posts_text']\n",
        "  label = data['annotations']\n",
        "  label_lookup = {'E': 1, 'I': 0, 'S': 1, 'N':0, 'T': 1, 'F': 0, 'J': 1, 'P':0}\n",
        "  persona_lookup = {}\n",
        "  poster_data = [{'posts': t, 'label0': label_lookup[list(label[i])[0]],\n",
        "          'label1': label_lookup[list(label[i])[1]],'label2': label_lookup[list(label[i])[2]],\n",
        "          'label3': label_lookup[list(label[i])[3]]} for i,t in enumerate(poster)]\n",
        "  return poster_data\n",
        "poster_data = process_data(data)\n",
        "texts = [item['posts'] for item in poster_data]\n",
        "labels = [item['label{}'.format(task)] for item in poster_data]"
      ],
      "metadata": {
        "id": "qKmEGADmXPqn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "texts[0]"
      ],
      "metadata": {
        "id": "vM7VC-DeeAzE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define Prompt"
      ],
      "metadata": {
        "id": "vvpbBX-cYRVJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if task == '0':\n",
        "  if not rev_label:\n",
        "    trait_choice = 'A: \"Extraversion\" or B: \"Introversion\"'\n",
        "  else:\n",
        "    trait_choice = 'A: \"Introversion\" or B: \"Extraversion\"'\n",
        "elif task == '1':\n",
        "  if not rev_label:\n",
        "    trait_choice = 'A: \"Sensing\" or B: \"Intuition\"'\n",
        "  else:\n",
        "    trait_choice = 'A: \"Intuition\" or B: \"Sensing\"'\n",
        "elif task == '2':\n",
        "  if not rev_label:\n",
        "    trait_choice = 'A: \"Thinking\" or B: \"Feeling\"'\n",
        "  else:\n",
        "    trait_choice = 'A: \"Feeling\" or B: \"Thinking\"'\n",
        "elif task == '3':\n",
        "  if not rev_label:\n",
        "    trait_choice = 'A: \"Judging\" or B: \"Perceiving\"'\n",
        "  else:\n",
        "    trait_choice = 'A: \"Perceiving\" or B: \"Judging\"'\n",
        "\n",
        "def cteat_agent(trait_choice):\n",
        "  perfix_temp = 'You are an AI assistant who specializes in text analysis. You will complete a text analysis task. '\\\n",
        "          'The task is as follows: according to a set of posts written by an author, '\\\n",
        "          'predicting whether the author is {}.'.format(trait_choice)\n",
        "  Prompt_temp = perfix_temp + \\\n",
        "          '\\nAUTHOR\\'S POSTS: {context}\\n' + \\\n",
        "          'The author is {}. Provide a choice in the format: \"CHOICE: <A/B>\" and do not give the explanation.'.format(trait_choice)\n",
        "  prompt = PromptTemplate(\n",
        "      input_variables=[\"context\"],\n",
        "      template=Prompt_temp)\n",
        "  llm = OpenAI(temperature=0.0, model_name='gpt-3.5-turbo-0301')\n",
        "  agent = LLMChain(llm=llm, prompt=prompt, verbose=False)\n",
        "  return agent"
      ],
      "metadata": {
        "id": "4XHmcQa2YVLh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import shuffle\n",
        "\n",
        "def shuffle_post(posts_list):\n",
        "  shuffle_index = shuffle([i for i in range(len(posts_list))], random_state=0)\n",
        "  shuffle_post_list = [posts_list[j] for j in shuffle_index]\n",
        "  return shuffle_post_list\n",
        "\n",
        "if not os.path.exists(file_name):\n",
        "  rest_sample = 0\n",
        "  head = {\"text\": [], 'gold': [], 'answers': []}\n",
        "  data = pd.DataFrame(head)\n",
        "  data.to_csv(file_name, mode='w', index=False, header=True)\n",
        "else:\n",
        "  data = pd.read_csv(file_name)\n",
        "  rest_sample = len(list(data['gold']))\n",
        "print('Done: {}/{}'.format(rest_sample, len(texts)))\n",
        "tokenizer = tiktoken.get_encoding(\"cl100k_base\")\n",
        "agent = cteat_agent(trait_choice)\n",
        "for i in range(rest_sample, len(texts)):\n",
        "  print('Done: {}/{}'.format(i, len(texts)))\n",
        "  posts = ''\n",
        "  count = 1\n",
        "  if shuffle:\n",
        "    posts_list = shuffle_post(texts[i])\n",
        "  else:\n",
        "    posts_list = texts[i]\n",
        "  for j in range(len(posts_list)):\n",
        "    if len(posts_list[j])>10:\n",
        "      post = tokenizer.decode(tokenizer.encode(posts_list[j].replace('{', '').replace('}', ''))[:80])\n",
        "      posts += 'Post{}: {}; '.format(count,post)\n",
        "      count += 1\n",
        "  print(posts)\n",
        "  answer = agent.predict(context=posts)\n",
        "  print(answer)\n",
        "  api_data = pd.DataFrame([[posts, labels[i], answer]])\n",
        "  api_data.to_csv(file_name, mode='a', index=False, header=False)"
      ],
      "metadata": {
        "id": "UG6y6kGQ2WaJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}