{"id":7360,"date":"2023-05-01T14:55:30","date_gmt":"2023-05-01T05:55:30","guid":{"rendered":"https:\/\/www.lancard.com\/blog\/?p=7360"},"modified":"2025-03-12T11:23:57","modified_gmt":"2025-03-12T02:23:57","slug":"chatgpt_langchain_titanic","status":"publish","type":"post","link":"https:\/\/www.lancard.com\/blog\/2023\/05\/01\/chatgpt_langchain_titanic\/","title":{"rendered":"ChatGPT\u3067\u30bf\u30a4\u30bf\u30cb\u30c3\u30af\u53f7\u306e\u751f\u5b58\u4e88\u6e2c\uff08LangChain\u7de8\uff09"},"content":{"rendered":"

\u30bf\u30a4\u30bf\u30cb\u30c3\u30af\u53f7\u306e\u751f\u5b58\u4e88\u6e2c\u3068\u306f\u3001Kaggle<\/a>\u3067\u3088\u304f\u77e5\u3089\u308c\u3066\u3044\u308b\u6a5f\u68b0\u5b66\u7fd2\u306e\u304a\u984c\u3067\u3059\u3002
\n\u30bf\u30a4\u30bf\u30cb\u30c3\u30af\u53f7\u3067\u751f\u304d\u6b8b\u308b\u306e\u306f\u8ab0\u304b\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002
\n\u4eca\u56de\u306f\u305d\u308c\u3092\u3059\u3079\u3066ChatGPT\u306b\u3084\u3063\u3066\u3082\u3089\u3044\u307e\u3059\u3002<\/p>\n

AI\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u3057\u3066\u3001LangChain<\/a>\u3092\u4f7f\u3044\u307e\u3059\u3002
\nLangChain\u306f\u3001LLM\u3092\u5229\u7528\u3057\u305f\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u958b\u767a\u3059\u308b\u305f\u3081\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3067\u3059\u3002
\nLangChain\u306eAgent\u3092\u4f7f\u3046\u3068\u3001LLM\u304c\u4f5c\u3063\u3066\u304f\u308c\u305fPython\u30d7\u30ed\u30b0\u30e9\u30e0\u3092Python REPL\u3067\u5b9f\u884c\u3057\u3001\u5b9f\u884c\u7d50\u679c\u3092\u30d7\u30ed\u30f3\u30d7\u30c8\u306b\u8ffd\u8a18\u3057\u3066LLM\u306b\u518d\u3073\u6295\u3052\u308b\u3001\u3068\u3044\u3046\u306e\u3092Final Answer\u3092\u898b\u3064\u3051\u308b\u307e\u3067\u81ea\u52d5\u5b9f\u884c\u3059\u308b\u3001\u3068\u8a00\u3046\u3088\u3046\u306a\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

<\/p>\n

\u30ed\u30fc\u30ab\u30eb\u74b0\u5883\u3067Python REPL\u3092\u81ea\u52d5\u5b9f\u884c\u3055\u308c\u308b\u306e\u306f\u5acc\u306a\u306e\u3067\u3001Google Colaboratory\u3067\u8a66\u3057\u307e\u3057\u305f\u3002<\/p>\n

\u5b9f\u88c5<\/h2>\n

\u6700\u521d\u306b\u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002<\/p>\n

!pip install openai\n!pip install langchain\n<\/code><\/pre>\n
OpenAI\u306eAPI\u30ad\u30fc\u3092\u74b0\u5883\u5909\u6570\u306b\u30bb\u30c3\u30c8\u3057\u307e\u3059\u3002<\/p>\n
import os\nos.environ[\"OPENAI_API_KEY\"] = \"**********************\"\n<\/code><\/pre>\n
\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002
\n\u516c\u5f0f\u30b5\u30a4\u30c8<\/a>\u3067\u306f\u3001LLM\u3092OpenAI()<\/code>\u3067\u751f\u6210\u3057\u3066\u307e\u3059\u304c\u3001\u3053\u308c\u3060\u3068GPT-3\uff08text-davinci-003\uff09\u306a\u306e\u3067\u3001ChatGPT\uff08gpt-3.5-turbo\uff09\u3092\u751f\u6210\u3059\u308b\u305f\u3081\u3001OpenAIChat()<\/code>\u3068\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
from langchain.agents.agent_toolkits import create_python_agent\nfrom langchain.tools.python.tool import PythonREPLTool\nfrom langchain.llms import OpenAIChat\n\nagent_executor = create_python_agent(\n    llm=OpenAIChat(temperature=0),\n    tool=PythonREPLTool(),\n    verbose=True\n)\n<\/code><\/pre>\n
\u6b21\u306bagent_executor.run(<\u8cea\u554f\u6587>)<\/code>\u3067\u3001\u8cea\u554f\u3092OpenAI API\u3067\u6295\u3052\u307e\u3059\u3002
\n\u3053\u308c\u306b\u3088\u308a\u4ee5\u4e0b\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u304cOpenAI API\u306b\u6295\u3052\u3089\u308c\u307e\u3059\u3002<\/p>\n
You are an agent designed to write and execute python code to answer questions.\nYou have access to a python REPL, which you can use to execute python code.\nIf you get an error, debug your code and try again.\nOnly use the output of your code to answer the question. \nYou might know the answer without running any code, but you should still run the code to get the answer.\nIf it does not seem like you can write code to answer the question, just return \\\\\"I don\\'t know\\\\\" as the answer.\n\n\nPython REPL: A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Python REPL]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought\/Action\/Action Input\/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: <\u5165\u529b\u3057\u305f\u8cea\u554f\u6587>\nThought:\n<\/code><\/pre>\n
\u4e0a\u8a18\u3092DeepL\u3067\u7ffb\u8a33\uff1a<\/p>\n
\u3042\u306a\u305f\u306f\u3001\u8cea\u554f\u306b\u7b54\u3048\u308b\u305f\u3081\u306bpython\u30b3\u30fc\u30c9\u3092\u66f8\u304d\u3001\u5b9f\u884c\u3059\u308b\u3088\u3046\u306b\u8a2d\u8a08\u3055\u308c\u305f\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u3067\u3059\u3002\n\u3042\u306a\u305f\u306fpython REPL\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u3001python\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\n\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3057\u305f\u5834\u5408\u306f\u3001\u30b3\u30fc\u30c9\u3092\u30c7\u30d0\u30c3\u30b0\u3057\u3066\u3001\u3082\u3046\u4e00\u5ea6\u3084\u308a\u76f4\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n\u8cea\u554f\u306b\u7b54\u3048\u308b\u306b\u306f\u3001\u30b3\u30fc\u30c9\u306e\u51fa\u529b\u306e\u307f\u3092\u4f7f\u7528\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3057\u306a\u304f\u3066\u3082\u7b54\u3048\u304c\u308f\u304b\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u304c\u3001\u305d\u308c\u3067\u3082\u7b54\u3048\u3092\u5f97\u308b\u305f\u3081\u306b\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u3079\u304d\u3067\u3059\u3002\n\u30b3\u30fc\u30c9\u3092\u66f8\u3044\u3066\u3082\u7b54\u3048\u3089\u308c\u305d\u3046\u306b\u306a\u3044\u5834\u5408\u306f\u3001\u7b54\u3048\u3068\u3057\u3066\u300cI don't know\u300d\u3092\u8fd4\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n\n\nPython REPL\uff1aPython\u306e\u30b7\u30a7\u30eb\u3002Python\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306b\u4f7f\u3046\u3002\u5165\u529b\u306f\u3001\u6709\u52b9\u306apython\u30b3\u30de\u30f3\u30c9\u3067\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3002\u5024\u306e\u51fa\u529b\u3092\u898b\u305f\u3044\u5834\u5408\u306f\u3001`print(...)`\u3067\u51fa\u529b\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\n\n\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u66f8\u5f0f\u3092\u4f7f\u7528\u3057\u307e\u3059\uff1a\n\nQuestion\uff1a\u3042\u306a\u305f\u304c\u7b54\u3048\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u5165\u529b\u306e\u8cea\u554f\nThought\uff1a\u5e38\u306b\u4f55\u3092\u3059\u3079\u304d\u304b\u3092\u8003\u3048\u308b\u3079\u304d\nAction: \u53d6\u308b\u3079\u304d\u884c\u52d5\u3002[Python REPL]\u306e\u3044\u305a\u308c\u304b\u3067\u3042\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\n\u30a2\u30af\u30b7\u30e7\u30f3\u30a4\u30f3\u30d7\u30c3\u30c8\uff1a\u30a2\u30af\u30b7\u30e7\u30f3\u3078\u306e\u5165\u529b\nObservation\uff1a\u884c\u52d5\u306e\u7d50\u679c\n...\uff08\u3053\u306e\u601d\u8003\u30fb\u884c\u52d5\u30fb\u884c\u70ba\u306e\u5165\u529b\u30fb\u89b3\u5bdf\u306f\u3001N\u56de\u7e70\u308a\u8fd4\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\uff09\nThought\uff1a \u6700\u7d42\u7684\u306a\u7b54\u3048\u304c\u308f\u304b\u3063\u305f\nFinal Answer\uff1a\u5165\u529b\u3055\u308c\u305f\u5143\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u6700\u7d42\u7684\u306a\u56de\u7b54\n\nBegin!\n\nQuestion\uff1a <\u5165\u529b\u3057\u305f\u8cea\u554f\u6587>\nThought\uff1a\n<\/code><\/pre>\n
\u3053\u308c\u3067ChatGPT\u304cFinal Answer\u3092\u51fa\u3059\u307e\u3067\u3001\u7e70\u308a\u8fd4\u3057ChatGPT\u306b\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u6295\u3052\u307e\u3059\u3002<\/p>\n
\u6700\u521d\u306btrain.csv<\/code>\u3068test.csv\u30d5<\/code>\u30a1\u30a4\u30eb\u3092data\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u4ee5\u4e0b\u306b\u7f6e\u304d\u3001
\n\u4ee5\u4e0b\u306e\u8cea\u554f\u3092\u6295\u3052\u307e\u3059\u3002<\/p>\n
question=\"\"\"\nIt is your task to predict if a passenger survived the crashing plane or not.\n\nThe data has been split into two groups:\n    training set (path: '.\/data\/train.csv')\n    test set (path: '.\/data\/test.csv')\nThe training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the \u201cground truth\u201d) for each passenger.\nThe test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes.\n\nYou should write a csv file with exactly 418 entries plus a header row.\nThe file should have exactly 2 columns:\n- PassengerId (sorted in any order)\n- Survived (contains your binary predictions: 1 for survived, 0 for deceased)\n\"\"\"\n\nagent_executor.run(question)\n<\/code><\/pre>\n
\u4e0a\u8a18\u306e\u8cea\u554f\u6587\u3092DeepL\u3067\u7ffb\u8a33:<\/p>\n
\u589c\u843d\u3057\u305f\u98db\u884c\u6a5f\u304b\u3089\u4e57\u5ba2\u304c\u751f\u9084\u3057\u305f\u304b\u3069\u3046\u304b\u3092\u4e88\u6e2c\u3059\u308b\u306e\u304c\u3042\u306a\u305f\u306e\u4ed5\u4e8b\u3067\u3059\u3002\n\n\u30c7\u30fc\u30bf\u306f2\u3064\u306e\u30b0\u30eb\u30fc\u30d7\u306b\u5206\u3051\u3089\u308c\u3066\u3044\u307e\u3059\uff1a\n    \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30bb\u30c3\u30c8\uff08\u30d1\u30b9\uff1a'.\/data\/train.csv')\n    \u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\uff08\u30d1\u30b9\uff1a'.\/data\/test.csv')\n\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30bb\u30c3\u30c8\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30bb\u30c3\u30c8\u3067\u306f\u3001\u5404\u4e57\u5ba2\u306e\u7d50\u679c\uff08\u300cground truth\u300d\u3068\u3082\u547c\u3070\u308c\u308b\uff09\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002\n\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u306f\u3001\u672a\u77e5\u306e\u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308b\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u3092\u78ba\u8a8d\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u3067\u306f\u3001\u5404\u4e57\u5ba2\u306e\u30b0\u30e9\u30f3\u30c9\u30c8\u30a5\u30eb\u30fc\u30b9\u306f\u63d0\u4f9b\u3055\u308c\u307e\u305b\u3093\u3002\u3053\u308c\u3089\u306e\u7d50\u679c\u3092\u4e88\u6e2c\u3059\u308b\u306e\u306f\u3042\u306a\u305f\u306e\u4ed5\u4e8b\u3067\u3059\u3002\n\n\u3042\u306a\u305f\u306f\u3001418\u500b\u306e\u30a8\u30f3\u30c8\u30ea\u3068\u30d8\u30c3\u30c0\u30fc\u884c\u3092\u6301\u3064csv\u30d5\u30a1\u30a4\u30eb\u3092\u4f5c\u6210\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\n\u3053\u306e\u30d5\u30a1\u30a4\u30eb\u306b\u306f2\u3064\u306e\u5217\u304c\u5fc5\u8981\u3067\u3059\uff1a\n- PassengerId (\u4efb\u610f\u306e\u9806\u5e8f\u3067\u30bd\u30fc\u30c8\u3055\u308c\u3066\u3044\u307e\u3059)\n- \u751f\u5b58\uff08\u751f\u5b58\u306e\u5834\u5408\u306f1\u3001\u6b7b\u4ea1\u306e\u5834\u5408\u306f0\u3068\u3044\u3046\u30d0\u30a4\u30ca\u30ea\u4e88\u6e2c\u304c\u542b\u307e\u308c\u307e\u3059\u3002\uff09\n<\/code><\/pre>\n
\u30bf\u30a4\u30bf\u30cb\u30c3\u30af\u53f7\u306e\u751f\u5b58\u4e88\u6e2c\u306f\u6709\u540d\u3067\u304a\u305d\u3089\u304fChatGPT\u306f\u77e5\u3063\u3066\u308b\u3068\u601d\u3046\u306e\u3067\u3001\u305d\u306e\u5834\u5408\u30ba\u30eb\u3092\u3059\u308b\u6050\u308c\u304c\u3042\u308b\u306e\u3067\u3001\u300c\u30bf\u30a4\u30bf\u30cb\u30c3\u30af\u300d\u3068\u3044\u3046\u30ef\u30fc\u30c9\u306f\u5165\u308c\u305a\u306b\u300c\u589c\u843d\u3057\u305f\u98db\u884c\u6a5f\u300d\u3068\u3057\u307e\u3057\u305f\u3002<\/p>\n
\u51fa\u529b\u7d50\u679c<\/h2>\n
\u4ee5\u4e0b\u3001\u5b9f\u884c\u3057\u305f\u51fa\u529b\u7d50\u679c\u3067\u3059\u3002
\n(head()\u5b9f\u884c\u90e8\u5206\u3001\u4e00\u90e8\u7701\u7565\u3057\u3066\u3044\u307e\u3059)<\/p>\n
> Entering new AgentExecutor chain...\nThis is a machine learning task. \nI need to build a model using the training set and use it to predict the outcomes for the test set. I should start by loading the data.\n\nAction: Python REPL\nAction Input:\nimport pandas as pd\ntrain_data = pd.read_csv('.\/data\/train.csv')\ntest_data = pd.read_csv('.\/data\/test.csv')\nprint(train_data.head())\nprint(test_data.head())\n\nObservation:    PassengerId  Survived  Pclass  Name     Sex   Age  SibSp Parch            Ticket     Fare Cabin Embarked \n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   <\u7701\u7565>\n\n\nThought:I need to explore the data to see what features are available and what they look like.\n\nAction: Python REPL\nAction Input:\nprint(train_data.info())\nprint(test_data.info())\n\nObservation: <class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   PassengerId  891 non-null    int64  \n 1   Survived     891 non-null    int64  \n 2   Pclass       891 non-null    int64  \n 3   Name         891 non-null    object \n 4   Sex          891 non-null    object \n 5   Age          714 non-null    float64\n 6   SibSp        891 non-null    int64  \n 7   Parch        891 non-null    int64  \n 8   Ticket       891 non-null    object \n 9   Fare         891 non-null    float64\n 10  Cabin        204 non-null    object \n 11  Embarked     889 non-null    object \ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\nNone\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 418 entries, 0 to 417\nData columns (total 11 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   PassengerId  418 non-null    int64  \n 1   Pclass       418 non-null    int64  \n 2   Name         418 non-null    object \n 3   Sex          418 non-null    object \n 4   Age          332 non-null    float64\n 5   SibSp        418 non-null    int64  \n 6   Parch        418 non-null    int64  \n 7   Ticket       418 non-null    object \n 8   Fare         417 non-null    float64\n 9   Cabin        91 non-null     object \n 10  Embarked     418 non-null    object \ndtypes: float64(2), int64(4), object(5)\nmemory usage: 36.0+ KB\nNone\n\nThought:I see that there are missing values in the Age, Cabin, and Embarked columns in the training set, and in the Age, Fare, and Cabin columns in the test set. I need to handle these missing values before building my model.\n\nAction: Python REPL\nAction Input:\nprint(train_data.isnull().sum())\nprint(test_data.isnull().sum())\n\nObservation: PassengerId      0\nSurvived         0\nPclass           0\nName             0\nSex              0\nAge            177\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          687\nEmbarked         2\ndtype: int64\nPassengerId      0\nPclass           0\nName             0\nSex              0\nAge             86\nSibSp            0\nParch            0\nTicket           0\nFare             1\nCabin          327\nEmbarked         0\ndtype: int64\n\nThought:I need to handle the missing values in the Age, Cabin, and Embarked columns in the training set, and in the Age, Fare, and Cabin columns in the test set. I can either drop the rows with missing values or fill in the missing values with some strategy.\n\nAction: Python REPL\nAction Input:\n# Fill in missing values in Age column with median age\ntrain_data['Age'].fillna(train_data['Age'].median(), inplace=True)\ntest_data['Age'].fillna(test_data['Age'].median(), inplace=True)\n\n# Fill in missing values in Embarked column with most common value\ntrain_data['Embarked'].fillna(train_data['Embarked'].mode()[0], inplace=True)\n\n# Fill in missing values in Fare column with median fare\ntest_data['Fare'].fillna(test_data['Fare'].median(), inplace=True)\n\n# Drop Cabin column since it has too many missing values\ntrain_data.drop('Cabin', axis=1, inplace=True)\ntest_data.drop('Cabin', axis=1, inplace=True)\n\nprint(train_data.isnull().sum())\nprint(test_data.isnull().sum())\n\nObservation: PassengerId    0\nSurvived       0\nPclass         0\nName           0\nSex            0\nAge            0\nSibSp          0\nParch          0\nTicket         0\nFare           0\nEmbarked       0\ndtype: int64\nPassengerId    0\nPclass         0\nName           0\nSex            0\nAge            0\nSibSp          0\nParch          0\nTicket         0\nFare           0\nEmbarked       0\ndtype: int64\n\nThought:Now that I have handled the missing values, I need to prepare the data for modeling. I should start by encoding categorical variables as numerical variables.\n\nAction: Python REPL\nAction Input:\n# Encode Sex column as binary variable\ntrain_data['Sex'] = train_data['Sex'].map({'male': 0, 'female': 1})\ntest_data['Sex'] = test_data['Sex'].map({'male': 0, 'female': 1})\n\n# Encode Embarked column as dummy variables\ntrain_data = pd.get_dummies(train_data, columns=['Embarked'])\ntest_data = pd.get_dummies(test_data, columns=['Embarked'])\n\nprint(train_data.head())\nprint(test_data.head())\n\nObservation:    PassengerId  Survived  Pclass  Name     Sex   Age  SibSp Parch \n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   <\u7701\u7565>\n\nThought:I see that the Name and Ticket columns are not useful for modeling, so I should drop them.\n\nAction: Python REPL\nAction Input:\ntrain_data.drop(['Name', 'Ticket'], axis=1, inplace=True)\ntest_data.drop(['Name', 'Ticket'], axis=1, inplace=True)\n\nprint(train_data.head())\nprint(test_data.head())\n\nObservation:    PassengerId  Survived  Pclass  Name     Sex   Age  SibSp Parch\n0            1         0       3    0  22.0      1      0   7.2500   \n1            2         1       1    1  38.0      1      0  71.2833   \n2            3         1       3    1  26.0      0      0   7.9250   \n3            4         1       1    1  35.0      1      0  53.1000   \n4            5         0       3    0  35.0      0      0   8.0500   <\u7701\u7565>\n\nThought:I need to split the training set into a training set and a validation set to evaluate my model.\n\nAction: Python REPL\nAction Input:\nfrom sklearn.model_selection import train_test_split\n\nX = train_data.drop('Survived', axis=1)\ny = train_data['Survived']\n\nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n\nprint(X_train.shape)\nprint(X_val.shape)\nprint(y_train.shape)\nprint(y_val.shape)\n\nObservation: (712, 10)\n(179, 10)\n(712,)\n(179,)\n\nThought:Now I can build and train my model. I will use a logistic regression model.\n\nAction: Python REPL\nAction Input:\nfrom sklearn.linear_model import LogisticRegression\n\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)\n\nprint(model.score(X_val, y_val))\n\nObservation: 0.7821229050279329\n\nThought:\/usr\/local\/lib\/python3.10\/dist-packages\/sklearn\/linear_model\/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https:\/\/scikit-learn.org\/stable\/modules\/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https:\/\/scikit-learn.org\/stable\/modules\/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\nMy model has an accuracy of 78.2% on the validation set. Now I can use it to make predictions on the test set.\n\nAction: Python REPL\nAction Input:\n# Make predictions on test set\npredictions = model.predict(test_data)\n\n# Create submission file\nsubmission = pd.DataFrame({'PassengerId': test_data['PassengerId'], 'Survived': predictions})\nsubmission.to_csv('submission.csv', index=False)\n\nprint(submission.head())\n\nObservation:    PassengerId  Survived\n0          892         0\n1          893         1\n2          894         0\n3          895         0\n4          896         1\n\nThought:\n---------------------------------------------------------------------------\nValueError                                Traceback (most recent call last)\n<ipython-input-93-1b4108d5832a> in <cell line: 1>()\n----> 1 agent_executor.run(prompt)\n\n6 frames\n\/usr\/local\/lib\/python3.10\/dist-packages\/langchain\/agents\/mrkl\/output_parser.py in parse(self, text)\n     18         match = re.search(regex, text, re.DOTALL)\n     19         if not match:\n---> 20             raise ValueError(f\"Could not parse LLM output: `{text}`\")\n     21         action = match.group(1).strip()\n     22         action_input = match.group(2)\n\nValueError: Could not parse LLM output: `I have successfully made predictions on the test set and created a submission file. My final answer is that I have predicted the survival outcomes for the test set passengers and saved the predictions in a csv file.`\n<\/code><\/pre>\n
\u51fa\u529b\u30d5\u30a1\u30a4\u30eb\u300csubmission.csv\u300d\u306f\u751f\u6210\u3055\u308c\u307e\u3057\u305f\u304c\u3001\u6700\u5f8c\u306b\u30a8\u30e9\u30fc\u306b\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u3002
\nChatGPT\u306f\u6700\u5f8c\u306b\u4ee5\u4e0b\u3092\u8fd4\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
Thought:\nI have successfully made predictions on the test set and created a submission file. My final answer is that I have predicted the survival outcomes for the test set passengers and saved the predictions in a csv file.\n<\/code><\/pre>\n
\u5fc5\u305aThought:<\/code>\u306e\u5f8c\u306bAction: <\/code>\u307e\u305f\u306fFinal Answer\uff1a<\/code>\u3092\u8fd4\u3057\u3066\u6b21\u306e\u30a2\u30af\u30b7\u30e7\u30f3\u304c\u6307\u5b9a\u3055\u308c\u3066\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u304c\u3001\u305d\u308c\u304c\u306a\u3044\u3067\u3059\u3002
\n\u4eca\u56de\u306f\u300csubmission.csv\u300d\u304c\u56de\u7b54\u3067\u3042\u308a\u3001Final Answer\uff1a<\/code>\u304c\u4e0d\u8981\u306a\u305f\u3081\u305d\u3046\u306a\u3063\u3066\u3057\u307e\u3063\u305f\u3088\u3046\u3067\u3059\u3002
\n\u3053\u308c\u3092\u30d5\u30a3\u30c3\u30af\u30b9\u3059\u308b\u306e\u306f\u3001\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3092\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3059\u308b<\/a>\u5fc5\u8981\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n
\u300csubmission.csv\u300d\u3092Kaggle\u306bsubmit\u3057\u305f\u3068\u3053\u308d\u3001\u30b9\u30b3\u30a2\u306f0.75\u3067\u3057\u305f\u3002<\/p>\n
\u51fa\u529b\u7d50\u679c\u306e\u6d41\u308c<\/h2>\n
\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u51e6\u7406\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n
    \n
  1. \u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u304a\u3088\u3073\u89b3\u5bdf<\/li>\n
  2. \u30c7\u30fc\u30bf\u306e\u6b20\u640d\u5024\u3092\u88dc\u5b8c<\/li>\n
  3. \u30ab\u30c6\u30b4\u30ea\u5909\u6570\u3092\u6570\u5024\u30c7\u30fc\u30bf\u306b\u7f6e\u63db<\/li>\n
  4. \u4e0d\u8981\u3068\u601d\u308f\u308c\u308b\u30ab\u30e9\u30e0\u3092\u524a\u9664\n