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2 Commits
| Author | SHA1 | Date | |
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f0731e2240 |
@@ -20,7 +20,7 @@
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:fire: [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) is released with support for ChatGPT and GPT-4, based on [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673).
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:fire: FLAML supports AutoML and Hyperparameter Tuning features in [Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/get-started/microsoft-fabric-overview) private preview. Sign up for these features at: https://aka.ms/fabric/data-science/sign-up.
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:fire: FLAML supports Code-First AutoML & Tuning – Private Preview in [Microsoft Fabric Data Science](https://learn.microsoft.com/en-us/fabric/data-science/).
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## What is FLAML
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|
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@@ -14,16 +14,16 @@ class AssistantAgent(ResponsiveAgent):
|
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"""
|
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|
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DEFAULT_SYSTEM_MESSAGE = """You are a helpful AI assistant.
|
||||
Solve tasks using your coding and language skills.
|
||||
If a plan is not provided, explain the plan first. Be clear which step uses code, and which step uses your language skill.
|
||||
In the following cases, suggest python code (in a python coding block) or shell script (in a sh coding block) for the user to execute.
|
||||
Solve tasks using your coding and language skills.
|
||||
In the following cases, suggest python code (in a python coding block) or shell script (in a sh coding block) for the user to execute.
|
||||
1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself.
|
||||
2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly. Solve the task step by step if you need to.
|
||||
You must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.
|
||||
If you want the user to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.
|
||||
If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
|
||||
When you find an answer, verify the answer carefully. If a function for planning is provided, call the function to make plans and verify the execution.
|
||||
Reply "TERMINATE" in the end when everything is done.
|
||||
2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.
|
||||
Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.
|
||||
When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.
|
||||
If you want the user to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.
|
||||
If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
|
||||
When you find an answer, verify the answer carefully. Include verifiable evidence in your response if possible.
|
||||
Reply "TERMINATE" in the end when everything is done.
|
||||
"""
|
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|
||||
def __init__(
|
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|
||||
@@ -895,10 +895,11 @@ class ResponsiveAgent(Agent):
|
||||
exitcode, logs, image = (
|
||||
1,
|
||||
f"unknown language {lang}",
|
||||
self._code_execution_config["use_docker"],
|
||||
None,
|
||||
)
|
||||
# raise NotImplementedError
|
||||
self._code_execution_config["use_docker"] = image
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||||
if image is not None:
|
||||
self._code_execution_config["use_docker"] = image
|
||||
logs_all += "\n" + logs
|
||||
if exitcode != 0:
|
||||
return exitcode, logs_all
|
||||
|
||||
@@ -1 +1 @@
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__version__ = "2.0.1"
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__version__ = "2.0.2"
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File diff suppressed because one or more lines are too long
@@ -35,7 +35,7 @@
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"outputs": [],
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"source": [
|
||||
"%%capture --no-stderr\n",
|
||||
"# %pip install flaml[autogen]~=2.0.1"
|
||||
"# %pip install flaml[autogen]~=2.0.2"
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||||
]
|
||||
},
|
||||
{
|
||||
@@ -138,9 +138,7 @@
|
||||
")\n",
|
||||
"bob = autogen.AssistantAgent(\n",
|
||||
" name=\"Bob\",\n",
|
||||
" system_message=\"Code and answer reviewer.\"\n",
|
||||
" \"For code, prevent code execution if unsafe or missing important details, e.g., sort order in arxiv API. Suggest changes. Otherwise, approve and return the final code to execute.\"\n",
|
||||
" \"For answer, carefully check the interpretation of code result and fix any errors. If the interpretation is correct, approve and return the final answer to the user.\",\n",
|
||||
" system_message=\"Scientist. Provide expert knowledge.\",\n",
|
||||
" llm_config=llm_config,\n",
|
||||
")\n",
|
||||
"groupchat = autogen.GroupChat(agents=[human, alice, bob], messages=[], max_round=12)\n",
|
||||
@@ -166,88 +164,62 @@
|
||||
"text": [
|
||||
"\u001b[33mHuman\u001b[0m (to chat_manager):\n",
|
||||
"\n",
|
||||
"Find a latest paper about gpt-4\n",
|
||||
"Find a latest paper about gpt-4 on arxiv and find its potential application.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mAlice\u001b[0m (to chat_manager):\n",
|
||||
"\n",
|
||||
"To find the latest papers about gpt-4, we can use APIs of scholarly databases like Google Scholar, PubMed, Arxiv, etc., or automation tools like Beautiful Soup to scrape the information from webpages. However, a significant number of these methods are in violation of the terms of service of these platforms. Therefore, it's recommended to manually search these databases. \n",
|
||||
"\n",
|
||||
"But in our case, we'll use the arXiv API, which is a freely accessible database. It holds a vast collection of articles in the field of computer science and many other disciplines. It is often used by researchers to share their papers before they are published, so it could include articles about GPT-4. \n",
|
||||
"\n",
|
||||
"The following Python code uses the requests and feedparser libraries to do this. Requests is used to make a GET request to the arXiv API with the search query as a parameter. Feedparser is used to parse the returned RSS feed. The result is the information of the most recent articles based on publication time related to gpt-4 from arXiv. \n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"# python code\n",
|
||||
"import requests\n",
|
||||
"import feedparser\n",
|
||||
"\n",
|
||||
"# Search arXiv API for papers related to gpt-4\n",
|
||||
"url = 'http://export.arxiv.org/api/query'\n",
|
||||
"params = {'search_query': 'all:gpt-4', 'sortBy': 'submittedDate', 'sortOrder': 'descending'}\n",
|
||||
"response = requests.get(url, params=params)\n",
|
||||
"\n",
|
||||
"# Parse the response\n",
|
||||
"feeds = feedparser.parse(response.content)\n",
|
||||
"\n",
|
||||
"# should check if feeds.entries is empty\n",
|
||||
"\n",
|
||||
"# Get the first paper's information\n",
|
||||
"latest_paper = feeds.entries[0]\n",
|
||||
"\n",
|
||||
"# Print the paper's title, authors, and published date\n",
|
||||
"print('Title: ', latest_paper['title'])\n",
|
||||
"print('Authors: ', latest_paper['author'])\n",
|
||||
"print('Published Date: ', latest_paper['published'])\n",
|
||||
"```\n",
|
||||
"You need to install requests and feedparser in your python environment, if they are not installed yet.\n",
|
||||
"To find the latest paper about GPT-4 on arxiv, I will use the arxiv API to query and filter the search results. First, I'll execute the following code to get the latest paper information.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mBob\u001b[0m (to chat_manager):\n",
|
||||
"\n",
|
||||
"The provided code is mostly correct, but it is missing some important error handling measures. As mentioned in the comments, it does not check if the feed entries are empty. It also does not check if the request was successful before trying to parse the response.\n",
|
||||
"\n",
|
||||
"Additionally, the code does not handle any exceptions that may occur during the process, such adding try/except clause would make it more robust.\n",
|
||||
"\n",
|
||||
"Here's a suggestion on how to improve it:\n",
|
||||
"\u001b[33mAlice\u001b[0m (to chat_manager):\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"# python code\n",
|
||||
"import requests\n",
|
||||
"import feedparser\n",
|
||||
"from bs4 import BeautifulSoup\n",
|
||||
"import re\n",
|
||||
"\n",
|
||||
"# Search arXiv API for papers related to gpt-4\n",
|
||||
"url = 'http://export.arxiv.org/api/query'\n",
|
||||
"params = {'search_query': 'all:gpt-4', 'sortBy': 'submittedDate', 'sortOrder': 'descending'}\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" response = requests.get(url, params=params)\n",
|
||||
" response.raise_for_status()\n",
|
||||
"except requests.HTTPError as http_err:\n",
|
||||
" print(f'HTTP error occurred: {http_err}')\n",
|
||||
"except requests.ConnectionError as conn_err:\n",
|
||||
" print(f'Error connecting: {conn_err}')\n",
|
||||
"except requests.Timeout as to_err:\n",
|
||||
" print(f'Timeout error: {to_err}')\n",
|
||||
"except requests.RequestException as err:\n",
|
||||
" print(f'An error occurred: {err}')\n",
|
||||
"def search_arxiv(query, sort_by=\"submittedDate\", order=\"descending\", max_results=1):\n",
|
||||
" search_url = \"http://export.arxiv.org/api/query\"\n",
|
||||
" query_string = f\"search_query=all:{query.replace(' ', '+')}&sortBy={sort_by}&sortOrder={order}&max_results={max_results}\"\n",
|
||||
" url = f\"{search_url}?{query_string}\"\n",
|
||||
" response = requests.get(url)\n",
|
||||
" return response.text\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def parse_paper_info(xml_response):\n",
|
||||
" soup = BeautifulSoup(xml_response, \"lxml-xml\")\n",
|
||||
" entry = soup.find(\"entry\")\n",
|
||||
" if not entry:\n",
|
||||
" return {}\n",
|
||||
"\n",
|
||||
" paper_info = {\n",
|
||||
" \"title\": entry.find(\"title\").text.strip(),\n",
|
||||
" \"authors\": [author.text for author in entry.find_all(\"author\")],\n",
|
||||
" \"published\": entry.find(\"published\").text,\n",
|
||||
" \"updated\": entry.find(\"updated\").text,\n",
|
||||
" \"summary\": entry.find(\"summary\").text.strip(),\n",
|
||||
" \"url\": entry.find(\"id\").text,\n",
|
||||
" }\n",
|
||||
" return paper_info\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"query = \"gpt-4\"\n",
|
||||
"arxiv_response = search_arxiv(query)\n",
|
||||
"paper_info = parse_paper_info(arxiv_response)\n",
|
||||
"\n",
|
||||
"if paper_info:\n",
|
||||
" print(f\"Title: {paper_info['title']}\")\n",
|
||||
" print(f\"Authors: {', '.join(paper_info['authors'])}\")\n",
|
||||
" print(f\"Published: {paper_info['published']}\")\n",
|
||||
" print(f\"Updated: {paper_info['updated']}\")\n",
|
||||
" print(f\"Abstract: {paper_info['summary']}\")\n",
|
||||
" print(f\"URL: {paper_info['url']}\")\n",
|
||||
"else:\n",
|
||||
" # Parse the response\n",
|
||||
" feeds = feedparser.parse(response.content)\n",
|
||||
"\n",
|
||||
" # Check if feeds.entries is empty\n",
|
||||
" if not feeds.entries:\n",
|
||||
" print(\"No results found.\")\n",
|
||||
" else: \n",
|
||||
" # Get the first paper's information\n",
|
||||
" latest_paper = feeds.entries[0]\n",
|
||||
"\n",
|
||||
" # Print the paper's title, authors, and published date\n",
|
||||
" print('Title: ', latest_paper['title'])\n",
|
||||
" print('Authors: ', latest_paper['author'])\n",
|
||||
" print('Published Date: ', latest_paper['published'])\n",
|
||||
" print(\"No papers about GPT-4 were found on arxiv.\")\n",
|
||||
"```\n",
|
||||
"This version of the script will handle the HTTP, Connection, and Timeout errors as well as any other request error. It also checks that there are results before trying to print.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
@@ -260,36 +232,56 @@
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"Code output: \n",
|
||||
"Title: Language as Reality: A Co-Creative Storytelling Game Experience in 1001\n",
|
||||
" Nights using Generative AI\n",
|
||||
"Authors: Ali Asadipour\n",
|
||||
"Published Date: 2023-08-24T16:42:23Z\n",
|
||||
"Title: Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance\n",
|
||||
"Authors: \n",
|
||||
"Lefteris Loukas\n",
|
||||
", \n",
|
||||
"Ilias Stogiannidis\n",
|
||||
", \n",
|
||||
"Prodromos Malakasiotis\n",
|
||||
", \n",
|
||||
"Stavros Vassos\n",
|
||||
"\n",
|
||||
"Published: 2023-08-28T15:04:16Z\n",
|
||||
"Updated: 2023-08-28T15:04:16Z\n",
|
||||
"Abstract: We propose the use of conversational GPT models for easy and quick few-shot\n",
|
||||
"text classification in the financial domain using the Banking77 dataset. Our\n",
|
||||
"approach involves in-context learning with GPT-3.5 and GPT-4, which minimizes\n",
|
||||
"the technical expertise required and eliminates the need for expensive GPU\n",
|
||||
"computing while yielding quick and accurate results. Additionally, we fine-tune\n",
|
||||
"other pre-trained, masked language models with SetFit, a recent contrastive\n",
|
||||
"learning technique, to achieve state-of-the-art results both in full-data and\n",
|
||||
"few-shot settings. Our findings show that querying GPT-3.5 and GPT-4 can\n",
|
||||
"outperform fine-tuned, non-generative models even with fewer examples. However,\n",
|
||||
"subscription fees associated with these solutions may be considered costly for\n",
|
||||
"small organizations. Lastly, we find that generative models perform better on\n",
|
||||
"the given task when shown representative samples selected by a human expert\n",
|
||||
"rather than when shown random ones. We conclude that a) our proposed methods\n",
|
||||
"offer a practical solution for few-shot tasks in datasets with limited label\n",
|
||||
"availability, and b) our state-of-the-art results can inspire future work in\n",
|
||||
"the area.\n",
|
||||
"URL: http://arxiv.org/abs/2308.14634v1\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mAlice\u001b[0m (to chat_manager):\n",
|
||||
"\n",
|
||||
"Based on the output of the code, the latest paper on GPT-4 found was \"Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights using Generative AI\" by Ali Asadipour, published on 24th August 2023. \n",
|
||||
"\n",
|
||||
"This shows the latest found document related to the search term \"GPT-4\" in the arXiv scholarly paper database. Please note that the search query includes all parts of the articles: the title, the abstract, and the full text if available, so it might not be necessarily about GPT-4 but includes the term in some section of the paper.\n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mBob\u001b[0m (to chat_manager):\n",
|
||||
"\n",
|
||||
"The interpretation of the code output is correct. The latest document retrieved related to \"GPT-4\" is indeed \"Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights using Generative AI\" by Ali Asadipour, published on 24th August 2023.\n",
|
||||
"The latest paper about GPT-4 on arxiv is titled \"Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance\" by Lefteris Loukas, Ilias Stogiannidis, Prodromos Malakasiotis, and Stavros Vassos. It was published on 2023-08-28.\n",
|
||||
"\n",
|
||||
"Make sure to understand that the document might not be solely about GPT-4 as the search used covered all aspects of the document. The term \"GPT-4\" could be mentioned anywhere in the paper.\n",
|
||||
"The potential application of GPT-4 in this paper is for few-shot text classification in the financial domain using the Banking77 dataset. The authors propose using conversational GPT models like GPT-3.5 and GPT-4 for in-context learning, which minimizes the technical expertise required and eliminates the need for expensive GPU computing while yielding quick and accurate results.\n",
|
||||
"\n",
|
||||
"APPROVED\n",
|
||||
"Additionally, the paper explores fine-tuning other pre-trained, masked language models with SetFit, a recent contrastive learning technique, to achieve state-of-the-art results in both full-data and few-shot settings.\n",
|
||||
"\n",
|
||||
"The findings of this paper show that querying GPT-3.5 and GPT-4 can outperform fine-tuned, non-generative models even with fewer examples. However, subscription fees associated with these solutions may be considered costly for small organizations. The authors also find that generative models perform better on the given task when shown representative samples selected by a human expert rather than when shown random ones. The proposed methods offer a practical solution for few-shot tasks in datasets with limited label availability, and the state-of-the-art results can inspire future work in the area.\n",
|
||||
"\n",
|
||||
"You can access the full paper [here](http://arxiv.org/abs/2308.14634v1).\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"human.initiate_chat(manager, message=\"Find a latest paper about gpt-4\")\n",
|
||||
"human.initiate_chat(manager, message=\"Find a latest paper about gpt-4 on arxiv and find its potential application.\")\n",
|
||||
"# type exit to terminate the chat"
|
||||
]
|
||||
}
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -45,7 +45,7 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install flaml[autogen]~=2.0.1 docker"
|
||||
"# %pip install flaml[autogen]~=2.0.2 docker"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -221,28 +221,31 @@
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"To suggest a fix to an open good first issue of FLAML, we first need to fetch the list of open issues labeled as \"good first issue\" from the FLAML GitHub repository. We can do this by using the GitHub API. Here is a Python script that uses the requests library to fetch the list of issues.\n",
|
||||
"To suggest a fix to an open good first issue of FLAML, we first need to fetch the list of open issues labeled as \"good first issue\" from the FLAML GitHub repository. We can do this using the GitHub API.\n",
|
||||
"\n",
|
||||
"Here is a Python script that uses the requests library to fetch the list of open issues labeled as \"good first issue\" from the FLAML GitHub repository.\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"# python code\n",
|
||||
"# filename: fetch_issues.py\n",
|
||||
"\n",
|
||||
"import requests\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"def fetch_issues():\n",
|
||||
" url = \"https://api.github.com/repos/microsoft/FLAML/issues\"\n",
|
||||
" response = requests.get(url, params={\"state\": \"open\", \"labels\": \"good first issue\"})\n",
|
||||
" params = {\n",
|
||||
" \"state\": \"open\",\n",
|
||||
" \"labels\": \"good first issue\"\n",
|
||||
" }\n",
|
||||
" response = requests.get(url, params=params)\n",
|
||||
" issues = response.json()\n",
|
||||
"\n",
|
||||
" for issue in issues:\n",
|
||||
" print(f\"Issue ID: {issue['id']}\")\n",
|
||||
" print(f\"Issue Title: {issue['title']}\")\n",
|
||||
" print(f\"Issue URL: {issue['html_url']}\")\n",
|
||||
" print(\"\\n\")\n",
|
||||
" print(f\"Issue ID: {issue['id']}, Title: {issue['title']}, URL: {issue['html_url']}\")\n",
|
||||
"\n",
|
||||
"fetch_issues()\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This script will print the ID, title, and URL of each open issue labeled as \"good first issue\". You can run this script to get the list of issues. After that, I can help you to suggest a fix for one of the issues.\n",
|
||||
"Please run this script to fetch the list of open issues. After that, we can select one issue and suggest a fix for it.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
@@ -253,106 +256,26 @@
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"Code output: \n",
|
||||
"Issue ID: 1809297895\n",
|
||||
"Issue Title: Moving function execution out of UserProxyAgent to be an openai util\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/1135\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1799114476\n",
|
||||
"Issue Title: use_label_encoder warning with xgboost\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/1120\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1705274482\n",
|
||||
"Issue Title: Use appropriate wait time for retry based on the error message. \n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/1034\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1702580697\n",
|
||||
"Issue Title: Issues with Adding Custom APIs in Auto Generation\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/1029\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1658981020\n",
|
||||
"Issue Title: Running flaml[tune] using \"-O\" flag for python interpreter (optimization - disables assertions) crashes\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/981\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1560969891\n",
|
||||
"Issue Title: Conditional parameter flow2 crash\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/903\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1538549388\n",
|
||||
"Issue Title: indentation space\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/884\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1531028010\n",
|
||||
"Issue Title: Check if openml version is required\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/882\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1470354491\n",
|
||||
"Issue Title: Adjust the indent\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/834\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1456950742\n",
|
||||
"Issue Title: pip install flaml FAIL\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/821\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1441047067\n",
|
||||
"Issue Title: Isolate the ensemble part and expose it to users\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/807\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1440171793\n",
|
||||
"Issue Title: how to pass categorical features names or indices to learner\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/805\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1429945686\n",
|
||||
"Issue Title: Flaml/LightGBM - Shouldn't I found better/faster or equal results from FLAML than direct LightGBM?\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/785\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1408240042\n",
|
||||
"Issue Title: Add an announcement of the discord channel\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/764\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1396515109\n",
|
||||
"Issue Title: Documentation about small budget\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/748\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1378268096\n",
|
||||
"Issue Title: Make zero-shot automl more discoverable\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/737\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1189515901\n",
|
||||
"Issue Title: New HCrystalBall release\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/509\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1114253143\n",
|
||||
"Issue Title: samples about conversion to ONNX\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/429\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1107488969\n",
|
||||
"Issue Title: support anomaly detection\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/413\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1061332179\n",
|
||||
"Issue Title: CatBoost Fails with Keyword 'groups'\n",
|
||||
"Issue URL: https://github.com/microsoft/FLAML/issues/304\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Issue ID: 1809297895, Title: Moving function execution out of UserProxyAgent to be an openai util, URL: https://github.com/microsoft/FLAML/issues/1135\n",
|
||||
"Issue ID: 1799114476, Title: use_label_encoder warning with xgboost, URL: https://github.com/microsoft/FLAML/issues/1120\n",
|
||||
"Issue ID: 1705274482, Title: Use appropriate wait time for retry based on the error message. , URL: https://github.com/microsoft/FLAML/issues/1034\n",
|
||||
"Issue ID: 1702580697, Title: Issues with Adding Custom APIs in Auto Generation, URL: https://github.com/microsoft/FLAML/issues/1029\n",
|
||||
"Issue ID: 1658981020, Title: Running flaml[tune] using \"-O\" flag for python interpreter (optimization - disables assertions) crashes, URL: https://github.com/microsoft/FLAML/issues/981\n",
|
||||
"Issue ID: 1560969891, Title: Conditional parameter flow2 crash, URL: https://github.com/microsoft/FLAML/issues/903\n",
|
||||
"Issue ID: 1538549388, Title: indentation space, URL: https://github.com/microsoft/FLAML/issues/884\n",
|
||||
"Issue ID: 1531028010, Title: Check if openml version is required, URL: https://github.com/microsoft/FLAML/issues/882\n",
|
||||
"Issue ID: 1470354491, Title: Adjust the indent, URL: https://github.com/microsoft/FLAML/issues/834\n",
|
||||
"Issue ID: 1456950742, Title: pip install flaml FAIL, URL: https://github.com/microsoft/FLAML/issues/821\n",
|
||||
"Issue ID: 1441047067, Title: Isolate the ensemble part and expose it to users, URL: https://github.com/microsoft/FLAML/issues/807\n",
|
||||
"Issue ID: 1440171793, Title: how to pass categorical features names or indices to learner, URL: https://github.com/microsoft/FLAML/issues/805\n",
|
||||
"Issue ID: 1429945686, Title: Flaml/LightGBM - Shouldn't I found better/faster or equal results from FLAML than direct LightGBM?, URL: https://github.com/microsoft/FLAML/issues/785\n",
|
||||
"Issue ID: 1408240042, Title: Add an announcement of the discord channel, URL: https://github.com/microsoft/FLAML/issues/764\n",
|
||||
"Issue ID: 1396515109, Title: Documentation about small budget, URL: https://github.com/microsoft/FLAML/issues/748\n",
|
||||
"Issue ID: 1378268096, Title: Make zero-shot automl more discoverable, URL: https://github.com/microsoft/FLAML/issues/737\n",
|
||||
"Issue ID: 1189515901, Title: New HCrystalBall release, URL: https://github.com/microsoft/FLAML/issues/509\n",
|
||||
"Issue ID: 1114253143, Title: samples about conversion to ONNX, URL: https://github.com/microsoft/FLAML/issues/429\n",
|
||||
"Issue ID: 1107488969, Title: support anomaly detection, URL: https://github.com/microsoft/FLAML/issues/413\n",
|
||||
"Issue ID: 1061332179, Title: CatBoost Fails with Keyword 'groups', URL: https://github.com/microsoft/FLAML/issues/304\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
@@ -361,7 +284,7 @@
|
||||
"\u001b[32m***** Suggested function Call: ask_planner *****\u001b[0m\n",
|
||||
"Arguments: \n",
|
||||
"{\n",
|
||||
"\"message\": \"Here are the open good first issues of FLAML. Please suggest a plan to fix one of them. The issues are: \\n\\n1. [Moving function execution out of UserProxyAgent to be an openai util](https://github.com/microsoft/FLAML/issues/1135)\\n2. [use_label_encoder warning with xgboost](https://github.com/microsoft/FLAML/issues/1120)\\n3. [Use appropriate wait time for retry based on the error message](https://github.com/microsoft/FLAML/issues/1034)\\n4. [Issues with Adding Custom APIs in Auto Generation](https://github.com/microsoft/FLAML/issues/1029)\\n5. [Running flaml[tune] using \\\"-O\\\" flag for python interpreter (optimization - disables assertions) crashes](https://github.com/microsoft/FLAML/issues/981)\\n6. [Conditional parameter flow2 crash](https://github.com/microsoft/FLAML/issues/903)\\n7. [indentation space](https://github.com/microsoft/FLAML/issues/884)\\n8. [Check if openml version is required](https://github.com/microsoft/FLAML/issues/882)\\n9. [Adjust the indent](https://github.com/microsoft/FLAML/issues/834)\\n10. [pip install flaml FAIL](https://github.com/microsoft/FLAML/issues/821)\\n11. [Isolate the ensemble part and expose it to users](https://github.com/microsoft/FLAML/issues/807)\\n12. [how to pass categorical features names or indices to learner](https://github.com/microsoft/FLAML/issues/805)\\n13. [Flaml/LightGBM - Shouldn't I found better/faster or equal results from FLAML than direct LightGBM?](https://github.com/microsoft/FLAML/issues/785)\\n14. [Add an announcement of the discord channel](https://github.com/microsoft/FLAML/issues/764)\\n15. [Documentation about small budget](https://github.com/microsoft/FLAML/issues/748)\\n16. [Make zero-shot automl more discoverable](https://github.com/microsoft/FLAML/issues/737)\\n17. [New HCrystalBall release](https://github.com/microsoft/FLAML/issues/509)\\n18. [samples about conversion to ONNX](https://github.com/microsoft/FLAML/issues/429)\\n19. [support anomaly detection](https://github.com/microsoft/FLAML/issues/413)\\n20. [CatBoost Fails with Keyword 'groups'](https://github.com/microsoft/FLAML/issues/304)\"\n",
|
||||
"\"message\": \"We have fetched a list of open issues labeled as 'good first issue' from the FLAML GitHub repository. Now, we need to select one issue and suggest a fix for it. Could you please provide a plan for this?\"\n",
|
||||
"}\n",
|
||||
"\u001b[32m************************************************\u001b[0m\n",
|
||||
"\n",
|
||||
@@ -372,115 +295,92 @@
|
||||
">>>>>>>> EXECUTING FUNCTION ask_planner...\u001b[0m\n",
|
||||
"\u001b[33mplanner_user\u001b[0m (to planner):\n",
|
||||
"\n",
|
||||
"Here are the open good first issues of FLAML. Please suggest a plan to fix one of them. The issues are: \n",
|
||||
"\n",
|
||||
"1. [Moving function execution out of UserProxyAgent to be an openai util](https://github.com/microsoft/FLAML/issues/1135)\n",
|
||||
"2. [use_label_encoder warning with xgboost](https://github.com/microsoft/FLAML/issues/1120)\n",
|
||||
"3. [Use appropriate wait time for retry based on the error message](https://github.com/microsoft/FLAML/issues/1034)\n",
|
||||
"4. [Issues with Adding Custom APIs in Auto Generation](https://github.com/microsoft/FLAML/issues/1029)\n",
|
||||
"5. [Running flaml[tune] using \"-O\" flag for python interpreter (optimization - disables assertions) crashes](https://github.com/microsoft/FLAML/issues/981)\n",
|
||||
"6. [Conditional parameter flow2 crash](https://github.com/microsoft/FLAML/issues/903)\n",
|
||||
"7. [indentation space](https://github.com/microsoft/FLAML/issues/884)\n",
|
||||
"8. [Check if openml version is required](https://github.com/microsoft/FLAML/issues/882)\n",
|
||||
"9. [Adjust the indent](https://github.com/microsoft/FLAML/issues/834)\n",
|
||||
"10. [pip install flaml FAIL](https://github.com/microsoft/FLAML/issues/821)\n",
|
||||
"11. [Isolate the ensemble part and expose it to users](https://github.com/microsoft/FLAML/issues/807)\n",
|
||||
"12. [how to pass categorical features names or indices to learner](https://github.com/microsoft/FLAML/issues/805)\n",
|
||||
"13. [Flaml/LightGBM - Shouldn't I found better/faster or equal results from FLAML than direct LightGBM?](https://github.com/microsoft/FLAML/issues/785)\n",
|
||||
"14. [Add an announcement of the discord channel](https://github.com/microsoft/FLAML/issues/764)\n",
|
||||
"15. [Documentation about small budget](https://github.com/microsoft/FLAML/issues/748)\n",
|
||||
"16. [Make zero-shot automl more discoverable](https://github.com/microsoft/FLAML/issues/737)\n",
|
||||
"17. [New HCrystalBall release](https://github.com/microsoft/FLAML/issues/509)\n",
|
||||
"18. [samples about conversion to ONNX](https://github.com/microsoft/FLAML/issues/429)\n",
|
||||
"19. [support anomaly detection](https://github.com/microsoft/FLAML/issues/413)\n",
|
||||
"20. [CatBoost Fails with Keyword 'groups'](https://github.com/microsoft/FLAML/issues/304)\n",
|
||||
"We have fetched a list of open issues labeled as 'good first issue' from the FLAML GitHub repository. Now, we need to select one issue and suggest a fix for it. Could you please provide a plan for this?\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mplanner\u001b[0m (to planner_user):\n",
|
||||
"\n",
|
||||
"Let's try to fix the \"use_label_encoder warning with xgboost\" issue from the list. The plan involves:\n",
|
||||
"Sure, here's a plan for selecting one issue from the list and suggesting a fix for it:\n",
|
||||
"\n",
|
||||
"1. Investigate why the warning is raised:\n",
|
||||
"Go through the FLAML repository's source code, specifically the part that deals with xgboost implementation. Check if the 'use_label_encoder' parameter is actually used during model training. If yes, inspect which value it has been assigned and try to find out why this parameter may be causing the warning.\n",
|
||||
"1. Import the fetched list of open issues labeled as 'good first issue' from the FLAML GitHub repository into your AI assistant. \n",
|
||||
"2. Examine the list for common issue attributes like 'title', 'description', 'labels', 'issue number', 'created at', and 'updated at'. \n",
|
||||
"3. To select a suitable issue for fixing, apply a selection criteria based on your preferences, such as prioritizing by the 'created at' attribute in descending order to pick the most recent issue, or filtering by a specific label in addition to 'good first issue'. Write code to filter and sort the issues accordingly.\n",
|
||||
"4. Inspect the execution result. If the selection criteria are not applied correctly, modify the code to fix any errors.\n",
|
||||
"5. Once the issue is selected, read the issue's title, description, and any linked resources or documents to understand the problem to be solved.\n",
|
||||
"6. Break down the issue into smaller tasks that can be addressed by writing code, and create a step-by-step plan.\n",
|
||||
"\n",
|
||||
"2. Understand the role of use_label_encoder in XGBoost:\n",
|
||||
"Understand what the 'use_label_encoder' parameter does and why it would be important to XGBoost. This might require studying XGBoost's documentation and community discussions, particularly any discussions related to this warning.\n",
|
||||
"For instance, the following could be smaller tasks to address the selected issue:\n",
|
||||
" a. Understand the issue's background and requirements.\n",
|
||||
" b. Write clear and concise instructions to reproduce the issue.\n",
|
||||
" c. Analyze existing code or tests related to the issue.\n",
|
||||
" d. Devise a solution to fix the issue.\n",
|
||||
" e. Implement the solution in separate code pieces.\n",
|
||||
" f. Verify that the solution addresses the issue.\n",
|
||||
" g. Write unit tests to ensure the solution is robust and handles edge cases.\n",
|
||||
"\n",
|
||||
"3. Suggest modification safely without affecting model performance and other functionalities:\n",
|
||||
"Once you understand the role and importance of the 'use_label_encoder parameter', think about how you could modify its usage in FLAML's XGBoost implementation to prevent the warning. The plan you design should preserve the current functionalities and not negatively impact the model's performance.\n",
|
||||
"7. Inspect the execution result. If the issue is misunderstood or the tasks' breakdown is incorrect, revise the understanding of the issue and modify the tasks accordingly.\n",
|
||||
"8. With the defined tasks and step-by-step plan, work on each task, and test the implemented code to ensure the issue is solved.\n",
|
||||
"9. If any issues arise during the task execution, analyze the errors and adjust the plan or code accordingly.\n",
|
||||
"10. Once the issue is fixed, prepare a pull request on GitHub, mentioning the issue number and giving a brief description of the solution in the merge request.\n",
|
||||
"\n",
|
||||
"4. Implement the plan:\n",
|
||||
"Once you have a modification plan, implement it in the code. Ensure to follow any code style guides or standards set by the FLAML project.\n",
|
||||
"\n",
|
||||
"5. Test the changes:\n",
|
||||
"After changing your code, thoroughly test it to make sure the warning is no longer appearing and that your changes haven't caused any other issues. This involves running existing unit tests and creating new tests if necessary.\n",
|
||||
"\n",
|
||||
"6. Evaluate the change:\n",
|
||||
"Check again to ensure that model performance and functionality haven't been negatively affected. \n",
|
||||
"\n",
|
||||
"7. Create a Pull Request:\n",
|
||||
"Having made your changes and ensured everything is working correctly, submit your modification as a pull request to the original FLAML repository and follow any steps they have for the contribution process. \n",
|
||||
"\n",
|
||||
"8. Respond to review:\n",
|
||||
"The reviewers may have comments or require changes, be ready to address any that come up until the solution is accepted. \n",
|
||||
"\n",
|
||||
"Please note that each step in this plan requires coding and/or reasoning, which are critical to an AI assistant's operations.\n",
|
||||
"Remember that this is meant to be a general plan, and the specific tasks may vary depending on the selected issue. Adjust the plan as needed, based on the selected issue's requirements and your problem-solving approach.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"\u001b[32m***** Response from calling function \"ask_planner\" *****\u001b[0m\n",
|
||||
"Let's try to fix the \"use_label_encoder warning with xgboost\" issue from the list. The plan involves:\n",
|
||||
"Sure, here's a plan for selecting one issue from the list and suggesting a fix for it:\n",
|
||||
"\n",
|
||||
"1. Investigate why the warning is raised:\n",
|
||||
"Go through the FLAML repository's source code, specifically the part that deals with xgboost implementation. Check if the 'use_label_encoder' parameter is actually used during model training. If yes, inspect which value it has been assigned and try to find out why this parameter may be causing the warning.\n",
|
||||
"1. Import the fetched list of open issues labeled as 'good first issue' from the FLAML GitHub repository into your AI assistant. \n",
|
||||
"2. Examine the list for common issue attributes like 'title', 'description', 'labels', 'issue number', 'created at', and 'updated at'. \n",
|
||||
"3. To select a suitable issue for fixing, apply a selection criteria based on your preferences, such as prioritizing by the 'created at' attribute in descending order to pick the most recent issue, or filtering by a specific label in addition to 'good first issue'. Write code to filter and sort the issues accordingly.\n",
|
||||
"4. Inspect the execution result. If the selection criteria are not applied correctly, modify the code to fix any errors.\n",
|
||||
"5. Once the issue is selected, read the issue's title, description, and any linked resources or documents to understand the problem to be solved.\n",
|
||||
"6. Break down the issue into smaller tasks that can be addressed by writing code, and create a step-by-step plan.\n",
|
||||
"\n",
|
||||
"2. Understand the role of use_label_encoder in XGBoost:\n",
|
||||
"Understand what the 'use_label_encoder' parameter does and why it would be important to XGBoost. This might require studying XGBoost's documentation and community discussions, particularly any discussions related to this warning.\n",
|
||||
"For instance, the following could be smaller tasks to address the selected issue:\n",
|
||||
" a. Understand the issue's background and requirements.\n",
|
||||
" b. Write clear and concise instructions to reproduce the issue.\n",
|
||||
" c. Analyze existing code or tests related to the issue.\n",
|
||||
" d. Devise a solution to fix the issue.\n",
|
||||
" e. Implement the solution in separate code pieces.\n",
|
||||
" f. Verify that the solution addresses the issue.\n",
|
||||
" g. Write unit tests to ensure the solution is robust and handles edge cases.\n",
|
||||
"\n",
|
||||
"3. Suggest modification safely without affecting model performance and other functionalities:\n",
|
||||
"Once you understand the role and importance of the 'use_label_encoder parameter', think about how you could modify its usage in FLAML's XGBoost implementation to prevent the warning. The plan you design should preserve the current functionalities and not negatively impact the model's performance.\n",
|
||||
"7. Inspect the execution result. If the issue is misunderstood or the tasks' breakdown is incorrect, revise the understanding of the issue and modify the tasks accordingly.\n",
|
||||
"8. With the defined tasks and step-by-step plan, work on each task, and test the implemented code to ensure the issue is solved.\n",
|
||||
"9. If any issues arise during the task execution, analyze the errors and adjust the plan or code accordingly.\n",
|
||||
"10. Once the issue is fixed, prepare a pull request on GitHub, mentioning the issue number and giving a brief description of the solution in the merge request.\n",
|
||||
"\n",
|
||||
"4. Implement the plan:\n",
|
||||
"Once you have a modification plan, implement it in the code. Ensure to follow any code style guides or standards set by the FLAML project.\n",
|
||||
"\n",
|
||||
"5. Test the changes:\n",
|
||||
"After changing your code, thoroughly test it to make sure the warning is no longer appearing and that your changes haven't caused any other issues. This involves running existing unit tests and creating new tests if necessary.\n",
|
||||
"\n",
|
||||
"6. Evaluate the change:\n",
|
||||
"Check again to ensure that model performance and functionality haven't been negatively affected. \n",
|
||||
"\n",
|
||||
"7. Create a Pull Request:\n",
|
||||
"Having made your changes and ensured everything is working correctly, submit your modification as a pull request to the original FLAML repository and follow any steps they have for the contribution process. \n",
|
||||
"\n",
|
||||
"8. Respond to review:\n",
|
||||
"The reviewers may have comments or require changes, be ready to address any that come up until the solution is accepted. \n",
|
||||
"\n",
|
||||
"Please note that each step in this plan requires coding and/or reasoning, which are critical to an AI assistant's operations.\n",
|
||||
"Remember that this is meant to be a general plan, and the specific tasks may vary depending on the selected issue. Adjust the plan as needed, based on the selected issue's requirements and your problem-solving approach.\n",
|
||||
"\u001b[32m********************************************************\u001b[0m\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"Based on the plan provided, here are the steps to fix the \"use_label_encoder warning with xgboost\" issue:\n",
|
||||
"Based on the plan, let's select the most recent issue from the list. The most recent issue is \"Moving function execution out of UserProxyAgent to be an openai util\" with the URL: https://github.com/microsoft/FLAML/issues/1135\n",
|
||||
"\n",
|
||||
"1. **Investigate why the warning is raised**: This step involves going through the FLAML repository's source code, specifically the part that deals with xgboost implementation. You need to check if the 'use_label_encoder' parameter is actually used during model training. If yes, inspect which value it has been assigned and try to find out why this parameter may be causing the warning.\n",
|
||||
"To suggest a fix for this issue, we need to understand the problem first. Please visit the issue URL and read the issue's description and any comments to understand the problem.\n",
|
||||
"\n",
|
||||
"2. **Understand the role of use_label_encoder in XGBoost**: This step requires studying XGBoost's documentation and community discussions, particularly any discussions related to this warning. You need to understand what the 'use_label_encoder' parameter does and why it would be important to XGBoost.\n",
|
||||
"Once you understand the problem, here are the steps you can follow to suggest a fix:\n",
|
||||
"\n",
|
||||
"3. **Suggest modification safely without affecting model performance and other functionalities**: Once you understand the role and importance of the 'use_label_encoder parameter', you need to think about how you could modify its usage in FLAML's XGBoost implementation to prevent the warning. The plan you design should preserve the current functionalities and not negatively impact the model's performance.\n",
|
||||
"1. Understand the issue's background and requirements: Read the issue description and any linked resources or documents to understand what needs to be done.\n",
|
||||
"\n",
|
||||
"4. **Implement the plan**: Once you have a modification plan, you need to implement it in the code. You should follow any code style guides or standards set by the FLAML project.\n",
|
||||
"2. Write clear and concise instructions to reproduce the issue: Based on the issue description, write down the steps to reproduce the issue. This will help you understand the problem better and will be useful when you test your fix.\n",
|
||||
"\n",
|
||||
"5. **Test the changes**: After changing your code, you need to thoroughly test it to make sure the warning is no longer appearing and that your changes haven't caused any other issues. This involves running existing unit tests and creating new tests if necessary.\n",
|
||||
"3. Analyze existing code or tests related to the issue: Look at the code in the FLAML repository that is related to the issue. Try to understand how it works and how it might be causing the issue.\n",
|
||||
"\n",
|
||||
"6. **Evaluate the change**: You need to check again to ensure that model performance and functionality haven't been negatively affected.\n",
|
||||
"4. Devise a solution to fix the issue: Based on your understanding of the problem and the code, come up with a solution to fix the issue. This might involve writing new code or modifying existing code.\n",
|
||||
"\n",
|
||||
"7. **Create a Pull Request**: Having made your changes and ensured everything is working correctly, you should submit your modification as a pull request to the original FLAML repository and follow any steps they have for the contribution process.\n",
|
||||
"5. Implement the solution in separate code pieces: Write the code for your solution. Make sure to break it down into small, manageable pieces. This will make it easier to understand and test.\n",
|
||||
"\n",
|
||||
"8. **Respond to review**: The reviewers may have comments or require changes, you should be ready to address any that come up until the solution is accepted.\n",
|
||||
"6. Verify that the solution addresses the issue: Test your code to make sure it solves the issue. You can do this by following the instructions you wrote to reproduce the issue.\n",
|
||||
"\n",
|
||||
"Please note that each step in this plan requires coding and/or reasoning, which are critical to an AI assistant's operations. \n",
|
||||
"7. Write unit tests to ensure the solution is robust and handles edge cases: In addition to testing your solution manually, write unit tests to automatically test your code. This will help ensure that your solution works in all cases and that future changes to the code won't break your solution.\n",
|
||||
"\n",
|
||||
"8. Prepare a pull request on GitHub: Once you're confident that your solution works, prepare a pull request on GitHub. In the pull request description, mention the issue number and give a brief description of your solution.\n",
|
||||
"\n",
|
||||
"Please note that this is a general plan and the specific steps may vary depending on the issue. Adjust the plan as needed based on the issue's requirements and your problem-solving approach. \n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"\n",
|
||||
|
||||
@@ -44,7 +44,7 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install flaml[autogen]~=2.0.1"
|
||||
"# %pip install flaml[autogen]~=2.0.2"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -141,7 +141,7 @@
|
||||
" expert.stop_reply_at_receive(assistant_for_expert)\n",
|
||||
" # expert.human_input_mode, expert.max_consecutive_auto_reply = \"NEVER\", 0\n",
|
||||
" # final message sent from the expert\n",
|
||||
" expert.send(\"summarize the solution\", assistant_for_expert)\n",
|
||||
" expert.send(\"summarize the solution and explain the answer in an easy-to-understand way\", assistant_for_expert)\n",
|
||||
" # return the last message the expert received\n",
|
||||
" return expert.last_message()[\"content\"]\n"
|
||||
]
|
||||
@@ -164,6 +164,7 @@
|
||||
"source": [
|
||||
"assistant_for_student = autogen.AssistantAgent(\n",
|
||||
" name=\"assistant_for_student\",\n",
|
||||
" system_message=\"You are a helpful assistant. Reply TERMINATE when the task is done.\",\n",
|
||||
" llm_config={\n",
|
||||
" \"request_timeout\": 600,\n",
|
||||
" \"seed\": 42,\n",
|
||||
@@ -232,71 +233,9 @@
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_student\u001b[0m (to student):\n",
|
||||
"\n",
|
||||
"To solve this task, we can use the method of substitution or elimination in linear algebra. We have three equations and three unknowns (a, b, c). We can solve these equations step by step using Python's sympy library, which provides a powerful interface for symbolic computation.\n",
|
||||
"To find the values of $a$, $b$, and $c$, we need to solve the system of equations. However, the system of equations you provided seems to be incomplete or incorrect. The equations are not properly formatted, and it's unclear how $x$ and $y$ are related to $a$, $b$, and $c$. \n",
|
||||
"\n",
|
||||
"Here is the plan:\n",
|
||||
"1. Define the symbols a, b, c, x, y using sympy.symbols.\n",
|
||||
"2. Define the equations using sympy.Eq.\n",
|
||||
"3. Use sympy.solve to solve the equations.\n",
|
||||
"\n",
|
||||
"Let's write the Python code to solve these equations.\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"# python code\n",
|
||||
"import sympy as sp\n",
|
||||
"\n",
|
||||
"# define the symbols\n",
|
||||
"a, b, c, x, y = sp.symbols('a b c x y')\n",
|
||||
"\n",
|
||||
"# define the equations\n",
|
||||
"eq1 = sp.Eq(a*x + b*y + c, x + 7)\n",
|
||||
"eq2 = sp.Eq(a + b*x + c*y, 2*x + 6*y)\n",
|
||||
"eq3 = sp.Eq(a*y + b + c*x, 4*x + y)\n",
|
||||
"\n",
|
||||
"# solve the equations\n",
|
||||
"solution = sp.solve((eq1, eq2, eq3), (a, b, c))\n",
|
||||
"\n",
|
||||
"# print the solution\n",
|
||||
"print(f\"a = {solution[a]}, b = {solution[b]}, c = {solution[c]}\")\n",
|
||||
"\n",
|
||||
"# calculate a + b + c\n",
|
||||
"sum_abc = solution[a] + solution[b] + solution[c]\n",
|
||||
"print(f\"a + b + c = {sum_abc}\")\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Please run this Python code to find the values of a, b, c and their sum.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33mstudent\u001b[0m (to assistant_for_student):\n",
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"Code output: \n",
|
||||
"a = (x**2 - 3*x*y + 2*x + y**2 - y)/(x**2 - x*y - x + y**2 - y + 1), b = (2*x**2 - 3*x + y)/(x**2 - x*y - x + y**2 - y + 1), c = (4*x**2 - 4*x*y - 6*x + 6*y**2 - 7*y + 7)/(x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"a + b + c = (2*x**2 - 3*x + y)/(x**2 - x*y - x + y**2 - y + 1) + (x**2 - 3*x*y + 2*x + y**2 - y)/(x**2 - x*y - x + y**2 - y + 1) + (4*x**2 - 4*x*y - 6*x + 6*y**2 - 7*y + 7)/(x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_student\u001b[0m (to student):\n",
|
||||
"\n",
|
||||
"The Python code has successfully solved the system of equations and found the values of a, b, c as well as their sum. The results are in terms of x and y. \n",
|
||||
"\n",
|
||||
"Here are the results:\n",
|
||||
"\n",
|
||||
"a = (x² - 3xy + 2x + y² - y) / (x² - xy - x + y² - y + 1)\n",
|
||||
"\n",
|
||||
"b = (2x² - 3x + y) / (x² - xy - x + y² - y + 1)\n",
|
||||
"\n",
|
||||
"c = (4x² - 4xy - 6x + 6y² - 7y + 7) / (x² - xy - x + y² - y + 1)\n",
|
||||
"\n",
|
||||
"a + b + c = (2x² - 3x + y) / (x² - xy - x + y² - y + 1) + (x² - 3xy + 2x + y² - y) / (x² - xy - x + y² - y + 1) + (4x² - 4xy - 6x + 6y² - 7y + 7) / (x² - xy - x + y² - y + 1)\n",
|
||||
"\n",
|
||||
"Please note that these solutions are valid for x + y ≠ -1, as stated in the problem.\n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"Could you please provide the correct system of equations?\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
@@ -308,20 +247,10 @@
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_student\u001b[0m (to student):\n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mstudent\u001b[0m (to assistant_for_student):\n",
|
||||
"\n",
|
||||
"I don't know if the answer is correct. It looks complicated.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_student\u001b[0m (to student):\n",
|
||||
"\n",
|
||||
"\u001b[32m***** Suggested function Call: ask_expert *****\u001b[0m\n",
|
||||
"Arguments: \n",
|
||||
"{\n",
|
||||
" \"message\": \"The user is unsure about the correctness of the solution provided for the system of equations. The solution is in terms of x and y, and it looks complicated. Here are the equations and the solution:\\n\\nEquations:\\n1. ax + by + c = x + 7\\n2. a + bx + cy = 2x + 6y\\n3. ay + b + cx = 4x + y\\n\\nSolution:\\na = (x² - 3xy + 2x + y² - y) / (x² - xy - x + y² - y + 1)\\nb = (2x² - 3x + y) / (x² - xy - x + y² - y + 1)\\nc = (4x² - 4xy - 6x + 6y² - 7y + 7) / (x² - xy - x + y² - y + 1)\\na + b + c = (2x² - 3x + y) / (x² - xy - x + y² - y + 1) + (x² - 3xy + 2x + y² - y) / (x² - xy - x + y² - y + 1) + (4x² - 4xy - 6x + 6y² - 7y + 7) / (x² - xy - x + y² - y + 1)\\n\\nCould you please verify if the solution is correct?\"\n",
|
||||
" \"message\": \"The user provided a system of equations and asked to find $a + b + c$, given that $x+y \\\\neq -1$ and \\n\\\\begin{align}\\n\\tax + by + c & = x + 7,\\\\\\n\\ta + bx + cy & = 2x + 6y,\\\\\\n\\tay + b + cx & = 4x + y.\\n\\\\end{align}. However, the system of equations seems to be incomplete or incorrectly formatted. How should I proceed?\"\n",
|
||||
"}\n",
|
||||
"\u001b[32m***********************************************\u001b[0m\n",
|
||||
"\n",
|
||||
@@ -332,49 +261,72 @@
|
||||
">>>>>>>> EXECUTING FUNCTION ask_expert...\u001b[0m\n",
|
||||
"\u001b[33mexpert\u001b[0m (to assistant_for_expert):\n",
|
||||
"\n",
|
||||
"The user is unsure about the correctness of the solution provided for the system of equations. The solution is in terms of x and y, and it looks complicated. Here are the equations and the solution:\n",
|
||||
"\n",
|
||||
"Equations:\n",
|
||||
"1. ax + by + c = x + 7\n",
|
||||
"2. a + bx + cy = 2x + 6y\n",
|
||||
"3. ay + b + cx = 4x + y\n",
|
||||
"\n",
|
||||
"Solution:\n",
|
||||
"a = (x² - 3xy + 2x + y² - y) / (x² - xy - x + y² - y + 1)\n",
|
||||
"b = (2x² - 3x + y) / (x² - xy - x + y² - y + 1)\n",
|
||||
"c = (4x² - 4xy - 6x + 6y² - 7y + 7) / (x² - xy - x + y² - y + 1)\n",
|
||||
"a + b + c = (2x² - 3x + y) / (x² - xy - x + y² - y + 1) + (x² - 3xy + 2x + y² - y) / (x² - xy - x + y² - y + 1) + (4x² - 4xy - 6x + 6y² - 7y + 7) / (x² - xy - x + y² - y + 1)\n",
|
||||
"\n",
|
||||
"Could you please verify if the solution is correct?\n",
|
||||
"The user provided a system of equations and asked to find $a + b + c$, given that $x+y \\neq -1$ and \n",
|
||||
"\\begin{align}\n",
|
||||
"\tax + by + c & = x + 7,\\\n",
|
||||
"\ta + bx + cy & = 2x + 6y,\\\n",
|
||||
"\tay + b + cx & = 4x + y.\n",
|
||||
"\\end{align}. However, the system of equations seems to be incomplete or incorrectly formatted. How should I proceed?\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_expert\u001b[0m (to expert):\n",
|
||||
"\n",
|
||||
"To verify the solution, we can substitute the given values of a, b, and c into the original equations and check if both sides of the equations are equal. If they are, then the solution is correct. If not, then the solution is incorrect.\n",
|
||||
"To proceed, we can first try to solve the given system of equations and see if we can find a unique solution for $a$, $b$, and $c$. If we can't find a unique solution, we can then determine if there's any additional information needed or if the system is indeed incomplete or incorrectly formatted.\n",
|
||||
"\n",
|
||||
"Here is the Python code to perform this verification:\n",
|
||||
"Let's solve the given system of equations using Python's SymPy library.\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"from sympy import symbols, simplify\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mexpert\u001b[0m (to assistant_for_expert):\n",
|
||||
"\n",
|
||||
"# Define the symbols\n",
|
||||
"x, y = symbols('x y')\n",
|
||||
"make sure you simplify the answer\n",
|
||||
"\n",
|
||||
"# Given solutions\n",
|
||||
"a = (x**2 - 3*x*y + 2*x + y**2 - y) / (x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"b = (2*x**2 - 3*x + y) / (x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"c = (4*x**2 - 4*x*y - 6*x + 6*y**2 - 7*y + 7) / (x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_expert\u001b[0m (to expert):\n",
|
||||
"\n",
|
||||
"# Original equations\n",
|
||||
"eq1 = simplify(a*x + b*y + c - x - 7)\n",
|
||||
"eq2 = simplify(a + b*x + c*y - 2*x - 6*y)\n",
|
||||
"eq3 = simplify(a*y + b + c*x - 4*x - y)\n",
|
||||
"Sure, I will simplify the answer. Let's solve the given system of equations using Python's SymPy library and simplify the result.\n",
|
||||
"\n",
|
||||
"# Check if the equations are satisfied\n",
|
||||
"print(eq1 == 0 and eq2 == 0 and eq3 == 0)\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[33mexpert\u001b[0m (to assistant_for_expert):\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_expert\u001b[0m (to expert):\n",
|
||||
"\n",
|
||||
"First, let's install the SymPy library if you haven't already. Execute the following command to install it:\n",
|
||||
"\n",
|
||||
"```sh\n",
|
||||
"pip install sympy\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This code uses the sympy library to handle symbolic computation. It first defines the symbols x and y. Then it defines a, b, and c as given in the solution. It then defines the original equations and simplifies them. Finally, it checks if all the equations are satisfied (i.e., equal to zero). If they are, it prints True, indicating that the solution is correct. If not, it prints False, indicating that the solution is incorrect.\n",
|
||||
"Now, let's solve the given system of equations using Python's SymPy library and simplify the result.\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"from sympy import symbols, Eq, solve, simplify\n",
|
||||
"\n",
|
||||
"a, b, c, x, y = symbols('a b c x y')\n",
|
||||
"\n",
|
||||
"eq1 = Eq(a * x + b * y + c, x + 7)\n",
|
||||
"eq2 = Eq(a + b * x + c * y, 2 * x + 6 * y)\n",
|
||||
"eq3 = Eq(a * y + b + c * x, 4 * x + y)\n",
|
||||
"\n",
|
||||
"solutions = solve((eq1, eq2, eq3), (a, b, c))\n",
|
||||
"\n",
|
||||
"simplified_solutions = {key: simplify(value) for key, value in solutions.items()}\n",
|
||||
"a_val, b_val, c_val = simplified_solutions[a], simplified_solutions[b], simplified_solutions[c]\n",
|
||||
"sum_abc = simplify(a_val + b_val + c_val)\n",
|
||||
"\n",
|
||||
"print(f\"a: {a_val}\")\n",
|
||||
"print(f\"b: {b_val}\")\n",
|
||||
"print(f\"c: {c_val}\")\n",
|
||||
"print(f\"a + b + c: {sum_abc}\")\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Please execute the above Python code to solve the system of equations and simplify the result.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
@@ -382,121 +334,123 @@
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is sh)...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33mexpert\u001b[0m (to assistant_for_expert):\n",
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"Code output: \n",
|
||||
"True\n",
|
||||
"Defaulting to user installation because normal site-packages is not writeable\n",
|
||||
"Requirement already satisfied: sympy in /home/vscode/.local/lib/python3.9/site-packages (1.12)\n",
|
||||
"Requirement already satisfied: mpmath>=0.19 in /home/vscode/.local/lib/python3.9/site-packages (from sympy) (1.3.0)\n",
|
||||
"\n",
|
||||
"a: (x**2 - 3*x*y + 2*x + y**2 - y)/(x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"b: (2*x**2 - 3*x + y)/(x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"c: (4*x**2 - 4*x*y - 6*x + 6*y**2 - 7*y + 7)/(x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"a + b + c: 7\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_expert\u001b[0m (to expert):\n",
|
||||
"\n",
|
||||
"The Python code has returned True, which means that the given solution for the system of equations is correct. The values of a, b, and c, as provided in the solution, satisfy all the original equations when substituted back into them. Therefore, the solution is verified and correct. \n",
|
||||
"The given system of equations has been solved and simplified. The result is:\n",
|
||||
"\n",
|
||||
"$$\n",
|
||||
"a = \\frac{x^2 - 3xy + 2x + y^2 - y}{x^2 - xy - x + y^2 - y + 1}, \\\\\n",
|
||||
"b = \\frac{2x^2 - 3x + y}{x^2 - xy - x + y^2 - y + 1}, \\\\\n",
|
||||
"c = \\frac{4x^2 - 4xy - 6x + 6y^2 - 7y + 7}{x^2 - xy - x + y^2 - y + 1}.\n",
|
||||
"$$\n",
|
||||
"\n",
|
||||
"The sum $a + b + c$ is equal to $7$. Since we were able to find a unique solution for $a$, $b$, and $c$, the system of equations is complete and correctly formatted.\n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mexpert\u001b[0m (to assistant_for_expert):\n",
|
||||
"\n",
|
||||
"simplify the answer\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_expert\u001b[0m (to expert):\n",
|
||||
"\n",
|
||||
"The solution provided for the system of equations is already in its simplest form. The expressions for a, b, and c are fractions where the numerator and denominator are polynomials in x and y. These polynomials cannot be further simplified or factored to reduce the complexity of the expressions.\n",
|
||||
"\n",
|
||||
"However, if you want to simplify the expression for a + b + c, we can do that. Here is the Python code to perform this simplification:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"from sympy import symbols, simplify\n",
|
||||
"\n",
|
||||
"# Define the symbols\n",
|
||||
"x, y = symbols('x y')\n",
|
||||
"\n",
|
||||
"# Given solutions\n",
|
||||
"a = (x**2 - 3*x*y + 2*x + y**2 - y) / (x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"b = (2*x**2 - 3*x + y) / (x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"c = (4*x**2 - 4*x*y - 6*x + 6*y**2 - 7*y + 7) / (x**2 - x*y - x + y**2 - y + 1)\n",
|
||||
"\n",
|
||||
"# Simplify a + b + c\n",
|
||||
"abc_simplified = simplify(a + b + c)\n",
|
||||
"\n",
|
||||
"# Print the simplified expression\n",
|
||||
"print(abc_simplified)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This code first defines the symbols x and y, and then defines a, b, and c as given in the solution. It then calculates the sum a + b + c and simplifies it using the sympy library's simplify function. Finally, it prints the simplified expression.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[33mexpert\u001b[0m (to assistant_for_expert):\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_expert\u001b[0m (to expert):\n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
|
||||
"\u001b[33mexpert\u001b[0m (to assistant_for_expert):\n",
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"Code output: \n",
|
||||
"7\n",
|
||||
"\n",
|
||||
"summarize the solution and explain the answer in an easy-to-understand way\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_expert\u001b[0m (to expert):\n",
|
||||
"\n",
|
||||
"The Python code has returned 7, which means that the sum of a, b, and c, as provided in the solution, simplifies to 7. Therefore, the simplified expression for a + b + c is 7.\n",
|
||||
"Sure, let me summarize the solution and explain the answer in an easy-to-understand way.\n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"We were given a system of three equations with three unknowns, $a$, $b$, and $c$. The equations were:\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mexpert\u001b[0m (to assistant_for_expert):\n",
|
||||
"1. $ax + by + c = x + 7$\n",
|
||||
"2. $a + bx + cy = 2x + 6y$\n",
|
||||
"3. $ay + b + cx = 4x + y$\n",
|
||||
"\n",
|
||||
"summarize the solution\n",
|
||||
"We were asked to find the sum $a + b + c$ given that $x + y \\neq -1$.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_expert\u001b[0m (to expert):\n",
|
||||
"We used Python's SymPy library to solve the system of equations and found the following solutions for $a$, $b$, and $c$:\n",
|
||||
"\n",
|
||||
"The solution provided for the system of equations is correct. The expressions for a, b, and c, when substituted back into the original equations, satisfy all of them. \n",
|
||||
"$$\n",
|
||||
"a = \\frac{x^2 - 3xy + 2x + y^2 - y}{x^2 - xy - x + y^2 - y + 1}, \\\\\n",
|
||||
"b = \\frac{2x^2 - 3x + y}{x^2 - xy - x + y^2 - y + 1}, \\\\\n",
|
||||
"c = \\frac{4x^2 - 4xy - 6x + 6y^2 - 7y + 7}{x^2 - xy - x + y^2 - y + 1}.\n",
|
||||
"$$\n",
|
||||
"\n",
|
||||
"The expressions for a, b, and c are:\n",
|
||||
"These solutions are expressed in terms of $x$ and $y$. However, we were able to find the sum $a + b + c$ without knowing the values of $x$ and $y$. The sum $a + b + c$ simplifies to $7$.\n",
|
||||
"\n",
|
||||
"a = (x² - 3xy + 2x + y² - y) / (x² - xy - x + y² - y + 1)\n",
|
||||
"b = (2x² - 3x + y) / (x² - xy - x + y² - y + 1)\n",
|
||||
"c = (4x² - 4xy - 6x + 6y² - 7y + 7) / (x² - xy - x + y² - y + 1)\n",
|
||||
"\n",
|
||||
"Moreover, the sum of a, b, and c simplifies to 7. This means that regardless of the values of x and y, the sum of a, b, and c will always be 7.\n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"In conclusion, the sum $a + b + c$ for the given system of equations is equal to $7$.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mstudent\u001b[0m (to assistant_for_student):\n",
|
||||
"\n",
|
||||
"\u001b[32m***** Response from calling function \"ask_expert\" *****\u001b[0m\n",
|
||||
"The solution provided for the system of equations is correct. The expressions for a, b, and c, when substituted back into the original equations, satisfy all of them. \n",
|
||||
"Sure, let me summarize the solution and explain the answer in an easy-to-understand way.\n",
|
||||
"\n",
|
||||
"The expressions for a, b, and c are:\n",
|
||||
"We were given a system of three equations with three unknowns, $a$, $b$, and $c$. The equations were:\n",
|
||||
"\n",
|
||||
"a = (x² - 3xy + 2x + y² - y) / (x² - xy - x + y² - y + 1)\n",
|
||||
"b = (2x² - 3x + y) / (x² - xy - x + y² - y + 1)\n",
|
||||
"c = (4x² - 4xy - 6x + 6y² - 7y + 7) / (x² - xy - x + y² - y + 1)\n",
|
||||
"1. $ax + by + c = x + 7$\n",
|
||||
"2. $a + bx + cy = 2x + 6y$\n",
|
||||
"3. $ay + b + cx = 4x + y$\n",
|
||||
"\n",
|
||||
"Moreover, the sum of a, b, and c simplifies to 7. This means that regardless of the values of x and y, the sum of a, b, and c will always be 7.\n",
|
||||
"We were asked to find the sum $a + b + c$ given that $x + y \\neq -1$.\n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"We used Python's SymPy library to solve the system of equations and found the following solutions for $a$, $b$, and $c$:\n",
|
||||
"\n",
|
||||
"$$\n",
|
||||
"a = \\frac{x^2 - 3xy + 2x + y^2 - y}{x^2 - xy - x + y^2 - y + 1}, \\\\\n",
|
||||
"b = \\frac{2x^2 - 3x + y}{x^2 - xy - x + y^2 - y + 1}, \\\\\n",
|
||||
"c = \\frac{4x^2 - 4xy - 6x + 6y^2 - 7y + 7}{x^2 - xy - x + y^2 - y + 1}.\n",
|
||||
"$$\n",
|
||||
"\n",
|
||||
"These solutions are expressed in terms of $x$ and $y$. However, we were able to find the sum $a + b + c$ without knowing the values of $x$ and $y$. The sum $a + b + c$ simplifies to $7$.\n",
|
||||
"\n",
|
||||
"In conclusion, the sum $a + b + c$ for the given system of equations is equal to $7$.\n",
|
||||
"\u001b[32m*******************************************************\u001b[0m\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant_for_student\u001b[0m (to student):\n",
|
||||
"\n",
|
||||
"The expert has confirmed that the solution provided for the system of equations is correct. The expressions for a, b, and c, when substituted back into the original equations, satisfy all of them. \n",
|
||||
"The solution to the system of equations you provided is:\n",
|
||||
"\n",
|
||||
"Moreover, the expert has pointed out that the sum of a, b, and c simplifies to 7. This means that regardless of the values of x and y (as long as x + y ≠ -1), the sum of a, b, and c will always be 7.\n",
|
||||
"1. $a = \\frac{x^2 - 3xy + 2x + y^2 - y}{x^2 - xy - x + y^2 - y + 1}$\n",
|
||||
"2. $b = \\frac{2x^2 - 3x + y}{x^2 - xy - x + y^2 - y + 1}$\n",
|
||||
"3. $c = \\frac{4x^2 - 4xy - 6x + 6y^2 - 7y + 7}{x^2 - xy - x + y^2 - y + 1}$\n",
|
||||
"\n",
|
||||
"So, the answer to your question \"Find $a + b + c$\" is 7.\n",
|
||||
"These solutions are expressed in terms of $x$ and $y$. However, we can find the sum $a + b + c$ without knowing the values of $x$ and $y$. The sum $a + b + c$ simplifies to $7$.\n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"So, the sum $a + b + c$ for the given system of equations is equal to $7$.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
|
||||
@@ -49,7 +49,7 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install flaml[autogen]~=2.0.1 docker"
|
||||
"# %pip install flaml[autogen]~=2.0.2 docker"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -183,21 +183,28 @@
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"To determine who should read this paper, I will first fetch the abstract and analyze its content. Then, I will provide a summary and suggest the target audience based on the paper's topic and focus.\n",
|
||||
"To determine who should read the paper, we need to first understand the content and context of the paper. We can do this by fetching the abstract of the paper from the provided URL and analyzing it. \n",
|
||||
"\n",
|
||||
"Please execute the following Python code to fetch the abstract:\n",
|
||||
"Here is a Python script that uses the BeautifulSoup library to scrape the abstract of the paper from the webpage. \n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"# Python script to scrape the abstract of the paper\n",
|
||||
"\n",
|
||||
"import requests\n",
|
||||
"from bs4 import BeautifulSoup\n",
|
||||
"\n",
|
||||
"def get_abstract(url):\n",
|
||||
" response = requests.get(url)\n",
|
||||
" soup = BeautifulSoup(response.text, 'html.parser')\n",
|
||||
" abstract = soup.find('blockquote', attrs={'class': 'abstract mathjax'}).text.strip()\n",
|
||||
" return abstract\n",
|
||||
"\n",
|
||||
"url = \"https://arxiv.org/abs/2306.01337\"\n",
|
||||
"response = requests.get(url)\n",
|
||||
"soup = BeautifulSoup(response.text, \"html.parser\")\n",
|
||||
"abstract = soup.find(\"blockquote\", {\"class\": \"abstract\"}).text.strip()\n",
|
||||
"abstract = get_abstract(url)\n",
|
||||
"print(abstract)\n",
|
||||
"```\n",
|
||||
"After you provide the abstract, I will analyze it and suggest the target audience.\n",
|
||||
"\n",
|
||||
"Please run this script and provide the output. Based on the abstract, I can suggest who might be interested in reading this paper.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
@@ -224,15 +231,21 @@
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"Based on the abstract, this paper discusses the use of Large Language Models (LLMs), specifically GPT-4, for solving complex and challenging math problems. The authors propose a conversational problem-solving framework called MathChat and evaluate its performance on difficult high school competition problems from the MATH dataset.\n",
|
||||
"Based on the abstract, the paper is about using Large Language Models (LLMs), specifically GPT-4, to solve complex mathematical problems. The paper introduces a new conversational problem-solving framework called MathChat and evaluates its performance on difficult high school competition problems from the MATH dataset.\n",
|
||||
"\n",
|
||||
"The target audience for this paper includes:\n",
|
||||
"Given this, the paper would be of interest to the following groups:\n",
|
||||
"\n",
|
||||
"1. Researchers and practitioners in the field of artificial intelligence, particularly those working with large language models like GPT-4.\n",
|
||||
"2. Mathematicians and educators interested in the application of AI for solving complex mathematical problems.\n",
|
||||
"3. Developers and engineers working on natural language processing and conversational AI systems for problem-solving tasks.\n",
|
||||
"1. **Researchers in Artificial Intelligence and Natural Language Processing**: The paper discusses the use of a large language model (GPT-4) for problem-solving, which is a key research area in AI and NLP.\n",
|
||||
"\n",
|
||||
"If you belong to any of these groups or have an interest in the intersection of AI and mathematics, this paper would be relevant to you.\n",
|
||||
"2. **Mathematicians and Math Educators**: The paper focuses on solving complex mathematical problems, so those with a background in mathematics might find the techniques and results interesting.\n",
|
||||
"\n",
|
||||
"3. **Data Scientists and Machine Learning Engineers**: These professionals often use models like GPT-4 in their work and might be interested in new applications and techniques.\n",
|
||||
"\n",
|
||||
"4. **Students studying AI, NLP, or Mathematics**: The paper could provide valuable insights for these students into how AI can be used in problem-solving.\n",
|
||||
"\n",
|
||||
"5. **Developers working on AI-based chatbots or conversational agents**: The paper introduces a new conversational problem-solving framework, which could be of interest to these developers.\n",
|
||||
"\n",
|
||||
"Please note that while the paper is likely to be of interest to these groups, the specific relevance will depend on the individual's specific interests and research needs.\n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"\n",
|
||||
@@ -247,7 +260,7 @@
|
||||
"user_proxy.initiate_chat(\n",
|
||||
" assistant,\n",
|
||||
" message=\"\"\"\n",
|
||||
"Who should read this paper: https://arxiv.org/abs/2306.01337\n",
|
||||
"Who should read this paper: https://arxiv.org/abs/2308.08155\n",
|
||||
"\"\"\",\n",
|
||||
")"
|
||||
]
|
||||
@@ -276,493 +289,296 @@
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"To get the YTD gain of the 10 largest technology companies, we will first need to fetch the list of these companies and their stock symbols. Then, we will use a financial API to get the YTD gain for each company. Here's the plan:\n",
|
||||
"To get the YTD gain of the 10 largest technology companies, we need to do the following:\n",
|
||||
"\n",
|
||||
"1. Use Python to scrape the list of the 10 largest technology companies and their stock symbols from a reliable source.\n",
|
||||
"2. Use a financial API to fetch the YTD gain for each company.\n",
|
||||
"3. Print the YTD gain for each company.\n",
|
||||
"1. Identify the 10 largest technology companies. We can use the list of the largest technology companies by market capitalization. This list can change frequently, so we need to get the latest data. We can use web scraping to get this data from a reliable source.\n",
|
||||
"\n",
|
||||
"First, let's fetch the list of the 10 largest technology companies and their stock symbols. We will use the BeautifulSoup library to scrape the data from a webpage. Please make sure you have the 'beautifulsoup4' and 'requests' packages installed. If not, you can install them using the following command:\n",
|
||||
"2. Get the YTD gain for each of these companies. We can use a financial data API to get this data. Yahoo Finance is a popular source for this kind of data.\n",
|
||||
"\n",
|
||||
"```sh\n",
|
||||
"pip install beautifulsoup4 requests\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Now, execute the following Python code to fetch the list of the 10 largest technology companies and their stock symbols:\n",
|
||||
"Here is a Python script that uses the BeautifulSoup library for web scraping and the yfinance library to get data from Yahoo Finance. This script will print the 10 largest technology companies and their YTD gains.\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"# filename: ytd_gain.py\n",
|
||||
"\n",
|
||||
"import requests\n",
|
||||
"from bs4 import BeautifulSoup\n",
|
||||
"\n",
|
||||
"url = \"https://www.investopedia.com/top-10-technology-stocks-4582443\"\n",
|
||||
"response = requests.get(url)\n",
|
||||
"soup = BeautifulSoup(response.text, \"html.parser\")\n",
|
||||
"\n",
|
||||
"company_symbols = []\n",
|
||||
"\n",
|
||||
"for symbol in soup.find_all(\"strong\"):\n",
|
||||
" if symbol.text.startswith(\"NASDAQ:\") or symbol.text.startswith(\"NYSE:\"):\n",
|
||||
" company_symbols.append(symbol.text.split(\":\")[1].strip())\n",
|
||||
"\n",
|
||||
"print(\"The 10 largest technology companies and their stock symbols are:\")\n",
|
||||
"for symbol in company_symbols:\n",
|
||||
" print(symbol)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"After you have the list of stock symbols, I will provide the code to fetch the YTD gain for each company.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is sh)...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"Code output: \n",
|
||||
"Defaulting to user installation because normal site-packages is not writeable\n",
|
||||
"Requirement already satisfied: beautifulsoup4 in /home/vscode/.local/lib/python3.9/site-packages (4.12.2)\n",
|
||||
"Requirement already satisfied: requests in /usr/local/lib/python3.9/site-packages (2.31.0)\n",
|
||||
"Requirement already satisfied: soupsieve>1.2 in /home/vscode/.local/lib/python3.9/site-packages (from beautifulsoup4) (2.4.1)\n",
|
||||
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.9/site-packages (from requests) (3.2.0)\n",
|
||||
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.9/site-packages (from requests) (2.0.3)\n",
|
||||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/site-packages (from requests) (2023.5.7)\n",
|
||||
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/site-packages (from requests) (3.4)\n",
|
||||
"\n",
|
||||
"The 10 largest technology companies and their stock symbols are:\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"It seems the code didn't output the stock symbols as expected. I apologize for the confusion. I will try a different approach to fetch the stock symbols.\n",
|
||||
"\n",
|
||||
"Please execute the following Python code to fetch the list of the 10 largest technology companies and their stock symbols:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"import requests\n",
|
||||
"from bs4 import BeautifulSoup\n",
|
||||
"\n",
|
||||
"url = \"https://www.suredividend.com/top-10-technology-stocks/\"\n",
|
||||
"response = requests.get(url)\n",
|
||||
"soup = BeautifulSoup(response.text, \"html.parser\")\n",
|
||||
"\n",
|
||||
"company_symbols = []\n",
|
||||
"\n",
|
||||
"for symbol in soup.find_all(\"a\"):\n",
|
||||
" if symbol.get(\"href\") and \"https://www.marketbeat.com/stocks/\" in symbol.get(\"href\"):\n",
|
||||
" company_symbols.append(symbol.text.strip())\n",
|
||||
"\n",
|
||||
"print(\"The 10 largest technology companies and their stock symbols are:\")\n",
|
||||
"for symbol in company_symbols:\n",
|
||||
" print(symbol)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"After you have the list of stock symbols, I will provide the code to fetch the YTD gain for each company.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"Code output: \n",
|
||||
"The 10 largest technology companies and their stock symbols are:\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"I apologize for the confusion again. It seems the code still didn't output the stock symbols as expected. I will try another approach to fetch the stock symbols.\n",
|
||||
"\n",
|
||||
"Please execute the following Python code to fetch the list of the 10 largest technology companies and their stock symbols:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"import requests\n",
|
||||
"from bs4 import BeautifulSoup\n",
|
||||
"\n",
|
||||
"url = \"https://www.marketbeat.com/market-data/largest-companies/technology/\"\n",
|
||||
"response = requests.get(url)\n",
|
||||
"soup = BeautifulSoup(response.text, \"html.parser\")\n",
|
||||
"\n",
|
||||
"company_symbols = []\n",
|
||||
"\n",
|
||||
"table = soup.find(\"table\", {\"class\": \"scroll-table\"})\n",
|
||||
"for row in table.find_all(\"tr\")[1:11]:\n",
|
||||
" cells = row.find_all(\"td\")\n",
|
||||
" symbol = cells[1].text.strip()\n",
|
||||
" company_symbols.append(symbol)\n",
|
||||
"\n",
|
||||
"print(\"The 10 largest technology companies and their stock symbols are:\")\n",
|
||||
"for symbol in company_symbols:\n",
|
||||
" print(symbol)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"After you have the list of stock symbols, I will provide the code to fetch the YTD gain for each company.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 1 (execution failed)\n",
|
||||
"Code output: \n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"\", line 11, in <module>\n",
|
||||
" for row in table.find_all(\"tr\")[1:11]:\n",
|
||||
"AttributeError: 'NoneType' object has no attribute 'find_all'\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"I apologize for the inconvenience. It seems the code still didn't work as expected. I will try a different source to fetch the stock symbols.\n",
|
||||
"\n",
|
||||
"Please execute the following Python code to fetch the list of the 10 largest technology companies and their stock symbols:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"import requests\n",
|
||||
"from bs4 import BeautifulSoup\n",
|
||||
"\n",
|
||||
"url = \"https://www.macrotrends.net/stocks/sector/technology/largest-market-cap\"\n",
|
||||
"response = requests.get(url)\n",
|
||||
"soup = BeautifulSoup(response.text, \"html.parser\")\n",
|
||||
"\n",
|
||||
"company_symbols = []\n",
|
||||
"\n",
|
||||
"table = soup.find(\"table\", {\"class\": \"historical_data_table\"})\n",
|
||||
"for row in table.find_all(\"tr\")[1:11]:\n",
|
||||
" cells = row.find_all(\"td\")\n",
|
||||
" symbol = cells[1].text.strip()\n",
|
||||
" company_symbols.append(symbol)\n",
|
||||
"\n",
|
||||
"print(\"The 10 largest technology companies and their stock symbols are:\")\n",
|
||||
"for symbol in company_symbols:\n",
|
||||
" print(symbol)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"After you have the list of stock symbols, I will provide the code to fetch the YTD gain for each company.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 1 (execution failed)\n",
|
||||
"Code output: \n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"\", line 11, in <module>\n",
|
||||
" for row in table.find_all(\"tr\")[1:11]:\n",
|
||||
"AttributeError: 'NoneType' object has no attribute 'find_all'\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"I apologize for the repeated issues. I will try a different approach to fetch the stock symbols using a different source.\n",
|
||||
"\n",
|
||||
"Please execute the following Python code to fetch the list of the 10 largest technology companies and their stock symbols:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"import requests\n",
|
||||
"from bs4 import BeautifulSoup\n",
|
||||
"\n",
|
||||
"url = \"https://www.tradingview.com/markets/stocks-usa/sectorandindustry-sector/technology/\"\n",
|
||||
"response = requests.get(url)\n",
|
||||
"soup = BeautifulSoup(response.text, \"html.parser\")\n",
|
||||
"\n",
|
||||
"company_symbols = []\n",
|
||||
"\n",
|
||||
"table = soup.find(\"table\", {\"class\": \"tv-data-table\"})\n",
|
||||
"for row in table.find_all(\"tr\")[1:11]:\n",
|
||||
" cells = row.find_all(\"td\")\n",
|
||||
" symbol = cells[0].text.strip()\n",
|
||||
" company_symbols.append(symbol)\n",
|
||||
"\n",
|
||||
"print(\"The 10 largest technology companies and their stock symbols are:\")\n",
|
||||
"for symbol in company_symbols:\n",
|
||||
" print(symbol)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"After you have the list of stock symbols, I will provide the code to fetch the YTD gain for each company.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 1 (execution failed)\n",
|
||||
"Code output: \n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"\", line 11, in <module>\n",
|
||||
" for row in table.find_all(\"tr\")[1:11]:\n",
|
||||
"AttributeError: 'NoneType' object has no attribute 'find_all'\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"I apologize for the continued issues. I will try one more approach to fetch the stock symbols using a different source.\n",
|
||||
"\n",
|
||||
"Please execute the following Python code to fetch the list of the 10 largest technology companies and their stock symbols:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"import requests\n",
|
||||
"from bs4 import BeautifulSoup\n",
|
||||
"\n",
|
||||
"url = \"https://www.slickcharts.com/sp500\"\n",
|
||||
"response = requests.get(url)\n",
|
||||
"soup = BeautifulSoup(response.text, \"html.parser\")\n",
|
||||
"\n",
|
||||
"company_symbols = []\n",
|
||||
"\n",
|
||||
"table = soup.find(\"table\", {\"class\": \"table table-hover table-borderless table-sm\"})\n",
|
||||
"for row in table.find_all(\"tr\")[1:11]:\n",
|
||||
" cells = row.find_all(\"td\")\n",
|
||||
" symbol = cells[2].text.strip()\n",
|
||||
" company_symbols.append(symbol)\n",
|
||||
"\n",
|
||||
"print(\"The 10 largest technology companies and their stock symbols are:\")\n",
|
||||
"for symbol in company_symbols:\n",
|
||||
" print(symbol)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Please note that this code will fetch the top 10 companies in the S&P 500, which may not be exclusively technology companies. However, it should provide a list of stock symbols that we can use to fetch the YTD gain for each company.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 1 (execution failed)\n",
|
||||
"Code output: \n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"\", line 11, in <module>\n",
|
||||
" for row in table.find_all(\"tr\")[1:11]:\n",
|
||||
"AttributeError: 'NoneType' object has no attribute 'find_all'\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"I apologize for the continued issues. It seems that web scraping is not a reliable method to fetch the stock symbols in this case. As an alternative, I will provide you with a list of the 10 largest technology companies and their stock symbols manually. Then, we can proceed with fetching the YTD gain for each company.\n",
|
||||
"\n",
|
||||
"Here is a list of the 10 largest technology companies and their stock symbols:\n",
|
||||
"\n",
|
||||
"1. Apple Inc. (AAPL)\n",
|
||||
"2. Microsoft Corporation (MSFT)\n",
|
||||
"3. Alphabet Inc. (GOOGL)\n",
|
||||
"4. Amazon.com, Inc. (AMZN)\n",
|
||||
"5. Meta Platforms, Inc. (FB)\n",
|
||||
"6. NVIDIA Corporation (NVDA)\n",
|
||||
"7. Tesla, Inc. (TSLA)\n",
|
||||
"8. Adobe Inc. (ADBE)\n",
|
||||
"9. PayPal Holdings, Inc. (PYPL)\n",
|
||||
"10. Cisco Systems, Inc. (CSCO)\n",
|
||||
"\n",
|
||||
"Now that we have the stock symbols, let's fetch the YTD gain for each company. We will use the Alpha Vantage API to get the stock data. Please sign up for a free API key at https://www.alphavantage.co/support/#api-key.\n",
|
||||
"\n",
|
||||
"Once you have the API key, replace `YOUR_API_KEY` in the following Python code with your actual API key and execute the code to fetch the YTD gain for each company:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"import requests\n",
|
||||
"import json\n",
|
||||
"from datetime import datetime\n",
|
||||
"\n",
|
||||
"api_key = \"YOUR_API_KEY\"\n",
|
||||
"stock_symbols = [\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"FB\", \"NVDA\", \"TSLA\", \"ADBE\", \"PYPL\", \"CSCO\"]\n",
|
||||
"\n",
|
||||
"def get_ytd_gain(symbol):\n",
|
||||
" url = f\"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={symbol}&apikey={api_key}&outputsize=full\"\n",
|
||||
" response = requests.get(url)\n",
|
||||
" data = json.loads(response.text)\n",
|
||||
"\n",
|
||||
" if \"Time Series (Daily)\" not in data:\n",
|
||||
" print(f\"Error fetching data for {symbol}: {data.get('Note', 'Unknown error')}\")\n",
|
||||
" return\n",
|
||||
"\n",
|
||||
" time_series = data[\"Time Series (Daily)\"]\n",
|
||||
" sorted_dates = sorted(time_series.keys(), reverse=True)\n",
|
||||
" latest_date = sorted_dates[0]\n",
|
||||
" start_of_year = f\"{datetime.now().year}-01-01\"\n",
|
||||
"\n",
|
||||
" if start_of_year not in sorted_dates:\n",
|
||||
" print(f\"Error: Data for {start_of_year} not found for {symbol}\")\n",
|
||||
" return\n",
|
||||
"\n",
|
||||
" latest_close = float(time_series[latest_date][\"4. close\"])\n",
|
||||
" start_of_year_close = float(time_series[start_of_year][\"4. close\"])\n",
|
||||
" ytd_gain = ((latest_close - start_of_year_close) / start_of_year_close) * 100\n",
|
||||
"\n",
|
||||
" return ytd_gain\n",
|
||||
"\n",
|
||||
"print(\"YTD gain for the 10 largest technology companies:\")\n",
|
||||
"for symbol in stock_symbols:\n",
|
||||
" ytd_gain = get_ytd_gain(symbol)\n",
|
||||
" if ytd_gain is not None:\n",
|
||||
" print(f\"{symbol}: {ytd_gain:.2f}%\")\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This code will print the YTD gain for each of the 10 largest technology companies.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"Code output: \n",
|
||||
"YTD gain for the 10 largest technology companies:\n",
|
||||
"Error fetching data for AAPL: Unknown error\n",
|
||||
"Error fetching data for MSFT: Unknown error\n",
|
||||
"Error fetching data for GOOGL: Unknown error\n",
|
||||
"Error fetching data for AMZN: Unknown error\n",
|
||||
"Error fetching data for FB: Unknown error\n",
|
||||
"Error fetching data for NVDA: Unknown error\n",
|
||||
"Error fetching data for TSLA: Unknown error\n",
|
||||
"Error fetching data for ADBE: Unknown error\n",
|
||||
"Error fetching data for PYPL: Unknown error\n",
|
||||
"Error fetching data for CSCO: Unknown error\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"I apologize for the issues with fetching the YTD gain using the Alpha Vantage API. It seems there might be a problem with the API or the API key. As an alternative, we can use the Yahoo Finance API to fetch the YTD gain for each company.\n",
|
||||
"\n",
|
||||
"Please install the `yfinance` package using the following command:\n",
|
||||
"\n",
|
||||
"```sh\n",
|
||||
"pip install yfinance\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"After installing the package, execute the following Python code to fetch the YTD gain for each of the 10 largest technology companies:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"import yfinance as yf\n",
|
||||
"from datetime import datetime\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"stock_symbols = [\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"FB\", \"NVDA\", \"TSLA\", \"ADBE\", \"PYPL\", \"CSCO\"]\n",
|
||||
"# Get the list of largest tech companies by market cap\n",
|
||||
"url = \"https://companiesmarketcap.com/technology/largest-technology-companies-by-market-cap/\"\n",
|
||||
"response = requests.get(url)\n",
|
||||
"soup = BeautifulSoup(response.text, 'html.parser')\n",
|
||||
"\n",
|
||||
"def get_ytd_gain(symbol):\n",
|
||||
" start_of_year = f\"{datetime.now().year}-01-01\"\n",
|
||||
" stock_data = yf.download(symbol, start=start_of_year)\n",
|
||||
" if stock_data.empty:\n",
|
||||
" print(f\"Error fetching data for {symbol}\")\n",
|
||||
" return\n",
|
||||
"# Find the table with the data\n",
|
||||
"table = soup.find('table')\n",
|
||||
"\n",
|
||||
" latest_close = stock_data.iloc[-1][\"Close\"]\n",
|
||||
" start_of_year_close = stock_data.iloc[0][\"Close\"]\n",
|
||||
" ytd_gain = ((latest_close - start_of_year_close) / start_of_year_close) * 100\n",
|
||||
"# Get the ticker symbols of the 10 largest companies\n",
|
||||
"tickers = []\n",
|
||||
"for row in table.find_all('tr')[1:11]: # Skip the header row\n",
|
||||
" ticker = row.find_all('td')[1].text\n",
|
||||
" tickers.append(ticker)\n",
|
||||
"\n",
|
||||
" return ytd_gain\n",
|
||||
"# Get the YTD gain for each company\n",
|
||||
"ytd_gains = {}\n",
|
||||
"for ticker in tickers:\n",
|
||||
" ticker_data = yf.Ticker(ticker)\n",
|
||||
" hist = ticker_data.history(period=\"ytd\")\n",
|
||||
" start_price = hist.iloc[0]['Close']\n",
|
||||
" end_price = hist.iloc[-1]['Close']\n",
|
||||
" ytd_gain = (end_price - start_price) / start_price * 100\n",
|
||||
" ytd_gains[ticker] = ytd_gain\n",
|
||||
"\n",
|
||||
"print(\"YTD gain for the 10 largest technology companies:\")\n",
|
||||
"for symbol in stock_symbols:\n",
|
||||
" ytd_gain = get_ytd_gain(symbol)\n",
|
||||
" if ytd_gain is not None:\n",
|
||||
" print(f\"{symbol}: {ytd_gain:.2f}%\")\n",
|
||||
"# Print the results\n",
|
||||
"for ticker, ytd_gain in ytd_gains.items():\n",
|
||||
" print(f\"{ticker}: {ytd_gain:.2f}%\")\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This code will print the YTD gain for each of the 10 largest technology companies.\n",
|
||||
"To run this script, save it to a file named `ytd_gain.py`, then run it with the command `python ytd_gain.py`.\n",
|
||||
"\n",
|
||||
"Please note that this script requires the BeautifulSoup, requests, yfinance, and pandas libraries. If you don't have these libraries installed, you can install them with the command `pip install beautifulsoup4 requests yfinance pandas`.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is sh)...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...\u001b[0m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"exitcode: 1 (execution failed)\n",
|
||||
"Code output: \n",
|
||||
"Defaulting to user installation because normal site-packages is not writeable\n",
|
||||
"Requirement already satisfied: yfinance in /home/vscode/.local/lib/python3.9/site-packages (0.2.26)\n",
|
||||
"Requirement already satisfied: frozendict>=2.3.4 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (2.3.8)\n",
|
||||
"Requirement already satisfied: appdirs>=1.4.4 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.4.4)\n",
|
||||
"Requirement already satisfied: beautifulsoup4>=4.11.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (4.12.2)\n",
|
||||
"Requirement already satisfied: pytz>=2022.5 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (2023.3)\n",
|
||||
"Requirement already satisfied: html5lib>=1.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.1)\n",
|
||||
"Requirement already satisfied: numpy>=1.16.5 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.25.1)\n",
|
||||
"Requirement already satisfied: multitasking>=0.0.7 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (0.0.11)\n",
|
||||
"Requirement already satisfied: lxml>=4.9.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (4.9.3)\n",
|
||||
"Requirement already satisfied: pandas>=1.3.0 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.3.3)\n",
|
||||
"Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.9/site-packages (from yfinance) (2.31.0)\n",
|
||||
"Requirement already satisfied: soupsieve>1.2 in /home/vscode/.local/lib/python3.9/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.4.1)\n",
|
||||
"Requirement already satisfied: webencodings in /home/vscode/.local/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
|
||||
"Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
|
||||
"Requirement already satisfied: python-dateutil>=2.7.3 in /home/vscode/.local/lib/python3.9/site-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
|
||||
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (3.4)\n",
|
||||
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (2.0.3)\n",
|
||||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (2023.5.7)\n",
|
||||
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (3.2.0)\n",
|
||||
"\n",
|
||||
"YTD gain for the 10 largest technology companies:\n",
|
||||
"[*********************100%***********************] 1 of 1 completed\n",
|
||||
"AAPL: 42.81%\n",
|
||||
"[*********************100%***********************] 1 of 1 completed\n",
|
||||
"MSFT: 34.81%\n",
|
||||
"[*********************100%***********************] 1 of 1 completed\n",
|
||||
"GOOGL: 45.74%\n",
|
||||
"[*********************100%***********************] 1 of 1 completed\n",
|
||||
"AMZN: 55.28%\n",
|
||||
"[*********************100%***********************] 1 of 1 completed\n",
|
||||
"Error fetching data for FB\n",
|
||||
"[*********************100%***********************] 1 of 1 completed\n",
|
||||
"NVDA: 221.47%\n",
|
||||
"[*********************100%***********************] 1 of 1 completed\n",
|
||||
"TSLA: 120.71%\n",
|
||||
"[*********************100%***********************] 1 of 1 completed\n",
|
||||
"ADBE: 55.84%\n",
|
||||
"[*********************100%***********************] 1 of 1 completed\n",
|
||||
"PYPL: -17.95%\n",
|
||||
"[*********************100%***********************] 1 of 1 completed\n",
|
||||
"CSCO: 16.19%\n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"ytd_gain.py\", line 18, in <module>\n",
|
||||
" for row in table.find_all('tr')[1:11]: # Skip the header row\n",
|
||||
"AttributeError: 'NoneType' object has no attribute 'find_all'\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"Great! The code has successfully fetched the YTD gain for the 10 largest technology companies. Here are the results:\n",
|
||||
"I apologize for the error. It seems that the script failed to find the table in the webpage. This could be due to changes in the webpage structure or because the page uses JavaScript to load the table, which BeautifulSoup can't handle.\n",
|
||||
"\n",
|
||||
"1. Apple Inc. (AAPL): 42.81%\n",
|
||||
"2. Microsoft Corporation (MSFT): 34.81%\n",
|
||||
"3. Alphabet Inc. (GOOGL): 45.74%\n",
|
||||
"4. Amazon.com, Inc. (AMZN): 55.28%\n",
|
||||
"5. Meta Platforms, Inc. (FB): Error fetching data\n",
|
||||
"6. NVIDIA Corporation (NVDA): 221.47%\n",
|
||||
"7. Tesla, Inc. (TSLA): 120.71%\n",
|
||||
"8. Adobe Inc. (ADBE): 55.84%\n",
|
||||
"9. PayPal Holdings, Inc. (PYPL): -17.95%\n",
|
||||
"10. Cisco Systems, Inc. (CSCO): 16.19%\n",
|
||||
"Let's try a different approach. We'll use the yfinance library to get the data for the 10 largest technology companies. These are currently: Apple (AAPL), Microsoft (MSFT), Alphabet (GOOGL), Amazon (AMZN), Facebook (FB), Tesla (TSLA), Nvidia (NVDA), Paypal (PYPL), Adobe (ADBE), and Netflix (NFLX).\n",
|
||||
"\n",
|
||||
"Please note that there was an error fetching data for Meta Platforms, Inc. (FB). This might be due to a temporary issue with the Yahoo Finance API or a change in the stock symbol.\n",
|
||||
"Here's the updated Python script:\n",
|
||||
"\n",
|
||||
"If you have any further questions or need assistance with another task, feel free to ask. Otherwise, if everything is done, you can reply with \"TERMINATE\" to end this session.\n",
|
||||
"```python\n",
|
||||
"# filename: ytd_gain.py\n",
|
||||
"\n",
|
||||
"import yfinance as yf\n",
|
||||
"\n",
|
||||
"# List of the 10 largest tech companies by market cap\n",
|
||||
"tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'FB', 'TSLA', 'NVDA', 'PYPL', 'ADBE', 'NFLX']\n",
|
||||
"\n",
|
||||
"# Get the YTD gain for each company\n",
|
||||
"ytd_gains = {}\n",
|
||||
"for ticker in tickers:\n",
|
||||
" ticker_data = yf.Ticker(ticker)\n",
|
||||
" hist = ticker_data.history(period=\"ytd\")\n",
|
||||
" start_price = hist.iloc[0]['Close']\n",
|
||||
" end_price = hist.iloc[-1]['Close']\n",
|
||||
" ytd_gain = (end_price - start_price) / start_price * 100\n",
|
||||
" ytd_gains[ticker] = ytd_gain\n",
|
||||
"\n",
|
||||
"# Print the results\n",
|
||||
"for ticker, ytd_gain in ytd_gains.items():\n",
|
||||
" print(f\"{ticker}: {ytd_gain:.2f}%\")\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"To run this script, save it to a file named `ytd_gain.py`, then run it with the command `python ytd_gain.py`.\n",
|
||||
"\n",
|
||||
"Please note that this script requires the yfinance library. If you don't have this library installed, you can install it with the command `pip install yfinance`.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 1 (execution failed)\n",
|
||||
"Code output: \n",
|
||||
"FB: No data found, symbol may be delisted\n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"ytd_gain.py\", line 13, in <module>\n",
|
||||
" start_price = hist.iloc[0]['Close']\n",
|
||||
" File \"/home/vscode/.local/lib/python3.9/site-packages/pandas/core/indexing.py\", line 931, in __getitem__\n",
|
||||
" return self._getitem_axis(maybe_callable, axis=axis)\n",
|
||||
" File \"/home/vscode/.local/lib/python3.9/site-packages/pandas/core/indexing.py\", line 1566, in _getitem_axis\n",
|
||||
" self._validate_integer(key, axis)\n",
|
||||
" File \"/home/vscode/.local/lib/python3.9/site-packages/pandas/core/indexing.py\", line 1500, in _validate_integer\n",
|
||||
" raise IndexError(\"single positional indexer is out-of-bounds\")\n",
|
||||
"IndexError: single positional indexer is out-of-bounds\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"I apologize for the error. It seems that the script failed to get the data for Facebook (FB). This could be due to a temporary issue with the Yahoo Finance API.\n",
|
||||
"\n",
|
||||
"Let's modify the script to handle this situation. We'll add a try-except block to skip any companies for which we can't get data.\n",
|
||||
"\n",
|
||||
"Here's the updated Python script:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"# filename: ytd_gain.py\n",
|
||||
"\n",
|
||||
"import yfinance as yf\n",
|
||||
"\n",
|
||||
"# List of the 10 largest tech companies by market cap\n",
|
||||
"tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'FB', 'TSLA', 'NVDA', 'PYPL', 'ADBE', 'NFLX']\n",
|
||||
"\n",
|
||||
"# Get the YTD gain for each company\n",
|
||||
"ytd_gains = {}\n",
|
||||
"for ticker in tickers:\n",
|
||||
" try:\n",
|
||||
" ticker_data = yf.Ticker(ticker)\n",
|
||||
" hist = ticker_data.history(period=\"ytd\")\n",
|
||||
" start_price = hist.iloc[0]['Close']\n",
|
||||
" end_price = hist.iloc[-1]['Close']\n",
|
||||
" ytd_gain = (end_price - start_price) / start_price * 100\n",
|
||||
" ytd_gains[ticker] = ytd_gain\n",
|
||||
" except IndexError:\n",
|
||||
" print(f\"Could not get data for {ticker}\")\n",
|
||||
"\n",
|
||||
"# Print the results\n",
|
||||
"for ticker, ytd_gain in ytd_gains.items():\n",
|
||||
" print(f\"{ticker}: {ytd_gain:.2f}%\")\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"To run this script, save it to a file named `ytd_gain.py`, then run it with the command `python ytd_gain.py`.\n",
|
||||
"\n",
|
||||
"Please note that this script requires the yfinance library. If you don't have this library installed, you can install it with the command `pip install yfinance`.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"Code output: \n",
|
||||
"Could not get data for FB\n",
|
||||
"AAPL: 46.41%\n",
|
||||
"MSFT: 37.63%\n",
|
||||
"GOOGL: 51.13%\n",
|
||||
"AMZN: 56.22%\n",
|
||||
"TSLA: 131.46%\n",
|
||||
"NVDA: 237.68%\n",
|
||||
"PYPL: -16.49%\n",
|
||||
"ADBE: 59.79%\n",
|
||||
"NFLX: 43.41%\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"The script has successfully fetched the Year-to-Date (YTD) gain for the largest technology companies, except for Facebook (FB). The YTD gains are as follows:\n",
|
||||
"\n",
|
||||
"- Apple (AAPL): 46.41%\n",
|
||||
"- Microsoft (MSFT): 37.63%\n",
|
||||
"- Alphabet (GOOGL): 51.13%\n",
|
||||
"- Amazon (AMZN): 56.22%\n",
|
||||
"- Tesla (TSLA): 131.46%\n",
|
||||
"- Nvidia (NVDA): 237.68%\n",
|
||||
"- Paypal (PYPL): -16.49%\n",
|
||||
"- Adobe (ADBE): 59.79%\n",
|
||||
"- Netflix (NFLX): 43.41%\n",
|
||||
"\n",
|
||||
"Unfortunately, the script could not fetch the data for Facebook (FB). This could be due to a temporary issue with the Yahoo Finance API or the ticker symbol might have changed.\n",
|
||||
"\n",
|
||||
"Please note that these values are subject to change as the stock market fluctuates. \n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n"
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"FB -> META\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"Thank you for the update. Facebook Inc. has indeed rebranded to Meta Platforms Inc. and its ticker symbol has changed from FB to META on the NASDAQ stock exchange.\n",
|
||||
"\n",
|
||||
"Let's update the script to reflect this change:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"# filename: ytd_gain.py\n",
|
||||
"\n",
|
||||
"import yfinance as yf\n",
|
||||
"\n",
|
||||
"# List of the 10 largest tech companies by market cap\n",
|
||||
"# Updated Facebook (FB) to Meta Platforms (META)\n",
|
||||
"tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'META', 'TSLA', 'NVDA', 'PYPL', 'ADBE', 'NFLX']\n",
|
||||
"\n",
|
||||
"# Get the YTD gain for each company\n",
|
||||
"ytd_gains = {}\n",
|
||||
"for ticker in tickers:\n",
|
||||
" try:\n",
|
||||
" ticker_data = yf.Ticker(ticker)\n",
|
||||
" hist = ticker_data.history(period=\"ytd\")\n",
|
||||
" start_price = hist.iloc[0]['Close']\n",
|
||||
" end_price = hist.iloc[-1]['Close']\n",
|
||||
" ytd_gain = (end_price - start_price) / start_price * 100\n",
|
||||
" ytd_gains[ticker] = ytd_gain\n",
|
||||
" except IndexError:\n",
|
||||
" print(f\"Could not get data for {ticker}\")\n",
|
||||
"\n",
|
||||
"# Print the results\n",
|
||||
"for ticker, ytd_gain in ytd_gains.items():\n",
|
||||
" print(f\"{ticker}: {ytd_gain:.2f}%\")\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"To run this script, save it to a file named `ytd_gain.py`, then run it with the command `python ytd_gain.py`.\n",
|
||||
"\n",
|
||||
"Please note that this script requires the yfinance library. If you don't have this library installed, you can install it with the command `pip install yfinance`.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
||||
"\n",
|
||||
"exitcode: 0 (execution succeeded)\n",
|
||||
"Code output: \n",
|
||||
"AAPL: 46.34%\n",
|
||||
"MSFT: 37.55%\n",
|
||||
"GOOGL: 51.08%\n",
|
||||
"AMZN: 56.17%\n",
|
||||
"META: 133.67%\n",
|
||||
"TSLA: 131.02%\n",
|
||||
"NVDA: 237.38%\n",
|
||||
"PYPL: -16.55%\n",
|
||||
"ADBE: 59.71%\n",
|
||||
"NFLX: 43.44%\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"The script has successfully fetched the Year-to-Date (YTD) gain for the largest technology companies, including Meta Platforms (formerly Facebook). The YTD gains are as follows:\n",
|
||||
"\n",
|
||||
"- Apple (AAPL): 46.34%\n",
|
||||
"- Microsoft (MSFT): 37.55%\n",
|
||||
"- Alphabet (GOOGL): 51.08%\n",
|
||||
"- Amazon (AMZN): 56.17%\n",
|
||||
"- Meta Platforms (META): 133.67%\n",
|
||||
"- Tesla (TSLA): 131.02%\n",
|
||||
"- Nvidia (NVDA): 237.38%\n",
|
||||
"- Paypal (PYPL): -16.55%\n",
|
||||
"- Adobe (ADBE): 59.71%\n",
|
||||
"- Netflix (NFLX): 43.44%\n",
|
||||
"\n",
|
||||
"Please note that these values are subject to change as the stock market fluctuates. \n",
|
||||
"\n",
|
||||
"TERMINATE\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[31m\n",
|
||||
">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
# AutoGen - Automated Multi Agent Chat
|
||||
<!-- Keep aligned with notebooks in docs/Use-Cases/Autogen#notebook-examples -->
|
||||
|
||||
`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance via multi-agent conversation.
|
||||
Please find documentation about this feature [here](/docs/Use-Cases/Autogen#agents).
|
||||
@@ -13,3 +14,4 @@ Links to notebook examples:
|
||||
* [Automated Chess Game Playing & Chitchatting by GPT-4 Agents](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_chess.ipynb)
|
||||
* [Automated Task Solving by Group Chat](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_groupchat.ipynb)
|
||||
* [Automated Continual Learning from New Data](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_stream.ipynb)
|
||||
* [Automated Code Generation and Question Answering with Retrieval Augemented Agents](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_RetrieveChat.ipynb)
|
||||
|
||||
@@ -146,6 +146,7 @@ user_proxy.initiate_chat(
|
||||
```
|
||||
|
||||
### Notebook Examples
|
||||
<!-- Keep aligned with notebooks in docs/Examples/AutoGen-AgentChat.md -->
|
||||
|
||||
*Interested in trying it yourself? Please check the following notebook examples:*
|
||||
* [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb)
|
||||
@@ -157,6 +158,7 @@ user_proxy.initiate_chat(
|
||||
* [Automated Chess Game Playing & Chitchatting by GPT-4 Agents](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_chess.ipynb)
|
||||
* [Automated Task Solving by Group Chat](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_groupchat.ipynb)
|
||||
* [Automated Continual Learning from New Data](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_stream.ipynb)
|
||||
* [Automated Code Generation and Question Answering with Retrieval Augemented Agents](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_RetrieveChat.ipynb)
|
||||
|
||||
## Enhanced Inference
|
||||
|
||||
@@ -550,3 +552,4 @@ The compact history is more efficient and the individual API call history contai
|
||||
*Interested in the research that leads to this package? Please check the following papers.*
|
||||
* [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673). Chi Wang, Susan Xueqing Liu, Ahmed H. Awadallah. ArXiv preprint arXiv:2303.04673 (2023).
|
||||
* [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337). Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2306.01337 (2023).
|
||||
* [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155). Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, Chi Wang. ArXiv preprint arXiv:2308.08155 (2023).
|
||||
|
||||
Reference in New Issue
Block a user