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autogen/notebook/research/math_level5counting.ipynb
Chi Wang c48babd02f
raise error when msg is invalid; fix docstr; improve ResponsiveAgent; update doc and packaging; capture ipython output; find code blocks with llm when regex fails. (#1154) 
* autogen.agent -> autogen.agentchat

* bug fix in portfolio

* notebook

* timeout

* timeout

* infer lang; close #1150

* timeout

* message context

* context handling

* add sender to generate_reply

* clean up the receive function

* move mathchat to contrib

* contrib

* last_message

* Add OptiGuide: agent and notebook

* Optiguide notebook: add figures and URL
1. figures and code points to remote URL
2. simplify the prompt for the interpreter, because
all information is already in the chat history.

* Update name: Agent -> GenericAgent

* Update notebook

* Rename: GenericAgent -> ResponsiveAgent

* Rebase to autogen.agentchat

* OptiGuide: Comment, sytle, and notebook updates

* simplify optiguide

* raise error when msg is invalid; fix docstr

* allow return None for generate_reply()

* update_system_message

* test update_system_message

* simplify optiguide

* simplify optiguide

* simplify optiguide

* simplify optiguide

* move test

* add test and fix bug

* doc update

* doc update

* doc update

* color

* optiguide

* prompt

* test danger case

* packaging

* docker

* remove path in traceback

* capture ipython output

* simplify

* find code blocks with llm

* find code with llm

* order

* order

* fix bug in context handling

* print executing msg

* print executing msg

* test find code

* test find code

* disable find_code

* default_auto_reply

* default auto reply

* remove optiguide

* remove -e

---------

Co-authored-by: Beibin Li <beibin79@gmail.com>
2023-08-01 02:22:30 +00:00

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"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
"\n",
"Licensed under the MIT License.\n",
"\n",
"# Math Study\n",
"\n",
"In this notebook, we study GPT-4 for math problem solving. We use [the MATH benchmark](https://crfm.stanford.edu/helm/latest/?group=math_chain_of_thought) for measuring mathematical problem solving on competition math problems with chain-of-thoughts style reasoning. \n",
"\n",
"## Requirements\n",
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai] option:\n",
"```bash\n",
"pip install flaml[openai]==1.2.2\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-13T23:40:52.317406Z",
"iopub.status.busy": "2023-02-13T23:40:52.316561Z",
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"shell.execute_reply": "2023-02-13T23:40:52.320628Z"
}
},
"outputs": [],
"source": [
"# %pip install flaml[openai]==1.2.2 datasets"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Set your OpenAI key:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-13T23:40:52.324240Z",
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"shell.execute_reply": "2023-02-13T23:40:52.329750Z"
}
},
"outputs": [],
"source": [
"import os\n",
"\n",
"if \"OPENAI_API_KEY\" not in os.environ:\n",
" os.environ[\"OPENAI_API_KEY\"] = \"<your OpenAI API key here>\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Uncomment the following to use Azure OpenAI:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-13T23:40:52.333547Z",
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"shell.execute_reply": "2023-02-13T23:40:52.335858Z"
}
},
"outputs": [],
"source": [
"# import openai\n",
"# openai.api_type = \"azure\"\n",
"# openai.api_base = \"https://<your_endpoint>.openai.azure.com/\"\n",
"# openai.api_version = \"2023-03-15-preview\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load dataset\n",
"\n",
"First, we load the competition_math dataset. We use a random sample of 50 examples for testing."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-13T23:40:52.339977Z",
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"shell.execute_reply": "2023-02-13T23:40:54.602630Z"
}
},
"outputs": [],
"source": [
"import datasets\n",
"\n",
"seed = 41\n",
"data = datasets.load_dataset(\"competition_math\")\n",
"train_data = data[\"train\"].shuffle(seed=seed)\n",
"test_data = data[\"test\"].shuffle(seed=seed)\n",
"n_tune_data = 20\n",
"tune_data = [\n",
" {\n",
" \"problem\": train_data[x][\"problem\"],\n",
" \"solution\": train_data[x][\"solution\"],\n",
" }\n",
" for x in range(len(train_data)) if train_data[x][\"level\"] == \"Level 5\" and train_data[x][\"type\"] == \"Counting & Probability\"\n",
"][:n_tune_data]\n",
"test_data = [\n",
" {\n",
" \"problem\": test_data[x][\"problem\"],\n",
" \"solution\": test_data[x][\"solution\"],\n",
" }\n",
" for x in range(len(test_data)) if test_data[x][\"level\"] == \"Level 5\" and test_data[x][\"type\"] == \"Counting & Probability\"\n",
"]\n",
"print(len(tune_data), len(test_data))\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Check a tuning example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-13T23:40:54.607152Z",
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"shell.execute_reply": "2023-02-13T23:40:54.609759Z"
},
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"outputs": [],
"source": [
"print(tune_data[1][\"problem\"])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is one example of the canonical solution:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-13T23:40:54.613590Z",
"iopub.status.busy": "2023-02-13T23:40:54.613168Z",
"iopub.status.idle": "2023-02-13T23:40:54.616873Z",
"shell.execute_reply": "2023-02-13T23:40:54.616193Z"
}
},
"outputs": [],
"source": [
"print(tune_data[1][\"solution\"])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Success Metric\n",
"\n",
"For each math task, we use voting to select a response with the most common answers out of all the generated responses. If it has an equivalent answer to the canonical solution, we consider the task as successfully solved. Then we can optimize the mean success rate of a collection of tasks."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-13T23:40:54.626998Z",
"iopub.status.busy": "2023-02-13T23:40:54.626593Z",
"iopub.status.idle": "2023-02-13T23:40:54.631383Z",
"shell.execute_reply": "2023-02-13T23:40:54.630770Z"
}
},
"outputs": [],
"source": [
"from flaml.autogen.math_utils import eval_math_responses"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Import the oai subpackage from flaml.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-13T23:40:54.634335Z",
"iopub.status.busy": "2023-02-13T23:40:54.633929Z",
"iopub.status.idle": "2023-02-13T23:40:56.105700Z",
"shell.execute_reply": "2023-02-13T23:40:56.105085Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"from flaml.autogen import oai"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"For (local) reproducibility and cost efficiency, we cache responses from OpenAI."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-13T23:40:56.109177Z",
"iopub.status.busy": "2023-02-13T23:40:56.108624Z",
"iopub.status.idle": "2023-02-13T23:40:56.112651Z",
"shell.execute_reply": "2023-02-13T23:40:56.112076Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"oai.ChatCompletion.set_cache(seed)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"This will create a disk cache in \".cache/{seed}\". You can change `cache_path` in `set_cache()`. The cache for different seeds are stored separately."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-13T23:40:56.115383Z",
"iopub.status.busy": "2023-02-13T23:40:56.114975Z",
"iopub.status.idle": "2023-02-13T23:41:55.045654Z",
"shell.execute_reply": "2023-02-13T23:41:55.044973Z"
}
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"outputs": [],
"source": [
"prompt = \"{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\\\boxed{{}}.\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evaluate the success rate on the test data\n",
"\n",
"You can use `oai.ChatCompletion.test` to evaluate the performance of an entire dataset with a config."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"\n",
"config_n1 = {\"model\": 'gpt-4', \"prompt\": prompt, \"max_tokens\": 600, \"n\": 1}\n",
"n1_result = oai.ChatCompletion.test(test_data[:50], eval_math_responses, **config_n1)\n",
"print(n1_result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"oai.ChatCompletion.request_timeout = 120\n",
"config_n10 = {\"model\": 'gpt-4', \"prompt\": prompt, \"max_tokens\": 600, \"n\": 10}\n",
"n10_result = oai.ChatCompletion.test(test_data[:50], eval_math_responses, logging_level=logging.INFO, **config_n10)\n",
"print(n10_result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"config_n30 = {\"model\": 'gpt-4', \"prompt\": prompt, \"max_tokens\": 600, \"n\": 30}\n",
"n30_result = oai.ChatCompletion.test(test_data[:50], eval_math_responses, logging_level=logging.INFO, **config_n30)\n",
"print(n30_result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from collections import defaultdict\n",
"import matplotlib.pyplot as plt\n",
"\n",
"prompts = [\"{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\\\boxed{{}}.\"]\n",
"markers = [\"o\", \"s\", \"D\", \"v\", \"p\", \"h\", \"d\", \"P\", \"X\", \"H\", \"8\", \"4\", \"3\", \"2\", \"1\", \"x\", \"+\", \">\", \"<\", \"^\", \"v\", \"1\", \"2\", \"3\", \"4\", \"8\", \"s\", \"p\", \"*\", \"h\", \"H\", \"d\", \"D\", \"|\", \"_\"]\n",
"for j, n in enumerate([10, 30]):\n",
" config = {\"model\": 'gpt-4', \"prompt\": prompts[0], \"max_tokens\": 600, \"n\": n}\n",
" metrics = []\n",
" x, y = [], []\n",
" votes_success = defaultdict(lambda: [0, 0])\n",
" for i, data_i in enumerate(test_data[:50]):\n",
" response = oai.ChatCompletion.create(context=data_i, **config)\n",
" responses = oai.ChatCompletion.extract_text(response)\n",
" metrics.append(eval_math_responses(responses, **data_i))\n",
" votes = metrics[-1][\"votes\"]\n",
" success = metrics[-1][\"success_vote\"]\n",
" votes_success[votes][0] += 1\n",
" votes_success[votes][1] += success\n",
" for votes in votes_success:\n",
" x.append(votes)\n",
" y.append(votes_success[votes][1] / votes_success[votes][0])\n",
"\n",
" plt.scatter(x, y, marker=markers[j])\n",
" plt.xlabel(\"top vote\")\n",
" plt.ylabel(\"success rate\")\n",
"plt.legend([\"n=10\", \"n=30\"])"
]
}
],
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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