mirror of
https://github.com/microsoft/autogen.git
synced 2025-07-25 01:41:01 +00:00
fa5ccea862
* extract code from text * solve_problem; request_timeout in config * improve * move import statement * improve code * generate assertions * constant * configs for implement; voting * doc * execute code in docker * success indicator of code executation in docker * success indicator * execute code * strip n * add cost in generate_code * add docstr * filename * bytes * check docker version * print log * python test * remove api key address * rename exit code * success exit code * datasets * exit code * recover openai tests * cache and pattern match * wait * wait * cache and test * timeout test * python image name and skip macos * windows image * docker images * volume path and yaml * win path -> posix * extensions * path * path * path * path * path * path * path * path * path * path * path * skip windows * path * timeout in windows * use_docker * use_docker * hot fix from #1000 --------- Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
785 lines
23 KiB
Plaintext
785 lines
23 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"Copyright (c) Microsoft Corporation. All rights reserved. \n",
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"\n",
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"Licensed under the MIT License.\n",
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"\n",
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"# Math Study\n",
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"\n",
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"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",
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"\n",
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"## Requirements\n",
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"\n",
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"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai] option:\n",
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"```bash\n",
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"pip install flaml[openai]==1.2.1\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-02-13T23:40:52.317406Z",
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"iopub.status.busy": "2023-02-13T23:40:52.316561Z",
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"iopub.status.idle": "2023-02-13T23:40:52.321193Z",
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"shell.execute_reply": "2023-02-13T23:40:52.320628Z"
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}
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},
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"outputs": [],
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"source": [
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"# %pip install flaml[openai]==1.2.1 datasets"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Set your OpenAI key:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-02-13T23:40:52.324240Z",
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"iopub.status.busy": "2023-02-13T23:40:52.323783Z",
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||
"iopub.status.idle": "2023-02-13T23:40:52.330570Z",
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"shell.execute_reply": "2023-02-13T23:40:52.329750Z"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"if \"OPENAI_API_KEY\" not in os.environ:\n",
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" os.environ[\"OPENAI_API_KEY\"] = \"<your OpenAI API key here>\""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Uncomment the following to use Azure OpenAI:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-02-13T23:40:52.333547Z",
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"iopub.status.busy": "2023-02-13T23:40:52.333249Z",
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||
"iopub.status.idle": "2023-02-13T23:40:52.336508Z",
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"shell.execute_reply": "2023-02-13T23:40:52.335858Z"
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}
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},
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"outputs": [],
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"source": [
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"# import openai\n",
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"# openai.api_type = \"azure\"\n",
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"# openai.api_base = \"https://<your_endpoint>.openai.azure.com/\"\n",
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"# openai.api_version = \"2023-03-15-preview\""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load dataset\n",
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"\n",
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"First, we load the competition_math dataset. We use a random sample of 50 examples for testing."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-02-13T23:40:52.339977Z",
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"iopub.status.busy": "2023-02-13T23:40:52.339556Z",
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"iopub.status.idle": "2023-02-13T23:40:54.603349Z",
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"shell.execute_reply": "2023-02-13T23:40:54.602630Z"
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}
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},
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"outputs": [],
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"source": [
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"import datasets\n",
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"\n",
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"seed = 41\n",
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"data = datasets.load_dataset(\"competition_math\")\n",
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"train_data = data[\"train\"].shuffle(seed=seed)\n",
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"test_data = data[\"test\"].shuffle(seed=seed)\n",
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"n_tune_data = 20\n",
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"tune_data = [\n",
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" {\n",
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" \"problem\": train_data[x][\"problem\"],\n",
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" \"solution\": train_data[x][\"solution\"],\n",
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" }\n",
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" for x in range(len(train_data)) if train_data[x][\"level\"] == \"Level 5\" and train_data[x][\"type\"] == \"Counting & Probability\"\n",
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"][:n_tune_data]\n",
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"test_data = [\n",
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" {\n",
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" \"problem\": test_data[x][\"problem\"],\n",
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" \"solution\": test_data[x][\"solution\"],\n",
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" }\n",
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" for x in range(len(test_data)) if test_data[x][\"level\"] == \"Level 5\" and test_data[x][\"type\"] == \"Counting & Probability\"\n",
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"]\n",
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"print(len(tune_data), len(test_data))\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"Check a tuning example:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-02-13T23:40:54.607152Z",
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"iopub.status.busy": "2023-02-13T23:40:54.606441Z",
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"iopub.status.idle": "2023-02-13T23:40:54.610504Z",
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"shell.execute_reply": "2023-02-13T23:40:54.609759Z"
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},
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"slideshow": {
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"slide_type": "subslide"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"print(tune_data[1][\"problem\"])"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Here is one example of the canonical solution:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-02-13T23:40:54.613590Z",
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||
"iopub.status.busy": "2023-02-13T23:40:54.613168Z",
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"iopub.status.idle": "2023-02-13T23:40:54.616873Z",
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"shell.execute_reply": "2023-02-13T23:40:54.616193Z"
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}
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},
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"outputs": [],
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"source": [
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"print(tune_data[1][\"solution\"])"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import Success Metric\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-02-13T23:40:54.626998Z",
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"iopub.status.busy": "2023-02-13T23:40:54.626593Z",
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"iopub.status.idle": "2023-02-13T23:40:54.631383Z",
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"shell.execute_reply": "2023-02-13T23:40:54.630770Z"
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}
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},
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"outputs": [],
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"source": [
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"from flaml.autogen.math_utils import eval_math_responses"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Import the oai and tune subpackages from flaml.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-02-13T23:40:54.634335Z",
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"iopub.status.busy": "2023-02-13T23:40:54.633929Z",
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"iopub.status.idle": "2023-02-13T23:40:56.105700Z",
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"shell.execute_reply": "2023-02-13T23:40:56.105085Z"
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},
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [],
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"source": [
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"from flaml import oai"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For (local) reproducibility and cost efficiency, we cache responses from OpenAI."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-02-13T23:40:56.109177Z",
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||
"iopub.status.busy": "2023-02-13T23:40:56.108624Z",
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||
"iopub.status.idle": "2023-02-13T23:40:56.112651Z",
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||
"shell.execute_reply": "2023-02-13T23:40:56.112076Z"
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},
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [],
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"source": [
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"oai.ChatCompletion.set_cache(seed)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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||
"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",
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||
"shell.execute_reply": "2023-02-13T23:41:55.044973Z"
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}
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},
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"outputs": [],
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"source": [
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"prompt = \"{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\\\boxed{{}}.\""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Evaluate the success rate on the test data\n",
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"\n",
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"You can use flaml's `oai.ChatCompletion.test` to evaluate the performance of an entire dataset with the tuned config."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"\n",
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"config_n1 = {\"model\": 'gpt-4', \"prompt\": prompt, \"max_tokens\": 600, \"n\": 1}\n",
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"n1_result = oai.ChatCompletion.test(test_data[:50], config_n1, eval_math_responses)\n",
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"print(n1_result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"oai.ChatCompletion.request_timeout = 120\n",
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"config_n10 = {\"model\": 'gpt-4', \"prompt\": prompts[0], \"max_tokens\": 600, \"n\": 10}\n",
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"n10_result = oai.ChatCompletion.test(test_data[:50], config_n10, eval_math_responses, logging_level=logging.INFO)\n",
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"print(n10_result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"config_n30 = {\"model\": 'gpt-4', \"prompt\": prompts[0], \"max_tokens\": 600, \"n\": 30}\n",
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"n30_result = oai.ChatCompletion.test(test_data[:50], config_n30, eval_math_responses, logging_level=logging.INFO)\n",
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"print(n30_result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import defaultdict\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"prompts = [\"{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\\\boxed{{}}.\"]\n",
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"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",
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"for j, n in enumerate([10, 30]):\n",
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" config = {\"model\": 'gpt-4', \"prompt\": prompts[0], \"max_tokens\": 600, \"n\": n}\n",
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" metrics = []\n",
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" x, y = [], []\n",
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" votes_success = defaultdict(lambda: [0, 0])\n",
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" for i, data_i in enumerate(test_data[:50]):\n",
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" response = oai.ChatCompletion.create(context=data_i, **config)\n",
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" responses = oai.ChatCompletion.extract_text(response)\n",
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" metrics.append(eval_math_responses(responses, **data_i))\n",
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" votes = metrics[-1][\"votes\"]\n",
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" success = metrics[-1][\"success_vote\"]\n",
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" votes_success[votes][0] += 1\n",
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" votes_success[votes][1] += success\n",
|
||
" for votes in votes_success:\n",
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" x.append(votes)\n",
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" y.append(votes_success[votes][1] / votes_success[votes][0])\n",
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"\n",
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" plt.scatter(x, y, marker=markers[j])\n",
|
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" plt.xlabel(\"top vote\")\n",
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" plt.ylabel(\"success rate\")\n",
|
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"plt.legend([\"n=10\", \"n=30\"])"
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]
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}
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],
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||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.16"
|
||
},
|
||
"vscode": {
|
||
"interpreter": {
|
||
"hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
|
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}
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {
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"2d910cfd2d2a4fc49fc30fbbdc5576a7": {
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"model_module": "@jupyter-widgets/base",
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"model_module_version": "2.0.0",
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"model_name": "LayoutModel",
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"state": {
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"_model_module": "@jupyter-widgets/base",
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"_model_module_version": "2.0.0",
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"_model_name": "LayoutModel",
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"_view_count": null,
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"_view_module": "@jupyter-widgets/base",
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"_view_module_version": "2.0.0",
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"_view_name": "LayoutView",
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"flex_flow": null,
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"grid_area": null,
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"grid_auto_columns": null,
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"grid_auto_flow": null,
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"grid_auto_rows": null,
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"grid_column": null,
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"grid_gap": null,
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"grid_row": null,
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"grid_template_areas": null,
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"height": null,
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"justify_content": null,
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"margin": null,
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