mirror of
https://github.com/microsoft/autogen.git
synced 2025-07-24 01:11:45 +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
{
|
||
"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]==1.2.1\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",
|
||
"iopub.status.idle": "2023-02-13T23:40:52.321193Z",
|
||
"shell.execute_reply": "2023-02-13T23:40:52.320628Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# %pip install flaml[openai]==1.2.1 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",
|
||
"iopub.status.busy": "2023-02-13T23:40:52.323783Z",
|
||
"iopub.status.idle": "2023-02-13T23:40:52.330570Z",
|
||
"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",
|
||
"iopub.status.busy": "2023-02-13T23:40:52.333249Z",
|
||
"iopub.status.idle": "2023-02-13T23:40:52.336508Z",
|
||
"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",
|
||
"iopub.status.busy": "2023-02-13T23:40:52.339556Z",
|
||
"iopub.status.idle": "2023-02-13T23:40:54.603349Z",
|
||
"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",
|
||
"iopub.status.busy": "2023-02-13T23:40:54.606441Z",
|
||
"iopub.status.idle": "2023-02-13T23:40:54.610504Z",
|
||
"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 and tune subpackages 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 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"
|
||
}
|
||
},
|
||
"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 flaml's `oai.ChatCompletion.test` to evaluate the performance of an entire dataset with the tuned 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], config_n1, eval_math_responses)\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\": prompts[0], \"max_tokens\": 600, \"n\": 10}\n",
|
||
"n10_result = oai.ChatCompletion.test(test_data[:50], config_n10, eval_math_responses, logging_level=logging.INFO)\n",
|
||
"print(n10_result)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"config_n30 = {\"model\": 'gpt-4', \"prompt\": prompts[0], \"max_tokens\": 600, \"n\": 30}\n",
|
||
"n30_result = oai.ChatCompletion.test(test_data[:50], config_n30, eval_math_responses, logging_level=logging.INFO)\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\"])"
|
||
]
|
||
}
|
||
],
|
||
"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"
|
||
}
|
||
},
|
||
"widgets": {
|
||
"application/vnd.jupyter.widget-state+json": {
|
||
"state": {
|
||
"2d910cfd2d2a4fc49fc30fbbdc5576a7": {
|
||
"model_module": "@jupyter-widgets/base",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "LayoutModel",
|
||
"state": {
|
||
"_model_module": "@jupyter-widgets/base",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "LayoutModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/base",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "LayoutView",
|
||
"align_content": null,
|
||
"align_items": null,
|
||
"align_self": null,
|
||
"border_bottom": null,
|
||
"border_left": null,
|
||
"border_right": null,
|
||
"border_top": null,
|
||
"bottom": null,
|
||
"display": null,
|
||
"flex": null,
|
||
"flex_flow": null,
|
||
"grid_area": null,
|
||
"grid_auto_columns": null,
|
||
"grid_auto_flow": null,
|
||
"grid_auto_rows": null,
|
||
"grid_column": null,
|
||
"grid_gap": null,
|
||
"grid_row": null,
|
||
"grid_template_areas": null,
|
||
"grid_template_columns": null,
|
||
"grid_template_rows": null,
|
||
"height": null,
|
||
"justify_content": null,
|
||
"justify_items": null,
|
||
"left": null,
|
||
"margin": null,
|
||
"max_height": null,
|
||
"max_width": null,
|
||
"min_height": null,
|
||
"min_width": null,
|
||
"object_fit": null,
|
||
"object_position": null,
|
||
"order": null,
|
||
"overflow": null,
|
||
"padding": null,
|
||
"right": null,
|
||
"top": null,
|
||
"visibility": null,
|
||
"width": null
|
||
}
|
||
},
|
||
"454146d0f7224f038689031002906e6f": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "HBoxModel",
|
||
"state": {
|
||
"_dom_classes": [],
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "HBoxModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/controls",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "HBoxView",
|
||
"box_style": "",
|
||
"children": [
|
||
"IPY_MODEL_e4ae2b6f5a974fd4bafb6abb9d12ff26",
|
||
"IPY_MODEL_577e1e3cc4db4942b0883577b3b52755",
|
||
"IPY_MODEL_b40bdfb1ac1d4cffb7cefcb870c64d45"
|
||
],
|
||
"layout": "IPY_MODEL_dc83c7bff2f241309537a8119dfc7555",
|
||
"tabbable": null,
|
||
"tooltip": null
|
||
}
|
||
},
|
||
"577e1e3cc4db4942b0883577b3b52755": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "FloatProgressModel",
|
||
"state": {
|
||
"_dom_classes": [],
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "FloatProgressModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/controls",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "ProgressView",
|
||
"bar_style": "success",
|
||
"description": "",
|
||
"description_allow_html": false,
|
||
"layout": "IPY_MODEL_2d910cfd2d2a4fc49fc30fbbdc5576a7",
|
||
"max": 1,
|
||
"min": 0,
|
||
"orientation": "horizontal",
|
||
"style": "IPY_MODEL_74a6ba0c3cbc4051be0a83e152fe1e62",
|
||
"tabbable": null,
|
||
"tooltip": null,
|
||
"value": 1
|
||
}
|
||
},
|
||
"6086462a12d54bafa59d3c4566f06cb2": {
|
||
"model_module": "@jupyter-widgets/base",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "LayoutModel",
|
||
"state": {
|
||
"_model_module": "@jupyter-widgets/base",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "LayoutModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/base",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "LayoutView",
|
||
"align_content": null,
|
||
"align_items": null,
|
||
"align_self": null,
|
||
"border_bottom": null,
|
||
"border_left": null,
|
||
"border_right": null,
|
||
"border_top": null,
|
||
"bottom": null,
|
||
"display": null,
|
||
"flex": null,
|
||
"flex_flow": null,
|
||
"grid_area": null,
|
||
"grid_auto_columns": null,
|
||
"grid_auto_flow": null,
|
||
"grid_auto_rows": null,
|
||
"grid_column": null,
|
||
"grid_gap": null,
|
||
"grid_row": null,
|
||
"grid_template_areas": null,
|
||
"grid_template_columns": null,
|
||
"grid_template_rows": null,
|
||
"height": null,
|
||
"justify_content": null,
|
||
"justify_items": null,
|
||
"left": null,
|
||
"margin": null,
|
||
"max_height": null,
|
||
"max_width": null,
|
||
"min_height": null,
|
||
"min_width": null,
|
||
"object_fit": null,
|
||
"object_position": null,
|
||
"order": null,
|
||
"overflow": null,
|
||
"padding": null,
|
||
"right": null,
|
||
"top": null,
|
||
"visibility": null,
|
||
"width": null
|
||
}
|
||
},
|
||
"74a6ba0c3cbc4051be0a83e152fe1e62": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "ProgressStyleModel",
|
||
"state": {
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "ProgressStyleModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/base",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "StyleView",
|
||
"bar_color": null,
|
||
"description_width": ""
|
||
}
|
||
},
|
||
"7d3f3d9e15894d05a4d188ff4f466554": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "HTMLStyleModel",
|
||
"state": {
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "HTMLStyleModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/base",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "StyleView",
|
||
"background": null,
|
||
"description_width": "",
|
||
"font_size": null,
|
||
"text_color": null
|
||
}
|
||
},
|
||
"b40bdfb1ac1d4cffb7cefcb870c64d45": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "HTMLModel",
|
||
"state": {
|
||
"_dom_classes": [],
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "HTMLModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/controls",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "HTMLView",
|
||
"description": "",
|
||
"description_allow_html": false,
|
||
"layout": "IPY_MODEL_f1355871cc6f4dd4b50d9df5af20e5c8",
|
||
"placeholder": "",
|
||
"style": "IPY_MODEL_ca245376fd9f4354af6b2befe4af4466",
|
||
"tabbable": null,
|
||
"tooltip": null,
|
||
"value": " 1/1 [00:00<00:00, 44.69it/s]"
|
||
}
|
||
},
|
||
"ca245376fd9f4354af6b2befe4af4466": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "HTMLStyleModel",
|
||
"state": {
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "HTMLStyleModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/base",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "StyleView",
|
||
"background": null,
|
||
"description_width": "",
|
||
"font_size": null,
|
||
"text_color": null
|
||
}
|
||
},
|
||
"dc83c7bff2f241309537a8119dfc7555": {
|
||
"model_module": "@jupyter-widgets/base",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "LayoutModel",
|
||
"state": {
|
||
"_model_module": "@jupyter-widgets/base",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "LayoutModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/base",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "LayoutView",
|
||
"align_content": null,
|
||
"align_items": null,
|
||
"align_self": null,
|
||
"border_bottom": null,
|
||
"border_left": null,
|
||
"border_right": null,
|
||
"border_top": null,
|
||
"bottom": null,
|
||
"display": null,
|
||
"flex": null,
|
||
"flex_flow": null,
|
||
"grid_area": null,
|
||
"grid_auto_columns": null,
|
||
"grid_auto_flow": null,
|
||
"grid_auto_rows": null,
|
||
"grid_column": null,
|
||
"grid_gap": null,
|
||
"grid_row": null,
|
||
"grid_template_areas": null,
|
||
"grid_template_columns": null,
|
||
"grid_template_rows": null,
|
||
"height": null,
|
||
"justify_content": null,
|
||
"justify_items": null,
|
||
"left": null,
|
||
"margin": null,
|
||
"max_height": null,
|
||
"max_width": null,
|
||
"min_height": null,
|
||
"min_width": null,
|
||
"object_fit": null,
|
||
"object_position": null,
|
||
"order": null,
|
||
"overflow": null,
|
||
"padding": null,
|
||
"right": null,
|
||
"top": null,
|
||
"visibility": null,
|
||
"width": null
|
||
}
|
||
},
|
||
"e4ae2b6f5a974fd4bafb6abb9d12ff26": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "HTMLModel",
|
||
"state": {
|
||
"_dom_classes": [],
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "HTMLModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/controls",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "HTMLView",
|
||
"description": "",
|
||
"description_allow_html": false,
|
||
"layout": "IPY_MODEL_6086462a12d54bafa59d3c4566f06cb2",
|
||
"placeholder": "",
|
||
"style": "IPY_MODEL_7d3f3d9e15894d05a4d188ff4f466554",
|
||
"tabbable": null,
|
||
"tooltip": null,
|
||
"value": "100%"
|
||
}
|
||
},
|
||
"f1355871cc6f4dd4b50d9df5af20e5c8": {
|
||
"model_module": "@jupyter-widgets/base",
|
||
"model_module_version": "2.0.0",
|
||
"model_name": "LayoutModel",
|
||
"state": {
|
||
"_model_module": "@jupyter-widgets/base",
|
||
"_model_module_version": "2.0.0",
|
||
"_model_name": "LayoutModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/base",
|
||
"_view_module_version": "2.0.0",
|
||
"_view_name": "LayoutView",
|
||
"align_content": null,
|
||
"align_items": null,
|
||
"align_self": null,
|
||
"border_bottom": null,
|
||
"border_left": null,
|
||
"border_right": null,
|
||
"border_top": null,
|
||
"bottom": null,
|
||
"display": null,
|
||
"flex": null,
|
||
"flex_flow": null,
|
||
"grid_area": null,
|
||
"grid_auto_columns": null,
|
||
"grid_auto_flow": null,
|
||
"grid_auto_rows": null,
|
||
"grid_column": null,
|
||
"grid_gap": null,
|
||
"grid_row": null,
|
||
"grid_template_areas": null,
|
||
"grid_template_columns": null,
|
||
"grid_template_rows": null,
|
||
"height": null,
|
||
"justify_content": null,
|
||
"justify_items": null,
|
||
"left": null,
|
||
"margin": null,
|
||
"max_height": null,
|
||
"max_width": null,
|
||
"min_height": null,
|
||
"min_width": null,
|
||
"object_fit": null,
|
||
"object_position": null,
|
||
"order": null,
|
||
"overflow": null,
|
||
"padding": null,
|
||
"right": null,
|
||
"top": null,
|
||
"visibility": null,
|
||
"width": null
|
||
}
|
||
}
|
||
},
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|