{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Data handling imports\n", "import numpy as np\n", "import pickle\n", "import random\n", "from tqdm.auto import tqdm\n", "import optuna" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "storage_name = \"mysql+pymysql://root:Ch31121992@192.168.1.10:3306/optuna_db\"\n", "study_name = \"Experiment 1\"\n", "study = optuna.load_study(\n", " study_name=study_name,\n", " storage=storage_name,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = study.trials_dataframe()\n", "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.dropna(inplace=True)\n", "df.sort_values(by=\"value\", inplace=True)\n", "df.drop(df[\"value\"].idxmax(), inplace=True)\n", "df.drop(df[\"value\"].idxmax(), inplace=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "pd.options.plotting.backend = \"plotly\"\n", "params = list(df.keys()[5:-1])\n", "for p in params:\n", " df.plot(x=p, y=\"value\", kind=\"scatter\", title=p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = list(df.keys()[5:-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!poetry add tabulate\n", "from tabulate import tabulate\n", "print(\n", " tabulate(\n", " (x[0] for x in sorted(list(df.groupby(params)), key=lambda x: x[1][\"value\"].mean())),\n", " headers = params,\n", " tablefmt = \"grid\",\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for p in params:\n", " df.plot(x=p, y=\"value\", kind=\"scatter\", title=p).show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.11.3" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }