inspect_study.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import optuna\n",
    "\n",
    "from optuna.visualization import plot_optimization_history\n",
    "from optuna.visualization import plot_param_importances\n",
    "from optuna.visualization import plot_rank\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "study_name = \"study\"  # Unique identifier of the study.\n",
    "storage_name = \"sqlite:///{}.db\".format(study_name)\n",
    "\n",
    "study = optuna.load_study(study_name=study_name, storage=storage_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "study.best_trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "study.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_optimization_history(study)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_rank(study, params=[\"r_max\", \"n_radial\", \"n_basis\", \"n_layers\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_rank(study, params=[\"n_radial\", \"n_basis\", \"basis\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_rank(study, params=[\"optimizer\", \"nn_lr\", \"weight_decay\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_param_importances(study)"
   ]
  }
 ],
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