tutorials_zh/04._Explain_the_Otimizer_in_Detail.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 04. 详解优化器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实例化优化器对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在Basic Tutorial中，我们知道UltraOpt中有如下优化器："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|优化器|描述|\n",
    "|-----|---|\n",
    "|ETPE| Embedding-Tree-Parzen-Estimator, 是UltraOpt作者自创的一种优化算法，在TPE算法[<sup>[4]</sup>](#refer-anchor-4)的基础上对类别变量采用Embedding降维为低维连续变量，<br>并在其他的一些方面也做了改进。ETPE在某些场景下表现比HyperOpt的TPE算法要好。 |\n",
    "|Forest |基于随机森林的贝叶斯优化算法。概率模型引用了`scikit-optimize`[<sup>[1]</sup>](#refer-anchor-1)包的`skopt.learning.forest`模型[<sup>[2]</sup>](#refer-anchor-2)，<br>并借鉴了`SMAC3`[<sup>[3]</sup>](#refer-anchor-3)中的局部搜索方法|\n",
    "|GBRT| 基于梯度提升回归树(Gradient Boosting Resgression Tree)的贝叶斯优化算法，<br>概率模型引用了`scikit-optimize`包的`skopt.learning.gbrt`模型 |\n",
    "|Random| 随机搜索。 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们在调用`ultraopt.fmin`函数进行优化时，在使用优化器默认参数的情况下，可以只传入优化器的名字，如：\n",
    "\n",
    "```python\n",
    "from ultraopt import fmin\n",
    "\n",
    "result = fmin(evaluate_function, config_space, optimizer=\"ETPE\")\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但如果我们要调整优化器的参数，如修改`ForestOptimizer`随机森林树的个数，或 `ETPEOptimizer` 的一些涉及冷启动和采样个数的参数时，就需要从`ultraopt.optimizer`包中引入相应的优化类，并对其实例化为优化器对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "引入`fmin`和你需要使用的优化器类："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultraopt import fmin\n",
    "from ultraopt.optimizer import ETPEOptimizer\n",
    "from ultraopt.optimizer import RandomOptimizer\n",
    "from ultraopt.optimizer import ForestOptimizer\n",
    "from ultraopt.optimizer import GBRTOptimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "引入一个测试用的配置空间和评价函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultraopt.tests.mock import config_space, evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据自己想要的参数实例化一个`ForestOptimizer`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = ForestOptimizer(n_estimators=20) # 代理模型为20棵树的随机森林"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把优化器对象传入fmin函数，开始优化过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:04<00:00, 20.13trial/s, best loss: 2.505]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "+---------------------------------+\n",
       "| HyperParameters | Optimal Value |\n",
       "+-----------------+---------------+\n",
       "| x0              | -0.5365       |\n",
       "| x1              | 0.3258        |\n",
       "+-----------------+---------------+\n",
       "| Optimal Loss    | 2.5050        |\n",
       "+-----------------+---------------+\n",
       "| Num Configs     | 100           |\n",
       "+-----------------+---------------+"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmin(evaluate, config_space, optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不用fmin函数自己实现一个优化过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "优化器有3个重要的函数：\n",
    "\n",
    "- `initialize(config_space, ...` : 参数为配置空间等，初始化优化器\n",
    "- `ask(n_points=None , ...` : 请求优化器推荐`n_points`个配置（默认为1个）\n",
    "- `tell(config, loss, ...)` : 告知优化器，之前推荐配置`config`的好坏（损失`loss`越小越好）\n",
    "\n",
    "优化器的运行流程如下图所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from graphviz import Digraph; g = Digraph()\n",
    "g.node(\"config space\", shape=\"ellipse\"); g.node(\"optimizer\", shape=\"box\")\n",
    "g.node(\"config\", shape=\"ellipse\"); g.node(\"loss\", shape=\"circle\"); g.node(\"evaluator\", shape=\"box\")\n",
    "g.edge(\"config space\", \"optimizer\", label=\"initialize\"); g.edge(\"optimizer\", \"config\", label=\"<<b>ask</b>>\", color='blue')\n",
    "g.edge(\"config\",\"evaluator\" , label=\"send to\"); g.edge(\"evaluator\",\"loss\" , label=\"evaluate\")\n",
    "g.edge(\"config\", \"optimizer\", label=\"<<b>tell</b>>\", color='red'); g.edge(\"loss\", \"optimizer\", label=\"<<b>tell</b>>\", color='red')\n",
    "g.graph_attr['rankdir'] = 'LR'; g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图中的`evaluator`评价器会对`config`配置进行评价，然后返回一个损失`loss`。\n",
    "\n",
    "举个例子，在AutoML问题中，评价器的工作流程如下：\n",
    "1. 将config转化为一个机器学习实例\n",
    "2. 在训练集上对机器学习实例进行训练\n",
    "3. 在验证集上得到相应的评价指标\n",
    "4. 对评价指标进行处理，使其`越小越好`，返回`loss`\n",
    "\n",
    "具体的评价器我们会在下个教程中实现。在学习了这些知识后， 我们能否脱离`fmin`函数，自己实现一个优化过程呢？答案是可以的。\n",
    "\n",
    "在UltraOpt的设计哲学中，优化器只需要具备上述的3个接口，评价器和分布式策略的设计都可以由用户完成。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 先从一次循环中体会整个过程\n",
    "\n",
    "> Step 1. 首先实例化和初始化优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = ETPEOptimizer()\n",
    "optimizer.initialize(config_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Step 2. 调用优化器的`ask`函数获取一个其推荐的配置："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x0': 6.632914250425159, 'x1': 1.830923578719112}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommend_config, config_info = optimizer.ask()\n",
    "recommend_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_based_pick': False, 'origin': 'Initial Design'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Step 3. 用评估器，在这是`evaluate`函数来评价配置的好坏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177817.31410476987"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss = evaluate(recommend_config)\n",
    "loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Step 4. 通过tell函数将观测结果 `config, loss` 传递给优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer.tell(recommend_config, loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 将上述过程整理为一个for循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = ETPEOptimizer()\n",
    "optimizer.initialize(config_space)\n",
    "losses = []\n",
    "best_losses = []\n",
    "for _ in range(100):\n",
    "    config, _ = optimizer.ask()\n",
    "    loss = evaluate(config)\n",
    "    optimizer.tell(config, loss)\n",
    "    losses.append(loss)\n",
    "    best_losses.append(min(losses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid(0.2)\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.plot(range(20, 100), best_losses[20:]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "您可能会有疑问，一次只能`ask`一个推荐配置吗，能不能`ask`多个呢？答案是可以的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MapReduce计算策略：ask多个配置 + 并行调用评价函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_parallels = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = ETPEOptimizer()\n",
    "optimizer.initialize(config_space)\n",
    "\n",
    "for _ in range(100 // n_parallels):\n",
    "    config_info_pairs = optimizer.ask(n_points=n_parallels)\n",
    "    losses = Parallel(n_jobs=n_parallels)(\n",
    "        delayed(evaluate)(config)\n",
    "        for config, _ in config_info_pairs\n",
    "    )\n",
    "    loss = evaluate(config)\n",
    "    for j, (loss, (config, _)) in enumerate(zip(losses, config_info_pairs)):\n",
    "        optimizer.tell(config, loss, update_model=(j == n_parallels - 1)) # 传入这批观测的最后一个观测时，更新模型\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**参考文献**\n",
    "\n",
    "<div id=\"refer-anchor-1\"></div>\n",
    "\n",
    "- [1] https://github.com/scikit-optimize/scikit-optimize\n",
    "\n",
    "<div id=\"refer-anchor-2\"></div>\n",
    "\n",
    "- [2] [Hutter, F. et al. “Algorithm runtime prediction: Methods & evaluation.” Artif. Intell. 206 (2014): 79-111.](https://arxiv.org/abs/1211.0906)\n",
    "\n",
    "<div id=\"refer-anchor-3\"></div>\n",
    "\n",
    "- [3] [Hutter F., Hoos H.H., Leyton-Brown K. (2011) Sequential Model-Based Optimization for General Algorithm Configuration. In: Coello C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg.](https://link.springer.com/chapter/10.1007/978-3-642-25566-3_40)\n",
    "\n",
    "<div id=\"refer-anchor-4\"></div>\n",
    "\n",
    "- [4] [James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011. Algorithms for hyper-parameter optimization. In Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS'11). Curran Associates Inc., Red Hook, NY, USA, 2546–2554.](https://dl.acm.org/doi/10.5555/2986459.2986743)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "auto-sklearn",
   "language": "python",
   "name": "auto-sklearn"
  },
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