blmoistawinde/active_learning1.ipynb

## active_learning1.ipynb
{
  "cells": [
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# 准备工作"
    },
    {
      "metadata": {
        "scrolled": true,
        "slideshow": {
          "slide_type": "skip"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# 若要在binder中执行这个notebook，请先执行下面的指令来安装依赖包\n!pip install scikit-learn numpy pandas matplotlib",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nRequirement already satisfied: scikit-learn in c:\\users\\kelen\\appdata\\roaming\\python\\python36\\site-packages (0.20.0)\nRequirement already satisfied: numpy in c:\\users\\kelen\\appdata\\roaming\\python\\python36\\site-packages (1.15.4)\nRequirement already satisfied: pandas in d:\\program files\\anaconda2\\envs\\py36\\lib\\site-packages (0.20.3)\nRequirement already satisfied: matplotlib in d:\\program files\\anaconda2\\envs\\py36\\lib\\site-packages (2.1.2)\nRequirement already satisfied: scipy>=0.13.3 in d:\\program files\\anaconda2\\envs\\py36\\lib\\site-packages (from scikit-learn) (1.1.0)\nRequirement already satisfied: python-dateutil>=2 in d:\\program files\\anaconda2\\envs\\py36\\lib\\site-packages (from pandas) (2.6.1)\nRequirement already satisfied: pytz>=2011k in d:\\program files\\anaconda2\\envs\\py36\\lib\\site-packages (from pandas) (2017.3)\nRequirement already satisfied: six>=1.10 in d:\\program files\\anaconda2\\envs\\py36\\lib\\site-packages (from matplotlib) (1.10.0)\nRequirement already satisfied: cycler>=0.10 in d:\\program files\\anaconda2\\envs\\py36\\lib\\site-packages (from matplotlib) (0.10.0)\nRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in d:\\program files\\anaconda2\\envs\\py36\\lib\\site-packages (from matplotlib) (2.2.0)\n"
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "You are using pip version 18.0, however version 18.1 is available.\nYou should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "假设我们正要完成手写数字识别的任务。我们可以使用著名的mnist数据集来训练这样的机器学习模型。"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt \nnp.random.seed(1)               # reproduce the result\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nfrom sklearn.datasets import load_digits",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "skip"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "digits = load_digits()\nprint(digits.data.shape)",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "(1797, 64)\n"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        }
      },
      "cell_type": "markdown",
      "source": "总共有1797个数字，每个数字使用一个64维的向量表示"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.gray() \nplt.matshow(digits.images[0]) \nplt.show() ",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x1f3efa088d0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAECCAYAAADesWqHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x1f3ec4ab5c0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "notes"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "x = digits.data\ny = digits.target",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# 效果检验"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        }
      },
      "cell_type": "markdown",
      "source": "先来看看使用完全数据集训练的模型能够达到什么样的效果（这里暂不区分训练测试集）"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.linear_model import LogisticRegression\nclf = LogisticRegression()\nclf.fit(x,y)\nclf.score(x,y)",
      "execution_count": 6,
      "outputs": [
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.993322203672788"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        }
      },
      "cell_type": "markdown",
      "source": "然而，要想得到完全数据，我们需要标注1797个数字，这是比较吃力的。这还只是一个很小的玩具数据集，对于更大的数据集，数量级数以百万计，我们又怎能标的过来？\n\n那么，我们能不能**只标注一小部分数据**，比如说3%(50个数字左右),让它也达到不错的效果？看看效果如何。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "clf2 = LogisticRegression()\nchosen_ids = np.random.choice(range(len(x)),50,replace=False)\nclf2.fit(x[chosen_ids],y[chosen_ids])\nclf2.score(x,y)",
      "execution_count": 7,
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7918753478018921"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "效果下降了很多，这还只是一个简单的任务，对于更难的任务模型的表现还会更差，那么我们就没有办法节省劳动力（偷懒）了吗？\n\n非也，我们还有**主动学习**。"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# 主动学习"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "何谓主动学习？这里我采用一种通俗的讲法：\n\n想象你面对百万大军，要想打败他们未必需要将其全部剿灭，有时只需要斩其**上将**首级即可。\n\n主动学习做的，就是帮助我们找到那个“上将”，解决重点问题，达到事半功倍的效果。看下面的图："
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        }
      },
      "cell_type": "markdown",
      "source": "![Fig1](https://img-blog.csdnimg.cn/20181213203559446.jpg) "
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "左图中红绿代表两种数据。现在我们只能标注其中有限个数据来训练分类器。中间的图显示的就是随机标注的样本和得到的分界线，准确率大约为70%。而右图就是主动学习方法找到的标注点，因为这些点几乎构成了完美分界线的边界，所以使用与中图同样的样本数，它能够取得90%左右的准确率！"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        }
      },
      "cell_type": "markdown",
      "source": "那么我们怎么找到这些关键的点呢？让我们从上例来考虑，上面找到的标注点都在中间区域，实际上都是相对比较**容易混淆的、难以确定类别的**样本。我们的目标就是找到这些样本。**分类器的预测概率**就可以帮助我们衡量这些特性，于是就有了下面的寻找策略："
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "- **RS策略**：随机选择，作为对照。\n- **LC策略**：寻找分类器最没有信心的预测样本，即预测的最可能类别的概率也很低的样本。\n- **BT策略**：寻找分类器最“左右为难”的预测样本，即预测的最可能的两个类别的概率很接近的样本。"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "下面用代码实验和验证这些策略的效果：\n\n我们进行10批5个样本的标注，对于不同策略选出的样本，使用直接抽取已知标签的方法来模拟手动标注。来看看随着标注的进行，模型的表现如何改善，以及其最终的效果。\n"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "notes"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "batch_size = 5\nrounds = 10",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "def RS(proba, batch_size):\n    return np.random.choice(range(proba.shape[0]),batch_size,replace=False)\n\ndef LC(proba, batch_size):\n    return np.argsort(np.max(proba,axis=1))[:batch_size]\n\ndef BT(proba, batch_size):\n    sorted_proba = np.sort(proba,axis=1)\n    return np.argsort(sorted_proba[:,-1]-sorted_proba[:,-2])[:batch_size]",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "strategies = {\"RS\":RS,\"LC\":LC,\"BT\":BT}\nresults = {\"RS\":[],\"LC\":[],\"BT\":[]}\nfor type0 in strategies:\n    clf = LogisticRegression()\n    anno_batch = RS(x,batch_size)    # 第一批标注样本只能随机选取\n    x_train = x[anno_batch]\n    y_train = y[anno_batch]\n    for i in range(rounds-1):\n        clf.fit(x_train,y_train)\n        prec = clf.score(x,y)\n        results[type0].append(prec)\n        proba = clf.predict_proba(x)\n        stategy0 = strategies[type0]      # 后面采用不同策略\n        anno_batch = stategy0(proba,batch_size)\n        x_train = np.concatenate([x_train,x[anno_batch]])\n        y_train = np.concatenate([y_train,y[anno_batch]])\n        \n    prec = clf.score(x,y)\n    results[type0].append(prec)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.DataFrame(results).plot()\nplt.ylabel(\"Accuracy\")\nplt.xlabel(\"epoches\")\nplt.show()",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6VBXEI6qrPz+AX982ZjLt+9pF01rnFph5eP5mcvIKmT+qC3W8XKsstONZx3l/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\nXNwL8+G7cbBzMUSNMtZUdpLBaEIIAdU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            "text/plain": "<matplotlib.figure.Figure at 0x1f3e627e048>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "以上只是一次随机实验的结果，受每一批的具体样本影响而有一些波动。不过进行多次实验也可以看到，两种主动学习方法利用同样多的样本，却能够达到接近90%的准确率。确实比RS更胜一筹，我们可以使用这个技术来降低达到一定准确度所需的标注量。妈妈再也不用担心我手动标注数据的辛苦了！"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# 体验手动标注"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "下面我也写了一段真正手动标注数据的代码，有兴趣的也不妨亲身尝试一下，用可以接受的时间量标注，打造出一个表现良好的模型："
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "skip"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from IPython import display\ndef human_annotate(batch_ids):\n    ret = []\n    plt.gray()\n    for id0 in batch_ids:\n        fig0 = plt.figure(figsize=(2,2)) \n        plt.matshow(digits.images[id0],fignum=0) \n        plt.show()\n        ret.append(int(input(\"请输入这个样本的标签：\")))\n        display.clear_output(wait=True)      # 动态刷新输出\n    return np.array(ret)",
      "execution_count": 18,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "skip"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "def annotate():\n    batch_size = 5\n    rounds = 3\n    results = []\n\n    clf = LogisticRegression()\n    anno_batch = RS(x,batch_size)\n    x_train = x[anno_batch]\n    y_train = human_annotate(anno_batch)\n    for i in range(rounds-1):\n        clf.fit(x_train,y_train)\n        prec = clf.score(x,y)\n        print(f\"round {i}, prec:{prec}\")\n        results.append(prec)\n        proba = clf.predict_proba(x)\n        sorted_proba = np.sort(proba,axis=1)\n        anno_batch = BT(proba,batch_size)\n        x_train = np.concatenate([x_train,x[anno_batch]])\n        y_train = np.concatenate([y_train,human_annotate(anno_batch)])\n    clf.fit(x_train,y_train)\n    prec = clf.score(x,y)\n    print(f\"round {rounds}, prec:{prec}\")\n    results.append(prec)\n    pd.Series(results).plot()\n    plt.show()",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "annotate()",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "round 3, prec:0.7006121313299944\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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