Python48-数据挖掘与机器学习作业.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 练习题解答"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 当 x=0 时，\n",
    "\n",
    "R(δ 1 |x = 0) = −80 × 0.3 − 20 × 0.6 + 10 × 0.7 = −29\n",
    "\n",
    "R(δ 2 |x = 0) = −40 × 0.3 − 10 × 0.6 − 5 × 0.7 = −21.5\n",
    "\n",
    "在 x=0 时，最小风险对应决策为转让专利。\n",
    "\n",
    "- 在 x=1 时，\n",
    "\n",
    "R(δ 1 |x = 1) = −80 × 0.7 − 20 × 0.4 + 10 × 0.3 = −61\n",
    "\n",
    "R(δ 2 |x = 1) = −40 × 0.7 − 10 × 0.4 − 5 × 0.3 = −33.5\n",
    "\n",
    "在 x=1 时，最小风险对应决策为转让专利。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业一\n",
    "![](https://ws1.sinaimg.cn/large/e59539f0ly1fpn5caxt79j20md0fon0q.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 6)\n",
      "w1的期望和方差的极大似然估计为： 4.26 0.22081632653061237\n",
      "w2的期望和方差的极大似然估计为： 5.552 0.304587755102041\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv('files/data/python48-data/iris.csv')\n",
    "print(data.shape)\n",
    "w1=data[data['Species']=='versicolor']\n",
    "w2=data[data['Species']=='virginica']\n",
    "x1=w1['Petal.Length'].mean()\n",
    "var1=w1['Petal.Length'].var()\n",
    "x2=w2['Petal.Length'].mean()\n",
    "var2=w2['Petal.Length'].var()\n",
    "print(\"w1的期望和方差的极大似然估计为：\",x1,var1)\n",
    "print(\"w2的期望和方差的极大似然估计为：\",x2,var2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  Sepal.Length  Sepal.Width  Petal.Length  Petal.Width     Species\n",
       "77  78           6.7          3.0           5.0          1.7  versicolor"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w1[w1['Petal.Length']==5.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>114</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>120</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "      id  Sepal.Length  Sepal.Width  Petal.Length  Petal.Width    Species\n",
       "113  114           5.7          2.5           5.0          2.0  virginica\n",
       "119  120           6.0          2.2           5.0          1.5  virginica\n",
       "146  147           6.3          2.5           5.0          1.9  virginica"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2[w2['Petal.Length']==5.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据密度分布最大原则，因为Petal.length出现的为5次数，w2>w1,所以x=5.0的判别类别为virginica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0999999999999996 0.7\n"
     ]
    }
   ],
   "source": [
    "P_w1,P_w2=0.3,0.7\n",
    "R_w1=P_w1*0+P_w2*3\n",
    "R_w2=P_w2*1+P_w1*0\n",
    "print(R_w1,R_w2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据期望损失最小原则，最优决策是w2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>4.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    Petal.Length     Species\n",
       "68           4.5  versicolor"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com_data=data[data['Species']!='setosa']\n",
    "com_data=com_data.sort_values(['Petal.Length'])\n",
    "com_data[['Petal.Length','Species']][29:30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分界点估计为4.5,当Petal.Length小于4.5时，判别为第一类versicolor，否则判别为第二类virginica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 数据集简介\n",
    "```iris以鸢尾花的特征作为数据来源，常用在分类操作中。该数据集由3种不同类型的鸢尾花的50个样本数据构成。其中的一个种类与另外两个种类是线性可分离的，后两个种类是非线性可分离的。```\n",
    "\n",
    "该数据集包含了5个属性：\n",
    "- Sepal.Length（花萼长度），单位是cm;\n",
    "- Sepal.Width（花萼宽度），单位是cm;\n",
    "- Petal.Length（花瓣长度），单位是cm;\n",
    "- Petal.Width（花瓣宽度），单位是cm;\n",
    "- 种类：Iris Setosa（山鸢尾）、Iris Versicolour（杂色鸢尾），以及Iris Virginica（维吉尼亚鸢尾）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 sklearn实现决策树\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "feature=[[140,1],[130,1],[150,0],[170,0]]\n",
    "labels=[0,0,1,1]\n",
    "clf=tree.DecisionTreeClassifier()\n",
    "clf=clf.fit(feature,labels)\n",
    "print(clf.predict([[150,0]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 使用鸢尾花训练决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 引入数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data\n",
      "target\n",
      "target_names\n",
      "DESCR\n",
      "feature_names\n"
     ]
    }
   ],
   "source": [
    "iris=load_iris()\n",
    "for key in iris:\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.1  3.5  1.4  0.2]\n",
      " [ 4.9  3.   1.4  0.2]\n",
      " [ 4.7  3.2  1.3  0.2]\n",
      " [ 4.6  3.1  1.5  0.2]\n",
      " [ 5.   3.6  1.4  0.2]] [0 0 0 0 0] ['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(iris['data'][:5],iris['target'][:5],iris['target_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_idx=[0,50,100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 训练集 和 测试集 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training data\n",
    "train_target=np.delete(iris.target,test_idx)\n",
    "train_data=np.delete(iris.data,test_idx,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n",
      "[0 1 2]\n"
     ]
    }
   ],
   "source": [
    "# testing data\n",
    "test_target=iris.target[test_idx]\n",
    "test_data=iris.data[test_idx]\n",
    "\n",
    "clf=tree.DecisionTreeClassifier()\n",
    "clf=clf.fit(train_data,train_target)\n",
    "\n",
    "print(test_target)\n",
    "print(clf.predict(test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 code可视化 制作一个简易可读的PDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# http://www.bubuko.com/infodetail-1778859.html\n",
    "# https://www.cnblogs.com/iamxyq/p/5918611.html\n",
    "from sklearn.externals.six import StringIO    \n",
    "import pydotplus   \n",
    "dot_data = StringIO()   \n",
    "tree.export_graphviz(clf, out_file=dot_data,  \n",
    "                      feature_names=iris.feature_names,    \n",
    "                         class_names=iris.target_names,    \n",
    "                         filled=True, rounded=True,    \n",
    "                         special_characters=True)   \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())   \n",
    "# graph.write_pdf(\"iris.pdf\")  \n",
    "graph.write_png('iris.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://ws1.sinaimg.cn/large/e59539f0ly1fpjtrw3pg9j20wr0ovwhl.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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