DS_course_from_stepik/2.4 iris.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<style>svg{width:100% !important;height:100% !important;}</style>"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy.distutils.system_info import dfftw_info\n",
    "from sklearn import tree\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics as skm\n",
    "\n",
    "import os\n",
    "\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:\\\\Program Files\\\\Graphviz\\\\bin\\\\'\n",
    "from IPython.display import SVG\n",
    "from graphviz import Source\n",
    "from IPython.display import display\n",
    "from IPython.display import HTML\n",
    "\n",
    "style = \"<style>svg{width:100% !important;height:100% !important;}</style>\"\n",
    "HTML(style)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "dfiris = pd.read_csv('https://stepik.org/media/attachments/course/4852/train_iris.csv',index_col=0)\n",
    "dfiris_test = pd.read_csv('https://stepik.org/media/attachments/course/4852/test_iris.csv',index_col=0)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal length  sepal width  petal length  petal width  species\n114           5.8          2.8           5.1          2.4        2\n70            5.9          3.2           4.8          1.8        1\n138           6.0          3.0           4.8          1.8        2\n59            5.2          2.7           3.9          1.4        1\n44            5.1          3.8           1.9          0.4        0\n125           7.2          3.2           6.0          1.8        2\n36            5.5          3.5           1.3          0.2        0\n141           6.9          3.1           5.1          2.3        2\n12            4.8          3.0           1.4          0.1        0\n63            6.1          2.9           4.7          1.4        1\n106           4.9          2.5           4.5          1.7        2\n96            5.7          2.9           4.2          1.3        1\n103           6.3          2.9           5.6          1.8        2\n22            4.6          3.6           1.0          0.2        0\n26            5.0          3.4           1.6          0.4        0\n104           6.5          3.0           5.8          2.2        2\n84            5.4          3.0           4.5          1.5        1\n32            5.2          4.1           1.5          0.1        0\n18            5.7          3.8           1.7          0.3        0\n67            5.8          2.7           4.1          1.0        1\n53            5.5          2.3           4.0          1.3        1\n137           6.4          3.1           5.5          1.8        2\n33            5.5          4.2           1.4          0.2        0\n6             4.6          3.4           1.4          0.3        0\n60            5.0          2.0           3.5          1.0        1\n29            4.7          3.2           1.6          0.2        0\n16            5.4          3.9           1.3          0.4        0\n83            6.0          2.7           5.1          1.6        1\n15            5.7          4.4           1.5          0.4        0\n2             4.7          3.2           1.3          0.2        0\n95            5.7          3.0           4.2          1.2        1\n52            6.9          3.1           4.9          1.5        1\n90            5.5          2.6           4.4          1.2        1\n81            5.5          2.4           3.7          1.0        1\n101           5.8          2.7           5.1          1.9        2\n92            5.8          2.6           4.0          1.2        1\n129           7.2          3.0           5.8          1.6        2\n71            6.1          2.8           4.0          1.3        1\n108           6.7          2.5           5.8          1.8        2\n143           6.8          3.2           5.9          2.3        2\n5             5.4          3.9           1.7          0.4        0\n56            6.3          3.3           4.7          1.6        1\n102           7.1          3.0           5.9          2.1        2\n97            6.2          2.9           4.3          1.3        1\n78            6.0          2.9           4.5          1.5        1\n128           6.4          2.8           5.6          2.1        2\n99            5.7          2.8           4.1          1.3        1\n131           7.9          3.8           6.4          2.0        2\n8             4.4          2.9           1.4          0.2        0\n54            6.5          2.8           4.6          1.5        1",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></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>114</th>\n      <td>5.8</td>\n      <td>2.8</td>\n      <td>5.1</td>\n      <td>2.4</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>5.9</td>\n      <td>3.2</td>\n      <td>4.8</td>\n      <td>1.8</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>4.8</td>\n      <td>1.8</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>5.2</td>\n      <td>2.7</td>\n      <td>3.9</td>\n      <td>1.4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>5.1</td>\n      <td>3.8</td>\n      <td>1.9</td>\n      <td>0.4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>125</th>\n      <td>7.2</td>\n      <td>3.2</td>\n      <td>6.0</td>\n      <td>1.8</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>5.5</td>\n      <td>3.5</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>6.9</td>\n      <td>3.1</td>\n      <td>5.1</td>\n      <td>2.3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>4.8</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>6.1</td>\n      <td>2.9</td>\n      <td>4.7</td>\n      <td>1.4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>4.9</td>\n      <td>2.5</td>\n      <td>4.5</td>\n      <td>1.7</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>5.7</td>\n      <td>2.9</td>\n      <td>4.2</td>\n      <td>1.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>6.3</td>\n      <td>2.9</td>\n      <td>5.6</td>\n      <td>1.8</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>4.6</td>\n      <td>3.6</td>\n      <td>1.0</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>5.0</td>\n      <td>3.4</td>\n      <td>1.6</td>\n      <td>0.4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>6.5</td>\n      <td>3.0</td>\n      <td>5.8</td>\n      <td>2.2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>5.4</td>\n      <td>3.0</td>\n      <td>4.5</td>\n      <td>1.5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>5.2</td>\n      <td>4.1</td>\n      <td>1.5</td>\n      <td>0.1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>5.7</td>\n      <td>3.8</td>\n      <td>1.7</td>\n      <td>0.3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>5.8</td>\n      <td>2.7</td>\n      <td>4.1</td>\n      <td>1.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>5.5</td>\n      <td>2.3</td>\n      <td>4.0</td>\n      <td>1.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>6.4</td>\n      <td>3.1</td>\n      <td>5.5</td>\n      <td>1.8</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>5.5</td>\n      <td>4.2</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>4.6</td>\n      <td>3.4</td>\n      <td>1.4</td>\n      <td>0.3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>3.5</td>\n      <td>1.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.6</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>5.4</td>\n      <td>3.9</td>\n      <td>1.3</td>\n      <td>0.4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>6.0</td>\n      <td>2.7</td>\n      <td>5.1</td>\n      <td>1.6</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>5.7</td>\n      <td>4.4</td>\n      <td>1.5</td>\n      <td>0.4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>5.7</td>\n      <td>3.0</td>\n      <td>4.2</td>\n      <td>1.2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>6.9</td>\n      <td>3.1</td>\n      <td>4.9</td>\n      <td>1.5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>5.5</td>\n      <td>2.6</td>\n      <td>4.4</td>\n      <td>1.2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>5.5</td>\n      <td>2.4</td>\n      <td>3.7</td>\n      <td>1.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>5.8</td>\n      <td>2.7</td>\n      <td>5.1</td>\n      <td>1.9</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>5.8</td>\n      <td>2.6</td>\n      <td>4.0</td>\n      <td>1.2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>129</th>\n      <td>7.2</td>\n      <td>3.0</td>\n      <td>5.8</td>\n      <td>1.6</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>6.1</td>\n      <td>2.8</td>\n      <td>4.0</td>\n      <td>1.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>6.7</td>\n      <td>2.5</td>\n      <td>5.8</td>\n      <td>1.8</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>143</th>\n      <td>6.8</td>\n      <td>3.2</td>\n      <td>5.9</td>\n      <td>2.3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5.4</td>\n      <td>3.9</td>\n      <td>1.7</td>\n      <td>0.4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>6.3</td>\n      <td>3.3</td>\n      <td>4.7</td>\n      <td>1.6</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>7.1</td>\n      <td>3.0</td>\n      <td>5.9</td>\n      <td>2.1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>6.2</td>\n      <td>2.9</td>\n      <td>4.3</td>\n      <td>1.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>6.0</td>\n      <td>2.9</td>\n      <td>4.5</td>\n      <td>1.5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>128</th>\n      <td>6.4</td>\n      <td>2.8</td>\n      <td>5.6</td>\n      <td>2.1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>5.7</td>\n      <td>2.8</td>\n      <td>4.1</td>\n      <td>1.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>131</th>\n      <td>7.9</td>\n      <td>3.8</td>\n      <td>6.4</td>\n      <td>2.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>4.4</td>\n      <td>2.9</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>6.5</td>\n      <td>2.8</td>\n      <td>4.6</td>\n      <td>1.5</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfiris_test"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "data": {
      "text/plain": "sepal length    0\nsepal width     0\npetal length    0\npetal width     0\nspecies         0\ndtype: int64"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfiris.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [
    "X = dfiris.drop(['species'],axis=1)\n",
    "y = dfiris.species\n",
    "\n",
    "X_test = dfiris_test.drop(['species'],axis=1)\n",
    "y_test = dfiris_test.species"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [],
   "source": [
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [],
   "source": [
    "np.random.seed(0)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [],
   "source": [
    "scores_data = pd.DataFrame()\n",
    "max_depth_range = range(1,100)\n",
    "for max_depth in max_depth_range:\n",
    "    clf = tree.DecisionTreeClassifier(criterion='entropy',max_depth=max_depth)\n",
    "    clf.fit(X,y)\n",
    "    train_score = clf.score(X, y)\n",
    "    test_score = clf.score(X_test,y_test)\n",
    "    # test_accuracy_score = accuracy_score(dfiris_test.drop(['species'],axis=1),\n",
    "    #                                      dfiris_test.species)\n",
    "\n",
    "    mean_cross_val_score = cross_val_score(clf,X,y, cv=5).mean()\n",
    "\n",
    "    skm_accurancy = skm.accuracy_score(clf.predict(X_test), y_test)\n",
    "\n",
    "    temp_score_date = pd.DataFrame({\n",
    "        'max_depth': [max_depth],\n",
    "        'train_score': [train_score],\n",
    "        'test_score': [test_score],\n",
    "        'skm_accurancy': [skm_accurancy],\n",
    "        'cross_val_score':mean_cross_val_score,\n",
    "        # 'test_accuracy_score': test_accuracy_score\n",
    "    })\n",
    "    #scores_data = scores_data.append(temp_score_date)\n",
    "    scores_data = pd.concat([scores_data, temp_score_date])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [],
   "source": [
    "scores_data_long = pd.melt(scores_data,\n",
    "                           id_vars=['max_depth'],\n",
    "                           value_vars=['train_score','test_score',\n",
    "                                       'skm_accurancy',\n",
    "                                       'cross_val_score'\n",
    "                                       ],\n",
    "                           var_name='set_type',\n",
    "                           value_name='score')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='max_depth', ylabel='score'>"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='max_depth',\n",
    "             y='score',\n",
    "             hue='set_type',\n",
    "             data=scores_data_long)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}