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{
"metadata": {
"name": "",
"signature": "sha256:9685c5d0f663be78b3bccac808e2fbc1c80e6546f15a0451c07ba555efc96e8b"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sklearn\n",
"import sklearn.tree\n",
"import sklearn.datasets\n",
"import sklearn.preprocessing\n",
"import sklearn.decomposition\n",
"import sklearn.ensemble\n",
"\n",
"import urllib2\n",
"import bs4\n",
"import itertools\n",
"\n",
"import pandas as pd\n",
"import pandas.tools.plotting \n",
"\n",
"import numpy as np\n",
"\n",
"import matplotlib\n",
"from matplotlib.colors import ListedColormap\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import seaborn as sns\n",
"sns.set_style(\"white\")\n",
"\n",
"cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF', '#000000'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 142
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data'\n",
"url = 'abalone.csv'\n",
"columns = ['sex','length','diameter','height','wholeWeight','shuckedWeight','visceraWeight','shellWeight','rings']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_orig = pd.io.parsers.read_csv(url, header=None, names=columns)\n",
"data_orig.head(3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sex</th>\n",
" <th>length</th>\n",
" <th>diameter</th>\n",
" <th>height</th>\n",
" <th>wholeWeight</th>\n",
" <th>shuckedWeight</th>\n",
" <th>visceraWeight</th>\n",
" <th>shellWeight</th>\n",
" <th>rings</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> M</td>\n",
" <td> 0.455</td>\n",
" <td> 0.365</td>\n",
" <td> 0.095</td>\n",
" <td> 0.5140</td>\n",
" <td> 0.2245</td>\n",
" <td> 0.1010</td>\n",
" <td> 0.15</td>\n",
" <td> 15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> M</td>\n",
" <td> 0.350</td>\n",
" <td> 0.265</td>\n",
" <td> 0.090</td>\n",
" <td> 0.2255</td>\n",
" <td> 0.0995</td>\n",
" <td> 0.0485</td>\n",
" <td> 0.07</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> F</td>\n",
" <td> 0.530</td>\n",
" <td> 0.420</td>\n",
" <td> 0.135</td>\n",
" <td> 0.6770</td>\n",
" <td> 0.2565</td>\n",
" <td> 0.1415</td>\n",
" <td> 0.21</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 165,
"text": [
" sex length diameter height wholeWeight shuckedWeight visceraWeight \\\n",
"0 M 0.455 0.365 0.095 0.5140 0.2245 0.1010 \n",
"1 M 0.350 0.265 0.090 0.2255 0.0995 0.0485 \n",
"2 F 0.530 0.420 0.135 0.6770 0.2565 0.1415 \n",
"\n",
" shellWeight rings \n",
"0 0.15 15 \n",
"1 0.07 7 \n",
"2 0.21 9 "
]
}
],
"prompt_number": 165
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## need to convert the label\n",
"#0:infant, 1:female, 2:male\n",
"def parse_label(label):\n",
" options = {'I': 0, 'F': 1, 'M': 2}\n",
" return options[label]\n",
"\n",
"data['sex'] = data_orig['sex'].map(parse_label)\n",
"data.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sex</th>\n",
" <th>length</th>\n",
" <th>diameter</th>\n",
" <th>height</th>\n",
" <th>wholeWeight</th>\n",
" <th>shuckedWeight</th>\n",
" <th>visceraWeight</th>\n",
" <th>shellWeight</th>\n",
" <th>rings</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 2</td>\n",
" <td> 0.455</td>\n",
" <td> 0.365</td>\n",
" <td> 0.095</td>\n",
" <td> 0.5140</td>\n",
" <td> 0.2245</td>\n",
" <td> 0.1010</td>\n",
" <td> 0.150</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> 0.350</td>\n",
" <td> 0.265</td>\n",
" <td> 0.090</td>\n",
" <td> 0.2255</td>\n",
" <td> 0.0995</td>\n",
" <td> 0.0485</td>\n",
" <td> 0.070</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 1</td>\n",
" <td> 0.530</td>\n",
" <td> 0.420</td>\n",
" <td> 0.135</td>\n",
" <td> 0.6770</td>\n",
" <td> 0.2565</td>\n",
" <td> 0.1415</td>\n",
" <td> 0.210</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 2</td>\n",
" <td> 0.440</td>\n",
" <td> 0.365</td>\n",
" <td> 0.125</td>\n",
" <td> 0.5160</td>\n",
" <td> 0.2155</td>\n",
" <td> 0.1140</td>\n",
" <td> 0.155</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0</td>\n",
" <td> 0.330</td>\n",
" <td> 0.255</td>\n",
" <td> 0.080</td>\n",
" <td> 0.2050</td>\n",
" <td> 0.0895</td>\n",
" <td> 0.0395</td>\n",
" <td> 0.055</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 166,
"text": [
" sex length diameter height wholeWeight shuckedWeight visceraWeight \\\n",
"0 2 0.455 0.365 0.095 0.5140 0.2245 0.1010 \n",
"1 2 0.350 0.265 0.090 0.2255 0.0995 0.0485 \n",
"2 1 0.530 0.420 0.135 0.6770 0.2565 0.1415 \n",
"3 2 0.440 0.365 0.125 0.5160 0.2155 0.1140 \n",
"4 0 0.330 0.255 0.080 0.2050 0.0895 0.0395 \n",
"\n",
" shellWeight rings \n",
"0 0.150 3 \n",
"1 0.070 0 \n",
"2 0.210 1 \n",
"3 0.155 2 \n",
"4 0.055 0 "
]
}
],
"prompt_number": 166
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.pairplot(data, hue='sex')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 98,
"text": [
"<seaborn.axisgrid.PairGrid at 0x40a13898>"
]
},
{
"metadata": {},
"output_type": "display_data",
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