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Python/machine_learning/NaiveBayes.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"iris = datasets.load_iris()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(iris.data)\n",
"df.columns = [\"sl\", \"sw\", 'pl', 'pw']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def abc(k, *val):\n",
" if k < val[0]:\n",
" return 0\n",
" else:\n",
" return 1"
]
},
{
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"execution_count": 5,
"metadata": {},
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]
},
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"metadata": {},
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}
],
"source": [
"df.sl.apply(abc, args=(5,))"
]
},
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"metadata": {
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},
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},
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"def label(val, *boundaries):\n",
" if (val < boundaries[0]):\n",
" return 'a'\n",
" elif (val < boundaries[1]):\n",
" return 'b'\n",
" elif (val < boundaries[2]):\n",
" return 'c'\n",
" else:\n",
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"\n",
"def toLabel(df, old_feature_name):\n",
" second = df[old_feature_name].mean()\n",
" minimum = df[old_feature_name].min()\n",
" first = (minimum + second)/2\n",
" maximum = df[old_feature_name].max()\n",
" third = (maximum + second)/2\n",
" return df[old_feature_name].apply(label, args= (first, second, third))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
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" <td>2.3</td>\n",
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" <td>5.6</td>\n",
" <td>2.4</td>\n",
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" <td>d</td>\n",
" <td>c</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>c</td>\n",
" <td>b</td>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td>6.9</td>\n",
" <td>3.1</td>\n",
" <td>5.4</td>\n",
" <td>2.1</td>\n",
" <td>d</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>6.7</td>\n",
" <td>3.1</td>\n",
" <td>5.6</td>\n",
" <td>2.4</td>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>d</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>d</td>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td>5.8</td>\n",
" <td>2.7</td>\n",
" <td>5.1</td>\n",
" <td>1.9</td>\n",
" <td>b</td>\n",
" <td>b</td>\n",
" <td>c</td>\n",
" <td>d</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>c</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>6.7</td>\n",
" <td>3.3</td>\n",
" <td>5.7</td>\n",
" <td>2.5</td>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>6.7</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.3</td>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>1.9</td>\n",
" <td>c</td>\n",
" <td>a</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.0</td>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>6.2</td>\n",
" <td>3.4</td>\n",
" <td>5.4</td>\n",
" <td>2.3</td>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>5.1</td>\n",
" <td>1.8</td>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>150 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" sl sw pl pw sl_labeled sw_labeled pl_labeled pw_labeled\n",
"0 5.1 3.5 1.4 0.2 b c a a\n",
"1 4.9 3.0 1.4 0.2 a b a a\n",
"2 4.7 3.2 1.3 0.2 a c a a\n",
"3 4.6 3.1 1.5 0.2 a c a a\n",
"4 5.0 3.6 1.4 0.2 a c a a\n",
"5 5.4 3.9 1.7 0.4 b d a a\n",
"6 4.6 3.4 1.4 0.3 a c a a\n",
"7 5.0 3.4 1.5 0.2 a c a a\n",
"8 4.4 2.9 1.4 0.2 a b a a\n",
"9 4.9 3.1 1.5 0.1 a c a a\n",
"10 5.4 3.7 1.5 0.2 b c a a\n",
"11 4.8 3.4 1.6 0.2 a c a a\n",
"12 4.8 3.0 1.4 0.1 a b a a\n",
"13 4.3 3.0 1.1 0.1 a b a a\n",
"14 5.8 4.0 1.2 0.2 b d a a\n",
"15 5.7 4.4 1.5 0.4 b d a a\n",
"16 5.4 3.9 1.3 0.4 b d a a\n",
"17 5.1 3.5 1.4 0.3 b c a a\n",
"18 5.7 3.8 1.7 0.3 b d a a\n",
"19 5.1 3.8 1.5 0.3 b d a a\n",
"20 5.4 3.4 1.7 0.2 b c a a\n",
"21 5.1 3.7 1.5 0.4 b c a a\n",
"22 4.6 3.6 1.0 0.2 a c a a\n",
"23 5.1 3.3 1.7 0.5 b c a a\n",
"24 4.8 3.4 1.9 0.2 a c a a\n",
"25 5.0 3.0 1.6 0.2 a b a a\n",
"26 5.0 3.4 1.6 0.4 a c a a\n",
"27 5.2 3.5 1.5 0.2 b c a a\n",
"28 5.2 3.4 1.4 0.2 b c a a\n",
"29 4.7 3.2 1.6 0.2 a c a a\n",
".. ... ... ... ... ... ... ... ...\n",
"120 6.9 3.2 5.7 2.3 d c d d\n",
"121 5.6 2.8 4.9 2.0 b b c d\n",
"122 7.7 2.8 6.7 2.0 d b d d\n",
"123 6.3 2.7 4.9 1.8 c b c c\n",
"124 6.7 3.3 5.7 2.1 c c d d\n",
"125 7.2 3.2 6.0 1.8 d c d c\n",
"126 6.2 2.8 4.8 1.8 c b c c\n",
"127 6.1 3.0 4.9 1.8 c b c c\n",
"128 6.4 2.8 5.6 2.1 c b d d\n",
"129 7.2 3.0 5.8 1.6 d b d c\n",
"130 7.4 2.8 6.1 1.9 d b d d\n",
"131 7.9 3.8 6.4 2.0 d d d d\n",
"132 6.4 2.8 5.6 2.2 c b d d\n",
"133 6.3 2.8 5.1 1.5 c b c c\n",
"134 6.1 2.6 5.6 1.4 c b d c\n",
"135 7.7 3.0 6.1 2.3 d b d d\n",
"136 6.3 3.4 5.6 2.4 c c d d\n",
"137 6.4 3.1 5.5 1.8 c c d c\n",
"138 6.0 3.0 4.8 1.8 c b c c\n",
"139 6.9 3.1 5.4 2.1 d c d d\n",
"140 6.7 3.1 5.6 2.4 c c d d\n",
"141 6.9 3.1 5.1 2.3 d c c d\n",
"142 5.8 2.7 5.1 1.9 b b c d\n",
"143 6.8 3.2 5.9 2.3 c c d d\n",
"144 6.7 3.3 5.7 2.5 c c d d\n",
"145 6.7 3.0 5.2 2.3 c b c d\n",
"146 6.3 2.5 5.0 1.9 c a c d\n",
"147 6.5 3.0 5.2 2.0 c b c d\n",
"148 6.2 3.4 5.4 2.3 c c d d\n",
"149 5.9 3.0 5.1 1.8 c b c c\n",
"\n",
"[150 rows x 8 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['sl_labeled'] = toLabel(df, 'sl')\n",
"df['sw_labeled'] = toLabel(df, 'sw')\n",
"df['pl_labeled'] = toLabel(df, 'pl')\n",
"df['pw_labeled'] = toLabel(df, 'pw')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"df.drop(['sl', 'sw', 'pl', 'pw'], axis = 1, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'a', 'b', 'c', 'd'}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set(df['sl_labeled'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"df[\"output\"] = iris.target"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <tr>\n",
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" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>d</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td>d</td>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td>b</td>\n",
" <td>b</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>d</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>d</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>c</td>\n",
" <td>a</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" <td>d</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>c</td>\n",
" <td>c</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>150 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" sl_labeled sw_labeled pl_labeled pw_labeled output\n",
"0 b c a a 0\n",
"1 a b a a 0\n",
"2 a c a a 0\n",
"3 a c a a 0\n",
"4 a c a a 0\n",
"5 b d a a 0\n",
"6 a c a a 0\n",
"7 a c a a 0\n",
"8 a b a a 0\n",
"9 a c a a 0\n",
"10 b c a a 0\n",
"11 a c a a 0\n",
"12 a b a a 0\n",
"13 a b a a 0\n",
"14 b d a a 0\n",
"15 b d a a 0\n",
"16 b d a a 0\n",
"17 b c a a 0\n",
"18 b d a a 0\n",
"19 b d a a 0\n",
"20 b c a a 0\n",
"21 b c a a 0\n",
"22 a c a a 0\n",
"23 b c a a 0\n",
"24 a c a a 0\n",
"25 a b a a 0\n",
"26 a c a a 0\n",
"27 b c a a 0\n",
"28 b c a a 0\n",
"29 a c a a 0\n",
".. ... ... ... ... ...\n",
"120 d c d d 2\n",
"121 b b c d 2\n",
"122 d b d d 2\n",
"123 c b c c 2\n",
"124 c c d d 2\n",
"125 d c d c 2\n",
"126 c b c c 2\n",
"127 c b c c 2\n",
"128 c b d d 2\n",
"129 d b d c 2\n",
"130 d b d d 2\n",
"131 d d d d 2\n",
"132 c b d d 2\n",
"133 c b c c 2\n",
"134 c b d c 2\n",
"135 d b d d 2\n",
"136 c c d d 2\n",
"137 c c d c 2\n",
"138 c b c c 2\n",
"139 d c d d 2\n",
"140 c c d d 2\n",
"141 d c c d 2\n",
"142 b b c d 2\n",
"143 c c d d 2\n",
"144 c c d d 2\n",
"145 c b c d 2\n",
"146 c a c d 2\n",
"147 c b c d 2\n",
"148 c c d d 2\n",
"149 c b c c 2\n",
"\n",
"[150 rows x 5 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fit(data):\n",
" output_name = data.columns[-1]\n",
" features = data.columns[0:-1]\n",
" counts = {}\n",
" possible_outputs = set(data[output_name])\n",
" for output in possible_outputs:\n",
" counts[output] = {}\n",
" smallData = data[data[output_name] == output]\n",
" counts[output][\"total_count\"] = len(smallData)\n",
" for f in features:\n",
" counts[output][f] = {}\n",
" possible_values = set(smallData[f])\n",
" for value in possible_values:\n",
" val_count = len(smallData[smallData[f] == value])\n",
" counts[output][f][value] = val_count\n",
" return counts"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: {'pl_labeled': {'a': 50},\n",
" 'pw_labeled': {'a': 50},\n",
" 'sl_labeled': {'a': 28, 'b': 22},\n",
" 'sw_labeled': {'a': 1, 'b': 7, 'c': 32, 'd': 10},\n",
" 'total_count': 50},\n",
" 1: {'pl_labeled': {'b': 7, 'c': 43},\n",
" 'pw_labeled': {'b': 10, 'c': 40},\n",
" 'sl_labeled': {'a': 3, 'b': 21, 'c': 24, 'd': 2},\n",
" 'sw_labeled': {'a': 13, 'b': 29, 'c': 8},\n",
" 'total_count': 50},\n",
" 2: {'pl_labeled': {'c': 20, 'd': 30},\n",
" 'pw_labeled': {'c': 16, 'd': 34},\n",
" 'sl_labeled': {'a': 1, 'b': 5, 'c': 29, 'd': 15},\n",
" 'sw_labeled': {'a': 5, 'b': 28, 'c': 15, 'd': 2},\n",
" 'total_count': 50}}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fit(df)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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