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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"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>preg</th>\n",
" <th>plas</th>\n",
" <th>pres</th>\n",
" <th>skin</th>\n",
" <th>test</th>\n",
" <th>mass</th>\n",
" <th>pedi</th>\n",
" <th>age</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>148</td>\n",
" <td>72</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>33.6</td>\n",
" <td>0.627</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>66</td>\n",
" <td>29</td>\n",
" <td>0</td>\n",
" <td>26.6</td>\n",
" <td>0.351</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8</td>\n",
" <td>183</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.3</td>\n",
" <td>0.672</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>89</td>\n",
" <td>66</td>\n",
" <td>23</td>\n",
" <td>94</td>\n",
" <td>28.1</td>\n",
" <td>0.167</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>168</td>\n",
" <td>43.1</td>\n",
" <td>2.288</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" preg plas pres skin test mass pedi age class\n",
"0 6 148 72 35 0 33.6 0.627 50 1\n",
"1 1 85 66 29 0 26.6 0.351 31 0\n",
"2 8 183 64 0 0 23.3 0.672 32 1\n",
"3 1 89 66 23 94 28.1 0.167 21 0\n",
"4 0 137 40 35 168 43.1 2.288 33 1"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Import the data set\n",
"\n",
"import pandas as pd\n",
"columns = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
"data = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/pima-indians-diabetes.csv', names=columns)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(768, 9)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 500\n",
"1 268\n",
"Name: class, dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['class'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#print bar chart\n",
"data['class'].value_counts().plot(kind='bar', title='Count (target)');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Down Sampling - Majority Class - Using Random Sampling"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class = 0 500\n",
"Class = 1 268\n"
]
}
],
"source": [
"count_class_0, count_class_1 = data['class'].value_counts()\n",
"\n",
"# Divide by class\n",
"df_class_0 = data[data['class'] == 0] #majority class\n",
"df_class_1 = data[data['class'] == 1] #minority class\n",
"\n",
"print('Class = 0 ', df_class_0.shape[0])\n",
"print('Class = 1 ', df_class_1.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(268, 9)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Sample Majority class (y=0, to have same number of records as minority calls (y=1)\n",
"df_class_0_under = df_class_0.sample(count_class_1)\n",
"\n",
"df_class_0_under.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random under-sampling:\n",
"0 268\n",
"1 268\n",
"Name: class, dtype: int64\n",
"Num records = 536\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# join the dataframes containing y=1 and y=0\n",
"df_test_under = pd.concat([df_class_0_under, df_class_1])\n",
"\n",
"print('Random under-sampling:')\n",
"print(df_test_under['class'].value_counts())\n",
"print(\"Num records = \", df_test_under.shape[0])\n",
"\n",
"df_test_under['class'].value_counts().plot(kind='bar', title='Count (target)');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Down Sampling - Majority Class - Using imblearn "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"imblearn over-sampling:\n",
"0 268\n",
"1 268\n",
"Name: class, dtype: int64\n",
"Num records = 536\n"
]
},
{
"data": {
"image/png": 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KcvxsnEuS9GSz9cz9RGBbVf28qn4LfAVYNUvnkiSNMG+WHncxsKPv/k7gZf07JFkDrOnu/jrJ7bM0y6FoAfCrQQ8xnvzzoCfQAPi9ObOeN9aG2Yr7uKpqPbB+UOdvWZLNVbVy0HNII/m9OXdm67LMLmBJ3/1juzVJ0hyYrbhfDyxPclySpwNnAhtn6VySpBFm5bJMVe1Nshb4NnAYcGFV3Tob59KovNylpyq/N+dIqmrQM0iSZpjvUJWkBhl3SWqQcZekBg3sde6aOUn+hN47gBd3S7uAjVW1dXBTSRokn7kf5JL8A71f7xDguu4jwKX+wjY9VSV566BnaJ2vljnIJfkZcEJV/W7E+tOBW6tq+WAmk8aW5H+qaumg52iZl2UOfo8BxwDbR6wv6rZJA5Fky1ibgIVzOcuhyLgf/N4JbEpyB0/8sralwAuAtYMaSqIX8FOA+0asB/jB3I9zaDHuB7mq+laSF9L7Ncv9P1C9vqoeHdxkEv8BHFFVN43ckOR7cz7NIcZr7pLUIF8tI0kNMu6S1CDjLkkNMu6S1CDjLkkN+n9FlP1ETWJfHAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from imblearn.under_sampling import RandomUnderSampler\n",
"\n",
"#separate the data in descriptive and target attributes\n",
"X = data.drop('class', axis=1)\n",
"Y = data['class']\n",
"\n",
"rus = RandomUnderSampler(random_state=42, replacement=True)\n",
"X_rus, Y_rus = rus.fit_resample(X, Y)\n",
"\n",
"df_rus = pd.concat([pd.DataFrame(X_rus), pd.DataFrame(Y_rus, columns=['class'])], axis=1)\n",
"\n",
"print('imblearn over-sampling:')\n",
"print(df_rus['class'].value_counts())\n",
"print(\"Num records = \", df_rus.shape[0])\n",
"\n",
"df_rus['class'].value_counts().plot(kind='bar', title='Count (target)');"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# we should have the same/similar results as previous. Although the selection of records could be different"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" #### Down/Under sampling the majority class y=1 using Sci-Kit Learn"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original Data distribution\n",
"0 500\n",
"1 268\n",
"Name: class, dtype: int64\n",
"Sci-Kit Learn : resample : Down Sampled data set\n",
"0 268\n",
"1 268\n",
"Name: class, dtype: int64\n",
"Num records = 536\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.utils import resample\n",
"\n",
"print(\"Original Data distribution\")\n",
"print(data['class'].value_counts())\n",
"\n",
"# Down Sample Majority class\n",
"down_sample = resample(data[data['class']==0],\n",
" replace = True, # sample with replacement\n",
" n_samples = data[data['class']==1].shape[0], # to match minority class\n",
" random_state=42) # reproducible results\n",
"\n",
"# Combine majority class with upsampled minority class\n",
"train_downsample = pd.concat([data[data['class']==1], down_sample])\n",
"\n",
"# Display new class counts\n",
"print('Sci-Kit Learn : resample : Down Sampled data set')\n",
"print(train_downsample['class'].value_counts())\n",
"print(\"Num records = \", train_downsample.shape[0])\n",
"train_downsample['class'].value_counts().plot(kind='bar', title='Count (target)');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Over sampling the minority call y=0 (using random sampling)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random over-sampling:\n",
"0 500\n",
"1 500\n",
"Name: class, dtype: int64\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_class_1_over = df_class_1.sample(count_class_0, replace=True)\n",
"\n",
"df_test_over = pd.concat([df_class_0, df_class_1_over], axis=0)\n",
"\n",
"print('Random over-sampling:')\n",
"print(df_test_over['class'].value_counts())\n",
"\n",
"df_test_over['class'].value_counts().plot(kind='bar', title='Count (target)');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Over sampling the minority call y=0 using SMOTE"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 500\n",
"1 268\n",
"Name: class, dtype: int64\n",
"SMOTE over-sampling:\n",
"0 500\n",
"1 500\n",
"Name: class, dtype: int64\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from imblearn.over_sampling import SMOTE\n",
"\n",
"print(data['class'].value_counts())\n",
"X = data.drop('class', axis=1)\n",
"Y = data['class']\n",
"\n",
"sm = SMOTE(random_state=42)\n",
"X_res, Y_res = sm.fit_resample(X, Y)\n",
"\n",
"df_smote_over = pd.concat([pd.DataFrame(X_res), pd.DataFrame(Y_res, columns=['class'])], axis=1)\n",
"\n",
"print('SMOTE over-sampling:')\n",
"print(df_smote_over['class'].value_counts())\n",
"\n",
"df_smote_over['class'].value_counts().plot(kind='bar', title='Count (target)');"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|