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Copyright (C) 2017 J. Patrick Hall, jphall@gwu.edu

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Simple oversampling - Pandas and imbalanced-learn

Imports


In [1]:
import pandas as pd              # pandas for handling mixed data sets 
import numpy as np               # numpy for basic math and matrix operations

# imbalanced-learn for oversampling
from imblearn.over_sampling import RandomOverSampler

Proportional oversampling

Create a sample data set


In [2]:
scratch_df = pd.DataFrame({'x': pd.Series(np.arange(0, 10)),
                           'y': [0, 1, 0, 0, 0, 0, 1, 0, 0, 0]})
scratch_df


Out[2]:
x y
0 0 0
1 1 1
2 2 0
3 3 0
4 4 0
5 5 0
6 6 1
7 7 0
8 8 0
9 9 0

If the event in a classification problem or the value in a prediction problem is imbalanced (usually toward zero) this can lead to biased models, single class predictions for classification models, and biased predictions for prediction models. The simplest approach for an imbalanced target is to oversample the data set.


In [3]:
# fit random oversampling function
# cannot pass single array for X, must use numpy.reshape(-1, 1)
ros = RandomOverSampler()
over_sample_x, over_sample_y = ros.fit_sample(scratch_df.x.get_values().reshape(-1, 1), 
                                              scratch_df.y)

# create Pandas dataframe from oversampling results
over_sample_df = pd.DataFrame({'over_sample_x': over_sample_x.reshape(16,),
                               'over_sample_y': over_sample_y})
over_sample_df


Out[3]:
over_sample_x over_sample_y
0 0 0
1 2 0
2 3 0
3 4 0
4 5 0
5 7 0
6 8 0
7 9 0
8 1 1
9 6 1
10 1 1
11 6 1
12 6 1
13 6 1
14 6 1
15 6 1