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