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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 License
Copyright (C) 2017 J. Patrick Hall, jphall@gwu.edu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions
Step1:  Proportional oversampling
Create a sample data set
Step2:  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.
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Python Code:
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                  
Explanation: License
Copyright (C) 2017 J. Patrick Hall, jphall@gwu.edu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Simple oversampling - Pandas and imbalanced-learn
Imports
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scratch_df = pd.DataFrame({'x': pd.Series(np.arange(0, 10)),
                           'y': [0, 1, 0, 0, 0, 0, 1, 0, 0, 0]})
scratch_df
Explanation: Proportional oversampling
Create a sample data set
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# 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
Explanation: 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.
End of explanation
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