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| def negative_down_sampling(data, random_state, target_variable): | |
| positive_data = data[data[target_variable] == 1] | |
| positive_ratio = float(len(positive_data)) / len(data) | |
| negative_data = data[data[target_variable] == 0].sample( | |
| frac=positive_ratio / (1 - positive_ratio), random_state=random_state) | |
| return pd.concat([positive_data, negative_data]) | |
| def multi_class_downsampling(data, random_state, target_variable): | |
| data0 = data[data[target_variable] == 0] | |
| data1 = data[data[target_variable] == 1] | |
| data2 = data[data[target_variable] == 2] | |
| data3 = data[data[target_variable] == 3] | |
| data0_ratio = float(len(data0)) / len(data) | |
| data1_ratio = float(len(data1)) / len(data) | |
| data2_ratio = float(len(data2)) / len(data) | |
| data3_ratio = float(len(data3)) / len(data) | |
| data0_sampled = data[data[target_variable] == 0].sample( | |
| frac=data3_ratio / data0_ratio, random_state=random_state) | |
| data1_sampled = data[data[target_variable] == 1].sample( | |
| frac=data3_ratio / data1_ratio, random_state=random_state) | |
| data2_sampled = data[data[target_variable] == 2].sample( | |
| frac=data3_ratio / data2_ratio, random_state=random_state) | |
| return pd.concat([data0_sampled, data1_sampled, data2_sampled, data3]) |
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