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52 行
1.9 KiB
Python
52 行
1.9 KiB
Python
import numpy as np
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import pandas as pd
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import pytest
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from ludwig.backend.base import LocalBackend
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from ludwig.constants import BALANCE_PERCENTAGE_TOLERANCE, NAME
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from ludwig.data.preprocessing import balance_data
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@pytest.mark.parametrize(
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"method, balance",
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[
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("oversample_minority", 0.25),
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("oversample_minority", 0.5),
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("oversample_minority", 0.75),
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("undersample_majority", 0.25),
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("undersample_majority", 0.5),
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("undersample_majority", 0.75),
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("undersample_majority", 0.9),
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],
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)
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def test_balance(method, balance):
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config = {
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"input_features": [
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{"name": "Index", "proc_column": "Index", "type": "number"},
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{"name": "random_1", "proc_column": "random_1", "type": "number"},
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{"name": "random_2", "proc_column": "random_2", "type": "number"},
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],
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"output_features": [{"name": "Label", "proc_column": "Label", "type": "binary"}],
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"preprocessing": {"oversample_minority": None, "undersample_majority": None},
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}
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input_df = pd.DataFrame(
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{
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"Index": np.arange(0, 200, 1),
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"random_1": np.random.randint(0, 50, 200),
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"random_2": np.random.choice(["Type A", "Type B", "Type C", "Type D"], 200),
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"Label": np.concatenate((np.zeros(180), np.ones(20))),
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"split": np.zeros(200),
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}
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)
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config["preprocessing"][method] = balance
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backend = LocalBackend()
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test_df = balance_data(input_df, config["output_features"], config["preprocessing"], backend, 42)
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target = config["output_features"][0][NAME]
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majority_class = test_df[target].value_counts()[test_df[target].value_counts().idxmax()]
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minority_class = test_df[target].value_counts()[test_df[target].value_counts().idxmin()]
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new_class_balance = round(minority_class / majority_class, 2)
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assert abs(balance - new_class_balance) < BALANCE_PERCENTAGE_TOLERANCE
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