64 lines
1.4 KiB
Python
64 lines
1.4 KiB
Python
import pandas as pd
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import quapy as qp
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from quapy.protocol import APP
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from sklearn.linear_model import LogisticRegression
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import quacc.evaluation as eval
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from quacc.estimator import MulticlassAccuracyEstimator
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from quacc.data import get_dataset
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qp.environ["SAMPLE_SIZE"] = 100
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pd.set_option("display.float_format", "{:.4f}".format)
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def test_2(dataset_name):
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train, test = get_dataset(dataset_name)
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model = LogisticRegression()
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print(f"fitting model {model.__class__.__name__}...", end=" ", flush=True)
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model.fit(*train.Xy)
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print("fit")
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estimator = MulticlassAccuracyEstimator(model)
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print(
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f"fitting qmodel {estimator.q_model.__class__.__name__}...", end=" ", flush=True
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)
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estimator.fit(train)
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print("fit")
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n_prevalences = 21
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repreats = 1000
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protocol = APP(test, n_prevalences=n_prevalences, repeats=repreats)
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print(
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f"Tests:\n\
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protocol={protocol.__class__.__name__}\n\
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n_prevalences={n_prevalences}\n\
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repreats={repreats}\n\
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executing...\n"
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)
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df = eval.evaluation_report(
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estimator,
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protocol,
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aggregate=True,
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)
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print(df.to_string())
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def main():
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for dataset_name in [
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"imdb",
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# "hp",
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# "spambase",
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]:
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print(dataset_name)
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test_2(dataset_name)
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print("*" * 50)
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if __name__ == "__main__":
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main()
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