import pandas as pd
import quapy as qp
from quapy.method.aggregative import SLD
from quapy.protocol import APP
from sklearn.svm import SVC

import quacc.evaluation as eval
from quacc.estimator import AccuracyEstimator

from .data import get_dataset

qp.environ["SAMPLE_SIZE"] = 100

pd.set_option("display.float_format", "{:.4f}".format)


def test_2(dataset_name):
    train, test = get_dataset(dataset_name)

    model = SVC(probability=True)

    print(f"fitting model {model.__class__.__name__}...", end=" ", flush=True)
    model.fit(*train.Xy)
    print("fit")

    qmodel = SLD(SVC(probability=True))
    estimator = AccuracyEstimator(model, qmodel)

    print(f"fitting qmodel {qmodel.__class__.__name__}...", end=" ", flush=True)
    estimator.fit(train)
    print("fit")

    n_prevalences = 21
    repreats = 1000
    protocol = APP(test, n_prevalences=n_prevalences, repeats=repreats)
    print(
        f"Tests:\n\
        protocol={protocol.__class__.__name__}\n\
        n_prevalences={n_prevalences}\n\
        repreats={repreats}\n\
        executing...\n"
    )
    df = eval.evaluation_report(
        estimator,
        protocol,
        aggregate=True,
    )
    print(df.to_string())


def main():
    for dataset_name in [
        "imdb",
        # "hp",
        # "spambase",
    ]:
        print(dataset_name)
        test_2(dataset_name)
        print("*" * 50)


if __name__ == "__main__":
    main()