diff --git a/quacc/main_test.py b/quacc/main_test.py
deleted file mode 100644
index 488f6fe..0000000
--- a/quacc/main_test.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import logging
-from logging.handlers import QueueHandler
-from multiprocessing import Manager, Queue
-from threading import Thread
-from time import sleep, time
-
-import numpy as np
-import scipy.sparse as sp
-from joblib import Parallel, delayed
-from quapy.protocol import APP
-from sklearn.linear_model import LinearRegression, LogisticRegression
-from sklearn.metrics import accuracy_score
-
-from baselines.mandoline import estimate_performance
-from quacc.dataset import Dataset
-from quacc.logger import logger, logger_manager, setup_logger, setup_worker_logger
-
-
-def test_lr():
-    d = Dataset(name="rcv1", target="CCAT", n_prevalences=1).get_raw()
-
-    classifier = LogisticRegression()
-    classifier.fit(*d.train.Xy)
-
-    val, _ = d.validation.split_stratified(0.5, random_state=0)
-    val_X, val_y = val.X, val.y
-    val_probs = classifier.predict_proba(val_X)
-
-    reg_X = sp.hstack([val_X, val_probs])
-    reg_y = val_probs[np.arange(val_probs.shape[0]), val_y]
-    reg = LinearRegression()
-    reg.fit(reg_X, reg_y)
-
-    _test_num = 10000
-    test_X = d.test.X[:_test_num, :]
-    test_probs = classifier.predict_proba(test_X)
-    test_reg_X = sp.hstack([test_X, test_probs])
-    reg_pred = reg.predict(test_reg_X)
-
-    def threshold(pred):
-        # return np.mean(
-        #     (reg.predict(test_reg_X) >= pred)
-        #     == (
-        #         test_probs[np.arange(_test_num), d.test.y[:_test_num]] == np.max(test_probs, axis=1)
-        #     )
-        # )
-        return np.mean(
-            (reg.predict(test_reg_X) >= pred)
-            == (np.argmax(test_probs, axis=1) == d.test.y[:_test_num])
-        )
-
-    max_p, max_acc = 0, 0
-    for p in reg_pred:
-        acc = threshold(p)
-        if acc > max_acc:
-            max_acc = acc
-            max_p = p
-
-    print(f"{max_p = }, {max_acc = }")
-    reg_pred = reg_pred - max_p + 0.5
-    print(reg_pred)
-    print(np.mean(reg_pred >= 0.5))
-    print(np.mean(np.argmax(test_probs, axis=1) == d.test.y[:_test_num]))
-
-
-def entropy(probas):
-    return -np.sum(np.multiply(probas, np.log(probas + 1e-20)), axis=1)
-
-
-def get_slices(probas):
-    ln, ncl = probas.shape
-    preds = np.argmax(probas, axis=1)
-    pred_slices = np.full((ln, ncl), fill_value=-1, dtype="<i8")
-    pred_slices[np.arange(ln), preds] = 1
-
-    ent = entropy(probas)
-    n_bins = 10
-    range_top = entropy(np.array([np.ones(ncl) / ncl]))[0]
-    bins = np.linspace(0, range_top, n_bins + 1)
-    bin_map = np.digitize(ent, bins=bins, right=True) - 1
-    ent_slices = np.full((ln, n_bins), fill_value=-1, dtype="<i8")
-    ent_slices[np.arange(ln), bin_map] = 1
-
-    return np.concatenate([pred_slices, ent_slices], axis=1)
-
-
-def test_mandoline():
-    d = Dataset(name="cifar10", target="dog", n_prevalences=1).get_raw()
-
-    tstart = time()
-    classifier = LogisticRegression()
-    classifier.fit(*d.train.Xy)
-
-    val_probs = classifier.predict_proba(d.validation.X)
-    val_preds = np.argmax(val_probs, axis=1)
-    D_val = get_slices(val_probs)
-    emprical_mat_list_val = (1.0 * (val_preds == d.validation.y))[:, np.newaxis]
-
-    protocol = APP(
-        d.test,
-        sample_size=1000,
-        n_prevalences=21,
-        repeats=100,
-        return_type="labelled_collection",
-    )
-    res = []
-    for test in protocol():
-        test_probs = classifier.predict_proba(test.X)
-        test_preds = np.argmax(test_probs, axis=1)
-        D_test = get_slices(test_probs)
-        wp = estimate_performance(D_val, D_test, None, emprical_mat_list_val)
-        score = wp.all_estimates[0].weighted[0]
-        res.append(abs(score - accuracy_score(test.y, test_preds)))
-        print(score)
-    res = np.array(res).reshape((21, 100))
-    print(res.mean(axis=1))
-    print(f"time: {time() - tstart}s")
-
-
-def joblib_queue():
-    def worker(q: Queue, i):
-        setup_worker_logger(q)
-        log = logger()
-        log.info(i)
-        sleep(2)
-        print(f"worker {i}")
-
-    setup_logger()
-    log = logger()
-    log.info("start")
-    Parallel(n_jobs=5)(delayed(worker)(logger_manager().q, i) for i in range(5))
-    log.info("end")
-    logger_manager().close()
-
-
-if __name__ == "__main__":
-    joblib_queue()