import unittest

import numpy as np
from sklearn.linear_model import LogisticRegression

import quapy as qp
import quapy.functional as F
from quapy.data import LabelledCollection
from quapy.functional import strprev
from quapy.method.aggregative import PACC
from quapy.tests._synthetic import make_dataset


class TestReplicability(unittest.TestCase):

    @classmethod
    def setUpClass(cls):
        cls.binary_dataset = make_dataset(
            n_train=180, n_test=80, n_classes=2, n_features=10, random_state=21, name='rep-binary'
        )
        cls.multiclass_dataset = make_dataset(
            n_train=180, n_test=80, n_classes=3, n_features=12, random_state=23, name='rep-multiclass'
        )

    def test_prediction_replicability(self):
        train, test = self.binary_dataset.train_test

        with qp.util.temp_seed(0):
            lr = LogisticRegression(random_state=0, max_iter=10000)
            pacc = PACC(lr)
            prev = pacc.fit(*train.Xy).predict(test.X)
            str_prev1 = strprev(prev, prec=5)

        with qp.util.temp_seed(0):
            lr = LogisticRegression(random_state=0, max_iter=10000)
            pacc = PACC(lr)
            prev2 = pacc.fit(*train.Xy).predict(test.X)
            str_prev2 = strprev(prev2, prec=5)

        self.assertEqual(str_prev1, str_prev2)

    def test_samping_replicability(self):

        def equal_collections(c1, c2, value=True):
            self.assertEqual(np.all(c1.X == c2.X), value)
            self.assertEqual(np.all(c1.y == c2.y), value)
            if value:
                self.assertEqual(np.all(c1.classes_ == c2.classes_), value)

        X = list(map(str, range(100)))
        y = np.random.randint(0, 2, 100)
        data = LabelledCollection(instances=X, labels=y)

        sample1 = data.sampling(50)
        sample2 = data.sampling(50)
        equal_collections(sample1, sample2, False)

        sample1 = data.sampling(50, random_state=0)
        sample2 = data.sampling(50, random_state=0)
        equal_collections(sample1, sample2, True)

        sample1 = data.sampling(50, *[0.7, 0.3], random_state=0)
        sample2 = data.sampling(50, *[0.7, 0.3], random_state=0)
        equal_collections(sample1, sample2, True)

        with qp.util.temp_seed(0):
            sample1 = data.sampling(50, *[0.7, 0.3])
        with qp.util.temp_seed(0):
            sample2 = data.sampling(50, *[0.7, 0.3])
        equal_collections(sample1, sample2, True)

        sample1_tr, sample1_te = data.split_stratified(train_prop=0.7, random_state=0)
        sample2_tr, sample2_te = data.split_stratified(train_prop=0.7, random_state=0)
        equal_collections(sample1_tr, sample2_tr, True)
        equal_collections(sample1_te, sample2_te, True)

    def test_parallel_replicability(self):
        train, test = self.multiclass_dataset.train_test
        test = test.sampling(60, *[0.2, 0.3, 0.5], random_state=4)

        with qp.util.temp_seed(10):
            pacc = PACC(LogisticRegression(max_iter=5000), val_split=.5, n_jobs=2)
            pacc.fit(*train.Xy)
            prev1 = F.strprev(pacc.predict(test.instances))

        with qp.util.temp_seed(0):
            pacc = PACC(LogisticRegression(max_iter=5000), val_split=.5, n_jobs=2)
            pacc.fit(*train.Xy)
            prev2 = F.strprev(pacc.predict(test.instances))

        with qp.util.temp_seed(0):
            pacc = PACC(LogisticRegression(max_iter=5000), val_split=.5, n_jobs=2)
            pacc.fit(*train.Xy)
            prev3 = F.strprev(pacc.predict(test.instances))

        self.assertNotEqual(prev1, prev2)
        self.assertEqual(prev2, prev3)


if __name__ == '__main__':
    unittest.main()