1
0
Fork 0
QuaPy/quapy/tests/test_replicability.py

79 lines
2.9 KiB
Python

import unittest
import quapy as qp
from quapy.data import LabelledCollection
from quapy.functional import strprev
from sklearn.linear_model import LogisticRegression
from quapy.method.aggregative import PACC
class MyTestCase(unittest.TestCase):
def test_prediction_replicability(self):
dataset = qp.datasets.fetch_UCIDataset('yeast')
with qp.util.temp_seed(0):
lr = LogisticRegression(random_state=0, max_iter=10000)
pacc = PACC(lr)
prev = pacc.fit(dataset.training).quantify(dataset.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(dataset.training).quantify(dataset.test.X)
str_prev2 = strprev(prev2, prec=5)
self.assertEqual(str_prev1, str_prev2) # add assertion here
def test_samping_replicability(self):
import numpy as np
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 = 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)
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)
with qp.util.temp_seed(0):
sample1_tr, sample1_te = data.split_stratified(train_prop=0.7)
with qp.util.temp_seed(0):
sample2_tr, sample2_te = data.split_stratified(train_prop=0.7)
equal_collections(sample1_tr, sample2_tr, True)
equal_collections(sample1_te, sample2_te, True)
if __name__ == '__main__':
unittest.main()