def test_permutations_length_of_output(self):
# asserts the length of output based off input size
assert len(permutations([])) == 0
assert len(permutations([1])) == 1
assert len(permutations([1, 2])) == 2
assert len(permutations([1, 2, 3])) == 6
assert len(permutations([1, 2, 3, 4])) == 24
assert len(permutations([1, 2, 3, 4, 5])) == 120
assert len(permutations([1, 2, 3, 4, 5, 6])) == 720
assert len(permutations([1, 2, 3, 4, 5, 6, 7])) == 5040
def test_generator_with_custom_start(self):
results = []
for p in permutations(3, Permutation.from_list([1, 2, 0])):
results.append(p)
self.assertEqual(len(results), 3)
self.assertEqual(results[0], Permutation.from_list([1, 2, 0]))
self.assertEqual(results[1], Permutation.from_list([2, 0, 1]))
self.assertEqual(results[2], Permutation.from_list([2, 1, 0]))
def test_generator_from_the_beginning(self):
results = []
for p in permutations(3):
results.append(p)
self.assertEqual(len(results), 6)
self.assertEqual(results[0], Permutation.from_list([0, 1, 2]))
self.assertEqual(results[1], Permutation.from_list([0, 2, 1]))
self.assertEqual(results[2], Permutation.from_list([1, 0, 2]))
self.assertEqual(results[3], Permutation.from_list([1, 2, 0]))
self.assertEqual(results[4], Permutation.from_list([2, 0, 1]))
self.assertEqual(results[5], Permutation.from_list([2, 1, 0]))
def test_permutations_with_integer_inputs(self):
# tests contains of outputs because order can be different
assert permutations([]) == []
assert permutations([1]) == [1]
self.assertCountEqual(permutations([1, 2]), self.perm_two)
self.assertCountEqual(permutations([1, 2, 3]), self.perm_three)
self.assertCountEqual(permutations([1, 2, 3, 4]), self.perm_four)
self.assertCountEqual(permutations([1, 2, 3, 4, 5]), self.perm_five)
def permutationTest(X, y, feature_list, dataset):
n_tests = 5
n_permutations = 100
scores = np.zeros(n_tests)
pvalues = np.zeros(n_tests)
permutation_scores = np.zeros(n_tests*n_permutations)
for i in range(n_tests):
# model = models.RF(feature_list)
model = xgb.XGBClassifier()
Xt = copy.deepcopy(X)
yt = copy.deepcopy(y)
rs = randint(1, 100)
score, permutation_score, pvalue = permutations(model, Xt, yt, scoring='f1',
cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=rs),
n_permuations=n_permutations)
print("\nPermutation {}:\n".format(i+1))
print("p-value is {:.2f}".format(pvalue))
print("Score is {:.2f}".format(score))
scores[i] = score
pvalues[i] = pvalue
permutation_scores[n_permutations*i:n_permutations*(i+1)] = permutation_score
p_mean = np.mean(pvalues)
p_stdev = np.std(pvalues)
s_mean = np.mean(scores)
print("\nMean p-value is {:.2f}".format(p_mean))
print("Stdev p-value is {:.2f}".format(p_stdev))
print("Mean score is {:.2f}\n".format(s_mean))
r = np.amax(permutation_scores) - np.amin(permutation_scores)
width = 0.03
print("Range is: {}".format(r))
plt.hist(permutation_scores, bins=ceil(r/width))
plt.axvline(x=s_mean, color='r', label='Average score value')
plt.title("Histogram plot of permutation score values for " + dataset)
plt.xlabel("permutation_scores")
plt.ylabel("Frequency of permutation_scores")
plt.legend()
plt.savefig('Histogram_' + dataset + '_.png')
plt.show()
def test_permutations_with_mixed_inputs(self):
self.assertCountEqual(permutations([2, 1]), self.perm_two)
self.assertCountEqual(permutations([2, 1, 3]), self.perm_three)
self.assertCountEqual(permutations([4, 2, 1, 3]), self.perm_four)
self.assertCountEqual(permutations([2, 5, 4, 3, 1]), self.perm_five)
def test_inverse(self):
e = Permutation(4)
for p in permutations(4):
pinv = p.inverse()
self.assertEqual(pinv * p, e)
self.assertEqual(p * pinv, e)
def test_cycle_decomposition(self):
for p in permutations(5):
cycle_list = p.as_cycles()
self.assertEqual(Permutation.from_cycles(cycle_list, 5), p)
