Esempio n. 1
0
def test_friedman():
    x = [[0, 0.1, 0.2, 0.3, 0.4, 0.5], [0, 0.1, 0.2, 0.3, 0.4, 0.5]]
    d = Distribution(x)

    output = friedman.friedman(d)

    assert output['arg0'] == ((10.76923076923077, 11), (0.0, (11, 0)))
Esempio n. 2
0
def test_std():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    d = Distribution(x)

    output = measure.std(d)

    assert output['arg0'] == 0.1707825127659933
Esempio n. 3
0
def test_var():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    d = Distribution(x)

    output = measure.var(d)

    assert output['arg0'] == 0.029166666666666664
Esempio n. 4
0
def test_rank():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    d = Distribution(x)

    output = measure.rank(d)

    assert len(output['arg0']) == 6
Esempio n. 5
0
def test_skewness():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    d = Distribution(x)

    output = measure.skewness(d)

    assert output['arg0'] == 5.804286057433026e-17
Esempio n. 6
0
def test_min():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    d = Distribution(x)

    output = measure.min(d)

    assert output['arg0'] == 0
Esempio n. 7
0
def test_kurtosis():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    d = Distribution(x)

    output = measure.kurtosis(d)

    assert output['arg0'] == -1.268571428571428
Esempio n. 8
0
def test_friedman_with_posthoc():
    x = [[0, 0.1, 0.2, 0.3, 0.4, 0.5], [0, 0.1, 0.2, 0.3, 0.4, 0.5]]
    d = Distribution(x)

    output = friedman.friedman_with_posthoc(d, axis=1)

    assert len(output['arg0'][0]) == 6
    assert output['arg0'][1] == 5.331310596344878
Esempio n. 9
0
def test_u_test():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    y = [0.07, 0.14, 0.72, 0.32, 0.59, 0.43]
    d = Distribution(x, y)

    output = mann_whitney.u_test(d)

    assert output['arg0-arg1'] == (0, 0.18923879662233944) or output['arg0-arg1'] == (0, 0.3939393939393939)
Esempio n. 10
0
def test_signed_rank():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    y = [0.07, 0.14, 0.72, 0.32, 0.59, 0.43]
    d = Distribution(x, y)

    output = wilcoxon.signed_rank(d)

    assert output['arg0-arg1'] == (0, 0.15625)
Esempio n. 11
0
def test_rank_sum():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    y = [0.07, 0.14, 0.72, 0.32, 0.59, 0.43]
    d = Distribution(x, y)

    output = wilcoxon.rank_sum(d)

    assert output['arg0-arg1'] == (0, 0.3366683676100388)
Esempio n. 12
0
def test_measure_pipeline():
    def f(x):
        return True

    d = Distribution([0.1, 0.2])

    output = wrappers.measure_pipeline(f, d)

    assert output['arg0'] == True
Esempio n. 13
0
def test_plot_critical_difference():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    y = [0.07, 0.14, 0.72, 0.32, 0.59, 0.43]

    d = Distribution(x, y)

    friedman_nemenyi = friedman.friedman_with_posthoc(d)

    critical.plot_critical_difference(friedman_nemenyi)
Esempio n. 14
0
def test_plot_h_index():
    x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
    y = [0.07, 0.14, 0.72, 0.32, 0.59, 0.43]
    z = [2.17, 9.14, 999.72, 8.32, 7.19, 9.43]

    d = Distribution(x, y, z)

    signed_rank = wilcoxon.signed_rank(d)

    significance.plot_h_index(signed_rank)
Esempio n. 15
0
def test_statistical_pipeline():
    def f(x, y):
        return [0, 0]

    d = Distribution([0.1, 0.2], [0.3, 0.4])
    alpha = 0.05

    output = wrappers.statistical_pipeline(f, d, alpha)

    assert output['arg0-arg1'] == (1, 0)
Esempio n. 16
0
import statys.plotters.significance as s
import statys.tests.wilcoxon as w
from statys.core import Distribution

# Defining input arguments
x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]
y = [0.07, 0.14, 0.72, 0.32, 0.59, 0.43]
z = [2.17, 9.14, 999.72, 8.32, 7.19, 9.43]

# Creating the distribution
d = Distribution(x, y, z)

# Calculating Wilcoxon's signed-rank test
signed_rank = w.signed_rank(d)

# Plots the p-values
s.plot_p_value(signed_rank, title='Wilcoxon Signed-Rank Test ($p$-values)')