本文整理汇总了Python中scipy.stats.friedmanchisquare方法的典型用法代码示例。如果您正苦于以下问题：Python stats.friedmanchisquare方法的具体用法？Python stats.friedmanchisquare怎么用？Python stats.friedmanchisquare使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy.stats的用法示例。
在下文中一共展示了stats.friedmanchisquare方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: friedman_test
# 需要导入模块: from scipy import stats [as 别名]
# 或者: from scipy.stats import friedmanchisquare [as 别名]
def friedman_test(self, metric_name=None):
"""
The Friedman test is a non-parametric statistical test used to
detect differences
in treatments across multiple test attempts. The procedure involves
ranking each row (or block) together,
then considering the values of ranks by columns.
Implementation used:
`scipy.stats <https://docs.scipy.org/doc/scipy-0.15.1/reference
/generated/scipy.stats.friedmanchisquare.html>`_.
"""
self._check_is_evaluated()
metric_name = self._validate_metric_name(metric_name)
metrics_per_estimator_dataset = \
self._get_metrics_per_estimator_dataset(
metric_name)
friedman_test = stats.friedmanchisquare(
*[metrics_per_estimator_dataset[k] for k in
metrics_per_estimator_dataset.keys()])
values = [friedman_test[0], friedman_test[1]]
values_df = pd.DataFrame([values], columns=["statistic", "p_value"])
return friedman_test, values_df 示例2: test_friedman
# 需要导入模块: from scipy import stats [as 别名]
# 或者: from scipy.stats import friedmanchisquare [as 别名]
def test_friedman(self):
"""Test function friedman"""
df = pd.DataFrame({'DV': np.r_[x, y, z],
'Time': np.repeat(['A', 'B', 'C'], 100),
'Subject': np.tile(np.arange(100), 3)})
friedman(data=df, dv='DV', subject='Subject', within='Time')
summary = friedman(data=df, dv='DV', within='Time', subject='Subject')
# Compare with SciPy built-in function
from scipy import stats
Q, p = stats.friedmanchisquare(x, y, z)
assert np.isclose(Q, summary.at['Friedman', 'Q'])
assert np.isclose(p, summary.at['Friedman', 'p-unc'])
# Test with NaN
df.at[10, 'DV'] = np.nan
friedman(data=df, dv='DV', subject='Subject', within='Time') 示例3: test_friedmanchisquare
# 需要导入模块: from scipy import stats [as 别名]
# 或者: from scipy.stats import friedmanchisquare [as 别名]
def test_friedmanchisquare():
# see ticket:113
# verified with matlab and R
# From Demsar "Statistical Comparisons of Classifiers over Multiple Data Sets"
# 2006, Xf=9.28 (no tie handling, tie corrected Xf >=9.28)
x1 = [array([0.763, 0.599, 0.954, 0.628, 0.882, 0.936, 0.661, 0.583,
0.775, 1.0, 0.94, 0.619, 0.972, 0.957]),
array([0.768, 0.591, 0.971, 0.661, 0.888, 0.931, 0.668, 0.583,
0.838, 1.0, 0.962, 0.666, 0.981, 0.978]),
array([0.771, 0.590, 0.968, 0.654, 0.886, 0.916, 0.609, 0.563,
0.866, 1.0, 0.965, 0.614, 0.9751, 0.946]),
array([0.798, 0.569, 0.967, 0.657, 0.898, 0.931, 0.685, 0.625,
0.875, 1.0, 0.962, 0.669, 0.975, 0.970])]
# From "Bioestadistica para las ciencias de la salud" Xf=18.95 p<0.001:
x2 = [array([4,3,5,3,5,3,2,5,4,4,4,3]),
array([2,2,1,2,3,1,2,3,2,1,1,3]),
array([2,4,3,3,4,3,3,4,4,1,2,1]),
array([3,5,4,3,4,4,3,3,3,4,4,4])]
# From Jerrorl H. Zar, "Biostatistical Analysis"(example 12.6), Xf=10.68, 0.005 < p < 0.01:
# Probability from this example is inexact using Chisquare aproximation of Friedman Chisquare.
x3 = [array([7.0,9.9,8.5,5.1,10.3]),
array([5.3,5.7,4.7,3.5,7.7]),
array([4.9,7.6,5.5,2.8,8.4]),
array([8.8,8.9,8.1,3.3,9.1])]
assert_array_almost_equal(stats.friedmanchisquare(x1[0],x1[1],x1[2],x1[3]),
(10.2283464566929, 0.0167215803284414))
assert_array_almost_equal(stats.friedmanchisquare(x2[0],x2[1],x2[2],x2[3]),
(18.9428571428571, 0.000280938375189499))
assert_array_almost_equal(stats.friedmanchisquare(x3[0],x3[1],x3[2],x3[3]),
(10.68, 0.0135882729582176))
np.testing.assert_raises(ValueError, stats.friedmanchisquare,x3[0],x3[1])
# test using mstats
assert_array_almost_equal(stats.mstats.friedmanchisquare(x1[0],x1[1],x1[2],x1[3]),
(10.2283464566929, 0.0167215803284414))
# the following fails
# assert_array_almost_equal(stats.mstats.friedmanchisquare(x2[0],x2[1],x2[2],x2[3]),
# (18.9428571428571, 0.000280938375189499))
assert_array_almost_equal(stats.mstats.friedmanchisquare(x3[0],x3[1],x3[2],x3[3]),
(10.68, 0.0135882729582176))
np.testing.assert_raises(ValueError,stats.mstats.friedmanchisquare,x3[0],x3[1]) 示例4: test_friedmanchisquare
# 需要导入模块: from scipy import stats [as 别名]
# 或者: from scipy.stats import friedmanchisquare [as 别名]
def test_friedmanchisquare():
# see ticket:113
# verified with matlab and R
# From Demsar "Statistical Comparisons of Classifiers over Multiple Data Sets"
# 2006, Xf=9.28 (no tie handling, tie corrected Xf >=9.28)
x1 = [array([0.763, 0.599, 0.954, 0.628, 0.882, 0.936, 0.661, 0.583,
0.775, 1.0, 0.94, 0.619, 0.972, 0.957]),
array([0.768, 0.591, 0.971, 0.661, 0.888, 0.931, 0.668, 0.583,
0.838, 1.0, 0.962, 0.666, 0.981, 0.978]),
array([0.771, 0.590, 0.968, 0.654, 0.886, 0.916, 0.609, 0.563,
0.866, 1.0, 0.965, 0.614, 0.9751, 0.946]),
array([0.798, 0.569, 0.967, 0.657, 0.898, 0.931, 0.685, 0.625,
0.875, 1.0, 0.962, 0.669, 0.975, 0.970])]
# From "Bioestadistica para las ciencias de la salud" Xf=18.95 p<0.001:
x2 = [array([4,3,5,3,5,3,2,5,4,4,4,3]),
array([2,2,1,2,3,1,2,3,2,1,1,3]),
array([2,4,3,3,4,3,3,4,4,1,2,1]),
array([3,5,4,3,4,4,3,3,3,4,4,4])]
# From Jerrorl H. Zar, "Biostatistical Analysis"(example 12.6), Xf=10.68, 0.005 < p < 0.01:
# Probability from this example is inexact using Chisquare approximation of Friedman Chisquare.
x3 = [array([7.0,9.9,8.5,5.1,10.3]),
array([5.3,5.7,4.7,3.5,7.7]),
array([4.9,7.6,5.5,2.8,8.4]),
array([8.8,8.9,8.1,3.3,9.1])]
assert_array_almost_equal(stats.friedmanchisquare(x1[0],x1[1],x1[2],x1[3]),
(10.2283464566929, 0.0167215803284414))
assert_array_almost_equal(stats.friedmanchisquare(x2[0],x2[1],x2[2],x2[3]),
(18.9428571428571, 0.000280938375189499))
assert_array_almost_equal(stats.friedmanchisquare(x3[0],x3[1],x3[2],x3[3]),
(10.68, 0.0135882729582176))
assert_raises(ValueError, stats.friedmanchisquare,x3[0],x3[1])
# test for namedtuple attribute results
attributes = ('statistic', 'pvalue')
res = stats.friedmanchisquare(*x1)
check_named_results(res, attributes)
# test using mstats
assert_array_almost_equal(mstats.friedmanchisquare(x1[0], x1[1],
x1[2], x1[3]),
(10.2283464566929, 0.0167215803284414))
# the following fails
# assert_array_almost_equal(mstats.friedmanchisquare(x2[0],x2[1],x2[2],x2[3]),
# (18.9428571428571, 0.000280938375189499))
assert_array_almost_equal(mstats.friedmanchisquare(x3[0], x3[1],
x3[2], x3[3]),
(10.68, 0.0135882729582176))
assert_raises(ValueError, mstats.friedmanchisquare,x3[0],x3[1]) 示例5: get_valid_tests
# 需要导入模块: from scipy import stats [as 别名]
# 或者: from scipy.stats import friedmanchisquare [as 别名]
def get_valid_tests(equal_var, independent, normal, num_samples):
'''
Get valid tests given number of samples and statistical characterization of
samples:
Equal variance
Indepenence
Normality
'''
if num_samples == 1:
valid_tests = {
'chisquare': stats.chisquare,
'power_divergence': stats.power_divergence,
'kstest': stats.kstest
}
if normal:
valid_tests['input']['one_sample_ttest'] = stats.ttest_1samp
elif num_samples == 2:
if independent:
valid_tests = {
'mannwhitneyu': stats.mannwhitneyu,
'kruskal': stats.kruskal,
'ks_2samp': stats.ks_2samp
}
if normal:
valid_tests['two_sample_ttest'] = stats.ttest_ind
if equal_var:
valid_tests['f_oneway'] = stats.f_oneway
else:
valid_tests = {
'two_sample_ks': stats.ks_2samp,
'wilcoxon': stats.wilcoxon
}
if normal:
valid_tests['two_sample_related_ttest'] = stats.ttest_rel
elif num_samples >= 3:
if independent:
valid_tests = {
'kruskal': stats.kruskal
}
if normal and equal_var:
valid_tests['f_oneway'] = stats.f_oneway
else:
valid_tests['friedmanchisquare'] = stats.friedmanchisquare
return valid_tests