def test_equal_mean_median(self):
x = np.linspace(-1, 1, 21)
y = x ** 3
W1, pval1 = stats.levene(x, y, center="mean")
W2, pval2 = stats.levene(x, y, center="median")
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_trimmed1(self):
# Test that center='trimmed' gives the same result as center='mean'
# when proportiontocut=0.
W1, pval1 = stats.levene(g1, g2, g3, center='mean')
W2, pval2 = stats.levene(g1, g2, g3, center='trimmed', proportiontocut=0.0)
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_equal_mean_median(self):
x = np.linspace(-1,1,21)
np.random.seed(1234)
x2 = np.random.permutation(x)
y = x**3
W1, pval1 = stats.levene(x, y, center='mean')
W2, pval2 = stats.levene(x2, y, center='median')
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_trimmed2(self):
x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0]
y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0]
# Use center='trimmed'
W1, pval1 = stats.levene(x, y, center="trimmed", proportiontocut=0.125)
# Trim the data here, and use center='mean'
W2, pval2 = stats.levene(x[1:-1], y[1:-1], center="mean")
# Result should be the same.
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_trimmed1(self):
# Test that center='trimmed' gives the same result as center='mean'
# when proportiontocut=0.
W1, pval1 = stats.levene(g1, g2, g3, center='mean')
W2, pval2 = stats.levene(g1,
g2,
g3,
center='trimmed',
proportiontocut=0.0)
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def main():
df = pd.read_json(sys.argv[1], lines=True)
reddit_df, weekends_df, weekdays_df = filterAndTransform(df)
weekend_counts = weekends_df['comment_count']
weekday_counts = weekdays_df['comment_count']
# T-test, normality test and variance test
ttest = stats.ttest_ind(weekend_counts, weekday_counts)
initial_ttest_p = ttest.pvalue
initial_weekday_normality_p = stats.normaltest(weekday_counts).pvalue
initial_weekend_normality_p = stats.normaltest(weekend_counts).pvalue
initial_levene_p = stats.levene(weekday_counts, weekend_counts).pvalue
#Fix 1
transformed_weekday_counts = np.sqrt(weekday_counts)
transformed_weekday_normality_p = stats.normaltest(
transformed_weekday_counts).pvalue
transformed_weekend_counts = np.sqrt(weekend_counts)
transformed_weekend_normality_p = stats.normaltest(
transformed_weekend_counts).pvalue
transformed_levene_p = stats.levene(transformed_weekend_counts,
transformed_weekday_counts).pvalue
#Fix 2
weekly_weekday_counts = weekdays_df.groupby(['year', 'week'
]).mean()['comment_count']
weekly_weekday_normality_p = stats.normaltest(weekly_weekday_counts).pvalue
weekly_weekend_counts = weekends_df.groupby(['year', 'week'
]).mean()['comment_count']
weekly_weekend_normality_p = stats.normaltest(weekly_weekend_counts).pvalue
weekly_levene_p = stats.levene(weekly_weekday_counts,
weekly_weekend_counts).pvalue
weekly_ttest_p = stats.ttest_ind(weekly_weekday_counts,
weekly_weekend_counts).pvalue
utest_p = stats.mannwhitneyu(weekday_counts,
weekend_counts,
alternative='two-sided').pvalue
# ...
print(
OUTPUT_TEMPLATE.format(
initial_ttest_p=initial_ttest_p,
initial_weekday_normality_p=initial_weekday_normality_p,
initial_weekend_normality_p=initial_weekend_normality_p,
initial_levene_p=initial_levene_p,
transformed_weekday_normality_p=transformed_weekday_normality_p,
transformed_weekend_normality_p=transformed_weekend_normality_p,
transformed_levene_p=transformed_levene_p,
weekly_weekday_normality_p=weekly_weekday_normality_p,
weekly_weekend_normality_p=weekly_weekend_normality_p,
weekly_levene_p=weekly_levene_p,
weekly_ttest_p=weekly_ttest_p,
utest_p=utest_p,
))
def stats_tests():
global errors
tests = ['Brown-Forsythe', 'Bartlett', 'Levene', 'Fligner-Killeen']
securities = list(container.index)
indicators = list(container.columns)
output = pd.DataFrame(index=pd.MultiIndex.from_product([securities, indicators]),
columns=tests)
for security in securities:
for indicator in indicators:
all = pd.Series(container.loc[security][indicator]['all'])
signal = pd.Series(container.loc[security][indicator]['signal'])
all = pd.to_numeric(all, errors='coerce')
signal = pd.to_numeric(signal, errors='coerce')
try:
output.loc[security, indicator][tests[0]] = stats.levene(
all, signal,
center='median'
)
except:
errors.append([security, indicator, tests[0]])
try:
output.loc[security, indicator][tests[1]] = stats.bartlett(
all, signal
)
except:
errors.append([security, indicator, tests[1]])
try:
output.loc[security, indicator][tests[2]] = stats.levene(
all, signal,
center='mean'
)
except:
errors.append([security, indicator, tests[2]])
try:
output.loc[security, indicator][tests[3]] = stats.fligner(
all, signal
)
except:
errors.append([security, indicator, tests[3]])
p_values = output.dropna().applymap(lambda x: x.pvalue).unstack()
p_values_container = output.dropna().applymap(lambda x: x.pvalue).unstack().melt()
p_values.to_pickle('p_values_full')
p_values_container.to_pickle('p_values_container_full')
def _varAnalysis(df, labels):
"""
"""
from scipy.stats import levene
if df.shape[0] != len(labels):
raise ValueError(
"The number of input samples is not equal to labels size")
return 0
label_ = np.unique(labels)
groups = _split(df, labels)
if len(label_) == 2:
print('Performing t-test analysis...')
from scipy.stats import ttest_ind
F, P = [], []
for i in range(df.shape[1]):
sample = [item[:, i] for item in groups]
stat, p = levene(*sample)
if p < 0.05:
f, p = ttest_ind(*sample, equal_var=False)
else:
f, p = ttest_ind(*sample, equal_var=True)
F.append(f)
P.append(p)
elif len(label_) > 2:
print('Performing anova analysis...')
F, P = [], []
for i in range(df.shape[1]):
sample = [item[:, i] for item in groups]
stat, p = levene(*sample)
if p < 0.05:
from pingouin import welch_anova
meta = pd.DataFrame(df.iloc[:, i])
meta.columns = ['feature']
meta['labels'] = labels
result = welch_anova(data=meta, dv='feature', between='labels')
f = result['F'].values[0]
p = result['p-unc'].values[0]
else:
from scipy.stats import f_oneway
f, p = f_oneway(*sample)
F.append(f)
P.append(p)
else:
raise ValueError("Groups for comparison are less than 2!")
F = pd.DataFrame(F)
P = pd.DataFrame(P)
F.index = df.columns
P.index = df.columns
return F, P
def test_trimmed2(self):
x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0]
y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0]
np.random.seed(1234)
x2 = np.random.permutation(x)
# Use center='trimmed'
W0, pval0 = stats.levene(x, y, center='trimmed', proportiontocut=0.125)
W1, pval1 = stats.levene(x2, y, center='trimmed', proportiontocut=0.125)
# Trim the data here, and use center='mean'
W2, pval2 = stats.levene(x[1:-1], y[1:-1], center='mean')
# Result should be the same.
assert_almost_equal(W0, W2)
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def anova_oneway():
''' One-way ANOVA: test if results from 3 groups are equal. '''
# Get the data
data = getData('altman_910.txt')
# Sort them into groups, according to column 1
group1 = data[data[:,1]==1,0]
group2 = data[data[:,1]==2,0]
group3 = data[data[:,1]==3,0]
# First, check if the variances are equal, with the "Levene"-test
(W,p) = stats.levene(group1, group2, group3)
if p<0.05:
print('Warning: the p-value of the Levene test is <0.05: p={0}'.format(p))
# Do the one-way ANOVA
F_statistic, pVal = stats.f_oneway(group1, group2, group3)
# Print the results
print 'Altman 910:'
print (F_statistic, pVal)
if pVal < 0.05:
print('One of the groups is significantly different.')
# Elegant alternative implementation, with pandas & statsmodels
df = pd.DataFrame(data, columns=['value', 'treatment'])
model = ols('value ~ C(treatment)', df).fit()
print anova_lm(model)
def apply_test(a1, a2, type):
#Todos los tests se hacen con un 95% de confianza.
if type == "shapiro":
_, p1 = stats.shapiro(a1)
_, p2 = stats.shapiro(a2)
return (p1 > 0.05 and p2 > 0.05)
elif type == "levene":
_, p = stats.levene(a1, a2)
return p > 0.05
elif type == "anova":
_, p = stats.f_oneway(a1, a2)
return p > 0.05
elif type == "welch":
_, p = stats.ttest_ind(a1, a2, equal_var=False)
return p > 0.05
elif type == "kruskal":
_, p = stats.kruskal(a1, a2)
return p > 0.05
else:
print("Test no identificado.")
return -1
def cep_anova(samples_dict):
'''
Perform ANOVAs for the samples listed in sample_list
'''
samples_list = samples_dict.values()
result_dict = {}
# First, perform a Levene test to determine the homogeneity of variance
equal_var_test = levene(*samples_list, center='mean')
# The significance stat is the second element in the result tuple
equal_var_test_sig = equal_var_test[1]
# Then, depending on the result, we'll perform either a standard or a Welch's test
# If there's no result, then end test here
if pd.isnull(equal_var_test_sig):
result_dict['test'] = 'N/A'
else:
if equal_var_test_sig >= SIG_LEVEL:
result_dict['test'] = 'Standard'
# Perform an ANOVA here
anova_result = f_oneway(*samples_list)
elif equal_var_test_sig < SIG_LEVEL:
result_dict['test'] = 'Welch'
# Perform a Welch test here
anova_result = welch_anova(*samples_list)
anova_result_sig = anova_result[1]
result_dict['anova_p'] = anova_result_sig
if anova_result_sig < SIG_LEVEL:
# If significant, we'll continue with posthoc tests
# First, split samples into pairs so we can perform tests
# on each pair
c = combinations(samples_dict.items(), 2)
pairs_dict = {}
for i in c:
# Get the value tuple first
val_tuple = i[0][0], i[1][0]
# Then the sample tuple
sample_tuple = i[0][1], i[1][1]
# Then assign all to pairs_dict
pairs_dict[val_tuple] = sample_tuple
# If we did standard test earlier, follow with Tukey posthoc
# If we did Welch earlier, follow with Games-Howell
# First, let's calculate msw, r, and df to feed into the posthoc
msw, r, df = get_msw_et_al(*samples_list)
kwargs_dict = {}
kwargs_dict['r'] = r
if result_dict['test'] == 'Standard':
result_dict['posthoc'] = 'Tukey'
posthoc = tukey
kwargs_dict['msw'] = msw
kwargs_dict['df'] = df
elif result_dict['test'] == 'Welch':
result_dict['posthoc'] = 'Games-Howell'
posthoc = gh
for key, sample_tuple in pairs_dict.items():
sample_a = sample_tuple[0]
sample_b = sample_tuple[1]
mean_diff, pval = posthoc(sample_a, sample_b, **kwargs_dict)
# Translate result into verdict, sign, and cohens_d
# And save this tuple in the key entry of the result_dict
result_dict[key] = translate_result(pval, mean_diff, sample_a, sample_b)
return result_dict
def get_p_value_by_feature(pd_train, pd_test, feature_name):
"""
对特征进行统计检验，保证在两个类别之间的特征是有差异的，没有差异的特征去除掉
:param pd_train: 可以是train 可以是label 为 1
:param pd_test: 可以是test 可以是label 为 0
:param feature_name: 特征的名字
:return: p值 小于 0.05 是有差异 大于 0.05 是无差异
"""
# pd_train = pd.read_csv(train_path)
# pd_test = pd.read_csv(test_path)
train_feature = pd_train[feature_name]
test_feature = pd_test[feature_name]
train_feature_class = len(set(train_feature))
test_feature_class = len(set(test_feature))
if train_feature_class > 2 and test_feature_class > 2:
# 说明这是连续变量，就使用T检验或者是Ｕ检验
train_feature_mean = np.mean(train_feature)
test_feature_mean = np.mean(test_feature)
train_feature_std = np.std(train_feature)
test_feature_std = np.std(test_feature)
# 进行正态性和方差齐性检验
sta_value, p_value = levene(train_feature, test_feature) # 方差齐性
sta_train, p_value_train = stats.kstest(
train_feature, "norm", (train_feature_mean, train_feature_std))
sta_train, p_value_test = stats.kstest(
test_feature, "norm", (test_feature_mean, test_feature_std))
# print(p_value_train, p_value_test, p_value)
if p_value_train >= 0.05 and p_value_test >= 0.05 and p_value >= 0.05:
statistic, pvalue_t = ttest_ind(train_feature, test_feature)
# print(feature_name + " t检验:", round(pvalue_t, 3))
return round(pvalue_t, 3)
else:
stat_num, p_m_value = mannwhitneyu(train_feature, test_feature)
# print(feature_name + " u检验:", round(p_m_value, 3))
return round(p_m_value, 3)
if train_feature_class == 2 and test_feature_class == 2:
# 进行卡方检验
train_class_1, train_class_2 = Counter(train_feature).most_common()
test_class_1, test_class_2 = Counter(test_feature).most_common()
kf_data = np.array(
[[np.array(train_class_1[-1]),
np.array(test_class_1[-1])],
[np.array(train_class_2[-1]),
np.array(test_class_2[-1])]])
# print(kf_data)
a, p_value, b, c = chi2_contingency(kf_data)
# print(feature_name+"卡方检验: p_value={:.4f}".format(p_value))
return round(p_value, 3)
def apply_anova(data):
p_values = []
genes = data.drop(columns=['group']).columns
for i, col in enumerate(genes):
group_names, groups = split_into_groups(data, col)
res = stats.f_oneway(*groups)
shapiro_res = stats.shapiro(np.concatenate(groups)) # normality test
levene_res = stats.levene(*groups, center='mean') # homodestacity test
p_values.append((col, res.pvalue, shapiro_res[1], levene_res.pvalue))
if i % 100 == 0:
print('Progress {:2.0%}'.format((i / (genes.shape[0]))), end='\r')
anova_table = pd.DataFrame(
p_values,
columns=['gene', 'p_value', 'shapiro_p_value', 'levene_p_value'])
print(
'Found {} genes that influence'.format(
(anova_table['p_value'] < ALPHA).sum()),
'health conditions according to ANOVA tests.')
print(
'Found {} genes that influence'.format(
((anova_table['p_value'] < ALPHA) &
(anova_table['shapiro_p_value'] > ALPHA) &
(anova_table['levene_p_value'] > ALPHA)).sum()),
'health conditions according to ANOVA Shapiro-Wilks and Levene tests.')
return anova_table
def _homogeneity_tests(self):
df = self.__df
homogeneityTests = pd.DataFrame(
{
"Test Statistic": [
stats.levene(df.iloc[:, 0], df.iloc[:, 1])[0],
stats.bartlett(df.iloc[:, 0], df.iloc[:, 1])[0]
],
"P-value": [
stats.levene(df.iloc[:, 0], df.iloc[:, 1])[1],
stats.bartlett(df.iloc[:, 0], df.iloc[:, 1])[1]
]
},
index=["Levene", "Bartlett"])
return round(homogeneityTests, 3)
def ttestForTwoChoiceQuestions(xValues, yValues): npArrayX = np.array(xValues) npArrayY = np.array(yValues) # http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html#scipy.stats.normaltest xIsNormal = isNormal(npArrayX) yIsNormal = isNormal(npArrayY) if xIsNormal and yIsNormal: # Levene test for equal variances # http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.levene.html#scipy.stats.levene l, lp = stats.levene(npArrayX, npArrayY) parametric = xIsNormal and yIsNormal and lp >- 0.05 else: parametric = False if parametric: # if levene test comes out well and samples are normal, can use standard t-test for independent samples # http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html#scipy.stats.ttest_ind t, tp = stats.ttest_ind(xValues, yValues, axis=0) else: # if not, use Kruskal-Wallis H-test instead # http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kruskal.html#scipy.stats.kruskal t, tp = stats.kruskal(npArrayX, npArrayY) t = t / 5.0 # these come out bigger than the t-test stats return parametric, t, tp
def Simulate(self):
res = Results()
res.origW2 = []
res.surrW2 = []
res.stats = {}
for i in range(self.nb_tree):
# Generate tree
t = self.treeGenerator.generate(self.tree_size)
# Sample a clade
allNodes = [
n for i, n in enumerate(
t.ageorder_node_iter(include_leaves=True, descending=True))
if self.cladeThrMin <= i <= self.cladeThrMax
]
for c in random.sample(allNodes, self.nb_clades):
res.origW2.append(computeW2(t, c))
res.surrW2.append(self.surrogateStrat.generate(t, c))
# Compute stats
res.stats['correl'], res.stats['correlPval'] = spearmanr(
res.origW2, res.surrW2)
res.stats['origW2Mean'] = np.mean(res.origW2)
res.stats['origW2Std'] = np.std(res.origW2)
res.stats['surrW2Mean'] = np.mean(res.surrW2)
res.stats['surrW2Std'] = np.std(res.surrW2)
leveneVal, res.stats['LevenePVal'] = levene(res.origW2, res.surrW2)
return res
def calc_ttest(data, exp_set, control_set, tags=()):
d = [ st.ttest_ind( data.ix[probeset, list(exp_set.filenames)],
data.ix[probeset, list(control_set.filenames)], equal_var=False) for probeset in data.index]
rs = pandas.DataFrame( index=data.index, data=d, columns=[ tm.e( tags+(("st", "t"),("tt", "welch ttest"))), tm.e( tags + (("st", "pval"), ("tt", "welch ttest"), ("mc", "nominal") ))])
rs[tm.e( tags + (("tt", "welch ttest"), ("st", "pval"), ("mc", "bonf")))] = statsmodels.sandbox.stats.multicomp.multipletests(rs.ix[:, tm.e( tags + (("st", "pval"), ("tt", "welch ttest"), ("mc", "nominal"))) ], method="bonferroni")[1]
rs[tm.e( tags + (("tt", "welch ttest"), ("st", "pval"), ("mc", "bh")))] = statsmodels.sandbox.stats.multicomp.multipletests(rs.ix[:, tm.e( tags + (("st", "pval"), ("tt", "welch ttest"), ("mc", "nominal")))], method="fdr_bh")[1]
d = [ st.ttest_ind( data.ix[probeset, list(exp_set.filenames)],
data.ix[probeset, list(control_set.filenames)], equal_var=True) for probeset in data.index]
rs[tm.e( tags+(("st", "t"),("tt", "student ttest")))] = [v[0] for v in d]
rs[tm.e( tags + (("st", "pval"), ("tt", "student ttest"), ("mc", "nominal") ))] = [v[1] for v in d]
rs[tm.e( tags + (("st", "pval"), ("tt", "student ttest"), ("mc", "bonf")))] = statsmodels.sandbox.stats.multicomp.multipletests(rs.ix[:, tm.e( tags + (("st", "pval"), ("tt", "student ttest"), ("mc", "nominal"))) ], method="bonferroni")[1]
rs[tm.e( tags + (("st", "pval"), ("tt", "student ttest"), ("mc", "bh")))] = statsmodels.sandbox.stats.multicomp.multipletests(rs.ix[:, tm.e( tags + (("st", "pval"), ("tt", "student ttest"), ("mc", "nominal")))], method="fdr_bh")[1]
# do diagnostic tests for heteroskedasticity
d = [st.levene( data.ix[probeset, list(exp_set.filenames)], data.ix[probeset, list(control_set.filenames)]) for probeset in data.index ]
rs[ tm.e( tags + (("tt", "levene"), ("st", "pval")))] = [z[1] for z in d]
# omnibus test for normality
# d = [st.normaltest( data.ix[probeset, list(exp_set.filenames)]) for probeset in data.index ]
# rs[ tm.e( tags + (("tt", "d-p omnibus"), ("st", "pval"), ("cg", "exp") ))] = [z[1] for z in d]
# d = [st.normaltest( data.ix[probeset, list(control_set.filenames)]) for probeset in data.index ]
# rs[ tm.e( tags + (("tt", "d-p omnibus"), ("st", "pval"), ("cg", "ctrl") ))] = [z[1] for z in d]
return rs
def test_data(self):
args = []
for k in range(1,11):
args.append(eval('g%d'%k))
W, pval = stats.levene(*args)
assert_almost_equal(W,1.7059176930008939,7)
assert_almost_equal(pval,0.0990829755522,7)
def test(self, arr1, arr2):
p_value = 0
if self.statistics == "auto":
# проверяем Левеном на равенство дисперсий. Если равны
if stats.levene(arr1, arr2)[1] > 0.05:
# Шапир на нормальность выборок. Если нормальные
if stats.shapiro(arr1)[1] > 0.05 and stats.shapiro(arr2)[1] > 0.05:
# p = Student
p_value = stats.ttest_ind(arr1, arr2)[1]
else:
# p = Mann
if equal(arr1, arr2):
p_value = 1
else:
p_value = stats.mannwhitneyu(arr1, arr2)[1]
else:
p_value = stats.ttest_ind(arr1, arr2, False)[1]
elif self.statistics == "student":
p_value = stats.ttest_ind(arr1, arr2)[1]
elif self.statistics == "welch":
p_value = stats.ttest_ind(arr1, arr2, False)[1]
elif self.statistics == "mann":
if equal(arr1, arr2):
p_value = 1
else:
p_value = stats.mannwhitneyu(arr1, arr2)[1]
return p_value
def check_different_feature_with_anova_limit2():
_student_data, headerArray = loadFeatureData()
for i in range(cluster_result_transferred.__len__() - 1):
for j in range(i + 1, cluster_result_transferred.__len__()):
print(i, j)
compare_array = [i, j]
print("feature,w,p,f,p_f")
for feature_index in range(1, headerArray.__len__()):
if headerArray[feature_index] == "unknownCount":
continue
_uid_to_feature_map = {}
for item in _student_data:
_uid_to_feature_map[item[0]] = item[feature_index]
cluster_with_feature = []
for cluster_index in compare_array:
feature_array = []
for uid in cluster_result_transferred[cluster_index]:
if uid in _uid_to_feature_map:
feature_array.append(_uid_to_feature_map[uid])
cluster_with_feature.append(feature_array)
w, p = stats.levene(*cluster_with_feature)
f, p_f = stats.f_oneway(*cluster_with_feature)
print(headerArray[feature_index], ",", w, ",", p, ",", f, ",",
p_f)
def levene(tamannoMuestras, poblacion):
results = st.levene(muestra(poblacion, tamannoMuestras),
muestra(poblacion, tamannoMuestras),
muestra(poblacion, tamannoMuestras),
muestra(poblacion, tamannoMuestras))
print("Levene Valor Estadistico %f" % results[0])
print("Levene Valor p %f" % results[1])
def homogeneity_class_covariances(X_train, y_train): dims = X_train.shape[1] y_u = np.unique(y_train) covs = [] for y in y_u: covs.append( X_train[y_train==y] ) levene_stat, levene_pval = [], [] levene_success = 0 for j in range(0, dims): L = [] for M in covs: L.append( M[:, j] ) l_stat, l_pval = levene(*L) levene_pval.append( l_pval ) if l_pval < 0.05: levene_success += 1 levene_stat.append( l_stat ) if levene_success > 0: levene_stat_avg = np.average( levene_stat, weights=levene_stat ) else: levene_stat_avg = np.nan levene_pval_avg = np.average( levene_pval, weights=levene_pval ) levene_success_ratio = levene_success / dims return levene_stat_avg, levene_pval_avg, levene_success_ratio
def test_for_side_levene(df_side, lvl=3, hue='value'):
from scipy import stats
columns = ['statistic', 'p-value']
index_0 = list("lvl_{}".format(i) for i in range(0, lvl))
index_1 = [
"start_cm", "rel_pt", "amp_max_cop", 'amp_max_pel', 'amp_max_c7',
"vel_max_cop", 'vel_max_pel', 'vel_max_c7', "overshoot", "dcm", "dtml",
"rcm"
]
index = pd.MultiIndex.from_product([index_0, index_1])
n_row = len(index_0) * len(index_1)
n_col = len(columns)
data = np.empty((n_row, n_col))
data[:] = np.nan
df = pd.DataFrame(data, index=index, columns=columns)
for i in range(0, len(index_0)):
for v in index_1:
i0, i1 = index_0[i], v
v_df = get_data_group_by_player_mean(df_side, i, v, hue=hue)
df.loc[(i0, i1)] = stats.levene(v_df['left'], v_df['right'])
return df
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--input',
type=str,
default='testData.csv',
help='path to the input csv file (default: testData.csv)')
parser.add_argument('--level',
type=float,
default=0.05,
help='level of significance (default: 0.05)')
args = parser.parse_args()
filename = args.input
level = args.level
group_list = data_reader(filename)
data_list = []
for group_xi in group_list:
data_list.append(group_xi.data_array)
data_array = np.array(data_list)
statistic, p_value = stats.levene(*(data_array[i, :]
for i in range(data_array.shape[0])))
if (p_value < level):
print('p-value = ' + str(p_value) + '*')
else:
print('p-value = ' + str(p_value))
def levene(cls, xa, xb):
print('2群間: 母平均の95%ルビーン検定による等分散性の検定-------------------start')
_, p = st.levene(xa, xb, center='mean')
if p >= 0.05:
print(f'p値 = {p:.3f} // 検定結果: 帰無仮説を採択して、2つの標本には等分散性なしとは言えない')
else:
print(f'p値 = {p:.3f} // 検定結果: 帰無仮説を棄却して、2つの標本には等分散性なし')
def find_feature_Ttest(Data, label, use_all=True):
# 两个都是list
Data = np.array(Data)
label = np.array(label)
positiver_f = Data[label == 1]
negative_f = Data[label == 0]
ksresult1 = check_normality(positiver_f)
ksresult2 = check_normality(negative_f)
if ((ksresult1[1] > 0.05) and (ksresult2[1] > 0.05)):
# 检验方差齐性
leveneresult = stats.levene(positiver_f, negative_f)
if leveneresult[1] >= 0.05:
ttestresult = stats.ttest_ind(positiver_f, negative_f)
static_value = ttestresult[0]
p_value = ttestresult[1]
method = 'ttest'
if leveneresult[1] < 0.05:
ttestresult = stats.ttest_ind(positiver_f,
negative_f,
equal_var=False)
static_value = ttestresult[0]
p_value = ttestresult[1]
method = 'ttes_adj'
elif use_all:
nontestresult = stats.mannwhitneyu(positiver_f, negative_f)
static_value = nontestresult[0]
p_value = nontestresult[1]
method = 'mannwhitneyu'
return [static_value, p_value, method]
def oneway_anova(df, x, y, W, H, use_hsd=True, plot=True):
# mean_compare_table
mean_compare_table = df.groupby(x, as_index=False)[[y]].mean()
print(mean_compare_table)
if plot:
# plot
plt.figure(figsize=(W, H))
sns.violinplot(x, y, data=df)
# set group
val_list = list(set(df[x]))
groups = []
for val in val_list:
groups.append(df.loc[df[x] == val, y].tolist())
# anova
levene_test = levene(*groups)
if levene_test.pvalue >= 0.05:
print("方差齐")
f_value, p_value = f_oneway(*groups)
else:
print("方差不齐")
f_value, p_value = f_oneway(*groups) # 实际都使用f_oneway
#h_value, p_value = kruskalwallis(*groups)
# 结论
print(p_value)
if use_hsd:
hsd = pairwise_tukeyhsd(endog=df[y], groups=df[x], alpha=0.05)
print(hsd.summary())
return mean_compare_table
def _anova_assumptions(self, cl):
arrays = [['Normality (Shapiro-Wilk)', 'Normality (Shapiro-Wilk)', 'Variance', 'Variance'],
['test stats', 'p-value', 'test stats', 'p-value']]
temp = np.zeros((4, 1+len(self.indep_var)))
index = [self.dep_var]
# Experimental errors are normally distributed
temp[0,0], temp[1,0] = ss.shapiro(self.ols_model.resid)
if temp[1,0] > cl: # test for equal variances using Bartlett's test
for i in range(len(self.indep_var)):
index.append(self.indep_var[i])
list_unique = self.df[self.indep_var[i]].unique()
args = [self.df.loc[self.df[self.indep_var[i]]== x].accuracy for x in list_unique]
temp[2,i+1], temp[3,i+1] = ss.bartlett(*args)
arrays[0][2] = arrays[0][2] + ' (Bartlett)'
arrays[0][3] = arrays[0][3] + ' (Bartlett)'
else: # test for equal variances using Levene's test
for i in range(len(self.indep_var)):
list_unique = self.df[self.indep_var[i]].unique()
args = [self.df.loc[self.df[self.indep_var[i]]== x].accuracy for x in list_unique]
temp[2,i+1], temp[3,i+1] = ss.levene(*args)
arrays[0][2] = arrays[0][2] + ' (Levene)'
arrays[0][3] = arrays[0][3] + ' (Levene)'
self.anova_assump_df = pd.DataFrame(temp, index=arrays, columns=index)
if self.print_output==True: print(' ------------------\n', 'ANOVA assumptions', '\n ------------------'),\
print(self.anova_assump_df, '\n')
return
def ttest(ds1, ds2, p = 0.05):
rlt_var, p_var = sp.levene(ds1, ds2)
eq = p_var > p
# If equal_variance is False, then Welch's ttest is performed
rlt_tt, p_tt = sp.ttest_ind(ds1, ds2, equal_var = eq)
return p_tt
def ANOVA_assumptions_test(R, N, H):
# RNCH are the provided groups
valid = False
# test for normality
ps = []
for i in [R, N, H]:
shapiro_test = stats.shapiro(i)
ps.append(shapiro_test.pvalue)
# test for equal variances
_, p = levene(R, N, H)
ps.append(p)
if (np.array(ps) > 0.05).all():
valid = True
if valid:
# stats f_oneway functions takes the groups as input and returns F and P-value
fvalue, pvalue = stats.f_oneway(R, N, H)
test = 'ANOVA'
else:
fvalue, pvalue = stats.kruskal(R, N, H)
test = 'kruskal'
return fvalue, pvalue, test
def compute(self, model):
Cexp = ad.cross_covariance(sts_exp, binsize = 150*ms)
# Now pipe model and exp into actual test
pvalue = levene(model.covar.ravel(), Cexp.ravel()).pvalue
self.score = LeveneScore(pvalue)
return self.score
def checkParametricConditions(accuracies,alpha):
print("Checking independence ")
print("Ok")
independence = True
print("Checking normality using Shapiro-Wilk's test for normality, alpha=0.05")
(W, p) = shapiro(accuracies)
print("W: %f, p:%f" % (W, p))
if p < alpha:
print("The null hypothesis (normality) is rejected")
normality = False
else:
print("The null hypothesis (normality) is accepted")
normality = True
print("Checking heteroscedasticity using Levene's test, alpha=0.05")
(W, p) = levene(*accuracies)
print("W: %f, p:%f" % (W, p))
if p < alpha:
print("The null hypothesis (heteroscedasticity) is rejected")
heteroscedasticity = False
else:
print("The null hypothesis (heteroscedasticity) is accepted")
heteroscedasticity = True
parametric = independence and normality and heteroscedasticity
return parametric
def compute_anova(df, clusters): clusters = clusters['labels'] set_cluster(df, clusters) clusters_ = set(clusters.values()) # print(stats.wilcoxon(list(df[df['#Cluster'] == '0']['SMSin']))) return stats.levene(*[df[df['#Cluster'] == c]['SMSin'] for c in clusters_])
def levene(data):
"""Test of equal variance. H0 = same variance.
@W: thev test statistics
@pval: the p-value
"""
W, pval = st.levene(*data)
return (W, pval)
def levene_by_column(df, dummy):
"""Iterate Levene's test for equality of variances for each column of a
DataFrame, after splitting the observations in two groups according to a
dummy variable.
Args:
df (pd.DataFrame): The dataframe on which to perform the test.
dummy (string): Name of *df* column (e.g. "Treatment"). Must represent
a dummy variable (take value 0 or 1). Observations where the dummy
value is missing are not considered.
Returns:
pd.DataFrame: A dataframe displaying, in each row,
the Levene's test statistic and p-value for each column.
"""
df1 = df[df[dummy] == 1].drop(dummy, axis=1)
df0 = df[df[dummy] == 0].drop(dummy, axis=1)
levene_outcome = []
for col in df1.columns:
levene_outcome.append(
stats.levene(df0[col].dropna(), df1[col].dropna()))
levene_df = pd.DataFrame(levene_outcome,
index=df1.columns,
columns=["test stat.", "p-value"])
return levene_df
def levenes_test(target, feature):
'''
This function does a Levene's Test for a categorical feature
PARAMETERS
----------
target: {pandas.Series} the response variable
feature: {pandas.Series} the categorical feature
RETURNS
-------
results: {pandas.DataFrame} dataframe containing the results of the Levene's Test
'''
categories = feature.unique()
feature_dict = {category: target[feature==category] for category in categories}
feature_tuple = (feature_dict[category] for category in categories)
stat, pval = levene(*feature_tuple)
results = pd.DataFrame({'Statistic': [stat], 'p-value': [pval]})
return results
def variance_test(self, group_a, group_b):
print('-----Variance test--------------------------------------------------')
df_a = self.df[group_a]
df_b = self.df[group_b]
t_l, p_l = levene(df_a, df_b)
print('Statistic: {} and p-value: {} of variance comparison'.format(t_l, p_l))
print('-----END Variance test----------------------------------------------\n')
def two_sample_ttest(df, x, y, val_1, val_2, W, H, plot=True):
# mean_compare_table
mean_compare_table = df.groupby(x, as_index=False)[[y]].mean()
print(mean_compare_table)
a = df.loc[df[x] == val_1, y].tolist()
b = df.loc[df[x] == val_2, y].tolist()
if plot:
# plot-1
plt.figure(figsize=(W, H))
sns.violinplot(x, y, data=df)
# plot-2
plt.figure(figsize=(W, H))
sns.kdeplot(a, shade=True, label=val_1)
sns.kdeplot(b, shade=True, label=val_2)
# T-test
groups = [a, b]
levene_test = levene(*groups)
if levene_test.pvalue >= 0.05:
t_test = ttest_ind(
a, b, equal_var=True) # standard independent 2 sample test
else:
t_test = ttest_ind(a, b, equal_var=False) # Welch's t-test
p_value = t_test.pvalue
# 结论
if p_value <= 0.05:
print(p_value)
print("%s 在 %s 上存在显著性差异" % (y, x))
else:
print(p_value)
print("%s 在 %s 上不存在显著性差异" % (y, x))
return mean_compare_table
def tTest(data, checking, group, group1, group2, nameGroup1, nameGroup2, x,
output):
output[x]['Variable'] = checking
leveneResult = stats.levene(data[checking][data[group] == group1],
data[checking][data[group] == group2],
center='mean')
summary, results = rp.ttest(group1=data[checking][data[group] == group1],
group1_name=nameGroup1,
group2=data[checking][data[group] == group2],
group2_name=nameGroup2)
output[x][nameGroup1 + ' N'] = round(summary.iloc[0]['N'], 2)
output[x][nameGroup2 + ' N'] = round(summary.iloc[1]['N'], 2)
output[x][nameGroup1 + ' Mean'] = round(summary.iloc[0]['Mean'], 2)
output[x][nameGroup2 + ' Mean'] = round(summary.iloc[1]['Mean'], 2)
output[x][nameGroup1 + ' SD'] = round(summary.iloc[0]['SD'], 2)
output[x][nameGroup2 + ' SD'] = round(summary.iloc[1]['SD'], 2)
output[x][nameGroup1 + ' SE'] = round(summary.iloc[0]['SE'], 2)
output[x][nameGroup2 + ' SE'] = round(summary.iloc[1]['SE'], 2)
if leveneResult.pvalue < 0.05:
output[x]['Leneve Value'] = str(round(leveneResult.pvalue, 2)) + "****"
else:
output[x]['Leneve Value'] = str(round(leveneResult.pvalue, 2))
values = results.results
output[x]["T-Test P Value"] = signifiant(float(values.loc[[3]]))
output[x]["Cohen Effect Size"] = effectSize(float(values.loc[[6]]))
def test_equal_var():
'''Levene test for independence
'''
d1 = self.d1
d2 = self.d2
#rewrite this, for now just use scipy.stats
return stats.levene(d1.data, d2.data)
def return_test_results(self, arr1, arr2):
test_name = ""
p_value = 0
t_value = 0
levene = stats.levene(arr1, arr2)[1]
if self.statistics == "auto":
# проверяем Левеном на равенство дисперсий. Если равны
if levene > 0.05:
# Шапир на нормальность выборок. Если нормальные
if stats.shapiro(arr1)[1] > 0.05 and stats.shapiro(arr2)[1] > 0.05:
# p = Student
test_name = "Student"
result = stats.ttest_ind(arr1, arr2)
t_value = result[0]
p_value = result[1]
else:
# p = Mann
test_name = "Mann"
if equal(arr1, arr2):
t_value = None
p_value = 1
else:
result = stats.mannwhitneyu(arr1, arr2)
t_value = result[0]
p_value = result[1]
else:
test_name = "Welch"
result = stats.ttest_ind(arr1, arr2, False)
t_value = result[0]
p_value = result[1]
elif self.statistics == "student":
test_name = "Student"
result = stats.ttest_ind(arr1, arr2)
t_value = result[0]
p_value = result[1]
elif self.statistics == "welch":
test_name = "Welch"
result = stats.ttest_ind(arr1, arr2, False)
t_value = result[0]
p_value = result[1]
elif self.statistics == "mann":
test_name = "Mann"
if equal(arr1, arr2):
t_value = None
p_value = 1
else:
result = stats.mannwhitneyu(arr1, arr2)
t_value = result[0]
p_value = result[1]
df = len(arr1) + len(arr2) - 2
return [test_name, t_value, p_value, df, levene]
def anova_oneway():
''' One-way ANOVA: test if results from 3 groups are equal.
Twenty-two patients undergoing cardiac bypass surgery were randomized to one of three ventilation groups:
Group I: Patients received 50% nitrous oxide and 50% oxygen mixture continuously for 24 h.
Group II: Patients received a 50% nitrous oxide and 50% oxygen mixture only dirng the operation.
Group III: Patients received no nitrous oxide but received 35-50% oxygen for 24 h.
The data show red cell folate levels for the three groups after 24h' ventilation.
'''
# Get the data
print('One-way ANOVA: -----------------')
inFile = 'altman_910.txt'
data = np.genfromtxt(inFile, delimiter=',')
# Sort them into groups, according to column 1
group1 = data[data[:,1]==1,0]
group2 = data[data[:,1]==2,0]
group3 = data[data[:,1]==3,0]
# --- >>> START stats <<< ---
# First, check if the variances are equal, with the "Levene"-test
(W,p) = stats.levene(group1, group2, group3)
if p<0.05:
print(('Warning: the p-value of the Levene test is <0.05: p={0}'.format(p)))
# Do the one-way ANOVA
F_statistic, pVal = stats.f_oneway(group1, group2, group3)
# --- >>> STOP stats <<< ---
# Print the results
print('Data form Altman 910:')
print((F_statistic, pVal))
if pVal < 0.05:
print('One of the groups is significantly different.')
# Elegant alternative implementation, with pandas & statsmodels
df = pd.DataFrame(data, columns=['value', 'treatment'])
model = ols('value ~ C(treatment)', df).fit()
anovaResults = anova_lm(model)
print(anovaResults)
# Check if the two results are equal. If they are, there is no output
np.testing.assert_almost_equal(F_statistic, anovaResults['F'][0])
return (F_statistic, pVal) # should be (3.711335988266943, 0.043589334959179327)
def run(self):
if len(self._data) < self._min_size:
pass
if len(self._data.groups.values()) <= 1:
raise NoDataError("Equal variance test requires at least two numeric vectors.")
if NormTest(self._data, display=False, alpha=self._alpha).p_value > self._alpha:
statistic, p_value = bartlett(*self._data.groups.values())
r = 'Bartlett'
self._results.update({'p value': p_value, self._statistic_name[r]: statistic, 'alpha': self._alpha})
else:
statistic, p_value = levene(*self._data.groups.values())
r = 'Levene'
self._results.update({'p value': p_value, self._statistic_name[r]: statistic, 'alpha': self._alpha})
self._test = r
self._name = self._names[r]
def cep_ttest(sample_a, sample_b):
'''
Sample A and Sample B are array-like data stores
Ideally they should be numpy arrays or pandas Series
So we can perform mean and standard deviation calculations with them
The function will return a dictionary with the following entries:
"test": "Standard" (equal variance) or "Welch" (not equal variance)
"pval": P-value of the test performed
"verdict": "Not significant" or effect size specified
"cohen": Cohen's d value
"sign": blank, ".", "*", "**", or "***" depending on p-value and significance
"g1_n": response count in sample_a
"g2_n": response count in sample_b
'''
# Construct a result_dict
result_dict = {}
# First, perform a Levene's test to determine whether the samples have equal variances
equal_var_test = levene(sample_a, sample_b, center='mean')
# The significance stat is the second element in the result tuple
equal_var_test_sig = equal_var_test[1]
# Then, depending on the result, we'll perform either a standard or a Welch's test
# If there's no result, then end test here
if pd.isnull(equal_var_test_sig):
result_dict['test'] = 'N/A'
else:
if equal_var_test_sig >= SIG_LEVEL:
equal_var_arg = True
result_dict['test'] = 'Standard'
elif equal_var_test_sig < SIG_LEVEL:
equal_var_arg = False
result_dict['test'] = 'Welch'
ttest_result = ttest_ind(sample_a, sample_b, axis=0, equal_var=equal_var_arg)
ttest_result_sig = ttest_result[1]
result_dict['pval'] = ttest_result_sig
# If it's not significant, end here
# Translate result here
mean_diff = sample_a.mean() - sample_b.mean()
verdict, sign, cohens_d = translate_result(ttest_result_sig, mean_diff, sample_a, sample_b)
result_dict['cohen'] = cohens_d
result_dict['verdict'] = verdict
result_dict['sign'] = sign
result_dict['g1_n'] = sample_a.count()
result_dict['g2_n'] = sample_b.count()
result_dict['g1_mean'] = sample_a.mean()
result_dict['g2_mean'] = sample_b.mean()
return result_dict
def apply_test(data, group, test):
'''applies test along axis=1
data - 2d data array
group - group identity (rows)
test - 'levene' for example
should accept functions too
'''
n_samples = data.shape[1]
if test == 'levene':
levene_W = np.zeros(n_samples)
levene_p = np.zeros(n_samples)
for t_ind in range(n_samples):
levene_W[t_ind], levene_p[t_ind] = stats.levene(
data[group == 0, t_ind], data[group == 1, t_ind])
return levene_W, levene_p
else:
raise NotImplementedError('Only levene is implemented currently...')
def anova_oneway():
''' One-way ANOVA: test if results from 3 groups are equal. '''
# Get the data
print('One-way ANOVA: -----------------')
data = getData('altman_910.txt', subDir='..\Data\data_altman')
# Sort them into groups, according to column 1
group1 = data[data[:, 1] == 1, 0]
group2 = data[data[:, 1] == 2, 0]
group3 = data[data[:, 1] == 3, 0]
# First, check if the variances are equal, with the "Levene"-test
(W, p) = stats.levene(group1, group2, group3)
if p < 0.05:
print('Warning: the p-value of the Levene test is <0.05: p={0}'.format(
p))
# Do the one-way ANOVA
F_statistic, pVal = stats.f_oneway(group1, group2, group3)
# Print the results
print('Data form Altman 910:')
print((F_statistic, pVal))
if pVal < 0.05:
print('One of the groups is significantly different.')
# Elegant alternative implementation, with pandas & statsmodels
df = pd.DataFrame(data, columns=['value', 'treatment'])
model = ols('value ~ C(treatment)', df).fit()
anovaResults = anova_lm(model)
print(anovaResults)
# Check if the two results are equal. If they are, there is no output
np.testing.assert_almost_equal(F_statistic, anovaResults['F'][0])
return (F_statistic,
pVal) # should be (3.711335988266943, 0.043589334959179327)
import os
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--infile", required=True, help="Tabular file.")
parser.add_argument("-o", "--outfile", required=True, help="Path to the output file.")
parser.add_argument("--sample_one_cols", help="Input format, like smi, sdf, inchi")
parser.add_argument("--sample_two_cols", help="Input format, like smi, sdf, inchi")
parser.add_argument("--sample_cols", help="Input format, like smi, sdf, inchi,separate arrays using ;")
parser.add_argument("--test_id", help="statistical test method")
parser.add_argument(
"--mwu_use_continuity",
action="store_true",
default=False,
help="Whether a continuity correction (1/2.) should be taken into account.",
)
parser.add_argument(
"--equal_var",
action="store_true",
default=False,
help="If set perform a standard independent 2 sample test that assumes equal population variances. If not set, perform Welch's t-test, which does not assume equal population variance.",
)
parser.add_argument(
"--reta", action="store_true", default=False, help="Whether or not to return the internally computed a values."
)
parser.add_argument("--fisher", action="store_true", default=False, help="if true then Fisher definition is used")
parser.add_argument(
"--bias",
action="store_true",
default=False,
help="if false,then the calculations are corrected for statistical bias",
)
parser.add_argument("--inclusive1", action="store_true", default=False, help="if false,lower_limit will be ignored")
parser.add_argument(
"--inclusive2", action="store_true", default=False, help="if false,higher_limit will be ignored"
)
parser.add_argument("--inclusive", action="store_true", default=False, help="if false,limit will be ignored")
parser.add_argument(
"--printextras",
action="store_true",
default=False,
help="If True, if there are extra points a warning is raised saying how many of those points there are",
)
parser.add_argument(
"--initial_lexsort",
action="store_true",
default="False",
help="Whether to use lexsort or quicksort as the sorting method for the initial sort of the inputs.",
)
parser.add_argument("--correction", action="store_true", default=False, help="continuity correction ")
parser.add_argument(
"--axis",
type=int,
default=0,
help="Axis can equal None (ravel array first), or an integer (the axis over which to operate on a and b)",
)
parser.add_argument(
"--n",
type=int,
default=0,
help="the number of trials. This is ignored if x gives both the number of successes and failures",
)
parser.add_argument("--b", type=int, default=0, help="The number of bins to use for the histogram")
parser.add_argument("--N", type=int, default=0, help="Score that is compared to the elements in a.")
parser.add_argument("--ddof", type=int, default=0, help="Degrees of freedom correction")
parser.add_argument("--score", type=int, default=0, help="Score that is compared to the elements in a.")
parser.add_argument("--m", type=float, default=0.0, help="limits")
parser.add_argument("--mf", type=float, default=2.0, help="lower limit")
parser.add_argument("--nf", type=float, default=99.9, help="higher_limit")
parser.add_argument(
"--p",
type=float,
default=0.5,
help="The hypothesized probability of success. 0 <= p <= 1. The default value is p = 0.5",
)
parser.add_argument("--alpha", type=float, default=0.9, help="probability")
parser.add_argument("--new", type=float, default=0.0, help="Value to put in place of values in a outside of bounds")
parser.add_argument(
"--proportiontocut",
type=float,
default=0.0,
help="Proportion (in range 0-1) of total data set to trim of each end.",
)
parser.add_argument(
"--lambda_",
type=float,
default=1.0,
help="lambda_ gives the power in the Cressie-Read power divergence statistic",
)
parser.add_argument(
"--imbda",
type=float,
default=0,
help="If lmbda is not None, do the transformation for that value.If lmbda is None, find the lambda that maximizes the log-likelihood function and return it as the second output argument.",
)
parser.add_argument("--base", type=float, default=1.6, help="The logarithmic base to use, defaults to e")
parser.add_argument("--dtype", help="dtype")
parser.add_argument("--med", help="med")
parser.add_argument("--cdf", help="cdf")
parser.add_argument("--zero_method", help="zero_method options")
parser.add_argument("--dist", help="dist options")
parser.add_argument("--ties", help="ties options")
parser.add_argument("--alternative", help="alternative options")
parser.add_argument("--mode", help="mode options")
parser.add_argument("--method", help="method options")
parser.add_argument("--md", help="md options")
parser.add_argument("--center", help="center options")
parser.add_argument("--kind", help="kind options")
parser.add_argument("--tail", help="tail options")
parser.add_argument("--interpolation", help="interpolation options")
parser.add_argument("--statistic", help="statistic options")
args = parser.parse_args()
infile = args.infile
outfile = open(args.outfile, "w+")
test_id = args.test_id
nf = args.nf
mf = args.mf
imbda = args.imbda
inclusive1 = args.inclusive1
inclusive2 = args.inclusive2
sample0 = 0
sample1 = 0
sample2 = 0
if args.sample_cols != None:
sample0 = 1
barlett_samples = []
for sample in args.sample_cols.split(";"):
barlett_samples.append(map(int, sample.split(",")))
if args.sample_one_cols != None:
sample1 = 1
sample_one_cols = args.sample_one_cols.split(",")
if args.sample_two_cols != None:
sample_two_cols = args.sample_two_cols.split(",")
sample2 = 1
for line in open(infile):
sample_one = []
sample_two = []
cols = line.strip().split("\t")
if sample0 == 1:
b_samples = columns_to_values(barlett_samples, line)
if sample1 == 1:
for index in sample_one_cols:
sample_one.append(cols[int(index) - 1])
if sample2 == 1:
for index in sample_two_cols:
sample_two.append(cols[int(index) - 1])
if test_id.strip() == "describe":
size, min_max, mean, uv, bs, bk = stats.describe(map(float, sample_one))
cols.append(size)
cols.append(min_max)
cols.append(mean)
cols.append(uv)
cols.append(bs)
cols.append(bk)
elif test_id.strip() == "mode":
vals, counts = stats.mode(map(float, sample_one))
cols.append(vals)
cols.append(counts)
elif test_id.strip() == "nanmean":
m = stats.nanmean(map(float, sample_one))
cols.append(m)
elif test_id.strip() == "nanmedian":
m = stats.nanmedian(map(float, sample_one))
cols.append(m)
elif test_id.strip() == "kurtosistest":
z_value, p_value = stats.kurtosistest(map(float, sample_one))
cols.append(z_value)
cols.append(p_value)
elif test_id.strip() == "variation":
ra = stats.variation(map(float, sample_one))
cols.append(ra)
elif test_id.strip() == "itemfreq":
freq = stats.itemfreq(map(float, sample_one))
for list in freq:
elements = ",".join(map(str, list))
cols.append(elements)
elif test_id.strip() == "nanmedian":
m = stats.nanmedian(map(float, sample_one))
cols.append(m)
elif test_id.strip() == "variation":
ra = stats.variation(map(float, sample_one))
cols.append(ra)
elif test_id.strip() == "boxcox_llf":
IIf = stats.boxcox_llf(imbda, map(float, sample_one))
cols.append(IIf)
elif test_id.strip() == "tiecorrect":
fa = stats.tiecorrect(map(float, sample_one))
cols.append(fa)
elif test_id.strip() == "rankdata":
r = stats.rankdata(map(float, sample_one), method=args.md)
cols.append(r)
elif test_id.strip() == "nanstd":
s = stats.nanstd(map(float, sample_one), bias=args.bias)
cols.append(s)
elif test_id.strip() == "anderson":
A2, critical, sig = stats.anderson(map(float, sample_one), dist=args.dist)
cols.append(A2)
for list in critical:
cols.append(list)
cols.append(",")
for list in sig:
cols.append(list)
elif test_id.strip() == "binom_test":
p_value = stats.binom_test(map(float, sample_one), n=args.n, p=args.p)
cols.append(p_value)
elif test_id.strip() == "gmean":
gm = stats.gmean(map(float, sample_one), dtype=args.dtype)
cols.append(gm)
elif test_id.strip() == "hmean":
hm = stats.hmean(map(float, sample_one), dtype=args.dtype)
cols.append(hm)
elif test_id.strip() == "kurtosis":
k = stats.kurtosis(map(float, sample_one), axis=args.axis, fisher=args.fisher, bias=args.bias)
cols.append(k)
elif test_id.strip() == "moment":
n_moment = stats.moment(map(float, sample_one), n=args.n)
cols.append(n_moment)
elif test_id.strip() == "normaltest":
k2, p_value = stats.normaltest(map(float, sample_one))
cols.append(k2)
cols.append(p_value)
elif test_id.strip() == "skew":
skewness = stats.skew(map(float, sample_one), bias=args.bias)
cols.append(skewness)
elif test_id.strip() == "skewtest":
z_value, p_value = stats.skewtest(map(float, sample_one))
cols.append(z_value)
cols.append(p_value)
elif test_id.strip() == "sem":
s = stats.sem(map(float, sample_one), ddof=args.ddof)
cols.append(s)
elif test_id.strip() == "zscore":
z = stats.zscore(map(float, sample_one), ddof=args.ddof)
for list in z:
cols.append(list)
elif test_id.strip() == "signaltonoise":
s2n = stats.signaltonoise(map(float, sample_one), ddof=args.ddof)
cols.append(s2n)
elif test_id.strip() == "percentileofscore":
p = stats.percentileofscore(map(float, sample_one), score=args.score, kind=args.kind)
cols.append(p)
elif test_id.strip() == "bayes_mvs":
c_mean, c_var, c_std = stats.bayes_mvs(map(float, sample_one), alpha=args.alpha)
cols.append(c_mean)
cols.append(c_var)
cols.append(c_std)
elif test_id.strip() == "sigmaclip":
c, c_low, c_up = stats.sigmaclip(map(float, sample_one), low=args.m, high=args.n)
cols.append(c)
cols.append(c_low)
cols.append(c_up)
elif test_id.strip() == "kstest":
d, p_value = stats.kstest(
map(float, sample_one), cdf=args.cdf, N=args.N, alternative=args.alternative, mode=args.mode
)
cols.append(d)
cols.append(p_value)
elif test_id.strip() == "chi2_contingency":
chi2, p, dof, ex = stats.chi2_contingency(
map(float, sample_one), correction=args.correction, lambda_=args.lambda_
)
cols.append(chi2)
cols.append(p)
cols.append(dof)
cols.append(ex)
elif test_id.strip() == "tmean":
if nf is 0 and mf is 0:
mean = stats.tmean(map(float, sample_one))
else:
mean = stats.tmean(map(float, sample_one), (mf, nf), (inclusive1, inclusive2))
cols.append(mean)
elif test_id.strip() == "tmin":
if mf is 0:
min = stats.tmin(map(float, sample_one))
else:
min = stats.tmin(map(float, sample_one), lowerlimit=mf, inclusive=args.inclusive)
cols.append(min)
elif test_id.strip() == "tmax":
if nf is 0:
max = stats.tmax(map(float, sample_one))
else:
max = stats.tmax(map(float, sample_one), upperlimit=nf, inclusive=args.inclusive)
cols.append(max)
elif test_id.strip() == "tvar":
if nf is 0 and mf is 0:
var = stats.tvar(map(float, sample_one))
else:
var = stats.tvar(map(float, sample_one), (mf, nf), (inclusive1, inclusive2))
cols.append(var)
elif test_id.strip() == "tstd":
if nf is 0 and mf is 0:
std = stats.tstd(map(float, sample_one))
else:
std = stats.tstd(map(float, sample_one), (mf, nf), (inclusive1, inclusive2))
cols.append(std)
elif test_id.strip() == "tsem":
if nf is 0 and mf is 0:
s = stats.tsem(map(float, sample_one))
else:
s = stats.tsem(map(float, sample_one), (mf, nf), (inclusive1, inclusive2))
cols.append(s)
elif test_id.strip() == "scoreatpercentile":
if nf is 0 and mf is 0:
s = stats.scoreatpercentile(
map(float, sample_one), map(float, sample_two), interpolation_method=args.interpolation
)
else:
s = stats.scoreatpercentile(
map(float, sample_one), map(float, sample_two), (mf, nf), interpolation_method=args.interpolation
)
for list in s:
cols.append(list)
elif test_id.strip() == "relfreq":
if nf is 0 and mf is 0:
rel, low_range, binsize, ex = stats.relfreq(map(float, sample_one), args.b)
else:
rel, low_range, binsize, ex = stats.relfreq(map(float, sample_one), args.b, (mf, nf))
for list in rel:
cols.append(list)
cols.append(low_range)
cols.append(binsize)
cols.append(ex)
elif test_id.strip() == "binned_statistic":
if nf is 0 and mf is 0:
st, b_edge, b_n = stats.binned_statistic(
map(float, sample_one), map(float, sample_two), statistic=args.statistic, bins=args.b
)
else:
st, b_edge, b_n = stats.binned_statistic(
map(float, sample_one),
map(float, sample_two),
statistic=args.statistic,
bins=args.b,
range=(mf, nf),
)
cols.append(st)
cols.append(b_edge)
cols.append(b_n)
elif test_id.strip() == "threshold":
if nf is 0 and mf is 0:
o = stats.threshold(map(float, sample_one), newval=args.new)
else:
o = stats.threshold(map(float, sample_one), mf, nf, newval=args.new)
for list in o:
cols.append(list)
elif test_id.strip() == "trimboth":
o = stats.trimboth(map(float, sample_one), proportiontocut=args.proportiontocut)
for list in o:
cols.append(list)
elif test_id.strip() == "trim1":
t1 = stats.trim1(map(float, sample_one), proportiontocut=args.proportiontocut, tail=args.tail)
for list in t1:
cols.append(list)
elif test_id.strip() == "histogram":
if nf is 0 and mf is 0:
hi, low_range, binsize, ex = stats.histogram(map(float, sample_one), args.b)
else:
hi, low_range, binsize, ex = stats.histogram(map(float, sample_one), args.b, (mf, nf))
cols.append(hi)
cols.append(low_range)
cols.append(binsize)
cols.append(ex)
elif test_id.strip() == "cumfreq":
if nf is 0 and mf is 0:
cum, low_range, binsize, ex = stats.cumfreq(map(float, sample_one), args.b)
else:
cum, low_range, binsize, ex = stats.cumfreq(map(float, sample_one), args.b, (mf, nf))
cols.append(cum)
cols.append(low_range)
cols.append(binsize)
cols.append(ex)
elif test_id.strip() == "boxcox_normmax":
if nf is 0 and mf is 0:
ma = stats.boxcox_normmax(map(float, sample_one))
else:
ma = stats.boxcox_normmax(map(float, sample_one), (mf, nf), method=args.method)
cols.append(ma)
elif test_id.strip() == "boxcox":
if imbda is 0:
box, ma, ci = stats.boxcox(map(float, sample_one), alpha=args.alpha)
cols.append(box)
cols.append(ma)
cols.append(ci)
else:
box = stats.boxcox(map(float, sample_one), imbda, alpha=args.alpha)
cols.append(box)
elif test_id.strip() == "histogram2":
h2 = stats.histogram2(map(float, sample_one), map(float, sample_two))
for list in h2:
cols.append(list)
elif test_id.strip() == "ranksums":
z_statistic, p_value = stats.ranksums(map(float, sample_one), map(float, sample_two))
cols.append(z_statistic)
cols.append(p_value)
elif test_id.strip() == "ttest_1samp":
t, prob = stats.ttest_1samp(map(float, sample_one), map(float, sample_two))
for list in t:
cols.append(list)
for list in prob:
cols.append(list)
elif test_id.strip() == "ansari":
AB, p_value = stats.ansari(map(float, sample_one), map(float, sample_two))
cols.append(AB)
cols.append(p_value)
elif test_id.strip() == "linregress":
slope, intercept, r_value, p_value, stderr = stats.linregress(
map(float, sample_one), map(float, sample_two)
)
cols.append(slope)
cols.append(intercept)
cols.append(r_value)
cols.append(p_value)
cols.append(stderr)
elif test_id.strip() == "pearsonr":
cor, p_value = stats.pearsonr(map(float, sample_one), map(float, sample_two))
cols.append(cor)
cols.append(p_value)
elif test_id.strip() == "pointbiserialr":
r, p_value = stats.pointbiserialr(map(float, sample_one), map(float, sample_two))
cols.append(r)
cols.append(p_value)
elif test_id.strip() == "ks_2samp":
d, p_value = stats.ks_2samp(map(float, sample_one), map(float, sample_two))
cols.append(d)
cols.append(p_value)
elif test_id.strip() == "mannwhitneyu":
mw_stats_u, p_value = stats.mannwhitneyu(
map(float, sample_one), map(float, sample_two), use_continuity=args.mwu_use_continuity
)
cols.append(mw_stats_u)
cols.append(p_value)
elif test_id.strip() == "zmap":
z = stats.zmap(map(float, sample_one), map(float, sample_two), ddof=args.ddof)
for list in z:
cols.append(list)
elif test_id.strip() == "ttest_ind":
mw_stats_u, p_value = stats.ttest_ind(
map(float, sample_one), map(float, sample_two), equal_var=args.equal_var
)
cols.append(mw_stats_u)
cols.append(p_value)
elif test_id.strip() == "ttest_rel":
t, prob = stats.ttest_rel(map(float, sample_one), map(float, sample_two), axis=args.axis)
cols.append(t)
cols.append(prob)
elif test_id.strip() == "mood":
z, p_value = stats.mood(map(float, sample_one), map(float, sample_two), axis=args.axis)
cols.append(z)
cols.append(p_value)
elif test_id.strip() == "shapiro":
W, p_value, a = stats.shapiro(map(float, sample_one), map(float, sample_two), args.reta)
cols.append(W)
cols.append(p_value)
for list in a:
cols.append(list)
elif test_id.strip() == "kendalltau":
k, p_value = stats.kendalltau(
map(float, sample_one), map(float, sample_two), initial_lexsort=args.initial_lexsort
)
cols.append(k)
cols.append(p_value)
elif test_id.strip() == "entropy":
s = stats.entropy(map(float, sample_one), map(float, sample_two), base=args.base)
cols.append(s)
elif test_id.strip() == "spearmanr":
if sample2 == 1:
rho, p_value = stats.spearmanr(map(float, sample_one), map(float, sample_two))
else:
rho, p_value = stats.spearmanr(map(float, sample_one))
cols.append(rho)
cols.append(p_value)
elif test_id.strip() == "wilcoxon":
if sample2 == 1:
T, p_value = stats.wilcoxon(
map(float, sample_one),
map(float, sample_two),
zero_method=args.zero_method,
correction=args.correction,
)
else:
T, p_value = stats.wilcoxon(
map(float, sample_one), zero_method=args.zero_method, correction=args.correction
)
cols.append(T)
cols.append(p_value)
elif test_id.strip() == "chisquare":
if sample2 == 1:
rho, p_value = stats.chisquare(map(float, sample_one), map(float, sample_two), ddof=args.ddof)
else:
rho, p_value = stats.chisquare(map(float, sample_one), ddof=args.ddof)
cols.append(rho)
cols.append(p_value)
elif test_id.strip() == "power_divergence":
if sample2 == 1:
stat, p_value = stats.power_divergence(
map(float, sample_one), map(float, sample_two), ddof=args.ddof, lambda_=args.lambda_
)
else:
stat, p_value = stats.power_divergence(map(float, sample_one), ddof=args.ddof, lambda_=args.lambda_)
cols.append(stat)
cols.append(p_value)
elif test_id.strip() == "theilslopes":
if sample2 == 1:
mpe, met, lo, up = stats.theilslopes(map(float, sample_one), map(float, sample_two), alpha=args.alpha)
else:
mpe, met, lo, up = stats.theilslopes(map(float, sample_one), alpha=args.alpha)
cols.append(mpe)
cols.append(met)
cols.append(lo)
cols.append(up)
elif test_id.strip() == "combine_pvalues":
if sample2 == 1:
stat, p_value = stats.combine_pvalues(
map(float, sample_one), method=args.med, weights=map(float, sample_two)
)
else:
stat, p_value = stats.combine_pvalues(map(float, sample_one), method=args.med)
cols.append(stat)
cols.append(p_value)
elif test_id.strip() == "obrientransform":
ob = stats.obrientransform(*b_samples)
for list in ob:
elements = ",".join(map(str, list))
cols.append(elements)
elif test_id.strip() == "f_oneway":
f_value, p_value = stats.f_oneway(*b_samples)
cols.append(f_value)
cols.append(p_value)
elif test_id.strip() == "kruskal":
h, p_value = stats.kruskal(*b_samples)
cols.append(h)
cols.append(p_value)
elif test_id.strip() == "friedmanchisquare":
fr, p_value = stats.friedmanchisquare(*b_samples)
cols.append(fr)
cols.append(p_value)
elif test_id.strip() == "fligner":
xsq, p_value = stats.fligner(center=args.center, proportiontocut=args.proportiontocut, *b_samples)
cols.append(xsq)
cols.append(p_value)
elif test_id.strip() == "bartlett":
T, p_value = stats.bartlett(*b_samples)
cols.append(T)
cols.append(p_value)
elif test_id.strip() == "levene":
w, p_value = stats.levene(center=args.center, proportiontocut=args.proportiontocut, *b_samples)
cols.append(w)
cols.append(p_value)
elif test_id.strip() == "median_test":
stat, p_value, m, table = stats.median_test(
ties=args.ties, correction=args.correction, lambda_=args.lambda_, *b_samples
)
cols.append(stat)
cols.append(p_value)
cols.append(m)
cols.append(table)
for list in table:
elements = ",".join(map(str, list))
cols.append(elements)
outfile.write("%s\n" % "\t".join(map(str, cols)))
outfile.close()
ejecucion['medRHV'] = numpy.mean(RHVs) ejecucion['stdRHV'] = numpy.std(RHVs) ejecucion['ksRHVpval'] = stats.kstest(RHVs,'norm', args=(ejecucion['medRHV'],ejecucion['stdRHV'])).pvalue if ejecucion['stdRHV'] else 0 ejecucion['shapiroRHVpval'] = stats.shapiro(RHVs)[1] if ejecucion['stdRHV'] else 0 ejecucion['medGD'] = numpy.mean(gds) ejecucion['stdGD'] = numpy.std(gds) ejecucion['ksGDpval'] = stats.kstest(gds,'norm', args=(ejecucion['medGD'],ejecucion['stdGD'])).pvalue if ejecucion['stdGD'] else 0 ejecucion['shapiroGDpval'] = stats.shapiro(gds)[1] if ejecucion['stdGD'] else 0 if RHVsAnteriores: ejecucion["tstudentRHV"] = stats.ttest_ind(RHVsAnteriores, RHVs, equal_var=False).pvalue ejecucion["leveneRHV"] = stats.levene(RHVsAnteriores, RHVs).pvalue else: ejecucion["tstudentRHV"] = "-" ejecucion["leveneRHV"] = "-" if gdsAnteriores: ejecucion["tstudentGD"] = stats.ttest_ind(gdsAnteriores, gds, equal_var=False).pvalue ejecucion["leveneGD"] = stats.levene(gdsAnteriores, gds).pvalue else: ejecucion["tstudentGD"] = "-" ejecucion["leveneGD"] = "-" RHVsAnteriores = RHVs gdsAnteriores = gds with open(PATH_JSON_EJECUCION,'w') as f:
def levene((x, y)):
return stats.levene(x, y)
def test_result_attributes(self):
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
res = stats.levene(*args)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes)
#hfmt = dates.DateFormatter('%H:%M')
#ax.xaxis.set_major_formatter(hfmt)
# y_formatter = mpl.ticker.ScalarFormatter(useOffset=False)
# ax.yaxis.set_major_formatter(y_formatter)
# ax.grid(True)
f.suptitle("Dichte der Leistungsgradienten")
f.autofmt_xdate()
plt.savefig("images/sonnenfinsternis-dichte-gradienten.png")#, bbox_inches='tight')
plt.clf()
friday_series, friday_vals = ecdf.get_ecdf(friday_momentum_df.momentum)
ecdf.plot_ecdf_curve(friday_series, friday_vals, color="b", label="Typischer Freitag")
eclipse_series, eclipse_vals = ecdf.get_ecdf(eclipse_momentum_df.momentum)
ecdf.plot_ecdf_curve(eclipse_series, eclipse_vals, color="r", label="Sonnenfinsternis")
print "Mittelwert alle Freitage: %f" % np.median(friday_momentum_df.momentum)
print "Mittelwert Sonnenfinsternis: %f" % np.median(eclipse_momentum_df.momentum)
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.levene.html#scipy.stats.levene
W, p_val = stats.levene(friday_momentum_df.momentum,
eclipse_momentum_df.momentum, center='median')
print ("Levenes Test auf Gleichheit der Varianz: P=%s (gleiche Varianz für p<=0.05)" % p_val)
W, p_val = stats.fligner(friday_momentum_df.momentum, eclipse_momentum_df.momentum)
print "Fliegners Test auf Gleichheit der Varianz: P=%s" % p_val
f.suptitle("ECDF der Leistungsgradienten: Ungleiche Varianzen (Levene, p=%f)" % p_val)
plt.savefig("images/sonnenfinsternis-ecdf-gradienten.png")#, bbox_inches='tight')
def test_data(self):
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
W, pval = stats.levene(*args)
assert_almost_equal(W,1.7059176930008939,7)
assert_almost_equal(pval,0.0990829755522,7)
#Report some descriptive statistics print stroop.describe() #visualizations (with seaborn style) import matplotlib.pyplot as plot import seaborn as sns # Do the boxplot plot.show(sns.boxplot(stroop)) # Do the violinplot plot.show(sns.violinplot(stroop, widths = 0.5)) """ # Do the distribution plot sns.distplot(stroop['Congruent'],kde=False, color= "b") """ from scipy.stats import levene print levene(stroop.Congruent, stroop.Incongruent) from scipy.stats import kstest print 'ks_con', kstest(stroop.Congruent, 'norm') print 'ks_inc', kstest(stroop.Incongruent, 'norm') from scipy.stats import ks_2samp ks_2samp(stroop.Congruent, stroop.Incongruent) # Do the t-test import scipy.stats as ss print ss.ttest_ind(stroop.Congruent, stroop.Incongruent)
def f_test(self,test_series):
"""F-test of equal variances."""
# print(stats.bartlett(self.series,test_series))
# return stats.f.sf(F, df1, df2)
return stats.levene(self.series,test_series)
def main():
def n_digits(num):
if num <= 1:
return 1
return math.ceil(math.log(num) / math.log(10))
db = sqlite.connect(db_fn)
dbc = db.cursor()
rows = []
integer_digits = {'best': 0,
'best_time': 0,
'mean': 0,
'stddev': 0}
allvals = []
allvals_dict = {}
for variant in VARIANTS:
query = ("select tw from (select min(treewidth) as tw from validationresults where variant='%(variant)s' and instance='%(instance)s' group by seed)")
result = dbc.execute(query % {'variant': variant, 'instance': instance})
vals = NP.array([row[0] for row in result])
min, mean, stddev = vals.min(), vals.mean(), vals.std()
# print('%s: vals=%r' % (variant, vals), file=sys.stderr)
W, p = STATS.shapiro(vals)
print('%s: normal distribution? shapiro-wilk: W=%s (p=%s) %s@5%% %s@2%%' % (variant, W, p, 'no' if W <= .905 else 'yes', 'no' if W <= .884 else 'yes'), file=sys.stderr)
z, p = STATS.skewtest(vals)
print('%s: normal distribution? skew test: (z=%s) p=%s => %s' % (variant, z, p, 'no' if p < .5 else 'yes'), file=sys.stderr)
allvals.append(vals)
allvals_dict[variant] = vals
query = ("select min(runtime_s)"
" from validationresults"
" where variant='%(variant)s' and instance='%(instance)s' and treewidth='%(treewidth)s'")
result = dbc.execute(query % {'variant': variant, 'instance': instance, 'treewidth': min})
best_time = [row[0] for row in result][0]
# print("%s: best=%s @ %ss, avg=%s +- %s" % (variant, min, best_time, mean, stddev), file=sys.stderr)
row = {'variant': variant,
'best': min,
'best_time': round(best_time, 1),
'mean': round(mean, 1),
'stddev': round(stddev, 1)}
rows.append(row)
integer_digits['best'] = max(integer_digits['best'], n_digits(row['best']))
integer_digits['best_time'] = max(integer_digits['best_time'], n_digits(row['best_time']))
integer_digits['mean'] = max(integer_digits['mean'], n_digits(row['mean']))
integer_digits['stddev'] = max(integer_digits['stddev'], n_digits(row['stddev']))
db.close()
T, p = STATS.bartlett(*allvals)
print('equal variances? bartlett: T=%s (p=%s) [vs Chi-Quadrat_{k-1=%s, alpha=.5}]' % (T, p, len(allvals) - 1), file=sys.stderr)
W, p = STATS.levene(*allvals, center='mean')
print('equal variances? levene (mean): (W=%s) p=%s' % (W, p), file=sys.stderr)
W, p = STATS.levene(*allvals, center='median')
print('equal variances? levene (median): (W=%s) p=%s' % (W, p), file=sys.stderr)
F, p = STATS.f_oneway(*allvals)
print('equal means? one-way ANOVA: F=%s, p=%s [vs F_{k-1=%s,n-k=%s}]' % (F, p, len(allvals) - 1, sum([len(x) for x in allvals]) - len(allvals)), file=sys.stderr)
try:
W, p = STATS.kruskal(*allvals)
print('equal means? kruskal wallis: W=%s, p=%s' % (W, p), file=sys.stderr)
except Exception as e:
print(e)
lsd = LSD.LSD(allvals, .05)
print('LSD: %r' % lsd, file=sys.stderr)
print(statsmodels.stats.multicomp.pairwise_tukeyhsd(NP.array(allvals).ravel(), NP.array([[x] * 20 for x in VARIANTS]).ravel(), alpha=.10), file=sys.stderr)
print(statsmodels.stats.multicomp.pairwise_tukeyhsd(NP.array(allvals).ravel(), NP.array([[x] * 20 for x in VARIANTS]).ravel(), alpha=.05), file=sys.stderr)
def welch(var1, var2):
res = STATS.ttest_ind(allvals_dict[var1], allvals_dict[var2], equal_var=False)
print('%4s vs %s t,p=%r => \t%s @a=10%%, %s @a=5%%'
% (var1, var2, res, 'NE' if res[1] < .01116 else ' E', 'NE' if res[1] < .00568 else ' E'), file=sys.stderr)
print('pairwise Welch\'s t-test with Bonferroni correction:', file=sys.stderr)
welch('IHA', 'MA1')
welch('IHA', 'MA2')
welch('IHA', 'MA3')
welch('GAtw', 'MA1')
welch('GAtw', 'MA2')
welch('GAtw', 'MA3')
welch('MA1', 'MA2')
welch('MA1', 'MA3')
welch('MA2', 'MA3')
def mannwhitneyu(var1, var2):
try:
res = STATS.mannwhitneyu(allvals_dict[var1], allvals_dict[var2])
print('%4s vs %s u,p=%r => \t%s @a=10%%, %s @a=5%%'
% (var1, var2, res, 'NE' if res[1] < .01116 else ' E', 'NE' if res[1] < .00568 else ' E'), file=sys.stderr)
except Exception as e:
print('%4s vs %s failed: %r' % (var1, var2, e))
print('pairwise Mann-Whitney U test with Bonferroni correction:', file=sys.stderr)
mannwhitneyu('IHA', 'MA1')
mannwhitneyu('IHA', 'MA2')
mannwhitneyu('IHA', 'MA3')
mannwhitneyu('GAtw', 'MA1')
mannwhitneyu('GAtw', 'MA2')
mannwhitneyu('GAtw', 'MA3')
mannwhitneyu('MA1', 'MA2')
mannwhitneyu('MA1', 'MA3')
mannwhitneyu('MA2', 'MA3')
#latex = [r'\begin{sidefigure}{caption={Results for instance \Instance{%(instanceTexEsc)s}},label={fig:%(instanceFileEsc)s-results},place={htbp}}''\n'
#r' \begin{center}''\n'
latex = [r'\begin{table}[hbtp]''\n'
r' \caption{Results for instance \Instance{%(instanceTexEsc)s}}''\n'
r' \label{fig:%(instanceFileEsc)s-results}''\n'
r' \centering\small''\n'
r' \begin{tabular}{l S[table-format=%(best)s] S[table-format=%(best_time)s.1]%%''\n'
r' S[table-format=%(mean)s.1,table-number-alignment=right] @{$\,\pm\,$} S[table-format=%(stddev)s.1,table-number-alignment=left]''\n'
r' S[table-format=2]} \toprule''\n'
r' & \multicolumn{2}{c}{\header{Best}} & \multicolumn{2}{c}{\header{Average}} & \\ \cmidrule(lr){2-3}\cmidrule(lr){4-5}''\n'
r' & \header{treewidth} & \header{seconds} & \multicolumn{2}{c}{\header{treewidth}} & \header{samples} \\ \midrule'
% dict(integer_digits.items() | dict(instanceTexEsc=instance.replace('_', r'\textunderscore{}'), instanceFileEsc=instance.replace('_', '-')).items())]
for row in rows:
latex.append(' ' * (3 * 3) + ' & '.join([row['variant'], str(row['best']), str(row['best_time']), str(row['mean']), str(row['stddev']), "20"]) + r'\\')
latex.append(r' \bottomrule''\n'
r' \end{tabular}''\n'
r'\end{table}')
#r' \end{center}''\n'
#r'\end{sidefigure}')
with open('validation-validationset-%s-results.tex' % instance.replace('_', '-'), 'w') as f:
print('\n'.join(latex), file=f)
def get_levene(group1, group2):
lev_w, lev_p_value = levene(group1, group2)
return (lev_p_value, lev_w)
for fiber in fiber_list:
mod = Model(lambda x, a, b: a * x + b)
slope_displ = mod.fit(fiber.binned_exp['static_fr_mean'],
x=fiber.binned_exp['displ_mean'],
a=1, b=1).best_values['a']
slope_force = mod.fit(fiber.binned_exp['static_fr_mean'],
x=fiber.binned_exp['force_mean'],
a=1, b=1).best_values['a']
slope_displ_list.append(slope_displ)
slope_force_list.append(slope_force)
slope_displ_arr = np.array(slope_displ_list)
slope_force_arr = np.array(slope_force_list)
sensitivity_df = pd.DataFrame(
np.c_[slope_displ_arr, slope_force_arr],
index=['#' + str(i+1) for i in range(slope_displ_arr.size)],
columns=['Displacement sensitivity (Hz/mm)',
'Force sensitivity (Hz/mN)'])
for column in sensitivity_df.columns:
sensitivity_df[column[:5] + '_normalized'] = sensitivity_df[column] /\
sensitivity_df[column].median()
sensitivity_df.transpose().to_excel('./csvs/sensitivity.xlsx')
print(sensitivity_df.var())
from scipy.stats import f, bartlett, levene
print(f.cdf(sensitivity_df['Displ_normalized'].var() /
sensitivity_df['Force_normalized'].var(),
sensitivity_df.shape[0], sensitivity_df.shape[0]))
print(bartlett(sensitivity_df['Displ_normalized'],
sensitivity_df['Force_normalized']))
print(levene(sensitivity_df['Displ_normalized'],
sensitivity_df['Force_normalized']))
