tTest.ttest() - Code Metrics - Inspection of "Migrate to Tornado 6 and other improvements (#329)" - tableau/TabPy - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( d71f34...c856ab )

by Oleksandr

created 2019-07-31 20:42 UTC

tTest.ttest() B

↳ Parent: tTest

Complexity

Conditions

Size

Total Lines	31
Code Lines	18

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	18
dl	0
loc	31
rs	8.5666
c	0
b	0
f	0
cc	6
nop	2

from scipy import stats
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).resolve().parent.parent.parent / 'models'))
from utils import setup_utils


def ttest(_arg1, _arg2):
    '''
    T-Test is a statistical hypothesis test that is used to compare
    two sample means or a sample’s mean against a known population mean.
    For more information on the function and how to use it please refer
    to tabpy-tools.md
    '''
    # one sample test with mean
    if len(_arg2) == 1:
        test_stat, p_value = stats.ttest_1samp(_arg1, _arg2)
        return p_value
    # two sample t-test where _arg1 is numeric and _arg2 is a binary factor
    elif len(set(_arg2)) == 2:
        # each sample in _arg1 needs to have a corresponding classification
        # in _arg2
        if not (len(_arg1) == len(_arg2)):
            raise ValueError
        class1, class2 = set(_arg2)
        sample1 = []
        sample2 = []
        for i in range(len(_arg1)):
            if _arg2[i] == class1:
                sample1.append(_arg1[i])
            else:
                sample2.append(_arg1[i])
        test_stat, p_value = stats.ttest_ind(sample1, sample2, equal_var=False)
        return p_value
    # arg1 is a sample and arg2 is a sample
    else:
        test_stat, p_value = stats.ttest_ind(_arg1, _arg2, equal_var=False)
        return p_value


if __name__ == '__main__':
    setup_utils.main('ttest',
                     ttest,
                     'Returns the p-value form a t-test')


1			from scipy import stats
2			import sys
3			from pathlib import Path
4			sys.path.append(str(Path(__file__).resolve().parent.parent.parent / 'models'))
5			from utils import setup_utils
6
7
8			def ttest(_arg1, _arg2):
9			'''
10			T-Test is a statistical hypothesis test that is used to compare
11			two sample means or a sample’s mean against a known population mean.
12			For more information on the function and how to use it please refer
13			to tabpy-tools.md
14			'''
15			# one sample test with mean
16			if len(_arg2) == 1:
17			test_stat, p_value = stats.ttest_1samp(_arg1, _arg2)
18			return p_value
19			# two sample t-test where _arg1 is numeric and _arg2 is a binary factor
20			elif len(set(_arg2)) == 2:
21			# each sample in _arg1 needs to have a corresponding classification
22			# in _arg2
23			if not (len(_arg1) == len(_arg2)):
24			raise ValueError
25			class1, class2 = set(_arg2)
26			sample1 = []
27			sample2 = []
28			for i in range(len(_arg1)):
29			if _arg2[i] == class1:
30			sample1.append(_arg1[i])
31			else:
32			sample2.append(_arg1[i])
33			test_stat, p_value = stats.ttest_ind(sample1, sample2, equal_var=False)
34			return p_value
35			# arg1 is a sample and arg2 is a sample
36			else:
37			test_stat, p_value = stats.ttest_ind(_arg1, _arg2, equal_var=False)
38			return p_value
39
40
41			if __name__ == '__main__':
42			setup_utils.main('ttest',
43			ttest,
44			'Returns the p-value form a t-test')
45

tableau / TabPy

Push — master ( d71f34...c856ab )

tTest.ttest() B

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