MeanDiffTesting - Code Metrics - Inspection of "complete one group proportion testing" - chen0040/pysie - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( b7c711...fe8c00 )

by Xianshun

created 2017-06-05 09:09 UTC

MeanDiffTesting A

↳ Parent: Project

Complexity

Total Complexity

Size/Duplication

Total Lines	33
Duplicated Lines	75.76 %

Importance

Changes

Metric	Value
dl	25
loc	33
rs	10
c	0
b	0
f	0
wmc	6

1 Method

Rating	Name	Duplication	Size	Complexity
B	__init__()	25	25	6

How to fix Duplicated Code

from pysie.stats.distributions import DistributionFamily
from scipy.stats import norm, t
import math


class MeanDiffTesting(object):
    sampling_distribution = None
    p_value_one_tail = None
    p_value_two_tail = None
    test_statistic = None
    significance_level = None
    reject_mean_same = None

    def __init__(self, sampling_distribution, significance_level=None):

        self.sampling_distribution = sampling_distribution
        if significance_level is not None:
            self.significance_level = significance_level

        if self.sampling_distribution.distribution_family == DistributionFamily.normal:
            Z = sampling_distribution.point_estimate / sampling_distribution.standard_error
            self.test_statistic = Z
            pf = norm.cdf(Z)
            if Z < 0:
                pf = 1 - pf
            self.p_value_one_tail = 1 - pf
            self.p_value_two_tail = self.p_value_one_tail * 2
        else:
            td_df = sampling_distribution.point_estimate / sampling_distribution.standard_error
            self.test_statistic = td_df
            pf = t.cdf(td_df, sampling_distribution.df)
            if td_df < 0:
                pf = 1 - pf
            self.p_value_one_tail = 1 - pf
            self.p_value_two_tail = self.p_value_one_tail * 2

        if significance_level is not None:
            self.reject_mean_same = (self.p_value_one_tail < significance_level,
                                     self.p_value_two_tail < significance_level)


class ProportionDiffTesting(object):
    sampling_distribution = None
    p_value_one_tail = None
    p_value_two_tail = None
    mean_null = None
    test_statistic = None
    significance_level = None
    reject_proportion_same = None

    def __init__(self, sampling_distribution, significance_level=None):

        self.sampling_distribution = sampling_distribution
        p_null = (sampling_distribution.grp1_point_estimate + sampling_distribution.grp2_point_estimate) / 2
        self.p_null = p_null
        if significance_level is not None:
            self.significance_level = significance_level

        if self.sampling_distribution.distribution_family == DistributionFamily.normal:
            standard_error_null = math.sqrt(p_null * (1 - p_null) / sampling_distribution.sample_size)
            Z = sampling_distribution.point_estimate / standard_error_null
            self.test_statistic = Z
            pf = norm.cdf(Z)
            if Z < 0:
                pf = 1 - pf
            self.p_value_one_tail = 1 - pf
            self.p_value_two_tail = self.p_value_one_tail * 2
        else:
            standard_error_null = math.sqrt(p_null * (1 - p_null) / sampling_distribution.sample_size)
            td_df = sampling_distribution.point_estimate / standard_error_null
            self.test_statistic = td_df
            pf = t.cdf(td_df, sampling_distribution.df)
            if td_df < 0:
                pf = 1 - pf
            self.p_value_one_tail = 1 - pf
            self.p_value_two_tail = self.p_value_one_tail * 2

        if significance_level is not None:
            self.reject_proportion_same = (self.p_value_one_tail < significance_level,
                                           self.p_value_two_tail < significance_level)

1		from pysie.stats.distributions import DistributionFamily
2		from scipy.stats import norm, t
3		import math
4
5
6		class MeanDiffTesting(object):
7		sampling_distribution = None
8		p_value_one_tail = None
9		p_value_two_tail = None
10		test_statistic = None
11		significance_level = None
12		reject_mean_same = None
13
14	View Code Duplication	def __init__(self, sampling_distribution, significance_level=None):
		0 ignored issues – show Duplication introduced 2017-06-05 09:10 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
15		self.sampling_distribution = sampling_distribution
16		if significance_level is not None:
17		self.significance_level = significance_level
18
19		if self.sampling_distribution.distribution_family == DistributionFamily.normal:
20		Z = sampling_distribution.point_estimate / sampling_distribution.standard_error
21		self.test_statistic = Z
22		pf = norm.cdf(Z)
23		if Z < 0:
24		pf = 1 - pf
25		self.p_value_one_tail = 1 - pf
26		self.p_value_two_tail = self.p_value_one_tail * 2
27		else:
28		td_df = sampling_distribution.point_estimate / sampling_distribution.standard_error
29		self.test_statistic = td_df
30		pf = t.cdf(td_df, sampling_distribution.df)
31		if td_df < 0:
32		pf = 1 - pf
33		self.p_value_one_tail = 1 - pf
34		self.p_value_two_tail = self.p_value_one_tail * 2
35
36		if significance_level is not None:
37		self.reject_mean_same = (self.p_value_one_tail < significance_level,
38		self.p_value_two_tail < significance_level)
39
40
41		class ProportionDiffTesting(object):
42		sampling_distribution = None
43		p_value_one_tail = None
44		p_value_two_tail = None
45		mean_null = None
46		test_statistic = None
47		significance_level = None
48		reject_proportion_same = None
49
50	View Code Duplication	def __init__(self, sampling_distribution, significance_level=None):
		0 ignored issues – show Duplication introduced 2017-06-05 09:10 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
51		self.sampling_distribution = sampling_distribution
52		p_null = (sampling_distribution.grp1_point_estimate + sampling_distribution.grp2_point_estimate) / 2
53		self.p_null = p_null
54		if significance_level is not None:
55		self.significance_level = significance_level
56
57		if self.sampling_distribution.distribution_family == DistributionFamily.normal:
58		standard_error_null = math.sqrt(p_null * (1 - p_null) / sampling_distribution.sample_size)
59		Z = sampling_distribution.point_estimate / standard_error_null
60		self.test_statistic = Z
61		pf = norm.cdf(Z)
62		if Z < 0:
63		pf = 1 - pf
64		self.p_value_one_tail = 1 - pf
65		self.p_value_two_tail = self.p_value_one_tail * 2
66		else:
67		standard_error_null = math.sqrt(p_null * (1 - p_null) / sampling_distribution.sample_size)
68		td_df = sampling_distribution.point_estimate / standard_error_null
69		self.test_statistic = td_df
70		pf = t.cdf(td_df, sampling_distribution.df)
71		if td_df < 0:
72		pf = 1 - pf
73		self.p_value_one_tail = 1 - pf
74		self.p_value_two_tail = self.p_value_one_tail * 2
75
76		if significance_level is not None:
77		self.reject_proportion_same = (self.p_value_one_tail < significance_level,
78		self.p_value_two_tail < significance_level)

chen0040 / pysie

Push — master ( b7c711...fe8c00 )

MeanDiffTesting A

Complexity

Size/Duplication

Importance

1 Method

How to fix Duplicated Code

Duplicated Code

Duplication Side-by-Side

Filter issues like