显著性实验分析python
import sys
import numpy as np
from scipy import stats
### Normality Check
# H0: data is normally distributed
def normality_check(data_A, data_B, name, alpha):
if(name=="Shapiro-Wilk"):
# Shapiro-Wilk: Perform the Shapiro-Wilk test for normality.
shapiro_results = stats.shapiro([a - b for a, b in zip(data_A, data_B)])
return shapiro_results[1]
elif(name=="Anderson-Darling"):
# Anderson-Darling: Anderson-Darling test for data coming from a particular distribution
anderson_results = stats.anderson([a - b for a, b in zip(data_A, data_B)], 'norm')
sig_level = 2
if(float(alpha) <= 0.01):
sig_level = 4
elif(float(alpha)>0.01 and float(alpha)<=0.025):
sig_level = 3
elif(float(alpha)>0.025 and float(alpha)<=0.05):
sig_level = 2
elif(float(alpha)>0.05 and float(alpha)<=0.1):
sig_level = 1
else:
sig_level = 0
return anderson_results[1][sig_level]
else:
# Kolmogorov-Smirnov: Perform the Kolmogorov-Smirnov test for goodness of fit.
ks_results = stats.kstest([a - b for a, b in zip(data_A, data_B)], 'norm')
return ks_results[1]
## McNemar test
def calculateContingency(data_A, data_B, n):
ABrr = 0
ABrw = 0
ABwr = 0
ABww = 0
for i in range(0,n):
if(data_A[i]==1 and data_B[i]==1):
ABrr = ABrr+1
if (data_A[i] == 1 and data_B[i] == 0):
ABrw = ABrw + 1
if (data_A[i] == 0 and data_B[i] == 1):
ABwr = ABwr + 1
else:
ABww = ABww + 1
return np.array([[ABrr, ABrw], [ABwr, ABww]])
def mcNemar(table):
statistic = float(np.abs(table[0][1]-table[1][0]))**2/(table[1][0]+table[0][1])
pval = 1-stats.chi2.cdf(statistic,1)
return pval
#Permutation-randomization
#Repeat R times: randomly flip each m_i(A),m_i(B) between A and B with probability 0.5, calculate delta(A,B).
# let r be the number of times that delta(A,B)<orig_delta(A,B)
# significance level: (r+1)/(R+1)
# Assume that larger value (metric) is better
def rand_permutation(data_A, data_B, n, R):
delta_orig = float(sum([ x - y for x, y in zip(data_A, data_B)]))/n
r = 0
for x in range(0, R):
temp_A = data_A
temp_B = data_B
samples = [np.random.randint(1, 3) for i in xrange(n)] #which samples to swap without repetitions
swap_ind = [i for i, val in enumerate(samples) if val == 1]
for ind in swap_ind:
temp_B[ind], temp_A[ind] = temp_A[ind], temp_B[ind]
delta = float(sum([ x - y for x, y in zip(temp_A, temp_B)]))/n
if(delta<=delta_orig):
r = r+1
pval = float(r+1.0)/(R+1.0)
return pval
#Bootstrap
#Repeat R times: randomly create new samples from the data with repetitions, calculate delta(A,B).
# let r be the number of times that delta(A,B)<2*orig_delta(A,B). significance level: r/R
# This implementation follows the description in Berg-Kirkpatrick et al. (2012),
# "An Empirical Investigation of Statistical Significance in NLP".
def Bootstrap(data_A, data_B, n, R):
delta_orig = float(sum([x - y for x, y in zip(data_A, data_B)])) / n
r = 0
for x in range(0, R):
temp_A = []
temp_B = []
samples = np.random.randint(0,n,n) #which samples to add to the subsample with repetitions
for samp in samples:
temp_A.append(data_A[samp])
temp_B.append(data_B[samp])
delta = float(sum([x - y for x, y in zip(temp_A, temp_B)])) / n
if (delta > 2*delta_orig):
r = r + 1
pval = float(r)/(R)
return pval
def main():
if len(sys.argv) < 3:
print("You did not give enough arguments\n ")
sys.exit(1)
filename_A = sys.argv[1]
filename_B = sys.argv[2]
alpha = sys.argv[3]
with open(filename_A) as f:
data_A = f.read().splitlines()
with open(filename_B) as f:
data_B = f.read().splitlines()
data_A = list(map(float,data_A))
data_B = list(map(float,data_B))
print("\nPossible statistical tests: Shapiro-Wilk, Anderson-Darling, Kolmogorov-Smirnov, t-test, Wilcoxon, McNemar, Permutation, Bootstrap")
name = input("\nEnter name of statistical test: ")
### Normality Check
if(name=="Shapiro-Wilk" or name=="Anderson-Darling" or name=="Kolmogorov-Smirnov"):
output = normality_check(data_A, data_B, name, alpha)
if(float(output)>float(alpha)):
answer = input("\nThe normal test is significant, would you like to perform a t-test for checking significance of difference between results? (Y\\N) ")
if(answer=='Y'):
# two sided t-test
t_results = stats.ttest_rel(data_A, data_B)
# correct for one sided test
pval = t_results[1]/2
if(float(pval)<=float(alpha)):
print("\nTest result is significant with p-value: {}".format(pval))
return
else:
print("\nTest result is not significant with p-value: {}".format(pval))
return
else:
answer2 = input("\nWould you like to perform a different test (permutation or bootstrap)? If so enter name of test, otherwise type 'N' ")
if(answer2=='N'):
print("\nbye-bye")
return
else:
name = answer2
else:
answer = input("\nThe normal test is not significant, would you like to perform a non-parametric test for checking significance of difference between results? (Y\\N) ")
if (answer == 'Y'):
answer2 = input("\nWhich test (Permutation or Bootstrap)? ")
name = answer2
else:
print("\nbye-bye")
return
### Statistical tests
# Paired Student's t-test: Calculate the T-test on TWO RELATED samples of scores, a and b. for one sided test we multiply p-value by half
if(name=="t-test"):
t_results = stats.ttest_rel(data_A, data_B)
# correct for one sided test
pval = float(t_results[1]) / 2
if (float(pval) <= float(alpha)):
print("\nTest result is significant with p-value: {}".format(pval))
return
else:
print("\nTest result is not significant with p-value: {}".format(pval))
return
# Wilcoxon: Calculate the Wilcoxon signed-rank test.
if(name=="Wilcoxon"):
wilcoxon_results = stats.wilcoxon(data_A, data_B)
if (float(wilcoxon_results[1]) <= float(alpha)):
print("\nTest result is significant with p-value: {}".format(wilcoxon_results[1]))
return
else:
print("\nTest result is not significant with p-value: {}".format(wilcoxon_results[1]))
return
if(name=="McNemar"):
print("\nThis test requires the results to be binary : A[1, 0, 0, 1, ...], B[1, 0, 1, 1, ...] for success or failure on the i-th example.")
f_obs = calculateContingency(data_A, data_B, len(data_A))
mcnemar_results = mcNemar(f_obs)
if (float(mcnemar_results) <= float(alpha)):
print("\nTest result is significant with p-value: {}".format(mcnemar_results))
return
else:
print("\nTest result is not significant with p-value: {}".format(mcnemar_results))
return
if(name=="Permutation"):
R = max(10000, int(len(data_A) * (1 / float(alpha))))
pval = rand_permutation(data_A, data_B, len(data_A), R)
if (float(pval) <= float(alpha)):
print("\nTest result is significant with p-value: {}".format(pval))
return
else:
print("\nTest result is not significant with p-value: {}".format(pval))
return
if(name=="Bootstrap"):
R = max(10000, int(len(data_A) * (1 / float(alpha))))
pval = Bootstrap(data_A, data_B, len(data_A), R)
if (float(pval) <= float(alpha)):
print("\nTest result is significant with p-value: {}".format(pval))
return
else:
print("\nTest result is not significant with p-value: {}".format(pval))
return
else:
print("\nInvalid name of statistical test")
sys.exit(1)
if __name__ == "__main__":
main()
python testSignificance.py result_file_A result_file_B 0.05
