def calcPairwisePvalues( tdf, factcol, groupcol=None, onetailed=False ) :
pvals = []
if groupcol is not None :
for grp in tdf[groupcol].unique():
lof = tdf[(tdf.Vclass=="LoF") & (tdf[groupcol] ==grp)][factcol].tolist()
rand = tdf[(tdf.Vclass=="Random") & (tdf[groupcol] ==grp)][factcol].tolist()
maxY = max(lof+rand)
ts,pv = ttest_ind(lof, rand)
#p/2 < alpha and t < 0
#if onetailed : print "Todo"
pvals.append( Series(data=[grp,pv,ts,"-2",maxY],
index=[groupcol,"pvalue","T-statistic","x","y"]))
else :
lof = tdf[(tdf.Vclass=="LoF")][factcol].tolist()
rand = tdf[(tdf.Vclass=="Random")][factcol].tolist()
maxY = max(lof+rand)
ts,pv = ttest_ind(lof, rand)
#p/2 < alpha and t < 0
#if onetailed : print "Todo"
pvals.append( Series(data=[pv,ts,"-2",maxY],
index=["pvalue","T-statistic","x","y"]))
pvals = DataFrame(pvals)
pvals["Pvalue"] = ["P-value: %.3g\nT-statistic: %.2f" %(x,y)
for x,y in pvals[["pvalue","T-statistic"]].values]
return pvals
def linreg2_err(t, x, wleft=5, wright=5, hop=None, use_l=True, use_r=True):
if hop is None:
hop=1
zs = np.zeros(len(x))
if wright<0:
wm = 0
else:
wm=wright
for ii in range(wleft,len(x)-wm,hop):
ts=[]
xl = x[ii-wleft:ii]
tl = t[ii-wleft:ii]
xr = x[ii:ii+wright]
tr = t[ii:ii+wright]
if use_l>0:
pl = np.polyfit(tl,xl,1)
residll = xl-np.polyval(pl,tl)
stdll = np.std(residll)
residlr = xr-np.polyval(pl,tr)
stdlr = np.std(residlr)
ttl,pvl = ttest_ind(residll,residlr)
ts.append(ttl)
if use_r>0:
pr = np.polyfit(tr,xr,1)
residrr = xr-np.polyval(pr,tr)
stdrr = np.std(residrr)
residrl = xl-np.polyval(pr,tl)
stdrl = np.std(residrl)
ttr,pvr = ttest_ind(residrr,residrl)
ts.append(-ttr)
zs[ii] = np.mean(ts)
return zs
def results_plots(name, min_n=2000):
frame = pd.DataFrame.from_csv(os.path.join(DATA_FOLDER, "storks_exp_rep", "out", name))
frame = frame[frame.N >= min_n]
# 0) Sig tests
behav_semi_sup = frame.AF_factor
juv_idx = frame.status == 'Juv'
adult_idx = frame.status == 'Adult'
print("semi: ", np.array([1, .5])*ttest_ind(behav_semi_sup[juv_idx], behav_semi_sup[adult_idx])) # [1, .5]*[t, p]
behav_sup = frame.AF_true_frac
print("sup: ", np.array([1, .5])*ttest_ind(behav_sup[juv_idx], behav_sup[adult_idx]))
# 1) scatter for AF_true_frac & AF_factor
frame.plot(kind='scatter', x='AF_true_frac', y='AF_factor')
plt.show()
# 2) compare Juv/Adults with AF_factor
m = frame.groupby(frame['status'])['AF_factor'].mean()
s = frame.groupby(frame['status'])['AF_factor'].sem()
m.plot(kind='bar', yerr=s)
plt.show()
# 2) compare Juv/Adults with AF_true_frac
m = frame.groupby(frame['status'])['AF_true_frac'].mean()
s = frame.groupby(frame['status'])['AF_true_frac'].sem()
m.plot(kind='bar', yerr=s)
plt.show()
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 plotHist():
TO, wmeanto= makeGaussian(20.74,1115,500,plot=False,dir="/Users/george/Dropbox/Astronomy/Oculus/25Oct2016/IMG00069.FIT")[3:]
SM, wmeansm= makeGaussian(20.64,710,885,plot=False,dir="/Users/george/Dropbox/Astronomy/Oculus/25Oct2016/IMG00074.FIT")[3:]
dif = np.median(20.74-2.5*np.log10(TO/wmeanto))-np.median(20.64-2.5*np.log10(SM/wmeansm))
pval= stats.ttest_ind(SM, TO)[1]
plt.ion()
plt.clf()
plt.figure(1)
data = np.vstack([TO,SM]).T
plt.xlim(2000,4000)
plt.ylim(0,2000)
#plt.hist(TO, bins=1000,label='Thacher Observatory',alpha=0.5,color='r')
#plt.hist(SM, bins=1000,label='Sulfur Mountain',alpha=0.5,color='b')
plt.axvline(x=rb.mean(TO), color ='red', linewidth = 2)
plt.axvline(x=rb.mean(SM), color = 'red', linewidth = 2)
plt.annotate(r'$dif$=%.2f mags/arcsec'u'\u00B2' %dif, [.01,.93], horizontalalignment='left', xycoords='axes fraction', fontsize='large', backgroundcolor='white')
plt.annotate(r'$\bar{{\sigma}_T}_O$=%.2f flux/px' %rb.mean(TO), [.01,0.86], horizontalalignment='left', xycoords='axes fraction', fontsize="large", color='midnightblue')
plt.annotate(r'$\bar{{\sigma}_S}_M$=%.2f flux/px'%rb.mean(SM), [0.01,0.79], horizontalalignment='left', xycoords='axes fraction', fontsize="large", color='darkgreen')
plt.annotate(r'$p-val$=%.2E' %pval, [.01,.72], horizontalalignment='left', xycoords='axes fraction', fontsize='large')
plt.hist(data, bins=1000,label=['Thacher Observatory (TO)','Sulphur Mountain (SM)'],alpha=0.5, width=40)
plt.title("Sky brightness")
plt.xlabel("Flux Value")
plt.ylabel("Frequency")
plt.legend(loc='upper right')
plt.show()
inds, = np.where(dif<=0)
pcent = len(inds,)
ttest=stats.ttest_ind(TO, SM)
#returns: T-statistic((estimated-hypothesis value)/standard error),
#p value(probability of an observed result assuming the null hypothesis is true)
print dif, pcent, ttest
def runTTest(col, df):
print "t-Test for %s" % col
d1 = df[df[col] == True]["rating"]
d2 = df[df[col] != True]["rating"]
print "Number of Samples: (%d, %d)" % (d1.shape[0], d2.shape[0])
print "Means: (%f, %f)" %(d1.mean(), d2.mean())
print stats.ttest_ind(d1, d2)
def ttest_PTC_divergence_different(divergence_file, PTC_file, blast_file):
'''
(file, file) -> None
Performs a t-test to compare the mean divergence between PTC and non-PTC genes, assuming unequal variance and print the results on screen
'''
divergence_data = PTC_divergence(divergence_file, PTC_file, blast_file)
divergence = divergence_data[0]
zero_dS = divergence_data[1]
omega_greater_1 = divergence_data[2]
from scipy import stats
# make a list of divergence values for PTC and non-PTC genes
dN_PTC = []
dN_non_PTC = []
dS_PTC = []
dS_non_PTC = []
omega_PTC = []
omega_non_PTC = []
for gene in divergence:
if divergence[gene][-1] == 'yes':
dN_PTC.append(divergence[gene][0])
dS_PTC.append(divergence[gene][1])
omega_PTC.append(divergence[gene][2])
else:
dN_non_PTC.append(divergence[gene][0])
dS_non_PTC.append(divergence[gene][1])
omega_non_PTC.append(divergence[gene][2])
dN = stats.ttest_ind(dN_PTC, dN_non_PTC, equal_var = False)
dS = stats.ttest_ind(dS_PTC, dS_non_PTC, equal_var = False)
omega = stats.ttest_ind(omega_PTC, omega_non_PTC, equal_var = False)
dN_ttest = (abs(round(float(dN[0]), 4)), float(dN[1]))
dS_ttest = (abs(round(float(dS[0]), 4)), float(dS[1]))
omega_ttest = (abs(round(float(omega[0]), 4)), float(omega[1]))
# compute mean and standard error
dN_stats_PTC = compute_mean_std_error(dN_PTC)
dN_stats_non_PTC = compute_mean_std_error(dN_non_PTC)
dS_stats_PTC = compute_mean_std_error(dS_PTC)
dS_stats_non_PTC = compute_mean_std_error(dS_non_PTC)
omega_stats_PTC = compute_mean_std_error(omega_PTC)
omega_stats_non_PTC = compute_mean_std_error(omega_non_PTC)
print('dN: t-test =\t {0},\t p-value =\t {1}'.format(dN_ttest[0], dN_ttest[1]))
print('dS: t-test =\t {0},\t p-value =\t {1}'.format(dS_ttest[0], dS_ttest[1]))
print('dN/dS: t-test =\t {0},\t p-value =\t {1}'.format(omega_ttest[0], omega_ttest[1]))
print('PTC: mean dN =\t %6.4f,\t standard error =\t %6.4f' % dN_stats_PTC)
print('nonPTC: mean dN =\t %6.4f,\t standard error =\t %6.4f' % dN_stats_non_PTC)
print('PTC: mean dS =\t %6.4f,\t standard error =\t %6.4f' % dS_stats_PTC)
print('nonPTC: mean dS =\t %6.4f,\t standard error =\t %6.4f' % dS_stats_non_PTC)
print('PTC: mean dN/dS =\t %6.4f,\t standard error =\t %6.4f' % omega_stats_PTC)
print('nonPTC: mean dN/dS =\t %6.4f,\t standard error =\t %6.4f' % omega_stats_non_PTC)
print('{0}\t genes with dS = 0 were excluded'.format(zero_dS))
print('{0}\t tgenes with dN/dS > 1 were excluded'.format(omega_greater_1))
def runCompare(self, objId, labelToAdd, expression1, expression2):
fh = open(self._getPath("report.txt"),'w')
self.experiment = self.readExperiment(self.inputExperiment.get().fnPKPD)
x1 = [float(x) for x in self.experiment.getSubGroupLabels(self.expression1.get(),self.labelToCompare.get())]
x2 = [float(x) for x in self.experiment.getSubGroupLabels(self.expression2.get(),self.labelToCompare.get())]
self.doublePrint(fh,"Values in SubGroup 1: %s"%str(x1))
self.doublePrint(fh,"Values in SubGroup 2: %s"%str(x2))
self.doublePrint(fh,"Testing H0: mu1=mu2")
self.doublePrint(fh," ")
try:
[t,pval] = stats.ttest_ind(np.asarray(x1,np.double),np.asarray(x2,np.double),True)
self.doublePrint(fh,"T-test two independent samples (same variance): t-statistic=%f p-value=%f"%(t,pval))
except:
pass
try:
[t,pval] = stats.ttest_ind(x1,x2, False)
self.doublePrint(fh,"T-test two independent samples (different variance, Welch's test): t-statistic=%f p-value=%f"%(t,pval))
except:
pass
try:
[u,pval] = stats.mannwhitneyu(x1, x2, True)
self.doublePrint(fh,"Mann-Whitney U test for two independent samples: u-statistic=%f p-value=%f"%(u,pval))
except:
pass
fh.close()
def pandas_boxplot():
'''Example from Altman "Practical statistics for medical research'''
# Get the data
inFile = 'altman_94.txt'
url_base = 'https://raw.github.com/thomas-haslwanter/statsintro/master/Data/data_altman/'
url = url_base + inFile
data = np.genfromtxt(urlopen(url), delimiter=',')
# Group them into "lean" and "obese"
lean = pd.Series(data[data[:,1]==1,0])
obese = pd.Series(data[data[:,1]==0,0])
# Combine them into a pandas DataFrame
df = pd.DataFrame({'lean':lean, 'obese':obese})
# Calculate the mean value, for each group
print(df.mean())
# Show a boxplot
df.boxplot()
plt.show()
# Perform a T-test between "lean" and "obese" subjects
stats.ttest_ind(lean, obese)
def check_distance_pvals(dist_dict, group_dict, group_frac=0.5, nreps=500):
groups = sorted(set(group_dict.values()))
assert len(groups) == 2
group_vals = defaultdict(list)
for (key1, key2), dist in dist_dict.items():
if group_dict[key1] == group_dict[key2]:
group_vals[group_dict[key1]].append(dist)
assert len(group_vals) == 2
_, raw_pval = ttest_ind(*group_vals.values())
nitems = int(group_frac*min(map(len, group_vals.values())))
cor_vals = []
for _ in range(nreps):
[shuffle(items) for items in group_vals.values()]
_, pval = ttest_ind(*[items[:nitems] for items in group_vals.values()])
cor_vals.append(pval)
odict = {
'RawPval': raw_pval,
'AdjPval': np.mean(cor_vals),
'Group1Name': groups[0],
'Group2Name': groups[1],
'Group1Mean': np.mean(group_vals[groups[0]]),
'Group2Mean': np.mean(group_vals[groups[1]]),
'Group1Std': np.std(group_vals[groups[0]]),
'Group2Std': np.std(group_vals[groups[1]])
}
return odict
def test_vs_nonmasked(self):
np.random.seed(1234567)
outcome = np.random.randn(20, 4) + [0, 0, 1, 2]
# 1-D inputs
res1 = stats.ttest_ind(outcome[:, 0], outcome[:, 1])
res2 = mstats.ttest_ind(outcome[:, 0], outcome[:, 1])
assert_allclose(res1, res2)
# 2-D inputs
res1 = stats.ttest_ind(outcome[:, 0], outcome[:, 1], axis=None)
res2 = mstats.ttest_ind(outcome[:, 0], outcome[:, 1], axis=None)
assert_allclose(res1, res2)
res1 = stats.ttest_ind(outcome[:, :2], outcome[:, 2:], axis=0)
res2 = mstats.ttest_ind(outcome[:, :2], outcome[:, 2:], axis=0)
assert_allclose(res1, res2)
# Check default is axis=0
res3 = mstats.ttest_ind(outcome[:, :2], outcome[:, 2:])
assert_allclose(res2, res3)
# Check equal_var
res4 = stats.ttest_ind(outcome[:, 0], outcome[:, 1], equal_var=True)
res5 = mstats.ttest_ind(outcome[:, 0], outcome[:, 1], equal_var=True)
assert_allclose(res4, res5)
res4 = stats.ttest_ind(outcome[:, 0], outcome[:, 1], equal_var=False)
res5 = mstats.ttest_ind(outcome[:, 0], outcome[:, 1], equal_var=False)
assert_allclose(res4, res5)
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 t_value(self, data, matched_tr_ind_list, matched_co_ind_list, treat_col_name):
"""
t値を計算する
"""
ps_tr, ps_co = data.ix[matched_tr_ind_list], data.ix[matched_co_ind_list]
rand_tr, rand_co = data.ix[data[treat_col_name]==1], data.ix[data[treat_col_name]==0]
del ps_tr[treat_col_name]
del ps_co[treat_col_name]
del rand_tr[treat_col_name]
del rand_co[treat_col_name]
ps_t_val_dict, rand_t_val_dict = {}, {}
for column in ps_tr.columns:
## ps scoreでマッチングを取った場合のT値
ps_tr_array, ps_co_array = numpy.array(ps_tr[column]), numpy.array(ps_co[column])
# ps_tr_n, ps_co_n = ps_tr.shape[0], ps_co.shape[0]
# ps_tr_mean, ps_co_mean = numpy.mean(ps_tr_array), numpy.mean(ps_co_array)
# ps_tr_var, ps_co_var = numpy.var(ps_tr_array), numpy.var(ps_co_array)
ps_t_val = stats.ttest_ind(ps_tr_array, ps_co_array)[0]
ps_t_val_dict.update({column:ps_t_val})
## マッチングしていない場合のT値
rand_tr_array, rand_co_array = numpy.array(rand_tr[column]), numpy.array(rand_co[column])
# rand_tr_n, rand_co_n = rand_tr.shape[0], rand_co.shape[0]
# rand_tr_mean, rand_co_mean = numpy.mean(rand_tr_array), numpy.mean(rand_co_array)
# rand_tr_var, rand_co_var = numpy.var(rand_tr_array), numpy.var(rand_co_array)
rand_t_val = stats.ttest_ind(rand_tr_array, rand_co_array)[0]
rand_t_val_dict.update({column:rand_t_val})
return ps_t_val_dict, rand_t_val_dict
def read_result2():
result1 = pickle.load(open('data/result/lab2/case1.obj', 'rb'))
result2 = pickle.load(open('data/result/lab2/case2.obj', 'rb'))
result3 = pickle.load(open('data/result/lab2/case3.obj', 'rb'))
print np.mean(result1), np.var(result1)
print np.mean(result2), np.var(result2), stats.ttest_ind(result1, result2)
print np.mean(result3), np.var(result3), stats.ttest_ind(result2, result3)
def significant(array1,array2):
try:
arr1 = np.array(array1)
arr2 = np.array(array2)
print(stats.ttest_ind(arr1,arr2)[1])
return stats.ttest_ind(arr1,arr2)[1] < 0.1
except:
print('PROBLEM!')
return None
def do_t_tests(): two_sample = stats.ttest_ind(sec_rating, not_rating) print "The t-statistic is %.3f and the p-value is %.3f." % two_sample # assuming unequal population variances two_sample_diff_var = stats.ttest_ind(sec_rating, not_rating) print "If we assume unequal variances than the t-statistic is %.3f and the p-value is %.3f." % two_sample_diff_var
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("top_data_dir")
parser.add_argument("performance_dir")
parser.add_argument("--basemodel","-bm",default="raw_f2exp")
parser.add_argument("--expansion_model","-em",default="raw_f2exp_fbDocs:10")
parser.add_argument("--threshold","-t",type=float,default=0.6)
args=parser.parse_args()
datasets = load_data(args.top_data_dir)
for collection_name in datasets:
for year in datasets[collection_name].years:
base = []
expansion = []
probs = []
performance_file = os.path.join(args.performance_dir,year.name)
performances = json.load(open(performance_file))
print "for year %s" %(year)
for day in datasets[collection_name]._prediction[year]:
if year==Year.y2016:
day_string = "201608%s" %(day.zfill(2))
elif year == Year.y2015:
day_string = "201507%s" %(day.zfill(2))
elif year == Year.y2017:
if int(day) < 10:
day_string = "201708%s" %(day.zfill(2))
else:
day_string = "201707%s" %(day.zfill(2))
else:
raise NotImplemented("year %s is not NotImplemented!" %(year.name))
for qid in datasets[collection_name]._prediction[year][day]:
try:
base_performance = performances[args.basemodel][qid][day_string]
except KeyError:
continue
else:
if datasets[collection_name].is_silent_day(year,day,qid):
base.append(.0)
expansion.append(.0)
else:
base.append(base_performance)
query_day_prob = datasets[collection_name].get_prob(year,day,qid)
probs.append(query_day_prob)
if query_day_prob > args.threshold:
expansion.append(base_performance)
else:
expansion.append(performances[args.expansion_model][qid][day_string])
print "There are %d pairs" %(len(base))
base_avg = sum(base) / len(base)
expansion_avg = sum(expansion) / len(expansion)
print "%s: %f, %s: %f" %(args.basemodel,base_avg,
args.expansion_model,expansion_avg)
# print probs
print ttest_ind(base,expansion)
print "-"*20
def motifStatsRandMarix(data, motifSize=3, degree=10, usetotal=False):
"""Outputs text file with stats on the motifs in data"""
filename = "result/t_test_Deg-{0}_Size-{1}.txt".format(degree,motifSize)
with open(filename,'w') as f:
f.write("Student's T test comparing both AD/NL/MCI to Random.\n\n")
for corr in ['corr']:
title = "P-values for "+corr+" data set compared to random generated graphs\n"
f.write(title)
data[('MCIR', corr)] = genRandomGraphs(data[('MCI', corr)], degree, len(data[('MCI', corr)]))
data[('ADR', corr)] = genRandomGraphs(data[('AD', corr)], degree, len(data[('AD', corr)]))
data[('NLR', corr)] = genRandomGraphs(data[('NL', corr)], degree, len(data[('NL', corr)]))
motifsNL=findMotifs(data,('NL',corr), motifSize, degree, usetotal)
motifsMCI=findMotifs(data,('MCI',corr), motifSize, degree, usetotal)
motifsAD=findMotifs(data,('AD',corr), motifSize, degree, usetotal)
motifsNLR=findMotifs(data,('NLR',corr), motifSize, degree, usetotal)
motifsMCIR=findMotifs(data,('MCIR',corr), motifSize, degree, usetotal)
motifsADR=findMotifs(data,('ADR',corr), motifSize, degree, usetotal)
allMotifs = list( set(motifsNL.keys())
& set(motifsAD.keys())
& set(motifsMCI.keys())
& set(motifsNLR.keys())
& set(motifsADR.keys())
& set(motifsMCIR.keys()) )
allMotifs.sort()
f.write("{0:>10}{1:>15}{2:>15}{3:>15}{4:>15}{5:>15}{6:>15}{7:>15}{8:>15}{9:>15}\n".format(
'MOTIF ID','NL', 'MCI','AD', 'NLR Mean','MCIR Mean','ADR Mean', 'NLR Std','MCIR Std', 'ADR Std'))
motifStats = []
for key in allMotifs:
tMCI, probMCI = stats.ttest_ind(motifsMCI[key], motifsMCIR[key])
tAD, probAD = stats.ttest_ind(motifsAD[key], motifsADR[key])
tNL, probNL = stats.ttest_ind(motifsNL[key], motifsNLR[key])
motifStats.append((key,probNL,probMCI,probAD))
motifStats.sort(key=lambda x: min(x))
for key, probNL, probMCI, probAD in motifStats:
normRMean = motifsNLR[key].mean()
mciRMean = motifsMCIR[key].mean()
adRMean = motifsADR[key].mean()
normRVar = motifsNLR[key].std()
mciRVar = motifsMCIR[key].std()
adRVar = motifsADR[key].std()
if probMCI<0.01 or probAD<0.01 or probNL<0.01:
star = "**"
elif probMCI<0.1 or probAD<0.1 or probNL<0.01:
star = "*"
else:
star = ""
line = star+"{0:>"+str(10-len(star))+"}{1:>15.3}{2:>15.3}{3:>15.3}{4:>15.3}{5:>15.3}{6:>15.3}{7:>15.3}{8:>15.3}{9:>15.3}\n"
f.write(line.format(str(int(key)), probNL, probMCI, probAD,normRMean,mciRMean,adRMean,normRVar,mciRVar,adRVar))
f.write("\n\n")
def generate_sequence_gene_expression_statistics(self, show_species_charts=True, show_chart=True):
i = -1
if self.multiple_networks:
for nw_ge_file in glob.glob(self.output_silix_nw_exp_data_folder_path + '/*.txt'):
i += 1
mapping_data = np.genfromtxt(nw_ge_file, delimiter=',', dtype=str)
if len(mapping_data) > 0:
print 'Network: ', i, mapping_data.shape
x = np.array(mapping_data[:, 2], dtype=float)
y = np.array(mapping_data[:, 3], dtype=float)
ca_stat = ca_pvalue = spike_stat = spike_pvalue = ind_stat = ind_pvalue = 0
if not np.all(x == 0):
ca_stat, ca_pvalue = stats.ttest_1samp(x[x != 0], 0)
spike_stat, spike_pvalue = stats.ttest_1samp(y[y != 0], 0)
ind_stat, ind_pvalue = stats.ttest_ind(x[x != 0], y[y != 0], equal_var=False)
nw_number = (int)(re.findall(r'\d+', nw_ge_file)[0])
nw_statistics = (
[nw_number, x[x != 0].mean(), x[x != 0].var(), x[x != 0].std(), y[y != 0].mean(),
y[y != 0].var(), y[y != 0].std(), ca_stat, ca_pvalue,
spike_stat, spike_pvalue, ind_stat, ind_pvalue])
self.network_gene_expressions.append(nw_statistics)
else:
mapping_data = np.genfromtxt(self.output_silix_nw_exp_data_folder_path + self.silix_nw_exp_data_filename,
delimiter=',', dtype=str)
if len(mapping_data) > 0:
print 'Network: ', mapping_data.shape
x = np.array(mapping_data[:, 2], dtype=float)
y = np.array(mapping_data[:, 3], dtype=float)
ca_stat = ca_pvalue = spike_stat = spike_pvalue = ind_stat = ind_pvalue = 0
if not np.all(x == 0):
ca_stat, ca_pvalue = stats.ttest_1samp(x[x != 0], 0)
spike_stat, spike_pvalue = stats.ttest_1samp(y[y != 0], 0)
ind_stat, ind_pvalue = stats.ttest_ind(x[x != 0], y[y != 0], equal_var=False)
nw_statistics = (
[0, x[x != 0].mean(), x[x != 0].var(), x[x != 0].std(), y[y != 0].mean(),
y[y != 0].var(), y[y != 0].std(), ca_stat, ca_pvalue,
spike_stat, spike_pvalue, ind_stat, ind_pvalue])
self.network_gene_expressions.append(nw_statistics)
# convert list into array
self.network_gene_expressions = np.asarray(self.network_gene_expressions)
# Save network gene expression statistics to csv file
gene_expression_statistics_file = self.output_silix_nw_exp_data_folder_path + 'gene_expression_statistics.csv'
with open(gene_expression_statistics_file, 'w') as f_handle:
f_handle.write(
'Network, 9mM CA Mean, 9mM CA Var, 9mM CA SD, Spike Mean, Spike Var, Spike SD, 9mM CA ttest-stat, 9mM CA ttest-pvalue, Spike ttest-stat, Spike ttest-pvalue, Ind ttest-stat, Ind ttest-pvalue \n')
np.savetxt(f_handle, self.network_gene_expressions, delimiter=',')
if show_species_charts:
self.generate_species_wise_gene_expression_statistics()
if self.multiple_networks and show_chart:
self.plot_all_nw_gene_expr_stats_chart()
elif show_chart and not self.multiple_networks:
self.plot_single_network_gene_expr_stats_chart()
def ttest(exp1,exp2,exp3):
print "=== exp1 vs exp2"
ts, pvalue = stats.ttest_ind(exp1, exp2,equal_var=False)
print "p value =", pvalue
print "=== exp1 vs exp3"
ts, pvalue = stats.ttest_ind(exp1, exp3,equal_var=False)
print "p value =", pvalue
print "=== exp2 vs exp3"
ts, pvalue = stats.ttest_ind(exp2, exp3,equal_var=False)
print "p value =", pvalue,
def motifStats(data, motifSize=3, degree=10, usetotal=False):
"""Outputs pdf file with stats on the motifs in data"""
filename = "result/t_test_Deg-{0}_Size-{1}.txt".format(degree,motifSize)
with open(filename,'w') as f:
f.write("Student's T test comparing both MCI and AD to NL.\n\n")
for corr in ('corr','lcorr','lacorr'):
title = "P-values for "+corr+" data set compared to normal patients\n"
f.write(title)
motifsNL=findMotifs(data,('NL',corr), motifSize, degree, usetotal)
motifsMCI=findMotifs(data,('MCI',corr), motifSize, degree, usetotal)
motifsAD=findMotifs(data,('AD',corr), motifSize, degree, usetotal)
#mats = []
#for i in xrange(108):
# x = np.random.rand(88,88)
# x -= np.diag(np.diag(x))
# mats.append(x)
#rand = {}
#rand['derp'] = mats
#motifsNL=findMotifs(rand,'derp', motifSize, degree, usetotal)
allMotifs = list( set(motifsNL.keys())
& set(motifsAD.keys())
& set(motifsMCI.keys()) )
f.write("{0:>10}{1:>15}{2:>15}{3:>15}{4:>15}{5:>15}{6:>15}{7:>15}{8:>15}\n".format(
'MOTIF ID','MCI','AD','Norm Mean','MCI Mean','AD Mean','NORM Std','MCI Std', 'AD Std'))
motifStats = []
for key in allMotifs:
tMCI, probMCI = stats.ttest_ind(motifsMCI[key], motifsNL[key])
tAD, probAD = stats.ttest_ind(motifsAD[key], motifsNL[key])
motifStats.append((key,probMCI,probAD))
motifStats.sort(key=lambda x: min(x))
for key, probMCI, probAD in motifStats:
normMean = motifsNL[key].mean()
mciMean = motifsMCI[key].mean()
adMean = motifsAD[key].mean()
normVar = motifsNL[key].std()
mciVar = motifsMCI[key].std()
adVar = motifsAD[key].std()
if probMCI<0.01 or probAD<0.01:
star = "**"
elif probMCI<0.1 or probAD<0.1:
star = "*"
else:
star = ""
line = star+"{0:>"+str(10-len(star))+"}{1:>15.3}{2:>15.3}{3:>15.3}{4:>15.3}{5:>15.3}{6:>15.3}{7:>15.3}{8:>15.3}\n"
f.write(line.format(str(int(key)), probMCI, probAD,normMean,mciMean,adMean,normVar,mciVar,adVar))
f.write("\n\n")
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 read_result():
fsc0 = pickle.load(open('data/result/fsc1.obj', 'rb'))
fsc25 = pickle.load(open('data/result/fsc2.obj', 'rb'))
fsc50 = pickle.load(open('data/result/fsc3.obj', 'rb'))
fsc75 = pickle.load(open('data/result/fsc4.obj', 'rb'))
fsc100 = pickle.load(open('data/result/fsc5.obj', 'rb'))
print np.mean(fsc0), np.var(fsc0)
print np.mean(fsc25), np.var(fsc25), stats.ttest_ind(fsc0, fsc25)
print np.mean(fsc50), np.var(fsc50), stats.ttest_ind(fsc25, fsc50)
print np.mean(fsc75), np.var(fsc75), stats.ttest_ind(fsc50, fsc75)
print np.mean(fsc100), np.var(fsc75), stats.ttest_ind(fsc100, fsc75)
def table_post_processing(results_collector):
stats_collector = []
print '\n Summary of the analysis:'
for line in results_collector:
class_name, _time_stamps = classify(line[0])
stats_collector.append([class_name, _time_stamps, line[6], line[7]])
stats_collector = np.array(stats_collector)
for class_name in classes:
class_set_filter = stats_collector[:, 0] == class_name
if any(class_set_filter):
class_set = stats_collector[class_set_filter, :]
print class_name
final_stats_collector_x = []
final_stats_collector_y = []
final_stats_collector_e = []
raw_times = {}
for time_stamp in time_stamp_coll:
time_stamp_filter = class_set[:, 1] == time_stamp
if any(time_stamp_filter):
time_stamp_set = class_set[time_stamp_filter, :]
mean = np.nanmean(time_stamp_set[:, 2].astype(np.float64))
err = np.nanstd(time_stamp_set[:, 2].astype(np.float64)) / \
np.sqrt(len(time_stamp_set[:, 2]))*1.96
raw_times[time_stamp] = rm_nans(time_stamp_set[:, 2].astype(np.float64))
print '\t time: %s, mean: %s, err: %s' % (time_stamp, mean, err)
final_stats_collector_x.append(time_stamps[time_stamp])
final_stats_collector_y.append(mean)
final_stats_collector_e.append(err)
time_translator = dict([(item, i) for i, item in enumerate(sorted(raw_times.keys()))])
samples_n = len(raw_times.keys())
print samples_n
p_val_array = np.array((samples_n, samples_n))
for time1, time2 in combinations(sorted(raw_times.keys()), 2):
print time1, time2
print time_translator[time1], time_translator[time2]
print ttest_ind(raw_times[time1], raw_times[time2])
_, p_val = ttest_ind(raw_times[time1], raw_times[time2])
p_val_array[time_translator[time1], time_translator[time2]] = p_val
print p_val_array
plt.errorbar(final_stats_collector_x, final_stats_collector_y, final_stats_collector_e,
label=class_name)
plt.legend()
plt.show()
def determine_significance(mesa1, mesa2):
""" Determines if two sets of values are statistically significant.
In the best case, we can determine a normal distribution, and equal
variance. Once determined we can use the independent t-test function if the
values are of equal variance. If we have normal data, but the variance is
unequal, the welch t-test is used.
http://en.wikipedia.org/wiki/Student%27s_t-test#Independent_two-sample_t-test
http://en.wikipedia.org/wiki/Student%27s_t-test#Equal_or_unequal_sample_sizes.2C_unequal_variances
In the case where we cannot determine normality the mann-whitney u-test is
desired to be used, but this test is only effective when there are greater
than 20 samples.
http://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test
"""
# FIXME: Is it possible to determine these things with fewer samples?
Distribution = Enum('Distribution', 'Normal, Non_normal Unknown')
normality = Distribution.Normal
try:
k2, normal = stats.normaltest(mesa1)
# FIXME: Unhardcode
if (normal < NORMAL_CI):
normality = Distribution.Non_normal
k2, normal = stats.normaltest(mesa2)
if (normal < NORMAL_CI):
normality = Distribution.Non_normal
except ValueError:
normality = Distribution.Unknown
equal_variance = is_equal_variance(mesa1, mesa2)
if args.ttest:
t, p = stats.ttest_ind(mesa1, mesa2, equal_var=equal_variance)
return (p, normality == Distribution.Normal,
"t-test" if equal_variance else "Welch's")
elif args.mannwhitney:
u, p = stats.mannwhitneyu(mesa1, mesa2)
p *= 2 # We want a 2-tailed p-value
return (p, len(mesa1) < 20 or len(mesa2) < 20, "Mann-Whitney")
if normality == Distribution.Normal:
error_handler='raise'
if np.var(mesa1) == 0 and equal_variance:
error_handler='ignore'
with np.errstate(divide=error_handler):
t, p = stats.ttest_ind(mesa1, mesa2, equal_var=equal_variance)
return (p, False, "t-test" if equal_variance else "Welch's")
else:
u, p = stats.mannwhitneyu(mesa1, mesa2)
p *= 2 # We want a 2-tailed p-value
flawed = len(mesa1) < 20 or len(mesa2) < 20
return (p, flawed, "Mann-Whitney")
def main():
f27_scan = open('sim_scan27.txt', 'r')
f27_table = open('sim_table27.txt', 'r')
f35932_scan = open('sim_scan35932.txt', 'r')
f35932_table = open('sim_table35932.txt', 'r')
ntests = 10
scan27 = [0 for i in range(ntests)]
table27 = [0 for i in range(ntests)]
scan35932 = [0 for i in range(ntests)]
table35932 = [0 for i in range(ntests)]
files = [f27_scan, f27_table, f35932_scan, f35932_table]
arrs = [scan27, table27, scan35932, table35932]
for i in range(ntests):
for j in range(4):
line = files[j].readline()
if line[len(line)-1] == '\n':
line = line[:len(line)-1]
arrs[j][i] = float(line)
for j in range(4):
files[j].close()
_, p27 = stats.ttest_ind(scan27, table27, equal_var=False)
mean27scan = stats.tmean(scan27)
mean27table = stats.tmean(table27)
var27scan = stats.tvar(scan27)
var27table = stats.tvar(table27)
_, p35932 = stats.ttest_ind(scan35932, table35932, equal_var=False)
mean35932scan = stats.tmean(scan35932)
mean35932table = stats.tmean(table35932)
var35932scan = stats.tvar(scan35932)
var35932table = stats.tvar(table35932)
f = open('sim_results_compare_scan_table.txt', 'w')
f.write('27\n')
f.write('scan mean: ' + str(mean27scan) + '\n')
f.write('scan var: ' + str(var27scan) + '\n')
f.write('table mean: ' + str(mean27table) + '\n')
f.write('table var: ' + str(var27table) + '\n')
f.write('p-value: ' + str(p27) + '\n\n')
f.write('35932\n')
f.write('scan mean: ' + str(mean35932scan) + '\n')
f.write('scan var: ' + str(var35932scan) + '\n')
f.write('table mean: ' + str(mean35932table) + '\n')
f.write('table var: ' + str(var35932table) + '\n')
f.write('p-value: ' + str(p35932) + '\n')
f.close()
def main(pkl_list, name_list, cut=sys.maxint):
pickles = plot_util.load_pickles(name_list, pkl_list)
best_dict, idx_dict, keys = plot_util.get_best_dict(name_list, pickles,
cut=cut)
for k in keys:
sys.stdout.write("%10s: %s experiment(s)\n" % (k, len(best_dict[k])))
sys.stdout.write("Unpaired t-tests-----------------------------------------------------\n")
# TODO: replace by itertools
for idx, k in enumerate(keys):
if len(keys) > 1:
for j in keys[idx+1:]:
t_true, p_true = stats.ttest_ind(best_dict[k], best_dict[j])
rounded_t_true, rounded_p_true = stats.ttest_ind(numpy.round(best_dict[k], 3),
numpy.round(best_dict[j], 3))
sys.stdout.write("%10s vs %10s\n" % (k, j))
sys.stdout.write("Standard independent 2 sample test, equal population variance\n")
sys.stdout.write(" "*24 + " T: %10.5e, p-value: %10.5e (%5.3f%%) \n" %
(t_true, p_true, p_true*100))
sys.stdout.write("Rounded: ")
sys.stdout.write(" T: %10.5e, p-value: %10.5e (%5.3f%%)\n" %
(rounded_t_true, rounded_p_true, rounded_p_true*100))
if tuple(map(int, (scipy.__version__.split(".")))) >= (0, 11, 0):
# print scipy.__version__ >= '0.11.0'
t_false, p_false = stats.ttest_ind(best_dict[k], best_dict[j], equal_var=False)
rounded_t_false, rounded_p_false = stats.ttest_ind(numpy.round(best_dict[k], 3),
numpy.round(best_dict[j], 3),
equal_var=False)
sys.stdout.write("Welch's t-test, no equal population variance\n")
sys.stdout.write(" "*24)
sys.stdout.write(": T: %10.5e, p-value: %10.5e (%5.3f%%)\n" %
(t_false, p_false, p_false*100))
sys.stdout.write("Rounded: ")
sys.stdout.write(": T: %10.5e, p-value: %10.5e (%5.3f%%)\n" %
(rounded_t_false, rounded_p_false, rounded_p_false*100))
sys.stdout.write("\n")
sys.stdout.write("Best Value-----------------------------------------------------------\n")
for k in keys:
sys.stdout.write("%10s: %10.5f (min: %10.5f, max: %10.5f, std: %5.3f)\n" %
(k, float(numpy.mean(best_dict[k])), float(numpy.min(best_dict[k])),
numpy.max(best_dict[k]), float(numpy.std(best_dict[k]))))
sys.stdout.write("Needed Trials--------------------------------------------------------\n")
for k in keys:
sys.stdout.write("%10s: %10.5f (min: %10.5f, max: %10.5f, std: %5.3f)\n" %
(k, float(numpy.mean(idx_dict[k])), float(numpy.min(idx_dict[k])),
numpy.max(idx_dict[k]), float(numpy.std(idx_dict[k]))))
sys.stdout.write("------------------------------------------------------------------------\n")
def runCompare(self, objId1, objId2, label1, label2, paired, expression1, expression2):
fh = open(self._getPath("report.txt"),'w')
self.experiment1 = self.readExperiment(self.inputExperiment1.get().fnPKPD)
self.experiment2 = self.readExperiment(self.inputExperiment2.get().fnPKPD)
label2ToUse = self.label1.get() if self.label2.get()=="" else self.label2.get()
if self.paired:
x1=[]
x2=[]
for sampleName, sample in self.experiment1.getSubGroup(self.expression1.get()).iteritems():
x1.append(float(sample.descriptors[self.label1.get()]))
if sampleName in self.experiment2.samples:
x2.append(float(self.experiment2.samples[sampleName].descriptors[label2ToUse]))
else:
raise "Cannot find sample %s in Experiment 2"%sample.sampleName
else:
expression2ToUse = self.expression1.get() if self.expression2.get()=="" else self.expression2.get()
x1 = [float(x) for x in self.experiment1.getSubGroupLabels(self.expression1.get(),self.label1.get())]
x2 = [float(x) for x in self.experiment2.getSubGroupLabels(expression2ToUse,label2ToUse)]
self.doublePrint(fh,"Values in SubGroup 1: %s"%str(x1))
self.doublePrint(fh,"Values in SubGroup 2: %s"%str(x2))
self.doublePrint(fh,"Testing H0: mu1=mu2")
self.doublePrint(fh," ")
try:
if self.paired:
[t,pval] = stats.ttest_rel(np.asarray(x1,np.double),np.asarray(x2,np.double),True)
self.doublePrint(fh,"T-test two paired samples: t-statistic=%f p-value=%f"%(t,pval))
else:
[t,pval] = stats.ttest_ind(np.asarray(x1,np.double),np.asarray(x2,np.double),True)
self.doublePrint(fh,"T-test two independent samples (same variance): t-statistic=%f p-value=%f"%(t,pval))
except:
pass
if not self.paired:
try:
[t,pval] = stats.ttest_ind(x1,x2, False)
self.doublePrint(fh,"T-test two independent samples (different variance, Welch's test): t-statistic=%f p-value=%f"%(t,pval))
except:
pass
try:
if self.paired:
[w,pval] = stats.wilcoxon(x1, x2, correction=True)
self.doublePrint(fh,"Wilcoxon signed rank test for two paired samples: w-statistic=%f p-value=%f"%(w,pval))
else:
[u,pval] = stats.mannwhitneyu(x1, x2, True)
self.doublePrint(fh,"Mann-Whitney U test for two independent samples: u-statistic=%f p-value=%f"%(u,pval))
except:
pass
fh.close()
def detect_hemizygous_markers(pop_file , coverage_file, nb_sample_required=0):
sample2pop, pop2sample = read_pop_file(pop_file)
if len(pop2sample)!=2:
logging.critical('Hemizygous Markers can only be search between two set of samples. Edit you population file to have two populations')
return -1
pop1,pop2 = pop2sample.keys()
samples_pop1 = pop2sample.get(pop1)
samples_pop2 = pop2sample.get(pop2)
all_markers, all_samples, all_samples_to_norm_coverage = get_normalize_coverage(coverage_file, nb_sample_required)
sample_errors = set(sample2pop.keys()).difference(set(all_samples))
if len(sample_errors)>0:
logging.critical('%s samples (%s) from the population file not found in the coverage file'%(len(sample_errors), ', '.join(sample_errors)))
return -2
header = ["#consensus", "mean_%s"%(pop1), "mean_%s"%(pop2), "fold_change",
"t_test_%s_eq_2X_%s"%(pop1,pop2), "t_test_%s_eq_%s"%(pop1,pop2)]
all_lines = [' '.join(header)]
for i, marker in enumerate(all_markers):
out=[marker]
out_pop1=[]
out_pop2=[]
pop1_values = []
pop2_values = []
for sample in samples_pop1:
cov = all_samples_to_norm_coverage.get(sample)[i]
pop1_values.append(cov)
out_pop1.append(str(cov))
for sample in samples_pop2:
cov = all_samples_to_norm_coverage.get(sample)[i]
pop2_values.append(cov)
out_pop2.append(str(cov))
pop1_nvalues = numpy.array(pop1_values)
pop1_nvalues_2 =pop1_nvalues*2
pop2_nvalues = numpy.array(pop2_values)
#This compare the normalized values, we assume they should not be equal
t_stat_eq, pvalue_eq = stats.ttest_ind(pop1_nvalues,pop2_nvalues)
#This compare the norm value with norm values * 2, we assume they should be equal
t_stat_2X, pvalue_2X = stats.ttest_ind(pop1_nvalues_2,pop2_nvalues)
fold=pop2_nvalues.mean()/pop1_nvalues.mean()
if pvalue_eq<.05 and pvalue_2X >0.5 and fold < 2.2 and fold >1.8:
out.append(str(pop1_nvalues.mean()))
out.append(str(pop2_nvalues.mean()))
out.append(str(fold))
out.append(str(pvalue_2X))
out.append(str(pvalue_eq))
all_lines.append(' '.join(out))
return '\n'.join(all_lines)
def stats(self, x, y):
if not self.diagonal:
xflatten = np.delete(x, [i*(x.shape[0]+1)for i in range(x.shape[0])])
yflatten = np.delete(y, [i*(y.shape[0]+1)for i in range(y.shape[0])])
p = np.corrcoef(xflatten,yflatten)
utils.printf('Pearson\'s correlation:\n{}'.format(p))
utils.printf('Z-Test:{}'.format(ztest(xflatten, yflatten)))
utils.printf('T-Test:{}'.format(ttest_ind(xflatten, yflatten)))
else:
p = np.corrcoef(x, y)
utils.printf('Pearson\'s correlation:\n{}'.format(p))
utils.printf('Z-Test:{}'.format(ztest(x, y)))
utils.printf('T-Test:{}'.format(ttest_ind(x, y)))
# if we leave in Aid Response all the distributions will test
# as the same
# comment out the next line to test with all Inc Types
row.pop(1)
# convert from strings
floats = []
for count in row:
floats.append(float(count))
# keep track of the values
hoodCounts.append(floats)
for index in range(len(hoodCounts)):
# get the current Neighborhood
compareHood = hoodNames[index]
compareSet = hoodCounts[index]
for next in range(len(hoodCounts)):
# compare to each
if index != next:
testHood = hoodNames[next]
testSet = hoodCounts[next]
# Run a t test on the incident distributions
pVals = stats.ttest_ind(compareSet, testSet, equal_var=False)
if pVals[1] < 0.05:
print testHood
print compareHood
print pVals
import numpy as np
from scipy import stats
N = 10
a = np.random.randn(N) + 2
b = np.random.randn(N)
var_a = a.var(ddof=1)
var_b = b.var(ddof=1)
s = np.sqrt((var_a + var_b) / 2)
t = ((a.mean() - b.mean()) / (s * np.sqrt(2.0 / N)))
df = 2*N - 2
p = 1 - stats.t.cdf(t, df=df)
print("t:\t", t, "p:\t", 2*p)
t2, p2 = stats.ttest_ind(a, b)
print("t2:\t", t2, "p2:\t", p2)
# Import independent two-sample t-test from scipy.stats import ttest_ind # Divide `df.brain_vol` by `df.skull_vol` df['adj_brain_vol'] = df.brain_vol / df.skull_vol # Select brain measures by Alzheimers group brain_alz = df.loc[df.alzheimers == True, 'adj_brain_vol'] brain_typ = df.loc[df.alzheimers == False, 'adj_brain_vol'] # Evaluate null hypothesis results = ttest_ind(brain_alz, brain_typ)
break
client_seconds = time.perf_counter() - start_seconds
print(f'client-time\t{query_index}\t{client_seconds}\t{server_seconds}')
# Run additional profiling queries to collect profile data, but only if test times appeared to be different.
# We have to do it after normal runs because otherwise it will affect test statistics too much
if len(all_server_times) != 2:
continue
if len(all_server_times[0]) < 3:
# Don't fail if for some reason there are not enough measurements.
continue
pvalue = stats.ttest_ind(all_server_times[0],
all_server_times[1],
equal_var=False).pvalue
median = [statistics.median(t) for t in all_server_times]
# Keep this consistent with the value used in report. Should eventually move
# to (median[1] - median[0]) / min(median), which is compatible with "times"
# difference we use in report (max(median) / min(median)).
relative_diff = (median[1] - median[0]) / median[0]
print(
f'diff\t{query_index}\t{median[0]}\t{median[1]}\t{relative_diff}\t{pvalue}'
)
if abs(relative_diff) < ignored_relative_change or pvalue > 0.05:
continue
# Perform profile runs for fixed amount of time. Don't limit the number
# of runs, because we also have short queries.
profile_start_seconds = time.perf_counter()
count2 = count2 - 1
print('After Sampling:\n')
print(sampled_native_df.describe())
print(sampled_singularity_df.describe())
#df = native_df.merge(singularity_df, how='left')
#print(df.describe())
print('p-value:\t 0.05\n')
print('degrees of freedom:\t ~60\n')
print('Critical t-val:\t 2.0\n')
t_val_rel = stats.ttest_rel(
sampled_native_df.loc[:, 'Native Runtime (Seconds)'],
sampled_singularity_df.loc[:, 'Singularity Runtime (Seconds)'])
print(t_val_rel)
t_val_ind = stats.ttest_ind(
sampled_native_df.loc[:, 'Native Runtime (Seconds)'],
sampled_singularity_df.loc[:, 'Singularity Runtime (Seconds)'])
print(t_val_ind)
ax = plt.gca()
native_df.plot(kind='hist', y='Native Runtime (Seconds)', color='red', ax=ax)
singularity_df.plot(kind='hist',
y='Singularity Runtime (Seconds)',
color='blue',
ax=ax)
plt.savefig('2gpu_Histogram.png')
plt.savefig('2gpu_Histogram.eps')
plt.figure(2)
ax2 = plt.gca()
sampled_native_df.plot(kind='hist',
# ## Use similarity across conditions as the 4th dimension ##########################################
print("Compute similarity via ttest.")
condition_names = list(np.unique(haxby_labels))
n_conds = len(condition_names)
n_compares = n_conds * (n_conds - 1) / 2
p_vectors = np.zeros((n_compares, masked_fmri_vectors.shape[1]))
comparison_text = []
comparison_img = []
idx = 0
for i, cond in enumerate(condition_names):
for j, cond2 in enumerate(condition_names[i + 1:]):
print("Computing ttest for %s vs. %s." % (cond, cond2))
_, p_vector = stats.ttest_ind(
masked_fmri_vectors[haxby_labels == cond, :],
masked_fmri_vectors[haxby_labels == cond2, :],
axis=0)
# Normalize and log-transform
p_vector /= p_vector.max() # normalize
p_vector = -np.log10(p_vector)
p_vector[np.isnan(p_vector)] = 0.
p_vector[p_vector > 10.] = 10.
p_img = epi_masker.inverse_transform(p_vector)
comparison_img.append(p_img)
comparison_text.append('%s vs. %s' % (cond, cond2))
p_vectors[idx, :] = p_vector
idx += 1
# ## Convert similarities into a single subject image (like a time-course) ################
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(get_value(cols, index))
if sample2 == 1:
for index in sample_two_cols:
sample_two.append(get_value(cols, index))
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()
clmtempf = ma.filled(clmtempf, fill_value=0.) clmtropfa = ma.masked_where(maitoatrop <= 0, clmtropfa) clmtempfa = ma.masked_where(maitoatemp <= 0, clmtempfa) clmtropfa = ma.filled(clmtropfa, fill_value=0.) clmtempfa = ma.filled(clmtempfa, fill_value=0.) clmhis = clmtropf + clmtempf clmfuture = clmtropfa + clmtempfa clmhis = ma.masked_where(clmhis[:, :] <= 0, clmhis) clmfuture = ma.masked_where(clmfuture[:, :] <= 0, clmfuture) clmhist = clmtrop + clmtemp clmfutt = clmtropa + clmtempa tc, pTc = ttest_ind(clmhist, clmfutt, axis=0, equal_var=False) tc = N.flipud(tc) pTc = N.flipud(pTc) yieldclm = clmfuture - clmhis yieldclm = ma.masked_where(yieldclm == 0., yieldclm) yieldclm1 = ma.masked_where(pTc[:, :] > 0.1, yieldclm) yieldf = N.zeros((10, 360, 720)) yieldf2 = N.zeros((10, 360, 720)) yieldfa = N.zeros((10, 360, 720)) yieldf2a = N.zeros((10, 360, 720)) yieldfb = N.zeros((10, 360, 720)) yieldf2b = N.zeros((10, 360, 720))
# Plotting by genotype and sex
gensex_fig = plt.figure(2)
mouse_mask_table[['MaskVolume', 'Genotype',
'Sex']].boxplot(by=['Genotype', 'Sex'])
plt.ylabel('$mm^3$')
plt.ylabel('')
plt.savefig(os.path.join(analysis_path, 'Boxplot_MaskVolumes_ByGenotypeSex'))
# plt.show()
# pval calculation, equal_var for now ;)
mouse_mask_table[mouse_mask_table['Genotype'] == 'WT']['MaskVolume']
cat2 = mouse_mask_table[mouse_mask_table['Genotype'] == 'KO']
print(
ttest_ind(
mouse_mask_table[mouse_mask_table['Genotype'] == 'WT']['MaskVolume'],
mouse_mask_table[mouse_mask_table['Genotype'] == 'KO']['MaskVolume'],
equal_var=True))
mouse_mask_table.to_csv(
os.path.join(analysis_path, 'Mouse_maskvolume_table.csv'))
## Function to compute volumes for image
def image2volumetable(image_path, voxel_volume):
# Compute voxel numbers and volumes and output to table
mouse_mask_image = nib.load(image_path)
mouse_mask_image_array = mouse_mask_image.get_fdata()
[mouse_volume_integer, mouse_voxel_number
] = np.unique(np.int64(np.round(mouse_mask_image_array)),
return_counts=True)
mouse_volume = mouse_voxel_number * voxel_volume
# descriptive statistics for rest of world sales df["Other_Sales"].describe() # In[6]: # descriptive statistics for global sales df["Global_Sales"].describe() # In[11]: # T-test for North American sales stats.ttest_ind(df["Global_Sales"], df["NA_Sales"]) # In[12]: # T-test for European sales stats.ttest_ind(df["Global_Sales"], df["EU_Sales"]) # In[13]: # T-test for Japanese sales stats.ttest_ind(df["Global_Sales"], df["JP_Sales"])
del current
currentInds.reset_index(drop=True)
#test if dataframe is empty to continue to next drug
if currentInds.empty:
continue
else:
#separate the concentrations
conc= np.unique(currentInds['concentration'])
for dose in conc:
test =[]
to_test = currentInds['concentration'] ==dose
testing = currentInds[to_test]
for feature in currentInds.columns[0:-3]:
test.append(stats.ttest_ind(testing[feature], controlMeans[rep][feature]))
ps = [(test[i][1]) for i in range(len(test))] #make into a list
ps.append(drug)
ps.append(dose)
temp = pd.DataFrame(ps).transpose()
pVals[rep] = pVals[rep].append(temp)
del temp, to_test, testing
del currentInds
#add in features
pVals[rep].columns = feats
pVals[rep] = pVals[rep].reset_index (drop=True)
#import module for multiple comparison correction
def q5():
# Retorne aqui o resultado da questão 5.
return False
# In[85]:
atletas = ["BRA", "USA", "CAN"]
amostra = athletes[athletes["nationality"].isin(atletas)]
brasileiros = athletes[athletes["nationality"].isin(["BRA"])]["height"]
americanos = athletes[athletes["nationality"].isin(["USA"])]["height"]
sct.ttest_ind(americanos.dropna(),brasileiros.dropna(), equal_var=False)
# ## Questão 6
#
# Repita o procedimento da questão 5, mas agora entre as alturas de `bra` e `can`. Podemos afimar agora que as médias são estatisticamente iguais? Reponda com um boolean (`True` ou `False`).
# In[11]:
def q6():
# Retorne aqui o resultado da questão 6.
return True
# In[88]:
def compara_media(str_1, str_2):
v1 = athletes[athletes["nationality"].isin([str(str_1)])]["height"]
v2 = athletes[athletes["nationality"].isin([str(str_2)])]["height"]
return sct.ttest_ind(v1.dropna(), v2.dropna(), equal_var=False)
# print("df2.income.sum() : ", df2.income.sum())
#
# print("+++++++++중앙값 구하기++++++++++")
# print("df2.income.median() : ", df2.income.median())
#
# print("+++++++++기초통계량 요약해서 출력하기++++++++++")
# print("df2.describe() : ", df2.describe())
# print("df2.income.describe() : ", df2.income.describe())
#
# print("df2.sex.value_counts() : ", df2.sex.value_counts())
# print("df2.groupby(df2.sex).mean()", df2.groupby(df2.sex).mean())
male = df2.income[df2.sex == 'm']
female = df2.income[df2.sex == 'f']
ttest_result = stats.ttest_ind(male, female)
print(ttest_result)
print("ttest_result[0]", ttest_result[0])
print("ttest_result[1]", ttest_result[1])
if ttest_result[1] > 0.05:
print(f'p-value는 {ttest_result[1]}로 95% 수준에서 유의하지않음')
else:
print(f'p-value는 {ttest_result[1]}로 95% 수준에서 유의함')
corr = df2.corr(method='spearman')
print("corr:", corr)
income_stress_corr = df2.income.corr(df2.stress)
print("income_stress_corr:", income_stress_corr)
def return_stats(
stock='jpm',
commission=2,
money=100000,
#inc=10,- can read this argument and change code below if doing absolute share-based
#original_shares=100, - can read this argument and change code below if doing absolute share-based
policies=[hold, random_action, rule_based, ols, qlearner]):
'''
Enacts every strategy and provides summary statistics and graphs
Inputs
stock:
money: original cash held
inc: increment of buy/sell permitted
original_shares: original number of shares held
Output
None
Provides numerous summary statistics and visualizations
'''
original_money = money
# generate stock table
stock_table = read_stock(stock, start, end)
# note stock name
stock_name = stock.upper()
# approximate 50/50 split in money-stock
original_shares = round(money / 2 / stock_table.values[0])
# recalculate money accordingly
money -= (stock_table.values[0] * original_shares)
# make share increment about 1% of original share holdings
inc = m.ceil(original_shares / 100)
# generate results
results = {
policy.__name__: policy(stock_table,
money=money,
inc=inc,
original_shares=original_shares,
commission=commission)
for policy in policies
}
# plot qtables only for qlearner (or any other strategies with Q table)
for policy in policies:
if results[policy.__name__][
'qtable'] is not None: #don't try to plot Q tables for benchmark strategies
# get state history and quantile length and qtable for normalization and averaging function
state_history = results[policy.__name__]['state_history']
quantile_length = len(results[policy.__name__]['BB_quantiles'])
qtab = results[policy.__name__]['qtable']
qtab_bb = weighted_average_and_normalize(qtab, state_history, 0,
quantile_length)
qtab_bb = qtab_bb.iloc[::
-1] # reverse order of rows for visualization purposes - now biggest value will be on top
qtab_bb.index = np.round(
np.flip(np.array(results[policy.__name__]['BB_quantiles'])), 5
) # define index as bb quantiles, reversing quantile order in kind so biggest value is first
# plot BB heatmap
plt.figure(figsize=(9, 7))
fig = heatmap(qtab_bb, cmap='Blues')
plt.title('Bollinger Band % Q-Table', size=16)
plt.gca().hlines([i + 1 for i in range(len(qtab_bb.index))],
xmin=0,
xmax=10,
linewidth=10,
color='white')
plt.xticks(fontsize=15)
plt.yticks(fontsize=14, rotation=0)
plt.gca().tick_params(axis='x', bottom=False, left=False)
plt.gca().tick_params(axis='y', bottom=False, left=False)
plt.show(fig)
# marginalize over SMA
# TODO - determine if this mean was taken correctly
qtab_sma = weighted_average_and_normalize(qtab, state_history, 1,
quantile_length)
qtab_sma = qtab_sma.iloc[::-1]
qtab_sma.index = np.round(
np.flip(np.array(results[policy.__name__]['SMA_quantiles'])),
5)
plt.figure(figsize=(9, 7))
fig = heatmap(qtab_sma, cmap='Blues')
plt.title('SMA Percentage Q-Table', size=16)
plt.gca().hlines([i + 1 for i in range(len(qtab_sma.index))],
xmin=0,
xmax=10,
linewidth=10,
color='white')
plt.xticks(fontsize=15)
plt.yticks(fontsize=14, rotation=0)
plt.gca().tick_params(axis='x', bottom=False, left=False)
plt.gca().tick_params(axis='y', bottom=False, left=False)
plt.show(fig)
# marginalize over MRDR
# TODO - determine if this mean was taken correctly
qtab_mrdr = weighted_average_and_normalize(qtab, state_history, 2,
quantile_length)
qtab_mrdr = qtab_mrdr.iloc[::-1]
qtab_mrdr.index = np.round(
np.flip(np.array(results[policy.__name__]['MRDR_quantiles'])),
5)
plt.figure(figsize=(9, 7))
fig = heatmap(qtab_mrdr, cmap='Blues')
plt.title('Market Relative Daily Return Q-Table', size=16)
plt.gca().hlines([i + 1 for i in range(len(qtab_mrdr.index))],
xmin=0,
xmax=10,
linewidth=10,
color='white')
plt.xticks(fontsize=15)
plt.yticks(fontsize=14, rotation=0)
plt.gca().tick_params(axis='x', bottom=False, left=False)
plt.gca().tick_params(axis='y', bottom=False, left=False)
plt.show(fig)
# get markov transition models
for policy in policies:
plt.figure(figsize=(6, 3))
plt.title('Transition Matrix For ' + policy.__name__, size=16)
mkv = results[policy.__name__]['markov']
fig = heatmap(mkv,
annot=True,
annot_kws={'size': 14},
cmap='Greens',
cbar=False)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14, rotation=0)
plt.gca().set(xlabel='Current Trading Day', ylabel='Last Trading Day')
plt.gca().tick_params(axis='x', bottom=False, left=False)
plt.gca().tick_params(axis='y', bottom=False, left=False)
plt.gca().hlines([1, 2], xmin=0, xmax=10, linewidth=10, color='white')
plt.show(fig)
# plot daily portfolio values
plt.figure(figsize=(14, 8))
for policy in policies:
plt.plot(results[policy.__name__]['final_vals'], label=policy.__name__)
plt.legend()
plt.xlabel("Date", fontsize=20)
plt.ylabel("Portfolio Value ($)", fontsize=20)
plt.title("Daily Portfolio Values For Different Trading Strategies: " +
stock.upper(),
fontsize=25)
plt.show()
# plot daily cash values
plt.figure(figsize=(14, 8))
for policy in policies:
plt.plot(results[policy.__name__]['cash'], label=policy.__name__)
plt.legend()
plt.xlabel("Date", fontsize=20)
plt.ylabel("Cash Held ($)", fontsize=20)
plt.title("Daily Cash Held For Different Trading Strategies: " +
stock.upper(),
fontsize=25)
plt.show()
# plot daily shares
plt.figure(figsize=(14, 8))
for policy in policies:
plt.plot(results[policy.__name__]['shares'], label=policy.__name__)
plt.legend()
plt.xlabel("Date", fontsize=20)
plt.ylabel("Shares Held", fontsize=20)
plt.title("Daily Share Holdings For Different Trading Strategies: " +
stock_name,
fontsize=25)
plt.show()
# plot daily portfolio values
for i, policy in enumerate(policies):
dic = results[policy.__name__]
if dic['state_history'] is not None:
print("States History for " + policy.__name__ + "is: ",
dic['state_history'])
del dic['state_history']
del dic['qtable']
del dic['markov']
try:
del dic['BB_quantiles']
del dic['SMA_quantiles']
del dic['MRDR_quantiles']
except:
pass
df = pd.DataFrame(dic)
plt.figure(figsize=(14, 8))
plt.plot([], label="BUY", color="orange", marker='o')
plt.plot([], label="SELL", color="black", marker='o')
plt.plot([], label="HOLD", color="red", marker='o')
buy_df = df[df.actions == 'BUY']
sell_df = df[df.actions == 'SELL']
hold_df = df[df.actions == 'HOLD']
plt.plot(results[policy.__name__]['final_vals'], label=policy.__name__)
plt.scatter(buy_df.index,
buy_df['final_vals'],
color='orange',
marker='^',
s=10)
plt.scatter(sell_df.index,
sell_df['final_vals'],
color='black',
marker='v',
s=10)
plt.scatter(hold_df.index,
hold_df['final_vals'],
color='red',
marker='s',
s=10)
plt.xlabel("Date", fontsize=20)
plt.ylabel("Portfolio Value ($)", fontsize=20)
plt.title("Daily Portfolio Values For Trading Strategies of " +
policy.__name__ + " for stock : " + stock.upper(),
fontsize=25)
plt.legend()
plt.show()
# display percentages
#TODO: display(res) has no display() function. Fix bug.
for policy in policies:
print('For ' + stock_name + ',', policy.__name__,
'action proportions were:')
res = results[policy.__name__]['actions'].value_counts()
res = res / res.sum()
print(res)
print('\n')
print('For ' + stock_name + ',', policy.__name__,
'average return based on action was:')
res = returns(results[policy.__name__]['final_vals']).groupby(
results[policy.__name__]['actions']).mean()
print(res)
print('\n')
# calculate final returns
for policy in policies:
print('Final porfolio value under', policy.__name__,
'strategy for ' + stock_name + ':',
round(results[policy.__name__]['final_vals'].values[-1], 0))
print('\n')
# calculate final percentage of money invested in stock
for policy in policies:
print(
'Final percentage of money invested in stock under',
policy.__name__, 'strategy for ' + stock_name + ':',
str(
round(
100 *
(1 - (results[policy.__name__]['cash'].values[-1] /
results[policy.__name__]['final_vals'].values[-1])),
1)) + '%')
print('\n')
# calculate returns
rets = {
policy: returns(results[policy.__name__]['final_vals'])
for policy in policies
}
# generate risk_free return for sharpe ratio - five-year treasury yield
rfs = returns(read_stock('^FVX'))
# find common indecies between stock tables and treasury yields
rfn = set(stock_table.index).intersection(set(rfs.index))
# now reindex
rfr = rfs.loc[rfn]
rfi = rfr.index
# generate baseline return for information ratio - s&p 500
bls = returns(read_stock('^GSPC')).values
# print summary stats for daily returns
for policy in policies:
nm = policy.__name__
# mean daily return
print('Mean daily return under', nm, 'for', stock_name + ':',
str(round(np.mean(rets[policy], axis=0), 5)))
# standard deviation of daily return
print('Standard deviation of daily return under', nm, 'for',
stock_name + ':', round(np.std(rets[policy], axis=0), 3))
# information ratio of daily return
checkhist(rets[policy].values, bls)
pr = np.mean(rets[policy].values)
br = np.mean(bls)
te = np.std(rets[policy].values - bls)
ir = round((pr - br) / (te) * np.sqrt(len(bls)), 2)
print('Information Ratio against S&P 500 under', nm, 'strategy for',
stock_name + ':', ir)
# sharpe ratio of daily return
dat = rets[policy].loc[
rfi].values # need to correct dates to line up with risk free return
checkhist(dat, rfr)
rp = np.mean(dat)
br = np.mean(rfr)
sd = np.std(rfr - dat)
sr = round((rp - br) / (sd) * np.sqrt(len(rfr)), 2)
print('Sharpe Ratio against five-year treasury yield under', nm,
'strategy for', stock_name + ':', sr)
print(
'Note: only used dates when five-year treasury yields were available in calculating RFR for Sharpe Ratio'
)
print('\n')
for policy1 in policies:
p1 = rets[policy1].loc[
rfi].values # filter to dates with five-year treasury yields available
n1 = policy1.__name__
# independent samples t-test vs. risk-free return
checkhist(p1, rfr)
t = ttest_ind(p1, rfr, equal_var=True)
gr = t[0] > 0
n2 = 'rfr'
p = round(t[1], 3) / 2 # make one-sided
if gr:
print('T-test for difference of mean returns in', n1, 'and', n2,
'finds', n1, '>', n2, 'with p-value', round(p, 3))
else:
print('T-test for difference of mean returns in', n2, 'and', n1,
'finds', n2, '>', n1, 'with p-value', round(p, 3))
# levene test vs. risk-free return
l = levene(rets[policy1].values, bls)
p = round(l[1], 3)
gr = np.std(rets[policy1].values) > np.std(bls)
n2 = 'bls'
if gr:
print('Levene test for difference of variances (volatility) in',
n1, 'and', n2, 'finds p-value of', round(p, 3), 'with', n1,
'showing more volatility')
else:
print('Levene test for difference of variances (volatility) in',
n1, 'and', n2, 'finds p-value of', round(p, 3), 'with', n2,
'showing more volatility')
print('\n')
for policy2 in policies:
if policy1 != policy2: #and hash(policy1) <= hash(policy2) - not necessary
p1 = rets[
policy1].values # no longer need to filter to dates with five-year treasury yields available
p2 = rets[policy2].values
checkhist(p1, p2)
n2 = policy2.__name__
# independent samples t-test
t = ttest_ind(p1, p2, equal_var=True)
gr = t[0] > 0
p = round(t[1], 3) / 2 # make one-sided
if gr:
print('T-test for difference of mean returns in', n1,
'and', n2, 'finds', n1, '>', n2, 'with p-value',
round(p, 3))
else:
print('T-test for difference of mean returns in', n2,
'and', n1, 'finds', n2, '>', n1, 'with p-value',
round(p, 3))
# levene test
l = levene(p1, p2)
p = round(l[1], 5)
gr = np.std(p1) > np.std(p2)
if gr:
print(
'Levene test for difference of variances (volatility) in',
n1, 'and', n2, 'finds p-value of', round(p, 3), 'with',
n1, 'showing more volatility')
else:
print(
'Levene test for difference of variances (volatility) in',
n1, 'and', n2, 'finds p-value of', round(p, 3), 'with',
n2, 'showing more volatility')
print('\n')
print('\n')
# TODO: add any additional desired visualizations
plt.show()
sdf = pd.DataFrame()
best_algo = "BL"
for e in error_metrics:
X = df.loc[df['algorithm'] == best_algo, e].to_numpy()
sdf = sdf.append(
{
'algorithm': best_algo,
'Error': np.mean(X),
'metric': e
},
ignore_index=True)
for algo in df['algorithm'].unique():
if algo in best_algo:
continue
Y = df.loc[df['algorithm'] == algo, e].to_numpy()
p_value = stats.ttest_ind(X, Y).pvalue
# print(p_value)
if p_value <= 0.05:
sdf = sdf.append(
{
'algorithm': algo,
'Error': np.mean(Y),
'metric': e
},
ignore_index=True)
else:
sdf = sdf.append(
{
'algorithm': algo,
'Error': np.mean(X),
'metric': e
def main(args):
# Loading data trought Interface
logger.info("Loading data with the Interface")
dat = wideToDesign(args.input,
args.design,
args.uniqueID,
group=args.group,
runOrder=args.order,
logger=logger)
# Treat everything as numeric
dat.wide = dat.wide.applymap(float)
# Cleaning from missing data
dat.dropMissing()
# SCENARIO 1: Unpaired t-test. In this case there can be as many groups as possible.
# Order variable is ignored and t-tests are performed pairwise for each pair of groups.
if args.pairing == "unpaired":
logger.info(
"Unpaired t-test will be performed for all groups pairwise.")
# Getting the uinique pairs and all pairwise prermutations
# son that we will feed them to pairwise unpaired t-tests.
group_values_series = dat.transpose()[dat.group].T.squeeze()
group_values_series_unique = group_values_series.unique()
number_of_unique_groups = group_values_series_unique.shape[0]
groups_pairwise = list(combinations(group_values_series_unique, 2))
number_of_groups_pairwise = len(groups_pairwise)
# Extracting data from the interface.
data_frame = dat.transpose()
# Extracting number of features. This will depend on whether the user has provided ordering variable or not.
# This variable is useless for unpared test. it just adds extra column to the data frame.
if args.order == False:
number_of_features = data_frame.shape[1] - 1
else:
number_of_features = data_frame.shape[1] - 2
# Saving treatment group name from the arguments.
# Computing overall summaries (mean and variance).
# This part just produces sumamry statistics for the output table.
# This has nothing to do with unpaired t-test. This is just summary for the table.
mean_value_all = [0] * number_of_features
variance_value_all = [0] * number_of_features
for j in range(0, number_of_features):
# Creating duplicate for manipulation.
data_frame_manipulate = data_frame
# Dropping columns that characterize group. Only feature columns will remain.
# We also trnaspose here so it will be easier to operate with.
# We should either drop 1 or 2 columns depending whether we fed the second one.
if args.order == False:
data_frame_manipulate_transpose = data_frame_manipulate.drop(
args.group, 1).transpose()
else:
data_frame_manipulate_transpose = data_frame_manipulate.drop(
[args.group, args.order], 1).transpose()
# Pulling indexes list from the current data frame.
indexes_list_complete = data_frame_manipulate_transpose.index.tolist(
)
# Computing dataset summaries.
mean_value_all[j] = np.mean(
data_frame_manipulate_transpose.loc[indexes_list_complete[j]])
variance_value_all[j] = np.var(
data_frame_manipulate_transpose.loc[indexes_list_complete[j]],
ddof=1)
# Creating the table and putting the results there.
summary_df = pd.DataFrame(data=mean_value_all,
columns=["GrandMean"],
index=indexes_list_complete)
summary_df['SampleVariance'] = variance_value_all
# Computing means for each group and outputting them.
# This part just produces summary statistics for the output table.
# This has nothing to do with unpaired t-test. This is just summary for the table.
for i in range(0, number_of_unique_groups):
# Extracting the pieces of the data frame that belong to the ith group.
data_frame_current_group = data_frame.loc[data_frame[
args.group].isin([group_values_series_unique[i]])]
# Dropping columns that characterize group. Only feature columns will remain.
# We also trnaspose here so it will be easier to operate with.
# We should either drop 1 or 2 columns depending whether we fed the second one.
if args.order == False:
data_frame_current_group = data_frame_current_group.drop(
args.group, 1).transpose()
else:
data_frame_current_group = data_frame_current_group.drop(
[args.group, args.order], 1).transpose()
# Pulling indexes list from the current group.
indexes_list = data_frame_current_group.index.tolist()
# Creating array of means for the current group that will be filled.
means_value = [0] * number_of_features
for j in range(0, number_of_features):
series_current = data_frame_current_group.loc[indexes_list[j]]
means_value[j] = series_current.mean()
# Adding current mean_value column to the data frame and assigning the name.
means_value_column_name_current = 'mean_treatment_' + group_values_series_unique[
i]
summary_df[means_value_column_name_current] = means_value
# Running pairwise unpaired (two-sample) t-test for all pairs of group levels that are saved in groups_pairwise.
for i in range(0, number_of_groups_pairwise):
# Extracting the pieces of the data frame that belong to groups saved in the i-th unique pair.
groups_subset = groups_pairwise[i]
data_frame_first_group = data_frame.loc[data_frame[
args.group].isin([groups_subset[0]])]
data_frame_second_group = data_frame.loc[data_frame[
args.group].isin([groups_subset[1]])]
# Dropping columns that characterize group. Only feature columns will remain.
# We also trnaspose here so it will be easier to operate with.
# We should either drop 1 or 2 columns depending whether we fed the second one.
if args.order == False:
data_frame_first_group = data_frame_first_group.drop(
args.group, 1).transpose()
data_frame_second_group = data_frame_second_group.drop(
args.group, 1).transpose()
else:
data_frame_first_group = data_frame_first_group.drop(
[args.group, args.order], 1).transpose()
data_frame_second_group = data_frame_second_group.drop(
[args.group, args.order], 1).transpose()
# Pulling indexes list from the first one (they are the same)
indexes_list = data_frame_first_group.index.tolist()
# Creating p_values, neg_log10_p_value, flag_values, difference_value lists filled wiht 0es.
p_value = [0] * number_of_features
t_value = [0] * number_of_features
neg_log10_p_value = [0] * number_of_features
flag_value_0p01 = [0] * number_of_features
flag_value_0p05 = [0] * number_of_features
flag_value_0p10 = [0] * number_of_features
difference_value = [0] * number_of_features
for j in range(0, number_of_features):
series_first = data_frame_first_group.loc[indexes_list[j]]
series_second = data_frame_second_group.loc[indexes_list[j]]
ttest_ind_args = [series_first, series_second]
p_value[j] = ttest_ind(*ttest_ind_args)[1]
t_value[j] = ttest_ind(*ttest_ind_args)[0]
# Possible alternative for two groups.
# p_value[j] = ttest_ind_args(series_first, series_second)[1]
neg_log10_p_value[j] = -np.log10(p_value[j])
difference_value[j] = series_first.mean() - series_second.mean(
)
if p_value[j] < 0.01: flag_value_0p01[j] = 1
if p_value[j] < 0.05: flag_value_0p05[j] = 1
if p_value[j] < 0.10: flag_value_0p10[j] = 1
# Creating column names for the data frame.
p_value_column_name_current = 'prob_greater_than_t_for_diff_' + groups_subset[
0] + '_' + groups_subset[1]
t_value_column_name_current = 't_value_for_diff_' + groups_subset[
0] + '_' + groups_subset[1]
neg_log10_p_value_column_name_current = 'neg_log10_p_value_' + groups_subset[
0] + '_' + groups_subset[1]
difference_value_column_name_current = 'diff_of_' + groups_subset[
0] + '_' + groups_subset[1]
flag_value_column_name_current_0p01 = 'flag_significant_0p01_on_' + groups_subset[
0] + '_' + groups_subset[1]
flag_value_column_name_current_0p05 = 'flag_significant_0p05_on_' + groups_subset[
0] + '_' + groups_subset[1]
flag_value_column_name_current_0p10 = 'flag_significant_0p10_on_' + groups_subset[
0] + '_' + groups_subset[1]
# Adding current p_value and flag_value column to the data frame and assigning the name.
# If the data frame has not been created yet we create it on the fly. i.e. if i == 0 create it.
if i == 0:
flag_df = pd.DataFrame(
data=flag_value_0p01,
columns=[flag_value_column_name_current_0p01],
index=indexes_list)
else:
flag_df[flag_value_column_name_current_0p01] = flag_value_0p01
# At this point data frame exists so only columns are added to the existing data frame.
summary_df[p_value_column_name_current] = p_value
summary_df[t_value_column_name_current] = t_value
summary_df[
neg_log10_p_value_column_name_current] = neg_log10_p_value
summary_df[difference_value_column_name_current] = difference_value
flag_df[flag_value_column_name_current_0p05] = flag_value_0p05
flag_df[flag_value_column_name_current_0p10] = flag_value_0p10
# SCENARIO 2: Paired t-test. In this case there should be EXACTLY TWO groups.
# Each sample in one group should have exacty one matching pair in the other group.
# The matching is controlled by args.order variable.
if args.pairing == "paired":
logger.info(
"Paired test will be performed for two groups pairwise based on pairing variable: {0}."
.format(args.order))
# Getting the number of unique groups. If it is bigger than 2 return the warning and exit.
group_values_series = dat.transpose()[dat.group].T.squeeze()
group_values_series_unique = group_values_series.unique()
number_of_unique_groups = group_values_series_unique.shape[0]
if number_of_unique_groups != 2:
logger.warning(
u"The number of unique groups is {0} and not 2 as expected. The paired t-test cannot be performed."
.format(number_of_unique_groups))
exit()
# This piece of code will be executed only if the number_of_unique_groups is exactly 2 so the group check is passed.
# Creating pairwise combination of our two groups that we will use in the future.
groups_pairwise = list(combinations(group_values_series_unique, 2))
number_of_groups_pairwise = len(groups_pairwise)
# Extracting data from the interface.
data_frame = dat.transpose()
# Extracting number of features. This will depend on whether the user has provided ordering variable or not.
# Checking that the requred pairing variable has been provided.
if args.order == False:
logger.info(
"The required t-test pairing variable has not been provided: The paired t-test cannot be performed."
)
exit()
# This piece of code will be executed only if the args.order has been provided and the check is passed.
# Defining the number of features. It should be the dimension of the data frame minus 2 columns that stand for arg.group and args.order
number_of_features = data_frame.shape[1] - 2
# At this point is is confirmed that there are only 2 groups and that pairing variable args.order has been provided.
# Now we need to check that pairing is correct i.e. that each pairID corresponds to only two samples from different groups.
# Getting the unique pairs and deleting those theat have more or less than three.
pairid_values_series = dat.transpose()[dat.runOrder].T.squeeze()
pairid_values_series_unique = pairid_values_series.unique()
number_of_unique_pairid = pairid_values_series_unique.shape[0]
# Extracting data from the interface.
data_frame = dat.transpose()
# Extracting the number of samples in the final frame.
number_of_samples = data_frame.shape[0]
# Performing the cleaning of the original data. We are removing samples that are not paired and not belonging to the two groups.
# If the dataset has 1 or 3 or more matches for a pairid those samples are removed with a warning.
# If pairdid corresponds to exactly two samples (which is correct) but groupid-s are NOT different those values will be also removed.
for i in range(0, number_of_unique_pairid):
# Extracting the pieces of the data frame that belong to ith unique pairid.
data_frame_current_pairid = data_frame.loc[data_frame[
args.order].isin([pairid_values_series_unique[i]])]
# We transpose here so it will be easier to operate with.
data_frame_current_pairid = data_frame_current_pairid.transpose()
sample_names_current_pairid = list(
data_frame_current_pairid.columns.values)
if data_frame_current_pairid.shape[1] != 2:
# Pulling indexes list from the current data frame.
logger.warning(
u"Number of samples for the pairID: {0} is equal to {1} and NOT equal to 2. Sample(s) {2} will be removed from further analysis."
.format(pairid_values_series_unique[i],
data_frame_current_pairid.shape[1],
sample_names_current_pairid))
# Getting indexes we are trying to delete.
boolean_indexes_to_delete = data_frame.index.isin(
sample_names_current_pairid)
# Deleting the indexes and in the for loop going to next iteration.
data_frame.drop(data_frame.index[boolean_indexes_to_delete],
inplace=True)
# This piece is executed if the numbe is correct i.e. data_frame_current_group.shape[1] == 2:
# Here we are checking if the groupID-s for the given pair are indeed different.
elif data_frame_current_pairid.transpose()[args.group][
0] == data_frame_current_pairid.transpose()[args.group][1]:
logger.warning(
u"Samples in pairID {0} have groupIDs: {1} and {2}. Should be different! Sample(s) {3} will be removed from further analysis."
.format(
pairid_values_series_unique[i],
data_frame_current_pairid.transpose()[args.group][1],
data_frame_current_pairid.transpose()[args.group][0],
sample_names_current_pairid))
# Getting indexes we are trying to delete.
boolean_indexes_to_delete = data_frame.index.isin(
sample_names_current_pairid)
# Deleting the indexes.
data_frame.drop(data_frame.index[boolean_indexes_to_delete],
inplace=True)
# Checking if the data frame became empty after cleaning.
if data_frame.shape[0] == 0:
logger.warning(
u"Number of paired samples in the final dataset is exactly 0! Please check the desing file for accuracy! Exiting the program."
)
exit()
# Computing overall summaries (mean and variance).
# This part just produces sumamry statistics for the output table.
# This has nothing to do with paired t-test. This is just summary for the table.
mean_value_all = [0] * number_of_features
variance_value_all = [0] * number_of_features
for j in range(0, number_of_features):
# Creating duplicate for manipulation.
data_frame_manipulate = data_frame
# Dropping columns that characterize group. Only feature columns will remain.
# We also trnaspose here so it will be easier to operate with.
data_frame_manipulate_transpose = data_frame_manipulate.drop(
[args.group, args.order], 1).transpose()
# Pulling indexes list from the current data frame.
indexes_list_complete = data_frame_manipulate_transpose.index.tolist(
)
# Computing dataset summaries.
mean_value_all[j] = np.mean(
data_frame_manipulate_transpose.loc[indexes_list_complete[j]])
variance_value_all[j] = np.var(
data_frame_manipulate_transpose.loc[indexes_list_complete[j]],
ddof=1)
# Creating the table and putting the results there.
summary_df = pd.DataFrame(data=mean_value_all,
columns=["GrandMean"],
index=indexes_list_complete)
summary_df['SampleVariance'] = variance_value_all
# Computing means for each group and outputting them.
# This part just produces summary statistics for the output table.
# This has nothing to do with paired t-test. This is just summary for the table.
for i in range(0, number_of_unique_groups):
# Extracting the pieces of the data frame that belong to the ith group.
data_frame_current_group = data_frame.loc[data_frame[
args.group].isin([group_values_series_unique[i]])]
# Dropping columns that characterize group. Only feature columns will remain.
data_frame_current_group = data_frame_current_group.drop(
[args.group, args.order], 1).transpose()
# Pulling indexes list from the current group.
indexes_list = data_frame_current_group.index.tolist()
# Creating array of means for the current group that will be filled.
means_value = [0] * number_of_features
for j in range(0, number_of_features):
series_current = data_frame_current_group.loc[indexes_list[j]]
means_value[j] = series_current.mean()
# Adding current mean_value column to the data frame and assigning the name.
means_value_column_name_current = 'mean_treatment_' + group_values_series_unique[
i]
summary_df[means_value_column_name_current] = means_value
# Performing paired t-test for the two groups and saving results.
# Creating p_values and flag_values empty list of length number_of_features.
# This will be used for the two groups in paired t-test.
p_value = [0] * number_of_features
t_value = [0] * number_of_features
flag_value_0p01 = [0] * number_of_features
flag_value_0p05 = [0] * number_of_features
flag_value_0p10 = [0] * number_of_features
neg_log10_p_value = [0] * number_of_features
difference_value = [0] * number_of_features
# Performing paired t-test for each pair of features.
for j in range(0, number_of_features):
# Extracting the pieces of the data frame that belong to 1st group.
data_frame_first_group = data_frame.loc[data_frame[
args.group].isin([group_values_series_unique[0]])]
data_frame_second_group = data_frame.loc[data_frame[
args.group].isin([group_values_series_unique[1]])]
# Sorting data frame by args.group index
# This will ensure datasets are aligned by pair when fed to the t-test.
data_frame_first_group = data_frame_first_group.sort(args.order)
data_frame_second_group = data_frame_second_group.sort(args.order)
# Sorting data frame by args.group index
data_frame_first_group = data_frame_first_group.drop(
[args.group, args.order], 1).transpose()
data_frame_second_group = data_frame_second_group.drop(
[args.group, args.order], 1).transpose()
# Pulling list of indexes. This is the same list for the first and for the second.
indexes_list = data_frame_first_group.index.tolist()
# Pullinng the samples out
series_first = data_frame_first_group.loc[indexes_list[j]]
series_second = data_frame_second_group.loc[indexes_list[j]]
# Running t-test for the two given samples
paired_ttest_args = [series_first, series_second]
p_value[j] = ttest_rel(*paired_ttest_args)[1]
t_value[j] = ttest_rel(*paired_ttest_args)[0]
neg_log10_p_value[j] = -np.log10(p_value[j])
difference_value[j] = series_first.mean() - series_second.mean()
if p_value[j] < 0.01: flag_value_0p01[j] = 1
if p_value[j] < 0.05: flag_value_0p05[j] = 1
if p_value[j] < 0.10: flag_value_0p10[j] = 1
# The loop over features has to be finished by now. Converting them into the data frame.
# Creating column names for the data frame.
p_value_column_name_current = 'prob_greater_than_t_for_diff_' + group_values_series_unique[
0] + '_' + group_values_series_unique[1]
t_value_column_name_current = 't_value_for_diff_' + group_values_series_unique[
0] + '_' + group_values_series_unique[1]
neg_log10_p_value_column_name_current = 'neg_log10_p_value_' + group_values_series_unique[
0] + '_' + group_values_series_unique[1]
difference_value_column_name_current = 'diff_of_' + group_values_series_unique[
0] + '_' + group_values_series_unique[1]
flag_value_column_name_current_0p01 = 'flag_value_diff_signif_' + group_values_series_unique[
0] + '_' + group_values_series_unique[1] + '_0p01'
flag_value_column_name_current_0p05 = 'flag_value_diff_signif_' + group_values_series_unique[
0] + '_' + group_values_series_unique[1] + '_0p05'
flag_value_column_name_current_0p10 = 'flag_value_diff_signif_' + group_values_series_unique[
0] + '_' + group_values_series_unique[1] + '_0p10'
summary_df[t_value_column_name_current] = t_value
summary_df[p_value_column_name_current] = p_value
summary_df[neg_log10_p_value_column_name_current] = neg_log10_p_value
summary_df[difference_value_column_name_current] = difference_value
flag_df = pd.DataFrame(data=flag_value_0p01,
columns=[flag_value_column_name_current_0p01],
index=indexes_list)
flag_df[flag_value_column_name_current_0p05] = flag_value_0p05
flag_df[flag_value_column_name_current_0p10] = flag_value_0p10
# Roundign the results up to 4 precision digits.
summary_df = summary_df.apply(lambda x: x.round(4))
# Adding name for the unique ID column that was there oroginally.
summary_df.index.name = args.uniqueID
flag_df.index.name = args.uniqueID
# Save summary_df to the ouptut
summary_df.to_csv(args.summaries, sep="\t")
# Save flag_df to the output
flag_df.to_csv(args.flags, sep="\t")
# Generating Indexing for volcano plots.
# Getting data for lpvals
lpvals = {col.split("_value_")[-1]:summary_df[col] for col in summary_df.columns.tolist() \
if col.startswith("neg_log10_p_value")}
# Gettign data for diffs
difs = {col.split("_of_")[-1]:summary_df[col] for col in summary_df.columns.tolist() \
if col.startswith("diff_of_")}
# The cutoff value for significance.
cutoff = 2
# Making volcano plots
with PdfPages(args.volcano) as pdf:
for i in range(0, number_of_groups_pairwise):
# Set Up Figure
volcanoPlot = figureHandler(proj="2d")
groups_subset = groups_pairwise[i]
current_key = groups_subset[0] + '_' + groups_subset[1]
# Plot all results
scatter.scatter2D(x=list(difs[current_key]),
y=list(lpvals[current_key]),
colorList=list('b'),
ax=volcanoPlot.ax[0])
# Color results beyond threshold red
cutLpvals = lpvals[current_key][lpvals[current_key] > cutoff]
if not cutLpvals.empty:
cutDiff = difs[current_key][cutLpvals.index]
scatter.scatter2D(x=list(cutDiff),
y=list(cutLpvals),
colorList=list('r'),
ax=volcanoPlot.ax[0])
# Drawing cutoffs
lines.drawCutoffHoriz(y=cutoff, ax=volcanoPlot.ax[0])
# Format axis (volcanoPlot)
volcanoPlot.formatAxis(
axTitle=current_key,
grid=False,
yTitle="-log10(p-value) for Diff of treatment means for {0}".
format(current_key),
xTitle="Difference of treatment means for {0}".format(
current_key))
# Add figure to PDF
volcanoPlot.addToPdf(pdfPages=pdf)
# Informing that the volcano plots are done
logger.info(u"Pairwise volcano plots have been created.")
# Ending script
logger.info(u"Finishing t-test run.")
sex = list(merged_df.gender.values)
site = list(merged_df.site.values)
age = list(merged_df.age.values)
train_index, test_index = None, None
while flag_selection:
splits = StratifiedShuffleSplit(n_splits=1, test_size=args.test_size)
for train_index, test_index in splits.split(np.zeros(len(site)), site):
age_test = [float(age[idx]) for idx in test_index]
age_train = [float(age[idx]) for idx in train_index]
sex_test = [sex_dict[sex[idx]] for idx in test_index]
sex_train = [sex_dict[sex[idx]] for idx in train_index]
t_age, p_age = ttest_ind(age_test, age_train)
T_sex = chi2(sex_test, sex_train)
print(p_age, T_sex)
if p_age > args.p_val_threshold and T_sex < args.t_val_threshold:
flag_selection = False
test_df = merged_df.iloc[test_index]
train_df = merged_df.iloc[train_index]
train_df.to_csv(train_path, sep='\t', index=False)
test_df.to_csv(test_path, sep='\t', index=False)
print("%20s %20s %20s %20s %20s %20s %20s" % (
begin_date, round(init_svi, 4), round(init_bs, 4),
round(portfolio_net_svi / init_svi, 4), round(portfolio_net_bs / init_bs, 4),
round(tradedamt_svi / holdamt_svi, 4), round(tradedamt_bs / holdamt_bs, 4)))
print('=' * 200)
print("%20s %20s %20s %20s %20s %20s %20s %20s" % (
"eval date", "spot", "delta", 'price_svi', 'price_bs', 'portfolio_svi', 'portfolio_bs',
'transaction'))
print('svi_pnl', sum(svi_pnl) / len(svi_pnl))
print('bs_pnl', sum(bs_pnl) / len(bs_pnl))
results = {}
results.update({'date': dates})
results.update({'pnl svi': svi_pnl})
results.update({'pnl bs': bs_pnl})
results.update({'option init svi': option_init_svi})
results.update({'option init bs': option_init_bs})
results.update({'transaction svi': transaction_svi})
results.update({'transaction bs': transaction_bs})
results.update({'holdings svi': holdings_svi})
results.update({'holdings bs': holdings_bs})
df = pd.DataFrame(data=results)
# print(df)
df.to_csv(os.path.abspath('..') + '/results4/dh_MA_'+barrier_type+'_r='
+str(rebalancerate) + '_b=' + str(barrier_pct) + 'f2.csv')
t,p = stats.ttest_ind(svi_pnl,bs_pnl)
t1,p1 = stats.wilcoxon(svi_pnl,bs_pnl)
print(barrier_type, ' ',barrier_pct)
print('t : ',t,p)
print('wilcoxom : ',t1,p1)
def generate_tstats_classes(df, dest_dir, params):
"""Computes t-test for each class.
This function computes a t-test for each class in the dataset.
The t-test is computed by comparing class level metrics for
a set of sparse model checkpoints to non-sparse model
checkppints.
Args:
df: input dataframe with class level metrics.
dest_dir: pathway to output directory.
params: dataset specific params.
"""
human_label_lookup = class_level_metrics.HumanLabelLookup()
label_dict = human_label_lookup.create_library()
class_names = list(label_dict.values())
df.drop(columns='Unnamed: 0')
df.reset_index(inplace=True, drop=True)
df['id'] = df.index
df_ = pd.wide_to_long(df,
stubnames=['precision', 'recall'],
i='id',
j='class',
sep='/',
suffix=r'\w+').reset_index()
data = pd.DataFrame([])
num_classes = params['num_classes']
mean_accuracy_dict = params['accuracy']
long_df_all = df_
for i in range(num_classes):
# adding label id ensures unique naming of classes
c = class_names[i] + '_' + str(i)
for p in [0.1, 0.3, 0.5, 0.7, 0.9]:
variant_mean_recall = long_df_all[(
(long_df_all['fraction_pruned'] == p) &
(long_df_all['class'] == c))]['recall'].mean()
baseline_mean_recall = long_df_all[(
(long_df_all['fraction_pruned'] == 0.0) &
(long_df_all['class'] == c))]['recall'].mean()
# normalize recall by model accuracy
baseline_set = long_df_all[(
(long_df_all['fraction_pruned'] == 0.0) &
(long_df_all['class']
== c))]['recall'] - mean_accuracy_dict[0.0]
variant_set = long_df_all[(
(long_df_all['fraction_pruned'] == p) &
(long_df_all['class'] == c))]['recall'] - mean_accuracy_dict[p]
t_stat = ttest_ind(baseline_set, variant_set, equal_var=False)
data = data.append(pd.DataFrame(
{
'class': c,
'pruning_fraction': p,
'baseline_mean_recall': baseline_mean_recall,
'variant_mean_recall': variant_mean_recall,
'pvalue_recall_norm': t_stat[1],
'statistic_recall_norm': t_stat[0],
},
index=[0]),
ignore_index=True)
time_ = str(time.time())
output_file = 'recall_t_statistic'
file_name = '_' + time_ + '_' + output_file + '.csv'
file_path = os.path.join(dest_dir, file_name)
with tf.gfile.Open(file_path, 'w') as f:
data.to_csv(f)
def two_cal(self, x, norm_res, homo_res, skews, paired=False):
'''Calculate and return two samples comparison tests results.
Parameters:
----------
x : list of numpy.ndarray
Variables to test on
norm_res : dict
Normality test results
homo_res : dict
Homogeneity test rerults
skews : list
Skewness values of all x variables
paired : bool
False for two independent variables. True Otherwise
Returns:
-------
res : dict
'Statistic': statistic value calculated by the test
'Pvalue': p-value calculated by the test
'Test': name of the test used
'Result': True if failed to reject null hypothesis, False otherwise
Notes:
-----
None'''
res = {}
if sum(norm_res.values()) == len(x) and all(abs(np.array(skews)) < .5):
# If all normal
if paired: # Paired samples
res['Statistic'] = ss.ttest_rel(x[0], x[1])[0]
res['Pvalue'] = ss.ttest_rel(x[0], x[1])[1]
res['Test'] = 'T-test with paired samples'
else: # Independent samples
if homo_res: # Variances equal
res['Statistic'] = ss.ttest_ind(x[0], x[1])[0]
res['Pvalue'] = ss.ttest_ind(x[0], x[1])[1]
res['Test'] = 'T-test with independent samples'
else: # Variances unequal
res['Statistic'] = ss.ttest_ind(x[0],
x[1],
equal_var=False)[0]
res['Pvalue'] = ss.ttest_ind(x[0], x[1],
equal_var=False)[1]
res['Test'] = 'Welch\'s T-test'
else: # If not all normal, use unparametric tests.
if paired: # Paired samples
res['Statistic'] = ss.wilcoxon(x[0].reshape(-1),
x[1].reshape(-1))[0]
res['Pvalue'] = ss.wilcoxon(x[0].reshape(-1),
x[1].reshape(-1))[1]
res['Test'] = 'Wilcoxon signed-rank Test with Paired Samples'
else: # Independent samples
if all([len(x[0]), len(x[1])]) >= 20: # Sample size > 20
res['Statistic'] = ss.mannwhitneyu(x[0], x[1])[0]
res['Pvalue'] = ss.mannwhitneyu(x[0], x[1])[1]
res['Test'] = 'Mann-Whitney U Test with Indepedent Samples'
else: # Sample size < 20
res['Statistic'] = ss.ranksums(x[0], x[1])[0]
res['Pvalue'] = ss.ranksums(x[0], x[1])[1]
res['Test'] = 'Wilcoxon rank-sum Test with Indepedent Samples'
# Get the results based on fixed significance level
if res['Pvalue'] >= .05:
res['Result'] = True
else:
res['Result'] = False
return res
mean_acc = cat2.loc[cat2[('platform', '')] == 'All',
('{}{}'.format(part, var), 'mean')].values[0]
std_rej = cat1.loc[cat1[('platform', '')] == 'All',
('{}{}'.format(part, var), 'std')].values[0]
std_acc = cat2.loc[cat2[('platform', '')] == 'All',
('{}{}'.format(part, var), 'std')].values[0]
count_rej = cat1.loc[cat1[('platform', '')] == 'All',
('{}{}'.format(part, var), 'count')].values[0]
count_acc = cat2.loc[cat2[('platform', '')] == 'All',
('{}{}'.format(part, var), 'count')].values[0]
grp1 = data.loc[data['accepted'] == 0,
'{}{}'.format(part, var)].dropna()
grp2 = data.loc[data['accepted'] == 1,
'{}{}'.format(part, var)].dropna()
ttest = stats.ttest_ind(grp1, grp2, equal_var=False)
pooled_std = np.sqrt(
((count_rej - 1) * std_rej**2 +
(count_acc - 1) * std_acc**2) / (count_rej + count_acc))
cohens_d = np.abs((mean_rej - mean_acc) / pooled_std)
results.loc[len(results)] = [
part, var, ttest[1], mean_acc - mean_rej, mean_rej, std_rej,
count_rej, mean_acc, std_acc, count_acc, cohens_d, has_edu
]
bar_i += 1
fig, ax = plt.subplots(figsize=(width, height))
nb_bars = len(ind)
acc = np.arange(0, nb_bars, 2)
rej = np.arange(1, nb_bars, 2)
early = df[df['assignment1_submission'] <= '2015-12-31']
late = df[df['assignment1_submission'] > '2015-12-31']
print(early)
# In[ ]:
early.mean()
# In[ ]:
late.mean()
# In[11]:
from scipy import stats
get_ipython().magic('pinfo stats.ttest_ind')
# In[14]:
stats.ttest_ind(early['assignment1_grade'], late['assignment1_grade'])
# In[ ]:
stats.ttest_ind(early['assignment2_grade'], late['assignment2_grade'])
# In[ ]:
stats.ttest_ind(early['assignment3_grade'], late['assignment3_grade'])
if "_" in id:
id = id.split("_")[1]
id = id[4:]
return id
correlationDict = {}
for i in correlationfh:
intA,intB,corr = i.split()
correlationDict[intA.lower()+"_"+intB.lower()]=float(corr)
inter_corr = []
count = 0
invalid = 0
for i in interologyfh:
intA,intB = i.split()
count += 1
try:
corr = correlationDict[intB.lower() + "_" + intA.lower()]
inter_corr.append(corr)
except KeyError:
try:
corr = correlationDict[intA.lower() + "_" + intB.lower()]
inter_corr.append(corr)
except KeyError:
print(intA.lower(),intB.lower())
invalid+=1
print(len(correlationDict.items()))
random_corr = random.sample(list(correlationDict.values()),1000)
print("total correlation from interologs= {}\ntotalcorrelation from random pairs= {}".format(sum(inter_corr)/count,sum(random_corr)/len(random_corr)))
print("p value between two correlations: {}".format(ttest_ind(inter_corr,random_corr)))
print("number of invalid keys: {} out of {} = {}".format(invalid,count,invalid/count))
def calculate_ttest(hyperpartisan_valid_predictions, joint_valid_predictions):
_, p_value = ttest_ind(hyperpartisan_valid_predictions,
joint_valid_predictions)
return p_value
feature_rep_train, feature_rep_test = datasets[0], datasets[1]
fingerprint_rep_train, fingerprint_rep_test = datasets[2], datasets[3]
#image_rep_train, image_rep_test = datasets[4], datasets[5]
######################################################################################
# LogisticRegression with MolecularDescriptors
X = feature_rep_train.iloc[:, 2:].values
Y = feature_rep_train.iloc[:, 1].values
model_scores, null_scores = assess_model(X, Y, [datasets[0], datasets[1]])
print("Null Model: ", np.mean(null_scores), np.std(null_scores))
print("Logistc Model with Molecular Descriptors performance: ",
np.mean(model_scores), np.std(model_scores))
t_stat, p_value = stats.ttest_ind(model_scores, null_scores)
print(p_value)
X_train, Y_train = X[:750, :], Y[:750]
X_test, Y_test = X[:750, :], Y[:750]
standard_scaler = StandardScaler()
X_train = standard_scaler.fit_transform(X_train)
X_test = standard_scaler.transform(X_test)
plot_learning_curve(LogisticRegression(),
"LogisticRegression_MolecularDescriptors",
X_train,
Y_train,
cv=10)
model = LogisticRegression()
def main():
reddit_counts = sys.argv[1]
station_fh = gzip.open(reddit_counts, 'rt', encoding='utf-8')
stations = pd.read_json(station_fh, lines=True)
stations = stations[stations.subreddit == "canada"]
stations = stations[(stations['date'].dt.year == 2012) |
(stations['date'].dt.year == 2013)].reset_index()
del stations["index"]
stations['weekday'] = stations['date'].dt.dayofweek
weekday = stations[(stations['weekday'] == 5) |
(stations['weekday'] == 6)].reset_index()
weekend = stations[(stations['weekday'] != 5)
& (stations['weekday'] != 6)].reset_index()
del weekday["index"]
del weekend["index"]
initial_ttest_p = ttest_ind(weekday['comment_count'],
weekend['comment_count'])
initial_weekday_normality_p = stats.normaltest(weekday['comment_count'])
initial_weekend_normality_p = stats.normaltest(weekend['comment_count'])
initial_levene_p = stats.levene(weekday['comment_count'],
weekend['comment_count'])
# Fix1
# sqrt
weekday_sqrt = np.sqrt(weekday["comment_count"])
weekend_sqrt = np.sqrt(weekend["comment_count"])
# print(weekday_sqrt)
transformed_weekday_normality_p = stats.normaltest(weekday_sqrt)
transformed_weekend_normality_p = stats.normaltest(weekend_sqrt)
transformed_levene_p = stats.levene(weekday_sqrt, weekend_sqrt)
# Fix2
# Logic copy from Prof Greg Baker
def week(dt):
isocal = dt.isocalendar()
return '%i-%i' % (isocal[0], isocal[1])
weekday_number = weekday.date.apply(week)
weekend_number = weekend.date.apply(week)
weekday["number"] = weekday_number
weekend["number"] = weekend_number
grouped_weekday = weekday.groupby(['number'])
weekly_weekday = grouped_weekday.aggregate('sum')
grouped_weekend = weekend.groupby(['number'])
weekly_weekend = grouped_weekend.aggregate('sum')
weekly_weekday_normality_p = stats.normaltest(
weekly_weekday['comment_count'])
weekly_weekend_normality_p = stats.normaltest(
weekly_weekend['comment_count'])
weekly_levene_p = stats.levene(weekly_weekday['comment_count'],
weekly_weekend['comment_count'])
weekly_ttest_p = ttest_ind(weekly_weekday['comment_count'],
weekly_weekend['comment_count'])
# Fix3
utest_p = mannwhitneyu(weekday['comment_count'], weekend['comment_count'])
print(
OUTPUT_TEMPLATE.format(
initial_ttest_p=initial_ttest_p.pvalue,
initial_weekday_normality_p=initial_weekday_normality_p.pvalue,
initial_weekend_normality_p=initial_weekend_normality_p.pvalue,
initial_levene_p=initial_levene_p.pvalue,
transformed_weekday_normality_p=transformed_weekday_normality_p.
pvalue,
transformed_weekend_normality_p=transformed_weekend_normality_p.
pvalue,
transformed_levene_p=transformed_levene_p.pvalue,
weekly_weekday_normality_p=weekly_weekday_normality_p.pvalue,
weekly_weekend_normality_p=weekly_weekend_normality_p.pvalue,
weekly_levene_p=weekly_levene_p.pvalue,
weekly_ttest_p=weekly_ttest_p.pvalue,
utest_p=utest_p.pvalue,
))
def main():
parser = argparse.ArgumentParser(description='read result from csv')
parser.add_argument('file_name_0',
metavar='file_name_0',
type=str,
help='input file name')
parser.add_argument('file_name_1',
metavar='file_name_1',
type=str,
help='input file name')
parser.add_argument('file_name_2',
metavar='file_name_2',
type=str,
help='input file name')
args = parser.parse_args()
result0 = np.loadtxt(args.file_name_0, delimiter=',')
result1 = np.loadtxt(args.file_name_1, delimiter=',')
result2 = np.loadtxt(args.file_name_2, delimiter=',')
plt.rcParams['font.family'] = 'Times new Roman'
method_name1 = "S. L. P."
method_name2 = "CADRL"
fig = plt.figure()
ax0 = fig.add_subplot(2, 2, 1)
ax1 = fig.add_subplot(2, 2, 2)
ax2 = fig.add_subplot(2, 2, 3)
ax3 = fig.add_subplot(2, 2, 4)
bp0 = ax0.boxplot((result0[:, 0], result1[:, 0], result2[:, 0]),
whis="range",
showmeans=True,
meanline=True)
ax0.set_xticklabels(['Our method', method_name1, method_name2])
ax0.set_title('(a) Travel distance[m]', y=-0.35, fontsize=10)
ax0.set_ylim(0, 50)
ax0.set_yticks(np.arange(0, ax0.get_ylim()[1] + 10, 10))
ax0.grid()
print("travel distance")
print("P. M. mean: " + str(result0[:, 0].mean()) + "[m]")
print("P. M. mean: " + str(result0[result0[:, 2] == 0, 0].mean()) + "[m]")
print("P. M. median: " + str(np.median(result0[:, 0])) + "[m]")
print("P. M. stddev: " + str(result0[:, 0].std()))
print(method_name1 + " mean: " + str(result1[:, 0].mean()) + "[m]")
print(method_name1 + " mean: " +
str(result1[result1[:, 2] == 0, 0].mean()) + "[m]")
print(method_name1 + " median: " + str(np.median(result1[:, 0])) + "[m]")
print(method_name1 + " stddev: " + str(result1[:, 0].std()))
print(stats.ttest_ind(result0[:, 0], result1[:, 0], equal_var=False))
print(
stats.ttest_ind(result0[result0[:, 2] == 0, 0],
result1[result1[:, 2] == 0, 0],
equal_var=False))
print(method_name2 + " mean: " + str(result2[:, 0].mean()) + "[m]")
print(method_name2 + " mean: " +
str(result2[result2[:, 2] == 0, 0].mean()) + "[m]")
print(method_name2 + " median: " + str(np.median(result2[:, 0])) + "[m]")
print(method_name2 + " stddev: " + str(result2[:, 0].std()))
print(stats.ttest_ind(result0[:, 0], result2[:, 0], equal_var=False))
print(
stats.ttest_ind(result0[result0[:, 2] == 0, 0],
result2[result2[:, 2] == 0, 0],
equal_var=False))
bp1 = ax1.boxplot((result0[:, 1], result1[:, 1], result2[:, 1]),
whis="range",
showmeans=True,
meanline=True)
ax1.set_xticklabels(['Our method', method_name1, method_name2])
ax1.set_title('(b) Travel time[s]', y=-0.35, fontsize=10)
ax1.set_ylim(0, 50)
ax1.set_yticks(np.arange(0, ax1.get_ylim()[1] + 10, 10))
ax1.grid()
print("travel time")
print("P. M. mean: " + str(result0[:, 1].mean()) + "[s]")
print("P. M. mean: " + str(result0[result0[:, 2] == 0, 1].mean()) + "[s]")
print("P. M. median: " + str(np.median(result0[:, 1])) + "[s]")
print("P. M. stddev: " + str(result0[:, 1].std()))
print(method_name1 + " mean: " + str(result1[:, 1].mean()) + "[s]")
print(method_name1 + " mean: " +
str(result1[result1[:, 2] == 0, 1].mean()) + "[s]")
print(method_name1 + " median: " + str(np.median(result1[:, 1])) + "[s]")
print(method_name1 + " stddev: " + str(result1[:, 1].std()))
print(stats.ttest_ind(result0[:, 1], result1[:, 1], equal_var=False))
print(
stats.ttest_ind(result0[result0[:, 2] == 0, 1],
result1[result1[:, 2] == 0, 1],
equal_var=False))
print(method_name2 + " mean: " + str(result2[:, 1].mean()) + "[s]")
print(method_name2 + " mean: " +
str(result2[result2[:, 2] == 0, 1].mean()) + "[s]")
print(method_name2 + " median: " + str(np.median(result2[:, 1])) + "[s]")
print(method_name2 + " stddev: " + str(result2[:, 1].std()))
print(stats.ttest_ind(result0[:, 1], result2[:, 1], equal_var=False))
print(
stats.ttest_ind(result0[result0[:, 2] == 0, 1],
result2[result2[:, 2] == 0, 1],
equal_var=False))
bp2 = ax2.boxplot((result0[:, 2], result1[:, 2], result2[:, 2]),
whis="range",
showmeans=True,
meanline=True)
ax2.set_xticklabels(['Our method', method_name1, method_name2])
ax2.set_title('(c) Number of collisions', y=-0.35, fontsize=10)
ax2.set_ylim(0, 5)
ax2.set_yticks(np.arange(0, ax2.get_ylim()[1] + 1, 1))
ax2.grid()
print("collision count")
print("P. M. mean: " + str(result0[:, 2].mean()))
print("P. M. median: " + str(np.median(result0[:, 2])))
print("P. M. stddev: " + str(result0[:, 2].std()))
print("P. M. c. rate: " + str(
np.where(result0[:, 2] > 0)[0].shape[0] /
float(result0[:, 2].shape[0])))
print(method_name1 + " mean: " + str(result1[:, 2].mean()))
print(method_name1 + " median: " + str(np.median(result1[:, 2])))
print(method_name1 + " stddev: " + str(result1[:, 2].std()))
print(method_name1 + " c. rate: " + str(
np.where(result1[:, 2] > 0)[0].shape[0] /
float(result1[:, 2].shape[0])))
print(stats.ttest_ind(result0[:, 2], result1[:, 2], equal_var=False))
print(method_name2 + " mean: " + str(result2[:, 2].mean()))
print(method_name2 + " median: " + str(np.median(result2[:, 2])))
print(method_name2 + " stddev: " + str(result2[:, 2].std()))
print(method_name2 + " c. rate: " + str(
np.where(result2[:, 2] > 0)[0].shape[0] /
float(result2[:, 2].shape[0])))
print(stats.ttest_ind(result0[:, 2], result2[:, 2], equal_var=False))
bp3 = ax3.boxplot((result0[:, 3], result1[:, 3], result2[:, 3]),
whis="range",
showmeans=True,
meanline=True)
ax3.set_xticklabels(['Our method', method_name1, method_name2])
ax3.set_title('(d) Minimum distance[m]', y=-0.35, fontsize=10)
ax3.set_ylim(0, 1.5)
ax3.set_yticks(np.arange(0, ax3.get_ylim()[1] + 0.5, 0.5))
ax3.axhline(0.6, c="r")
ax3.text(3.6, 0.6, "$R_{col}$", size=10, color="red")
ax3.grid()
print("min distance")
print("P. M. mean: " + str(result0[:, 3].mean()) + "[m]")
print("P. M. mean: " + str(result0[result0[:, 2] == 0, 3].mean()) + "[m]")
print("P. M. median: " + str(np.median(result0[:, 3])) + "[m]")
print("P. M. stddev: " + str(result0[:, 3].std()))
print(method_name1 + " mean: " + str(result1[:, 3].mean()) + "[m]")
print(method_name1 + " mean: " +
str(result1[result1[:, 2] == 0, 3].mean()) + "[m]")
print(method_name1 + " median: " + str(np.median(result1[:, 3])) + "[m]")
print(method_name1 + " stddev: " + str(result1[:, 3].std()))
print(stats.ttest_ind(result0[:, 3], result1[:, 3], equal_var=False))
print(
stats.ttest_ind(result0[result0[:, 2] == 0, 3],
result1[result1[:, 2] == 0, 3],
equal_var=False))
print(method_name2 + " mean: " + str(result2[:, 3].mean()) + "[m]")
print(method_name2 + " mean: " +
str(result2[result2[:, 2] == 0, 3].mean()) + "[m]")
print(method_name2 + " median: " + str(np.median(result2[:, 3])) + "[m]")
print(method_name2 + " stddev: " + str(result2[:, 3].std()))
print(stats.ttest_ind(result0[:, 3], result2[:, 3], equal_var=False))
print(
stats.ttest_ind(result0[result0[:, 2] == 0, 3],
result2[result2[:, 2] == 0, 3],
equal_var=False))
plt.subplots_adjust(wspace=0.4, hspace=0.4)
plt.show()
data1, data2, data3 = sdpt, sdkt, sdbt
stat, p = f_oneway(data1, data2, data3)
f.write("\nanova small dense: ")
f.write("stat: " + str(stat) + " ")
f.write("p: " + str(p))
data1, data2, data3 = mspt, mskt, msbt
stat, p = f_oneway(data1, data2, data3)
f.write("\nanova medium sparse: ")
f.write("stat: " + str(stat) + " ")
f.write("p: " + str(p))
data1, data2, data3 = lspt, lskt, lsbt
stat, p = f_oneway(data1, data2, data3)
f.write("\nanova medium dense: ")
f.write("stat: " + str(stat) + " ")
f.write("p: " + str(p))
data1, data2 = lspt, lskt
stat, p = ttest_ind(data1, data2)
f.write("\n ttest p and k med dense: ")
f.write("stat: " + str(stat) + " ")
f.write("p: " + str(p))
data1, data2, data3 = lspt, lskt, lsbt
stat, p = f_oneway(data1, data2, data3)
f.write("\nanova large sparse: ")
f.write("stat: " + str(stat) + " ")
f.write("p: " + str(p))
f.close()
def t_test2(x1, x2, eq_Var=True):
t, p = stats2.ttest_ind(x1, x2, equal_var=eq_Var)
return p, p > alpha # 如果p小，是拒绝原假设，认为二者不同，即可以通过该属性区分