def run(dataDefFile, var1, var2, datapoints):
tau = TAU
print('Generating data using: ', dataDefFile)
fileExt = dataDefFile.split('.')[-1]
if fileExt == 'py':
# Data Definition File
outFile = synthDataGen.run(dataDefFile, datapoints)
elif fileExt == 'csv':
# Data file
outFile = dataDefFile
else:
print('*** Invalid file type = ', fileExt)
return
d = getData.DataReader(outFile, limit=datapoints)
fig = plt.figure()
ax = fig.add_axes([.1,.1,.8,.8],projection='3d')
# If var1 and var2 are not specified, then build a master manifold out of three vars at a time. Otherwise, build shadow manifolds
# from the two specified vars
if var1 is None:
vars = d.getSeriesNames()
vars.sort()
print('Vars = ', vars)
colors = ['b', 'g', 'r','o']
for i in range(4):
if len(vars) < i*3+3:
break
X = d.getSeries(vars[i*3])
Y = d.getSeries(vars[i*3+1])
Z = d.getSeries(vars[i*3+2])
if standard:
X = standardize.standardize(X)
Y = standardize.standardize(Y)
Z = standardize.standardize(Z)
color = colors[i]
ax.plot(X, Y, Z)
else:
var1D = d.getSeries(var1)
if standard:
var1D = standardize.standardize(var1D)
X1 = var1D[:-2*tau]
Y1 = var1D[tau:-tau]
Z1 = var1D[2*tau:]
var2D = d.getSeries(var2)
if standard:
var2D = standardize.standardize(var2D)
X2 = var2D[:-2*tau]
Y2 = var2D[tau:-tau]
Z2 = var2D[2*tau:]
#plotData(d, datapoints)
ax.plot(X1,Y1,Z1)
ax.plot(X2,Y2,Z2, 'r')
#ax.plot(X1, Y1, Z2, 'g')
# `ax` is a 3D-aware axis instance because of the projection='3d' keyword argument to add_subplot
#ax = fig.add_subplot(1, 2, 1, projection='3d')
plt.show()
def run(s1, s2):
global firstTime, CUM_DEPENDENCE
global SNR, SNR_DB, CUM_TIME
reset = True
if firstTime:
#print('std ratio = ', s1D.std() / s2D.std())
firstTime = False
else:
reset = False
try:
synthDataGen.run(FILE + '.py',
samples=DATA_POINTS,
maxDifficulty=MAX_DIFFICULTY,
reset=reset)
except:
pass
dr = getData.DataReader(input=FILE + '.csv', limit=DATA_POINTS)
s1D = np.array(dr.getSeries(s1))
s2D = np.array(dr.getSeries(s2))
dependence = IndHSIC.scoreDependence(s1D, s2D)
#dependence = IndLn.scoreDependence(s1D, s2D)
CUM_DEPENDENCE = CUM_DEPENDENCE + dependence
#print('s1 = ', s1D.mean(), s1D.std())
#print('s2 = ', s2D.mean(), s2D.std())
if firstTime:
#print('std ratio = ', s1D.std() / s2D.std())
firstTime = False
coefs = np.polyfit(s1D, s2D.T, 1)
x1coef, x0coef = coefs
Dprint('coefs = ', coefs)
cs2D = s1D * x1coef + x0coef
# coef = lstsq(np.array([list(s1D)]).T, s2D)[0][0]
# Dprint('coef = ', coef)
# cs2D = s1D * coef
res = s2D - cs2D
signal = cs2D.var(ddof=1)
noise = res.var(ddof=1)
snr = signal / noise
if snr < SNR:
SNR = snr
SNR_DB = 10 * math.log(snr, 10)
start = time.time()
if IND_TYPE == 'PL' or IND_TYPE == 'CCM':
dep = scoreDependence(s1D, s2D)
else:
dep = scoreDependence(s1D, res)
end = time.time()
duration = end - start
CUM_TIME += duration
Dprint('Residual Dependence for ', s1, '-->', s2, ' = ', dep)
return dep
def analyzeOneLingam(self, dataCount, gen=True, valData=None):
if gen:
synthDataGen.run(self.genFile, dataCount)
dr = getData.DataReader(self.testFile, dataCount)
vars = dr.getSeriesNames()
if valData is None:
valData = synthDataGen.getValidation()
lowestSNR = 10**100
cumSNR = 0
pairCount = 0
lowestPair = None
stats = {}
nodeDiffs = {}
for tuple in valData:
successor, predecessors = tuple
if len(predecessors) == 0:
continue
for predecessor in predecessors:
nodeDiffs[predecessor] = {}
pD = np.array(dr.getSeries(predecessor))
sD = np.array(dr.getSeries(successor))
coefs = np.polyfit(pD, sD.T, 1)
x1coef, x0coef = coefs
#Dprint('coefs = ', coefs)
cs2D = pD * x1coef + x0coef
res = sD - cs2D
# Note that signal and noise are reversed from what you might expect.
# In this case, the variance of the error term is the signal we are looking for,
# while the linear component is actually the noise
noise = cs2D.var(ddof=1)
signal = res.var(ddof=1)
#print('pair = ', predecessor, '--->', successor)
#print('noise = ', noise)
#print('signal = ', signal)
snr = signal / noise
#print('snr = ', snr)
nodeDiffs[predecessor][successor] = 10* math.log(1.0/snr, 10)
if snr < lowestSNR:
lowestSNR = snr
lowestPair = (predecessor, successor)
cumSNR += snr
pairCount += 1
stats['minSnr'] = 10*math.log(lowestSNR,10)
avgSNR = cumSNR/float(pairCount)
stats['avgSnr'] = 10*math.log(avgSNR, 10)
stats['weakestPair'] = lowestPair
difficulty = max([10 * math.log(1.0 / lowestSNR, 10),0.0])
stats['difficulty'] = difficulty
stats['weakestPairing'] = lowestPair
stats['variableDifficulty'] = nodeDiffs
stats['normDifficulty'] = 100.0 * difficulty / (dataCount**.5)
return stats
def __init__(self, dataFile, limit=None, prune=True, adjustData=True):
if limit is None:
limit = dataLimit
self.limit = limit
self.doPrune = prune
self.adjustData = adjustData
self.dataFile = dataFile
self.dataCache = {}
self.data = getData.DataReader(dataFile, limit=limit)
self.corrMatrix = None
self.varIndexes = {}
return
def run(dataDefFile, datapoints):
print('Generating data using: ', dataDefFile)
fileExt = dataDefFile.split('.')[-1]
if fileExt == 'py':
# Data Definition File
outFile = synthDataGen.run(dataDefFile, datapoints)
elif fileExt == 'csv':
# Data file
outFile = dataDefFile
else:
print('*** Invalid file type = ', fileExt)
return
d = getData.DataReader(outFile, limit=datapoints)
print('Vars = ', d.getSeriesNames())
plotData(d, datapoints)
def run():
#d = getData.DataReader(input='..\data\experiment1.csv', limit=2000)
# d = getData.DataReader(input='..\Tests\IS1c.csv', limit=2000)
# X = d.getSeries('R11')
d = getData.DataReader(input='..\data\experiment1.csv', limit=2000)
X = d.getSeries('Io')
print('acf = ', acf(X), ', pacf = ', pacf(X))
print('arma = ', arma_order_select_ic(X))
result = adfuller(X)
pvalue = result[1]
print('result = ', result)
if pvalue > .05:
# Non stationary -- Difference the series and try again
Xa = np.array(X)
X2 = [0]
X2.extend(X[:-1])
X2a = np.array(X2)
X3a = Xa - X2a
result = adfuller(X3a)
print('result2 = ', result)
def calibrateOneCITest(self, testType, filePath):
synthDataGen.run(filePath + '.py', samples=self.datacount)
exec('import ' + testType)
module = eval(testType)
SA = analyzeSEM.SemAnalyzer(filePath + '.py', self.datacount)
reader = getData.DataReader(filePath + '.csv', self.datacount)
dependencies, independencies = SA.getCondDependencies()
# print('dependencies = ', dependencies)
# print('independencies = ', independencies)
errors = 0
errorTerms = {}
items = 0
for item in dependencies:
x, y, z = item
X = reader.getSeries(x)
Y = reader.getSeries(y)
Z = reader.getSeries(z)
ind = module.isIndependent(X, Y, Z)
if ind:
print('Error -- ', x, 'and', y, 'Should be dependent given', z)
self.err1Count += 1
errors += 1
errorTerms[item] = 1
self.testCount += 1
for item in independencies:
x, y, z = item
X = reader.getSeries(x)
Y = reader.getSeries(y)
Z = reader.getSeries(z)
ind = module.isIndependent(X, Y, Z)
if not ind:
print('Error -- ', x, 'and', y, 'Should be independent given',
z)
self.err2Count += 1
errors += 1
errorTerms[item] = 1
self.testCount += 1
#print('Rating = ', (1 - (errors / items))*100, '%')
print('Errors for file: ', filePath, '=', errors,
list(errorTerms.keys()))
return
