def _exec(self, Lambda):
Y_lambda = ot.BoxCoxTransform(Lambda)(self.Y_i_)
algo = ot.LinearLeastSquares(self.a_i_, Y_lambda)
algo.run()
beta0 = algo.getConstant()[0]
beta1 = algo.getLinear()[0, 0]
sigma2 = (self.N_ - 1.0) / (self.N_ - 2.0) * (Y_lambda - (self.a_i_ * [beta1] + [beta0])).computeVariance()[0]
return [-0.5 * self.N_ * m.log(sigma2) + (Lambda[0] - 1) * self.sumLogY_i]
def build(self, dataX, dataY):
logLikelihood = ot.NumericalMathFunction(ReducedLogLikelihood(dataX, dataY))
xlb = np.linspace(self.lambdaMin_,self.lambdaMax_,num=500)
lambdax = [logLikelihood([x])[0] for x in xlb]
algo = ot.TNC(logLikelihood)
algo.setStartingPoint([xlb[np.array(lambdax).argmax()]])
algo.setBoundConstraints(ot.Interval(self.lambdaMin_, self.lambdaMax_))
algo.setOptimizationProblem(ot.BoundConstrainedAlgorithmImplementationResult.MAXIMIZATION)
algo.run()
optimalLambda = algo.getResult().getOptimizer()[0]
# graph
optimalLogLikelihood = algo.getResult().getOptimalValue()
graph = logLikelihood.draw(0.01 * optimalLambda, 10.0 * optimalLambda)
c = ot.Cloud([[optimalLambda, optimalLogLikelihood]])
c.setColor("red")
c.setPointStyle("circle")
graph.add(c)
return ot.BoxCoxTransform([optimalLambda]), graph
def _run(self, inputSample, outputSample, detection, noiseThres,
saturationThres, boxCox, censored):
"""
Run common preliminary analysis to all methods to build POD.
"""
#################### Filter censored data ##############################
if censored:
# Filter censored data
inputSample, inputSampleNoise, inputSampleSat, signals = \
DataHandling.filterCensoredData(inputSample, outputSample,
noiseThres, saturationThres)
else:
inputSample, signals = inputSample, outputSample
inputSampleNoise, inputSampleSat = None, None
###################### Box Cox transformation ##########################
# Compute Box Cox if enabled
if boxCox:
# optimization required, get optimal lambda without graph
self._lambdaBoxCox, self._graphBoxCox = computeBoxCox(
inputSample, signals)
# Transformation of data
boxCoxTransform = ot.BoxCoxTransform([self._lambdaBoxCox])
signals = boxCoxTransform(signals)
if censored:
if noiseThres is not None:
noiseThres = boxCoxTransform([noiseThres])[0]
if saturationThres is not None:
saturationThres = boxCoxTransform([saturationThres])[0]
detectionBoxCox = boxCoxTransform([detection])[0]
else:
detectionBoxCox = detection
self._lambdaBoxCox = None
boxCoxTransform = None
return {
'inputSample': inputSample,
'signals': signals,
'detectionBoxCox': detectionBoxCox,
'boxCoxTransform': boxCoxTransform
}
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
# Create a Box Cox transformation
myLambda = ot.NumericalPoint([0.0, 0.1, 1.0, 1.5])
graph = ot.Graph()
for i in range(myLambda.getDimension()):
myBoxCox = ot.BoxCoxTransform(myLambda[i])
graph.add(myBoxCox.draw(0.1, 2.1))
graph.setColors(['red', 'blue', 'black', 'green'])
graph.setLegends(['lambda = ' + str(lam) for lam in myLambda])
graph.setLegendPosition("bottomright")
fig = plt.figure(figsize=(8, 4))
plt.suptitle("Box Cox transformations")
axis = fig.add_subplot(111)
axis.set_xlim(auto=True)
View(graph, figure=fig, axes=[axis], add_legend=True)
def _run(self):
"""
Run the analysis :
- Computes the Box Cox parameter if *boxCox* is True,
- Computes the transformed signals if *boxCox* is True or a float,
- Builds the univariate linear regression model on the data,
- Computes the linear regression parameters for censored data if needed,
- Computes the residuals,
- Runs all hypothesis tests.
"""
#################### Filter censored data ##############################
if self._censored:
# Filter censored data
# Returns:
# defects in the non censored area
# defectsNoise in the noisy area
# defectsSat in the saturation area
# signals in the non censored area
# check if one the threshold is None
defects, defectsNoise, defectsSat, signals = \
DataHandling.filterCensoredData(self._inputSample, self._outputSample,
self._noiseThres, self._saturationThres)
else:
defects, signals = self._inputSample, self._outputSample
defectsSize = defects.getSize()
###################### Box Cox transformation ##########################
# Compute Box Cox if enabled
if self._boxCox:
if self._lambdaBoxCox is None:
# optimization required, get optimal lambda and graph
self._lambdaBoxCox, self._graphBoxCox = computeBoxCox(
defects, signals)
# Transformation of data
boxCoxTransform = ot.BoxCoxTransform([self._lambdaBoxCox])
signals = boxCoxTransform(signals)
if self._noiseThres is not None:
noiseThres = boxCoxTransform([self._noiseThres])[0]
else:
noiseThres = self._noiseThres
if self._saturationThres is not None:
saturationThres = boxCoxTransform([self._saturationThres])[0]
else:
saturationThres = self._saturationThres
else:
noiseThres = self._noiseThres
saturationThres = self._saturationThres
######################### Linear Regression model ######################
# Linear regression with statsmodels module
# Create the X matrix : [1, inputSample]
X = ot.NumericalSample(defectsSize, [1, 0])
X[:, 1] = defects
self._algoLinear = OLS(np.array(signals), np.array(X)).fit()
self._resultsUnc.intercept = self._algoLinear.params[0]
self._resultsUnc.slope = self._algoLinear.params[1]
# get standard error estimates (residuals standard deviation)
self._resultsUnc.stderr = np.sqrt(self._algoLinear.scale)
# get confidence interval at level 95%
self._resultsUnc.confInt = self._algoLinear.conf_int(0.05)
if self._censored:
# define initial starting point for MLE optimization
initialStartMLE = [
self._resultsUnc.intercept, self._resultsUnc.slope,
self._resultsUnc.stderr
]
# MLE optimization
res = computeLinearParametersCensored(initialStartMLE, defects,
defectsNoise, defectsSat,
signals, noiseThres,
saturationThres)
self._resultsCens.intercept = res[0]
self._resultsCens.slope = res[1]
self._resultsCens.stderr = res[2]
############################ Residuals #################################
# get residuals from algoLinear
self._resultsUnc.residuals = ot.NumericalSample(
np.vstack(self._algoLinear.resid))
# compute residuals distribution
self._resultsUnc.resDist = self._resDistFact.build(
self._resultsUnc.residuals)
if self._censored:
# create linear model function for censored case
def CensLinModel(x):
return self._resultsCens.intercept + self._resultsCens.slope * x
# compute the residuals for the censored case.
self._resultsCens.fittedSignals = CensLinModel(defects)
self._resultsCens.residuals = signals - self._resultsCens.fittedSignals
# compute residuals distribution.
self._resultsCens.resDist = self._resDistFact.build(
self._resultsCens.residuals)
########################## Compute tests ###############################
self._resultsUnc.testResults = \
self._computeTests(defects, signals, self._resultsUnc.residuals,
self._resultsUnc.resDist)
if self._censored:
self._resultsCens.testResults = \
self._computeTests(defects, signals, self._resultsCens.residuals,
self._resultsCens.resDist)
################ Build the result lists to be printed ##################
self._buildPrintResults()
def _computeLinearModel(inputSample, outputSample, detection, noiseThres,
saturationThres, boxCox, censored):
"""
Run filerCensoredData and build the linear regression model.
It is defined as a simple function because it is also needed in a loop for
the bootstrap based POD.
"""
#################### Filter censored data ##############################
if censored:
# Filter censored data
defects, defectsNoise, defectsSat, signals = \
DataHandling.filterCensoredData(inputSample, outputSample,
noiseThres, saturationThres)
else:
defects, signals = inputSample, outputSample
defectsSize = defects.getSize()
###################### Box Cox transformation ##########################
# Compute Box Cox if enabled
if boxCox:
if signals.getMin()[0] < 0:
shift = -signals.getMin()[0] + 100
else:
shift = 0.
# optimization required, get optimal lambda without graph
lambdaBoxCox, graphBoxCox = computeBoxCox(defects, signals, shift)
# Transformation of data
boxCoxTransform = ot.BoxCoxTransform([lambdaBoxCox])
signals = boxCoxTransform(signals + shift)
if censored:
if noiseThres is not None:
noiseThres = boxCoxTransform([noiseThres + shift])[0]
if saturationThres is not None:
saturationThres = boxCoxTransform([saturationThres + shift])[0]
detectionBoxCox = boxCoxTransform([detection + shift])[0]
else:
detectionBoxCox = detection
lambdaBoxCox = None
graphBoxCox = None
######################### Linear Regression model ######################
# Linear regression with statsmodels module
# Create the X matrix : [1, inputSample]
X = ot.Sample(defectsSize, [1, 0])
X[:, 1] = defects
algoLinear = OLS(np.array(signals), np.array(X)).fit()
intercept = algoLinear.params[0]
slope = algoLinear.params[1]
# get standard error estimates (residuals standard deviation)
stderr = np.sqrt(algoLinear.scale)
# get residuals from algoLinear
residuals = ot.Sample(np.vstack(algoLinear.resid))
if censored:
# define initial starting point for MLE optimization
initialStartMLE = [intercept, slope, stderr]
# MLE optimization
res = computeLinearParametersCensored(initialStartMLE, defects,
defectsNoise, defectsSat,
signals, noiseThres,
saturationThres)
intercept = res[0]
slope = res[1]
stderr = res[2]
residuals = signals - (intercept + slope * defects)
return {
'defects': defects,
'signals': signals,
'intercept': intercept,
'slope': slope,
'stderr': stderr,
'residuals': residuals,
'detection': detectionBoxCox,
'lambdaBoxCox': lambdaBoxCox,
'graphBoxCox': graphBoxCox
}
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
# Create a Box Cox transformation
lambdas = [0.0, 0.1, 1.0, 1.5]
graph = ot.Graph("Box Cox transformations", 'x', 'y', True)
for i in range(len(lambdas)):
boxCoxT = ot.BoxCoxTransform(lambdas[i])
graph.add(boxCoxT.draw(0.1, 2.1))
graph.setColors(['red', 'blue', 'black', 'green'])
graph.setLegends(['lambda = ' + str(lam) for lam in lambdas])
graph.setLegendPosition("bottomright")
fig = plt.figure(figsize=(8, 4))
axis = fig.add_subplot(111)
axis.set_xlim(auto=True)
View(graph, figure=fig, axes=[axis], add_legend=True)