class LR(QObject):
"""
Implements Logistic Regression model definition and calibration
(maximum liklihood parameter estimation).
"""
rangeChanged = pyqtSignal(str, int)
updateProgress = pyqtSignal()
processFinished = pyqtSignal()
samplingFinished = pyqtSignal()
finished = pyqtSignal()
logMessage = pyqtSignal(str)
errorReport = pyqtSignal(str)
def __init__(self, ns=0, logreg=None):
QObject.__init__(self)
if logreg:
self.logreg = logreg
else:
self.logreg = MLR()
self.state = None
self.factors = None
self.output = None
self.mode = "All"
self.samples = None
self.catlist = None
self.ns = ns # Neighbourhood size of training rasters.
self.data = None # Training data
self.maxiter = 100 # Maximum of fitting iterations
self.sampler = None # Sampler
# Results of the LR prediction
self.prediction = None # Raster of the LR prediction results
self.confidence = None # Raster of the LR results confidence (1 = the maximum confidence, 0 = the least confidence)
self.Kappa = 0 # Kappa value
self.pseudoR = 0 # Pseudo R-squared (Count) (http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm)
self.transitionPotentials = None # Dictionary of transition potencial maps: {category1: map1, category2: map2, ...}
def getCoef(self):
return self.logreg.get_weights().T
def getConfidence(self):
return self.confidence
def getIntercept(self):
return self.logreg.get_intercept()
def getKappa(self):
return self.Kappa
def getStdErrIntercept(self):
X = np.column_stack( (self.data['state'], self.data['factors']) )
return self.logreg.get_stderr_intercept(X)
def getStdErrWeights(self):
X = np.column_stack( (self.data['state'], self.data['factors']) )
return self.logreg.get_stderr_weights(X).T
def get_PvalIntercept(self):
X = np.column_stack( (self.data['state'], self.data['factors']) )
return self.logreg.get_pval_intercept(X)
def get_PvalWeights(self):
X = np.column_stack( (self.data['state'], self.data['factors']) )
return self.logreg.get_pval_weights(X).T
def getPrediction(self, state, factors, calcTransitions=False):
self._predict(state, factors, calcTransitions)
return self.prediction
def getPseudoR(self):
return self.pseudoR
def getTransitionPotentials(self):
return self.transitionPotentials
def _outputConfidence(self, input):
'''
Return confidence (difference between 2 biggest probabilities) of the LR output.
1 = the maximum confidence, 0 = the least confidence
'''
out_scl = self.logreg.predict_proba(input)[0]
# Calculate the confidence:
out_scl.sort()
return int(100 * (out_scl[-1] - out_scl[-2]) )
def outputTransitions(self, input):
'''
Return transition potential of the outputs
'''
out_scl = self.logreg.predict_proba(input)[0]
out_scl = [int(100 * x) for x in out_scl]
result = {}
for r, v in enumerate(out_scl):
cat = self.catlist[r]
result[cat] = v
return result
def _predict(self, state, factors, calcTransitions=False):
'''
Calculate output and confidence rasters using LR model and input rasters
@param state Raster of the current state (categories) values.
@param factors List of the factor rasters (predicting variables).
'''
try:
self.rangeChanged.emit(self.tr("Initialize model %p%"), 1)
geodata = state.getGeodata()
rows, cols = geodata['ySize'], geodata['xSize']
for r in factors:
if not state.geoDataMatch(r):
raise LRError('Geometries of the input rasters are different!')
self.transitionPotentials = None # Reset tr.potentials if they exist
# Normalize factors before prediction:
for f in factors:
f.normalize(mode = 'mean')
predicted_band = np.zeros([rows, cols], dtype=np.uint8)
confidence_band = np.zeros([rows, cols], dtype=np.uint8)
if calcTransitions:
self.transitionPotentials = {}
for cat in self.catlist:
self.transitionPotentials[cat] = np.zeros([rows, cols], dtype=np.uint8)
self.sampler = Sampler(state, factors, ns=self.ns)
mask = state.getBand(1).mask.copy()
if mask.shape == ():
mask = np.zeros([rows, cols], dtype=np.bool)
self.updateProgress.emit()
self.rangeChanged.emit(self.tr("Prediction %p%"), rows)
for i in xrange(rows):
for j in xrange(cols):
if not mask[i,j]:
input = self.sampler.get_inputs(state, i,j)
if input != None:
input = np.array([input])
out = self.logreg.predict(input)
predicted_band[i,j] = out
confidence = self._outputConfidence(input)
confidence_band[i, j] = confidence
if calcTransitions:
potentials = self.outputTransitions(input)
for cat in self.catlist:
map = self.transitionPotentials[cat]
map[i, j] = potentials[cat]
else: # Input sample is incomplete => mask this pixel
mask[i, j] = True
self.updateProgress.emit()
predicted_bands = [np.ma.array(data = predicted_band, mask = mask, dtype=np.uint8)]
confidence_bands = [np.ma.array(data = confidence_band, mask = mask, dtype=np.uint8)]
self.prediction = Raster()
self.prediction.create(predicted_bands, geodata)
self.confidence = Raster()
self.confidence.create(confidence_bands, geodata)
if calcTransitions:
for cat in self.catlist:
band = [np.ma.array(data=self.transitionPotentials[cat], mask=mask, dtype=np.uint8)]
self.transitionPotentials[cat] = Raster()
self.transitionPotentials[cat].create(band, geodata)
except MemoryError:
self.errorReport.emit(self.tr("The system out of memory during LR prediction"))
raise
except:
self.errorReport.emit(self.tr("An unknown error occurs during LR prediction"))
raise
finally:
self.processFinished.emit()
def __propagateSamplerSignals(self):
self.sampler.rangeChanged.connect(self.__samplerProgressRangeChanged)
self.sampler.updateProgress.connect(self.__samplerProgressChanged)
self.sampler.samplingFinished.connect(self.__samplerFinished)
def __samplerFinished(self):
self.sampler.rangeChanged.disconnect(self.__samplerProgressRangeChanged)
self.sampler.updateProgress.disconnect(self.__samplerProgressChanged)
self.sampler.samplingFinished.disconnect(self.__samplerFinished)
self.samplingFinished.emit()
def __samplerProgressRangeChanged(self, message, maxValue):
self.rangeChanged.emit(message, maxValue)
def __samplerProgressChanged(self):
self.updateProgress.emit()
def save(self):
pass
def saveSamples(self, fileName):
self.sampler.saveSamples(fileName)
def setMaxIter(self, maxiter):
self.maxiter = maxiter
def setTrainingData(self):
state, factors, output, mode, samples = self.state, self.factors, self.output, self.mode, self.samples
if not self.logreg:
raise LRError('You must create a Logistic Regression model before!')
# Normalize factors before sampling:
for f in factors:
f.normalize(mode = 'mean')
self.sampler = Sampler(state, factors, output, ns=self.ns)
self.__propagateSamplerSignals()
self.sampler.setTrainingData(state, output, shuffle=False, mode=mode, samples=samples)
outputVecLen = self.sampler.outputVecLen
stateVecLen = self.sampler.stateVecLen
factorVectLen = self.sampler.factorVectLen
size = len(self.sampler.data)
self.data = self.sampler.data
self.catlist = np.unique(self.data['output'])
def train(self):
X = np.column_stack( (self.data['state'], self.data['factors']) )
Y = self.data['output']
self.labelCodes = np.unique(Y)
self.logreg.fit(X, Y, maxiter=self.maxiter)
out = self.logreg.predict(X)
depCoef = DependenceCoef(np.ma.array(out), np.ma.array(Y), expand=True)
self.Kappa = depCoef.kappa(mode=None)
self.pseudoR = depCoef.correctness(percent = False)
def setState(self, state):
self.state = state
def setFactors(self, factors):
self.factors = factors
def setOutput(self, output):
self.output = output
def setMode(self, mode):
self.mode = mode
def setSamples(self, samples):
self.samples = samples
def startTrain(self):
try:
self.setTrainingData()
self.train()
except MemoryError:
self.errorReport.emit(self.tr("The system out of memory during LR training"))
raise
except:
self.errorReport.emit(self.tr("An unknown error occurs during LR trainig"))
raise
finally:
self.finished.emit()