def train(self):
"""
Train the model
"""
self.transitionPotentials = {}
try:
iterCount = len(self.codes) * len(self.factors)
self.rangeChanged.emit(self.tr("Training WoE... %p%"), iterCount)
changeMap = self.changeMap.getBand(1)
for code in self.codes:
sites = binaryzation(changeMap, [code])
# Reclass factors (continuous factor -> ordinal factor)
wMap = np.ma.zeros(changeMap.shape) # The map of summary weight of the all factors
self.weights[code] = {} # Dictionary for storing wheights of every raster's band
for k in xrange(len(self.factors)):
fact = self.factors[k]
self.weights[code][k] = {} # Weights of the factor
factorW = self.weights[code][k]
if self.bins: # Get bins of the factor
bin = self.bins[k]
if (bin != None) and fact.getBandsCount() != len(bin):
raise WoeManagerError("Count of bins list for multiband factor is't equal to band count!")
else:
bin = None
for i in range(1, fact.getBandsCount() + 1):
band = fact.getBand(i)
if bin and bin[i - 1]: #
band = reclass(band, bin[i - 1])
band, sites = masks_identity(band, sites, dtype=np.uint8) # Combine masks of the rasters
woeRes = woe(
band, sites, self.unit_cell
) # WoE for the 'code' (initState->finalState) transition and current 'factor'.
weights = woeRes["map"]
wMap = wMap + weights
factorW[i] = woeRes["weights"]
self.updateProgress.emit()
# Reclassification finished => set WoE coefficients
self.woe[code] = wMap # WoE for all factors and the transition code.
# Potentials are WoE map rescaled to 0--100 percents
band = (sigmoid(wMap) * 100).astype(np.uint8)
p = Raster()
p.create([band], self.geodata)
self.transitionPotentials[code] = p
gc.collect()
except MemoryError:
self.errorReport.emit("The system out of memory during WoE trainig")
raise
except:
self.errorReport.emit(self.tr("An unknown error occurs during WoE trainig"))
raise
finally:
self.processFinished.emit()
def __init__(self, factors, areaAnalyst, unit_cell=1, bins = None):
'''
@param factors List of the pattern rasters used for prediction of point objects (sites).
@param areaAnalyst AreaAnalyst that contains map of the changes, encodes and decodes class numbers.
@param unit_cell Method parameter, pixelsize of resampled rasters.
@param bins Dictionary of bins. Bins are binning boundaries that used for reduce count of classes.
For example if factors = [f0, f1], then bins could be (for example) {0:[bins for f0], 1:[bins for f1]} = {0:[[10, 100, 250]],1:[[0.2, 1, 1.5, 4]]}.
List of list used because a factor can be a multiband raster, we need get a list of bins for every band. For example:
factors = [f0, 2-band-factor], bins= {0: [[10, 100, 250]], 1:[[0.2, 1, 1.5, 4], [3, 4, 7]] }
'''
self.factors = factors
self.analyst = areaAnalyst
self.changeMap = areaAnalyst.getChangeMap()
self.prediction = None
self.confidence = None
if (bins != None) and (len(factors) != len(bins.keys())):
raise WoeManagerError('Lengths of bins and factors are different!')
for r in self.factors:
if not self.changeMap.geoDataMatch(r):
raise WoeManagerError('Geometries of the input rasters are different!')
if self.changeMap.getBandsCount() != 1:
raise WoeManagerError('Change map must have one band!')
# Get list of codes from the changeMap raster
classes = self.changeMap.getBandStat(1)['gradation']
cMap = self.changeMap.getBand(1)
self.codes = [int(c) for c in classes] # Codes of transitions initState->finalState (see AreaAnalyst.encode)
self.woe = {}
for code in self.codes:
sites = binaryzation(cMap, [code])
# TODO: reclass factors (continuous factor -> ordinal factor)
wMap = np.ma.zeros(cMap.shape)
for k in xrange(len(factors)):
fact = factors[k]
if bins: # Get bins of the factor
bin = bins[k]
if (bin != None) and fact.getBandsCount() != len(bin):
raise WoeManagerError("Count of bins list for multiband factor is't equal to band count!")
else: bin = None
for i in range(1, fact.getBandsCount()+1):
band = fact.getBand(i)
if bin:
band = reclass(band, bin[i-1])
band, sites = masks_identity(band, sites) # Combine masks of the rasters
weights = woe(band, sites, unit_cell) # WoE for the 'code' (initState->finalState) transition and current 'factor'.
wMap = wMap + weights
self.woe[code]=wMap # WoE for all factors and the transition.
def train(self):
'''
Train the model
'''
self.transitionPotentials = {}
try:
iterCount = len(self.codes)*len(self.factors)
self.rangeChanged.emit(self.tr("Training WoE... %p%"), iterCount)
changeMap = self.changeMap.getBand(1)
for code in self.codes:
sites = binaryzation(changeMap, [code])
# Reclass factors (continuous factor -> ordinal factor)
wMap = np.ma.zeros(changeMap.shape) # The map of summary weight of the all factors
self.weights[code] = {} # Dictionary for storing wheights of every raster's band
for k in xrange(len(self.factors)):
fact = self.factors[k]
self.weights[code][k] = {} # Weights of the factor
factorW = self.weights[code][k]
if self.bins: # Get bins of the factor
bin = self.bins[k]
if (bin != None) and fact.getBandsCount() != len(bin):
raise WoeManagerError("Count of bins list for multiband factor is't equal to band count!")
else: bin = None
for i in range(1, fact.getBandsCount()+1):
band = fact.getBand(i)
if bin and bin[i-1]: #
band = reclass(band, bin[i-1])
band, sites = masks_identity(band, sites, dtype=np.uint8) # Combine masks of the rasters
woeRes = woe(band, sites, self.unit_cell) # WoE for the 'code' (initState->finalState) transition and current 'factor'.
weights = woeRes['map']
wMap = wMap + weights
factorW[i] = woeRes['weights']
self.updateProgress.emit()
# Reclassification finished => set WoE coefficients
self.woe[code]=wMap # WoE for all factors and the transition code.
# Potentials are WoE map rescaled to 0--100 percents
band = (sigmoid(wMap)*100).astype(np.uint8)
p = Raster()
p.create([band], self.geodata)
self.transitionPotentials[code] = p
gc.collect()
except MemoryError:
self.errorReport.emit('The system out of memory during WoE trainig')
raise
except:
self.errorReport.emit(self.tr("An unknown error occurs during WoE trainig"))
raise
finally:
self.processFinished.emit()