def validateModel(self, printToScreen=False):
""" take the validation data and test it on the network at any given point.
returns a single numeric error value which describes the goodness of the model"""
if NoneType in (type(self.validationFeatures),
type(self.validationLabels), type(self.meta)):
raise Exception("Specify validation features and labels")
predictedOutput = list()
for loopCounter, (rowIndex, row) in enumerate(
self.validationFeatures.iterrows()):
predictedOutput.append([item for item in self.predict(row)])
predictedOutput = pd.DataFrame(
np.array(predictedOutput),
columns=[self.meta.categoricalLabelColumns])
#convert predictions back into a human readable format
predictedMatrix = DataCleaner.deNormalize(
DataCleaner.categoricalToNominal(
self.validationFeatures.join(predictedOutput), self.meta),
self.meta)
expectedMatrix = DataCleaner.deNormalize(
DataCleaner.categoricalToNominal(
self.validationFeatures.join(self.validationLabels),
self.meta), self.meta)
error = comparePrediction(predictedMatrix, expectedMatrix, self.meta,
printToScreen)
return error
def validateModel(self, printToScreen=False):
""" take the validation data and test it on the network at any given point.
returns a single numeric error value which describes the goodness of the model"""
if NoneType in (type(self.validationFeatures), type(self.validationLabels), type(self.meta)):
raise Exception("Specify validation features and labels")
predictedOutput = list()
for loopCounter, (rowIndex, row) in enumerate(self.validationFeatures.iterrows()):
predictedOutput.append([item for item in self.predict(row)])
predictedOutput = pd.DataFrame(np.array(predictedOutput), columns=[self.meta.categoricalLabelColumns])
#convert predictions back into a human readable format
predictedMatrix = DataCleaner.deNormalize(DataCleaner.categoricalToNominal(self.validationFeatures.join(predictedOutput), self.meta), self.meta)
expectedMatrix = DataCleaner.deNormalize(DataCleaner.categoricalToNominal(self.validationFeatures.join(self.validationLabels), self.meta), self.meta)
error = comparePrediction(predictedMatrix, expectedMatrix, self.meta, printToScreen)
return error
