def run_internal(self):
'''
Builds a model using the internally defined machine learning tools.
All input parameters are extracted from self.param.
The main output is an instance of basemodel saved in
the model folder as a pickle (model.pkl) and used for prediction.
The results of building and validation are added to results,
but also saved to the model folder as a pickle (info.pkl)
for being displayed in manage tools.
'''
# check suitability of Y matrix
if not self.param.getVal('quantitative') :
success, yresult = utils.qualitative_Y(self.Y)
if not success:
self.conveyor.setError(yresult)
return
# expand with new methods here:
registered_methods = [('RF', RF),
('SVM', SVM),
('GNB', GNB),
('PLSR', PLSR),
('PLSDA', PLSDA), ]
# instantiate an appropriate child of base_model
model = None
for imethod in registered_methods:
if imethod[0] == self.param.getVal('model'):
model = imethod[1](self.X, self.Y, self.param)
LOG.debug('Recognized learner: '
f"{self.param.getVal('model')}")
break
if not model:
self.conveyor.setError(f'Modeling method {self.param.getVal("model")}'
'not recognized')
LOG.error(f'Modeling method {self.param.getVal("model")}'
'not recognized')
return
# build model
LOG.info('Starting model building')
success, model_building_results = model.build()
if not success:
self.conveyor.setError(model_building_results)
return
self.conveyor.addVal(
model_building_results,
'model_build_info',
'model building information',
'method',
'single',
'Information about the model')
# self.results['model_build'] = results
# validate model
LOG.info('Starting model validation')
success, model_validation_results = model.validate()
if not success:
self.conveyor.setError(model_validation_results)
return
# model_validation_results is a dictionary which contains model_validation_info and
# (optionally) Y_adj and Y_pred, depending on the model type
self.conveyor.addVal(
model_validation_results['quality'],
'model_valid_info',
'model validation information',
'method',
'single',
'Information about the model validation')
# non-conformal qualitative and quantitative models
if 'Y_adj' in model_validation_results:
self.conveyor.addVal(
model_validation_results['Y_adj'],
'Y_adj',
'Y fitted',
'result',
'objs',
'Y values of the training series fitted by the model')
if 'Y_pred' in model_validation_results:
self.conveyor.addVal(
model_validation_results['Y_pred'],
'Y_pred',
'Y predicted',
'result',
'objs',
'Y values of the training series predicted by the model')
# conformal qualitative models produce a list of tuples, indicating
# if the object is predicted to belong to class 0 and 1
if 'classes' in model_validation_results:
for i in range(len(model_validation_results['classes'][0])):
class_key = 'c' + str(i)
class_label = 'Class ' + str(i)
class_list = model_validation_results['classes'][:, i].tolist()
self.conveyor.addVal( class_list,
class_key, class_label,
'result', 'objs',
'Conformal class assignment',
'main')
# conformal quantitataive models produce a list of tuples, indicating
# the minumum and maximum value
# if 'interval' in model_validation_results:
# mean1 = np.mean(model_validation_results['classes'], axis=1)
# lower_limit = model_validation_results['classes'][:, 0]
# upper_limit = model_validation_results['classes'][:, 1]
# utils.add_result(results, mean1, 'values', 'Prediction',
# 'result', 'objs',
# 'Results of the prediction', 'main')
# utils.add_result(results, lower_limit, 'lower_limit',
# 'Lower limit', 'confidence', 'objs',
# 'Lower limit of the conformal prediction')
# utils.add_result(results, upper_limit, 'upper_limit',
# 'Upper limit', 'confidence', 'objs',
# 'Upper limit of the conformal prediction')
# TODO: compute AD (when applicable)
LOG.info('Model finished successfully')
# save model
try:
model.save_model()
except Exception as e:
LOG.error(f'Error saving model with exception {e}')
return False, 'An error ocurred saving the model'
return
def external_validation(self):
''' when experimental values are available for the predicted compounds,
run external validation '''
ext_val_results = []
# Ye are the y values present in the input file
Ye = np.asarray(self.conveyor.getVal("ymatrix"))
# For qualitative models, make sure the Y is qualitative as well
if not self.param.getVal("quantitative"):
qy, message = utils.qualitative_Y(Ye)
if not qy:
self.conveyor.setWarning(
f'No qualitative activity suitable for external validation "{message}". Skipping.'
)
LOG.warning(
f'No qualitative activity suitable for external validation "{message}". Skipping.'
)
return
# there are four variants of external validation, depending if the method
# if conformal or non-conformal and the model is qualitative and quantitative
if not self.param.getVal("conformal"):
# non-conformal
if not self.param.getVal("quantitative"):
# non-conformal & qualitative
Yp = np.asarray(self.conveyor.getVal("values"))
if Ye.size == 0:
raise ValueError("Experimental activity vector is empty")
if Yp.size == 0:
raise ValueError("Predicted activity vector is empty")
# the use of labels is compulsory to inform the confusion matrix that
# it must return a 2x2 confussion matrix. Otherwise it will fail when
# a single class is represented (all TP, for example)
TN, FP, FN, TP = confusion_matrix(Ye, Yp, labels=[0,
1]).ravel()
# protect to avoid warnings in special cases (div by zero)
MCC = mcc(Ye, Yp)
if (TP + FN) > 0:
sensitivity = (TP / (TP + FN))
else:
sensitivity = 0.0
if (TN + FP) > 0:
specificity = (TN / (TN + FP))
else:
specificity = 0.0
ext_val_results.append(
('TP', 'True positives in external-validation', float(TP)))
ext_val_results.append(
('TN', 'True negatives in external-validation', float(TN)))
ext_val_results.append(
('FP', 'False positives in external-validation',
float(FP)))
ext_val_results.append(
('FN', 'False negatives in external-validation',
float(FN)))
ext_val_results.append(
('Sensitivity', 'Sensitivity in external-validation',
float(sensitivity)))
ext_val_results.append(
('Specificity', 'Specificity in external-validation',
float(specificity)))
ext_val_results.append(
('MCC',
'Mattews Correlation Coefficient in external-validation',
float(MCC)))
else:
# non-conformal & quantitative
Yp = np.asarray(self.conveyor.getVal("values"))
if Ye.size == 0:
raise ValueError("Experimental activity vector is empty")
if Yp.size == 0:
raise ValueError("Predicted activity vector is empty")
Ym = np.mean(Ye)
nobj = len(Yp)
SSY0_out = np.sum(np.square(Ym - Ye))
SSY_out = np.sum(np.square(Ye - Yp))
scoringP = mean_squared_error(Ye, Yp)
SDEP = np.sqrt(SSY_out / (nobj))
if SSY0_out == 0:
Q2 = 0.0
else:
Q2 = 1.00 - (SSY_out / SSY0_out)
ext_val_results.append(('scoringP', 'Scoring P', scoringP))
ext_val_results.append(
('Q2', 'Determination coefficient in cross-validation',
Q2))
ext_val_results.append(
('SDEP', 'Standard Deviation Error of the Predictions',
SDEP))
self.conveyor.addVal(ext_val_results, 'external-validation',
'external validation', 'method', 'single',
'External validation results')
else:
# conformal external validation
if not self.param.getVal("quantitative"):
# conformal & qualitative
Yp = np.concatenate(
(np.asarray(self.conveyor.getVal('c0')).reshape(-1, 1),
np.asarray(self.conveyor.getVal('c1')).reshape(-1, 1)),
axis=1)
if Ye.size == 0:
raise ValueError("Experimental activity vector is empty")
if Yp.size == 0:
raise ValueError("Predicted activity vector is empty")
c0_correct = 0
c1_correct = 0
not_predicted = 0
c0_incorrect = 0
c1_incorrect = 0
Ye1 = []
Yp1 = []
for i in range(len(Ye)):
real = float(Ye[i])
predicted = Yp[i]
if predicted[0] != predicted[1]:
Ye1.append(real)
if predicted[0]:
Yp1.append(0)
else:
Yp1.append(1)
if real == 0 and predicted[0] == True:
c0_correct += 1
if real == 0 and predicted[1] == True:
c0_incorrect += 1
if real == 1 and predicted[1] == True:
c1_correct += 1
if real == 1 and predicted[0] == True:
c1_incorrect += 1
else:
not_predicted += 1
MCC = mcc(Ye1, Yp1)
TN = c0_correct
FP = c0_incorrect
TP = c1_correct
FN = c1_incorrect
coverage = float((len(Yp) - not_predicted) / len(Yp))
try:
# Compute accuracy (% of correct predictions)
conformal_accuracy = (float(TN + TP) /
float(FP + FN + TN + TP))
except Exception as e:
LOG.error(f'Failed to compute conformal accuracy with'
f'exception {e}')
conformal_accuracy = '-'
if (TP + FN) > 0:
sensitivity = (TP / (TP + FN))
else:
sensitivity = 0.0
if (TN + FP) > 0:
specificity = (TN / (TN + FP))
else:
specificity = 0.0
ext_val_results.append(
('TP', 'True positives in external-validation', float(TP)))
ext_val_results.append(
('TN', 'True negatives in external-validation', float(TN)))
ext_val_results.append(
('FP', 'False positives in external-validation',
float(FP)))
ext_val_results.append(
('FN', 'False negatives in external-validation',
float(FN)))
ext_val_results.append(
('Sensitivity', 'Sensitivity in external-validation',
float(sensitivity)))
ext_val_results.append(
('Specificity', 'Specificity in external-validation',
float(specificity)))
ext_val_results.append(
('MCC',
'Mattews Correlation Coefficient in external-validation',
float(MCC)))
ext_val_results.append(
('Conformal_coverage',
'Conformal coverage in external-validation',
float(coverage)))
ext_val_results.append(
('Conformal_accuracy',
'Conformal accuracy in external-validation',
float(conformal_accuracy)))
self.conveyor.addVal(ext_val_results, 'external-validation',
'external validation', 'method', 'single',
'External validation results')
else:
# conformal & quantitative
Yp_lower = self.conveyor.getVal('lower_limit')
Yp_upper = self.conveyor.getVal('upper_limit')
mean_interval = np.mean(np.abs(Yp_lower) - np.abs(Yp_upper))
inside_interval = (Yp_lower.reshape(-1, 1) <
Ye) & (Yp_upper.reshape(-1, 1) > Ye)
accuracy = len(inside_interval) / len(Ye)
conformal_accuracy = float("{0:.2f}".format(accuracy))
conformal_mean_interval = float(
"{0:.2f}".format(mean_interval))
ext_val_results.append(
('Conformal_mean_interval', 'Conformal mean interval',
conformal_mean_interval))
ext_val_results.append(
('Conformal_accuracy', 'Conformal accuracy',
conformal_accuracy))
self.conveyor.addVal(ext_val_results, 'external-validation',
'external validation', 'method', 'single',
'External validation results')
def run_internal(self):
'''
Builds a model using the internally defined machine learning tools.
All input parameters are extracted from self.param.
The main output is an instance of basemodel saved in
the model folder as a pickle (model.pkl) and used for prediction.
The results of building and validation are added to results,
but also saved to the model folder as a pickle (info.pkl)
for being displayed in manage tools.
'''
# expand with new methods here:
# registered_methods = [('RF', RF),
# ('SVM', SVM),
# ('GNB', GNB),
# ('PLSR', PLSR),
# ('PLSDA', PLSDA),
# ('median', median),
# ('mean', mean),
# ('majority', majority),
# ('logicalOR', logicalOR),
# ('matrix', matrix)]
if self.param.getVal('model') == 'XGBOOST':
from flame.stats.XGboost import XGBOOST
self.registered_methods.append(('XGBOOST', XGBOOST))
# check suitability of Y matrix
if not self.param.getVal('quantitative'):
success, yresult = utils.qualitative_Y(self.Y)
if not success:
self.conveyor.setError(yresult)
return
# print (np.shape(self.X))
# collect model information from parameters
model_type_info = []
model_type_info.append(
('quantitative', 'True if the endpoint is quantitative',
self.param.getVal('quantitative')))
model_type_info.append(
('conformal', 'True if the endpoint is conformal',
self.param.getVal('conformal')))
model_type_info.append(
('confidential', 'True if the model is confidential',
self.param.getVal('confidential')))
model_type_info.append(
('secret',
'True for barebone models exported by a confidential models',
False))
model_type_info.append(
('ensemble', 'True if the model is an ensemble of models',
self.param.getVal('input_type') == 'model_ensemble'))
model_type_info.append(('ensemble_names', 'List of ensemble models',
self.param.getVal('ensemble_names')))
model_type_info.append(
('ensemble_versions', 'List of ensemble versions',
self.param.getVal('ensemble_versions')))
model_type_info.append(
('conformal_confidence', 'Confidence of the conformal model',
self.param.getVal('conformalConfidence')))
self.conveyor.addVal(model_type_info, 'model_type_info',
'model type information', 'method', 'single',
'Information about the type of model')
# instantiate an appropriate child of base_model
model = None
for imethod in self.registered_methods:
if imethod[0] == self.param.getVal('model'):
# we instantiate the subtype of base_model,
# passing
# - preteated X and Y matrices for model building
# - model parameters (param)
# - already obtained results (conveyor)
model = imethod[1](self.X, self.Y, self.param, self.conveyor)
LOG.debug('Recognized learner: '
f"{self.param.getVal('model')}")
break
if not model:
self.conveyor.setError(
f'Modeling method {self.param.getVal("model")}'
'not recognized')
LOG.error(f'Modeling method {self.param.getVal("model")}'
'not recognized')
return
if self.conveyor.getError():
return
# build model
LOG.debug('Starting model building')
success, model_building_results = model.build()
if not success:
self.conveyor.setError(model_building_results)
return
self.conveyor.addVal(model_building_results, 'model_build_info',
'model building information', 'method', 'single',
'Information about the model building')
if hasattr(model, 'feature_importances'):
self.conveyor.addVal(
model.feature_importances, 'feature_importances',
'feature importances', 'method', 'vars',
'Information about the relative importance of the model variables'
)
if hasattr(model, 'feature_importances_method'):
self.conveyor.addVal(
model.feature_importances_method, 'feature_importances_method',
'feature importances_method', 'method', 'single',
'Method used to compute the relative importance of the model variables'
)
# validate model
if self.param.getVal('input_type') == 'model_ensemble':
validation_method = 'ensemble validation'
else:
validation_method = self.param.getVal("ModelValidationCV")
LOG.info(f'Validating the model using method: {validation_method}')
success, model_validation_results = model.validate()
if not success:
self.conveyor.setError(model_validation_results)
return
# model_validation_results is a dictionary which contains model_validation_info and
# (optionally) Y_adj and Y_pred, depending on the model type
self.conveyor.addVal(model_validation_results['quality'],
'model_valid_info',
'model validation information', 'method',
'single',
'Information about the model validation')
# non-conformal qualitative and quantitative models
if 'Y_adj' in model_validation_results:
self.conveyor.addVal(
model_validation_results['Y_adj'], 'Y_adj', 'Y fitted',
'result', 'objs',
'Y values of the training series fitted by the model')
if 'Y_pred' in model_validation_results:
self.conveyor.addVal(
model_validation_results['Y_pred'], 'Y_pred', 'Y predicted',
'result', 'objs',
'Y values of the training series predicted by the model')
if 'Conformal_prediction_ranges' in model_validation_results:
self.conveyor.addVal(
model_validation_results['Conformal_prediction_ranges'],
'Conformal_prediction_ranges', 'Conformal prediction ranges',
'method', 'objs',
'Interval for the cross-validated predictions')
if 'Conformal_prediction_ranges_fitting' in model_validation_results:
self.conveyor.addVal(
model_validation_results[
'Conformal_prediction_ranges_fitting'],
'Conformal_prediction_ranges_fitting',
'Conformal prediction ranges fitting', 'method', 'objs',
'Interval for the predictions in fitting')
# conformal qualitative models produce a list of tuples, indicating
# if the object is predicted to belong to class 0 and 1
if 'classes' in model_validation_results:
for i in range(len(model_validation_results['classes'][0])):
class_key = 'c' + str(i)
class_label = 'Class ' + str(i)
class_list = model_validation_results['classes'][:, i].tolist()
self.conveyor.addVal(class_list, class_key, class_label,
'result', 'objs',
'Conformal class assignment', 'main')
# conformal quantitataive models produce a list of tuples, indicating
# the minumum and maximum value
dimRed = self.param.getVal("dimensionality_reduction")
if dimRed is None:
nobj, nvarx = np.shape(self.X)
if nvarx > 300:
dimRed = 't-SNE'
else:
dimRed = 'PCA'
if dimRed == 'PCA':
generatePCASpace(self.X, self.param, self.conveyor)
elif dimRed == 't-SNE':
generateManifoldSpace(self.X, self.param, self.conveyor)
# TODO: compute AD (when applicable)
LOG.info('Model finished successfully')
# save model
model.save_model()
return
def external_validation(self):
''' when experimental values are available for the predicted compounds,
run external validation '''
if self.conveyor.getVal("values") is None:
LOG.error("Predicted activity vector is empty")
return
if self.conveyor.getVal("ymatrix") is None:
LOG.error("External activity vector is empty")
return
ext_val_results = []
# Ye are the y values present in the input file
Ye = np.asarray(self.conveyor.getVal("ymatrix"))
# For qualitative models, make sure the Y is qualitative as well
if not self.param.getVal("quantitative"):
qy, message = utils.qualitative_Y(Ye)
if not qy:
self.conveyor.setWarning(
f'No qualitative activity suitable for external validation "{message}". Skipping.'
)
LOG.warning(
f'No qualitative activity suitable for external validation "{message}". Skipping.'
)
return
# there are four variants of external validation, depending if the variable is qualitative or quantitative
if not self.param.getVal("quantitative"):
# qualitative
Yp = np.asarray(self.conveyor.getVal("values"))
if len(Yp[Yp == -1]) > 0:
pseudo_conformal = True
nobj = len(Ye)
Ye = Ye[Yp != -1]
Yp = Yp[Yp != -1]
coverage = len(Ye) / nobj
ext_val_results.append(
('Conformal_coverage',
'Conformal coverage in external-validation', coverage))
else:
pseudo_conformal = False
if Ye.size == 0:
LOG.error("Experimental activity vector is empty")
return
if Yp.size == 0:
LOG.error("Predicted activity vector is empty")
return
# the use of labels is compulsory to inform the confusion matrix that
# it must return a 2x2 confussion matrix. Otherwise it will fail when
# a single class is represented (all TP, for example)
TN, FP, FN, TP = confusion_matrix(Ye, Yp, labels=[0, 1]).ravel()
# protect to avoid warnings in special cases (div by zero)
MCC = matthews_corrcoef(Ye, Yp)
if (TP + FN) > 0:
sensitivity = (TP / (TP + FN))
else:
sensitivity = 0.0
if (TN + FP) > 0:
specificity = (TN / (TN + FP))
else:
specificity = 0.0
ext_val_results.append(
('TP', 'True positives in external-validation', float(TP)))
ext_val_results.append(
('TN', 'True negatives in external-validation', float(TN)))
ext_val_results.append(
('FP', 'False positives in external-validation', float(FP)))
ext_val_results.append(
('FN', 'False negatives in external-validation', float(FN)))
ext_val_results.append(
('Sensitivity', 'Sensitivity in external-validation',
float(sensitivity)))
ext_val_results.append(
('Specificity', 'Specificity in external-validation',
float(specificity)))
ext_val_results.append(
('MCC',
'Mattews Correlation Coefficient in external-validation',
float(MCC)))
if pseudo_conformal:
try:
conformal_accuracy = (float(TN + TP) /
float(FP + FN + TN + TP))
except Exception as e:
LOG.error(f'Failed to compute conformal accuracy with'
f'exception {e}')
conformal_accuracy = '-'
ext_val_results.append(
('Conformal_accuracy',
'Conformal accuracy in external-validation',
conformal_accuracy))
else:
# quantitative
Yp = np.asarray(self.conveyor.getVal("values"))
if Yp.size == 0:
LOG.error("Predicted activity vector is empty")
return
if Ye.size == 0:
LOG.error("Experimental activity vector is empty")
return
Ym = np.mean(Ye)
nobj = len(Yp)
SSY0_out = np.sum(np.square(Ym - Ye))
SSY_out = np.sum(np.square(Ye - Yp))
scoringP = mean_squared_error(Ye, Yp)
SDEP = np.sqrt(SSY_out / (nobj))
if SSY0_out == 0:
Q2 = 0.0
else:
Q2 = 1.00 - (SSY_out / SSY0_out)
ext_val_results.append(('scoringP', 'Scoring P', scoringP))
ext_val_results.append(
('Q2', 'Determination coefficient in cross-validation', Q2))
ext_val_results.append(
('SDEP', 'Standard Deviation Error of the Predictions', SDEP))
self.conveyor.addVal(ext_val_results, 'external-validation',
'external validation', 'method', 'single',
'External validation results')
def run_internal(self):
'''
Builds a model using the internally defined machine learning tools.
All input parameters are extracted from self.param.
The main output is an instance of basemodel saved in
the model folder as a pickle (model.pkl) and used for prediction.
The results of building and validation are added to results,
but also saved to the model folder as a pickle (info.pkl)
for being displayed in manage tools.
'''
# check suitability of Y matrix
if not self.param.getVal('quantitative') :
success, yresult = utils.qualitative_Y(self.Y)
if not success:
self.conveyor.setError(yresult)
return
# pre-process data
success, message = self.preprocess()
if not success:
self.conveyor.setError(message)
return
# collect model information from parameters
model_type_info = []
model_type_info.append(('quantitative', 'True if the endpoint is quantitative', self.param.getVal('quantitative')))
model_type_info.append(('conformal', 'True if the endpoint is conformal', self.param.getVal('conformal')))
model_type_info.append(('ensemble', 'True if the model is an ensemble of models', self.param.getVal('input_type') == 'model_ensemble'))
model_type_info.append(('ensemble_names', 'List of ensemble models', self.param.getVal('ensemble_names')))
model_type_info.append(('ensemble_versions', 'List of ensemble versions', self.param.getVal('ensemble_versions')))
model_type_info.append(('conformal_confidence', 'Confidence of the conformal model', self.param.getVal('conformalConfidence')))
self.conveyor.addVal(
model_type_info,
'model_type_info',
'model type information',
'method',
'single',
'Information about the type of model')
# instantiate an appropriate child of base_model
model = None
for imethod in self.registered_methods:
if imethod[0] == self.param.getVal('model'):
# we instantiate the subtype of base_model,
# passing
# - preteated X and Y matrices for model building
# - model parameters (param)
# - already obtained results (conveyor)
model = imethod[1](self.X, self.Y, self.param, self.conveyor)
LOG.debug('Recognized learner: '
f"{self.param.getVal('model')}")
break
if not model:
self.conveyor.setError(f'Modeling method {self.param.getVal("model")}'
'not recognized')
LOG.error(f'Modeling method {self.param.getVal("model")}'
'not recognized')
return
if self.conveyor.getError():
return
# build model
LOG.debug('Starting model building')
success, model_building_results = model.build()
if not success:
self.conveyor.setError(model_building_results)
return
self.conveyor.addVal(
model_building_results,
'model_build_info',
'model building information',
'method',
'single',
'Information about the model building')
# validate model
if self.param.getVal('input_type') == 'model_ensemble':
validation_method = 'ensemble validation'
else:
validation_method = self.param.getVal("ModelValidationCV")
LOG.info(f'Validating the model using method: {validation_method}')
success, model_validation_results = model.validate()
if not success:
self.conveyor.setError(model_validation_results)
return
# model_validation_results is a dictionary which contains model_validation_info and
# (optionally) Y_adj and Y_pred, depending on the model type
self.conveyor.addVal(
model_validation_results['quality'],
'model_valid_info',
'model validation information',
'method',
'single',
'Information about the model validation')
# non-conformal qualitative and quantitative models
if 'Y_adj' in model_validation_results:
self.conveyor.addVal(
model_validation_results['Y_adj'],
'Y_adj',
'Y fitted',
'result',
'objs',
'Y values of the training series fitted by the model')
if 'Y_pred' in model_validation_results:
self.conveyor.addVal(
model_validation_results['Y_pred'],
'Y_pred',
'Y predicted',
'result',
'objs',
'Y values of the training series predicted by the model')
if 'Conformal_prediction_ranges' in model_validation_results:
self.conveyor.addVal(
model_validation_results['Conformal_prediction_ranges'],
'Conformal_prediction_ranges',
'Conformal prediction ranges',
'method',
'objs',
'Interval for the cross-validated predictions')
if 'Conformal_prediction_ranges_fitting' in model_validation_results:
self.conveyor.addVal(
model_validation_results['Conformal_prediction_ranges_fitting'],
'Conformal_prediction_ranges_fitting',
'Conformal prediction ranges fitting',
'method',
'objs',
'Interval for the predictions in fitting')
# conformal qualitative models produce a list of tuples, indicating
# if the object is predicted to belong to class 0 and 1
if 'classes' in model_validation_results:
for i in range(len(model_validation_results['classes'][0])):
class_key = 'c' + str(i)
class_label = 'Class ' + str(i)
class_list = model_validation_results['classes'][:, i].tolist()
self.conveyor.addVal( class_list,
class_key, class_label,
'result', 'objs',
'Conformal class assignment',
'main')
# conformal quantitataive models produce a list of tuples, indicating
# the minumum and maximum value
# TODO: compute AD (when applicable)
# generate a proyected space and use it to generate graphics
generateProjectedSpace(self.X, self.param, self.conveyor)
LOG.info('Model finished successfully')
# save model
model.save_model()
return
