def run(self):
folds_test = mask.compute_folds(self.I,self.J,self.K,self.M)
folds_training = mask.compute_Ms(folds_test)
for i,(train,test) in enumerate(zip(folds_training,folds_test)):
print "Fold %s of nested cross-validation." % (i+1)
# Run the cross-validation
crossval = ParallelMatrixCrossValidation(
#crossval = MatrixCrossValidation(
method=self.method,
X=self.X,
M=train,
K=self.K,
parameter_search=self.parameter_search,
train_config=self.train_config,
file_performance=self.files_nested_performances[i],
P=self.P
)
crossval.run()
(best_parameters,_) = crossval.find_best_parameters(evaluation_criterion='MSE',low_better=True)
print "Best parameters for fold %s were %s." % (i+1,best_parameters)
# Train the model and test the performance on the test set
performance_dict = self.run_model(train,test,best_parameters)
self.store_performances(performance_dict)
print "Finished fold %s, with performances %s." % (i+1,performance_dict)
self.log()
def run(self):
folds_test = mask.compute_folds(self.I, self.J, self.K, self.M)
folds_training = mask.compute_Ms(folds_test)
for i, (train, test) in enumerate(zip(folds_training, folds_test)):
print "Fold %s of nested cross-validation." % (i + 1)
# Run the cross-validation
crossval = ParallelMatrixCrossValidation(
#crossval = MatrixCrossValidation(
method=self.method,
X=self.X,
M=train,
K=self.K,
parameter_search=self.parameter_search,
train_config=self.train_config,
file_performance=self.files_nested_performances[i],
P=self.P)
crossval.run()
(best_parameters,
_) = crossval.find_best_parameters(evaluation_criterion='MSE',
low_better=True)
print "Best parameters for fold %s were %s." % (i + 1,
best_parameters)
# Train the model and test the performance on the test set
performance_dict = self.run_model(train, test, best_parameters)
self.store_performances(performance_dict)
print "Finished fold %s, with performances %s." % (
i + 1, performance_dict)
self.log()
def run(self):
folds_test = mask.compute_folds_attempts(I=self.I,J=self.J,no_folds=self.K,attempts=attempts_generate_M,M=self.M)
folds_training = mask.compute_Ms(folds_test)
for i,(train,test) in enumerate(zip(folds_training,folds_test)):
print "Fold %s of nested cross-validation." % (i+1)
# Run the cross-validation
crossval = ParallelMatrixCrossValidation(
#crossval = MatrixCrossValidation(
method=self.method,
X=self.X,
M=train,
K=self.K,
parameter_search=self.parameter_search,
train_config=self.train_config,
file_performance=self.files_nested_performances[i],
P=self.P
)
crossval.run()
try:
(best_parameters,_) = crossval.find_best_parameters(evaluation_criterion='MSE',low_better=True)
print "Best parameters for fold %s were %s." % (i+1,best_parameters)
except KeyError:
best_parameters = self.parameter_search[0]
print "Found no performances, dataset too sparse? Use first values instead for fold %s, %s." % (i+1,best_parameters)
# Train the model and test the performance on the test set
performance_dict = self.run_model(train,test,best_parameters)
self.store_performances(performance_dict)
print "Finished fold %s, with performances %s." % (i+1,performance_dict)
self.log()
def run(self):
folds_test = mask.compute_folds_attempts(I=self.I,J=self.J,no_folds=self.K,attempts=attempts_generate_M,M=self.M)
folds_training = mask.compute_Ms(folds_test)
for i,(train,test) in enumerate(zip(folds_training,folds_test)):
print "Fold %s of nested cross-validation." % (i+1)
# Run the cross-validation
crossval = ParallelMatrixCrossValidation(
#crossval = MatrixCrossValidation(
method=self.method,
X=self.X,
M=train,
K=self.K,
parameter_search=self.parameter_search,
train_config=self.train_config,
predict_config=self.predict_config,
file_performance=self.files_nested_performances[i],
P=self.P
)
crossval.run()
try:
(best_parameters,_) = crossval.find_best_parameters(evaluation_criterion='MSE',low_better=True)
print "Best parameters for fold %s were %s." % (i+1,best_parameters)
except KeyError:
best_parameters = self.parameter_search[0]
print "Found no performances, dataset too sparse? Use first values instead for fold %s, %s." % (i+1,best_parameters)
# Train the model and test the performance on the test set
performance_dict = self.run_model(train,test,best_parameters)
self.store_performances(performance_dict)
print "Finished fold %s, with performances %s." % (i+1,performance_dict)
self.log()
