def test_cv_reproducibility(self):
"""
Tests that the splitting of the data by a cv splitter node
is deterministic given the run_number
"""
second_cv_splitter = CrossValidationSplitterNode(splits = 10)
second_cv_splitter.register_input_node(self.source)
# Test whether the two splitter give the same results for an
# arbitrary run number (say 7)
second_cv_splitter.set_run_number(7)
self.cv_splitter.set_run_number(7)
train_data1 = list(self.cv_splitter.request_data_for_training(False))
train_data2 = list(second_cv_splitter.request_data_for_training(False))
# Check that all data points in the training set generated by
# the first splitter are also in the set of the second splitter
all_contained = True
for datapoint1, label1 in train_data1:
this_contained = False
for datapoint2, label2 in train_data2:
this_contained |= (datapoint1.view(numpy.ndarray) == datapoint2.view(numpy.ndarray)).all() and (label1 == label2)
if this_contained: break
all_contained &= this_contained
if not all_contained: break
self.assert_(all_contained,
"CV Splitter generated different splits for the same run numbers")
def test_cv_dependance_on_run_number(self):
"""
Tests that the splitting of the data by a cv splitter node
is randomized by the run number
"""
second_cv_splitter = CrossValidationSplitterNode(splits = 10)
second_cv_splitter.register_input_node(self.source)
# Test whether the two splitter give different results for two
# arbitrary run numbers (say 7 and 8)
second_cv_splitter.set_run_number(7)
self.cv_splitter.set_run_number(8)
train_data1 = list(self.cv_splitter.request_data_for_training(False))
train_data2 = list(second_cv_splitter.request_data_for_training(False))
# Check that there is a data point in the training set generated by
# the first splitter that is not in the set of the second splitter
# NOTE: The small chance that they produce the same split for the
# specific numbers 7 and 8 but not for all run numbers is neglected...
one_not_contained = False
for datapoint1, label1 in train_data1:
this_contained = False
for datapoint2, label2 in train_data2:
this_contained |= (datapoint1.view(numpy.ndarray) == datapoint2.view(numpy.ndarray)).all() and (label1 == label2)
if this_contained: break
one_not_contained |= (not this_contained)
if one_not_contained: break
self.assert_(one_not_contained,
"CV Splitter generated the same split for two run numbers")
def test_cv_reproducibility(self):
"""
Tests that the splitting of the data by a cv splitter node
is deterministic given the run_number
"""
second_cv_splitter = CrossValidationSplitterNode(splits = 10)
second_cv_splitter.register_input_node(self.source)
# Test whether the two splitter give the same results for an
# arbitrary run number (say 7)
second_cv_splitter.set_run_number(7)
self.cv_splitter.set_run_number(7)
train_data1 = list(self.cv_splitter.request_data_for_training(False))
train_data2 = list(second_cv_splitter.request_data_for_training(False))
# Check that all data points in the training set generated by
# the first splitter are also in the set of the second splitter
all_contained = True
for datapoint1, label1 in train_data1:
this_contained = False
for datapoint2, label2 in train_data2:
this_contained |= (datapoint1.view(numpy.ndarray) == datapoint2.view(numpy.ndarray)).all() and (label1 == label2)
if this_contained: break
all_contained &= this_contained
if not all_contained: break
self.assert_(all_contained,
"CV Splitter generated different splits for the same run numbers")
def test_cv_dependance_on_run_number(self):
"""
Tests that the splitting of the data by a cv splitter node
is randomized by the run number
"""
second_cv_splitter = CrossValidationSplitterNode(splits = 10)
second_cv_splitter.register_input_node(self.source)
# Test whether the two splitter give different results for two
# arbitrary run numbers (say 7 and 8)
second_cv_splitter.set_run_number(7)
self.cv_splitter.set_run_number(8)
train_data1 = list(self.cv_splitter.request_data_for_training(False))
train_data2 = list(second_cv_splitter.request_data_for_training(False))
# Check that there is a data point in the training set generated by
# the first splitter that is not in the set of the second splitter
# NOTE: The small chance that they produce the same split for the
# specific numbers 7 and 8 but not for all run numbers is neglected...
one_not_contained = False
for datapoint1, label1 in train_data1:
this_contained = False
for datapoint2, label2 in train_data2:
this_contained |= (datapoint1.view(numpy.ndarray) == datapoint2.view(numpy.ndarray)).all() and (label1 == label2)
if this_contained: break
one_not_contained |= (not this_contained)
if one_not_contained: break
self.assert_(one_not_contained,
"CV Splitter generated the same split for two run numbers")
class CrossValidationSplitterTestCase(unittest.TestCase):
def setUp(self):
self.source = SimpleTimeSeriesSourceNode()
self.cv_splitter = CrossValidationSplitterNode(splits = 10)
self.cv_splitter.register_input_node(self.source)
def test_cv_coverage_by_testdata(self):
"""
Tests that all data points are contained at least once in a test set
"""
all_testdata = []
# For every split of the dataset
while True: # As long as more splits are available
# Append all test data of the current split
all_testdata.extend(self.cv_splitter.request_data_for_testing())
# If no more splits are available
if not self.cv_splitter.use_next_split():
break
# Check that every data point from the source was once in a test set
for orig_data, orig_label in self.source.time_series:
found = False
for test_data, test_label in all_testdata:
found |= (orig_data.view(numpy.ndarray) == test_data.view(numpy.ndarray)).all() \
and (orig_label == test_label)
if found: break
self.assert_(found,
"One data point is never used for testing in cv splitting")
def test_cv_coverage_by_split(self):
"""
Tests that each split during crossvalidation covers the whole data set
"""
# For every split of the dataset
while True: # As long as more splits are available
split_data = []
# Append all data of the current split
split_data.extend(self.cv_splitter.request_data_for_training(False))
split_data.extend(self.cv_splitter.request_data_for_testing())
# Check that every data point from the source was once in a test set
for orig_datapoint, orig_label in self.source.time_series:
found = False
for split_datapoint, split_label in split_data:
found |= (orig_datapoint.view(numpy.ndarray) == split_datapoint.view(numpy.ndarray)).all() \
and (orig_label == split_label)
if found: break
self.assert_(found,
"One data point is neither used for training nor for testing in one cv split")
# If no more splits are available
if not self.cv_splitter.use_next_split():
break
def test_cv_train_test_seperation(self):
""" Test that no data point is contained in train and test set """
#For every split of the dataset
while True: # As long as more splits are available
# Check that no data point in the training is used for testing
train_data = list(self.cv_splitter.request_data_for_training(False))
test_data = list(self.cv_splitter.request_data_for_testing())
for training_datapoint, train_label in train_data:
doublet = False
for test_datapoint, test_label in test_data:
doublet |= (training_datapoint.view(numpy.ndarray) == test_datapoint.view(numpy.ndarray)).all() \
and (train_label == test_label)
if doublet:
break
self.assert_(not doublet,
"In one split of the cv splitter, a sample is used for training and testing")
# If no more splits are available
if not self.cv_splitter.use_next_split():
break
def test_cv_no_iterated_splitters(self):
""" Splitter cannot be applied to a node chain, that has already been split """
second_cv_splitter = CrossValidationSplitterNode(splits = 10)
second_cv_splitter.register_input_node(self.cv_splitter)
#check that the proper Exception is raised
#catch the exception and then do assertEqual
try:
second_cv_splitter.request_data_for_training(use_test_data=False)
self.assert_(False,"Concatenation of several splitters should not be possible!")
except Exception as e:
#cv_splitter just use raise Exception(msg)but Exception.message has been deprecated
#possible solution is to define own Exception subclass
self.assertEqual(str(e), "No iterated splitting of data sets allowed\n " +
"(Calling a splitter on a data set that is " +
"already split)",
"Concatenation of several splitters should not be possible!")
def test_cv_dependance_on_run_number(self):
"""
Tests that the splitting of the data by a cv splitter node
is randomized by the run number
"""
second_cv_splitter = CrossValidationSplitterNode(splits = 10)
second_cv_splitter.register_input_node(self.source)
# Test whether the two splitter give different results for two
# arbitrary run numbers (say 7 and 8)
second_cv_splitter.set_run_number(7)
self.cv_splitter.set_run_number(8)
train_data1 = list(self.cv_splitter.request_data_for_training(False))
train_data2 = list(second_cv_splitter.request_data_for_training(False))
# Check that there is a data point in the training set generated by
# the first splitter that is not in the set of the second splitter
# NOTE: The small chance that they produce the same split for the
# specific numbers 7 and 8 but not for all run numbers is neglected...
one_not_contained = False
for datapoint1, label1 in train_data1:
this_contained = False
for datapoint2, label2 in train_data2:
this_contained |= (datapoint1.view(numpy.ndarray) == datapoint2.view(numpy.ndarray)).all() and (label1 == label2)
if this_contained: break
one_not_contained |= (not this_contained)
if one_not_contained: break
self.assert_(one_not_contained,
"CV Splitter generated the same split for two run numbers")
def test_cv_reproducibility(self):
"""
Tests that the splitting of the data by a cv splitter node
is deterministic given the run_number
"""
second_cv_splitter = CrossValidationSplitterNode(splits = 10)
second_cv_splitter.register_input_node(self.source)
# Test whether the two splitter give the same results for an
# arbitrary run number (say 7)
second_cv_splitter.set_run_number(7)
self.cv_splitter.set_run_number(7)
train_data1 = list(self.cv_splitter.request_data_for_training(False))
train_data2 = list(second_cv_splitter.request_data_for_training(False))
# Check that all data points in the training set generated by
# the first splitter are also in the set of the second splitter
all_contained = True
for datapoint1, label1 in train_data1:
this_contained = False
for datapoint2, label2 in train_data2:
this_contained |= (datapoint1.view(numpy.ndarray) == datapoint2.view(numpy.ndarray)).all() and (label1 == label2)
if this_contained: break
all_contained &= this_contained
if not all_contained: break
self.assert_(all_contained,
"CV Splitter generated different splits for the same run numbers")
class CrossValidationSplitterTestCase(unittest.TestCase):
def setUp(self):
self.source = SimpleTimeSeriesSourceNode()
self.cv_splitter = CrossValidationSplitterNode(splits = 10)
self.cv_splitter.register_input_node(self.source)
def test_cv_coverage_by_testdata(self):
"""
Tests that all data points are contained at least once in a test set
"""
all_testdata = []
# For every split of the dataset
while True: # As long as more splits are available
# Append all test data of the current split
all_testdata.extend(self.cv_splitter.request_data_for_testing())
# If no more splits are available
if not self.cv_splitter.use_next_split():
break
# Check that every data point from the source was once in a test set
for orig_data, orig_label in self.source.time_series:
found = False
for test_data, test_label in all_testdata:
found |= (orig_data.view(numpy.ndarray) == test_data.view(numpy.ndarray)).all() \
and (orig_label == test_label)
if found: break
self.assert_(found,
"One data point is never used for testing in cv splitting")
def test_cv_coverage_by_split(self):
"""
Tests that each split during crossvalidation covers the whole data set
"""
# For every split of the dataset
while True: # As long as more splits are available
split_data = []
# Append all data of the current split
split_data.extend(self.cv_splitter.request_data_for_training(False))
split_data.extend(self.cv_splitter.request_data_for_testing())
# Check that every data point from the source was once in a test set
for orig_datapoint, orig_label in self.source.time_series:
found = False
for split_datapoint, split_label in split_data:
found |= (orig_datapoint.view(numpy.ndarray) == split_datapoint.view(numpy.ndarray)).all() \
and (orig_label == split_label)
if found: break
self.assert_(found,
"One data point is neither used for training nor for testing in one cv split")
# If no more splits are available
if not self.cv_splitter.use_next_split():
break
def test_cv_train_test_seperation(self):
""" Test that no data point is contained in train and test set """
#For every split of the dataset
while True: # As long as more splits are available
# Check that no data point in the training is used for testing
train_data = list(self.cv_splitter.request_data_for_training(False))
test_data = list(self.cv_splitter.request_data_for_testing())
for training_datapoint, train_label in train_data:
doublet = False
for test_datapoint, test_label in test_data:
doublet |= (training_datapoint.view(numpy.ndarray) == test_datapoint.view(numpy.ndarray)).all() \
and (train_label == test_label)
if doublet:
break
self.assert_(not doublet,
"In one split of the cv splitter, a sample is used for training and testing")
# If no more splits are available
if not self.cv_splitter.use_next_split():
break
def test_cv_no_iterated_splitters(self):
""" Splitter cannot be applied to a node chain, that has already been split """
second_cv_splitter = CrossValidationSplitterNode(splits = 10)
second_cv_splitter.register_input_node(self.cv_splitter)
#check that the proper Exception is raised
#catch the exception and then do assertEqual
try:
second_cv_splitter.request_data_for_training(use_test_data=False)
self.assert_(False,"Concatenation of several splitters should not be possible!")
except Exception as e:
#cv_splitter just use raise Exception(msg)but Exception.message has been deprecated
#possible solution is to define own Exception subclass
self.assertEqual(str(e), "No iterated splitting of data sets allowed\n " +
"(Calling a splitter on a data set that is " +
"already split)",
"Concatenation of several splitters should not be possible!")
def test_cv_dependance_on_run_number(self):
"""
Tests that the splitting of the data by a cv splitter node
is randomized by the run number
"""
second_cv_splitter = CrossValidationSplitterNode(splits = 10)
second_cv_splitter.register_input_node(self.source)
# Test whether the two splitter give different results for two
# arbitrary run numbers (say 7 and 8)
second_cv_splitter.set_run_number(7)
self.cv_splitter.set_run_number(8)
train_data1 = list(self.cv_splitter.request_data_for_training(False))
train_data2 = list(second_cv_splitter.request_data_for_training(False))
# Check that there is a data point in the training set generated by
# the first splitter that is not in the set of the second splitter
# NOTE: The small chance that they produce the same split for the
# specific numbers 7 and 8 but not for all run numbers is neglected...
one_not_contained = False
for datapoint1, label1 in train_data1:
this_contained = False
for datapoint2, label2 in train_data2:
this_contained |= (datapoint1.view(numpy.ndarray) == datapoint2.view(numpy.ndarray)).all() and (label1 == label2)
if this_contained: break
one_not_contained |= (not this_contained)
if one_not_contained: break
self.assert_(one_not_contained,
"CV Splitter generated the same split for two run numbers")
def test_cv_reproducibility(self):
"""
Tests that the splitting of the data by a cv splitter node
is deterministic given the run_number
"""
second_cv_splitter = CrossValidationSplitterNode(splits = 10)
second_cv_splitter.register_input_node(self.source)
# Test whether the two splitter give the same results for an
# arbitrary run number (say 7)
second_cv_splitter.set_run_number(7)
self.cv_splitter.set_run_number(7)
train_data1 = list(self.cv_splitter.request_data_for_training(False))
train_data2 = list(second_cv_splitter.request_data_for_training(False))
# Check that all data points in the training set generated by
# the first splitter are also in the set of the second splitter
all_contained = True
for datapoint1, label1 in train_data1:
this_contained = False
for datapoint2, label2 in train_data2:
this_contained |= (datapoint1.view(numpy.ndarray) == datapoint2.view(numpy.ndarray)).all() and (label1 == label2)
if this_contained: break
all_contained &= this_contained
if not all_contained: break
self.assert_(all_contained,
"CV Splitter generated different splits for the same run numbers")