def test_normal_run(self):
self.docfreq_args.update(self.extractor_args)
weight = 2
for key in self.docfreq_args:
if "_weight" in key:
self.docfreq_args[key] *= weight # make not 1
arguments = dict_to_arguments(self.docfreq_args)
with patch.dict(sourced.ml.extractors.__extractors__,
self.extractors,
clear=True):
result = modify_feature_weights(deepcopy(self.batches), arguments)
self.assertEqual(len(result), len(self.batches))
for bathc_res, batch_init in zip(result, self.batches):
bathc_res.matrix.sort_indices()
batch_init.matrix.sort_indices()
self.assertTrue(
numpy.array_equal(bathc_res.matrix.indices,
batch_init.matrix.indices))
self.assertTrue(
numpy.array_equal(bathc_res.matrix.data,
batch_init.matrix.data * weight))
self.assertTrue(
numpy.array_equal(bathc_res.matrix.indptr,
batch_init.matrix.indptr))
pass
def test_empty_batches(self):
self.docfreq_args.update(self.extractor_args)
arguments = dict_to_arguments(self.docfreq_args)
with patch.dict(sourced.ml.extractors.__extractors__,
self.extractors,
clear=True):
result = modify_feature_weights([], arguments)
self.assertEqual(len(result), 0)
def test_no_docfreq(self):
no_file = tempfile.NamedTemporaryFile(prefix="test_weighting", delete=False)
no_file.close()
try:
os.remove(no_file.name)
except:
pass
no_docfreq = {"docfreq": no_file.name}
no_docfreq.update(self.extractor_args)
arguments = dict_to_arguments(self.docfreq_args)
with patch.dict(sourced.ml.extractors.__extractors__, self.extractors, clear=True):
self.assertRaises(Exception, modify_feature_weights(self.batches, arguments))
def test_empty_extractors(self):
arguments = dict_to_arguments(self.docfreq_args)
with patch.dict(sourced.ml.extractors.__extractors__, self.extractors, clear=True):
result = modify_feature_weights(deepcopy(self.batches), arguments)
self.assertEqual(len(result), len(self.batches))
for bathc_res, batch_init in zip(result, self.batches):
bathc_res.matrix.sort_indices()
batch_init.matrix.sort_indices()
self.assertTrue(numpy.array_equal(bathc_res.matrix.indices,
batch_init.matrix.indices))
self.assertTrue(numpy.array_equal(bathc_res.matrix.data, batch_init.matrix.data))
self.assertTrue(numpy.array_equal(bathc_res.matrix.indptr,
batch_init.matrix.indptr))
def test_no_docfreq(self):
no_file = tempfile.NamedTemporaryFile(prefix="test_weighting",
delete=False)
no_file.close()
try:
os.remove(no_file.name)
except:
pass
no_docfreq = {"docfreq": no_file.name}
no_docfreq.update(self.extractor_args)
arguments = dict_to_arguments(self.docfreq_args)
with patch.dict(sourced.ml.extractors.__extractors__,
self.extractors,
clear=True):
self.assertRaises(Exception,
modify_feature_weights(self.batches, arguments))
def test_empty_extractors(self):
arguments = dict_to_arguments(self.docfreq_args)
with patch.dict(sourced.ml.extractors.__extractors__,
self.extractors,
clear=True):
result = modify_feature_weights(deepcopy(self.batches), arguments)
self.assertEqual(len(result), len(self.batches))
for bathc_res, batch_init in zip(result, self.batches):
bathc_res.matrix.sort_indices()
batch_init.matrix.sort_indices()
self.assertTrue(
numpy.array_equal(bathc_res.matrix.indices,
batch_init.matrix.indices))
self.assertTrue(
numpy.array_equal(bathc_res.matrix.data,
batch_init.matrix.data))
self.assertTrue(
numpy.array_equal(bathc_res.matrix.indptr,
batch_init.matrix.indptr))
def test_normal_run(self):
self.docfreq_args.update(self.extractor_args)
weight = 2
for key in self.docfreq_args:
if "_weight" in key:
self.docfreq_args[key] *= weight # make not 1
arguments = dict_to_arguments(self.docfreq_args)
with patch.dict(sourced.ml.extractors.__extractors__, self.extractors, clear=True):
result = modify_feature_weights(deepcopy(self.batches), arguments)
self.assertEqual(len(result), len(self.batches))
for bathc_res, batch_init in zip(result, self.batches):
bathc_res.matrix.sort_indices()
batch_init.matrix.sort_indices()
self.assertTrue(numpy.array_equal(bathc_res.matrix.indices,
batch_init.matrix.indices))
self.assertTrue(numpy.array_equal(bathc_res.matrix.data,
batch_init.matrix.data * weight))
self.assertTrue(numpy.array_equal(bathc_res.matrix.indptr,
batch_init.matrix.indptr))
pass
def test_empty_batches(self):
self.docfreq_args.update(self.extractor_args)
arguments = dict_to_arguments(self.docfreq_args)
with patch.dict(sourced.ml.extractors.__extractors__, self.extractors, clear=True):
result = modify_feature_weights([], arguments)
self.assertEqual(len(result), 0)