def test_same_data_arrays(self):
first_array = np.array([[1, 2], [3, 4]])
second_array = np.array([[1, 2], [3, 4]])
first_hash = hash_numpy_array(first_array)
second_hash = hash_numpy_array(second_array)
self.assertEqual(first_hash, second_hash)
def test_different_data_arrays(self):
first_array = np.array([[1, 2], [3, 4]])
second_array = np.array([[1, 3], [2, 4]])
first_hash = hash_numpy_array(first_array)
second_hash = hash_numpy_array(second_array)
self.assertNotEqual(first_hash, second_hash)
def test_different_data_arrays(self):
first_array = np.array([[1, 2], [3, 4]])
second_array = np.array([[1, 3], [2, 4]])
first_hash = hash_numpy_array(first_array)
second_hash = hash_numpy_array(second_array)
self.assertNotEqual(first_hash, second_hash)
def test_same_data_arrays(self):
first_array = np.array([[1, 2], [3, 4]])
second_array = np.array([[1, 2], [3, 4]])
first_hash = hash_numpy_array(first_array)
second_hash = hash_numpy_array(second_array)
self.assertEqual(first_hash, second_hash)
def test_transpose_arrays(self):
c_array = np.array([[1, 2], [3, 4]])
f_array = np.array([[1, 3], [2, 4]])
f_array = np.asfortranarray(f_array)
c_hash = hash_numpy_array(c_array)
f_hash = hash_numpy_array(f_array)
self.assertEqual(c_hash, f_hash)
def test_transpose_arrays(self):
c_array = np.array([[1, 2], [3, 4]])
f_array = np.array([[1, 3], [2, 4]])
f_array = np.asfortranarray(f_array)
c_hash = hash_numpy_array(c_array)
f_hash = hash_numpy_array(f_array)
self.assertEqual(c_hash, f_hash)
def test_f_contiguous_array(self):
array = np.array([[1, 2], [3, 4]])
array = np.asfortranarray(array)
hash = hash_numpy_array(array)
self.assertIsNotNone(hash)
def test_f_contiguous_array(self):
array = np.array([[1, 2], [3, 4]])
array = np.asfortranarray(array)
hash = hash_numpy_array(array)
self.assertIsNotNone(hash)
def fit(self,
X,
y,
task=MULTICLASS_CLASSIFICATION,
metric='acc_metric',
feat_type=None,
dataset_name=None):
if not self._shared_mode:
self._backend.context.delete_directories()
else:
# If this fails, it's likely that this is the first call to get
# the data manager
try:
D = self._backend.load_datamanager()
dataset_name = D.name
except IOError:
pass
self._backend.context.create_directories()
if dataset_name is None:
dataset_name = hash_numpy_array(X)
self._backend.save_start_time(self._seed)
self._stopwatch = StopWatch()
self._dataset_name = dataset_name
self._stopwatch.start_task(self._dataset_name)
self._logger = self._get_logger(dataset_name)
if isinstance(metric, str):
metric = STRING_TO_METRIC[metric]
if feat_type is not None and len(feat_type) != X.shape[1]:
raise ValueError('Array feat_type does not have same number of '
'variables as X has features. %d vs %d.' %
(len(feat_type), X.shape[1]))
if feat_type is not None and not all(
[isinstance(f, str) for f in feat_type]):
raise ValueError('Array feat_type must only contain strings.')
if feat_type is not None:
for ft in feat_type:
if ft.lower() not in ['categorical', 'numerical']:
raise ValueError('Only `Categorical` and `Numerical` are '
'valid feature types, you passed `%s`' %
ft)
self._data_memory_limit = None
loaded_data_manager = XYDataManager(X,
y,
task=task,
metric=metric,
feat_type=feat_type,
dataset_name=dataset_name,
encode_labels=False)
return self._fit(loaded_data_manager)
def fit(self, X, y,
task=MULTICLASS_CLASSIFICATION,
metric='acc_metric',
feat_type=None,
dataset_name=None):
if not self._shared_mode:
self._backend.context.delete_directories()
else:
# If this fails, it's likely that this is the first call to get
# the data manager
try:
D = self._backend.load_datamanager()
dataset_name = D.name
except IOError:
pass
self._backend.context.create_directories()
if dataset_name is None:
dataset_name = hash_numpy_array(X)
self._backend.save_start_time(self._seed)
self._stopwatch = StopWatch()
self._dataset_name = dataset_name
self._stopwatch.start_task(self._dataset_name)
self._logger = self._get_logger(dataset_name)
if isinstance(metric, str):
metric = STRING_TO_METRIC[metric]
if feat_type is not None and len(feat_type) != X.shape[1]:
raise ValueError('Array feat_type does not have same number of '
'variables as X has features. %d vs %d.' %
(len(feat_type), X.shape[1]))
if feat_type is not None and not all([isinstance(f, str)
for f in feat_type]):
raise ValueError('Array feat_type must only contain strings.')
if feat_type is not None:
for ft in feat_type:
if ft.lower() not in ['categorical', 'numerical']:
raise ValueError('Only `Categorical` and `Numerical` are '
'valid feature types, you passed `%s`' % ft)
self._data_memory_limit = None
loaded_data_manager = XYDataManager(X, y,
task=task,
metric=metric,
feat_type=feat_type,
dataset_name=dataset_name,
encode_labels=False)
return self._fit(loaded_data_manager)
