Remove pg tests from fit and eval transition
JIRA: MADLIB-1438
We don't really need to test for pg because nothing in any of the transition
functions care about postgres
also our previous way of mocking is_platform_pg wasn't working correctly
Also removed the code for postgres from
get_image_count_per_seg_from_array since current_seg_id is always passed
in as 0 for pg. So just indexing it from the array should be good enough
For fit multiple, removed the call to is_platform_pg() while setting the
gp_segment_id_col because we don't support pg for fit multiple.
Co-authored-by: Ekta Khanna <ekhanna@vmware.com>
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.py_in
index 9287524..8a5b2b3 100644
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.py_in
+++ b/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.py_in
@@ -208,7 +208,7 @@
self.msts_for_schedule = self.msts
random.shuffle(self.msts_for_schedule)
self.grand_schedule = self.generate_schedule(self.msts_for_schedule)
- self.gp_segment_id_col = '0' if is_platform_pg() else GP_SEGMENT_ID_COLNAME
+ self.gp_segment_id_col = GP_SEGMENT_ID_COLNAME
self.unlogged_table = "UNLOGGED" if is_platform_gp6_or_up() else ''
if self.warm_start:
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in
index be9a1f9..cf030e1 100644
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in
+++ b/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in
@@ -110,14 +110,10 @@
def get_image_count_per_seg_from_array(current_seg_id, images_per_seg):
"""
Get the image count from the array containing all the images
- per segment. Based on the platform, we find the index of the current segment.
+ per segment.
This function is only called from inside the transition function.
"""
- if is_platform_pg():
- total_images = images_per_seg[0]
- else:
- total_images = images_per_seg[current_seg_id]
- return total_images
+ return images_per_seg[current_seg_id]
def get_image_count_per_seg_for_minibatched_data_from_db(table_name):
"""
diff --git a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in b/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
index 1b0ee8d..7cdd83c 100644
--- a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
+++ b/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
@@ -99,8 +99,7 @@
self.module_patcher.stop()
self.subject.K.clear_session()
- def _test_fit_transition_first_buffer_pass(self, is_platform_pg, **kwargs):
- self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
+ def _test_fit_transition_first_buffer_pass(self, **kwargs):
ending_image_count = len(self.dependent_var_int)
previous_state = np.array(self.model_weights, dtype=np.float32)
@@ -152,8 +151,7 @@
self.assertTrue(k['GD']['x_train'])
self.assertTrue(k['GD']['y_train'])
- def _test_fit_transition_middle_buffer_pass(self, is_platform_pg, **kwargs):
- self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
+ def _test_fit_transition_middle_buffer_pass(self, **kwargs):
starting_image_count = len(self.dependent_var_int)
ending_image_count = starting_image_count + len(self.dependent_var_int)
@@ -214,8 +212,7 @@
self.assertTrue(k['GD']['x_train'])
self.assertTrue(k['GD']['y_train'])
- def _test_fit_transition_last_buffer_pass(self, is_platform_pg, **kwargs):
- self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
+ def _test_fit_transition_last_buffer_pass(self, **kwargs):
starting_image_count = 2*len(self.dependent_var_int)
ending_image_count = starting_image_count + len(self.dependent_var_int)
@@ -237,10 +234,9 @@
self.assertTrue((weights == multiplied_weights).all())
self.assertEqual(ending_image_count, image_count)
- def _test_internal_keras_eval_transition_first_buffer(self, is_platform_pg,
+ def _test_internal_keras_eval_transition_first_buffer(self,
last_iteration = False,
**kwargs):
- self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
ending_image_count = len(self.dependent_var_int)
state = [0,0,0]
@@ -260,11 +256,9 @@
self.assertAlmostEqual(self.loss * image_count, agg_loss, 4)
self.assertAlmostEqual(self.accuracy * image_count, agg_accuracy, 4)
- def _test_internal_keras_eval_transition_last_buffer(self, is_platform_pg,
+ def _test_internal_keras_eval_transition_last_buffer(self,
last_iteration = False,
**kwargs):
- self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
-
starting_image_count = 2*len(self.dependent_var_int)
ending_image_count = starting_image_count + len(self.dependent_var_int)
@@ -448,24 +442,6 @@
self.assertTrue('x_train' not in k['GD'])
self.assertTrue('y_train' not in k['GD'])
- def test_fit_transition_first_buffer_pass_pg(self):
- self._test_fit_transition_first_buffer_pass(True)
-
- def test_fit_transition_first_buffer_pass_gpdb(self):
- self._test_fit_transition_first_buffer_pass(False)
-
- def test_fit_transition_middle_buffer_pass_pg(self):
- self._test_fit_transition_middle_buffer_pass(True)
-
- def test_fit_transition_middle_buffer_pass_gpdb(self):
- self._test_fit_transition_middle_buffer_pass(False)
-
- def test_fit_transition_last_buffer_pass_pg(self):
- self._test_fit_transition_last_buffer_pass(True)
-
- def test_fit_transition_last_buffer_pass_gpdb(self):
- self._test_fit_transition_last_buffer_pass(False)
-
############### GRAPH AND SESSION TESTS ################################
def test_fit_eval_2_iterations_mcf_null_gpdb(self):
kwargs = {'GD': {}}
