khannaekta commented on a change in pull request #506:
URL: https://github.com/apache/madlib/pull/506#discussion_r465932779
##########
File path: src/ports/postgres/modules/deep_learning/madlib_keras_wrapper.py_in
##########
@@ -201,13 +201,14 @@ def parse_and_validate_compile_params(str_of_args):
"""
literal_eval_compile_params = ['metrics', 'loss_weights',
'weighted_metrics', 'sample_weight_mode']
- accepted_compile_params = literal_eval_compile_params + ['optimizer',
'loss']
+ accepted_compile_params = literal_eval_compile_params + ['optimizer',
'loss', 'optimizer_params_list']
compile_dict = convert_string_of_args_to_dict(str_of_args)
compile_dict = validate_and_literal_eval_keys(compile_dict,
literal_eval_compile_params,
accepted_compile_params)
- _assert('optimizer' in compile_dict, "optimizer is a required parameter
for compile")
+ # _assert('optimizer' in compile_dict, "optimizer is a required parameter
for compile")
Review comment:
Remove these commented out code.
##########
File path:
src/ports/postgres/modules/deep_learning/madlib_keras_model_selection.py_in
##########
@@ -442,28 +468,43 @@ class MstSearch():
if self.random_state:
np.random.seed(self.random_state+seed_changes)
seed_changes += 1
-
param_values = params_dict[cp]
-
- # sampling from a distribution
- if param_values[-1] in ['linear', 'log']:
- _assert(len(param_values) == 3,
- "DL: {0} should have exactly 3 elements if picking
from a distribution".format(cp))
- _assert(param_values[1] > param_values[0],
- "DL: {0} should be of the format [lower_bound,
uppper_bound, distribution_type]".format(cp))
- if param_values[-1] == 'linear':
- config_dict[cp] = np.random.uniform(param_values[0],
param_values[1])
- elif param_values[-1] == 'log':
- config_dict[cp] = np.power(10,
np.random.uniform(np.log10(param_values[0]),
-
np.log10(param_values[1])))
- else:
- plpy.error("DL: Choose a valid distribution type!
('linear' or 'log')")
- # random sampling
+ if cp == ModelSelectionSchema.OPTIMIZER_PARAMS_LIST:
+ opt_dict = np.random.choice(param_values)
+ opt_combination = {}
+ for i in opt_dict:
+ opt_values = opt_dict[i]
+ if self.random_state:
+ np.random.seed(self.random_state+seed_changes)
+ seed_changes += 1
+ opt_combination[i] = self.sample_val(cp, opt_values)
+ config_dict[cp] = opt_combination
else:
- config_dict[cp] = np.random.choice(params_dict[cp])
-
+ config_dict[cp] = self.sample_val(cp, param_values)
return config_dict, seed_changes
+ def sample_val(self, cp, param_value_list):
Review comment:
Good to have a comment for what this function does.
##########
File path:
src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras_model_selection_table.py_in
##########
@@ -77,6 +373,8 @@ class LoadModelSelectionTableTestCase(unittest.TestCase):
"batch_size=10,epochs=1"
]
+ # plpy.execute("SELECT * FROM invalid_table;")
Review comment:
we can remove this comment code
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