[GitHub] [madlib] Advitya17 commented on a change in pull request #518: DL: [AutoML] Add support for Hyperopt on top of MOP for MPP + AutoML best-so-far

[GitBox <gi...@apache.org>]

Advitya17 commented on a change in pull request #518:
URL: https://github.com/apache/madlib/pull/518#discussion_r493918484



##########
File path: src/ports/postgres/modules/deep_learning/madlib_keras_automl.py_in
##########
@@ -17,27 +17,41 @@
 # specific language governing permissions and limitations
 # under the License.
 
+from ast import literal_eval
 from datetime import datetime
-import plpy
+from hyperopt import hp, rand, tpe, atpe, Trials, STATUS_OK, STATUS_RUNNING
+from hyperopt.base import Domain
 import math
+import numpy as np
+import plpy
 from time import time
 
 from madlib_keras_validator import MstLoaderInputValidator
-from utilities.utilities import unique_string, add_postfix, extract_keyvalue_params, \
-    _assert, _assert_equal, rename_table
+from utilities.utilities import get_seg_number, get_segments_per_host, unique_string, add_postfix, \
+    extract_keyvalue_params, _assert, _assert_equal, rename_table
 from utilities.control import MinWarning, SetGUC
 from madlib_keras_fit_multiple_model import FitMultipleModel
 from madlib_keras_model_selection import MstSearch, ModelSelectionSchema
 from keras_model_arch_table import ModelArchSchema
 from utilities.validate_args import table_exists, drop_tables
 
+import inspect
+
+def decallmethods(decorator, prefix='test_'):

Review comment:
       I inserted this to make the class inheritance work earlier. On further inspection, it seems I may not need this function.

##########
File path: src/ports/postgres/modules/deep_learning/madlib_keras_automl.py_in
##########
@@ -158,11 +174,10 @@ class HyperbandSchedule():
                                       **locals())
             plpy.execute(insert_query)
 
-@MinWarning("warning")
-class KerasAutoML():
-    """The core AutoML function for running AutoML algorithms such as Hyperband.
-    This function executes the hyperband rounds 'diagonally' to evaluate multiple configurations together
-    and leverage the compute power of MPP databases such as Greenplum.
+# @MinWarning("warning")

Review comment:
       They error out if I do that:
   ```
   ERROR:  TypeError: Error when calling the metaclass bases
       function() argument 1 must be code, not str (plpy_elog.c:121)
   ```




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