orhankislal commented on a change in pull request #518:
URL: https://github.com/apache/madlib/pull/518#discussion_r494129949
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File path: src/ports/postgres/modules/deep_learning/madlib_keras_automl.py_in
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@@ -291,9 +338,100 @@ class KerasAutoML():
(self.metrics_compute_frequency >= 1 and \
self.metrics_compute_frequency <= num_iterations)
+ def print_best_so_far(self):
+ """
+ Prints mst keys with best train/val losses at a given point.
+ """
+ best_so_far = '\n'
+ best_so_far += self.print_best_helper('training')
+ if self.validation_table:
+ best_so_far += self.print_best_helper('validation')
+ plpy.info(best_so_far)
+
+ def print_best_helper(self, keyword):
+ """
+ Helper function to Prints mst keys with best train/val losses at a
given point.
+ :param keyword: column prefix ('training' or 'validation')
+ :return:
+ """
+ metrics_word, loss_word = keyword + '_metrics_final', keyword +
'_loss_final'
+
+ res_str = 'Best {keyword} loss so far:\n'.format(keyword=keyword)
+ best_value = plpy.execute("SELECT {ModelSelectionSchema.MST_KEY},
{metrics_word}, " \
+ "{loss_word} FROM {self.model_info_table}
ORDER BY " \
+ "{loss_word} LIMIT 1".format(self=self,
ModelSelectionSchema=ModelSelectionSchema,
+
metrics_word=metrics_word, loss_word=loss_word))[0]
+ mst_key_value, metric_value, loss_value =
best_value[ModelSelectionSchema.MST_KEY], \
+ best_value[metrics_word],
best_value[loss_word]
+ res_str += ModelSelectionSchema.MST_KEY + '=' + str(mst_key_value) +
': metric=' + str(metric_value) + \
+ ', loss=' + str(loss_value) + '\n'
+ return res_str
+
+ def get_current_timestamp(self):
+ """for start and end times for the chosen AutoML algorithm. Showcased
in the output summary table"""
+ return datetime.fromtimestamp(time()).strftime('%Y-%m-%d %H:%M:%S')
+
+ def remove_temp_tables(self, model_training):
+ """
+ Remove all intermediate tables created for AutoML runs/updates.
+ :param model_training: Fit Multiple function call object.
+ """
+ drop_tables([model_training.original_model_output_table,
model_training.model_info_table,
+ model_training.model_summary_table,
AutoMLSchema.TEMP_MST_TABLE,
+ AutoMLSchema.TEMP_MST_SUMMARY_TABLE])
+
+# @MinWarning("warning")
+class AutoMLHyperband(KerasAutoML):
+ """
+ This class implements Hyperband, an infinite-arm bandit based algorithm
that speeds up random search
+ through adaptive resource allocation, successive halving (SHA) and early
stopping.
+
+ This class showcases and novel hyperband implementation by executing the
hyperband rounds 'diagonally'
Review comment:
I meant this:
`This class showcases and novel hyperband` -> `This class showcases a novel
hyperband`
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