orhankislal commented on a change in pull request #518:
URL: https://github.com/apache/madlib/pull/518#discussion_r493744195
##########
File path: src/ports/postgres/modules/deep_learning/madlib_keras_automl.py_in
##########
@@ -500,63 +648,432 @@ class KerasAutoML():
"b (key integer, s_val integer, i_val integer) WHERE
t.mst_key=b.key".format(self=self, l=l)
plpy.execute(query)
- def update_model_selection_table(self):
+# @MinWarning("warning")
+class AutoMLHyperopt(KerasAutoML):
+ """
+ This class implements Hyperopt, another automl method that explores
awkward search spaces using
+ Random Search, Tree-structured Parzen Estimator (TPE), or Adaptive TPE.
+
+ This function executes hyperopt on top of our multiple model training
infrastructure powered with
+ Model hOpper Parallelism (MOP), a hybrid of data and task parallelism.
+
+ This automl method inherits qualities from the automl class.
+ """
+ def __init__(self, schema_madlib, source_table, model_output_table,
model_arch_table, model_selection_table,
+ model_id_list, compile_params_grid, fit_params_grid,
automl_method='hyperopt',
+ automl_params='num_models=20, num_iters=5, algorithm=tpe',
random_state=None, object_table=None,
+ use_gpus=False, validation_table=None,
metrics_compute_frequency=None,
+ name=None, description=None, **kwargs):
+ KerasAutoML.__init__(self, schema_madlib, source_table,
model_output_table, model_arch_table,
+ model_selection_table, model_id_list,
compile_params_grid, fit_params_grid,
+ automl_method, automl_params, random_state,
object_table, use_gpus,
+ validation_table, metrics_compute_frequency,
name, description, **kwargs)
+ self.compile_params_grid = self.compile_params_grid.replace('\n',
'').replace(' ', '')
+ self.fit_params_grid = self.fit_params_grid.replace('\n',
'').replace(' ', '')
+ try:
+ self.compile_params_grid = literal_eval(self.compile_params_grid)
+
+ except:
+ plpy.error("Invalid syntax in 'compile_params_dict'")
+ try:
+ self.fit_params_grid = literal_eval(self.fit_params_grid)
+ except:
+ plpy.error("Invalid syntax in 'fit_params_dict'")
+ self.validate_and_define_inputs()
+
+ self.num_workers = get_seg_number() * get_segments_per_host()
+
+ self.create_model_output_table()
+ self.create_model_output_info_table()
+ self.find_hyperopt_config()
+
+ def validate_and_define_inputs(self):
+ automl_params_dict = extract_keyvalue_params(self.automl_params,
+
default_values={'num_models': 20,
+
'num_iters': 5,
+
'algorithm': 'tpe'},
+ lower_case_names=True)
+ # casting relevant values to int
+ for i in automl_params_dict:
+ try:
+ automl_params_dict[i] = int(automl_params_dict[i])
+ except ValueError:
+ pass
+ _assert(len(automl_params_dict) >= 1 and len(automl_params_dict) <= 3,
+ "DL: Only num_models, num_iters, and algorithm may be
specified")
+ for i in automl_params_dict:
+ if i == AutoMLSchema.NUM_MODELS:
+ self.num_models = automl_params_dict[AutoMLSchema.NUM_MODELS]
+ elif i == AutoMLSchema.NUM_ITERS:
+ self.num_iters = automl_params_dict[AutoMLSchema.NUM_ITERS]
+ elif i == AutoMLSchema.ALGORITHM:
+ if automl_params_dict[AutoMLSchema.ALGORITHM].lower() ==
'rand':
+ self.algorithm = rand
+ elif automl_params_dict[AutoMLSchema.ALGORITHM].lower() ==
'tpe':
+ self.algorithm = tpe
+ # elif automl_params_dict[AutoMLSchema.ALGORITHM].lower() ==
'atpe':
+ # self.algorithm = atpe
+ # uncomment the above lines after atpe works
+ else:
+ plpy.error("DL: algorithm in 'automl_params' must be
'rand', 'tpe'") # , or 'atpe'
+ else:
+ plpy.error("DL: {0} is an invalid automl param".format(i))
+ _assert(self.num_models > 0 and self.num_iters > 0, "DL: num_models
and num_iters in 'automl_params' "
+ "must be positive")
+ _assert(self._is_valid_metrics_compute_frequency(self.num_iters), "DL:
'metrics_compute_frequency' "
+ "out
of iteration range")
+
+ def find_hyperopt_config(self):
"""
- Drops and re-create the mst table to only include the best performing
model configuration.
+ Executes hyperopt on top of MOP.
"""
- drop_tables([self.model_selection_table])
- # only retaining best performing config
- plpy.execute("CREATE TABLE {self.model_selection_table} AS SELECT
mst_key, model_id, compile_params, " \
- "fit_params FROM {self.model_info_table} " \
- "ORDER BY {AutoMLSchema.LOSS_METRIC} LIMIT
1".format(self=self, AutoMLSchema=AutoMLSchema))
+ make_mst_summary = True
+ trials = Trials()
+ domain = Domain(None, self.get_search_space())
+ rand_state = np.random.RandomState(self.random_state)
+ configs_lst = self.get_configs_list()
- def generate_model_output_summary_table(self, model_training):
+ self.start_training_time = self.get_current_timestamp()
+ for low, high in configs_lst:
+ i, n = low, high - low + 1
+
+ # Using HyperOpt TPE/ATPE to generate parameters
+ hyperopt_params = []
+ sampled_params = []
+ for j in range(i, i + n):
+ new_param = self.algorithm.suggest([j], domain, trials,
rand_state.randint(0, 2 ** 31 - 1))
Review comment:
I don't think we should calculate 2**31 every time we run this. Setting
an INT_MAX constant would be better.
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