src/ports/postgres/modules/deep_learning/test/madlib_keras_model_selection_e2e.sql_in - madlib - Git at Google

 /* ---------------------------------------------------------------------*//**
  *
  * Licensed to the Apache Software Foundation (ASF) under one
  * or more contributor license agreements.  See the NOTICE file
  * distributed with this work for additional information
  * regarding copyright ownership.  The ASF licenses this file
  * to you under the Apache License, Version 2.0 (the
  * "License"); you may not use this file except in compliance
  * with the License.  You may obtain a copy of the License at
  *
  *   http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License is distributed on an
  * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  * KIND, either express or implied.  See the License for the
  * specific language governing permissions and limitations
  * under the License.
  *
  *//* ---------------------------------------------------------------------*/

 m4_include(`SQLCommon.m4')

 \i m4_regexp(MODULE_PATHNAME,
              `\(.*\)libmadlib\.so',
              `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
 )

 \i m4_regexp(MODULE_PATHNAME,
              `\(.*\)libmadlib\.so',
              `\1../../modules/deep_learning/test/madlib_keras_custom_function.setup.sql_in'
 )

 m4_changequote(`<!', `!>')
 m4_ifdef(<!__POSTGRESQL__!>, <!!>, <!
 -- Multiple models End-to-End test
 -- Prepare model selection table with four rows
 DROP TABLE IF EXISTS pg_temp.mst_table, pg_temp.mst_table_summary;
 SELECT load_model_selection_table(
     'iris_model_arch',
     'pg_temp.mst_table',
     ARRAY[1],
     ARRAY[
         $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$,
         $$loss='categorical_crossentropy', optimizer='Adam(lr=0.001)', metrics=['accuracy']$$,
         $$loss='categorical_crossentropy', optimizer='Adam(lr=0.0001)', metrics=['accuracy']$$
     ],
     ARRAY[
         $$batch_size=16, epochs=1$$
     ]
 );

 CREATE SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__;
 DROP TABLE if exists __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model,
                      __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_summary,
                      __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_info;
 SELECT madlib_keras_fit_multiple_model(
 	'iris_data_packed',
 	'__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model',
 	'pg_temp.mst_table',
 	3,
 	FALSE
 );

 SELECT assert(
         model_arch_table = 'iris_model_arch' AND
         validation_table is NULL AND
         model_info = '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_info' AND
         source_table = 'iris_data_packed' AND
         model = '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model' AND
         dependent_varname[0] = 'class_text' AND
         independent_varname[0] = 'attributes' AND
         madlib_version is NOT NULL AND
         num_iterations = 3 AND
         start_training_time < now() AND
         end_training_time < now() AND
         num_classes[0] = 3 AND
         class_text_class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
         dependent_vartype[0] LIKE '%char%' AND
         normalizing_const = 1,
         'Keras Fit Multiple Output Summary Validation failed. Actual:' || __to_char(summary))
 FROM (SELECT * FROM __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_summary) summary;

 -- Run Predict
 DROP TABLE IF EXISTS pg_temp.iris_predict;
 SELECT madlib_keras_predict(
     '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model',
     'iris_data',
     'id',
     'attributes',
     'pg_temp.iris_predict',
     'prob',
     NULL,
     1);

 -- Run Evaluate
 DROP TABLE IF EXISTS pg_temp.evaluate_out;
 SELECT madlib_keras_evaluate(
     '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model',
     'iris_data_val',
     'pg_temp.evaluate_out',
     NULL,
     1);

 SELECT assert(loss >= 0 AND
         metric >= 0 AND
         metrics_type = '{accuracy}', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
 FROM pg_temp.evaluate_out;

 -- Test for one-hot encoded user input data
 DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
 SELECT madlib_keras_fit_multiple_model(
 	'iris_data_one_hot_encoded_packed',
 	'iris_multiple_model',
 	'pg_temp.mst_table',
 	3,
 	FALSE
 );

 SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;

 SELECT assert(
         model_arch_table = 'iris_model_arch' AND
         validation_table is NULL AND
         model_info = 'iris_multiple_model_info' AND
         source_table = 'iris_data_one_hot_encoded_packed' AND
         model = 'iris_multiple_model' AND
         dependent_varname[0] = 'class_one_hot_encoded' AND
         independent_varname[0] = 'attributes' AND
         madlib_version is NOT NULL AND
         num_iterations = 3 AND
         start_training_time < now() AND
         end_training_time < now() AND
         dependent_vartype[0] = 'integer[]' AND
         num_classes[0] = NULL AND
         normalizing_const = 1,
         'Keras Fit Multiple Output Summary Validation failed when user passes in 1-hot encoded label vector. Actual:' || __to_char(summary))
 FROM (SELECT * FROM iris_multiple_model_summary) summary;

 -- Run Predict
 DROP TABLE IF EXISTS iris_predict;
 SELECT madlib_keras_predict(
     'iris_multiple_model',
     'iris_data_one_hot_encoded',
     'id',
     'attributes',
     'iris_predict',
     'prob',
     NULL,
     1);
 SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;

 -- Run Evaluate
 DROP TABLE IF EXISTS evaluate_out;
 SELECT madlib_keras_evaluate(
     'iris_multiple_model',
     'iris_data_one_hot_encoded_val',
     'evaluate_out',
     NULL,
     1);
 SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;

 SELECT assert(loss >= 0 AND
         metric >= 0 AND
         metrics_type = '{accuracy}', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
 FROM evaluate_out;

 -- TEST custom loss function and

 DROP TABLE IF EXISTS test_custom_function_table;
 SELECT load_custom_function('test_custom_function_table', custom_function_zero_object(), 'test_custom_fn', 'returns test_custom_fn');

 -- Prepare model selection table with four rows
 DROP TABLE IF EXISTS mst_object_table, mst_object_table_summary;
 SELECT load_top_k_accuracy_function('test_custom_function_table', 4);
 SELECT load_model_selection_table(
     'iris_model_arch',
     'mst_object_table',
     ARRAY[1],
     ARRAY[
         $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$,
         $$loss='test_custom_fn', optimizer='Adam(lr=0.001)', metrics=['top_4_accuracy']$$
     ],
     ARRAY[
         $$batch_size=16, epochs=1$$
     ],
     'test_custom_function_table'
 );

 DROP TABLE if exists iris_multiple_model_custom_fn, iris_multiple_model_custom_fn_summary, iris_multiple_model_custom_fn_info;
 SELECT madlib_keras_fit_multiple_model(
 	'iris_data_packed',
 	'iris_multiple_model_custom_fn',
 	'mst_object_table',
 	3,
 	FALSE,
 	'iris_data_one_hot_encoded_packed',
 	1
 );

 SELECT assert(
         model_arch_table = 'iris_model_arch' AND
         validation_table = 'iris_data_one_hot_encoded_packed' AND
         model_info = 'iris_multiple_model_custom_fn_info' AND
         source_table = 'iris_data_packed' AND
         model = 'iris_multiple_model_custom_fn' AND
         dependent_varname[0] = 'class_text' AND
         independent_varname[0] = 'attributes' AND
         madlib_version is NOT NULL AND
         num_iterations = 3 AND
         start_training_time < now() AND
         end_training_time < now() AND
         num_classes[0] = 3 AND
         class_text_class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
         dependent_vartype[0] LIKE '%char%' AND
         normalizing_const = 1,
         'Keras Fit Multiple Output Summary Validation failed. Actual:' || __to_char(summary))
 FROM (SELECT * FROM iris_multiple_model_custom_fn_summary) summary;

 SELECT assert(
         model_type = 'madlib_keras' AND
         model_size > 0 AND
         fit_params = $MAD$batch_size=16, epochs=1$MAD$::text AND
         metrics_type = '{top_4_accuracy}' AND
         training_metrics_final >= 0  AND
         training_loss_final  = 0  AND
         training_loss = '{0,0,0}' AND
         array_upper(training_metrics, 1) = 3 AND
         array_upper(training_loss, 1) = 3 AND
         validation_metrics_final >= 0  AND
         validation_loss_final  = 0  AND
         array_upper(validation_metrics, 1) = 3 AND
         array_upper(validation_loss, 1) = 3 AND
         array_upper(metrics_elapsed_time, 1) = 3,
         'Keras Fit Multiple Output Info Validation failed. Actual:' || __to_char(info))
 FROM (SELECT * FROM iris_multiple_model_custom_fn_info where compile_params like '%test_custom_fn%') info;

 -- Run Predict
 DROP TABLE IF EXISTS iris_predict;
 SELECT madlib_keras_predict(
     'iris_multiple_model_custom_fn',
     'iris_data',
     'id',
     'attributes',
     'pg_temp.iris_predict',
     'prob',
     NULL,
     1);

 -- Run Evaluate
 DROP TABLE IF EXISTS evaluate_out;
 SELECT madlib_keras_evaluate(
     'iris_multiple_model_custom_fn',
     'iris_data_val',
     'evaluate_out',
     NULL,
     2);

 SELECT assert(loss = 0 AND
         metric >= 0 AND
         metrics_type = '{top_4_accuracy}' AND
         loss_type = 'test_custom_fn', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
 FROM evaluate_out;


 DROP SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__ CASCADE;
 !>)
	/* ---------------------------------------------------------------------//*
	*
	* Licensed to the Apache Software Foundation (ASF) under one
	* or more contributor license agreements. See the NOTICE file
	* distributed with this work for additional information
	* regarding copyright ownership. The ASF licenses this file
	* to you under the Apache License, Version 2.0 (the
	* "License"); you may not use this file except in compliance
	* with the License. You may obtain a copy of the License at
	*
	* http://www.apache.org/licenses/LICENSE-2.0
	*
	* Unless required by applicable law or agreed to in writing,
	* software distributed under the License is distributed on an
	* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
	* KIND, either express or implied. See the License for the
	* specific language governing permissions and limitations
	* under the License.
	*
	// ---------------------------------------------------------------------*/

	m4_include(`SQLCommon.m4')

	\i m4_regexp(MODULE_PATHNAME,
	`\(.*\)libmadlib\.so',
	`\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
	)

	\i m4_regexp(MODULE_PATHNAME,
	`\(.*\)libmadlib\.so',
	`\1../../modules/deep_learning/test/madlib_keras_custom_function.setup.sql_in'
	)

	m4_changequote(`<!', `!>')
	m4_ifdef(<!__POSTGRESQL__!>, <!!>, <!
	-- Multiple models End-to-End test
	-- Prepare model selection table with four rows
	DROP TABLE IF EXISTS pg_temp.mst_table, pg_temp.mst_table_summary;
	SELECT load_model_selection_table(
	'iris_model_arch',
	'pg_temp.mst_table',
	ARRAY[1],
	ARRAY[
	$$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$,
	$$loss='categorical_crossentropy', optimizer='Adam(lr=0.001)', metrics=['accuracy']$$,
	$$loss='categorical_crossentropy', optimizer='Adam(lr=0.0001)', metrics=['accuracy']$$
	],
	ARRAY[
	$$batch_size=16, epochs=1$$
	]
	);

	CREATE SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__;
	DROP TABLE if exists __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model,
	__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_summary,
	__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_info;
	SELECT madlib_keras_fit_multiple_model(
	'iris_data_packed',
	'__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model',
	'pg_temp.mst_table',
	3,
	FALSE
	);

	SELECT assert(
	model_arch_table = 'iris_model_arch' AND
	validation_table is NULL AND
	model_info = '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_info' AND
	source_table = 'iris_data_packed' AND
	model = '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model' AND
	dependent_varname[0] = 'class_text' AND
	independent_varname[0] = 'attributes' AND
	madlib_version is NOT NULL AND
	num_iterations = 3 AND
	start_training_time < now() AND
	end_training_time < now() AND
	num_classes[0] = 3 AND
	class_text_class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
	dependent_vartype[0] LIKE '%char%' AND
	normalizing_const = 1,
	'Keras Fit Multiple Output Summary Validation failed. Actual:' \|\| __to_char(summary))
	FROM (SELECT * FROM __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_summary) summary;

	-- Run Predict
	DROP TABLE IF EXISTS pg_temp.iris_predict;
	SELECT madlib_keras_predict(
	'__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model',
	'iris_data',
	'id',
	'attributes',
	'pg_temp.iris_predict',
	'prob',
	NULL,
	1);

	-- Run Evaluate
	DROP TABLE IF EXISTS pg_temp.evaluate_out;
	SELECT madlib_keras_evaluate(
	'__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model',
	'iris_data_val',
	'pg_temp.evaluate_out',
	NULL,
	1);

	SELECT assert(loss >= 0 AND
	metric >= 0 AND
	metrics_type = '{accuracy}', 'Evaluate output validation failed. Actual:' \|\| __to_char(evaluate_out))
	FROM pg_temp.evaluate_out;

	-- Test for one-hot encoded user input data
	DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
	SELECT madlib_keras_fit_multiple_model(
	'iris_data_one_hot_encoded_packed',
	'iris_multiple_model',
	'pg_temp.mst_table',
	3,
	FALSE
	);

	SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;

	SELECT assert(
	model_arch_table = 'iris_model_arch' AND
	validation_table is NULL AND
	model_info = 'iris_multiple_model_info' AND
	source_table = 'iris_data_one_hot_encoded_packed' AND
	model = 'iris_multiple_model' AND
	dependent_varname[0] = 'class_one_hot_encoded' AND
	independent_varname[0] = 'attributes' AND
	madlib_version is NOT NULL AND
	num_iterations = 3 AND
	start_training_time < now() AND
	end_training_time < now() AND
	dependent_vartype[0] = 'integer[]' AND
	num_classes[0] = NULL AND
	normalizing_const = 1,
	'Keras Fit Multiple Output Summary Validation failed when user passes in 1-hot encoded label vector. Actual:' \|\| __to_char(summary))
	FROM (SELECT * FROM iris_multiple_model_summary) summary;

	-- Run Predict
	DROP TABLE IF EXISTS iris_predict;
	SELECT madlib_keras_predict(
	'iris_multiple_model',
	'iris_data_one_hot_encoded',
	'id',
	'attributes',
	'iris_predict',
	'prob',
	NULL,
	1);
	SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;

	-- Run Evaluate
	DROP TABLE IF EXISTS evaluate_out;
	SELECT madlib_keras_evaluate(
	'iris_multiple_model',
	'iris_data_one_hot_encoded_val',
	'evaluate_out',
	NULL,
	1);
	SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;

	SELECT assert(loss >= 0 AND
	metric >= 0 AND
	metrics_type = '{accuracy}', 'Evaluate output validation failed. Actual:' \|\| __to_char(evaluate_out))
	FROM evaluate_out;

	-- TEST custom loss function and

	DROP TABLE IF EXISTS test_custom_function_table;
	SELECT load_custom_function('test_custom_function_table', custom_function_zero_object(), 'test_custom_fn', 'returns test_custom_fn');

	-- Prepare model selection table with four rows
	DROP TABLE IF EXISTS mst_object_table, mst_object_table_summary;
	SELECT load_top_k_accuracy_function('test_custom_function_table', 4);
	SELECT load_model_selection_table(
	'iris_model_arch',
	'mst_object_table',
	ARRAY[1],
	ARRAY[
	$$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$,
	$$loss='test_custom_fn', optimizer='Adam(lr=0.001)', metrics=['top_4_accuracy']$$
	],
	ARRAY[
	$$batch_size=16, epochs=1$$
	],
	'test_custom_function_table'
	);

	DROP TABLE if exists iris_multiple_model_custom_fn, iris_multiple_model_custom_fn_summary, iris_multiple_model_custom_fn_info;
	SELECT madlib_keras_fit_multiple_model(
	'iris_data_packed',
	'iris_multiple_model_custom_fn',
	'mst_object_table',
	3,
	FALSE,
	'iris_data_one_hot_encoded_packed',
	1
	);

	SELECT assert(
	model_arch_table = 'iris_model_arch' AND
	validation_table = 'iris_data_one_hot_encoded_packed' AND
	model_info = 'iris_multiple_model_custom_fn_info' AND
	source_table = 'iris_data_packed' AND
	model = 'iris_multiple_model_custom_fn' AND
	dependent_varname[0] = 'class_text' AND
	independent_varname[0] = 'attributes' AND
	madlib_version is NOT NULL AND
	num_iterations = 3 AND
	start_training_time < now() AND
	end_training_time < now() AND
	num_classes[0] = 3 AND
	class_text_class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
	dependent_vartype[0] LIKE '%char%' AND
	normalizing_const = 1,
	'Keras Fit Multiple Output Summary Validation failed. Actual:' \|\| __to_char(summary))
	FROM (SELECT * FROM iris_multiple_model_custom_fn_summary) summary;

	SELECT assert(
	model_type = 'madlib_keras' AND
	model_size > 0 AND
	fit_params = $MAD$batch_size=16, epochs=1$MAD$::text AND
	metrics_type = '{top_4_accuracy}' AND
	training_metrics_final >= 0 AND
	training_loss_final = 0 AND
	training_loss = '{0,0,0}' AND
	array_upper(training_metrics, 1) = 3 AND
	array_upper(training_loss, 1) = 3 AND
	validation_metrics_final >= 0 AND
	validation_loss_final = 0 AND
	array_upper(validation_metrics, 1) = 3 AND
	array_upper(validation_loss, 1) = 3 AND
	array_upper(metrics_elapsed_time, 1) = 3,
	'Keras Fit Multiple Output Info Validation failed. Actual:' \|\| __to_char(info))
	FROM (SELECT * FROM iris_multiple_model_custom_fn_info where compile_params like '%test_custom_fn%') info;

	-- Run Predict
	DROP TABLE IF EXISTS iris_predict;
	SELECT madlib_keras_predict(
	'iris_multiple_model_custom_fn',
	'iris_data',
	'id',
	'attributes',
	'pg_temp.iris_predict',
	'prob',
	NULL,
	1);

	-- Run Evaluate
	DROP TABLE IF EXISTS evaluate_out;
	SELECT madlib_keras_evaluate(
	'iris_multiple_model_custom_fn',
	'iris_data_val',
	'evaluate_out',
	NULL,
	2);

	SELECT assert(loss = 0 AND
	metric >= 0 AND
	metrics_type = '{top_4_accuracy}' AND
	loss_type = 'test_custom_fn', 'Evaluate output validation failed. Actual:' \|\| __to_char(evaluate_out))
	FROM evaluate_out;


	DROP SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__ CASCADE;
	!>)