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 *
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
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 * 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
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 *//* ---------------------------------------------------------------------*/

m4_include(`SQLCommon.m4')

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

m4_changequote(`<!', `!>')
m4_ifdef(<!__POSTGRESQL__!>, <!!>, <!

--------------------------- HYPEROPT TEST CASES ---------------------------
-- test table dimensions / happy path (algorithm = rand)
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
automl_mst_table_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
                           ARRAY[1], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [{'optimizer': ['Adam', 'SGD'],
    'lr': [0.01, 0.011, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [50], 'epochs': [1]}$$,
    'hyperopt', 'num_configs=5, num_iterations=6, algorithm=rand', NULL, NULL, FALSE, NULL, 1, 'test1', 'test1 descr');

SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_mst_table;
SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_mst_table_summary;
SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_output_summary;
SELECT assert(COUNT(*)=5, 'The length of table does not match with the inputs') FROM automl_output;
SELECT assert(COUNT(*)=5, 'The length of table does not match with the inputs') FROM automl_output_info;
-- Validate model output summary table
SELECT assert(
    source_table = 'iris_data_packed' AND
    validation_table IS NULL AND
    model = 'automl_output' AND
    model_info = 'automl_output_info' AND
    dependent_varname[0] = 'class_text' AND
    independent_varname[0] = 'attributes' AND
    model_arch_table = 'iris_model_arch' AND
    model_selection_table = 'automl_mst_table' AND
    automl_method = 'hyperopt' AND
    automl_params = 'num_configs=5, num_iterations=6, algorithm=rand' AND
    random_state IS NULL AND
    object_table IS NULL AND
    use_gpus = FALSE AND
    metrics_compute_frequency = 1 AND
    name = 'test1' AND
    description = 'test1 descr' AND
    start_training_time < now() AND
    end_training_time < now() AND
    madlib_version IS NOT NULL AND
    num_classes[0] = 3 AND
    class_text_class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
    dependent_vartype[0] = 'character varying' AND
    normalizing_const = 1, 'Output summary table validation failed. Actual:' || __to_char(summary)
) FROM (SELECT * FROM automl_output_summary) summary;

-- caching test case
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
automl_mst_table_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
    ARRAY[1], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [{'optimizer': ['Adam', 'SGD'],
    'lr': [0.01, 0.011, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [50], 'epochs': [1]}$$,
    'hyperopt', 'num_configs=5, num_iterations=6, algorithm=rand', NULL, NULL, FALSE, NULL, 1, 'test1', 'test1 descr', TRUE);
SELECT assert(
    source_table = 'iris_data_packed' AND
    validation_table IS NULL AND
    model = 'automl_output' AND
    model_info = 'automl_output_info' AND
    dependent_varname[0] = 'class_text' AND
    independent_varname[0] = 'attributes' AND
    model_arch_table = 'iris_model_arch' AND
    model_selection_table = 'automl_mst_table' AND
    automl_method = 'hyperopt' AND
    automl_params = 'num_configs=5, num_iterations=6, algorithm=rand' AND
    random_state IS NULL AND
    object_table IS NULL AND
    use_gpus = FALSE AND
    metrics_compute_frequency = 1 AND
    name = 'test1' AND
    description = 'test1 descr' AND
    start_training_time < now() AND
    end_training_time < now() AND
    madlib_version IS NOT NULL AND
    num_classes[0] = 3 AND
    class_text_class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
    dependent_vartype[0] = 'character varying' AND
    normalizing_const = 1, 'Output summary table validation failed. Actual:' || __to_char(summary)
) FROM (SELECT * FROM automl_output_summary) summary;

-- Validate output info table for metrics_iters NOT NULL
SELECT assert(
    metrics_iters = ARRAY[1,2,3,4,5,6], 'Invalid metrics_iters value in output info table. Actual:' || __to_char(info)
) FROM (SELECT * FROM automl_output_info) info;

-- Validate mst summary table
SELECT assert(
    model_arch_table = 'iris_model_arch' AND
    object_table IS NULL , 'mst summary table validation failed. Actual:' || __to_char(summary)
) FROM (SELECT * FROM automl_mst_table_summary) summary;

-- Validating the best model selected learns (training loss goes down and accuracy improves)
-- TODO: Keep a look for flaky test
SELECT assert(
    training_loss[6]-training_loss[1] < 10e-4 AND
    training_metrics[6]-training_metrics[1] > 0,
    'The loss and accuracy should have improved with more iterations.'
)
FROM automl_output_info
WHERE mst_key = (SELECT mst_key from automl_mst_table);

-- algorithm = tpe
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
automl_mst_table_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
                           ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperopt', 'num_configs=4, num_iterations=1, algorithm=tpe');

SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_mst_table;
SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_mst_table_summary;
SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_output_summary;
SELECT assert(COUNT(*)=4, 'The length of table does not match with the inputs') FROM automl_output;
SELECT assert(COUNT(*)=4, 'The length of table does not match with the inputs') FROM automl_output_info;

-- test invalid source table
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('invalid_source_table', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperopt', 'num_configs=5, num_iterations=2, algorithm=tpe', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid source table');

-- test preexisting output table
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
CREATE TABLE automl_output(a int);

DROP TABLE IF EXISTS automl_mst_table, automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperopt', 'num_configs=5, num_iterations=2, algorithm=tpe', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for preexisting output table');

-- test preexisting selection table
CREATE TABLE automl_mst_table(a int);
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperopt', 'num_configs=5, num_iterations=2, algorithm=tpe', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for preexisting selection table');

-- test invalid model id
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[2,-1], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperopt', 'num_configs=5, num_iterations=2, algorithm=tpe', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid model id');

-- test invalid distribution
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperopt', 'num_configs=5, num_iterations=2, algorithm=tpe', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for preexisting selection table');

-- test invalid automl method
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyper', 'num_configs=5, num_iterations=2, algorithm=tpe', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid automl method');

-- test invalid automl params for hyperopt: {num_configs, num_iterations, algorithm}
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperopt', 'num_configs=-2, num_iterations=5, algorithm=rand', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid automl params');

SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperopt', 'num_configs=2, num_iterations=0, algorithm=tpe', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid automl params');

SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperopt', 'num_configs=5, num_iterations=2, algorithm=random', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid automl params');

-- test invalid object table
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperopt', 'num_configs=5, num_iterations=2, algorithm=tpe', NULL, 'invalid_object_table', FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid object table');

-- test invalid validation table
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperopt', 'num_configs=5, num_iterations=2, algorithm=tpe', NULL, NULL, FALSE, 'invalid_validation_table', NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid validation table');

-- test config reproducibility
DROP TABLE IF EXISTS automl_output1, automl_output1_info, automl_output1_summary, automl_mst_table1,
    automl_mst_table1_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output1', 'iris_model_arch', 'automl_mst_table1',
                           ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
                           'hyperopt', 'num_configs=4, num_iterations=2, algorithm=tpe', 42, NULL, FALSE, NULL, NULL, NULL, NULL);

DROP TABLE IF EXISTS automl_output2, automl_output2_info, automl_output2_summary, automl_mst_table2,
    automl_mst_table2_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output2', 'iris_model_arch', 'automl_mst_table2',
                           ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
                           'hyperopt', 'num_configs=4, num_iterations=2, algorithm=tpe', 42, NULL, FALSE, NULL, NULL, NULL, NULL);

DROP TABLE IF EXISTS automl_output3, automl_output3_info, automl_output3_summary, automl_mst_table3,
    automl_mst_table3_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output3', 'iris_model_arch', 'automl_mst_table3',
                           ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'linear']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
                           'hyperopt', 'num_configs=4, num_iterations=2, algorithm=tpe', 42, NULL, FALSE, NULL, NULL, NULL, NULL);

SELECT assert(model_id=(SELECT model_id FROM automl_output2_info WHERE mst_key=3) AND
              compile_params=(SELECT compile_params FROM automl_output2_info WHERE mst_key=3) AND
              fit_params=(SELECT fit_params FROM automl_output2_info WHERE mst_key=3), 'invalid config uniformity')
FROM (SELECT model_id, compile_params, fit_params FROM automl_output1_info WHERE mst_key=3) output1;
SELECT assert(model_id=(SELECT model_id FROM automl_output2_info WHERE mst_key=3) AND
              compile_params=(SELECT compile_params FROM automl_output2_info WHERE mst_key=3) AND
              fit_params=(SELECT fit_params FROM automl_output2_info WHERE mst_key=3), 'invalid config uniformity')
FROM (SELECT model_id, compile_params, fit_params FROM automl_output3_info WHERE mst_key=3) output3;

-- Test for metrics_elapsed_time for 2 configs per trial (total 3 trials)
-- Setup for distributing data only on 2 segments
DROP TABLE IF EXISTS segments_to_use;
CREATE TABLE segments_to_use(
    dbid INTEGER,
    hostname TEXT
);
INSERT INTO segments_to_use SELECT dbid, hostname
	FROM gp_segment_configuration
	WHERE content>=0 AND preferred_role='p' limit 2;

DROP TABLE IF EXISTS iris_data_2seg_packed, iris_data_2seg_packed_summary;
SELECT training_preprocessor_dl('iris_data',         -- Source table
                                'iris_data_2seg_packed',  -- Output table
                                'class_text',         -- Dependent variable
                                'attributes',         -- Independent variable
								NULL, 255, NULL,
								'segments_to_use'     -- Distribution rules
                                );

DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
automl_mst_table_summary;
SELECT madlib_keras_automl('iris_data_2seg_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
                           ARRAY[1], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [{'optimizer': ['Adam', 'SGD'],
    'lr': [0.01, 0.011, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [50], 'epochs': [1]}$$,
    'hyperopt', 'num_configs=5, num_iterations=2, algorithm=rand', NULL, NULL, FALSE, NULL, 1);

SELECT assert(
	t1.metrics_elapsed_time[2] < t2.metrics_elapsed_time[1] AND
	t2.metrics_elapsed_time[2] < t3.metrics_elapsed_time[1] ,
	'metrics_elapsed_time should be cumulative for each trial.'
) FROM (SELECT * FROM automl_output_info WHERE mst_key=1) t1,
 (SELECT * FROM automl_output_info WHERE mst_key=3) t2,
 (SELECT * FROM automl_output_info WHERE mst_key=5) t3;

--------------------------- HYPERBAND TEST CASES ---------------------------

-- test table dimensions / happy path with default automl_params
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$);

SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_mst_table;
SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_mst_table_summary;
SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_output_summary;
SELECT assert(COUNT(*)=5, 'The length of table does not match with the inputs') FROM automl_output;
SELECT assert(COUNT(*)=5, 'The length of table does not match with the inputs') FROM automl_output_info;

-- Validate model output summary table
SELECT assert(
    source_table = 'iris_data_packed' AND
    validation_table IS NULL AND
    model = 'automl_output' AND
    model_info = 'automl_output_info' AND
    dependent_varname[0] = 'class_text' AND
    independent_varname[0] = 'attributes' AND
    model_arch_table = 'iris_model_arch' AND
    model_selection_table = 'automl_mst_table' AND
    automl_method = 'hyperband' AND
    automl_params = 'R=6, eta=3, skip_last=0' AND
    random_state IS NULL AND
    object_table IS NULL AND
    use_gpus = FALSE AND
    metrics_compute_frequency = 6 AND
    name IS NULL AND
    description IS NULL AND
    start_training_time < now() AND
    end_training_time < now() AND
    madlib_version IS NOT NULL AND
    num_classes[0] = 3 AND
    class_text_class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
    dependent_vartype[0] = 'character varying' AND
    normalizing_const = 1, 'Output summary table validation failed. Actual:' || __to_char(summary)
) FROM (SELECT * FROM automl_output_summary) summary;

-- caching test case
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
	ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
	'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'], 'lr': [0.6, 0.65, 'log']} ],
	'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$, 'hyperband', 'R=5, eta=5, skip_last=1',
	NULL, NULL, FALSE, NULL, NULL, NULL, NULL, TRUE);
SELECT assert(
    source_table = 'iris_data_packed' AND
    validation_table IS NULL AND
    model = 'automl_output' AND
    model_info = 'automl_output_info' AND
    dependent_varname[0] = 'class_text' AND
    independent_varname[0] = 'attributes' AND
    model_arch_table = 'iris_model_arch' AND
    model_selection_table = 'automl_mst_table' AND
    automl_method = 'hyperband' AND
    automl_params = 'R=5, eta=5, skip_last=1' AND
    random_state IS NULL AND
    object_table IS NULL AND
    use_gpus = FALSE AND
    metrics_compute_frequency = 1 AND
    name IS NULL AND
    description IS NULL AND
    start_training_time < now() AND
    end_training_time < now() AND
    madlib_version IS NOT NULL AND
    num_classes[0] = 3 AND
    class_text_class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
    dependent_vartype[0] = 'character varying' AND
    normalizing_const = 1, 'Output summary table validation failed. Actual:' || __to_char(summary)
) FROM (SELECT * FROM automl_output_summary) summary;

-- Validate metrics_elapsed_time
-- We know that the schedule for the above automl query looks like
--  s | i | n_i | r_i
-- ---+---+-----+-----
--  1 | 0 |   3 |   2
--  1 | 1 |   1 |   6
--  0 | 0 |   2 |   6
-- So we can make 3 assertions:
-- 1. The metrics_elapsed_time array should have cumulative time for each mst
-- 2. Within the same bracket, we know that one mst is run for 8 iterations and
--    the other two are run for 2 iterations. So we can assert that the 3rd iteration
--    time for (s=1,i=1) > the 2nd iteration time of any of the other two (s=1,i=0)
-- 3. Within different brackets (s=1,i=0) and (s=0,i=0), we know that the 1st iteration of
--    the former > last/2nd iteration time of the latter

SELECT  assert(metrics_elapsed_time = ARRAY(SELECT unnest(metrics_elapsed_time) ORDER BY 1),
     'metrics_elapsed_time is not cumulative over iterations for one or more msts') FROM automl_output_info  metrics_iters;
SELECT assert(t1.metrics_elapsed_time[2] < t2.metrics_elapsed_time[3] , 'metrics_elapsed_time is not
    cumulative within the same bracket.') FROM (SELECT * FROM automl_output_info WHERE s=1 and i = 0) t1, (SELECT * FROM automl_output_info WHERE s=1 and i = 1) t2;
SELECT assert(t1.metrics_elapsed_time[2] < t2.metrics_elapsed_time[1] , 'metrics_elapsed_time is not
    cumulative between two brackets.') FROM (SELECT * FROM automl_output_info WHERE s=1 and i = 0) t1,
    (SELECT * FROM automl_output_info WHERE s=0 and i =0) t2;

-- Validate output info table for s and i NOT NULL
SELECT assert(
    metrics_iters IS NOT NULL AND
    s = ANY(ARRAY[0,1]) AND
    i = ANY(ARRAY[0,1]) , 'Invalid metrics_iters, s and i value in output info table. Actual:' || __to_char(info)
) FROM (SELECT * FROM automl_output_info) info;

-- Validate mst summary table
SELECT assert(
    model_arch_table = 'iris_model_arch' AND
    object_table IS NULL , 'mst summary table validation failed. Actual:' || __to_char(summary)
) FROM (SELECT * FROM automl_mst_table_summary) summary;

-- test invalid source table
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('invalid_source_table', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperband', 'R=9, eta=3, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid source table');

-- test preexisting output table
CREATE TABLE automl_output(a int);
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperband', 'R=9, eta=3, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for preexisting output table');

-- test preexisting selection table
CREATE TABLE automl_mst_table(a int);
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperband', 'R=9, eta=3, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for preexisting selection table');

-- test test invalid model id
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[2,-1], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperband', 'R=9, eta=3, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid model id');

-- test invalid automl params {R, eta, skip_last}
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperband', 'R=2, eta=3, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid automl params: R');

DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperband', 'R=9, eta=1, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid automl params: eta');

SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperband', 'R=9, eta=3, skip_last=3', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid automl params: skip_last');

-- test invalid object table
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperband', 'R=9, eta=3, skip_last=0', NULL, 'invalid_object_table', FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid object table');

-- test invalid validation table
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
        ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        'hyperband', 'R=9, eta=3, skip_last=0', NULL, NULL, FALSE, 'invalid_validation_table', NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid validation table');

-- test automl_params vals {R, eta, skip_last}
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperband', 'R=5, eta=5, skip_last=1', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);

SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_mst_table;
SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_mst_table_summary;
SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_output_summary;
SELECT assert(COUNT(*)=5, 'The length of table does not match with the inputs') FROM automl_output;
SELECT assert(COUNT(*)=5, 'The length of table does not match with the inputs') FROM automl_output_info;

DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table,
    automl_mst_table_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output', 'iris_model_arch', 'automl_mst_table',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperband', 'R=5, eta=5, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);

SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_mst_table;
SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_mst_table_summary;
SELECT assert(COUNT(*)=1, 'The length of table does not match with the inputs') FROM automl_output_summary;
SELECT assert(COUNT(*)=7, 'The length of table does not match with the inputs') FROM automl_output;
SELECT assert(COUNT(*)=7, 'The length of table does not match with the inputs') FROM automl_output_info;

-- test config reproducibility
DROP TABLE IF EXISTS automl_output1, automl_output1_info, automl_output1_summary, automl_mst_table1,
    automl_mst_table1_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output1', 'iris_model_arch', 'automl_mst_table1',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperband', 'R=3, eta=2, skip_last=1', 42, NULL, FALSE, NULL, NULL, NULL, NULL);

DROP TABLE IF EXISTS automl_output2, automl_output2_info, automl_output2_summary, automl_mst_table2,
    automl_mst_table2_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output2', 'iris_model_arch', 'automl_mst_table2',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperband', 'R=3, eta=2, skip_last=1', 42, NULL, FALSE, NULL, NULL, NULL, NULL);

DROP TABLE IF EXISTS automl_output3, automl_output3_info, automl_output3_summary, automl_mst_table3,
    automl_mst_table3_summary;
SELECT madlib_keras_automl('iris_data_packed', 'automl_output3', 'iris_model_arch', 'automl_mst_table3',
    ARRAY[1,2], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperband', 'R=3, eta=2, skip_last=1', 42, NULL, FALSE, NULL, NULL, NULL, NULL);

SELECT assert(model_id=(SELECT model_id FROM automl_output2_info WHERE mst_key=2) AND
              compile_params=(SELECT compile_params FROM automl_output2_info WHERE mst_key=2) AND
              fit_params=(SELECT fit_params FROM automl_output2_info WHERE mst_key=2), 'invalid config uniformity')
FROM (SELECT model_id, compile_params, fit_params FROM automl_output1_info WHERE mst_key=2) output1;
SELECT assert(model_id=(SELECT model_id FROM automl_output2_info WHERE mst_key=2) AND
              compile_params=(SELECT compile_params FROM automl_output2_info WHERE mst_key=2) AND
              fit_params=(SELECT fit_params FROM automl_output2_info WHERE mst_key=2), 'invalid config uniformity')
FROM (SELECT model_id, compile_params, fit_params FROM automl_output3_info WHERE mst_key=2) output3;

DROP TABLE IF EXISTS automl_output1, automl_output1_info, automl_output1_summary, automl_mst_table1,
    automl_mst_table1_summary, automl_output2, automl_output2_info, automl_output2_summary, automl_mst_table2,
    automl_mst_table2_summary, automl_output3, automl_output3_info, automl_output3_summary, automl_mst_table3,
    automl_mst_table3_summary;

--------------------------- HYPERBAND SCHEDULE TEST CASES ---------------------------
-- Testing happy path with default values
DROP TABLE IF EXISTS schedule_table;
SELECT hyperband_schedule(
               'schedule_table',
               81
        );
SELECT assert(
    COUNT(*)=15,
    'The length of mst table does not match with the inputs'
)
FROM schedule_table;

-- checking table existence
SELECT assert(trap_error($TRAP$
    SELECT hyperband_schedule(
               'schedule_table',
               81
        );
$TRAP$)=1, 'Should error out if schedule_table already exists');
!>)