/* ---------------------------------------------------------------------*//**
 *
 * 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'
)

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

--------------------------- MADLIB KERAS AUTOML HYPERBAND TEST CASES ---------------------------

-- test table dimensions / happy path
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;

-- 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
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=9, eta=3, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);

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], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
        'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
        'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
        '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
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=9, eta=3, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);

DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_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, 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 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], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyperbrand', 'R=9, eta=3, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for 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], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hb', 'R=9, eta=3, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid automl method');

-- 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');

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=0, eta=3, skip_last=0', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid automl params');

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');

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=3', NULL, NULL, FALSE, NULL, NULL, NULL, NULL);
$TRAP$)=1, 'Should error out for invalid automl params');

-- 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_method val
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], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyper', 'R=9, eta=3, 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(*)=15, 'The length of table does not match with the inputs') FROM automl_output;
SELECT assert(COUNT(*)=15, '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], $${'loss': ['categorical_crossentropy'], 'optimizer_params_list': [ {'optimizer': ['Adagrad', 'Adam'],
    'lr': [0.9, 0.95, 'log'], 'epsilon': [0.3, 0.5, 'log_near_one']}, {'optimizer': ['Adam', 'SGD'],
    'lr': [0.6, 0.65, 'log']} ], 'metrics':['accuracy'] }$$, $${'batch_size': [2, 4], 'epochs': [3]}$$,
    'hyp', 'R=9, eta=3, 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(*)=15, 'The length of table does not match with the inputs') FROM automl_output;
SELECT assert(COUNT(*)=15, 'The length of table does not match with the inputs') FROM automl_output_info;

-- 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=10, eta=3, 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(*)=15, 'The length of table does not match with the inputs') FROM automl_output;
SELECT assert(COUNT(*)=15, '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=3, 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(*)=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=10, eta=4, 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(*)=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;

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;

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=9, eta=3, skip_last=2', 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(*)=9, 'The length of table does not match with the inputs') FROM automl_output;
SELECT assert(COUNT(*)=9, '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=11, eta=2, skip_last=3', 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(*)=8, 'The length of table does not match with the inputs') FROM automl_output;
SELECT assert(COUNT(*)=8, 'The length of table does not match with the inputs') FROM automl_output_info;

-- test name and description
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=11, eta=2, skip_last=3', NULL, NULL, FALSE, NULL, NULL, 'test1', 'test1 descr');
SELECT assert(name='test1' AND description='test1 descr',
    'invalid name/description') FROM (SELECT * FROM automl_output_summary) summary;

-- 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=9, eta=3, 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=9, eta=3, 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=9, eta=3, skip_last=1', 42, NULL, FALSE, NULL, NULL, NULL, NULL);

SELECT assert(model_id=(SELECT model_id FROM automl_output2_info WHERE mst_key=7) AND
              compile_params=(SELECT compile_params FROM automl_output2_info WHERE mst_key=7) AND
              fit_params=(SELECT fit_params FROM automl_output2_info WHERE mst_key=7), 'invalid config uniformity')
FROM (SELECT model_id, compile_params, fit_params FROM automl_output1_info WHERE mst_key=7) output1;
SELECT assert(model_id=(SELECT model_id FROM automl_output2_info WHERE mst_key=7) AND
              compile_params=(SELECT compile_params FROM automl_output2_info WHERE mst_key=7) AND
              fit_params=(SELECT fit_params FROM automl_output2_info WHERE mst_key=7), 'invalid config uniformity')
FROM (SELECT model_id, compile_params, fit_params FROM automl_output3_info WHERE mst_key=7) output3;

--------------------------- 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');
!>)