/* ---------------------------------------------------------------------*//**
 *
 * 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_cifar.setup.sql_in'
)

-- -- Please do not break up the compile_params string
-- -- It might break the assertion
DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
SELECT madlib_keras_fit(
    'cifar_10_sample_batched',
    'keras_saved_out',
    'model_arch',
    1,
    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['mae']$$::text,
    $$ batch_size=2, epochs=1, verbose=0 $$::text,
    3);

-- Test that evaluate works as expected:
DROP TABLE IF EXISTS evaluate_out;
SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val', 'evaluate_out', FALSE);

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

-- Test that passing NULL / None instead of 0 for gpus_per_host works
DROP TABLE IF EXISTS evaluate_out;
SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val', 'evaluate_out');
SELECT assert(loss  >= 0 AND
        metric  >= 0 AND
        metrics_type = '{mae}', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
FROM evaluate_out;

-- Test that evaluate errors out correctly if mst_key is given for non-multi model tables
DROP TABLE IF EXISTS evaluate_out;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val', 'evaluate_out', FALSE ,1);
    $TRAP$) = 1, 'Should error out if mst_key is given for non-multi model tables');

DROP TABLE IF EXISTS evaluate_out;
SELECT assert(test_input_table($test$SELECT madlib_keras_evaluate(
    NULL, 'cifar_10_sample_val', 'evaluate_out', FALSE)$test$),
    'Failed to assert the correct error message for null source table');

SELECT assert(test_input_table($test$SELECT madlib_keras_evaluate(
    'keras_saved_out', NULL, 'evaluate_out', FALSE)$test$),
    'Failed to assert the correct error message for null source table');

-- Test that evaluate errors out correctly if model_arch field missing from fit output
DROP TABLE IF EXISTS evaluate_out;
ALTER TABLE keras_saved_out DROP COLUMN model_arch;
SELECT assert(trap_error($TRAP$
	SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val', 'evaluate_out');
	$TRAP$) = 1, 'Should error out if model_arch column is missing from model_table');


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

DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT load_model_selection_table(
    'model_arch',
    'mst_table',
    ARRAY[1],
    ARRAY[
        $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,
        $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']$$,
        $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$
    ],
    ARRAY[
        $$batch_size=5,epochs=1$$,
        $$batch_size=10,epochs=1$$
    ]
);

DROP TABLE if exists cifar_10_multiple_model, cifar_10_multiple_model_summary,
                     cifar_10_multiple_model_info;
SELECT setseed(0);
SELECT madlib_keras_fit_multiple_model(
    'cifar_10_sample_batched',
    'cifar_10_multiple_model',
    'mst_table',
    6,
    FALSE
);

DROP TABLE IF EXISTS evaluate_out;
SELECT madlib_keras_evaluate('cifar_10_multiple_model', 'cifar_10_sample_batched', 'evaluate_out', FALSE, 2);
SELECT assert(
    (
        (e.metric < 0.00001 AND i.training_metrics_final < 0.00001) OR
         relative_error(e.metric,i.training_metrics_final) < 0.00001
    ) AND (
        (e.loss < 0.00001 AND i.training_loss_final < 0.00001) OR
         relative_error(e.loss,i.training_loss_final)  < 0.00001
    ) AND
         e.metrics_type = '{accuracy}', 'Evaluate output validation failed.')
FROM evaluate_out e, cifar_10_multiple_model_info i WHERE i.mst_key = 2;


-- Test that evaluate errors out correctly if mst_key is missing for multi model tables
DROP TABLE IF EXISTS evaluate_out;
SELECT assert(trap_error($TRAP$
    SELECT madlib_keras_evaluate('cifar_10_multiple_model', 'cifar_10_sample_val', 'evaluate_out');
    $TRAP$) = 1, 'Should error out if mst_key is missing for multi model tables');


!>)