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

DROP TABLE if exists pg_temp.iris_model, pg_temp.iris_model_summary;
SELECT madlib_keras_fit(
	'iris_data_packed',
	'pg_temp.iris_model',
	'iris_model_arch',
	1,
	$$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$,
  $$batch_size=16, epochs=1$$,
	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
        source_table = 'iris_data_packed' AND
        model = 'pg_temp.iris_model' AND
        dependent_varname = 'class_text' AND
        independent_varname = 'attributes' AND
        madlib_version is NOT NULL AND
        num_iterations = 3 AND
        start_training_time < now() AND
        end_training_time < now() AND
        num_classes = 3 AND
        class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
        dependent_vartype LIKE '%char%' AND
        normalizing_const = 1,
        'Keras Fit Multiple Output Summary Validation failed. Actual:' || __to_char(summary))
FROM (SELECT * FROM pg_temp.iris_model_summary) summary;

-- Run Predict
DROP TABLE IF EXISTS pg_temp.iris_predict;
SELECT madlib_keras_predict(
    'pg_temp.iris_model',
    'iris_data',
    'id',
    'attributes',
    'pg_temp.iris_predict',
    'prob',
    FALSE);
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 pg_temp.evaluate_out;
SELECT madlib_keras_evaluate(
    'pg_temp.iris_model',
    'iris_data_val',
    'pg_temp.evaluate_out',
    FALSE);

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

-- Test for one-hot encoded user input data
DROP TABLE if exists iris_model, iris_model_summary, iris_model_info;
SELECT madlib_keras_fit(
	'iris_data_one_hot_encoded_packed',
	'iris_model',
	'iris_model_arch',
	1,
	$$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$,
  $$batch_size=16, epochs=1$$,
	3,
	FALSE
);

SELECT assert(
        model_arch_table = 'iris_model_arch' AND
        validation_table is NULL AND
        source_table = 'iris_data_one_hot_encoded_packed' AND
        model = 'iris_model' AND
        dependent_varname = 'class_one_hot_encoded' AND
        independent_varname = 'attributes' AND
        madlib_version is NOT NULL AND
        num_iterations = 3 AND
        start_training_time < now() AND
        end_training_time < now() AND
        dependent_vartype = 'integer[]' AND
        num_classes = NULL AND
        class_values = 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_model_summary) summary;

-- Run Predict
DROP TABLE IF EXISTS iris_predict;
SELECT madlib_keras_predict(
    'iris_model',
    'iris_data_one_hot_encoded',
    'id',
    'attributes',
    'iris_predict',
    'prob',
    FALSE);

-- Run Evaluate
DROP TABLE IF EXISTS evaluate_out;
SELECT madlib_keras_evaluate(
    'iris_model',
    'iris_data_one_hot_encoded_val',
    'evaluate_out',
    FALSE);

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

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');
SELECT load_custom_function('test_custom_function_table', custom_function_one_object(), 'test_custom_fn1', 'returns test_custom_fn1');

DROP TABLE if exists iris_model, iris_model_summary, iris_model_info;
SELECT madlib_keras_fit(
    'iris_data_packed',
    'iris_model',
    'iris_model_arch',
    1,
    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='test_custom_fn', metrics=['mae']$$::text,
    $$ batch_size=2, epochs=1, verbose=0 $$::text,
    3,
    FALSE, NULL, 1, NULL, NULL, NULL,
    'test_custom_function_table'
);
DROP TABLE if exists iris_model, iris_model_summary, iris_model_info;
SELECT madlib_keras_fit(
    'iris_data_packed',
    'iris_model',
    'iris_model_arch',
    1,
    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['test_custom_fn1']$$::text,
    $$ batch_size=2, epochs=1, verbose=0 $$::text,
    3,
    FALSE, NULL, 1, NULL, NULL, NULL,
    'test_custom_function_table'
);
DROP TABLE if exists iris_model, iris_model_summary, iris_model_info;
-- Test for load_top_k_accuracy with a custom k value
SELECT load_top_k_accuracy_function('test_custom_function_table', 3);
SELECT madlib_keras_fit(
    'iris_data_packed',
    'iris_model',
    'iris_model_arch',
    1,
    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='test_custom_fn', metrics=['top_3_accuracy']$$::text,
    $$ batch_size=2, epochs=1, verbose=0 $$::text,
    3,
    FALSE, NULL, 1, NULL, NULL, NULL,
    'test_custom_function_table'
);

SELECT assert(
        model_arch_table = 'iris_model_arch' AND
        model_id = 1 AND
        model_type = 'madlib_keras' AND
        source_table = 'iris_data_packed' AND
        model = 'iris_model' AND
        dependent_varname = 'class_text' AND
        independent_varname = 'attributes' AND
        dependent_vartype LIKE '%char%' AND
        normalizing_const = 1 AND
        pg_typeof(normalizing_const) = 'real'::regtype AND
        name is NULL AND
        description is NULL AND
        model_size > 0 AND
        madlib_version is NOT NULL AND
        compile_params = $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='test_custom_fn', metrics=['top_3_accuracy']$$::text AND
        fit_params = $$ batch_size=2, epochs=1, verbose=0 $$::text AND
        num_iterations = 3 AND
        metrics_compute_frequency = 1 AND
        num_classes = 3 AND
        class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
        metrics_type = '{top_3_accuracy}' AND
        array_upper(training_metrics, 1) = 3 AND
        training_loss = '{0,0,0}' AND
        array_upper(metrics_elapsed_time, 1) = 3 ,
        'Keras model output Summary Validation failed. Actual:' || __to_char(summary))
FROM (SELECT * FROM iris_model_summary) summary;

SELECT assert(
        model_weights IS NOT NULL AND
        model_arch IS NOT NULL, 'Keras model output validation failed. Actual:' || __to_char(k))
FROM (SELECT * FROM iris_model) k;

DROP TABLE IF EXISTS evaluate_out;
SELECT madlib_keras_evaluate(
    'iris_model',
    'iris_data_val',
    'evaluate_out',
    FALSE);

SELECT assert(loss >= 0 AND
        metric >= 0 AND
        metrics_type = '{top_3_accuracy}' AND
        loss_type = 'test_custom_fn', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
FROM evaluate_out;
SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;

DROP TABLE if exists iris_model, iris_model_summary, iris_model_info;
SELECT assert(trap_error($TRAP$SELECT madlib_keras_fit(
    'iris_data_packed',
    'iris_model',
    'iris_model_arch',
    1,
    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='fail_test_custom_fn', metrics=['mae']$$::text,
    $$ batch_size=2, epochs=1, verbose=0 $$::text,
    3,
    FALSE, NULL, 1, NULL, NULL, NULL,
    'test_custom_function_table'
);$TRAP$) = 1,
'custom function in compile_params not defined in Object table.');

DROP TABLE if exists iris_model, iris_model_summary, iris_model_info;
SELECT assert(trap_error($TRAP$SELECT madlib_keras_fit(
    'iris_data_packed',
    'iris_model',
    'iris_model_arch',
    1,
    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='accuracy', metrics=['fail_test_custom_fn']$$::text,
    $$ batch_size=2, epochs=1, verbose=0 $$::text,
    3,
    FALSE, NULL, 1, NULL, NULL, NULL,
    'test_custom_function_table'
);$TRAP$) = 1,
'custom function in compile_params not defined in Object table.');