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 *
 * 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.
 *
 *//* ---------------------------------------------------------------------*/

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

DROP TABLE IF EXISTS iris_model, iris_model_summary;
SELECT madlib_keras_fit('iris_data_packed',   -- source table
                        'iris_model',          -- model output table
                        'iris_model_arch',  -- model arch table
                         1,                    -- model arch id
                         $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                         $$ batch_size=5, epochs=3 $$,  -- fit_params
                         3);                    -- num_iterations

DROP TABLE IF EXISTS iris_predict;
SELECT madlib_keras_predict('iris_model', -- model
                            'iris_test',  -- test_table
                            'id',  -- id column
                            'attributes', -- independent var
                            'iris_predict',  -- output table
                            'response'
                            );

-- Copy weights that were learnt from the previous run, for transfer
-- learning. Copy it now, because using warm_start will overwrite it.
UPDATE iris_model_arch set model_weights = (select model_weights from iris_model)  WHERE model_id = 2;

-- class_values not NULL, pred_type is response
DROP TABLE IF EXISTS iris_predict_byom;
SELECT madlib_keras_predict_byom(
                                 'iris_model_arch',
                                 2,
                                 'iris_test',
                                 'id',
                                 'attributes',
                                 'iris_predict_byom',
                                 'response',
                                 NULL,
                                 ARRAY[ARRAY['Iris-setosa', 'Iris-versicolor',
                                  'Iris-virginica']::text[]]
                                 );

SELECT assert(
  p0.class_value = p1.class_value,
  'Predict byom failure for non null class value and response pred_type.')
FROM iris_predict AS p0,  iris_predict_byom AS p1
WHERE p0.id=p1.id;

DROP TABLE IF EXISTS cifar10_predict;
SELECT assert(test_input_table($test$SELECT madlib_keras_predict_byom(
     'iris_model_arch',
     2,
     NULL,
     'id',
     'attributes',
     'iris_predict_byom',
     'response',
     NULL,
     ARRAY[ARRAY['Iris-setosa', 'Iris-versicolor',
      'Iris-virginica']::text[]]
     )$test$), 'Failed to assert the correct error message for null test table');

SELECT assert(test_input_table($test$SELECT madlib_keras_predict_byom(
     NULL,
     2,
     'iris_test',
     'id',
     'attributes',
     'iris_predict_byom',
     'response',
     NULL,
     ARRAY[ARRAY['Iris-setosa', 'Iris-versicolor',
      'Iris-virginica']::text[]]
     )$test$), 'Failed to assert the correct error message for null model table');

-- class_values NULL, pred_type is NULL (response)
DROP TABLE IF EXISTS iris_predict_byom;
SELECT madlib_keras_predict_byom(
                                 'iris_model_arch',
                                 2,
                                 'iris_test',
                                 'id',
                                 'attributes',
                                 'iris_predict_byom'
                                 );
SELECT assert(
  p1.class_value IN ('0', '1', '2'),
  'Predict byom failure for null class value and null pred_type.')
FROM iris_predict_byom AS p1;

-- class_values not NULL, pred_type is prob
DROP TABLE IF EXISTS iris_predict_byom;
SELECT madlib_keras_predict_byom(
                                 'iris_model_arch',
                                 2,
                                 'iris_test',
                                 'id',
                                 'attributes',
                                 'iris_predict_byom',
                                 'prob',
                                 NULL,
                                 ARRAY[ARRAY['Iris-setosa', 'Iris-versicolor',
                                  'Iris-virginica']::text[]],
                                 1.0
                                 );

SELECT assert(
  sum(prob) - 1 < 1e-6,
    'Predict byom failure for non null class value and prob pred_type.')
FROM iris_predict_byom AS p1 GROUP BY id;

-- class_values NULL, pred_type is prob
DROP TABLE IF EXISTS iris_predict_byom;
SELECT madlib_keras_predict_byom(
                                 'iris_model_arch',
                                 2,
                                 'iris_test',
                                 'id',
                                 'attributes',
                                 'iris_predict_byom',
                                 'prob',
                                 NULL,
                                 NULL
                                 );
SELECT assert(
  sum(prob) - 1 < 1e-6,
    'Predict byom failure for null class value and prob pred_type.')
FROM iris_predict_byom GROUP BY id;