src/ports/postgres/modules/deep_learning/test/madlib_keras_predict_byom.sql_in

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\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
                            );

-- 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['Iris-setosa', 'Iris-versicolor',
                                  'Iris-virginica']
                                 );

SELECT assert(
  p0.estimated_class_text = p1.estimated_dependent_var,
  '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;
SELECT assert(UPPER(pg_typeof(estimated_dependent_var)::TEXT) = 'TEXT',
	'Predict byom failure for non null class value and response pred_type.
	 Expeceted estimated_dependent_var to be of type TEXT')
FROM  iris_predict_byom LIMIT 1;

-- 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.estimated_dependent_var IN ('0', '1', '2'),
  'Predict byom failure for null class value and null pred_type.')
FROM iris_predict_byom AS p1;
SELECT assert(UPPER(pg_typeof(estimated_dependent_var)::TEXT) = 'TEXT',
	'Predict byom failure for non null class value and response pred_type.
	 Expeceted estimated_dependent_var to be of type TEXT')
FROM  iris_predict_byom LIMIT 1;

-- 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['Iris-setosa', 'Iris-versicolor',
                                  'Iris-virginica'],
                                 1.0
                                 );

SELECT assert(
  (p1."prob_Iris-setosa" + p1."prob_Iris-virginica" + p1."prob_Iris-versicolor") - 1 < 1e-6,
    'Predict byom failure for non null class value and prob pred_type.')
FROM iris_predict_byom AS p1;
SELECT assert(UPPER(pg_typeof("prob_Iris-setosa")::TEXT) = 'DOUBLE PRECISION',
	'Predict byom failure for non null class value and prob pred_type.
	Expeceted "prob_Iris-setosa" to be of type DOUBLE PRECISION')
FROM  iris_predict_byom LIMIT 1;

-- 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(
  (prob[1] + prob[2] + prob[3]) - 1 < 1e-6,
    'Predict byom failure for null class value and prob pred_type.')
FROM iris_predict_byom;
SELECT assert(UPPER(pg_typeof(prob)::TEXT) = 'DOUBLE PRECISION[]',
	'Predict byom failure for null class value and prob pred_type. Expeceted prob to
	be of type DOUBLE PRECISION[]')
FROM  iris_predict_byom LIMIT 1;