src/ports/postgres/modules/deep_learning/test/madlib_keras_model_selection_e2e.sql_in

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

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__!>, <!!>, <!
-- Multiple models End-to-End test
-- Prepare model selection table with four rows
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT load_model_selection_table(
    'iris_model_arch',
    'mst_table',
    ARRAY[1],
    ARRAY[
        $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$,
        $$loss='categorical_crossentropy', optimizer='Adam(lr=0.001)', metrics=['accuracy']$$,
        $$loss='categorical_crossentropy', optimizer='Adam(lr=0.0001)', metrics=['accuracy']$$
    ],
    ARRAY[
        $$batch_size=16, epochs=1$$
    ]
);

DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
SELECT madlib_keras_fit_multiple_model(
	'iris_data_packed',
	'iris_multiple_model',
	'mst_table',
	3,
	0
);

SELECT assert(
        model_arch_table = 'iris_model_arch' AND
        validation_table is NULL AND
        model_info = 'iris_multiple_model_info' AND
        source_table = 'iris_data_packed' AND
        model = 'iris_multiple_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 iris_multiple_model_summary) summary;

-- Run Predict
DROP TABLE IF EXISTS iris_predict;
SELECT madlib_keras_predict(
    'iris_multiple_model',
    'iris_data',
    'id',
    'attributes',
    'iris_predict',
    'prob',
    NULL,
    1);

-- Run Evaluate
DROP TABLE IF EXISTS evaluate_out;
SELECT madlib_keras_evaluate(
    'iris_multiple_model',
    'iris_data_val',
    'evaluate_out',
    NULL,
    1);

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

-- Test for one-hot encoded user input data
DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
SELECT madlib_keras_fit_multiple_model(
	'iris_data_one_hot_encoded_packed',
	'iris_multiple_model',
	'mst_table',
	3,
	0
);

SELECT assert(
        model_arch_table = 'iris_model_arch' AND
        validation_table is NULL AND
        model_info = 'iris_multiple_model_info' AND
        source_table = 'iris_data_one_hot_encoded_packed' AND
        model = 'iris_multiple_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_multiple_model_summary) summary;

-- Run Predict
DROP TABLE IF EXISTS iris_predict;
SELECT madlib_keras_predict(
    'iris_multiple_model',
    'iris_data_one_hot_encoded',
    'id',
    'attributes',
    'iris_predict',
    'prob',
    NULL,
    1);

-- Run Evaluate
DROP TABLE IF EXISTS evaluate_out;
SELECT madlib_keras_evaluate(
    'iris_multiple_model',
    'iris_data_one_hot_encoded_val',
    'evaluate_out',
    NULL,
    1);

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