blob: 59c74d209233ee50f32feee07c2effbcddb92b7f [file] [log] [blame]
#
# 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.
#
from ml.classifiers import GradientBoostClassifier, MLP, RandomForestClassifier, SVC
from ml.registry import MLRegistry
from django.test import TestCase
import inspect
test_data = {
"age": 22,
"sex": "female",
"job": 2,
"housing": "own",
"credit_amount": 5951,
"duration": 48,
"purpose": "radio/TV"
}
expected_output = 'bad'
class MLTests(TestCase):
def test_rf_algorithm(self):
my_alg = RandomForestClassifier()
response = my_alg.compute_prediction(test_data)
# self.assertEqual('OK', response['status'])
self.assertTrue('label' in response)
self.assertEqual(expected_output, response['label'])
# def test_svc_algorithm(self):
# my_alg = SVC()
# response = my_alg.compute_prediction(test_data)
# self.assertEqual('OK', response['status'])
# self.assertTrue('label' in response)
# self.assertEqual(expected_output, response['label'])
def test_mlp_algorithm(self):
my_alg = MLP()
response = my_alg.compute_prediction(test_data)
# self.assertEqual('OK', response['status'])
self.assertTrue('label' in response)
self.assertEqual(expected_output, response['label'])
def test_gb_algorithm(self):
my_alg = GradientBoostClassifier()
response = my_alg.compute_prediction(test_data)
# self.assertEqual('OK', response['status'])
self.assertTrue('label' in response)
self.assertEqual(expected_output, response['label'])
def test_registry(self):
registry = MLRegistry()
self.assertEqual(len(registry.classifiers), 0)
# Random Forest classifier
rf_algo = {
'classifier': RandomForestClassifier(),
'description': "Random Forest with simple pre and post-processing",
'status': "production",
'version': "0.0.1",
'dataset': 'German',
'region': 'Germany',
'created_by': "xurror"
}
# add to registry
registry.add_algorithms([rf_algo])
# there should be one endpoint available
self.assertEqual(len(registry.classifiers), 1)