def test_create_original_split_fact_value_given(self):
payload = {
"description": "Original index splitting",
"indices": [{
"name": self.test_index_name
}],
"train_index": INDEX_SPLITTING_TRAIN_INDEX,
"test_index": INDEX_SPLITTING_TEST_INDEX,
"distribution": "original",
"test_size": 20,
"fact": self.FACT,
"str_val": "FUBAR"
}
response = self.client.post(self.url, data=payload, format="json")
print_output(
'test_create_original_split_fact_value_given:response.data',
response.data)
splitter_obj = IndexSplitter.objects.get(id=response.data['id'])
sleep(5)
original_distribution = ElasticAggregator(
indices=self.test_index_name).get_fact_values_distribution(
self.FACT)
test_distribution = ElasticAggregator(
indices=INDEX_SPLITTING_TEST_INDEX).get_fact_values_distribution(
self.FACT)
train_distribution = ElasticAggregator(
indices=INDEX_SPLITTING_TRAIN_INDEX).get_fact_values_distribution(
self.FACT)
print_output(
'original_dist, test_dist, train_dist',
[original_distribution, test_distribution, train_distribution])
for label, quant in original_distribution.items():
if label == "FUBAR":
self.assertTrue(
self.is_between_limits(test_distribution[label], quant,
0.2))
self.assertTrue(
self.is_between_limits(train_distribution[label], quant,
0.8))
def test_create_equal_split(self):
payload = {
"description": "Original index splitting",
"indices": [{
"name": self.test_index_name
}],
"train_index": INDEX_SPLITTING_TRAIN_INDEX,
"test_index": INDEX_SPLITTING_TEST_INDEX,
"distribution": "equal",
"test_size": 20,
"fact": self.FACT
}
response = self.client.post(self.url, data=payload)
print_output('test_create_equal_split:response.data', response.data)
splitter_obj = IndexSplitter.objects.get(id=response.data['id'])
# Assert Task gets completed
self.assertEqual(Task.STATUS_COMPLETED, Task.STATUS_COMPLETED)
print_output("Task status", Task.STATUS_COMPLETED)
sleep(5)
original_distribution = ElasticAggregator(
indices=self.test_index_name).get_fact_values_distribution(
self.FACT)
test_distribution = ElasticAggregator(
indices=INDEX_SPLITTING_TEST_INDEX).get_fact_values_distribution(
self.FACT)
train_distribution = ElasticAggregator(
indices=INDEX_SPLITTING_TRAIN_INDEX).get_fact_values_distribution(
self.FACT)
print_output(
'original_dist, test_dist, train_dist',
[original_distribution, test_distribution, train_distribution])
for label, quant in original_distribution.items():
if (quant > 20):
self.assertEqual(test_distribution[label], 20)
self.assertEqual(train_distribution[label], quant - 20)
else:
self.assertEqual(test_distribution[label], quant)
self.assertTrue(label not in train_distribution)