def test_multiple_ratio_metrics_as_reward(self):
with requests_mock.mock(real_http=True) as m:
file_path = os.path.join(os.path.dirname(__file__),
'../../../data/prom_responses',
'prometheus_sample_response.json')
m.get(metrics_endpoint, json=json.load(open(file_path)))
try:
eg = copy.deepcopy(eip_example)
eg["criteria"].append({
"id": "1",
"metric_id": "iter8_error_rate",
"is_reward": True
})
eg["criteria"].append({
"id": "2",
"metric_id": "conversion_rate",
"is_reward": True
})
eip = ExperimentIterationParameters(**eg)
exp = Experiment(eip)
exp.run()
except HTTPException as he:
pass
def test_start_time_with_current_time(self):
with requests_mock.mock(real_http=True) as m:
file_path = os.path.join(os.path.dirname(__file__),
'../../../data/prom_responses',
'prometheus_sample_response.json')
m.get(metrics_endpoint, json=json.load(open(file_path)))
eip = ExperimentIterationParameters(**eip_with_percentile)
eip.start_time = datetime.now(timezone.utc)
exp = Experiment(eip)
exp.run()
def test_get_ratio_max_min(self):
with requests_mock.mock(real_http=True) as m:
file_path = os.path.join(os.path.dirname(__file__),
'../../../data/prom_responses',
'prometheus_sample_response.json')
m.get(metrics_endpoint, json=json.load(open(file_path)))
eip = ExperimentIterationParameters(**eip_example)
exp = Experiment(eip)
exp.run()
eip = ExperimentIterationParameters(
**reviews_example_with_last_state)
exp = Experiment(eip)
exp.run()
eip = ExperimentIterationParameters(
**reviews_example_with_partial_last_state)
exp = Experiment(eip)
exp.run()
eip = ExperimentIterationParameters(
**reviews_example_with_ratio_max_mins)
exp = Experiment(eip)
exp.run()
def test_relative_threshold(self):
with requests_mock.mock(real_http=True) as m:
m.get(metrics_endpoint,
json=json.load(
open("tests/data/prometheus_no_data_response.json")))
eip_with_relative = copy.deepcopy(eip_with_assessment)
eip_with_relative["criteria"][0] = {
"id": "iter8_mean_latency",
"metric_id": "iter8_mean_latency",
"is_reward": False,
"threshold": {
"threshold_type": "relative",
"value": 1.6
}
}
eip = ExperimentIterationParameters(**eip_with_relative)
exp = Experiment(eip)
res = exp.run()
for c in res.candidate_assessments:
if c.id == 'productpage-v3':
assert c.win_probability == 1.0
assert res.last_state
assert res.last_state["traffic_split_recommendation"]
assert res.last_state["aggregated_counter_metrics"]
assert res.last_state["aggregated_ratio_metrics"]
assert res.last_state["ratio_max_mins"]
def test_counter_metric_as_reward(self):
with requests_mock.mock(real_http=True) as m:
m.get(metrics_endpoint,
json=json.load(
open("tests/data/prometheus_sample_response.json")))
try:
eg = copy.deepcopy(eip_example)
eg["criteria"].append({
"id": "1",
"metric_id": "conversion_count",
"is_reward": True
})
eip = ExperimentIterationParameters(**eg)
exp = Experiment(eip)
exp.run()
except HTTPException as he:
pass
def test_start_time_with_current_time(self):
with requests_mock.mock(real_http=True) as m:
m.get(metrics_endpoint,
json=json.load(
open("tests/data/prometheus_sample_response.json")))
eip = ExperimentIterationParameters(**eip_with_percentile)
eip.start_time = datetime.now(timezone.utc)
exp = Experiment(eip)
# print("mert")
# time this run
# increase number of versions by changing version in 1) eipwithpercentile 2) sample response
# logger.info(f"mert:{time.time()}")
current_time = time.time()
for i in range(10):
exp.run()
finished = time.time()
f = open("demofile2.txt", "a")
f.write(f"{finished-current_time}")
f.close()
def test_aa_experiment(self):
with requests_mock.mock(real_http=True) as m:
file_path = os.path.join(os.path.dirname(__file__),
'../../../data/prom_responses',
'prometheus_sample_response.json')
m.get(metrics_endpoint, json=json.load(open(file_path)))
eip_aa = ExperimentIterationParameters(**reviews_example_aa)
exp_aa = Experiment(eip_aa)
res = exp_aa.run()
logger.info("AA Result")
logger.info(pformat(res.dict(), indent=2))
assert (res.baseline_assessment.request_count ==
res.candidate_assessments[0].request_count)
def test_missing_iter8_request_count(self):
with requests_mock.mock(real_http=True) as m:
m.get(metrics_endpoint,
json=json.load(
open("tests/data/prometheus_sample_response.json")))
eip = ExperimentIterationParameters(
**reviews_example_without_request_count)
exp = Experiment(eip)
resp = exp.run()
assert resp.baseline_assessment.request_count is None
for assessment in resp.candidate_assessments:
assert assessment.request_count is None
def test_assessment(self):
with requests_mock.mock(real_http=True) as m:
m.get(metrics_endpoint,
json=json.load(
open("tests/data/prometheus_no_data_response.json")))
eip = ExperimentIterationParameters(**eip_with_assessment)
exp = Experiment(eip)
res = exp.run()
assert res.last_state
assert res.last_state["traffic_split_recommendation"]
assert res.last_state["aggregated_counter_metrics"]
assert res.last_state["aggregated_ratio_metrics"]
assert res.last_state["ratio_max_mins"]
def test_relative_win_probability_and_threshold_assessment(self):
with requests_mock.mock(real_http=True) as m:
m.get(metrics_endpoint,
json=json.load(
open("tests/data/prometheus_no_data_response.json")))
eip = copy.deepcopy(eip_with_relative_assessments)
eip = ExperimentIterationParameters(**eip)
exp = Experiment(eip)
res = exp.run()
for c in res.candidate_assessments:
if c.id == 'productpage-v3':
assert c.win_probability == 1.0
assert res.last_state
assert res.last_state["traffic_split_recommendation"]
assert res.last_state["aggregated_counter_metrics"]
assert res.last_state["aggregated_ratio_metrics"]
assert res.last_state["ratio_max_mins"]
def test_get_ratio_max_min(self):
with requests_mock.mock(real_http=True) as m:
m.get(metrics_endpoint,
json=json.load(
open("tests/data/prometheus_sample_response.json")))
eip = ExperimentIterationParameters(**eip_example)
exp = Experiment(eip)
exp.run()
eip = ExperimentIterationParameters(
**reviews_example_with_last_state)
exp = Experiment(eip)
exp.run()
eip = ExperimentIterationParameters(
**reviews_example_with_partial_last_state)
exp = Experiment(eip)
exp.run()
eip = ExperimentIterationParameters(
**reviews_example_with_ratio_max_mins)
exp = Experiment(eip)
exp.run()