def test_dsjson_with_metrics():
vw = pyvw.vw(
'--extra_metrics metrics.json --cb_explore_adf --epsilon 0.2 --dsjson')
ex_l_str = '{"_label_cost":-0.9,"_label_probability":0.5,"_label_Action":1,"_labelIndex":0,"o":[{"v":1.0,"EventId":"38cbf24f-70b2-4c76-aa0c-970d0c8d388e","ActionTaken":false}],"Timestamp":"2020-11-15T17:09:31.8350000Z","Version":"1","EventId":"38cbf24f-70b2-4c76-aa0c-970d0c8d388e","a":[1,2],"c":{ "GUser":{"id":"person5","major":"engineering","hobby":"hiking","favorite_character":"spock"}, "_multi": [ { "TAction":{"topic":"SkiConditions-VT"} }, { "TAction":{"topic":"HerbGarden"} } ] },"p":[0.5,0.5],"VWState":{"m":"N/A"}}\n'
ex_l = vw.parse(ex_l_str)
vw.learn(ex_l)
pred = ex_l[0].get_action_scores()
expected = [0.5, 0.5]
assert len(pred) == len(expected)
for a, b in zip(pred, expected):
assert isclose(a, b)
vw.finish_example(ex_l)
ex_p = '{"_label_cost":-1.0,"_label_probability":0.5,"_label_Action":1,"_labelIndex":0,"o":[{"v":1.0,"EventId":"38cbf24f-70b2-4c76-aa0c-970d0c8d388e","ActionTaken":false}],"Timestamp":"2020-11-15T17:09:31.8350000Z","Version":"1","EventId":"38cbf24f-70b2-4c76-aa0c-970d0c8d388e","a":[1,2],"c":{ "GUser":{"id":"person5","major":"engineering","hobby":"hiking","favorite_character":"spock"}, "_multi": [ { "TAction":{"topic":"SkiConditions-VT"} }, { "TAction":{"topic":"HerbGarden"} } ] },"p":[0.5,0.5],"VWState":{"m":"N/A"}}\n'
pred = vw.predict(ex_p)
expected = [0.9, 0.1]
assert len(pred) == len(expected)
for a, b in zip(pred, expected):
assert isclose(a, b)
learner_metric_dict = vw.get_learner_metrics()
assert (learner_metric_dict["total_predict_calls"] == 2)
assert (learner_metric_dict["total_learn_calls"] == 1)
assert (learner_metric_dict["cbea_labeled_ex"] == 1)
assert (learner_metric_dict["cbea_predict_in_learn"] == 0)
assert (learner_metric_dict["cbea_label_first_action"] == 1)
assert (learner_metric_dict["cbea_label_not_first"] == 0)
assert (pytest.approx(learner_metric_dict["cbea_sum_cost"]) == -0.9)
assert (pytest.approx(
learner_metric_dict["cbea_sum_cost_baseline"]) == -0.9)
assert (len(vw.get_learner_metrics()) == 8)
def test_dsjson():
vw = pyvw.vw('--cb_explore_adf --epsilon 0.2 --dsjson')
ex_l_str = '{"_label_cost":-1.0,"_label_probability":0.5,"_label_Action":1,"_labelIndex":0,"o":[{"v":1.0,"EventId":"38cbf24f-70b2-4c76-aa0c-970d0c8d388e","ActionTaken":false}],"Timestamp":"2020-11-15T17:09:31.8350000Z","Version":"1","EventId":"38cbf24f-70b2-4c76-aa0c-970d0c8d388e","a":[1,2],"c":{ "GUser":{"id":"person5","major":"engineering","hobby":"hiking","favorite_character":"spock"}, "_multi": [ { "TAction":{"topic":"SkiConditions-VT"} }, { "TAction":{"topic":"HerbGarden"} } ] },"p":[0.5,0.5],"VWState":{"m":"N/A"}}\n'
ex_l = vw.parse(ex_l_str)
vw.learn(ex_l)
pred = ex_l[0].get_action_scores()
expected = [0.5, 0.5]
assert len(pred) == len(expected)
for a, b in zip(pred, expected):
assert isclose(a, b)
vw.finish_example(ex_l)
ex_p = '{"_label_cost":-1.0,"_label_probability":0.5,"_label_Action":1,"_labelIndex":0,"o":[{"v":1.0,"EventId":"38cbf24f-70b2-4c76-aa0c-970d0c8d388e","ActionTaken":false}],"Timestamp":"2020-11-15T17:09:31.8350000Z","Version":"1","EventId":"38cbf24f-70b2-4c76-aa0c-970d0c8d388e","a":[1,2],"c":{ "GUser":{"id":"person5","major":"engineering","hobby":"hiking","favorite_character":"spock"}, "_multi": [ { "TAction":{"topic":"SkiConditions-VT"} }, { "TAction":{"topic":"HerbGarden"} } ] },"p":[0.5,0.5],"VWState":{"m":"N/A"}}\n'
pred = vw.predict(ex_p)
expected = [0.9, 0.1]
assert len(pred) == len(expected)
for a, b in zip(pred, expected):
assert isclose(a, b)