def test_multi_output(artifact_dir):
records_train = gen_records(NUM_SAMPLES_TRAIN)
records_validation = gen_records(NUM_SAMPLES_VALIDATION)
records_score = gen_records(NUM_SAMPLES_SCORE)
loc = os.path.abspath(os.path.dirname(__file__))
cfg = io_utils.load_json("config_multi_output.json", loc)
bm = BarrageModel(artifact_dir)
bm.train(cfg, records_train, records_validation)
scores = bm.predict(records_score)
classification = [np.argmax(score["classification"]) for score in scores]
regression_1 = [score["regression"][0] for score in scores]
regression_2 = [score["regression"][1] for score in scores]
df_scores = pd.DataFrame({
"classification": classification,
"regression_1": regression_1,
"regression_2": regression_2,
})
assert (df_scores["classification"] == records_score["y_cls"]).mean() > 0.5
assert abs(
(df_scores["regression_1"] - records_score["y_reg_1"]).mean()) < 0.5
assert abs(
(df_scores["regression_2"] - records_score["y_reg_2"]).mean()) < 0.5
def test_simple_output(artifact_dir, records_train, records_validation,
records_score):
loc = os.path.abspath(os.path.dirname(__file__))
cfg = io_utils.load_json("config_single_output.json", loc)
bm = BarrageModel(artifact_dir)
bm.train(cfg, records_train, records_validation)
scores = bm.predict(records_score)
df_scores = pd.DataFrame(scores)
assert (df_scores["softmax"] == records_score["label"]).mean() >= 0.90
def test_simple_output(artifact_dir):
records_train = gen_records(NUM_SAMPLES_TRAIN)
records_validation = gen_records(NUM_SAMPLES_VALIDATION)
records_score = gen_records(NUM_SAMPLES_SCORE)
loc = os.path.abspath(os.path.dirname(__file__))
cfg = io_utils.load_json("config_single_output.json", loc)
bm = BarrageModel(artifact_dir)
bm.train(cfg, records_train, records_validation)
scores = bm.predict(records_score)
df_scores = pd.DataFrame(scores)
records_score = pd.DataFrame(records_score)
assert (df_scores["softmax"] == records_score["label"]).mean() >= 0.90
