def test_inference(payload):
save_module_name = const.XLMR_MODULE
save_model_name = const.XLMR_MULTI_CLASS_MODEL
const.XLMR_MODULE = "tests.plugin.text.classification.test_xlmr"
const.XLMR_MULTI_CLASS_MODEL = "MockClassifier"
directory = "/tmp"
file_path = os.path.join(directory, const.LABELENCODER_FILE)
if os.path.exists(file_path):
os.remove(file_path)
transcripts = payload.get("input")
intent = payload["expected"]["label"]
xlmr_clf = XLMRMultiClass(
model_dir=directory,
dest="output.intents",
debug=False,
)
merge_asr_output_plugin = MergeASROutputPlugin(dest="input.clf_feature",
debug=False)
workflow = Workflow([merge_asr_output_plugin, xlmr_clf])
train_df = pd.DataFrame([
{
"data": json.dumps([[{
"transcript": "yes"
}]]),
"labels": "_confirm_",
},
{
"data": json.dumps([[{
"transcript": "yea"
}]]),
"labels": "_confirm_",
},
{
"data": json.dumps([[{
"transcript": "no"
}]]),
"labels": "_cancel_",
},
{
"data": json.dumps([[{
"transcript": "nope"
}]]),
"labels": "_cancel_",
},
])
workflow.train(train_df)
assert isinstance(
xlmr_clf.model,
MockClassifier), "model should be a MockClassifier after training."
_, output = workflow.run(input_=Input(utterances=[[{
"transcript": transcript
} for transcript in transcripts]]))
assert output[const.INTENTS][0]["name"] == intent
assert output[const.INTENTS][0]["score"] > 0.9
if os.path.exists(file_path):
os.remove(file_path)
const.XLMR_MODULE = save_module_name
const.XLMR_MULTI_CLASS_MODEL = save_model_name
def test_inference(payload):
directory = "/tmp"
file_path = os.path.join(directory, const.MLPMODEL_FILE)
if os.path.exists(file_path):
os.remove(file_path)
USE = "use"
fake_args = {
const.TRAIN: {
const.NUM_TRAIN_EPOCHS: 5,
const.USE_GRIDSEARCH: {
USE: False,
const.CV: 2,
const.VERBOSE_LEVEL: 2,
const.PARAMS: {
"activation": ["relu", "tanh"],
"hidden_layer_sizes": [(10, ), (2, 2)],
"ngram_range": [(1, 1), (1, 2)],
"max_iter": [20, 2],
},
},
},
const.TEST: {},
const.PRODUCTION: {},
}
transcripts = payload.get("input")
intent = payload["expected"]["label"]
mlp_clf = MLPMultiClass(
model_dir=directory,
dest="output.intents",
args_map=fake_args,
debug=False,
)
merge_asr_output_plugin = MergeASROutputPlugin(
dest="input.clf_feature",
debug=False,
)
workflow = Workflow([merge_asr_output_plugin, mlp_clf])
train_df = pd.DataFrame([
{
"data": json.dumps([[{
"transcript": "yes"
}]]),
"labels": "_confirm_",
},
{
"data": json.dumps([[{
"transcript": "ye"
}]]),
"labels": "_confirm_",
},
{
"data": json.dumps([[{
"transcript": "<s> yes </s> <s> ye </s>"
}]]),
"labels": "_confirm_",
},
{
"data": json.dumps([[{
"transcript": "no"
}]]),
"labels": "_cancel_",
},
{
"data": json.dumps([[{
"transcript": "new"
}]]),
"labels": "_cancel_",
},
{
"data": json.dumps([[{
"transcript": "<s> new </s> <s> no </s>"
}]]),
"labels": "_cancel_",
},
])
workflow.train(train_df)
_, output = workflow.run(
Input(utterances=[[{
"transcript": transcript
} for transcript in transcripts]]))
assert output[const.INTENTS][0]["name"] == intent
assert output[const.INTENTS][0]["score"] > 0.5
if os.path.exists(file_path):
os.remove(file_path)
