def test_intent():
#load model
interpreter = Interpreter.load(model_location)
# get true target of the testing data
targets = evaluate.get_intent_targets(testing_data)
# get predictions of the testing data
predictions = evaluate.get_intent_predictions(interpreter, testing_data)
#create a confusion matrix and summary statistics for intent predictions
evaluate.evaluate_intents(targets, predictions)
#generate classification report, precision, f1 score and accuary
report, precision, f1, accuracy = evaluate.get_evaluation_metrics(
targets, predictions)
print("F1-Score: {}\n".format(f1), "Precision: {}\n".format(precision),
"Accuracy: {}\n".format(accuracy),
"Classification report: \n{}".format(report))
def test_intent_evaluation_report(tmpdir_factory):
path = tmpdir_factory.mktemp("evaluation").strpath
report_folder = os.path.join(path, "reports")
report_filename = os.path.join(report_folder, "intent_report.json")
utils.create_dir(report_folder)
intent_results = [
IntentEvaluationResult("", "restaurant_search", "I am hungry",
0.12345),
IntentEvaluationResult("greet", "greet", "hello", 0.98765)
]
result = evaluate_intents(intent_results,
report_folder,
successes_filename=None,
errors_filename=None,
confmat_filename=None,
intent_hist_filename=None)
report = json.loads(utils.read_file(report_filename))
greet_results = {
"precision": 1.0,
"recall": 1.0,
"f1-score": 1.0,
"support": 1
}
prediction = {
'text': 'hello',
'intent': 'greet',
'predicted': 'greet',
'confidence': 0.98765
}
assert len(report.keys()) == 4
assert report["greet"] == greet_results
assert result["predictions"][0] == prediction