def test_LabelTokenizerSingleStateFeaturizer():
f = LabelTokenizerSingleStateFeaturizer()
f.user_labels = ["a_d"]
f.bot_labels = ["c_b"]
f.user_vocab = {"a": 0, "d": 1}
f.bot_vocab = {"b": 1, "c": 0}
f.num_features = len(f.user_vocab) + len(f.slot_labels) + len(f.bot_vocab)
# Dictionary: 0 1 2 3
# "a" "d" "c" "b"
encoded = f.encode({
"a_d": 1.0,
"prev_c_b": 0.0,
"e": 1.0,
"prev_action_listen": 1.0
})
assert list(encoded) == [1, 1, 0, 0]
# "a_d" -> ["a", "d"] -> 1 1 0 0
# "prev_c_b" -> ["prev", "c", "b"] --> only "c_b" count: --> [0 0 0 0]
# "prev_action_listen" --> ["prev", "action", "listen"] --> ignored because not in labels
encoded = f.encode({
"a_d": 1.7,
"prev_c_b": 2.0,
"e": 1.0,
"prev_action_listen": 1.0
})
assert encoded == [1.7, 1.7, 2.0, 2.0]
def test_label_tokenizer_featurizer_uses_correct_dtype_float():
f = LabelTokenizerSingleStateFeaturizer()
f.user_labels = ["a_d"]
f.bot_labels = ["c_b"]
f.user_vocab = {"a": 0, "d": 1}
f.bot_vocab = {"b": 1, "c": 0}
f.num_features = len(f.user_vocab) + len(f.slot_labels) + len(f.bot_vocab)
encoded = f.encode({
"a_d": 0.2,
"prev_c_b": 0.0,
"prev_action_listen": 1.0
})
assert encoded.dtype == np.float64
def test_label_tokenizer_featurizer_handles_on_non_existing_features():
f = LabelTokenizerSingleStateFeaturizer()
f.user_labels = ["a_d"]
f.bot_labels = ["c_b"]
f.user_vocab = {"a": 0, "d": 1}
f.bot_vocab = {"b": 1, "c": 0}
f.num_features = len(f.user_vocab) + len(f.slot_labels) + len(f.bot_vocab)
encoded = f.encode({
"a_d": 1.0,
"prev_c_b": 0.0,
"e": 1.0,
"prev_action_listen": 1.0
})
assert (encoded == np.array([1, 1, 0, 0])).all()
def test_label_tokenizer_featurizer_handles_probabilistic_intents():
f = LabelTokenizerSingleStateFeaturizer()
f.user_labels = ["intent_a", "intent_d"]
f.bot_labels = ["c", "b"]
f.user_vocab = {"intent": 2, "a": 0, "d": 1}
f.bot_vocab = {"b": 1, "c": 0}
f.num_features = len(f.user_vocab) + len(f.slot_labels) + len(f.bot_vocab)
encoded = f.encode({
"intent_a": 0.5,
"prev_b": 0.2,
"intent_d": 1.0,
"prev_action_listen": 1.0
})
assert (encoded == np.array([0.5, 1.0, 1.5, 0.0, 0.2])).all()