Add evaluation script for the multiwoz RuleDST and BERTNLU-RuleDST models

convlab/dst/rule/multiwoz/evaluate.py

+# -*- coding: utf-8 -*-
+# Copyright 2023 DSML Group, Heinrich Heine University, Düsseldorf
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MultiWOZ Test data inference for RuleDST and BERTNLU+RuleDST"""
+
+import json
+from copy import deepcopy
+import os
+
+from tqdm import tqdm
+
+from convlab.util import load_dataset, load_dst_data
+from convlab.dst.rule.multiwoz.dst import RuleDST
+from convlab.nlu.jointBERT.unified_datasets.nlu import BERTNLU
+
+BERTNLU_PATH = "https://huggingface.co/ConvLab/bert-base-nlu/resolve/main/bertnlu_unified_multiwoz21_user_context3.zip"
+
+
+def flatten_act(acts: dict) -> list:
+    acts_list = list()
+    for act_type, _acts in acts.items():
+        for act in _acts:
+            if 'value' in act:
+                _act = [act['intent'], act['domain'], act['slot'], act['value']]
+            else:
+                _act = [act['intent'], act['domain'], act['slot'], '']
+            acts_list.append(_act)
+    return acts_list
+
+
+def load_act_data(dataset: dict) -> list:
+    data = list()
+    for dialogue in tqdm(dataset['test']):
+        dial = []
+        for _turn in dialogue['turns']:
+            if _turn['speaker'] == 'user':
+                turn = {'user_acts': flatten_act(_turn['dialogue_acts']),
+                        'state': _turn['state']}
+                dial.append(turn)
+        data.append(dial)
+    return data
+
+
+def load_text_data(dataset: dict) -> list:
+    data = list()
+    for dialogue in tqdm(dataset['test']):
+        dial = []
+        turn = {'user': '', 'system': 'Start', 'state': None}
+        for _turn in dialogue['turns']:
+            if _turn['speaker'] == 'user':
+                turn['user'] = _turn['utterance']
+                turn['state'] = _turn['state']
+            elif _turn['speaker'] == 'system':
+                turn['system'] = _turn['utterance']
+            if turn['user'] and turn['system']:
+                if turn['system'] == 'Start':
+                    turn['system'] = ''
+                dial.append(deepcopy(turn))
+                turn = {'user': '', 'system': '', 'state': None}
+        data.append(dial)
+    return data
+
+
+def predict_acts(data: list, nlu: BERTNLU) -> list:
+    processed_data = list()
+    for dialogue in tqdm(data):
+        context = list()
+        dial = list()
+        for turn in dialogue:
+            context.append(['sys', turn['system']])
+            acts = nlu.predict(turn['user'], context=context)
+            context.append(['usr', turn['user']])
+            dial.append({'user_acts': deepcopy(acts), 'state': turn['state']})
+        processed_data.append(dial)
+    return processed_data
+
+
+def predict_states(data: list):
+    dst = RuleDST()
+    processed_data = list()
+    for dialogue in tqdm(data):
+        dst.init_session()
+        for turn in dialogue:
+            pred = dst.update(turn['user_acts'])
+            dial = {'state': turn['state'],
+                    'predictions': {'state': deepcopy(pred['belief_state'])}}
+            processed_data.append(dial)
+    return processed_data
+
+
+if __name__ == '__main__':
+    dataset = load_dataset(dataset_name='multiwoz21')
+    dataset = load_dst_data(dataset, data_split='test', speaker='all', dialogue_acts=True, split_to_turn=False)
+
+    data = load_text_data(dataset)
+    nlu = BERTNLU(mode='user', config_file='multiwoz21_user_context3.json', model_file=BERTNLU_PATH)
+    bertnlu_data = predict_acts(data, nlu)
+
+    golden_data = load_act_data(dataset)
+
+    bertnlu_data = predict_states(bertnlu_data)
+    golden_data = predict_states(golden_data)
+
+    path = os.path.dirname(os.path.realpath(__file__))
+    writer = open(os.path.join(path, f"predictions_BERTNLU-RuleDST.json"), 'w')
+    json.dump(bertnlu_data, writer)
+    writer.close()
+
+    writer = open(os.path.join(path, f"predictions_RuleDST.json"), 'w')
+    json.dump(golden_data, writer)
+    writer.close()