dstc9 xldst evaluation

convlab2/dst/dst.py

 """Dialog State Tracker Interface"""
-from convlab2.util.module import Module
 import copy
+from abc import abstractmethod
+
+from convlab2.util.module import Module
 
 
 class DST(Module):
@@ -18,6 +20,21 @@ class DST(Module):
         """
         pass
 
+    @abstractmethod
+    def update_turn(self, sys_utt, user_utt):
+        """ Update the internal dialog state variable with .
+
+        Args:
+            sys_utt (str):
+                system utterance of current turn, set to `None` for the first turn
+            user_utt (str):
+                user utterance of current turn
+        Returns:
+            new_state (dict):
+                Updated dialog state, with the same form of previous state.
+        """
+        pass
+
     def to_cache(self, *args, **kwargs):
         return copy.deepcopy(self.state)
 

convlab2/dst/dstc9/eval_file.py

+"""
+    evaluate output file
+"""
+
+from convlab2.dst.dstc9.utils import prepare_data, eval_states
+
+if __name__ == '__main__':
+    import os
+    import json
+    from argparse import ArgumentParser
+    parser = ArgumentParser()
+    parser.add_argument('subtask', type=str, choices=['multiwoz', 'crosswoz'])
+    args = parser.parse_args()
+
+    gt = {
+        dialog_id: [state for _, _, state in turns]
+        for dialog_id, turns in prepare_data(args.subtask).items()
+    }
+    # json.dump(gt, open('gt-crosswoz.json', 'w'), ensure_ascii=False, indent=4)
+
+    results = {}
+    for i in range(1, 6):
+        filename = f'submission{i}.json'
+        if not os.path.exists(filename):
+            continue
+        pred = json.load(open(filename))
+        results[filename] = eval_states(gt, pred)
+
+    json.dump(results, open('results.json', 'w'), indent=4, ensure_ascii=False)

convlab2/dst/dstc9/eval_model.py

+"""
+    evaluate DST model
+"""
+
+import os
+import json
+import importlib
+
+from convlab2.dst import DST
+from convlab2.dst.dstc9.utils import prepare_data, eval_states
+
+
+def evaluate(model_name, subtask):
+    subdir = 'multiwoz_zh' if subtask == 'multiwoz' else 'crosswoz_en'
+    module = importlib.import_module(f'{model_name}.{subdir}')
+    assert 'Model' in dir(module), 'please import your model as name `Model` in your subtask module root'
+    model_cls = module.__getattribute__('Model')
+    assert issubclass(model_cls, DST), 'the model must implement DST interface'
+    # load weights, set eval() on default
+    model = model_cls()
+    gt = {}
+    pred = {}
+    for dialog_id, turns in prepare_data(subtask).items():
+        gt_dialog = []
+        pred_dialog = []
+        model.init_session()
+        for sys_utt, user_utt, gt_turn in turns:
+            gt_dialog.append(gt_turn)
+            pred_dialog.append(model.update_turn(sys_utt, user_utt))
+        gt[dialog_id] = gt_dialog
+        pred[dialog_id] = pred_dialog
+    result = eval_states(gt, pred)
+    print(result)
+    json.dump(result, open(os.path.join(model_name, subdir, 'result.json'), 'w'), indent=4, ensure_ascii=False)
+
+
+if __name__ == '__main__':
+    from argparse import ArgumentParser
+    parser = ArgumentParser()
+    parser.add_argument('subtask', type=str, choices=['multiwoz', 'crosswoz'])
+    args = parser.parse_args()
+    evaluate('example', args.subtask)

convlab2/dst/dstc9/example/crosswoz_en/__init__.py

+from .model import ExampleModel as Model

convlab2/dst/dstc9/example/crosswoz_en/model.py

+from convlab2.dst import DST
+
+
+class ExampleModel(DST):
+    def update_turn(self, sys_utt, user_utt):
+        return {
+            "Attraction": {
+                "name": "",
+                "fee": "",
+                "duration": "",
+                "rating": "",
+                "nearby attract.": "",
+                "nearby rest.": "",
+                "nearby hotels": ""
+            },
+            "Restaurant": {
+                "name": "",
+                "dishes": "",
+                "cost": "",
+                "rating": "",
+                "nearby attract.": "",
+                "nearby rest.": "",
+                "nearby hotels": ""
+            },
+            "Hotel": {
+                "name": "",
+                "type": "",
+                "Hotel Facilities": "",
+                "price": "",
+                "rating": "",
+                "nearby attract.": "",
+                "nearby rest.": "",
+                "nearby hotels": ""
+            },
+            "Metro": {
+                "from": "",
+                "to": ""
+            },
+            "Taxi": {
+                "from": "",
+                "to": ""
+            }
+        }

convlab2/dst/dstc9/example/multiwoz_zh/__init__.py

+from .model import ExampleModel as Model

convlab2/dst/dstc9/example/multiwoz_zh/model.py

+from convlab2.dst import DST
+
+
+class ExampleModel(DST):
+    def update_turn(self, sys_utt, user_utt):
+        return {
+            "出租车": {
+                "出发时间": "",
+                "目的地": "",
+                "出发地": "",
+                "到达时间": "",
+            },
+            "餐厅": {
+                "时间": "",
+                "日期": "",
+                "人数": "",
+                "食物": "",
+                "价格范围": "",
+                "名称": "",
+                "区域": "",
+            },
+            "公共汽车": {
+                "人数": "",
+                "出发时间": "",
+                "目的地": "",
+                "日期": "",
+                "到达时间": "",
+                "出发地": "",
+            },
+            "旅馆": {
+                "停留天数": "",
+                "日期": "",
+                "人数": "",
+                "名称": "",
+                "区域": "",
+                "停车处": "",
+                "价格范围": "",
+                "星级": "",
+                "互联网": "",
+                "类型": "",
+            },
+            "景点": {
+                "类型": "",
+                "名称": "",
+                "区域": "",
+            },
+            "列车": {
+                "票价": "",
+                "人数": "",
+                "出发时间": "",
+                "目的地": "",
+                "日期": "",
+                "到达时间": "未提及",
+                "出发地": "未提及",
+            },
+        }

convlab2/dst/dstc9/utils.py

+import os
+import json
+import zipfile
+
+
+def load_test_data(subtask):
+    from convlab2 import DATA_ROOT
+    data_dir = os.path.join(DATA_ROOT, 'multiwoz_zh' if subtask == 'multiwoz' else 'crosswoz_en')
+    # the filename will change to test file during testing phase
+    zip_filename = os.path.join(data_dir, 'human_val.json.zip')
+    return json.load(zipfile.ZipFile(zip_filename).open('human_val.json'))
+
+
+def prepare_data(subtask):
+    test_data = load_test_data(subtask)
+    data = {}
+    if subtask == 'multiwoz':
+        for dialog_id, dialog in test_data.items():
+            dialog_data = []
+            turns = dialog['log']
+            for i in range(0, len(turns), 2):
+                sys_utt = turns[i - 1]['text'] if i else None
+                user_utt = turns[i]['text']
+                state = {}
+                for domain_name, domain in turns[i + 1]['metadata'].items():
+                    if domain_name in ['警察机关', '医院']:
+                        continue
+                    domain_state = {}
+                    for slots in domain.values():
+                        for slot_name, value in slots.items():
+                            domain_state[slot_name] = value
+                    state[domain_name] = domain_state
+                dialog_data.append((sys_utt, user_utt, state))
+            data[dialog_id] = dialog_data
+    else:
+        for dialog_id, dialog in test_data.items():
+            dialog_data = []
+            turns = dialog['messages']
+            for i in range(0, len(turns), 2):
+                sys_utt = turns[i - 1]['content'] if i else None
+                user_utt = turns[i]['content']
+                state = {}
+                for domain_name, domain in turns[i + 1]['sys_state_init'].items():
+                    domain_state = {}
+                    for slot_name, value in domain.items():
+                        if slot_name == 'selectedResults':
+                            continue
+                        domain_state[slot_name] = value
+                    state[domain_name] = domain_state
+                dialog_data.append((sys_utt, user_utt, state))
+            data[dialog_id] = dialog_data
+
+    return data
+
+
+def eval_states(gt, pred):
+    def exception(description, **kargs):
+        ret = {
+            'status': 'exception',
+            'description': description,
+        }
+        for k, v in kargs.items():
+            ret[k] = v
+        return ret
+
+    joint_acc, joint_tot = 0, 0
+    slot_acc, slot_tot = 0, 0
+    tp, fp, fn = 0, 0, 0
+    for dialog_id, gt_states in gt.items():
+        if dialog_id not in pred:
+            return exception('some dialog not found', dialog_id=dialog_id)
+
+        pred_states = pred[dialog_id]
+        if len(gt_states) != len(pred_states):
+            return exception(f'turns number incorrect, {len(gt_states)} expected, {len(pred_states)} found', dialog_id=dialog_id)
+
+        for turn_id, (gt_state, pred_state) in enumerate(zip(gt_states, pred_states)):
+            joint_tot += 1
+            turn_result = True
+            for domain_name, gt_domain in gt_state.items():
+                if domain_name not in pred_state:
+                    return exception('domain missing', dialog_id=dialog_id, turn_id=turn_id, domain=domain_name)
+
+                pred_domain = pred_state[domain_name]
+                for slot_name, gt_value in gt_domain.items():
+                    if slot_name not in pred_domain:
+                        return exception('slot missing', dialog_id=dialog_id, turn_id=turn_id, domain=domain_name, slot=slot_name)
+                    pred_value = pred_domain[slot_name]
+                    slot_tot += 1
+                    if gt_value == pred_value:
+                        slot_acc += 1
+                        tp += 1
+                    else:
+                        turn_result = False
+                        # for class of gt_value
+                        fn += 1
+                        # for class of pred_value
+                        fp += 1
+            joint_acc += turn_result
+
+    precision = tp / (tp + fp)
+    recall = tp / (tp + fn)
+    f1 = 2 * tp / (2 * tp + fp + fn)
+    return {
+        'status': 'ok',
+        'joint accuracy': joint_acc / joint_tot,
+        'slot': {
+            'accuracy': slot_acc / slot_tot,
+            'precision': precision,
+            'recall': recall,
+            'f1': f1,
+        }
+    }

convlab2/dst/dstc9_eval.py

-import random
-from argparse import ArgumentParser
-
-import numpy as np
-import torch
-
-parser = ArgumentParser()
-parser.add_argument('--seed', type=int, default=23333)
-parser.add_argument('--subtask', type=str, required=True, choices=['multiwoz', 'crosswoz'])
-args = parser.parse_args()
-
-# make your model's behavior deterministic
-seed = args.seed
-random.seed(seed)
-np.random.seed(seed)
-torch.manual_seed(seed)
-torch.cuda.manual_seed_all(seed)
-torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
-print(f'fix random seed: {seed}')
-
-subtask = args.subtask
-
-if __name__ == '__main__':
-    pass
\ No newline at end of file