From 622284b8da9ee97ecfb3c69199b8585283a692ec Mon Sep 17 00:00:00 2001
From: Hsien-Chin Lin <linh@hhu.de>
Date: Mon, 6 Feb 2023 00:01:03 +0100
Subject: [PATCH] wip
---
convlab/policy/ussT5/emowoz_evaluate.py | 68 ++++++++++++++++---------
1 file changed, 44 insertions(+), 24 deletions(-)
diff --git a/convlab/policy/ussT5/emowoz_evaluate.py b/convlab/policy/ussT5/emowoz_evaluate.py
index 01cae7a8..5c2586f8 100644
--- a/convlab/policy/ussT5/emowoz_evaluate.py
+++ b/convlab/policy/ussT5/emowoz_evaluate.py
@@ -28,34 +28,54 @@ def build_data(raw_data):
for sentiment, index in json.load(open("convlab/policy/emoTUS/sentiment.json")).items():
sentiments[int(index)] = sentiment
data = {"input_text": [], "target_text": []}
- prefix = "satisfaction score: "
- for d in raw_data:
- utt = ""
- turn_len = len(d["turns"])
- for index, turn in enumerate(d["turns"]):
- if turn["speaker"] == "user":
- if index == turn_len - 2:
- break
- if index == 0:
- utt = prefix + turn["utterance"]
+ for prefix in ["satisfaction score: ", "action prediction: ", "utterance generation: "]:
+ for d in raw_data:
+ utt = ""
+ turn_len = len(d["turns"])
+ for index, turn in enumerate(d["turns"]):
+ if turn["speaker"] == "user":
+ if index == turn_len - 2:
+ break
+ if index == 0:
+ utt = prefix + turn["utterance"]
+ else:
+ utt += ' ' + turn["utterance"]
else:
+ if index == 0:
+ print("this should no happen (index == 0)")
+ utt = prefix + turn["utterance"]
+ if index == turn_len - 1:
+ print("this should no happen (index == turn_len - 1)")
+ continue
+
utt += ' ' + turn["utterance"]
- else:
- if index == 0:
- print("this should no happen (index == 0)")
- utt = prefix + turn["utterance"]
- if index == turn_len - 1:
- print("this should no happen (index == turn_len - 1)")
- continue
-
- utt += ' ' + turn["utterance"]
-
- data["input_text"].append(utt)
- data["target_text"].append(
- sentiments[d["turns"][index+1]["emotion"][-1]["sentiment"]])
+
+ data["input_text"].append(utt)
+ if prefix == "satisfaction score: ":
+ data["target_text"].append(
+ sentiments[d["turns"][index+1]["emotion"][-1]["sentiment"]])
+ elif prefix == "action prediction: ":
+ data["target_text"].append(
+ get_action(d["turns"][index+1]["dialogue_acts"]))
+ else:
+ data["target_text"].append(
+ d["turns"][index+1]["utterance"])
+
+ json.dump(data, open("convlab/policy/ussT5/emowoz-test.json", 'w'), indent=2)
return data
+def get_action(dialogue_acts):
+ acts = []
+ for _, act in dialogue_acts.items():
+ for a in act:
+ acts.append(
+ f"{a['domain'].capitalize()}-{a['intent'].capitalize()}")
+ if not acts:
+ return "None"
+ return ','.join(acts)
+
+
def generate_result(model_checkpoint, data, stop=-1):
tokenizer = T5Tokenizer.from_pretrained(model_checkpoint)
model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
@@ -81,7 +101,7 @@ def generate_result(model_checkpoint, data, stop=-1):
"preds": output,
"label": target_text})
json.dump(results, open(os.path.join(
- model_checkpoint, "emowoz_result.json"), 'w'))
+ model_checkpoint, "emowoz_result.json"), 'w'), indent=2)
return results
--
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