Merge pull request #282 from yangheng95/dev

2.1.12

Author: yangheng95

examples-v2/aspect_opinion_sentiment_category_extraction/inference.py

@@ -10,7 +10,9 @@
 from pyabsa import ABSAInstruction
 
 if __name__ == "__main__":
-    generator = ABSAInstruction.ABSAGenerator("multilingual")
+    generator = ABSAInstruction.ABSAGenerator(
+        "checkpoints/multitask/googleflan-t5-base-instruction/checkpoint-2745"
+    )
     example = [
         "The food is good, but the service is bad.",
         "The laptop is good, but the battery life is bad.",

examples-v2/aspect_opinion_sentiment_category_extraction/multitask_train.py

@@ -11,17 +11,18 @@
 
 import findfile
 from pyabsa import ABSAInstruction as absa_instruction
+
 warnings.filterwarnings("ignore")
 import pandas as pd
 
 
 task_name = "multitask"
 experiment_name = "instruction"
 # model_checkpoint = 'allenai/tk-instruct-base-def-pos'
-model_checkpoint = "kevinscaria/ate_tk-instruct-base-def-pos-neg-neut-combined"
+# model_checkpoint = "kevinscaria/ate_tk-instruct-base-def-pos-neg-neut-combined"
 # model_checkpoint = 'allenai/tk-instruct-large-def-pos'
 # model_checkpoint = 'allenai/tk-instruct-3b-def-pos'
-# model_checkpoint = 'google/mt5-base'
+model_checkpoint = "google/flan-t5-base"
 
 print("Experiment Name: ", experiment_name)
 model_out_path = "checkpoints"
@@ -33,12 +34,12 @@
 # Load the data
 # id_train_file_path = './integrated_datasets'
 # id_test_file_path = './integrated_datasets'
-# id_train_file_path = "./integrated_datasets/acos_datasets/"
-# id_test_file_path = "./integrated_datasets/acos_datasets"
-id_train_file_path = './integrated_datasets/acos_datasets/501.Laptop14'
-id_test_file_path = './integrated_datasets/acos_datasets/501.Laptop14'
-# id_train_file_path = './integrated_datasets/acos_datasets/504.Restaurant16'
-# id_test_file_path = './integrated_datasets/acos_datasets/504.Restaurant16'
+id_train_file_path = "./integrated_datasets/acos_datasets/"
+id_test_file_path = "./integrated_datasets/acos_datasets"
+# id_train_file_path = './integrated_datasets/acos_datasets/501.Laptop14'
+# id_test_file_path = './integrated_datasets/acos_datasets/501.Laptop14'
+# id_train_file_path = './integrated_datasets/acos_datasets/502.Restaurant14'
+# id_test_file_path = './integrated_datasets/acos_datasets/502.Restaurant14'
 
 
 id_tr_df = absa_instruction.data_utils.read_json(id_train_file_path, "train")
@@ -72,9 +73,9 @@
     "evaluation_strategy": "epoch",
     "save_strategy": "epoch",
     "learning_rate": 5e-5,
-    "per_device_train_batch_size": 4,
+    "per_device_train_batch_size": 16,
     "per_device_eval_batch_size": 16,
-    "num_train_epochs": 6,
+    "num_train_epochs": 3,
     "weight_decay": 0.01,
     "warmup_ratio": 0.1,
     "load_best_model_at_end": True,

pyabsa/__init__.py

@@ -7,7 +7,7 @@
 # Copyright (C) 2021. All Rights Reserved.
 
 __name__ = "pyabsa"
-__version__ = "2.1.11"
+__version__ = "2.1.12"
 
 from pyabsa.framework.flag_class import *
 

pyabsa/tasks/ABSAInstruction/data_utils.py

@@ -48,57 +48,67 @@ def prepare_instruction_dataloader(self, df):
         cat_instructor = CategoryInstruction()
         alldata = []
         for i, data in df.iterrows():
-            _aspects = [label["aspect"] for label in data["labels"]]
+            _aspects = ["aspect:" + label["aspect"] for label in data["labels"]]
             aspects = []
             for asp in _aspects:
                 if asp.strip() not in aspects:
                     aspects.append(asp.strip())
-            aspects = ", ".join(aspects)
-            alldata.append(
-                {"text": ate_instructor.prepare_input(data["text"]), "labels": aspects}
-            )
+            aspects = "|".join(aspects)
 
-            opinions = ", ".join(
+            polarities = []
+            _polarities = [
+                "{}:{}".format(label["aspect"], label["polarity"])
+                for label in data["labels"]
+            ]
+            for pol in _polarities:
+                if pol not in polarities:
+                    polarities.append(pol)
+            polarities = "|".join(polarities)
+
+            opinions = "|".join(
                 [
                     "{}:{}".format(label["aspect"], label["opinion"])
                     for label in data["labels"]
                 ]
             )
-            alldata.append(
-                {
-                    "text": op_instructor.prepare_input(data["text"], aspects),
-                    "labels": opinions,
-                }
-            )
 
-            polarities = ", ".join(
+            categories = "|".join(
                 [
-                    "{}:{}".format(label["aspect"], label["polarity"])
+                    "{}:{}".format(label["aspect"], label["category"])
                     for label in data["labels"]
                 ]
             )
+
+            # ATE task
+            alldata.append(
+                {"text": ate_instructor.prepare_input(data["text"]), "labels": aspects}
+            )
+
+            # APC task
             alldata.append(
                 {
                     "text": apc_instructor.prepare_input(data["text"], aspects),
                     "labels": polarities,
                 }
             )
 
-            categories = ", ".join(
-                [
-                    "{}:{}".format(
-                        label["aspect"], label["category"].replace("NULL", "")
-                    )
-                    for label in data["labels"]
-                ]
-            )
+            # Opinion task
             alldata.append(
                 {
-                    "text": cat_instructor.prepare_input(data["text"], aspects),
-                    "labels": categories,
+                    "text": op_instructor.prepare_input(data["text"], aspects),
+                    "labels": opinions,
                 }
             )
-            # print(alldata[-1]['labels'])
+
+            # Category task
+            if "NULL" not in categories:
+                alldata.append(
+                    {
+                        "text": cat_instructor.prepare_input(data["text"], aspects),
+                        "labels": categories,
+                    }
+                )
+
         alldata = pd.DataFrame(alldata)
         return alldata
 
@@ -163,6 +173,7 @@ def read_json(data_path, data_type="train"):
 
     files = findfile.find_files(data_path, [data_type, ".jsonl"], exclude_key=[".txt"])
     for f in files:
+        print(f)
         with open(f, "r", encoding="utf8") as fin:
             for line in fin:
                 data.append(json.loads(line))

pyabsa/tasks/ABSAInstruction/instruction.py

@@ -31,12 +31,12 @@ def __init__(self, bos_instruction=None, eos_instruction=None):
 example 1-
 input: I charge it at night and skip taking the cord with me because of the good battery life.
 {self.eos_instruction}
-battery life, cord
+aspect:battery life|aspect:cord
 
 example 2-
 input: Great food, good size menu, great service and an unpretensious setting.
 {self.eos_instruction}
-food, menu, service, setting
+aspect:food|aspect:menu|aspect:service|aspect:setting
 
 Now extract aspects from the following example:
 input: """
@@ -64,13 +64,13 @@ def __init__(self, bos_instruction=None, eos_instruction=None):
 input: I charge it at night and skip taking the cord with me because of the good battery life.
 The aspects are: battery life, cord
 {self.eos_instruction}
-battery life:positive, cord:positive
+battery life:positive|cord:positive
 
 example 2-
 input: Great food, good size menu, great service and an unpretensious setting.
 The aspects are: food, menu, service, setting
 {self.eos_instruction}
-food:positive, menu:positive, service:positive, setting:positive
+food:positive|menu:positive|service:positive|setting:positive
     
 Now predict aspect sentiments from the following example:
 
@@ -103,13 +103,13 @@ def __init__(self, bos_instruction=None, eos_instruction=None):
 input: I charge it at night and skip taking the cord with me because of the good battery life.
 The aspects are: battery life, cord
 {self.eos_instruction}
-battery life:good, cord:NULL
+battery life:good|cord:NULL
     
 example 2-
 input: Great food, good size menu, great service and an unpretensious setting.
 The aspects are: food, menu, service, setting
 {self.eos_instruction}
-food:great, menu:good, service:great, setting:unpretensious
+food:great|menu:good|service:great|setting:unpretensious
 
 Now extract opinions for the following example:
 input:"""
@@ -141,11 +141,11 @@ def __init__(self, bos_instruction=None, eos_instruction=None):
 input: I charge it at night and skip taking the cord with me because of the good battery life.
 The aspects are: battery life, cord
 {self.eos_instruction}
-battery life:POWER_SUPPLY#GENERAL, cord:NULL
+battery life:POWER_SUPPLY#GENERAL|cord:NULL
 
 example 2-
 input: Great food, good size menu, great service and an unpretensious setting.
-The aspects are: food, menu, service, setting
+The aspects are: food:FOOD#QUALITY| menu:RESTAURANT#GENERAL|service:SERVICE#GENERAL|setting:SERVICE#GENERAL
 {self.eos_instruction}
 food:FOOD#QUALITY, menu:RESTAURANT#GENERAL, service:SERVICE#GENERAL, setting:SERVICE#GENERAL
 

pyabsa/tasks/ABSAInstruction/model.py

@@ -1,4 +1,5 @@
 import autocuda
+import sklearn
 import torch
 from pyabsa.framework.checkpoint_class.checkpoint_template import CheckpointManager
 from torch.utils.data import DataLoader
@@ -32,6 +33,7 @@ def __init__(self, checkpoint):
 
         self.tokenizer = AutoTokenizer.from_pretrained(checkpoint)
         self.model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
+        self.model.config.max_length = 128
         self.data_collator = DataCollatorForSeq2Seq(self.tokenizer)
         self.device = autocuda.auto_cuda()
         self.model.to(self.device)
@@ -94,7 +96,7 @@ def predict(self, text, **kwargs):
         ate_outputs = self.tokenizer.batch_decode(
             ate_outputs, skip_special_tokens=True
         )[0]
-        result["aspect"] = [asp.strip() for asp in ate_outputs.split(",")]
+        result["aspect"] = [asp.strip() for asp in ate_outputs.split("|")]
 
         # APC inference
         inputs = self.tokenizer(
@@ -106,7 +108,7 @@ def predict(self, text, **kwargs):
         apc_outputs = self.tokenizer.batch_decode(
             apc_outputs, skip_special_tokens=True
         )[0]
-        result["sentiment"] = [sent.strip() for sent in apc_outputs.split(",")]
+        result["sentiment"] = [sent.strip() for sent in apc_outputs.split("|")]
 
         # Opinion inference
         inputs = self.tokenizer(
@@ -118,7 +120,7 @@ def predict(self, text, **kwargs):
         op_outputs = self.tokenizer.batch_decode(op_outputs, skip_special_tokens=True)[
             0
         ]
-        result["opinion"] = [op.strip() for op in op_outputs.split(",")]
+        result["opinion"] = [op.strip() for op in op_outputs.split("|")]
 
         # Category inference
         inputs = self.tokenizer(
@@ -130,7 +132,7 @@ def predict(self, text, **kwargs):
         cat_outputs = self.tokenizer.batch_decode(
             cat_outputs, skip_special_tokens=True
         )[0]
-        result["category"] = [cat.strip() for cat in cat_outputs.split(",")]
+        result["category"] = [cat.strip() for cat in cat_outputs.split("|")]
         ensemble_result = {
             "text": text,
             "Quadruples": [
@@ -207,26 +209,43 @@ def get_aspect_metrics(self, true_aspects, pred_aspects):
         return aspect_p, aspect_r, aspect_f1
 
     def get_classic_metrics(self, y_true, y_pred):
-        total_pred = 0
-        total_gt = 0
-        tp = 1e-6
+        valid_gts = []
+        valid_preds = []
         for gt, pred in zip(y_true, y_pred):
-            print(gt)
-            print(pred)
-
-            gt_list = gt.split(", ")
-            pred_list = pred.split(", ")
-            total_pred += len(pred_list)
-            total_gt += len(gt_list)
-            for gt_val in gt_list:
+            gt_list = gt.split("|")
+            pred_list = pred.split("|")
+            while gt_list:
+                gt_val = gt_list[-1].strip().lower()
                 for pred_val in pred_list:
-                    gt_val = gt_val.replace(" ", "")
-                    pred_val = pred_val.replace(" ", "")
-                    if pred_val.strip().lower() == gt_val.strip().lower():
-                        tp += 1
-        p = tp / total_pred
-        r = tp / total_gt
-        return {"precision": p, "recall": r, "f1": 2 * p * r / (p + r)}
+                    pred_val = pred_val.strip().lower()
+                    gt_key, _, gt_label = gt_val.partition(":")
+                    pred_key, _, pred_label = pred_val.partition(":")
+                    if gt_key.startswith(pred_key):
+                        if gt_label:
+                            valid_gts.append(gt_label)
+                        else:
+                            break
+                        if pred_label:
+                            valid_preds.append(pred_label)
+                        else:
+                            valid_preds.append("")
+                        break
+
+                gt_list.pop()
+
+        report = sklearn.metrics.classification_report(valid_gts, valid_preds)
+        print(report)
+        accuracy = sklearn.metrics.accuracy_score(valid_gts, valid_preds)
+        precision = precision_score(valid_gts, valid_preds, average="macro")
+        recall = recall_score(valid_gts, valid_preds, average="macro")
+        f1 = f1_score(valid_gts, valid_preds, average="macro")
+
+        return {
+            "accuracy": accuracy,
+            "precision": precision,
+            "recall": recall,
+            "f1": f1,
+        }
 
     # def get_classic_metrics(self, y_true, y_pred):
     #