Add ruff linting and formatting

- Added ruff as development dependency
- Added ruff.toml configuration
- Fixed all linting issues automatically
- Reformatted code with consistent style

dataset_statistics.py

@@ -1,13 +1,13 @@
 import json
-from pathlib import Path
-from collections import Counter
 import statistics as stats
+from collections import Counter
+from pathlib import Path
 
 
 def load_jsonl(file_path):
     """Load JSONL file and return list of items."""
     items = []
-    with open(file_path, 'r', encoding='utf-8') as f:
+    with open(file_path, encoding="utf-8") as f:
         for line in f:
             items.append(json.loads(line.strip()))
     return items
@@ -15,36 +15,36 @@ def load_jsonl(file_path):
 
 def calculate_text_statistics(items):
     """Calculate statistics for text fields."""
-    text_lengths = [len(item['text'].split()) for item in items]
-    char_lengths = [len(item['text']) for item in items]
+    text_lengths = [len(item["text"].split()) for item in items]
+    char_lengths = [len(item["text"]) for item in items]
 
     return {
-        'avg_words': stats.mean(text_lengths),
-        'min_words': min(text_lengths),
-        'max_words': max(text_lengths),
-        'median_words': stats.median(text_lengths),
-        'avg_chars': stats.mean(char_lengths),
-        'min_chars': min(char_lengths),
-        'max_chars': max(char_lengths),
-        'median_chars': stats.median(char_lengths),
+        "avg_words": stats.mean(text_lengths),
+        "min_words": min(text_lengths),
+        "max_words": max(text_lengths),
+        "median_words": stats.median(text_lengths),
+        "avg_chars": stats.mean(char_lengths),
+        "min_chars": min(char_lengths),
+        "max_chars": max(char_lengths),
+        "median_chars": stats.median(char_lengths),
     }
 
 
 def analyze_classification_subset():
     """Analyze classification subset statistics."""
-    print("\n" + "="*60)
+    print("\n" + "=" * 60)
     print("CLASSIFICATION SUBSET ANALYSIS")
-    print("="*60)
+    print("=" * 60)
 
-    for split in ['train', 'test']:
-        file_path = Path(f'data/classification/{split}.jsonl')
+    for split in ["train", "test"]:
+        file_path = Path(f"data/classification/{split}.jsonl")
         items = load_jsonl(file_path)
 
         print(f"\n{split.upper()} Split:")
         print(f"  Total examples: {len(items)}")
 
         # Label distribution
-        label_counter = Counter(item['label'] for item in items)
+        label_counter = Counter(item["label"] for item in items)
         print("\n  Label Distribution:")
         for label, count in label_counter.most_common():
             percentage = (count / len(items)) * 100
@@ -53,29 +53,33 @@ def analyze_classification_subset():
         # Text statistics
         text_stats = calculate_text_statistics(items)
         print("\n  Text Statistics:")
-        print(f"    Words per text - Avg: {text_stats['avg_words']:.1f}, "
-              f"Min: {text_stats['min_words']}, Max: {text_stats['max_words']}, "
-              f"Median: {text_stats['median_words']:.1f}")
-        print(f"    Chars per text - Avg: {text_stats['avg_chars']:.1f}, "
-              f"Min: {text_stats['min_chars']}, Max: {text_stats['max_chars']}, "
-              f"Median: {text_stats['median_chars']:.1f}")
+        print(
+            f"    Words per text - Avg: {text_stats['avg_words']:.1f}, "
+            f"Min: {text_stats['min_words']}, Max: {text_stats['max_words']}, "
+            f"Median: {text_stats['median_words']:.1f}"
+        )
+        print(
+            f"    Chars per text - Avg: {text_stats['avg_chars']:.1f}, "
+            f"Min: {text_stats['min_chars']}, Max: {text_stats['max_chars']}, "
+            f"Median: {text_stats['median_chars']:.1f}"
+        )
 
 
 def analyze_sentiment_subset():
     """Analyze sentiment subset statistics."""
-    print("\n" + "="*60)
+    print("\n" + "=" * 60)
     print("SENTIMENT SUBSET ANALYSIS")
-    print("="*60)
+    print("=" * 60)
 
-    for split in ['train', 'test']:
-        file_path = Path(f'data/sentiment/{split}.jsonl')
+    for split in ["train", "test"]:
+        file_path = Path(f"data/sentiment/{split}.jsonl")
         items = load_jsonl(file_path)
 
         print(f"\n{split.upper()} Split:")
         print(f"  Total examples: {len(items)}")
 
         # Sentiment distribution
-        sentiment_counter = Counter(item['sentiment'] for item in items)
+        sentiment_counter = Counter(item["sentiment"] for item in items)
         print("\n  Sentiment Distribution:")
         for sentiment, count in sentiment_counter.most_common():
             percentage = (count / len(items)) * 100
@@ -84,44 +88,50 @@ def analyze_sentiment_subset():
         # Text statistics
         text_stats = calculate_text_statistics(items)
         print("\n  Text Statistics:")
-        print(f"    Words per text - Avg: {text_stats['avg_words']:.1f}, "
-              f"Min: {text_stats['min_words']}, Max: {text_stats['max_words']}, "
-              f"Median: {text_stats['median_words']:.1f}")
+        print(
+            f"    Words per text - Avg: {text_stats['avg_words']:.1f}, "
+            f"Min: {text_stats['min_words']}, Max: {text_stats['max_words']}, "
+            f"Median: {text_stats['median_words']:.1f}"
+        )
 
 
 def analyze_aspect_sentiment_subset():
     """Analyze aspect-sentiment subset statistics."""
-    print("\n" + "="*60)
+    print("\n" + "=" * 60)
     print("ASPECT-SENTIMENT SUBSET ANALYSIS")
-    print("="*60)
+    print("=" * 60)
 
-    for split in ['train', 'test']:
-        file_path = Path(f'data/aspect_sentiment/{split}.jsonl')
+    for split in ["train", "test"]:
+        file_path = Path(f"data/aspect_sentiment/{split}.jsonl")
         items = load_jsonl(file_path)
 
         print(f"\n{split.upper()} Split:")
         print(f"  Total examples: {len(items)}")
 
         # Multi-aspect analysis
-        single_aspect = sum(1 for item in items if len(item['aspects']) == 1)
-        multi_aspect = sum(1 for item in items if len(item['aspects']) > 1)
-        max_aspects = max(len(item['aspects']) for item in items)
-
-        print(f"\n  Aspect Coverage:")
-        print(f"    Single aspect: {single_aspect} ({(single_aspect/len(items))*100:.1f}%)")
-        print(f"    Multi-aspect: {multi_aspect} ({(multi_aspect/len(items))*100:.1f}%)")
+        single_aspect = sum(1 for item in items if len(item["aspects"]) == 1)
+        multi_aspect = sum(1 for item in items if len(item["aspects"]) > 1)
+        max_aspects = max(len(item["aspects"]) for item in items)
+
+        print("\n  Aspect Coverage:")
+        print(
+            f"    Single aspect: {single_aspect} ({(single_aspect / len(items)) * 100:.1f}%)"
+        )
+        print(
+            f"    Multi-aspect: {multi_aspect} ({(multi_aspect / len(items)) * 100:.1f}%)"
+        )
         print(f"    Max aspects per example: {max_aspects}")
 
         # Aspect-sentiment pair distribution
         aspect_sentiment_pairs = []
         for item in items:
-            for asp in item['aspects']:
+            for asp in item["aspects"]:
                 aspect_sentiment_pairs.append(f"{asp['aspect']}#{asp['sentiment']}")
 
         pair_counter = Counter(aspect_sentiment_pairs)
         print("\n  Top 10 Aspect-Sentiment Pairs:")
         for pair, count in pair_counter.most_common(10):
-            aspect, sentiment = pair.split('#')
+            aspect, sentiment = pair.split("#")
             percentage = (count / len(aspect_sentiment_pairs)) * 100
             print(f"    {aspect:20s} + {sentiment:8s}: {count:4d} ({percentage:5.1f}%)")
 
@@ -130,9 +140,9 @@ def analyze_aspect_sentiment_subset():
         sentiment_by_aspect = {}
 
         for item in items:
-            for asp in item['aspects']:
-                aspect = asp['aspect']
-                sentiment = asp['sentiment']
+            for asp in item["aspects"]:
+                aspect = asp["aspect"]
+                sentiment = asp["sentiment"]
                 aspect_counter[aspect] += 1
 
                 if aspect not in sentiment_by_aspect:
@@ -147,7 +157,7 @@ def analyze_aspect_sentiment_subset():
             # Sentiment breakdown for this aspect
             sentiments = sentiment_by_aspect[aspect]
             total_aspect = sum(sentiments.values())
-            for sentiment in ['positive', 'negative', 'neutral']:
+            for sentiment in ["positive", "negative", "neutral"]:
                 if sentiment in sentiments:
                     sent_count = sentiments[sentiment]
                     sent_pct = (sent_count / total_aspect) * 100
@@ -156,18 +166,18 @@ def analyze_aspect_sentiment_subset():
 
 def generate_summary_statistics():
     """Generate overall summary statistics."""
-    print("\n" + "="*60)
+    print("\n" + "=" * 60)
     print("DATASET SUMMARY")
-    print("="*60)
+    print("=" * 60)
 
-    total_train = len(load_jsonl('data/classification/train.jsonl'))
-    total_test = len(load_jsonl('data/classification/test.jsonl'))
+    total_train = len(load_jsonl("data/classification/train.jsonl"))
+    total_test = len(load_jsonl("data/classification/test.jsonl"))
 
     print("\nTotal Dataset Size:")
     print(f"  Train: {total_train} examples")
     print(f"  Test: {total_test} examples")
     print(f"  Total: {total_train + total_test} examples")
-    print(f"  Train/Test Ratio: {total_train/total_test:.2f}:1")
+    print(f"  Train/Test Ratio: {total_train / total_test:.2f}:1")
 
     # Available subsets
     print("\nAvailable Subsets:")
@@ -208,7 +218,7 @@ def save_statistics_report():
     sys.stdout = old_stdout
 
     # Save to file
-    with open('statistics_report.md', 'w', encoding='utf-8') as f:
+    with open("statistics_report.md", "w", encoding="utf-8") as f:
         f.write("# UTS2017_Bank Dataset Statistics Report\n\n")
         f.write("```\n")
         f.write(output)
@@ -223,5 +233,5 @@ if __name__ == "__main__":
     analyze_sentiment_subset()
     analyze_aspect_sentiment_subset()
 
-    print("\n" + "="*60)
-    save_statistics_report()
+    print("\n" + "=" * 60)
+    save_statistics_report()

preprocess_data.py

@@ -1,5 +1,5 @@
-import re
 import json
+import re
 from pathlib import Path
 
 
@@ -15,18 +15,20 @@ def process_banking_data(input_file, output_dir):
     sentiment_data = []
     aspect_sentiment_data = []
 
-    with open(input_file, 'r', encoding='utf-8') as f:
+    with open(input_file, encoding="utf-8") as f:
         for line_num, line in enumerate(f, 1):
             line = line.strip()
             if not line:
                 continue
 
             # Extract labels and sentiments
-            label_pattern = r'__label__([A-Z_]+)#(positive|negative|neutral)'
+            label_pattern = r"__label__([A-Z_]+)#(positive|negative|neutral)"
             matches = re.findall(label_pattern, line)
 
             # Remove labels from text
-            text = re.sub(r'__label__[A-Z_]+#(positive|negative|neutral)\s*', '', line).strip()
+            text = re.sub(
+                r"__label__[A-Z_]+#(positive|negative|neutral)\s*", "", line
+            ).strip()
 
             if not text or not matches:
                 print(f"Skipping line {line_num}: No valid data found")
@@ -37,10 +39,7 @@ def process_banking_data(input_file, output_dir):
             sentiments = [m[1] for m in matches]
 
             # 1. Classification subset (first aspect as main label)
-            classification_data.append({
-                "text": text,
-                "label": aspects[0]
-            })
+            classification_data.append({"text": text, "label": aspects[0]})
 
             # 2. Sentiment-only subset (overall sentiment)
             # If multiple sentiments, use the first one or most frequent
@@ -54,23 +53,18 @@ def process_banking_data(input_file, output_dir):
                     sentiment_counts[s] = sentiment_counts.get(s, 0) + 1
                 overall_sentiment = max(sentiment_counts, key=sentiment_counts.get)
 
-            sentiment_data.append({
-                "text": text,
-                "sentiment": overall_sentiment
-            })
+            sentiment_data.append({"text": text, "sentiment": overall_sentiment})
 
             # 3. Aspect-Sentiment subset
             aspect_sentiment_pairs = []
-            for aspect, sentiment in zip(aspects, sentiments):
-                aspect_sentiment_pairs.append({
-                    "aspect": aspect,
-                    "sentiment": sentiment
-                })
+            for aspect, sentiment in zip(aspects, sentiments, strict=False):
+                aspect_sentiment_pairs.append(
+                    {"aspect": aspect, "sentiment": sentiment}
+                )
 
-            aspect_sentiment_data.append({
-                "text": text,
-                "aspects": aspect_sentiment_pairs
-            })
+            aspect_sentiment_data.append(
+                {"text": text, "aspects": aspect_sentiment_pairs}
+            )
 
     # Save the three subsets
     output_dir = Path(output_dir)
@@ -82,26 +76,30 @@ def process_banking_data(input_file, output_dir):
     # Save classification subset
     classification_file = output_dir / "classification" / f"{split}.jsonl"
     classification_file.parent.mkdir(parents=True, exist_ok=True)
-    with open(classification_file, 'w', encoding='utf-8') as f:
+    with open(classification_file, "w", encoding="utf-8") as f:
         for item in classification_data:
-            f.write(json.dumps(item, ensure_ascii=False) + '\n')
-    print(f"Saved {len(classification_data)} classification examples to {classification_file}")
+            f.write(json.dumps(item, ensure_ascii=False) + "\n")
+    print(
+        f"Saved {len(classification_data)} classification examples to {classification_file}"
+    )
 
     # Save sentiment subset
     sentiment_file = output_dir / "sentiment" / f"{split}.jsonl"
     sentiment_file.parent.mkdir(parents=True, exist_ok=True)
-    with open(sentiment_file, 'w', encoding='utf-8') as f:
+    with open(sentiment_file, "w", encoding="utf-8") as f:
         for item in sentiment_data:
-            f.write(json.dumps(item, ensure_ascii=False) + '\n')
+            f.write(json.dumps(item, ensure_ascii=False) + "\n")
     print(f"Saved {len(sentiment_data)} sentiment examples to {sentiment_file}")
 
     # Save aspect-sentiment subset
     aspect_sentiment_file = output_dir / "aspect_sentiment" / f"{split}.jsonl"
     aspect_sentiment_file.parent.mkdir(parents=True, exist_ok=True)
-    with open(aspect_sentiment_file, 'w', encoding='utf-8') as f:
+    with open(aspect_sentiment_file, "w", encoding="utf-8") as f:
         for item in aspect_sentiment_data:
-            f.write(json.dumps(item, ensure_ascii=False) + '\n')
-    print(f"Saved {len(aspect_sentiment_data)} aspect-sentiment examples to {aspect_sentiment_file}")
+            f.write(json.dumps(item, ensure_ascii=False) + "\n")
+    print(
+        f"Saved {len(aspect_sentiment_data)} aspect-sentiment examples to {aspect_sentiment_file}"
+    )
 
     # Print statistics
     print("\n=== Statistics ===")
@@ -110,7 +108,7 @@ def process_banking_data(input_file, output_dir):
     # Label distribution
     label_counts = {}
     for item in classification_data:
-        label = item['label']
+        label = item["label"]
         label_counts[label] = label_counts.get(label, 0) + 1
 
     print("\nLabel distribution:")
@@ -120,15 +118,19 @@ def process_banking_data(input_file, output_dir):
     # Sentiment distribution
     sentiment_counts = {}
     for item in sentiment_data:
-        sentiment = item['sentiment']
+        sentiment = item["sentiment"]
         sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1
 
     print("\nSentiment distribution:")
-    for sentiment, count in sorted(sentiment_counts.items(), key=lambda x: x[1], reverse=True):
+    for sentiment, count in sorted(
+        sentiment_counts.items(), key=lambda x: x[1], reverse=True
+    ):
         print(f"  {sentiment}: {count}")
 
     # Multi-aspect examples
-    multi_aspect_count = sum(1 for item in aspect_sentiment_data if len(item['aspects']) > 1)
+    multi_aspect_count = sum(
+        1 for item in aspect_sentiment_data if len(item["aspects"]) > 1
+    )
     print(f"\nExamples with multiple aspects: {multi_aspect_count}")
 
 
@@ -138,6 +140,6 @@ if __name__ == "__main__":
     process_banking_data("raw_data/train.txt", "data")
 
     # Process test data
-    print("\n" + "="*50)
+    print("\n" + "=" * 50)
     print("Processing test data...")
-    process_banking_data("raw_data/test.txt", "data")
+    process_banking_data("raw_data/test.txt", "data")

pyproject.toml

@@ -6,3 +6,8 @@ requires-python = ">=3.13"
 dependencies = [
     "datasets>=4.1.1",
 ]
+
+[dependency-groups]
+dev = [
+    "ruff>=0.13.1",
+]

ruff.toml

@@ -0,0 +1,23 @@
+[lint]
+select = [
+    "E",  # pycodestyle errors
+    "W",  # pycodestyle warnings
+    "F",  # pyflakes
+    "I",  # isort
+    "B",  # flake8-bugbear
+    "C4", # flake8-comprehensions
+    "UP", # pyupgrade
+]
+
+ignore = [
+    "E501",  # line too long, handled by formatter
+]
+
+[format]
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = false
+line-ending = "auto"
+
+[lint.isort]
+known-first-party = []

uv.lock

@@ -522,6 +522,32 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6", size = 64738, upload-time = "2025-08-18T20:46:00.542Z" },
 ]
 
+[[package]]
+name = "ruff"
+version = "0.13.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ab/33/c8e89216845615d14d2d42ba2bee404e7206a8db782f33400754f3799f05/ruff-0.13.1.tar.gz", hash = "sha256:88074c3849087f153d4bb22e92243ad4c1b366d7055f98726bc19aa08dc12d51", size = 5397987, upload-time = "2025-09-18T19:52:44.33Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f3/41/ca37e340938f45cfb8557a97a5c347e718ef34702546b174e5300dbb1f28/ruff-0.13.1-py3-none-linux_armv6l.whl", hash = "sha256:b2abff595cc3cbfa55e509d89439b5a09a6ee3c252d92020bd2de240836cf45b", size = 12304308, upload-time = "2025-09-18T19:51:56.253Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/84/ba378ef4129415066c3e1c80d84e539a0d52feb250685091f874804f28af/ruff-0.13.1-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:4ee9f4249bf7f8bb3984c41bfaf6a658162cdb1b22e3103eabc7dd1dc5579334", size = 12937258, upload-time = "2025-09-18T19:52:00.184Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/b6/ec5e4559ae0ad955515c176910d6d7c93edcbc0ed1a3195a41179c58431d/ruff-0.13.1-py3-none-macosx_11_0_arm64.whl", hash = "sha256:5c5da4af5f6418c07d75e6f3224e08147441f5d1eac2e6ce10dcce5e616a3bae", size = 12214554, upload-time = "2025-09-18T19:52:02.753Z" },
+    { url = "https://files.pythonhosted.org/packages/70/d6/cb3e3b4f03b9b0c4d4d8f06126d34b3394f6b4d764912fe80a1300696ef6/ruff-0.13.1-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:80524f84a01355a59a93cef98d804e2137639823bcee2931f5028e71134a954e", size = 12448181, upload-time = "2025-09-18T19:52:05.279Z" },
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+    { url = "https://files.pythonhosted.org/packages/2d/3e/05f72f4c3d3a69e65d55a13e1dd1ade76c106d8546e7e54501d31f1dc54a/ruff-0.13.1-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c55d84715061f8b05469cdc9a446aa6c7294cd4bd55e86a89e572dba14374f8c", size = 13791178, upload-time = "2025-09-18T19:52:10.189Z" },
+    { url = "https://files.pythonhosted.org/packages/81/e7/01b1fc403dd45d6cfe600725270ecc6a8f8a48a55bc6521ad820ed3ceaf8/ruff-0.13.1-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:ac57fed932d90fa1624c946dc67a0a3388d65a7edc7d2d8e4ca7bddaa789b3b0", size = 14814474, upload-time = "2025-09-18T19:52:12.866Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/92/d9e183d4ed6185a8df2ce9faa3f22e80e95b5f88d9cc3d86a6d94331da3f/ruff-0.13.1-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c366a71d5b4f41f86a008694f7a0d75fe409ec298685ff72dc882f882d532e36", size = 14217531, upload-time = "2025-09-18T19:52:15.245Z" },
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+]
+
 [[package]]
 name = "six"
 version = "1.17.0"
@@ -572,15 +598,23 @@ wheels = [
 
 [[package]]
 name = "uts2017-bank"
-version = "0.1.0"
+version = "1.0.0"
 source = { virtual = "." }
 dependencies = [
     { name = "datasets" },
 ]
 
+[package.dev-dependencies]
+dev = [
+    { name = "ruff" },
+]
+
 [package.metadata]
 requires-dist = [{ name = "datasets", specifier = ">=4.1.1" }]
 
+[package.metadata.requires-dev]
+dev = [{ name = "ruff", specifier = ">=0.13.1" }]
+
 [[package]]
 name = "xxhash"
 version = "3.5.0"