Upload scripts/show_tokens_distribution.py with huggingface_hub

scripts/show_tokens_distribution.py

@@ -0,0 +1,177 @@
+#!/usr/bin/env python3
+"""
+Analyze git diff token distribution for tangled commits.
+Purpose: Find token size distribution to optimize LLM input.
+"""
+
+import pandas as pd
+import numpy as np
+import tiktoken
+from pathlib import Path
+
+# Constants
+ENCODING_NAME = "cl100k_base"
+CSV_FILE = "../data/tangled_ccs_dataset_test.csv"
+DIFF_COLUMN = "diff"
+OUTPUT_FILE = "../token_distribution_results.csv"
+
+
+def count_tokens(text: str) -> int:
+    """Count tokens using tiktoken."""
+    try:
+        encoding = tiktoken.get_encoding(ENCODING_NAME)
+        return len(encoding.encode(text))
+    except Exception:
+        return len(text) // 4
+
+
+def get_token_range(token_count: int) -> str:
+    """Get token range label."""
+    if token_count <= 1024:
+        return "≤1024"
+    elif token_count <= 2048:
+        return "1025-2048"
+    elif token_count <= 4096:
+        return "2049-4096"
+    elif token_count <= 8192:
+        return "4097-8192"
+    elif token_count <= 16384:
+        return "8193-16384"
+    elif token_count <= 32768:
+        return "16385-32768"
+    else:
+        return ">32768"
+
+
+def analyze_token_distribution(df: pd.DataFrame) -> pd.DataFrame:
+    """Analyze token distribution of tangled commits."""
+    results = []
+
+    for idx, row in df.iterrows():
+        diff_text = row[DIFF_COLUMN]
+
+        if pd.isna(diff_text):
+            continue
+
+        token_count = count_tokens(str(diff_text))
+        token_range = get_token_range(token_count)
+
+        results.append(
+            {
+                "row_idx": idx,
+                "token_count": token_count,
+                "token_range": token_range,
+            }
+        )
+
+    return pd.DataFrame(results)
+
+
+def create_distribution_summary(results_df: pd.DataFrame) -> pd.DataFrame:
+    """Create token distribution summary."""
+    token_ranges = [
+        "≤1024",
+        "1025-2048",
+        "2049-4096",
+        "4097-8192",
+        "8193-16384",
+        "16385-32768",
+        ">32768",
+    ]
+
+    total_count = len(results_df)
+    distribution = results_df["token_range"].value_counts()
+    token_counts = results_df["token_count"].values
+
+    summary_data = []
+    for range_label in token_ranges:
+        count = distribution.get(range_label, 0)
+        percentage = (count / total_count) * 100
+        summary_data.append(
+            {
+                "token_range": range_label,
+                "count": count,
+                "percentage": round(percentage, 1),
+            }
+        )
+
+    # Add overall statistics
+    stats = {
+        "total_samples": total_count,
+        "median_tokens": int(np.median(token_counts)),
+        "mean_tokens": int(np.mean(token_counts)),
+        "min_tokens": int(np.min(token_counts)),
+        "max_tokens": int(np.max(token_counts)),
+    }
+
+    return pd.DataFrame(summary_data), stats
+
+
+def main() -> None:
+    """Main analysis function."""
+    csv_file = Path(CSV_FILE)
+    output_file = Path(OUTPUT_FILE)
+
+    if not csv_file.exists():
+        print(f"Error: CSV file not found at {csv_file}")
+        return
+
+    print(f"Tangled Commits Token Distribution Analyzer")
+    print(f"Loading dataset from {csv_file}...")
+
+    try:
+        df = pd.read_csv(csv_file)
+        print(f"Loaded {len(df)} tangled commits")
+        print(f"Using tiktoken {ENCODING_NAME} encoding")
+
+        print("Analyzing token distribution...")
+        results_df = analyze_token_distribution(df)
+        print(f"Processed {len(results_df)} diffs")
+
+        print("Creating distribution summary...")
+        summary_df, stats = create_distribution_summary(results_df)
+
+        # Save detailed results
+        results_df.to_csv(output_file, index=False)
+        print(f"Results saved to {output_file}")
+
+        # Print summary
+        print("\nToken Distribution Summary:")
+        print("=" * 60)
+        print(f"Total samples: {stats['total_samples']}")
+        print(f"Median tokens: {stats['median_tokens']}")
+        print(f"Mean tokens: {stats['mean_tokens']}")
+        print(f"Min tokens: {stats['min_tokens']}")
+        print(f"Max tokens: {stats['max_tokens']}")
+        print("-" * 60)
+
+        for _, row in summary_df.iterrows():
+            print(
+                f"{row['token_range']:>12}: {row['count']:>4} samples ({row['percentage']:>5.1f}%)"
+            )
+
+        # Key thresholds
+        le_1024 = summary_df[summary_df["token_range"] == "≤1024"]["percentage"].iloc[0]
+        le_4096 = summary_df[
+            summary_df["token_range"].isin(["≤1024", "1025-2048", "2049-4096"])
+        ]["percentage"].sum()
+        le_8192 = summary_df[
+            summary_df["token_range"].isin(
+                ["≤1024", "1025-2048", "2049-4096", "4097-8192"]
+            )
+        ]["percentage"].sum()
+
+        print("-" * 60)
+        print(f"≤1024 tokens: {le_1024:.1f}%")
+        print(f"≤4096 tokens: {le_4096:.1f}%")
+        print(f"≤8192 tokens: {le_8192:.1f}%")
+
+    except Exception as e:
+        print(f"Error processing file: {e}")
+        import traceback
+
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()