Delete view_parquet.py

view_parquet.py

@@ -1,151 +0,0 @@
-#!/usr/bin/env python3
-
-import pandas as pd
-import argparse
-from pathlib import Path
-from collections import Counter
-
-
-def analyze_parquet(parquet_file, rows_to_display=10):
-    """View and analyze the operator_input_models parquet file."""
-    
-    print(f"Reading parquet file: {parquet_file}")
-    df = pd.read_parquet(parquet_file)
-    
-    print(f"\n{'='*80}")
-    print("DATASET OVERVIEW")
-    print(f"{'='*80}")
-    print(f"Total rows: {len(df):,}")
-    print(f"Columns: {list(df.columns)}")
-    print(f"Memory usage: {df.memory_usage(deep=True).sum() / 1024**2:.2f} MB")
-    
-    print(f"\n{'='*80}")
-    print("UNIQUE COUNTS")
-    print(f"{'='*80}")
-    print(f"Unique operators: {df['operator name'].nunique():,}")
-    print(f"Unique models: {df['used in model'].nunique():,}")
-    print(f"Unique argument configurations: {df['args'].nunique():,}")
-    
-    print(f"\n{'='*80}")
-    print("MODEL SOURCE BREAKDOWN")
-    print(f"{'='*80}")
-    
-    # Extract model sources
-    model_sources = {
-        'HuggingFace': [],
-        'TorchBench': [],
-        'Timm': [],
-        'Other': []
-    }
-    
-    unique_models = df['used in model'].unique()
-    
-    for model in unique_models:
-        if model.startswith('HuggingFace/'):
-            model_sources['HuggingFace'].append(model)
-        elif model.startswith('TorchBench/'):
-            model_sources['TorchBench'].append(model)
-        elif model.startswith('Timm/'):
-            model_sources['Timm'].append(model)
-        else:
-            model_sources['Other'].append(model)
-    
-    # Print source statistics
-    for source, models in model_sources.items():
-        if models:
-            print(f"\n{source}: {len(models)} models")
-            # Count total rows per source
-            source_rows = df[df['used in model'].isin(models)]
-            print(f"  - Total operator instances: {len(source_rows):,}")
-            print(f"  - Unique operators used: {source_rows['operator name'].nunique()}")
-            # Show sample models
-            sample_models = sorted(models)[:5]
-            for model in sample_models:
-                print(f"    • {model}")
-            if len(models) > 5:
-                print(f"    ... and {len(models) - 5} more")
-    
-    print(f"\n{'='*80}")
-    print("TOP OPERATORS BY USAGE")
-    print(f"{'='*80}")
-    operator_counts = df['operator name'].value_counts().head(10)
-    for i, (op, count) in enumerate(operator_counts.items(), 1):
-        print(f"{i:2}. {op:<50} {count:5} uses")
-    
-    print(f"\n{'='*80}")
-    print("TOP MODELS BY OPERATOR COUNT")
-    print(f"{'='*80}")
-    model_counts = df['used in model'].value_counts().head(10)
-    for i, (model, count) in enumerate(model_counts.items(), 1):
-        print(f"{i:2}. {model:<50} {count:5} operators")
-    
-    print(f"\n{'='*80}")
-    print(f"SAMPLE DATA (first {rows_to_display} rows)")
-    print(f"{'='*80}")
-    
-    # Display sample with truncated args for readability
-    sample_df = df.head(rows_to_display).copy()
-    sample_df['args'] = sample_df['args'].apply(lambda x: x[:100] + '...' if len(x) > 100 else x)
-    
-    pd.set_option('display.max_columns', None)
-    pd.set_option('display.width', None)
-    pd.set_option('display.max_colwidth', 50)
-    
-    print(sample_df.to_string(index=False))
-    
-    return df, model_sources
-
-
-def main():
-    parser = argparse.ArgumentParser(description='View and analyze operator_input_models parquet file')
-    parser.add_argument('--input', '-i', 
-                        default='operator_input_models_mapping.parquet',
-                        help='Input Parquet file (default: operator_input_models_mapping.parquet)')
-    parser.add_argument('--rows', '-r',
-                        type=int,
-                        default=10,
-                        help='Number of sample rows to display (default: 10)')
-    parser.add_argument('--query', '-q',
-                        help='Filter by operator name (partial match)')
-    parser.add_argument('--model', '-m',
-                        help='Filter by model name (partial match)')
-    
-    args = parser.parse_args()
-    
-    # Check if input file exists
-    if not Path(args.input).exists():
-        print(f"Error: Input file '{args.input}' not found")
-        return 1
-    
-    # Analyze the parquet file
-    df, model_sources = analyze_parquet(args.input, args.rows)
-    
-    # Apply filters if specified
-    if args.query or args.model:
-        print(f"\n{'='*80}")
-        print("FILTERED RESULTS")
-        print(f"{'='*80}")
-        
-        filtered_df = df.copy()
-        
-        if args.query:
-            filtered_df = filtered_df[filtered_df['operator name'].str.contains(args.query, case=False)]
-            print(f"Filtering for operator containing: '{args.query}'")
-        
-        if args.model:
-            filtered_df = filtered_df[filtered_df['used in model'].str.contains(args.model, case=False)]
-            print(f"Filtering for model containing: '{args.model}'")
-        
-        if len(filtered_df) > 0:
-            print(f"\nFound {len(filtered_df)} matching entries")
-            sample_df = filtered_df.head(args.rows).copy()
-            sample_df['args'] = sample_df['args'].apply(lambda x: x[:100] + '...' if len(x) > 100 else x)
-            print(sample_df.to_string(index=False))
-        else:
-            print("No matching entries found")
-    
-    return 0
-
-
-if __name__ == "__main__":
-    exit(main())