File size: 5,360 Bytes
79c9403 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | #!/usr/bin/env python3
"""
Phase 3: Dataset Preparation
Creates structured training dataset from repository contents
"""
import os
from pathlib import Path
# Configuration
REPOSITORIES_SRC_DIR = Path('/app/repositories')
OUTPUT_DATASET_FILE = Path('/app/output/dataset.txt')
EXCLUDED_DIRS = ['.git']
INCLUDED_EXTENSIONS = [
# Code
'.py', '.rs', '.js', '.ts', '.java', '.c', '.h', '.cpp', '.go', '.sh',
# Config/Data
'.json', '.yaml', '.yml', '.toml', '.xml', '.ini',
# Docs
'.md', '.txt', '.rst'
]
# Special tokens for structuring the dataset
REPO_START_TOKEN = "<|repo_start|>"
REPO_END_TOKEN = "<|repo_end|>"
FILE_START_TOKEN = "<|file_start|>"
FILE_END_TOKEN = "<|file_end|>"
def run_phase3():
"""Execute Phase 3: Dataset Preparation"""
print("Starting dataset creation process...")
print("=" * 60)
processed_files_count = 0
processed_repos_count = 0
skipped_files_count = 0
if not REPOSITORIES_SRC_DIR.exists():
print(f"ERROR: Source directory not found at '{REPOSITORIES_SRC_DIR}'")
return False
# Curriculum Learning Order
priority_order = [
'asi-ecosystem',
'symbiotic-core-library',
'asi-protosymbiotic-signal',
'asi-symbiotic-signal',
'asi-core-protocol',
'eco-benchmark',
'eco-datacenter'
]
last_order = [
'emergence-engine',
'asi-backups'
]
all_repos_on_disk = {d.name: d for d in REPOSITORIES_SRC_DIR.iterdir() if d.is_dir()}
sorted_repo_paths = []
# 1. Add priority repos in their specified order
for repo_name in priority_order:
if repo_name in all_repos_on_disk:
sorted_repo_paths.append(all_repos_on_disk.pop(repo_name))
# 2. Add the remaining repos (alphabetically), excluding the ones for the end
middle_repos_names = sorted([
name for name in all_repos_on_disk
if name not in last_order
])
for repo_name in middle_repos_names:
sorted_repo_paths.append(all_repos_on_disk.pop(repo_name))
# 3. Add the last repos in their specified order
for repo_name in last_order:
if repo_name in all_repos_on_disk:
sorted_repo_paths.append(all_repos_on_disk.pop(repo_name))
print(f"Found {len(sorted_repo_paths)} repositories to process in curriculum order.")
with open(OUTPUT_DATASET_FILE, 'w', encoding='utf-8') as outfile:
for repo_path in sorted_repo_paths:
repo_name = repo_path.name
print(f"[Processing] '{repo_name}'...")
processed_repos_count += 1
# Write the repository start token and its name
outfile.write(f"{REPO_START_TOKEN}{repo_name}\n")
# Use rglob to recursively find all files
files_in_repo = list(repo_path.rglob('*'))
print(f" Found {len(files_in_repo)} total items (files/dirs). Filtering...")
repo_file_count = 0
for file_path in files_in_repo:
# Skip directories and files in excluded directories
if not file_path.is_file() or any(d in file_path.parts for d in EXCLUDED_DIRS):
continue
# Filter by extension if the list is not empty
if INCLUDED_EXTENSIONS and file_path.suffix.lower() not in INCLUDED_EXTENSIONS:
skipped_files_count += 1
continue
try:
# Get relative path to store in the dataset
relative_path = file_path.relative_to(repo_path)
with open(file_path, 'r', encoding='utf-8', errors='ignore') as infile:
content = infile.read()
# Write the file start token and its path
outfile.write(f"{FILE_START_TOKEN}{relative_path}\n")
# Write the file content
outfile.write(content)
# Write the file end token
outfile.write(f"\n{FILE_END_TOKEN}\n")
processed_files_count += 1
repo_file_count += 1
except Exception as e:
print(f" [!] Warning: Could not process file {file_path}. Reason: {e}")
skipped_files_count += 1
print(f" -> Added content from {repo_file_count} files.")
# Write the repository end token
outfile.write(f"{REPO_END_TOKEN}\n\n")
print("\n" + "=" * 60)
print("Dataset Creation Summary")
print("=" * 60)
print(f" Total repositories processed: {processed_repos_count}")
print(f" Total text files added: {processed_files_count}")
print(f" Total files skipped (binary/extension/error): {skipped_files_count}")
print(f"Dataset successfully created at: {OUTPUT_DATASET_FILE}")
# Verify the created dataset
file_size_kb = OUTPUT_DATASET_FILE.stat().st_size / 1024
print(f"Dataset size: {file_size_kb:.2f} KB")
# Load dataset into variable for verification
try:
with open(OUTPUT_DATASET_FILE, 'r', encoding='utf-8') as f:
training_data = f.read()
print(f"Dataset loaded into memory: {len(training_data)} characters")
except Exception as e:
print(f"Error verifying dataset: {e}")
return False
return True
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