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"""
This module contains all configuration parameters for the VCF processing pipeline
"""
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Any
import json
import os
@dataclass
class ModelConfig:
"""Configurations"""
# Embedding dimensions
embed_dim: int = 32
transformer_dim: int = 128
# Transformer parameters
nhead: int = 8
num_layers: int = 2
dropout: float = 0.1
# Model architecture
num_classes: int = 2
hidden_dims: List[int] = field(default_factory=lambda: [256, 128])
# Training parameters
learning_rate: float = 1e-4
batch_size: int = 16
max_epochs: int = 100
early_stopping_patience: int = 10
# Data processing
max_mutations_per_gene: int = 100
max_genes_per_chromosome: int = 1000
max_chromosomes_per_pathway: int = 50
max_pathways_per_sample: int = 100
@dataclass
class DataConfig:
"""Configurations"""
# File paths
vcf_file_path: Optional[str] = None
gene_annotation_path: Optional[str] = None
pathway_mapping_path: Optional[str] = None
output_dir: str = "./outputs"
cache_dir: str = "./cache"
# VCF processing
supported_impacts: List[str] = field(default_factory=lambda: [
"HIGH", "MODERATE", "LOW", "MODIFIER"
])
supported_chromosomes: List[str] = field(default_factory=lambda: [
"1", "2", "3", "4", "5", "6", "7", "8", "9", "10",
"11", "12", "13", "14", "15", "16", "17", "18", "19", "20",
"21", "22", "X", "Y", "MT"
])
# Tokenization
special_tokens: Dict[str, str] = field(default_factory=lambda: {
"pad_token": "[PAD]",
"unk_token": "[UNK]",
"sep_token": "[SEP]",
"cls_token": "[CLS]"
})
# Data validation
min_mutations_per_sample: int = 1
max_mutations_per_sample: int = 10000
@dataclass
class HuggingFaceConfig:
"""Configurations"""
model_name: str = "GvEM"
model_version: str = "1.0.0"
model_description: str = "Genomic Variant Embedding Model"
# Hub configuration
push_to_hub: bool = False
hub_model_id: Optional[str] = None
hub_token: Optional[str] = None
# Model card information
license: str = "apache-2.0"
tags: List[str] = field(default_factory=lambda: [
"genomics", "vcf", "transformer", "hierarchical", "mutations"
])
# Repository information
repository_url: Optional[str] = None
paper_url: Optional[str] = None
class ConfigManager:
"""Manage configurations"""
def __init__(self, config_path: Optional[str] = None):
self.config_path = config_path or "config.json"
self.model_config = ModelConfig()
self.data_config = DataConfig()
self.hf_config = HuggingFaceConfig()
def load_config(self, config_path: Optional[str] = None) -> None:
path = config_path or self.config_path
if os.path.exists(path):
with open(path, 'r') as f:
config_dict = json.load(f)
# Update configurations
if 'model' in config_dict:
self._update_dataclass(self.model_config, config_dict['model'])
if 'data' in config_dict:
self._update_dataclass(self.data_config, config_dict['data'])
if 'huggingface' in config_dict:
self._update_dataclass(self.hf_config, config_dict['huggingface'])
def save_config(self, config_path: Optional[str] = None) -> None:
path = config_path or self.config_path
config_dict = {
'model': self._dataclass_to_dict(self.model_config),
'data': self._dataclass_to_dict(self.data_config),
'huggingface': self._dataclass_to_dict(self.hf_config)
}
os.makedirs(os.path.dirname(path), exist_ok=True)
with open(path, 'w') as f:
json.dump(config_dict, f, indent=2)
def _update_dataclass(self, dataclass_obj: Any, update_dict: Dict) -> None:
"""Update dataclass fields from dictionary."""
for key, value in update_dict.items():
if hasattr(dataclass_obj, key):
setattr(dataclass_obj, key, value)
def _dataclass_to_dict(self, dataclass_obj: Any) -> Dict:
"""Convert dataclass to dictionary."""
result = {}
for key, value in dataclass_obj.__dict__.items():
if not key.startswith('_'):
result[key] = value
return result
def validate_config(self) -> bool:
"""Validate configuration parameters."""
# Model validation
assert self.model_config.embed_dim > 0, "embed_dim must be positive"
assert self.model_config.nhead > 0, "nhead must be positive"
assert self.model_config.num_classes > 1, "num_classes must be > 1"
assert 0 <= self.model_config.dropout <= 1, "dropout must be in [0, 1]"
# Data validation
assert self.data_config.min_mutations_per_sample > 0, "min_mutations_per_sample must be positive"
assert self.data_config.max_mutations_per_sample > self.data_config.min_mutations_per_sample, \
"max_mutations_per_sample must be > min_mutations_per_sample"
return True
def get_model_config_dict(self) -> Dict:
return {
'architectures': ['HierarchicalVCFModel'],
'model_type': 'hierarchical-vcf',
**self._dataclass_to_dict(self.model_config)
}
default_config = ConfigManager()
EXAMPLE_CONFIG = {
"model": {
"embed_dim": 64,
"transformer_dim": 256,
"nhead": 8,
"num_layers": 3,
"num_classes": 5,
"learning_rate": 5e-4,
"batch_size": 32
},
"data": {
"vcf_file_path": "/path/to/variants.vcf",
"gene_annotation_path": "/path/to/gene_annotations.json",
"pathway_mapping_path": "/path/to/pathway_mappings.json",
"output_dir": "./results",
"min_mutations_per_sample": 5,
"max_mutations_per_sample": 5000
},
"huggingface": {
"model_name": "my-vcf-model",
"push_to_hub": True,
"hub_model_id": "username/my-vcf-model",
"license": "mit"
}
} |