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run_validation.py
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run_validation.py
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import itertools
import time
from functools import partial
from multiprocessing import Pool
from multiprocessing.managers import SyncManager
from typing import List, Iterable, Callable
from cdisc_rules_engine.config import config
from cdisc_rules_engine.enums.progress_parameter_options import ProgressParameterOptions
from cdisc_rules_engine.models.library_metadata_container import (
LibraryMetadataContainer,
)
from cdisc_rules_engine.models.rule_conditions import ConditionCompositeFactory
from cdisc_rules_engine.models.rule_validation_result import RuleValidationResult
from cdisc_rules_engine.models.validation_args import Validation_args
from cdisc_rules_engine.rules_engine import RulesEngine
from cdisc_rules_engine.services import logger as engine_logger
from cdisc_rules_engine.services.cache import (
InMemoryCacheService,
RedisCacheService,
)
from cdisc_rules_engine.services.data_services import (
DataServiceFactory,
)
from cdisc_rules_engine.models.dataset import PandasDataset
from scripts.script_utils import (
fill_cache_with_dictionaries,
get_cache_service,
get_library_metadata_from_cache,
get_rules,
get_datasets,
get_max_dataset_size,
)
from cdisc_rules_engine.services.reporting import BaseReport, ReportFactory
from cdisc_rules_engine.utilities.progress_displayers import get_progress_displayer
from warnings import simplefilter
import os
simplefilter(
action="ignore", category=FutureWarning
) # Suppress warnings coming from numpy
"""
Sync manager used to manage instances of the cache between processes.
Cache types are registered to this manager, and only one instance of the
cache is created at startup and provided to each process.
"""
class CacheManager(SyncManager):
pass
def validate_single_rule(
cache,
datasets,
args: Validation_args,
library_metadata: LibraryMetadataContainer,
rule: dict = None,
):
rule["conditions"] = ConditionCompositeFactory.get_condition_composite(
rule["conditions"]
)
max_dataset_size = max(datasets, key=lambda x: x["size"])["size"]
# call rule engine
engine = RulesEngine(
cache=cache,
standard=args.standard,
standard_version=args.version.replace(".", "-"),
ct_packages=args.controlled_terminology_package,
meddra_path=args.meddra,
whodrug_path=args.whodrug,
define_xml_path=args.define_xml_path,
library_metadata=library_metadata,
max_dataset_size=max_dataset_size,
dataset_paths=args.dataset_paths,
)
results = []
validated_domains = set()
for dataset in datasets:
# Check if the domain has been validated before
# This addresses the case where a domain is split
# and appears multiple times within the list of datasets
if dataset["domain"] not in validated_domains:
validated_domains.add(dataset["domain"])
results.append(
engine.validate_single_rule(
rule, dataset["full_path"], datasets, dataset["domain"]
)
)
results = list(itertools.chain(*results))
if args.progress == ProgressParameterOptions.VERBOSE_OUTPUT.value:
engine_logger.log(f"{rule['core_id']} validation complete")
return RuleValidationResult(rule, results)
def set_log_level(args):
if args.log_level.lower() == "disabled":
engine_logger.disabled = True
else:
engine_logger.setLevel(args.log_level.lower())
if args.progress == ProgressParameterOptions.VERBOSE_OUTPUT.value:
engine_logger.disabled = False
engine_logger.setLevel("verbose")
def initialize_logger(disabled, log_level):
if disabled:
engine_logger.disabled = True
else:
engine_logger.disabled = False
engine_logger.setLevel(log_level)
def run_validation(args: Validation_args):
set_log_level(args)
# fill cache
CacheManager.register("RedisCacheService", RedisCacheService)
CacheManager.register("InMemoryCacheService", InMemoryCacheService)
manager = CacheManager()
manager.start()
shared_cache = get_cache_service(manager)
engine_logger.info(f"Populating cache, cache path: {args.cache}")
library_metadata: LibraryMetadataContainer = get_library_metadata_from_cache(args)
# install dictionaries if needed
fill_cache_with_dictionaries(shared_cache, args)
rules = get_rules(args)
max_dataset_size = get_max_dataset_size(args.dataset_paths)
standard = args.standard
standard_version = args.version.replace(".", "-")
data_service = DataServiceFactory(
config,
shared_cache,
max_dataset_size=max_dataset_size,
standard=standard,
standard_version=standard_version,
library_metadata=library_metadata,
).get_data_service()
large_dataset_validation: bool = (
data_service.dataset_implementation != PandasDataset
)
datasets = get_datasets(data_service, args.dataset_paths)
created_files = []
if large_dataset_validation:
# convert all files to parquet temp files
engine_logger.warning(
"Large datasets must use parquet format, converting all datasets to parquet"
)
for dataset in datasets:
file_path = dataset.get("full_path")
if file_path.endswith(".parquet"):
continue
num_rows, new_file = data_service.to_parquet(file_path)
created_files.append(new_file)
dataset["full_path"] = new_file
dataset["length"] = num_rows
dataset["original_path"] = file_path
engine_logger.info(f"Running {len(rules)} rules against {len(datasets)} datasets")
start = time.time()
results = []
# instantiate logger in each child process to maintain log level
initializer = partial(
initialize_logger, engine_logger.disabled, engine_logger._logger.level
)
# run each rule in a separate process
with Pool(args.pool_size, initializer=initializer) as pool:
validation_results: Iterable[RuleValidationResult] = pool.imap_unordered(
partial(
validate_single_rule, shared_cache, datasets, args, library_metadata
),
rules,
)
progress_handler: Callable = get_progress_displayer(args)
results = progress_handler(rules, validation_results, results)
# build all desired reports
end = time.time()
elapsed_time = end - start
reporting_factory = ReportFactory(
datasets, results, elapsed_time, args, data_service
)
reporting_services: List[BaseReport] = reporting_factory.get_report_services()
for reporting_service in reporting_services:
reporting_service.write_report(args.define_xml_path)
engine_logger.info("Cleaning up intermediate files")
for file in created_files:
engine_logger.info(f"Deleting file {file}")
os.remove(file)