Note: The Windows commands provided in this README are written for PowerShell. While most commands are compatible with both PowerShell and Command Prompt, some adjustments may be necessary when using Command Prompt. If you encounter any issues running these commands in Command Prompt, try using PowerShell or consult the Command Prompt documentation for equivalent commands.
Open source offering of the CDISC Rules Engine, a tool designed for validating clinical trial data against data standards.
To learn more, visit our official CDISC website or for other implementation options, see our DockerHub repository:
CDISC Website
CDISC Rules Engine on DockerHub
To quickly get up and running with CORE, users can download the latest executable version of the engine for their operating system from here: https://github.com/cdisc-org/cdisc-rules-engine/releases
Once downloaded, simply unzip the file and run the following command based on your Operating System:
Windows:
.\core.exe validate -s <standard> -v <standard_version> -d path/to/datasets
# ex: .\core.exe validate -s sdtmig -v 3-4 -d .\xpt\
Linux/Mac:
./core validate -s <standard> -v <standard_version> -d path/to/datasets
# ex: ./core validate -s sdtmig -v 3-4 -d .\xpt\
This project uses the black code formatter, flake8 linter for python and prettier for JSON, YAML and MD.
It also uses pre-commit to run black, flake8 and prettier when you commit.
Both dependencies are added to requirements.txt.
Required
Setting up pre-commit requires one extra step. After installing it you have to run
pre-commit install
This installs pre-commit in your .git/hooks directory.
These steps should be run before running any tests or core commands using the non compiled version.
- Create a virtual environment:
python -m venv <virtual_environment_name> - Activate the virtual environment:
./<virtual_environment_name>/bin/activate -- on linux/mac
.\<virtual_environment_name>\Scripts\Activate -- on windows
- Install the requirements.
python -m pip install -r requirements.txt # From the root directory
From the root of the project run the following command (this will run both the unit and regression tests):
python -m pytest tests
Clone the repository and run python core.py --help to see the full list of commands.
Run python core.py validate --help to see the list of validation options.
-ca, --cache TEXT Relative path to cache files containing pre
loaded metadata and rules
-ps, --pool-size INTEGER Number of parallel processes for validation
-d, --data TEXT Path to directory containing data files
-dp, --dataset-path TEXT Absolute path to dataset file. Can be specified multiple times.
-dxp, --define_xml_path TEXT Path to Define-XML
-l, --log-level [info|debug|error|critical|disabled|warn]
Sets log level for engine logs, logs are
disabled by default
-rt, --report-template TEXT File path of report template to use for
excel output
-s, --standard TEXT CDISC standard to validate against
[required]
-v, --version TEXT Standard version to validate against
[required]
-ct, --controlled-terminology-package TEXT
Controlled terminology package to validate
against, can provide more than one
-o, --output TEXT Report output file destination and name. Path will be
relative to the validation execution directory
and should end in the desired output filename
without file extension
'/user/reports/result' will be 'user/report' directory
with the filename as 'result'
-of, --output-format [JSON|XLSX]
Output file format
-rr, --raw-report Report in a raw format as it is generated by
the engine. This flag must be used only with
--output-format JSON.
-dv, --define-version TEXT Define-XML version used for validation
-dxp, --define-xml-path Path to define-xml file.
--whodrug TEXT Path to directory with WHODrug dictionary
files
--meddra TEXT Path to directory with MedDRA dictionary
files
--loinc TEXT Path to directory with LOINC dictionary
files
--medrt TEXT Path to directory with MEDRT dictionary
files
-r, --rules TEXT Specify rule core ID ex. CORE-000001. Can be specified multiple times.
-lr, --local_rules TEXT Specify relative path to directory containing
local rule yml and/or json rule files.
-lrc, --local_rules_cache Adding this flag tells engine to use local rules
uploaded to the cache instead of published rules
in the cache for the validation run.
-lri, --local_rule_id TEXT Specify ID for custom, local rules in the cache
you wish to run a validation with.
-vo, --verbose-output Specify this option to print rules as they
are completed
-p, --progress [verbose_output|disabled|percents|bar]
Defines how to display the validation
progress. By default a progress bar like
"[████████████████████████████--------]
78%"is printed.
--help Show this message and exit.
debug- Display all logsinfo- Display info, warnings, and error logswarn- Display warnings and errorserror- Display only error logscritical- Display critical logs
To validate a folder using rules for SDTM-IG version 3.4 use the following command:
`python core.py validate -s sdtmig -v 3-4 -d path/to/datasets`
The rules report tab displays the run status of each rule selected for validation
The possible rule run statuses are:
SUCCESS- The rule ran and data was validated against the rule. May or may not produce resultsSKIPPED- The rule was unable to be run. Usually due to missing required data, but could also be cause by rule execution errors.
- update-cache - update locally stored cache data (Requires an environment variable - CDISC_LIBRARY_API_KEY)
`python core.py update-cache`
To obtain an api key, please follow the instructions found here: https://wiki.cdisc.org/display/LIBSUPRT/Getting+Started%3A+Access+to+CDISC+Library+API+using+API+Key+Authentication. Please note it can take up to an hour after sign up to have an api key issued
-
an additional local rule
-lrflag can be added to the update-cache command that points to a directory of local rules. This adds the rules contained in the directory to the cache. It will not update the cache from library when-lris specified. A-lrilocal rules ID must be given when -lr is used to ID your rules in the cache. NOTE: local rules must contain a 'custom_id' key to be added to the cache. This should replace the Core ID field in the rule.`python core.py update-cache -lr 'path/to/directory' -lri 'CUSTOM123'` -
to remove local rules from to the cache, remove rules
-rlris added to update-cache to remove local rules from the cache. A previously used local_rules_id can be specified to remove all local rules with that ID from the cache or the keyword 'ALL' is reserved to remove all local rules from the cache.`python core.py update-cache -rlr 'CUSTOM123'`
- list-rules - list published rules available in the cache
-
list all published rules:
`python core.py list-rules` -
list rules for standard:
`python core.py list-rules -s sdtmig -v 3-4`
-list all local rules:
`python core.py list-rules -lr`
-list local rules with a specific local rules id:
`python core.py list-rules -lr -lri 'CUSTOM1'`
- list-rule-sets - lists all standards and versions for which rules are available:
python core.py list-rule-sets
- test - Test authored rule given dataset in json format
-ca, --cache TEXT Relative path to cache files containing pre
loaded metadata and rules
-dp, --dataset-path TEXT Absolute path to dataset file
-d, --data TEXT Path to directory containing data files
-l, --log-level [info|debug|error|critical|disabled|warn]
Sets log level for engine logs, logs are
disabled by default
-s, --standard TEXT CDISC standard to validate against
[required]
-v, --version TEXT Standard version to validate against
[required]
-ct, --controlled-terminology-package TEXT
Controlled terminology package to validate
against, can provide more than one
-dv, --define-version TEXT Define-XML version used for validation
--whodrug TEXT Path to directory with WHODrug dictionary
files
--meddra TEXT Path to directory with MedDRA dictionary
files
--loinc TEXT Path to directory with LOINC dictionary
files
-r, --rule TEXT Path to rule json file.
-dxp Path to define-xml file.
--help Show this message and exit.
EX: python core.py test -s sdtmig -v 3-4 -dp <path to dataset json file> -r <path to rule json file> --meddra ./meddra/ --whodrug ./whodrug/
Note: JSON dataset should match the format provided by the rule editor:
{
"datasets": [{
"filename": "cm.xpt",
"label": "Concomitant/Concurrent medications",
"domain": "CM",
"variables": [
{
"name": "STUDYID",
"label": "Study Identifier",
"type": "Char",
"length": 10
}
],
"records": {
"STUDYID": [
"CDISC-TEST",
"CDISC-TEST",
"CDISC-TEST",
"CDISC-TEST"
],
}
}
]
}
- list-ct - list ct packages available in the cache
Usage: python core.py list-ct [OPTIONS]
Command to list the ct packages available in the cache.
Options:
-c, --cache_path TEXT Relative path to cache files containing pre loaded
metadata and rules
-s, --subsets TEXT CT package subset type. Ex: sdtmct. Multiple values
allowed
--help Show this message and exit.
An alternative to running the validation from the command line is to instead import the rules engine library in python and run rules against data directly (without needing your data to be in .xpt format).
pip install cdisc-rules-engine
In addition to installing the library, you'll also want to download the rules cache (found in the resources/cache folder of this repository) and store them somewhere in your project.
The rules can be loaded into an in-memory cache by doing the following:
import os
import pathlib
from multiprocessing.managers import SyncManager
from cdisc_rules_engine.services.cache import InMemoryCacheService
class CacheManager(SyncManager):
pass
# If you're working from a terminal you may need to
# use SyncManager directly rather than define CacheManager
CacheManager.register("InMemoryCacheService", InMemoryCacheService)
def load_rules_cache(path_to_rules_cache):
cache_path = pathlib.Path(path_to_rules_cache)
manager = CacheManager()
manager.start()
cache = manager.InMemoryCacheService()
files = next(os.walk(cache_path), (None, None, []))[2]
for fname in files:
with open(cache_path / fname, "rb") as f:
cache.add_all(pickle.load(f))
return cacheRules in this cache can be accessed by standard and version using the get_rules_cache_key function.
from cdisc_rules_engine.utilities.utils import get_rules_cache_key
cache = load_rules_cache()
# Note that the standard version is separated by a dash, not a period
cache_key_prefix = get_rules_cache_key("sdtmig", "3-4")
rules = cache.get_all_by_prefix(cache_key_prefix)rules will now be a list of dictionaries the following keys
core_id- e.g. "CORE-000252"
domains- e.g.
{'Include': ['DM'], 'Exclude': []}or{'Include': ['ALL']}
- e.g.
authorreferencesensitivityexecutabilitydescriptionauthoritiesstandardsclassesrule_typeconditionsactionsdatasetsoutput_variables
In order to pass your data through the rules engine, it must be a pandas dataframe of an SDTM dataset. For example:
>>> data
STUDYID DOMAIN USUBJID AESEQ AESER AETERM ... AESDTH AESLIFE AESHOSP
0 AE 001 0 Y Headache ... N N N
[1 rows x 19 columns]
Before passing this into the rules engine, we need to wrap it in a DatasetVariable.
from cdisc_rules_engine.models.dataset_variable import DatasetVariable
dataset = DatasetVariable(data)Next, we need to actually run the rules. We can select which rules we want to run based on the domain of the data we're checking and the "Include" and "Exclude" domains of the rule.
# Get the rules for the domain AE
# (Note: we're ignoring ALL domain rules here)
ae_rules = [
rule for rule in rules
if "AE" in rule["domains"].get("Include", [])
]There's one last thing we need before we can actually run the rule, and that's a COREActions object. This object will handle generating error messages should the rule fail.
To instantiate a COREActions object, we need to pass in the following:
results: An array to which errors will be appendedvariable: Our DatasetVariabledomain: e.g. "AE"rule: Our rule
from cdisc_rules_engine.models.actions import COREActions
rule = ae_rules[0]
results = []
core_actions = COREActions(
results,
variable=dataset,
domain="AE",
rule=rule
)All that's left is to run the rule!
from business_rules.engine import run
was_triggered = run(
rule=rule,
defined_variables=dataset_variable,
defined_actions=core_actions,
)The return value of run will tell us if the rule was triggered.
- A
Falsevalue means that there were no errors - A
Truevalue means that there were errors
If there were errors, they will have been appended to the results array passed into your COREActions instance. Here's an example error:
{
'executionStatus': 'success',
'domain': 'AE',
'variables': ['AESLIFE'],
'message': 'AESLIFE is completed, but not equal to "N" or "Y"',
'errors': [
{'value': {'AESLIFE': 'Maybe'}, 'row': 1}
]
}Linux
pyinstaller core.py --add-data=venv/lib/python3.9/site-packages/xmlschema/schemas:xmlschema/schemas --add-data=resources/cache:resources/cache --add-data=resources/templates:resources/templates
Windows
pyinstaller core.py --add-data=".venv/Lib/site-packages/xmlschema/schemas;xmlschema/schemas" --add-data="resources/cache;resources/cache" --add-data="resources/templates;resources/templates"
Note .venv should be replaced with path to python installation or virtual environment
This will create an executable version in the dist folder. The version does not require having Python installed and
can be launched by running core script with all necessary CLI arguments.
All non-python files should be listed in MANIFEST.in to be included in the distribution.
Files must be in python package.
Unix/MacOS
python3 -m pip install --upgrade build
python3 -m build
To install from dist folder
pip3 install {path_to_file}/cdisc_rules_engine-{version}-py3-none-any.whl
To upload built distributive to pypi
python3 -m pip install --upgrade twine
python3 -m twine upload --repository {repository_name} dist/*
Windows(Untested)
py -m pip install --upgrade build
py -m build
To install from dist folder
pip install {path_to_file}/cdisc_rules_engine-{version}-py3-none-any.whl
To upload built distributive to pypi
py -m pip install --upgrade twine
py -m twine upload --repository {repository_name} dist/*