Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.3.7] - 2020-04-14

Added

  • IO operations demo and literal_eval operation.

  • Python prompts >>> can now be enabled or disabled for easy copying of code into interactive sessions.

  • Whitespace check now checks .rst and .md files too.

  • GetMulti operation which gets all Inputs of a given definition

  • Python usage example for LogisticRegression and its related tests.

  • Support for async generator operations

  • Example CLI commands and Python code for SLRModel

  • save function in high level API to quickly save all given records to a source

  • Ability to configure sources and models for HTTP API from command line when starting server

  • Documentation page for command line usage of HTTP API

  • Usage of HTTP API to the quickstart to use trained model

Changed

  • Renamed "arg" to "plugin".

  • CSV source sorts feature names within headers when saving

  • Moved HTTP service testing code to HTTP service util.testing

Fixed

  • Exporting plugins

  • Issue parsing string values when using the dataflow run command and specifying extra inputs.

Removed

  • Unused imports

[0.3.6] - 2020-04-04

Added

  • Operations for taking input from the user AcceptUserInput and for printing the output print_output

  • Hugging Face Transformers tensorflow based NER models.

  • PNG ConfigLoader for reading images as arrays to predict using MNIST trained models

  • Docstrings and doctestable examples to record.py.

  • Inputs can be validated using operations

    • validate parameter in Input takes Operation.instance_name

  • New db source can utilize any database that inherits from BaseDatabase

  • Logistic Regression with SAG optimizer

  • Test tensorflow DNNEstimator documentation examples in CI

  • shouldi got an operation to run cargo-audit on rust code.

  • Moved all the downloads to tests/downloads to speed the CI test.

  • Test tensorflow DNNEstimator documentation exaples in CI

  • Add python code for tensorflow DNNEstimator

  • Ability to run a subflow as if it were an operation using the dffml.dataflow.run operation.

  • Support for operations without inputs.

  • Partial doctestable examples to features.py

  • Doctestable examples for BaseSource

  • Instructions for setting up debugging environment in VSCode

Fixed

  • New model tutorial mentions file paths that should be edited.

  • DataFlow is no longer a dataclass to prevent it from being exported incorrectly.

  • operations_parameter_set_pairs moved to MemoryOrchestratorContext

  • Ignore generated files in docs/plugins/

  • Treat "~" as the the home directory rather than a literal

  • Windows support by selecting asyncio.ProactorEventLoop and not using asyncio.FastChildWatcher.

  • Moved SLR into the main dffml package and removed scratch:slr.

Changed

  • Refactor model/tensroflow

[0.3.5] - 2020-03-10

Added

  • Parent flows can now forward inputs to active contexts of subflows.

    • forward parameter in DataFlow

    • subflow in OperationImplementationContext

  • Documentation on writing examples and running doctests

  • Doctestable Examples to high-level API.

  • Shouldi got an operation to run npm-audit on JavaScript code

  • Docstrings and doctestable examples for record.py (features and evaluated)

  • Simplified model API with SimpleModel

  • Documentation on how DataFlows work conceptually.

  • Style guide now contains information on class, variable, and function naming.

Changed

  • Restructured contributing documentation

  • Use randomly generated data for scikit tests

  • Change Core to Official to clarify who maintains each plugin

  • Name of output of unsupervised model from “Prediction” to “cluster”

  • Test scikit LR documentation examples in CI

  • Create a fresh archive of the git repo for release instead of cleaning existing repo with git clean for development service release command.

  • Simplified SLR tests for scratch model

  • Test tensorflow DNNClassifier documentation exaples in CI

  • config directories and files associated with ConfigLoaders have been renamed to configloader.

  • Model config directory parameters are now pathlib.Path objects

  • New model tutorial and skel/model use simplifeid model API.

[0.3.4] - 2020-02-28

Added

  • Tensorflow hub NLP models.

  • Notes on development dependencies in setup.py files to codebase notes.

  • Test for cached_download

  • dffml.util.net.cached_download_unpack_archive to run a cached download and unpack the archive, very useful for testing. Documented on the Networking Helpers API docs page.

  • Directions on how to read the CI under the Git and GitHub page of the contributing documentation.

  • HTTP API

    • Static file serving from a dirctory with -static

    • api.js file serving with the -js flag

    • Docs page for JavaScript example

  • shouldi got an operation to run golangci-lint on Golang code

  • Note about using black via VSCode

Fixed

  • Port assignment for the HTTP API via the -port flag

Changed

  • repo/Repo to record/Record

  • Definitions with a spec can use the subspec parameter to declare that they are a list or a dict where the values are of the spec type. Rather than the list or dict itself being of the spec type.

  • Fixed the URL mentioned in example to configure a model.

  • Sphinx doctests are now run in the CI in the DOCS task.

  • Lint JavaScript files with js-beautify and enforce with CI

Removed

  • Unused imports

[0.3.3] - 2020-02-10

Added

  • Moved from TensorFlow 1 to TensorFlow 2.

  • IDX Sources to read binary data files and train models on MNIST Dataset

  • scikit models

    • Clusterers

      • KMeans

      • Birch

      • MiniBatchKMeans

      • AffinityPropagation

      • MeanShift

      • SpectralClustering

      • AgglomerativeClustering

      • OPTICS

  • allowempty added to source config parameters.

  • Quickstart document to show how to use models from Python.

  • The latest release of the documentation now includes a link to the documentation for the master branch (on GitHub pages).

  • Virtual environment, GitPod, and Docker development environment setup notes to the CONTRIBUTING.md file.

  • Changelog now included in documenation website.

  • Database abstraction dffml.db

    • SQLite connector

    • MySQL connector

  • Documented style for imports.

  • Documented use of numpy docstrings.

  • Inputs can now be sanitized using function passed in validate parameter

  • Helper utilities to take callables with numpy style docstrings and create config classes out of them using make_config.

  • File listing endpoint to HTTP service.

  • When an operation throws an exception the name of the instance and the parameters it was executed with will be thrown via an OperationException.

  • Network utilities to preformed cached downloads with hash validation.

  • Development service got a new command, which can retrieve an argument passed to setuptools setup function within a setup.py file.

Changed

  • All instances of src_url changed to key.

  • readonly parameter in source config is now changed to readwrite.

  • predict parameter of all model config classes has been changed from str to Feature.

  • Defining features on the command line no longer requires that defined features be prefixed with def:

  • The model predict operation will now raise an exception if the model it is passed via it’s config is a class rather than an instance.

  • entry_point and friends have been renamed to entrypoint.

  • Use FastChildWatcher when run via the CLI to prevent BlockingIOErrors.

  • TensorFlow based neural network classifier had the classification parameter in it’s config changed to predict.

  • SciKit models use make_config_numpy.

  • Predictions in repos are now dictionary.

  • All instances of label changed to tag

  • Subclasses of BaseConfigurable will now auto instantiate their respective config classes using kwargs if the config argument isn’t given and keyword arguments are.

  • The quickstart documentation was improved as well as the structure of docs.

Fixed

  • CONTRIBUTING.md has -e in the wrong place in the getting setup section.

  • Since moving to auto args() and config(), BaseConfigurable no longer produces odd typenames in conjunction with docs.py.

  • Autoconvert Definitions with spec into their spec

Removed

  • The model predict operation erroneously had a msg parameter in it’s config.

  • Unused imports identified by deepsource.io

  • Evaluation code from feature.py file as well as tests for those evaluations.

[0.3.2] - 2020-01-03

Added

  • scikit models

    • Classifiers

      • LogisticRegression

      • GradientBoostingClassifier

      • BernoulliNB

      • ExtraTreesClassifier

      • BaggingClassifier

      • LinearDiscriminantAnalysis

      • MultinomialNB

    • Regressors

      • ElasticNet

      • BayesianRidge

      • Lasso

      • ARDRegression

      • RANSACRegressor

      • DecisionTreeRegressor

      • GaussianProcessRegressor

      • OrthogonalMatchingPursuit

      • Lars

      • Ridge

  • AsyncExitStackTestCase which instantiates and enters async and non-async contextlib exit stacks. Provides temporary file creation.

  • Automatic releases to PyPi via GitHub Actions

  • Automatic documentation deployment to GitHub Pages

  • Function to create a config class dynamically, analogous to make_dataclass

  • ConfigLoaders class which loads config files from a file or directory to a dictionary.

Changed

  • CLI tests and integration tests derive from AsyncExitStackTestCase

  • SciKit models now use the auto args and config methods.

Fixed

  • Correctly identify when functions decorated with op use self to reference the OperationImplementationContext.

  • shouldi safety operation uses subprocess communicate method instead of stdin pipe writes.

  • Negative values are correctly parsed when input via the command line.

  • Do not lowercase development mode install location when reporting version.

[0.3.1] - 2019-12-12

Added

  • Integration tests using the command line interface.

  • Operation run_dataflow to run a dataflow and test for the same.

Changed

  • Features were moved from ModelContext to ModelConfig

  • CI is now run via GitHub Actions

  • CI testing script is now verbose

  • args and config methods of all classes no longer require implementation. BaseConfigurable handles exporting of arguments and creation of config objects for each class based off of the CONFIG property of that class. The CONFIG property is a class which has been decorated with dffml.base.config to make it a dataclass.

  • Speed up development service install of all plugins in development mode

  • Speed up named plugin load times

Fixed

  • DataFlows with multiple possibilities for a source for an input, now correctly look through all possible sources instead of just the first one.

  • DataFlow MemoryRedundancyCheckerContext was using all inputs in an input set and all their ancestors to check redundancy (a hold over from pre uid days). It now correctly only uses the inputs in the parameter set. This fixes a major performance issue.

  • MySQL packaging issue.

  • Develop service running one off operations correctly json-loads dict types.

  • Operations with configs can be run via the development service

  • JSON dumping numpy int* and float* caused crash on dump.

  • CSV source always loads src_urls as strings.

Removed

  • CLI command operations removed in favor of dataflow run

  • Duplicate dataflow diagram code from development service

[0.3.0] - 2019-10-26

Added

  • Real DataFlows, see operations tutorial and usage examples

  • Async helper concurrently nocancel optional keyword argument which, if set is a set of tasks not to cancel when the concurrently execution loop completes.

  • FileSourceTest has a test_label method which checks that a FileSource knows how to properly load and save repos under a given label.

  • Test case for Merge CLI command

  • Repo.feature method to select a single piece of feature data within a repo.

  • Dev service to help with hacking on DFFML and to create models from templates in the skel/ directory.

  • Classification type parameter to DNNClassifierModelConfig to specifiy data type of given classification options.

  • util.cli CMD classes have their argparse description set to their docstring.

  • util.cli CMD classes can specify the formatter class used in argparse.ArgumentParser via the CLI_FORMATTER_CLASS property.

  • Skeleton for service creation was added

  • Simple Linear Regression model from scratch

  • Scikit Linear Regression model

  • Community link in CONTRIBUTING.md.

  • Explained three main parts of DFFML on docs homepage

  • Documentation on how to use ML models on docs Models plugin page.

  • Mailing list info

  • Issue template for questions

  • Multiple Scikit Models with dynamic config

  • Entrypoint listing command to development service to aid in debugging issues with entrypoints.

  • HTTP API service to enable interacting with DFFML over HTTP. Currently includes APIs for configuring and using Sources and Models.

  • MySQL protocol source to work with data from a MySQL protocol compatible db

  • shouldi example got a bandit operation which tells users not to install if there are more than 5 issues of high severity and confidence.

  • dev service got the ability to run a single operation in a standalone fashion.

  • About page to docs.

  • Tensorflow DNNEstimator based regression model.

Changed

  • feature/codesec became it’s own branch, binsec

  • BaseOrchestratorContext run_operations strict is default to true. With strict as true errors will be raised and not just logged.

  • MemoryInputNetworkContext got an sadd method which is shorthand for creating a MemoryInputSet with a StringInputSetContext.

  • MemoryOrchestrator basic_config method takes list of operations and optional config for them.

  • shouldi example uses updated MemoryOrchestrator.basic_config method and includes more explanation in comments.

  • CSVSource allows for setting the Repo’s src_url from a csv column

  • util Entrypoint defines a new class for each loaded class and sets the ENTRY_POINT_LABEL parameter within the newly defined class.

  • Tensorflow model removed usages of repo.classifications methods.

  • Entrypoint prints traceback of loaded classes to standard error if they fail to load.

  • Updated Tensorflow model README.md to match functionality of DNNClassifierModel.

  • DNNClassifierModel no longer splits data for the user.

  • Update pip in Dockerfile.

  • Restructured documentation

  • Ran black on whole codebase, including all submodules

  • CI style check now checks whole codebase

  • Merged HACKING.md into CONTRIBUTING.md

  • shouldi example runs bandit now in addition to safety

  • The way safety gets called

  • Switched documentation to Read The Docs theme

  • Models yield only a repo object instead of the value and confidence of the prediction as well. Models are not responsible for calling the predicted method on the repo. This will ease the process of making predict feature specific.

  • Updated Tensorflow model README.md to include usage of regression model

Fixed

  • Docs get version from dffml.version.VERSION.

  • FileSource zipfiles are wrapped with TextIOWrapper because CSVSource expects the underlying file object to return str instances rather than bytes.

  • FileSourceTest inherits from SourceTest and is used to test json and csv sources.

  • A temporary directory is used to replicate mktemp -u functionality so as to provide tests using a FileSource with a valid tempfile name.

  • Labels for JSON sources

  • Labels for CSV sources

  • util.cli CMD’s correcly set the description of subparsers instead of their help, they also accept the CLI_FORMATTER_CLASS property.

  • CSV source now has entrypoint decoration

  • JSON source now has entrypoint decoration

  • Strict flag in df.memory is now on by default

  • Dynamically created scikit models get config args correctly

  • Renamed DNNClassifierModelContext first init arg from config to features

  • BaseSource now has base_entry_point decoration

Removed

  • Repo objects are no longer classification specific. Their classify, classified, and classification methods were removed.

[0.2.1] - 2019-06-07

Added

  • Definition spec field to specify a class representative of key value pairs for definitions with primitives which are dictionaries

  • Auto generation of documentation for operation implementations, models, and sources. Generated docs include information on configuration options and inputs and outputs for operation implementations.

  • Async helpers got an aenter_stack method which creates and returns and contextlib.AsyncExitStack after entering all the context’s passed to it.

  • Example of how to use Data Flow Facilitator / Orchestrator / Operations by writing a Python meta static analysis tool, shouldi

Changed

  • OperationImplementation add_label and add_orig_label methods now use op.name instead of ENTRY_POINT_ORIG_LABEL and ENTRY_POINT_NAME.

  • Make output specs and remap arguments optional for Operations CLI commands.

  • Feature skeleton project is now operations skeleton project

Fixed

  • MemoryOperationImplementationNetwork instantiates OperationImplementations using their withconfig() method.

  • MemorySource now decorated with entrypoint

  • MemorySource takes arguments correctly via config_set and config_get

  • skel modules have long_description_content_type set to “text/markdown”

  • Base Orchestrator __aenter__ and __aexit__ methods were moved to the Memory Orchestrator because they are specific to that config.

  • Async helper aenter_stack uses inspect.isfunction so it will bind lambdas

[0.2.0] - 2019-05-23

Added

  • Support for zip file source

  • Async helper for running tasks concurrently

  • Gitter badge to README

  • Documentation on the Data Flow Facilitator subsystem

  • codesec plugin containing operations which gather security related metrics on code and binaries.

  • auth plugin containing an scrypt operation as an example of thread pool usage.

Changed

  • Standardized the API for most classes in DFFML via inheritance from dffml.base

  • Configuration of classes is now done via the args() and config() methods

  • Documentation is now generated using Sphinx

Fixed

  • Corrected maxsplit in util.cli.parser

  • Check that dtype is a class in Tensorlfow DNN

  • CI script no longer always exits 0 for plugin tests

  • Corrected render type in setup.py to markdown

[0.1.2] - 2019-03-29

Added

  • Contribution guidelines

  • Logging documentation

  • Example usage of Git features

  • New Model and Feature creation script

  • New Feature skeleton directory

  • New Model skeleton directory

  • New Feature creation tutorial

  • New Model creation tutorial

  • Update functionality to the CSV source

  • Support for Gzip file source

  • Support for bz2 file source

  • Travis checks for additions to CHANGELOG.md

  • Travis checks for trailing whitespace

  • Support for lzma file source

  • Support for xz file source

  • Data Flow Facilitator

Changed

  • Restructured documentation to docs folder and moved from rST to markdown

  • Git feature cloc logs if no binaries are in path

Fixed

  • Enable source.file to read from /dev/fd/XX

[0.1.1] - 2019-03-08

Changed

  • Corrected formatting in README for PyPi

[0.1.0] - 2019-03-07

Added

  • Feature class to collect a feature in a dataset

  • Git features to collect feature data from Git repos

  • Model class to wrap implementations of machine learning models

  • Tensorflow DNN model for generic usage of the DNN estimator

  • CLI interface and framework

  • Source class to manage dataset storage