Datasets:

erishabh

/

hf-tmp-c1-repos

like 0

capitalone_repos

capitalone_repos/rubicon-ml/.pre-commit-config.yaml

repos: - repo: https://github.com/psf/black rev: 23.7.0 hooks: - id: black exclude: (versioneer.py|_version.py) - repo: https://github.com/timothycrosley/isort rev: 5.12.0 hooks: - id: isort - repo: https://github.com/pycqa/flake8 rev: 6.1.0 hooks: - id: flake8

capitalone_repos

capitalone_repos/rubicon-ml/setup.cfg

[metadata] name = rubicon-ml description = "an ML library for model development and governance" long_description = file: README.md long_description_content_type = text/markdown author = "Joe Wolfe, Ryan Soley, Diane Lee, Mike McCarty, CapitalOne" license = "Apache License, Version 2.0" url = https://github.com/capitalone/rubicon-ml python_requires = >=3.8.0 project_urls = Documentation = https://capitalone.github.io/rubicon-ml/ Bug Tracker = https://github.com/capitalone/rubicon-ml/issues Source Code = https://github.com/capitalone/rubicon-ml classifiers = Development Status :: 4 - Beta Intended Audience :: Developers Intended Audience :: Science/Research Topic :: Scientific/Engineering Topic :: Scientific/Engineering :: Information Analysis Topic :: Software Development :: Build Tools Topic :: Software Development :: Documentation License :: OSI Approved :: Apache Software License Programming Language :: Python :: 3 [options] zip_safe = False include_package_data = True packages = find: install_requires = click<=8.1.7,>=7.1 fsspec<=2023.9.2,>=2021.4.0 intake[dataframe]<=0.7.0,>=0.5.2 jsonpath-ng<=1.6.0,>=1.5.3 numpy<=1.26.0,>=1.22.0 pandas<=2.1.1,>=1.0.0 pyarrow<=13.0.0,>=0.18.0 PyYAML<=6.0.1,>=5.4.0 scikit-learn<=1.3.1,>=0.22.0 [options.extras_require] prefect = prefect<=1.2.4,>=0.12.0 s3 = s3fs<=2023.9.2,>=0.4 ui = dash<=2.14.0,>=2.0.0 dash-bootstrap-components<=1.5.0,>=1.0.0 viz = dash<=2.14.0,>=2.0.0 dash-bootstrap-components<=1.5.0,>=1.0.0 all = dash<=2.14.0,>=2.0.0 dash-bootstrap-components<=1.5.0,>=1.0.0 prefect<=1.2.4,>=0.12.0 s3fs<=2023.9.2,>=0.4 [options.entry_points] console_scripts = rubicon_ml = rubicon_ml.cli:cli intake.drivers = rubicon_ml_experiment = rubicon_ml.intake_rubicon.experiment:ExperimentSource [versioneer] vcs = git style = pep440 versionfile_source = rubicon_ml/_version.py versionfile_build = rubicon_ml/_version.py tag_prefix = "" parentdir_prefix = rubicon-ml- [flake8] exclude = versioneer.py, rubicon_ml/_version.py, docs, .ipynb_checkpoints max-line-length = 88 ignore = E731 E741 W503 E203 E501 [isort] line_length = 88 skip = versioneer.py, rubicon_ml/_version.py, rubicon_ml/client/__init__.py filter_files = True multi_line_output = 3 include_trailing_comma = True force_grid_wrap = 0 combine_as_imports = True [tool:pytest] markers = run_notebooks: tests that run Jupyter notebooks write_files: tests that physically write files to local and S3 filesystems addopts = --cov=./rubicon_ml --cov-report=term-missing --cov-fail-under=90 -m="not write_files" minversion = 3.2 xfail_strict = True [edgetest.envs.core] python_version = 3.9 deps = dask jupyterlab kaleido nodejs nbconvert nbformat palmerpenguins Pillow pytest pytest-cov prefect xgboost extras = all upgrade = click dash dash-bootstrap-components fsspec intake[dataframe] jsonpath-ng numpy pandas pyarrow PyYAML s3fs scikit-learn command = pytest -m 'not run_notebooks and not write_files'

capitalone_repos

capitalone_repos/rubicon-ml/.coveragerc

[report] exclude_lines = pragma: no cover raise AssertionError raise NotImplementedError if __name__ == .__main__.: omit = versioneer.py rubicon_ml/_version.py

capitalone_repos

capitalone_repos/rubicon-ml/versioneer.py

# Version: 0.19 """The Versioneer - like a rocketeer, but for versions. The Versioneer ============== * like a rocketeer, but for versions! * https://github.com/python-versioneer/python-versioneer * Brian Warner * License: Public Domain * Compatible with: Python 3.6, 3.7, 3.8, 3.9 and pypy3 * [![Latest Version][pypi-image]][pypi-url] * [![Build Status][travis-image]][travis-url] This is a tool for managing a recorded version number in distutils-based python projects. The goal is to remove the tedious and error-prone "update the embedded version string" step from your release process. Making a new release should be as easy as recording a new tag in your version-control system, and maybe making new tarballs. ## Quick Install * `pip install versioneer` to somewhere in your $PATH * add a `[versioneer]` section to your setup.cfg (see [Install](INSTALL.md)) * run `versioneer install` in your source tree, commit the results * Verify version information with `python setup.py version` ## Version Identifiers Source trees come from a variety of places: * a version-control system checkout (mostly used by developers) * a nightly tarball, produced by build automation * a snapshot tarball, produced by a web-based VCS browser, like github's "tarball from tag" feature * a release tarball, produced by "setup.py sdist", distributed through PyPI Within each source tree, the version identifier (either a string or a number, this tool is format-agnostic) can come from a variety of places: * ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows about recent "tags" and an absolute revision-id * the name of the directory into which the tarball was unpacked * an expanded VCS keyword ($Id$, etc) * a `_version.py` created by some earlier build step For released software, the version identifier is closely related to a VCS tag. Some projects use tag names that include more than just the version string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool needs to strip the tag prefix to extract the version identifier. For unreleased software (between tags), the version identifier should provide enough information to help developers recreate the same tree, while also giving them an idea of roughly how old the tree is (after version 1.2, before version 1.3). Many VCS systems can report a description that captures this, for example `git describe --tags --dirty --always` reports things like "0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the 0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has uncommitted changes). The version identifier is used for multiple purposes: * to allow the module to self-identify its version: `myproject.__version__` * to choose a name and prefix for a 'setup.py sdist' tarball ## Theory of Operation Versioneer works by adding a special `_version.py` file into your source tree, where your `__init__.py` can import it. This `_version.py` knows how to dynamically ask the VCS tool for version information at import time. `_version.py` also contains `$Revision$` markers, and the installation process marks `_version.py` to have this marker rewritten with a tag name during the `git archive` command. As a result, generated tarballs will contain enough information to get the proper version. To allow `setup.py` to compute a version too, a `versioneer.py` is added to the top level of your source tree, next to `setup.py` and the `setup.cfg` that configures it. This overrides several distutils/setuptools commands to compute the version when invoked, and changes `setup.py build` and `setup.py sdist` to replace `_version.py` with a small static file that contains just the generated version data. ## Installation See [INSTALL.md](./INSTALL.md) for detailed installation instructions. ## Version-String Flavors Code which uses Versioneer can learn about its version string at runtime by importing `_version` from your main `__init__.py` file and running the `get_versions()` function. From the "outside" (e.g. in `setup.py`), you can import the top-level `versioneer.py` and run `get_versions()`. Both functions return a dictionary with different flavors of version information: * `['version']`: A condensed version string, rendered using the selected style. This is the most commonly used value for the project's version string. The default "pep440" style yields strings like `0.11`, `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section below for alternative styles. * `['full-revisionid']`: detailed revision identifier. For Git, this is the full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". * `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the commit date in ISO 8601 format. This will be None if the date is not available. * `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that this is only accurate if run in a VCS checkout, otherwise it is likely to be False or None * `['error']`: if the version string could not be computed, this will be set to a string describing the problem, otherwise it will be None. It may be useful to throw an exception in setup.py if this is set, to avoid e.g. creating tarballs with a version string of "unknown". Some variants are more useful than others. Including `full-revisionid` in a bug report should allow developers to reconstruct the exact code being tested (or indicate the presence of local changes that should be shared with the developers). `version` is suitable for display in an "about" box or a CLI `--version` output: it can be easily compared against release notes and lists of bugs fixed in various releases. The installer adds the following text to your `__init__.py` to place a basic version in `YOURPROJECT.__version__`: from ._version import get_versions __version__ = get_versions()['version'] del get_versions ## Styles The setup.cfg `style=` configuration controls how the VCS information is rendered into a version string. The default style, "pep440", produces a PEP440-compliant string, equal to the un-prefixed tag name for actual releases, and containing an additional "local version" section with more detail for in-between builds. For Git, this is TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags --dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and that this commit is two revisions ("+2") beyond the "0.11" tag. For released software (exactly equal to a known tag), the identifier will only contain the stripped tag, e.g. "0.11". Other styles are available. See [details.md](details.md) in the Versioneer source tree for descriptions. ## Debugging Versioneer tries to avoid fatal errors: if something goes wrong, it will tend to return a version of "0+unknown". To investigate the problem, run `setup.py version`, which will run the version-lookup code in a verbose mode, and will display the full contents of `get_versions()` (including the `error` string, which may help identify what went wrong). ## Known Limitations Some situations are known to cause problems for Versioneer. This details the most significant ones. More can be found on Github [issues page](https://github.com/python-versioneer/python-versioneer/issues). ### Subprojects Versioneer has limited support for source trees in which `setup.py` is not in the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are two common reasons why `setup.py` might not be in the root: * Source trees which contain multiple subprojects, such as [Buildbot](https://github.com/buildbot/buildbot), which contains both "master" and "slave" subprojects, each with their own `setup.py`, `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI distributions (and upload multiple independently-installable tarballs). * Source trees whose main purpose is to contain a C library, but which also provide bindings to Python (and perhaps other languages) in subdirectories. Versioneer will look for `.git` in parent directories, and most operations should get the right version string. However `pip` and `setuptools` have bugs and implementation details which frequently cause `pip install .` from a subproject directory to fail to find a correct version string (so it usually defaults to `0+unknown`). `pip install --editable .` should work correctly. `setup.py install` might work too. Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in some later version. [Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking this issue. The discussion in [PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the issue from the Versioneer side in more detail. [pip PR#3176](https://github.com/pypa/pip/pull/3176) and [pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve pip to let Versioneer work correctly. Versioneer-0.16 and earlier only looked for a `.git` directory next to the `setup.cfg`, so subprojects were completely unsupported with those releases. ### Editable installs with setuptools <= 18.5 `setup.py develop` and `pip install --editable .` allow you to install a project into a virtualenv once, then continue editing the source code (and test) without re-installing after every change. "Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a convenient way to specify executable scripts that should be installed along with the python package. These both work as expected when using modern setuptools. When using setuptools-18.5 or earlier, however, certain operations will cause `pkg_resources.DistributionNotFound` errors when running the entrypoint script, which must be resolved by re-installing the package. This happens when the install happens with one version, then the egg_info data is regenerated while a different version is checked out. Many setup.py commands cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into a different virtualenv), so this can be surprising. [Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes this one, but upgrading to a newer version of setuptools should probably resolve it. ## Updating Versioneer To upgrade your project to a new release of Versioneer, do the following: * install the new Versioneer (`pip install -U versioneer` or equivalent) * edit `setup.cfg`, if necessary, to include any new configuration settings indicated by the release notes. See [UPGRADING](./UPGRADING.md) for details. * re-run `versioneer install` in your source tree, to replace `SRC/_version.py` * commit any changed files ## Future Directions This tool is designed to make it easily extended to other version-control systems: all VCS-specific components are in separate directories like src/git/ . The top-level `versioneer.py` script is assembled from these components by running make-versioneer.py . In the future, make-versioneer.py will take a VCS name as an argument, and will construct a version of `versioneer.py` that is specific to the given VCS. It might also take the configuration arguments that are currently provided manually during installation by editing setup.py . Alternatively, it might go the other direction and include code from all supported VCS systems, reducing the number of intermediate scripts. ## Similar projects * [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time dependency * [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of versioneer ## License To make Versioneer easier to embed, all its code is dedicated to the public domain. The `_version.py` that it creates is also in the public domain. Specifically, both are released under the Creative Commons "Public Domain Dedication" license (CC0-1.0), as described in https://creativecommons.org/publicdomain/zero/1.0/ . [pypi-image]: https://img.shields.io/pypi/v/versioneer.svg [pypi-url]: https://pypi.python.org/pypi/versioneer/ [travis-image]: https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg [travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer """ import configparser import errno import json import os import re import subprocess import sys class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_root(): """Get the project root directory. We require that all commands are run from the project root, i.e. the directory that contains setup.py, setup.cfg, and versioneer.py . """ root = os.path.realpath(os.path.abspath(os.getcwd())) setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): # allow 'python path/to/setup.py COMMAND' root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): err = ("Versioneer was unable to run the project root directory. " "Versioneer requires setup.py to be executed from " "its immediate directory (like 'python setup.py COMMAND'), " "or in a way that lets it use sys.argv[0] to find the root " "(like 'python path/to/setup.py COMMAND').") raise VersioneerBadRootError(err) try: # Certain runtime workflows (setup.py install/develop in a setuptools # tree) execute all dependencies in a single python process, so # "versioneer" may be imported multiple times, and python's shared # module-import table will cache the first one. So we can't use # os.path.dirname(__file__), as that will find whichever # versioneer.py was first imported, even in later projects. me = os.path.realpath(os.path.abspath(__file__)) me_dir = os.path.normcase(os.path.splitext(me)[0]) vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0]) if me_dir != vsr_dir: print("Warning: build in %s is using versioneer.py from %s" % (os.path.dirname(me), versioneer_py)) except NameError: pass return root def get_config_from_root(root): """Read the project setup.cfg file to determine Versioneer config.""" # This might raise EnvironmentError (if setup.cfg is missing), or # configparser.NoSectionError (if it lacks a [versioneer] section), or # configparser.NoOptionError (if it lacks "VCS="). See the docstring at # the top of versioneer.py for instructions on writing your setup.cfg . setup_cfg = os.path.join(root, "setup.cfg") parser = configparser.ConfigParser() with open(setup_cfg, "r") as f: parser.read_file(f) VCS = parser.get("versioneer", "VCS") # mandatory def get(parser, name): if parser.has_option("versioneer", name): return parser.get("versioneer", name) return None cfg = VersioneerConfig() cfg.VCS = VCS cfg.style = get(parser, "style") or "" cfg.versionfile_source = get(parser, "versionfile_source") cfg.versionfile_build = get(parser, "versionfile_build") cfg.tag_prefix = get(parser, "tag_prefix") if cfg.tag_prefix in ("''", '""'): cfg.tag_prefix = "" cfg.parentdir_prefix = get(parser, "parentdir_prefix") cfg.verbose = get(parser, "verbose") return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" # these dictionaries contain VCS-specific tools LONG_VERSION_PY = {} HANDLERS = {} def register_vcs_handler(vcs, method): # decorator """Create decorator to mark a method as the handler of a VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None): """Call the given command(s).""" assert isinstance(commands, list) p = None for c in commands: try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git p = subprocess.Popen([c] + args, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except EnvironmentError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %s" % dispcmd) print(e) return None, None else: if verbose: print("unable to find command, tried %s" % (commands,)) return None, None stdout = p.communicate()[0].strip().decode() if p.returncode != 0: if verbose: print("unable to run %s (error)" % dispcmd) print("stdout was %s" % stdout) return None, p.returncode return stdout, p.returncode LONG_VERSION_PY['git'] = r''' # This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build # directories (produced by setup.py build) will contain a much shorter file # that just contains the computed version number. # This file is released into the public domain. Generated by # versioneer-0.19 (https://github.com/python-versioneer/python-versioneer) """Git implementation of _version.py.""" import errno import os import re import subprocess import sys def get_keywords(): """Get the keywords needed to look up the version information.""" # these strings will be replaced by git during git-archive. # setup.py/versioneer.py will grep for the variable names, so they must # each be defined on a line of their own. _version.py will just call # get_keywords(). git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s" keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} return keywords class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_config(): """Create, populate and return the VersioneerConfig() object.""" # these strings are filled in when 'setup.py versioneer' creates # _version.py cfg = VersioneerConfig() cfg.VCS = "git" cfg.style = "%(STYLE)s" cfg.tag_prefix = "%(TAG_PREFIX)s" cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" cfg.verbose = False return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" LONG_VERSION_PY = {} HANDLERS = {} def register_vcs_handler(vcs, method): # decorator """Create decorator to mark a method as the handler of a VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None): """Call the given command(s).""" assert isinstance(commands, list) p = None for c in commands: try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git p = subprocess.Popen([c] + args, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except EnvironmentError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %%s" %% dispcmd) print(e) return None, None else: if verbose: print("unable to find command, tried %%s" %% (commands,)) return None, None stdout = p.communicate()[0].strip().decode() if p.returncode != 0: if verbose: print("unable to run %%s (error)" %% dispcmd) print("stdout was %%s" %% stdout) return None, p.returncode return stdout, p.returncode def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. We will also support searching up two directory levels for an appropriately named parent directory """ rootdirs = [] for i in range(3): dirname = os.path.basename(root) if dirname.startswith(parentdir_prefix): return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None, "date": None} else: rootdirs.append(root) root = os.path.dirname(root) # up a level if verbose: print("Tried directories %%s but none started with prefix %%s" %% (str(rootdirs), parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: f = open(versionfile_abs, "r") for line in f.readlines(): if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) if line.strip().startswith("git_date ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["date"] = mo.group(1) f.close() except EnvironmentError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if not keywords: raise NotThisMethod("no keywords at all, weird") date = keywords.get("date") if date is not None: # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601 # -like" string, which we must then edit to make compliant), because # it's been around since git-1.5.3, and it's too difficult to # discover which version we're using, or to work around using an # older one. date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = set([r.strip() for r in refnames.strip("()").split(",")]) # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %%d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = set([r for r in refs if re.search(r'\d', r)]) if verbose: print("discarding '%%s', no digits" %% ",".join(refs - tags)) if verbose: print("likely tags: %%s" %% ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] if verbose: print("picking %%s" %% r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None, "date": date} # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags", "date": None} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True) if rc != 0: if verbose: print("Directory %%s not under git control" %% root) raise NotThisMethod("'git rev-parse --git-dir' returned error") # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%%s*" %% tag_prefix], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%%s'" %% describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%%s' doesn't start with prefix '%%s'" print(fmt %% (full_tag, tag_prefix)) pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" %% (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits # commit date: see ISO-8601 comment in git_versions_from_keywords() date = run_command(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip() # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) return pieces def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_pre(pieces): """TAG[.post0.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post0.devDISTANCE """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post0.dev%%d" %% pieces["distance"] else: # exception #1 rendered = "0.post0.dev%%d" %% pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%%s" %% pieces["short"] else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%%s" %% pieces["short"] return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"], "date": None} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%%s'" %% style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None, "date": pieces.get("date")} def get_versions(): """Get version information or return default if unable to do so.""" # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have # __file__, we can work backwards from there to the root. Some # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which # case we can only use expanded keywords. cfg = get_config() verbose = cfg.verbose try: return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) except NotThisMethod: pass try: root = os.path.realpath(__file__) # versionfile_source is the relative path from the top of the source # tree (where the .git directory might live) to this file. Invert # this to find the root from __file__. for i in cfg.versionfile_source.split('/'): root = os.path.dirname(root) except NameError: return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to find root of source tree", "date": None} try: pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) return render(pieces, cfg.style) except NotThisMethod: pass try: if cfg.parentdir_prefix: return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) except NotThisMethod: pass return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version", "date": None} ''' @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: f = open(versionfile_abs, "r") for line in f.readlines(): if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) if line.strip().startswith("git_date ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["date"] = mo.group(1) f.close() except EnvironmentError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if not keywords: raise NotThisMethod("no keywords at all, weird") date = keywords.get("date") if date is not None: # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 # -like" string, which we must then edit to make compliant), because # it's been around since git-1.5.3, and it's too difficult to # discover which version we're using, or to work around using an # older one. date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = set([r.strip() for r in refnames.strip("()").split(",")]) # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = set([r for r in refs if re.search(r'\d', r)]) if verbose: print("discarding '%s', no digits" % ",".join(refs - tags)) if verbose: print("likely tags: %s" % ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] if verbose: print("picking %s" % r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None, "date": date} # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags", "date": None} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True) if rc != 0: if verbose: print("Directory %s not under git control" % root) raise NotThisMethod("'git rev-parse --git-dir' returned error") # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%s*" % tag_prefix], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%s'" % describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" % (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits # commit date: see ISO-8601 comment in git_versions_from_keywords() date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) return pieces def do_vcs_install(manifest_in, versionfile_source, ipy): """Git-specific installation logic for Versioneer. For Git, this means creating/changing .gitattributes to mark _version.py for export-subst keyword substitution. """ GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] files = [manifest_in, versionfile_source] if ipy: files.append(ipy) try: me = __file__ if me.endswith(".pyc") or me.endswith(".pyo"): me = os.path.splitext(me)[0] + ".py" versioneer_file = os.path.relpath(me) except NameError: versioneer_file = "versioneer.py" files.append(versioneer_file) present = False try: f = open(".gitattributes", "r") for line in f.readlines(): if line.strip().startswith(versionfile_source): if "export-subst" in line.strip().split()[1:]: present = True f.close() except EnvironmentError: pass if not present: f = open(".gitattributes", "a+") f.write("%s export-subst\n" % versionfile_source) f.close() files.append(".gitattributes") run_command(GITS, ["add", "--"] + files) def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. We will also support searching up two directory levels for an appropriately named parent directory """ rootdirs = [] for i in range(3): dirname = os.path.basename(root) if dirname.startswith(parentdir_prefix): return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None, "date": None} else: rootdirs.append(root) root = os.path.dirname(root) # up a level if verbose: print("Tried directories %s but none started with prefix %s" % (str(rootdirs), parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") SHORT_VERSION_PY = """ # This file was generated by 'versioneer.py' (0.19) from # revision-control system data, or from the parent directory name of an # unpacked source archive. Distribution tarballs contain a pre-generated copy # of this file. import json version_json = ''' %s ''' # END VERSION_JSON def get_versions(): return json.loads(version_json) """ def versions_from_file(filename): """Try to determine the version from _version.py if present.""" try: with open(filename) as f: contents = f.read() except EnvironmentError: raise NotThisMethod("unable to read _version.py") mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", contents, re.M | re.S) if not mo: mo = re.search(r"version_json = '''\r\n(.*)''' # END VERSION_JSON", contents, re.M | re.S) if not mo: raise NotThisMethod("no version_json in _version.py") return json.loads(mo.group(1)) def write_to_version_file(filename, versions): """Write the given version number to the given _version.py file.""" os.unlink(filename) contents = json.dumps(versions, sort_keys=True, indent=1, separators=(",", ": ")) with open(filename, "w") as f: f.write(SHORT_VERSION_PY % contents) print("set %s to '%s'" % (filename, versions["version"])) def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_pre(pieces): """TAG[.post0.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post0.devDISTANCE """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post0.dev%d" % pieces["distance"] else: # exception #1 rendered = "0.post0.dev%d" % pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%s" % pieces["short"] return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"], "date": None} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%s'" % style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None, "date": pieces.get("date")} class VersioneerBadRootError(Exception): """The project root directory is unknown or missing key files.""" def get_versions(verbose=False): """Get the project version from whatever source is available. Returns dict with two keys: 'version' and 'full'. """ if "versioneer" in sys.modules: # see the discussion in cmdclass.py:get_cmdclass() del sys.modules["versioneer"] root = get_root() cfg = get_config_from_root(root) assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" handlers = HANDLERS.get(cfg.VCS) assert handlers, "unrecognized VCS '%s'" % cfg.VCS verbose = verbose or cfg.verbose assert cfg.versionfile_source is not None, \ "please set versioneer.versionfile_source" assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" versionfile_abs = os.path.join(root, cfg.versionfile_source) # extract version from first of: _version.py, VCS command (e.g. 'git # describe'), parentdir. This is meant to work for developers using a # source checkout, for users of a tarball created by 'setup.py sdist', # and for users of a tarball/zipball created by 'git archive' or github's # download-from-tag feature or the equivalent in other VCSes. get_keywords_f = handlers.get("get_keywords") from_keywords_f = handlers.get("keywords") if get_keywords_f and from_keywords_f: try: keywords = get_keywords_f(versionfile_abs) ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) if verbose: print("got version from expanded keyword %s" % ver) return ver except NotThisMethod: pass try: ver = versions_from_file(versionfile_abs) if verbose: print("got version from file %s %s" % (versionfile_abs, ver)) return ver except NotThisMethod: pass from_vcs_f = handlers.get("pieces_from_vcs") if from_vcs_f: try: pieces = from_vcs_f(cfg.tag_prefix, root, verbose) ver = render(pieces, cfg.style) if verbose: print("got version from VCS %s" % ver) return ver except NotThisMethod: pass try: if cfg.parentdir_prefix: ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) if verbose: print("got version from parentdir %s" % ver) return ver except NotThisMethod: pass if verbose: print("unable to compute version") return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version", "date": None} def get_version(): """Get the short version string for this project.""" return get_versions()["version"] def get_cmdclass(cmdclass=None): """Get the custom setuptools/distutils subclasses used by Versioneer. If the package uses a different cmdclass (e.g. one from numpy), it should be provide as an argument. """ if "versioneer" in sys.modules: del sys.modules["versioneer"] # this fixes the "python setup.py develop" case (also 'install' and # 'easy_install .'), in which subdependencies of the main project are # built (using setup.py bdist_egg) in the same python process. Assume # a main project A and a dependency B, which use different versions # of Versioneer. A's setup.py imports A's Versioneer, leaving it in # sys.modules by the time B's setup.py is executed, causing B to run # with the wrong versioneer. Setuptools wraps the sub-dep builds in a # sandbox that restores sys.modules to it's pre-build state, so the # parent is protected against the child's "import versioneer". By # removing ourselves from sys.modules here, before the child build # happens, we protect the child from the parent's versioneer too. # Also see https://github.com/python-versioneer/python-versioneer/issues/52 cmds = {} if cmdclass is None else cmdclass.copy() # we add "version" to both distutils and setuptools from distutils.core import Command class cmd_version(Command): description = "report generated version string" user_options = [] boolean_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): vers = get_versions(verbose=True) print("Version: %s" % vers["version"]) print(" full-revisionid: %s" % vers.get("full-revisionid")) print(" dirty: %s" % vers.get("dirty")) print(" date: %s" % vers.get("date")) if vers["error"]: print(" error: %s" % vers["error"]) cmds["version"] = cmd_version # we override "build_py" in both distutils and setuptools # # most invocation pathways end up running build_py: # distutils/build -> build_py # distutils/install -> distutils/build ->.. # setuptools/bdist_wheel -> distutils/install ->.. # setuptools/bdist_egg -> distutils/install_lib -> build_py # setuptools/install -> bdist_egg ->.. # setuptools/develop -> ? # pip install: # copies source tree to a tempdir before running egg_info/etc # if .git isn't copied too, 'git describe' will fail # then does setup.py bdist_wheel, or sometimes setup.py install # setup.py egg_info -> ? # we override different "build_py" commands for both environments if 'build_py' in cmds: _build_py = cmds['build_py'] elif "setuptools" in sys.modules: from setuptools.command.build_py import build_py as _build_py else: from distutils.command.build_py import build_py as _build_py class cmd_build_py(_build_py): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() _build_py.run(self) # now locate _version.py in the new build/ directory and replace # it with an updated value if cfg.versionfile_build: target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) cmds["build_py"] = cmd_build_py if "setuptools" in sys.modules: from setuptools.command.build_ext import build_ext as _build_ext else: from distutils.command.build_ext import build_ext as _build_ext class cmd_build_ext(_build_ext): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() _build_ext.run(self) if self.inplace: # build_ext --inplace will only build extensions in # build/lib<..> dir with no _version.py to write to. # As in place builds will already have a _version.py # in the module dir, we do not need to write one. return # now locate _version.py in the new build/ directory and replace # it with an updated value target_versionfile = os.path.join(self.build_lib, cfg.versionfile_source) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) cmds["build_ext"] = cmd_build_ext if "cx_Freeze" in sys.modules: # cx_freeze enabled? from cx_Freeze.dist import build_exe as _build_exe # nczeczulin reports that py2exe won't like the pep440-style string # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g. # setup(console=[{ # "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION # "product_version": versioneer.get_version(), # ... class cmd_build_exe(_build_exe): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() target_versionfile = cfg.versionfile_source print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) _build_exe.run(self) os.unlink(target_versionfile) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) cmds["build_exe"] = cmd_build_exe del cmds["build_py"] if 'py2exe' in sys.modules: # py2exe enabled? from py2exe.distutils_buildexe import py2exe as _py2exe class cmd_py2exe(_py2exe): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() target_versionfile = cfg.versionfile_source print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) _py2exe.run(self) os.unlink(target_versionfile) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) cmds["py2exe"] = cmd_py2exe # we override different "sdist" commands for both environments if 'sdist' in cmds: _sdist = cmds['sdist'] elif "setuptools" in sys.modules: from setuptools.command.sdist import sdist as _sdist else: from distutils.command.sdist import sdist as _sdist class cmd_sdist(_sdist): def run(self): versions = get_versions() self._versioneer_generated_versions = versions # unless we update this, the command will keep using the old # version self.distribution.metadata.version = versions["version"] return _sdist.run(self) def make_release_tree(self, base_dir, files): root = get_root() cfg = get_config_from_root(root) _sdist.make_release_tree(self, base_dir, files) # now locate _version.py in the new base_dir directory # (remembering that it may be a hardlink) and replace it with an # updated value target_versionfile = os.path.join(base_dir, cfg.versionfile_source) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, self._versioneer_generated_versions) cmds["sdist"] = cmd_sdist return cmds CONFIG_ERROR = """ setup.cfg is missing the necessary Versioneer configuration. You need a section like: [versioneer] VCS = git style = pep440 versionfile_source = src/myproject/_version.py versionfile_build = myproject/_version.py tag_prefix = parentdir_prefix = myproject- You will also need to edit your setup.py to use the results: import versioneer setup(version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), ...) Please read the docstring in ./versioneer.py for configuration instructions, edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. """ SAMPLE_CONFIG = """ # See the docstring in versioneer.py for instructions. Note that you must # re-run 'versioneer.py setup' after changing this section, and commit the # resulting files. [versioneer] #VCS = git #style = pep440 #versionfile_source = #versionfile_build = #tag_prefix = #parentdir_prefix = """ INIT_PY_SNIPPET = """ from ._version import get_versions __version__ = get_versions()['version'] del get_versions """ def do_setup(): """Do main VCS-independent setup function for installing Versioneer.""" root = get_root() try: cfg = get_config_from_root(root) except (EnvironmentError, configparser.NoSectionError, configparser.NoOptionError) as e: if isinstance(e, (EnvironmentError, configparser.NoSectionError)): print("Adding sample versioneer config to setup.cfg", file=sys.stderr) with open(os.path.join(root, "setup.cfg"), "a") as f: f.write(SAMPLE_CONFIG) print(CONFIG_ERROR, file=sys.stderr) return 1 print(" creating %s" % cfg.versionfile_source) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) ipy = os.path.join(os.path.dirname(cfg.versionfile_source), "__init__.py") if os.path.exists(ipy): try: with open(ipy, "r") as f: old = f.read() except EnvironmentError: old = "" if INIT_PY_SNIPPET not in old: print(" appending to %s" % ipy) with open(ipy, "a") as f: f.write(INIT_PY_SNIPPET) else: print(" %s unmodified" % ipy) else: print(" %s doesn't exist, ok" % ipy) ipy = None # Make sure both the top-level "versioneer.py" and versionfile_source # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so # they'll be copied into source distributions. Pip won't be able to # install the package without this. manifest_in = os.path.join(root, "MANIFEST.in") simple_includes = set() try: with open(manifest_in, "r") as f: for line in f: if line.startswith("include "): for include in line.split()[1:]: simple_includes.add(include) except EnvironmentError: pass # That doesn't cover everything MANIFEST.in can do # (http://docs.python.org/2/distutils/sourcedist.html#commands), so # it might give some false negatives. Appending redundant 'include' # lines is safe, though. if "versioneer.py" not in simple_includes: print(" appending 'versioneer.py' to MANIFEST.in") with open(manifest_in, "a") as f: f.write("include versioneer.py\n") else: print(" 'versioneer.py' already in MANIFEST.in") if cfg.versionfile_source not in simple_includes: print(" appending versionfile_source ('%s') to MANIFEST.in" % cfg.versionfile_source) with open(manifest_in, "a") as f: f.write("include %s\n" % cfg.versionfile_source) else: print(" versionfile_source already in MANIFEST.in") # Make VCS-specific changes. For git, this means creating/changing # .gitattributes to mark _version.py for export-subst keyword # substitution. do_vcs_install(manifest_in, cfg.versionfile_source, ipy) return 0 def scan_setup_py(): """Validate the contents of setup.py against Versioneer's expectations.""" found = set() setters = False errors = 0 with open("setup.py", "r") as f: for line in f.readlines(): if "import versioneer" in line: found.add("import") if "versioneer.get_cmdclass()" in line: found.add("cmdclass") if "versioneer.get_version()" in line: found.add("get_version") if "versioneer.VCS" in line: setters = True if "versioneer.versionfile_source" in line: setters = True if len(found) != 3: print("") print("Your setup.py appears to be missing some important items") print("(but I might be wrong). Please make sure it has something") print("roughly like the following:") print("") print(" import versioneer") print(" setup( version=versioneer.get_version(),") print(" cmdclass=versioneer.get_cmdclass(), ...)") print("") errors += 1 if setters: print("You should remove lines like 'versioneer.VCS = ' and") print("'versioneer.versionfile_source = ' . This configuration") print("now lives in setup.cfg, and should be removed from setup.py") print("") errors += 1 return errors if __name__ == "__main__": cmd = sys.argv[1] if cmd == "setup": errors = do_setup() errors += scan_setup_py() if errors: sys.exit(1)

capitalone_repos

capitalone_repos/rubicon-ml/conftest.py

pytest_plugins = [ "tests.fixtures", ] def pytest_addoption(parser): parser.addoption("--s3-path", dest="s3-path")

capitalone_repos

capitalone_repos/rubicon-ml/LICENSE

Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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capitalone_repos

capitalone_repos/rubicon-ml/pyproject.toml

[tool.black] line-length = 100 exclude = "(versioneer.py|_version.py)"

capitalone_repos

capitalone_repos/rubicon-ml/CODEOWNERS

* @capitalone/rubicon-admin-team

capitalone_repos

capitalone_repos/rubicon-ml/MANIFEST.in

graft rubicon_ml/viz/assets graft rubicon_ml/viz/assets/css include versioneer.py include rubicon_ml/_version.py recursive-include rubicon_ml/schema *.yaml

capitalone_repos

capitalone_repos/rubicon-ml/README.md

# rubicon-ml [](https://github.com/capitalone/rubicon-ml/actions/workflows/test-package.yml) [](https://github.com/capitalone/rubicon-ml/actions/workflows/publish-package.yml) [](https://github.com/capitalone/rubicon-ml/actions/workflows/publish-docs.yml) [](https://github.com/capitalone/rubicon-ml/actions/workflows/edgetest.yml) [](https://anaconda.org/conda-forge/rubicon-ml) [](https://pypi.org/project/rubicon-ml/) [](https://mybinder.org/v2/gh/capitalone/rubicon-ml/main?labpath=binder%2Fwelcome.ipynb) ## Purpose rubicon-ml is a data science tool that captures and stores model training and execution information, like parameters and outcomes, in a repeatable and searchable way. Its `git` integration associates these inputs and outputs directly with the model code that produced them to ensure full auditability and reproducibility for both developers and stakeholders alike. While experimenting, the dashboard makes it easy to explore, filter, visualize, and share recorded work. p.s. If you're looking for Rubicon, the Java/ObjC Python bridge, visit [this](https://pypi.org/project/rubicon/) instead. --- ## Components rubicon-ml is composed of three parts: * A Python library for storing and retrieving model inputs, outputs, and analyses to filesystems that’s powered by [`fsspec`](https://filesystem-spec.readthedocs.io/en/latest/?badge=latest) * A dashboard for exploring, comparing, and visualizing logged data built with [`dash`](https://dash.plotly.com/) * And a process for sharing a selected subset of logged data with collaborators or reviewers that leverages [`intake`](https://intake.readthedocs.io/en/latest/) ## Workflow Use `rubicon_ml` to capture model inputs and outputs over time. It can be easily integrated into existing Python models or pipelines and supports both concurrent logging (so multiple experiments can be logged in parallel) and asynchronous communication with S3 (so network reads and writes won’t block). Meanwhile, periodically review the logged data within the Rubicon dashboard to steer the model tweaking process in the right direction. The dashboard lets you quickly spot trends by exploring and filtering your logged results and visualizes how the model inputs impacted the model outputs. When the model is ready for review, Rubicon makes it easy to share specific subsets of the data with model reviewers and stakeholders, giving them the context necessary for a complete model review and approval. ## Use Check out the [interactive notebooks in this Binder](https://mybinder.org/v2/gh/capitalone/rubicon-ml/main?labpath=binder%2Fwelcome.ipynb) to try `rubicon_ml` for yourself. Here's a simple example: ```python from rubicon_ml import Rubicon rubicon = Rubicon( persistence="filesystem", root_dir="/rubicon-root", auto_git_enabled=True ) project = rubicon.create_project( "Hello World", description="Using rubicon to track model results over time." ) experiment = project.log_experiment( training_metadata=[SklearnTrainingMetadata("sklearn.datasets", "my-data-set")], model_name="My Model Name", tags=["my_model_name"], ) experiment.log_parameter("n_estimators", n_estimators) experiment.log_parameter("n_features", n_features) experiment.log_parameter("random_state", random_state) accuracy = rfc.score(X_test, y_test) experiment.log_metric("accuracy", accuracy) ``` Then explore the project by running the dashboard: ``` rubicon_ml ui --root-dir /rubicon-root ``` ## Documentation For a full overview, visit the [docs](https://capitalone.github.io/rubicon-ml/). If you have suggestions or find a bug, [please open an issue](https://github.com/capitalone/rubicon-ml/issues/new/choose). ## Install The Python library is available on Conda Forge via `conda` and PyPi via `pip`. ``` conda config --add channels conda-forge conda install rubicon-ml ``` or ``` pip install rubicon-ml ``` ## Develop The project uses conda to manage environments. First, install [conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html). Then use conda to setup a development environment: ```bash conda env create -f environment.yml conda activate rubicon-ml-dev ``` Finally, install `rubicon_ml` locally into the newly created environment. ```bash pip install -e ".[all]" ``` ## Testing The tests are separated into unit and integration tests. They can be run directly in the activated dev environment via `pytest tests/unit` or `pytest tests/integration`. Or by simply running `pytest` to execute all of them. **Note**: some integration tests are intentionally `marked` to control when they are run (i.e. not during CICD). These tests include: * Integration tests that write to physical filesystems - local and S3. Local files will be written to `./test-rubicon` relative to where the tests are run. An S3 path must also be provided to run these tests. By default, these tests are disabled. To enable them, run: ``` pytest -m "write_files" --s3-path "s3://my-bucket/my-key" ``` * Integration tests that run Jupyter notebooks. These tests are a bit slower than the rest of the tests in the suite as they need to launch Jupyter servers. By default, they are enabled. To disable them, run: ``` pytest -m "not run_notebooks and not write_files" ``` **Note**: When simply running `pytest`, `-m "not write_files"` is the default. So, we need to also apply it when disabling notebook tests. ## Code Formatting Install and configure pre-commit to automatically run `black`, `flake8`, and `isort` during commits: * [install pre-commit](https://pre-commit.com/#installation) * run `pre-commit install` to set up the git hook scripts Now `pre-commit` will run automatically on git commit and will ensure consistent code format throughout the project. You can format without committing via `pre-commit run` or skip these checks with `git commit --no-verify`.

capitalone_repos

capitalone_repos/rubicon-ml/environment.yml

name: rubicon-ml-dev channels: - conda-forge dependencies: - python>=3.8 - pip - click<=8.1.7,>=7.1 - fsspec<=2023.9.2,>=2021.4.0 - intake[dataframe]<=0.7.0,>=0.5.2 - jsonpath-ng<=1.6.0,>=1.5.3 - numpy<=1.26.0,>=1.22.0 - pandas<=2.1.1,>=1.0.0 - pyarrow<=13.0.0,>=0.18.0 - PyYAML<=6.0.1,>=5.4.0 - scikit-learn<=1.3.1,>=0.22.0 # for prefect extras - prefect<=1.2.4,>=0.12.0 # for s3fs extras - s3fs<=2023.9.2,>=0.4 # for viz extras - dash<=2.14.0,>=2.0.0 - dash-bootstrap-components<=1.5.0,>=1.0.0 # for testing - black - dask - flake8 - ipykernel - isort - jupyterlab - lightgbm - nbconvert - pytest - pytest-cov - xgboost # for versioning - versioneer # for packaging - setuptools - wheel # for edgetest - edgetest - edgetest-conda

capitalone_repos

capitalone_repos/rubicon-ml/setup.py

"""Setup file for the package. For configuration information, see the ``setup.cfg``.""" from setuptools import setup import versioneer setup( version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), )

capitalone_repos/rubicon-ml

capitalone_repos/rubicon-ml/notebooks/user-environment.yml

name: rubicon-ml channels: - conda-forge dependencies: - python>=3.8 - pip - jupyterlab - rubicon-ml

capitalone_repos/rubicon-ml

capitalone_repos/rubicon-ml/notebooks/README.md

# rubicon-ml notebooks These notebooks are interactive versions of the examples found in our documentation. You can clone the repo and run the examples on your own, or just take a look at their outputs here! If you're a rubicon-ml user that wants to run the examples, check out the first section, **Users** to get set up. If you're a developer looking to create new examples, take a look at the second section, **Developers**. ## Users To ensure these examples work with your version of rubicon-ml, clone this repository at the tag corresponding to the verison of rubicon-ml you'll be using by replacing `X.X.X` in the command below with that version. ``` git clone https://github.com/capitalone/rubicon-ml.git --branch X.X.X --single-branch ``` Then, create and activate the "rubicon-ml" `conda` environment in the `notebooks` directory. ``` cd rubicon_ml conda env create -f notebooks/user-environment.yml conda activate rubicon-ml ``` The example notebooks can be viewed with either the `jupyter notebook` or `lab` command. ``` jupyter notebook notebooks/ ``` ``` jupyter lab notebooks/ ``` ## Developers When adding examples, make sure to commit any notebooks with their cells executed in order. These example notebooks are rendered as-is within the [documentation](https://capitalone.github.io/rubicon-ml/examples.html). To develop examples off the latest on the `main` branch, use the "rubicon-ml-dev" environment in `environment.yml` at the root of the rubicon-ml repository. ``` conda env create -f environment.yml conda activate rubicon-ml-dev ``` You'll need to install Jupyterlab and a local copy of the library as well. ``` conda install jupyterlab pip install -e .[all] ```

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/tagging.ipynb

from rubicon_ml import Rubicon import pandas as pd rubicon = Rubicon(persistence="memory") project = rubicon.get_or_create_project("Tagging") #logging experiments with tags experiment1 = project.log_experiment(name="experiment1", tags=["odd_num_exp"]) experiment2 = project.log_experiment(name="experiment2", tags=["even_num_exp"]) #logging artifacts, dataframes, features, metrics and parameters with tags first_artifact = experiment1.log_artifact(data_bytes=b"bytes", name="data_path", tags=["data"]) confusion_matrix = pd.DataFrame([[5, 0, 0], [0, 5, 1], [0, 0, 4]], columns=["x", "y", "z"]) first_dataframe = experiment1.log_dataframe(confusion_matrix, tags=["three_column"]) first_feature = experiment1.log_feature("year", tags=["time"]) first_metric = experiment1.log_metric("accuracy", .8, tags=["scalar"]) #can add multiple tags at logging (works for all objects) first_parameter = experiment1.log_parameter("n_estimators", tags=["tag1", "tag2"])print(experiment1.tags) print(experiment2.tags) print(first_artifact.tags) print(first_dataframe.tags) print(first_feature.tags) print(first_metric.tags) print(first_parameter.tags)experiment1.add_tags(["linear regression"]) experiment2.add_tags(["random forrest"]) first_artifact.add_tags(["added_tag"]) first_dataframe.add_tags(["added_tag"]) first_feature.add_tags(["added_tag"]) first_metric.add_tags(["added_tag"]) #can add multiple tags (works for all objects) first_parameter.add_tags(["added_tag1", "added_tag2"]) print(experiment1.tags) print(experiment2.tags) print(first_artifact.tags) print(first_dataframe.tags) print(first_feature.tags) print(first_metric.tags) print(first_parameter.tags)experiment1.remove_tags(["linear regression"]) experiment2.remove_tags(["random forrest"]) first_artifact.remove_tags(["added_tag"]) first_dataframe.remove_tags(["added_tag"]) first_feature.remove_tags(["added_tag"]) first_metric.remove_tags(["added_tag"]) #can remove multiple tags (works for all objects) first_parameter.remove_tags(["added_tag2", "added_tag1"]) print(experiment1.tags) print(experiment2.tags) print(first_artifact.tags) print(first_dataframe.tags) print(first_feature.tags) print(first_metric.tags) print(first_parameter.tags)experiment1.add_tags(["old_exp"]) experiment2.add_tags(["old_exp"]) experiment3 = project.log_experiment(name="experiment3", tags=["odd_num_exp","new_exp"]) #want just old experiments old_experiments = project.experiments(tags=["old_exp"]) #want just new experiments new_experiments = project.experiments(tags=["new_exp"]) #want just the odd number experiments odd_experiments = project.experiments(tags=["odd_num_exp"]) #this will return the same result as above since qtype="or" by default same_experiments = project.experiments(tags=["odd_num_exp", "new_exp"]) #this will return just experiment3 expected_experiment = project.experiments(tags=["odd_num_exp", "new_exp"], qtype="and") #getting both the old experiments 1 and 2 print("old experiments: " + str(old_experiments[0].name) + ", " + str(old_experiments[1].name) + "\n") #getting just the new experiment 3 print("new experiments: " + str(new_experiments[0].name) + "\n") #getting both odd experiments 1 and 3 print("odd experiments: " + str(odd_experiments[0].name) + ", " + str(odd_experiments[1].name) + "\n") #again getting both experiments 1 and 3 print("same experiments: " + str(same_experiments[0].name) + ", " + str(same_experiments[1].name) + "\n") #getting just experiment 3 print("expected experiment: " + str(expected_experiment[0].name) + "\n")

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/logging-plots.ipynb

import os from rubicon_ml import Rubicon rubicon = Rubicon(persistence="memory") project = rubicon.get_or_create_project("Artifact Plots")import plotly.express as px from plotly import data df = data.wind() df.head()scatter_plot = px.scatter(df, x="direction", y="frequency", color="strength") scatter_plot.write_image("saved-scatter-plot-for-logging.png")bar_plot = px.bar(df, x="direction", y="frequency", color="strength") bar_plot_bytes = bar_plot.to_image(format="png")project.log_artifact( name="scatter plot", data_path="saved-scatter-plot-for-logging.png", description="saved scatter plot with path", ) artifact_plot_from_file = project.artifact(name="scatter plot")project.log_artifact(name="bar plot", data_bytes=bar_plot_bytes) artifact_plot_bytes = project.artifact(name="bar plot")import io from PIL import Image imageScatterPlotStream = io.BytesIO(artifact_plot_from_file.data) scatter_plot_image = Image.open(imageScatterPlotStream) imageBarPlotStream = io.BytesIO(artifact_plot_bytes.data) bar_plot_image = Image.open(imageBarPlotStream)display(scatter_plot_image)display(bar_plot_image)

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/set-schema.ipynb

from rubicon_ml.schema import registry available_schema = registry.available_schema() available_schemaimport pprint rfc_schema = registry.get_schema("sklearn__RandomForestClassifier") pprint.pprint(rfc_schema)from rubicon_ml import Rubicon rubicon = Rubicon(persistence="memory") project = rubicon.create_project(name="apply schema") projectproject.set_schema(rfc_schema)

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/multiple-backend.ipynb

from rubicon_ml import Rubicon#rb = Rubicon(persistence="memory") #or #rb = Rubicon(persistence="filesystem")#example multiple backend instantiaiton rb = Rubicon(composite_config=[ {"persistence": "filesystem", "root_dir": "./rubicon-root/rootA"}, {"persistence": "filesystem", "root_dir": "./rubicon-root/rootB"}, ])project = rb.create_project("example_project") experiment = project.log_experiment("example_experiment") artifact = experiment.log_artifact(data_bytes=b"bytes", name="example_artifact") import pandas as pd dataframe = experiment.log_dataframe(pd.DataFrame([[5, 0, 0], [0, 5, 1], [0, 0, 4]], columns=["x", "y", "z"])) feature = experiment.log_feature("year") metric = experiment.log_metric("accuracy", .8) parameter = experiment.log_parameter("n_estimators")projects = rb.projects() print("projects: " + str(projects)) print("\n") experiments = project.experiments() print("experiments: " + str(experiments)) print("\n") artifacts = experiment.artifacts() print("artifacts: " + str(artifacts)) print("\n") dataframes = experiment.dataframes() print("dataframes: " + str(dataframes)) print("\n") features = experiment.features() print("features: " + str(features)) print("\n") metrics = experiment.metrics() print("metrics: " + str(metrics)) print("\n") parameters = experiment.parameters() print("parameters: " + str(parameters)) print("\n")rm -rf rubicon-root/rootArm -rf rubicon-root/rootB

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/logging_concurrently.py

from collections import namedtuple SklearnTrainingMetadata = namedtuple("SklearnTrainingMetadata", "module_name method") def run_experiment(project, classifier_cls, wine_datasets, feature_names, **kwargs): X_train, X_test, y_train, y_test = wine_datasets experiment = project.log_experiment( training_metadata=[ SklearnTrainingMetadata("sklearn.datasets", "load_wine"), ], model_name=classifier_cls.__name__, tags=[classifier_cls.__name__], ) for key, value in kwargs.items(): experiment.log_parameter(key, value) for name in feature_names: experiment.log_feature(name) classifier = classifier_cls(**kwargs) classifier.fit(X_train, y_train) classifier.predict(X_test) accuracy = classifier.score(X_test, y_test) experiment.log_metric("accuracy", accuracy) if accuracy >= 0.95: experiment.add_tags(["success"]) else: experiment.add_tags(["failure"])

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/visualizing-logged-dataframes.ipynb

import os from rubicon_ml import Rubicon rubicon = Rubicon(persistence="memory") project = rubicon.get_or_create_project("Plotting Example") projectimport pandas as pd df = pd.DataFrame.from_records( [ ["Walmart", 514405], ["Exxon Mobil", 290212], ["Apple", 265595], ["Berkshire Hathaway", 247837], ["Amazon.com", 232887] ], columns=["Company", "Revenue (in millions)"] ) experiment = project.log_experiment() dataframe = experiment.log_dataframe(df, name="sample revenue df") dataframerevenue_line = dataframe.plot(x="Company", y="Revenue (in millions)") revenue_lineimport plotly.express as px revenue_scatter = dataframe.plot( plotting_func=px.scatter, x="Company", y="Revenue (in millions)", ) revenue_scatterfrom rubicon_ml.viz import DataframePlot DataframePlot( experiments=[experiment], dataframe_name="sample revenue df", ).show()

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/logging-concurrently.ipynb

import os from rubicon_ml import Rubicon root_dir = os.environ.get("RUBICON_ROOT", "rubicon-root") root_path = f"{os.path.dirname(os.getcwd())}/{root_dir}" rubicon = Rubicon(persistence="filesystem", root_dir=root_path) project = rubicon.get_or_create_project( "Concurrent Experiments", description="training multiple models in parallel", ) projectfrom sklearn.datasets import load_wine from sklearn.model_selection import train_test_split wine = load_wine() wine_feature_names = wine.feature_names wine_datasets = train_test_split( wine["data"], wine["target"], test_size=0.25, )from collections import namedtuple SklearnTrainingMetadata = namedtuple("SklearnTrainingMetadata", "module_name method") def run_experiment(project, classifier_cls, wine_datasets, feature_names, **kwargs): X_train, X_test, y_train, y_test = wine_datasets experiment = project.log_experiment( training_metadata=[ SklearnTrainingMetadata("sklearn.datasets", "load_wine"), ], model_name=classifier_cls.__name__, tags=[classifier_cls.__name__], ) for key, value in kwargs.items(): experiment.log_parameter(key, value) for name in feature_names: experiment.log_feature(name) classifier = classifier_cls(**kwargs) classifier.fit(X_train, y_train) classifier.predict(X_test) accuracy = classifier.score(X_test, y_test) experiment.log_metric("accuracy", accuracy) if accuracy >= .95: experiment.add_tags(["success"]) else: experiment.add_tags(["failure"])from logging_concurrently import run_experimentimport multiprocessing from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier processes = [] for n_estimators in [10, 20, 30, 40]: processes.append(multiprocessing.Process( target=run_experiment, args=[project, RandomForestClassifier, wine_datasets, wine_feature_names], kwargs={"n_estimators": n_estimators}, )) for n_neighbors in [5, 10, 15, 20]: processes.append(multiprocessing.Process( target=run_experiment, args=[project, KNeighborsClassifier, wine_datasets, wine_feature_names], kwargs={"n_neighbors": n_neighbors}, )) for criterion in ["gini", "entropy"]: for splitter in ["best", "random"]: processes.append(multiprocessing.Process( target=run_experiment, args=[project, DecisionTreeClassifier, wine_datasets, wine_feature_names], kwargs={ "criterion": criterion, "splitter": splitter, }, ))for process in processes: process.start() for process in processes: process.join()len(project.experiments())for e in project.experiments(tags=["success"]): print(f"experiment {e.id[:8]} was successful using a {e.model_name}")first_experiment = project.experiments()[0] training_metadata = SklearnTrainingMetadata(*first_experiment.training_metadata) tags = first_experiment.tags parameters = [(p.name, p.value) for p in first_experiment.parameters()] metrics = [(m.name, m.value) for m in first_experiment.metrics()] print( f"experiment {first_experiment.id}\n" f"training metadata: {training_metadata}\ntags: {tags}\n" f"parameters: {parameters}\nmetrics: {metrics}" )ddf = rubicon.get_project_as_df("Concurrent Experiments", df_type="dask") ddf.compute()

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/logging-training-metadata.ipynb

s3_config = { "region_name": "us-west-2", "signature_version": "v4", "retries": { "max_attempts": 10, "mode": "standard", } } bucket_name = "my-bucket" key = "path/to/my/data.parquet"def read_from_s3(config, bucket, key, local_output_path): import boto3 from botocore.config import Config config = Config(**config) # assuming credentials are correct in `~/.aws` or set in environment variables client = boto3.client("s3", config=config) with open(local_output_path, "wb") as f: s3.download_fileobj(bucket, key, f)def read_from_s3(config, bucket, key, local_output_path): return Nonefrom rubicon_ml import Rubicon rubicon = Rubicon(persistence="memory") project = rubicon.get_or_create_project("Storing Training Metadata") projecttraining_metadata = (s3_config, bucket_name, key) experiment = project.log_experiment( training_metadata=training_metadata, tags=["S3", "training metadata"] ) # then run the experiment and log everything to rubicon! experiment.training_metadataexperiment = project.experiments(tags=["S3", "training metadata"], qtype="and")[0] training_metadata = experiment.training_metadata read_from_s3( training_metadata[0], training_metadata[1], training_metadata[2], "./local_output.parquet", )training_metadata = [ (s3_config, bucket_name, "path/to/my/data_0.parquet"), (s3_config, bucket_name, "path/to/my/data_1.parquet"), (s3_config, bucket_name, "path/to/my/data_2.parquet"), ] experiment = project.log_experiment(training_metadata=training_metadata) experiment.training_metadatatraining_metadata = ( s3_config, bucket_name, [ "path/to/my/data_0.parquet", "path/to/my/data_1.parquet", "path/to/my/data_2.parquet", ], ) experiment = project.log_experiment(training_metadata=training_metadata) experiment.training_metadatafrom collections import namedtuple S3TrainingMetadata = namedtuple("S3TrainingMetadata", "config bucket keys") training_metadata = S3TrainingMetadata( s3_config, bucket_name, [ "path/to/my/data_0.parquet", "path/to/my/data_1.parquet", "path/to/my/data_2.parquet", ], ) experiment = project.log_experiment(training_metadata=training_metadata) experiment.training_metadataS3Config = namedtuple("S3Config", "region_name signature_version retries") S3DatasetMetadata = namedtuple("S3DatasetMetadata", "bucket key") project = rubicon.get_or_create_project( "S3 Training Metadata", training_metadata=S3Config(**s3_config), ) for key in [ "path/to/my/data_0.parquet", "path/to/my/data_1.parquet", "path/to/my/data_2.parquet", ]: experiment = project.log_experiment( training_metadata=S3DatasetMetadata(bucket=bucket_name, key=key) ) # then run the experiment and log everything to rubicon!project = rubicon.get_project("S3 Training Metadata") s3_config = S3Config(*project.training_metadata) print(s3_config) for experiment in project.experiments(): s3_dataset_metadata = S3DatasetMetadata(*experiment.training_metadata) print(s3_dataset_metadata) training_data = read_from_s3( s3_config._asdict(), s3_dataset_metadata.bucket, s3_dataset_metadata.key, "./local_output.parquet" )

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/rubiconJSON-querying.ipynb

from rubicon_ml import Rubicon from sklearn.datasets import load_wine from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, make_scorer, precision_score, recall_score from sklearn.model_selection import ParameterGrid, train_test_splitX, y = load_wine(return_X_y=True, as_frame=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=0)rubicon = Rubicon(persistence="memory", auto_git_enabled=True) project = rubicon.get_or_create_project(name="jsonpath querying")for parameters in ParameterGrid({ "n_estimators": [5, 50, 500], "min_samples_leaf": [1, 10, 100], }): rfc = RandomForestClassifier(random_state=0, **parameters) tags = ["large"] if parameters["n_estimators"] > 10 else [] experiment = project.log_experiment(model_name=rfc.__class__.__name__, tags=tags) for name, value in parameters.items(): experiment.log_parameter(name=name, value=value) for name in X_train.columns: experiment.log_feature(name=name) rfc.fit(X_train, y_train) precision_scorer = make_scorer(precision_score, average="weighted", zero_division=0.0) precision = precision_scorer(rfc, X_test, y_test) recall_scorer = make_scorer(recall_score, average="weighted") recall = recall_scorer(rfc, X_test, y_test) experiment.log_metric(name="precision", value=precision) experiment.log_metric(name="recall", value=recall) experiment.log_artifact(data_object=rfc, name=rfc.__class__.__name__, tags=["trained"])from rubicon_ml import RubiconJSON rubicon_json = RubiconJSON(experiments=project.experiments()) rubicon_json.json["experiment"][0]experiment_query = "$..experiment[?(@.tags[*]=='large')]" for match in rubicon_json.search(experiment_query): print(match.value)experiment_query += ".id" for match in rubicon_json.search(experiment_query): print(match.value)metric_query = "$..experiment[*].metric" for match in rubicon_json.search(metric_query): print(match.value)best_metric_query = "$..experiment[*].metric[?(@.name=='precision' & @.value>=0.96)]" for match in rubicon_json.search(best_metric_query): print(match.value)best_experiment_query = "$..experiment[?(@.metric[?(@.name=='precision' & @.value>=0.96)])].id" for match in rubicon_json.search(best_experiment_query): print(match.value)for match in rubicon_json.search(best_experiment_query): experiment = project.experiment(id=match.value) print(experiment.artifact(name="RandomForestClassifier").get_data(unpickle=True))

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/logging-feature-plots.ipynb

import shap import sklearn from sklearn.datasets import load_wine from sklearn.ensemble import GradientBoostingRegressor from sklearn.preprocessing import StandardScaler from rubicon_ml import Rubicon from rubicon_ml.sklearn import make_pipeline rubicon = Rubicon(persistence="memory") project = rubicon.get_or_create_project("Logging Feature Plots") X, y = load_wine(return_X_y=True) reg = GradientBoostingRegressor(random_state=1) pipeline = make_pipeline(project, reg) pipeline.fit(X, y)explainer = shap.Explainer(pipeline[0]) shap_values = explainer.shap_values(X)import io import matplotlib.pyplot as pl experiment = pipeline.experiment for i in range(reg.n_features_in_): feature_name = f"feature {i}" experiment.log_feature(name=feature_name, tags=[feature_name]) shap.dependence_plot(i, shap_values, X, interaction_index=None, show=False) fig = pl.gcf() buf = io.BytesIO() fig.savefig(buf, format="png") buf.seek(0) experiment.log_artifact( data_bytes=buf.read(), name=feature_name, tags=[feature_name], ) buf.close()import io from PIL import Image experiment = pipeline.experiment for feature in experiment.features(): artifact = experiment.artifact(name=feature.name) buf = io.BytesIO(artifact.data) scatter_plot_image = Image.open(buf) print(feature.name) display(scatter_plot_image)

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/register-custom-schema.ipynb

import os os.environ["RUNTIME_ENV"] = "AWS" ! echo $RUNTIME_ENVimport pprint extended_schema = { "name": "sklearn__RandomForestClassifier__ext", "extends": "sklearn__RandomForestClassifier", "parameters": [ {"name": "runtime_environment", "value_env": "RUNTIME_ENV"}, ], } pprint.pprint(extended_schema)from rubicon_ml import Rubicon rubicon = Rubicon(persistence="memory", auto_git_enabled=True) project = rubicon.create_project(name="apply schema") projectproject.set_schema(extended_schema)from sklearn.datasets import load_wine X, y = load_wine(return_X_y=True, as_frame=True)from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier( ccp_alpha=5e-3, criterion="log_loss", max_features="log2", n_estimators=24, oob_score=True, random_state=121, ) rfc.fit(X, y) print(rfc)experiment = project.log_with_schema( rfc, experiment_kwargs={ "name": "log with extended schema", "model_name": "RandomForestClassifier", "description": "logged with an extended `rubicon_schema`", }, ) experimentfor parameter in experiment.parameters(): print(f"{parameter.name}: {parameter.value}")del os.environ["RUNTIME_ENV"]

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/log-with-schema.ipynb

from rubicon_ml import Rubicon rubicon = Rubicon(persistence="memory", auto_git_enabled=True) project = rubicon.create_project(name="apply schema") projectfrom sklearn.datasets import load_wine X, y = load_wine(return_X_y=True, as_frame=True)from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier( ccp_alpha=5e-3, criterion="log_loss", max_features="log2", n_estimators=24, oob_score=True, random_state=121, ) rfc.fit(X, y) print(rfc)experiment = project.log_with_schema( rfc, experiment_kwargs={ # additional kwargs to be passed to `project.log_experiment` "name": "log with schema", "model_name": "RandomForestClassifier", "description": "logged with the `RandomForestClassifier` `rubicon_schema`", }, ) print(f"inferred schema name: {project.schema_['name']}") experimentvars(experiment._domain)project.schema_["features"]for feature in experiment.features(): print(f"{feature.name} ({feature.importance})")project.schema_["parameters"]for parameter in experiment.parameters(): print(f"{parameter.name}: {parameter.value}")project.schema_["metrics"]import numpy as np for metric in experiment.metrics(): if np.isscalar(metric.value): print(f"{metric.name}: {metric.value}") else: # don't print long metrics print(f"{metric.name}: ...")project.schema_["artifacts"]for artifact in experiment.artifacts(): print(f"{artifact.name}:\n{artifact.get_data(unpickle=True)}")

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/logging-examples/logging-experiment-failures.ipynb

from sklearn.base import BaseEstimator import random class BadEstimator(BaseEstimator): def __init__(self): super().__init__() self.knn = KNeighborsClassifier(n_neighbors=2) def fit(self, X, y): self.knn.fit(X, y) output=random.random() if output>.3: self.state_=output def score(self, X): knn_score = self.knn.score(X) return knn_scorefrom rubicon_ml.sklearn import make_pipeline from sklearn.neighbors import KNeighborsClassifier from sklearn.impute import SimpleImputer from rubicon_ml import Rubicon random.seed(17) rubicon = Rubicon( persistence="memory", ) project = rubicon.get_or_create_project(name="Failed Experiments")X = [[1], [1], [1], [1]] y = [1, 1, 1, 1] for _ in range(20): pipe=make_pipeline(project, SimpleImputer(strategy="mean"),BadEstimator()) pipe.fit(X,y) if not hasattr(pipe["badestimator"],"state_"): pipe.experiment.add_tags(["failed"]) else: pipe.experiment.add_tags(["passed"])for exp in project.experiments(tags=["failed"]): print(exp)len(project.experiments(tags=["failed"]))len(project.experiments(tags=["passed"]))

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/quick-look/visualizing-experiments.ipynb

import intake import rubicon_ml catalog = intake.open_catalog("./penguin_catalog.yml") for source in catalog: catalog[source].discover() experiments = [catalog[source].read() for source in catalog]from rubicon_ml.viz import ExperimentsTable ExperimentsTable(experiments=experiments).show()from rubicon_ml.viz import Dashboard Dashboard(experiments=experiments).serve(run_server_kwargs={"port": 8051})

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/quick-look/logging-experiments.ipynb

from palmerpenguins import load_penguins penguins_df = load_penguins() target_values = penguins_df['species'].unique() print(f"target classes (species): {target_values}") penguins_df.head()from sklearn.preprocessing import LabelEncoder for column in ["species", "island", "sex"]: penguins_df[column] = LabelEncoder().fit_transform(penguins_df[column]) print(f"target classes (species): {penguins_df['species'].unique()}") penguins_df.head()from sklearn.model_selection import train_test_split train_penguins_df, test_penguins_df = train_test_split(penguins_df, test_size=.30) target_name = "species" feature_names = [c for c in train_penguins_df.columns if c != target_name] X_train, y_train = train_penguins_df[feature_names], train_penguins_df[target_name] X_test, y_test = test_penguins_df[feature_names], test_penguins_df[target_name] X_train.shape, y_train.shape, X_test.shape, y_test.shapefrom sklearn.impute import SimpleImputer from sklearn.neighbors import KNeighborsClassifier from sklearn.pipeline import Pipeline imputer_strategy = "mean" classifier_n_neighbors = 5 steps = [ ("si", SimpleImputer(strategy=imputer_strategy)), ("kn", KNeighborsClassifier(n_neighbors=classifier_n_neighbors)), ] penguin_pipeline = Pipeline(steps=steps) penguin_pipeline.fit(X_train, y_train) score = penguin_pipeline.score(X_test, y_test) scorefrom rubicon_ml import Rubicon rubicon = Rubicon( persistence="filesystem", root_dir="./rubicon-root", auto_git_enabled=True, ) project = rubicon.get_or_create_project(name="classifying penguins") experiment = project.log_experiment() for feature_name in feature_names: experiment.log_feature(name=feature_name) _ = experiment.log_parameter(name="strategy", value=imputer_strategy) _ = experiment.log_parameter(name="n_neighbors", value=classifier_n_neighbors) _ = experiment.log_metric(name="accuracy", value=score)print(experiment) print() print(f"git info:") print(f"\tbranch name: {experiment.branch_name}\n\tcommit hash: {experiment.commit_hash}") print(f"features: {[f.name for f in experiment.features()]}") print(f"parameters: {[(p.name, p.value) for p in experiment.parameters()]}") print(f"metrics: {[(m.name, m.value) for m in experiment.metrics()]}")from sklearn.base import clone for imputer_strategy in ["mean", "median", "most_frequent"]: for classifier_n_neighbors in [5, 10, 15, 20]: pipeline = clone(penguin_pipeline) pipeline.set_params( si__strategy=imputer_strategy, kn__n_neighbors=classifier_n_neighbors, ) pipeline.fit(X_train, y_train) score = pipeline.score(X_test, y_test) experiment = project.log_experiment(tags=["parameter search"]) for feature_name in feature_names: experiment.log_feature(name=feature_name) experiment.log_parameter(name="strategy", value=imputer_strategy) experiment.log_parameter(name="n_neighbors", value=classifier_n_neighbors) experiment.log_metric(name="accuracy", value=score)print("experiments:") for experiment in project.experiments(tags=["parameter search"]): print( f"\tid: {experiment.id}, " f"parameters: {[(p.name, p.value) for p in experiment.parameters()]}, " f"metrics: {[(m.name, m.value) for m in experiment.metrics()]}" )import pandas as pd from sklearn.metrics import confusion_matrix experiment = project.experiments(tags=["parameter search"])[-1] trained_model = pipeline._final_estimator experiment.log_artifact(data_object=trained_model, name="trained model") y_pred = pipeline.predict(X_test) confusion_matrix_df = pd.DataFrame( confusion_matrix(y_test, y_pred), columns=target_values, index=target_values, ) experiment.log_dataframe(confusion_matrix_df, name="confusion matrix") print(experiment.artifact(name="trained model").get_data(unpickle=True)) experiment.dataframe(name="confusion matrix").get_data()

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/quick-look/sharing-experiments.ipynb

from rubicon_ml import Rubicon rubicon = Rubicon(persistence="filesystem", root_dir="./rubicon-root") project = rubicon.get_project(name="classifying penguins") projectfrom rubicon_ml import publish catalog = publish( project.experiments(tags=["parameter search"]), output_filepath="./penguin_catalog.yml", ) !head -7 penguin_catalog.ymlimport intake catalog = intake.open_catalog("./penguin_catalog.yml") for source in catalog: catalog[source].discover() shared_experiments = [catalog[source].read() for source in catalog] print("shared experiments:") for experiment in shared_experiments: print( f"\tid: {experiment.id}, " f"parameters: {[(p.name, p.value) for p in experiment.parameters()]}, " f"metrics: {[(m.name, m.value) for m in experiment.metrics()]}" )new_project = rubicon.get_or_create_project(name="update catalog example") new_experiments = [new_project.log_experiment() for _ in range(2)] updated_catalog = publish( base_catalog_filepath="./penguin_catalog.yml", experiments = new_experiments, ) !head -7 penguin_catalog.yml

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/demos/classification.ipynb

from palmerpenguins import load_penguins penguins_df = load_penguins() penguins_df.head()import plotly.express as px px.scatter(penguins_df, x="flipper_length_mm", y="bill_length_mm", color="species")from sklearn.preprocessing import LabelEncoder penguin_encoder = LabelEncoder() for column in ["species", "island", "sex"]: penguins_df[column] = penguin_encoder.fit_transform(penguins_df[column]) penguins_df.head()from sklearn.model_selection import train_test_split train_penguins_df, test_penguins_df = train_test_split(penguins_df, test_size=.30) target_column = "species" feature_columns = [c for c in train_penguins_df.columns if c != target_column] X_train, y_train = train_penguins_df[feature_columns], train_penguins_df[target_column] X_test, y_test = test_penguins_df[feature_columns], test_penguins_df[target_column] X_train.shape, y_train.shape, X_test.shape, y_test.shapefrom sklearn.impute import SimpleImputer from sklearn.neighbors import KNeighborsClassifier from sklearn.pipeline import Pipeline steps = [ ("si", SimpleImputer(strategy="mean")), ("kn", KNeighborsClassifier(n_neighbors=5)), ] penguin_pipeline = Pipeline(steps=steps) penguin_pipeline.fit(X_train, y_train) score = penguin_pipeline.score(X_test, y_test) scorefrom rubicon_ml import Rubicon rubicon = Rubicon( persistence="filesystem", root_dir="./rubicon-root", auto_git_enabled=True, ) project = rubicon.get_or_create_project(name="demo") experiment = project.log_experiment(name="classifying penguins") parameter_strategy = experiment.log_parameter(name="strategy", value="mean") parameter_n_neighbors = experiment.log_parameter(name="n_neighbors", value=5) metric_accuracy = experiment.log_metric(name="accuracy", value=score)print(experiment) print(experiment.branch_name, experiment.commit_hash) print() print([(p.name, p.value) for p in experiment.parameters()]) print([(m.name, m.value) for m in experiment.metrics()])from rubicon_ml.sklearn import RubiconPipeline rubicon_penguin_pipeline = RubiconPipeline( project=project, experiment_kwargs={"name": "KNeighborsClassifier", "tags": ["knn"]}, steps=steps, ) rubicon_penguin_pipeline.fit(X_train, y_train) pipeline_experiment = rubicon_penguin_pipeline.experiment rubicon_penguin_pipeline.score(X_test, y_test)print(pipeline_experiment) print() print([(p.name, p.value) for p in pipeline_experiment.parameters()]) print([(m.name, m.value) for m in pipeline_experiment.metrics()])from sklearn.model_selection import GridSearchCV parameters = { "si__strategy": ["mean", "median", "most_frequent"], "kn__n_neighbors": [2, 4, 8, 16, 32, 64], } grid_search_project = rubicon.get_or_create_project(name="grid search demo") rubicon_penguin_pipeline.project = grid_search_project grid_search = GridSearchCV( rubicon_penguin_pipeline, cv=2, param_grid=parameters, refit=False, verbose=True, ) grid_search.fit(X_train, y_train) grid_search_project.experiments()from rubicon_ml.viz import ExperimentsTable ExperimentsTable(experiments=grid_search_project.experiments()).show()import intake catalog = intake.open_catalog("./rubicon-ml-catalog.yml") for source in catalog: catalog[source].discover() shared_experiments = [catalog[source].read() for source in catalog] [(e.id, e.metric(name="score").value) for e in shared_experiments]from sklearn.ensemble import RandomForestClassifier new_steps = [ ("si", SimpleImputer(strategy="mean")), ("rf", RandomForestClassifier(n_estimators=100)), ] new_rubicon = Rubicon( persistence="filesystem", root_dir="./new-rubicon-root", auto_git_enabled=True, ) new_project = new_rubicon.get_or_create_project(name="demo") new_pipeline = RubiconPipeline( project=new_project, experiment_kwargs={"name": "RandomForestClassifier", "tags": ["rf"]}, steps=new_steps, ) new_parameters = { "si__strategy": ["mean", "median", "most_frequent"], "rf__n_estimators": [25, 50, 100, 200, 400], } new_grid_search = GridSearchCV( new_pipeline, cv=2, param_grid=new_parameters, refit=False, verbose=True, ) new_grid_search.fit(X_train, y_train)from rubicon_ml.viz import Dashboard, MetricCorrelationPlot Dashboard( experiments=new_project.experiments(), widgets=[ [ExperimentsTable()], [MetricCorrelationPlot(parameter_names=["si__strategy", "rf__n_estimators"])], ], ).serve()from rubicon_ml import publish combined_catalog = publish( shared_experiments + new_project.experiments(), "./combined-catalog.yml", )from sklearn.base import BaseEstimator class ComboEstimator(BaseEstimator): def __init__(self, n_neighbors=2, n_estimators=25): super().__init__() self.n_neighbors = n_neighbors self.n_estimators = n_estimators self.knn = KNeighborsClassifier(n_neighbors=n_neighbors) self.rf = RandomForestClassifier(n_estimators=n_estimators) def fit(self, X, y): self.knn.fit(X, y) self.rf.fit(X, y) def score(self, X): knn_score = self.knn.score(X) rf_score = self.rf.score(X) return (knn_score + (rf_score * 2)) / 3import pickle from rubicon_ml.sklearn.estimator_logger import EstimatorLogger class ModelLogger(EstimatorLogger): def log_parameters(self): super().log_parameters() self.experiment.log_artifact(data_bytes=pickle.dumps(self.estimator.knn), name="knn") self.experiment.log_artifact(data_bytes=pickle.dumps(self.estimator.rf), name="rf")from rubicon_ml.sklearn import make_pipeline another_pipeline = make_pipeline( new_project, SimpleImputer(strategy="mean"), (ComboEstimator(n_neighbors=16, n_estimators=100), ModelLogger()), ) another_pipeline.fit(X_train, y_train) [(a.name, a) for a in another_pipeline.experiment.artifacts()]for artifact in another_pipeline.experiment.artifacts(): print(pickle.loads(artifact.data))

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/integrations/integration-prefect-workflows.ipynb

from rubicon_ml.workflow.prefect import ( get_or_create_project_task, create_experiment_task, log_artifact_task, log_dataframe_task, log_feature_task, log_metric_task, log_parameter_task, )from prefect import task @task def load_data(): from sklearn.datasets import load_wine return load_wine()@task def split_data(dataset): from sklearn.model_selection import train_test_split return train_test_split( dataset.data, dataset.target, test_size=0.25, )@task def get_feature_names(dataset): return dataset.feature_names@task def fit_pred_model( train_test_split_result, n_components, n_neighbors, is_standardized ): from sklearn import metrics from sklearn.decomposition import PCA from sklearn.neighbors import KNeighborsClassifier from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler X_train, X_test, y_train, y_test = train_test_split_result if is_standardized: classifier = make_pipeline( StandardScaler(), PCA(n_components=n_components), KNeighborsClassifier(n_neighbors=n_neighbors), ) else: classifier = make_pipeline( PCA(n_components=n_components), KNeighborsClassifier(n_neighbors=n_neighbors), ) classifier.fit(X_train, y_train) pred_test = classifier.predict(X_test) accuracy = metrics.accuracy_score(y_test, pred_test) return accuracyfrom prefect import Flow n_components = 2 n_neighbors = 5 is_standardized = True with Flow("Wine Classification") as flow: wine_dataset = load_data() feature_names = get_feature_names(wine_dataset) train_test_split = split_data(wine_dataset) accuracy = fit_pred_model( train_test_split, n_components, n_neighbors, is_standardized, )flow_id = flow.register(project_name="Wine Classification")import time from prefect import Client prefect_client = Client() def run_flow(client, flow_id): flow_run_id = client.create_flow_run(flow_id=flow_id) is_finished = False while not is_finished: state = client.get_flow_run_info(flow_run_id).state print(f"{state.message.strip('.')}. Waiting...") time.sleep(3) is_finished = state.is_finished() assert state.is_successful() print(f"Flow run {flow_run_id} was successful!") return flow_run_id flow_run_id = run_flow(prefect_client, flow_id)info = prefect_client.get_flow_run_info(flow_run_id) slugs = [t.task_slug for t in info.task_runs] index = slugs.index(accuracy.slug) result = info.task_runs[index].state._result.read( info.task_runs[index].state._result.location, ) result.valueimport os from prefect import unmapped root_dir = os.environ.get("RUBICON_ROOT", "rubicon-root") root_path = f"{os.path.dirname(os.getcwd())}/{root_dir}" n_components = 2 n_neighbors = 5 is_standardized = True with Flow("Wine Classification with Rubicon") as flow: project = get_or_create_project_task( "filesystem", root_path, "Wine Classification with Prefect", ) experiment = create_experiment_task( project, name="logged from a Prefect task", ) wine_dataset = load_data() feature_names = get_feature_names(wine_dataset) train_test_split = split_data(wine_dataset) log_feature_task.map(unmapped(experiment), feature_names) log_parameter_task(experiment, "n_components", n_components) log_parameter_task(experiment, "n_neighbors", n_neighbors) log_parameter_task(experiment, "is_standardized", is_standardized) accuracy = fit_pred_model( train_test_split, n_components, n_neighbors, is_standardized, ) log_metric_task(experiment, "accuracy", accuracy)flow_with_rubicon_id = flow.register(project_name="Wine Classification") flow_run_with_rubicon_id = run_flow(prefect_client, flow_with_rubicon_id)from rubicon_ml import Rubicon rubicon = Rubicon(persistence="filesystem", root_dir=root_path) project = rubicon.get_project("Wine Classification with Prefect") experiment = project.experiments()[0] features = [f.name for f in experiment.features()] parameters = [(p.name, p.value) for p in experiment.parameters()] metrics = [(m.name, m.value) for m in experiment.metrics()] print( f"experiment {experiment.id}\n" f"features: {features}\nparameters: {parameters}\n" f"metrics: {metrics}" )import dask.distributed from prefect.executors import DaskExecutor dask_client = dask.distributed.Client() dask_executor = DaskExecutor(address=dask_client.cluster.scheduler.address)@task def log_feature_set(experiment, feature_names): """log a set of features to a rubicon experiment Parameters ---------- experiment : rubicon.Experiment the experiment to log the feature set to feature_names : list of str the names of the features to log to `experiment` """ features = [] for name in feature_names: features.append(experiment.log_feature(name=name)) return featuresn_components = [2, 2, 2, 2, 4, 4, 4, 4 ] n_neighbors = [5, 5, 10, 10, 5, 5, 10, 10 ] is_standardized = [True, False, True, False, True, False, True, False] experiment_names = [f"mapped run {i}" for i in range(len(n_components))] with Flow( "Wine Classification with Rubicon - Mapped", executor=dask_executor, ) as mapped_flow: project = get_or_create_project_task( "filesystem", root_path, "Wine Classification with Prefect - Mapped", ) experiments = create_experiment_task.map( unmapped(project), name=experiment_names, description=unmapped("concurrent example with Prefect"), ) wine_dataset = load_data() feature_names = get_feature_names(wine_dataset) train_test_split = split_data(wine_dataset) log_feature_set.map(experiments, unmapped(feature_names)) log_parameter_task.map(experiments, unmapped("n_components"), n_components) log_parameter_task.map(experiments, unmapped("n_neighbors"), n_neighbors) log_parameter_task.map(experiments, unmapped("is_standardized"), is_standardized) accuracies = fit_pred_model.map( unmapped(train_test_split), n_components, n_neighbors, is_standardized, ) log_metric_task.map(experiments, unmapped("accuracy"), accuracies)flow_with_concurrent_rubicon_id = mapped_flow.register( project_name="Wine Classification", ) flow_run_with_concurrent_rubicon_id = run_flow( prefect_client, flow_with_concurrent_rubicon_id, )from rubicon_ml.viz import Dashboard project = get_project( "filesystem", root_path, "Wine Classification with Prefect - Mapped", ) Dashboard(project.experiments()).show()

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/integrations/integration-git.ipynb

from rubicon_ml import Rubicon rubicon = Rubicon(persistence="memory", auto_git_enabled=True)project = rubicon.create_project("Automatic Git Integration") project.github_urlexperiment = project.log_experiment(model_name="GitHub Model") experiment.branch_name, experiment.commit_hashfrom rubicon_ml.viz import Dashboard experiment.log_parameter(name="input", value=True) experiment.log_metric(name="output", value=1.0) Dashboard([experiment]).show()

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/integrations/integration-sklearn.ipynb

from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from rubicon_ml import Rubicon from rubicon_ml.sklearn import RubiconPipeline rubicon = Rubicon(persistence="memory") project = rubicon.get_or_create_project("Rubicon Pipeline Example") X, y = make_classification(random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) pipe = RubiconPipeline( project, [('scaler', StandardScaler()), ('svc', SVC())], ) print(pipe)pipe.fit(X_train, y_train) pipe.score(X_test, y_test)for experiment in project.experiments(): parameters = [(p.name, p.value) for p in experiment.parameters()] metrics = [(m.name, m.value) for m in experiment.metrics()] print( f"experiment {experiment.id}\n" f"parameters: {parameters}\nmetrics: {metrics}" )from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.linear_model import SGDClassifier from sklearn.model_selection import GridSearchCV categories = ["alt.atheism", "talk.religion.misc"] data = fetch_20newsgroups(subset='train', categories=categories)import os from rubicon_ml import Rubicon from rubicon_ml.sklearn import FilterEstimatorLogger, RubiconPipeline root_dir = os.environ.get("RUBICON_ROOT", "rubicon-root") root_path = f"{os.path.dirname(os.getcwd())}/{root_dir}" rubicon = Rubicon(persistence="filesystem", root_dir=root_path) project = rubicon.get_or_create_project("Grid Search") pipeline = RubiconPipeline( project, [ ("vect", CountVectorizer()), ("tfidf", TfidfTransformer()), ("clf", SGDClassifier()), ], user_defined_loggers = { "vect": FilterEstimatorLogger(select=["max_df"]), "tfidf": FilterEstimatorLogger(ignore_all=True), "clf": FilterEstimatorLogger(select=["max_iter", "alpha", "penalty"]), }, experiment_kwargs={ "name": "logged from a RubiconPipeline", "model_name": SGDClassifier.__name__, }, )parameters = { "vect__max_df": (0.5, 0.75, 1.0), "vect__ngram_range": ((1, 1), (1, 2)), "clf__max_iter": (10, 20), "clf__alpha": (0.00001, 0.000001), "clf__penalty": ("l2", "elasticnet"), } grid_search = GridSearchCV(pipeline, parameters, cv=2, n_jobs=-1, refit=False) grid_search.fit(data.data, data.target) print(grid_search)print(f"Best score: {grid_search.best_score_}") full_results = grid_search.cv_results_from rubicon_ml.viz import Dashboard Dashboard(project.experiments()).serve(in_background=True)pipe_toggle_warnings = RubiconPipeline( project, [('scaler', StandardScaler()), ('svc', SVC())], ignore_warnings=True )pipe_toggle_warnings.ignore_warnings = False

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/viz/dataframe-plot.ipynb

import random import numpy as np import pandas as pd import plotly.express as px from rubicon_ml import Rubicon from rubicon_ml.viz import DataframePlotDISPLAY_DFS = False rubicon = Rubicon(persistence="memory", auto_git_enabled=True) project = rubicon.get_or_create_project("plot comparison") num_experiments_to_log = 6 data_ranges = [ (random.randint(0, 15000), random.randint(0, 15000)) for _ in range(num_experiments_to_log) ] dates = pd.date_range(start="1/1/2010", end="12/1/2020", freq="MS") for start, stop in data_ranges: data = np.array([list(dates), np.linspace(start, stop, len(dates))]) data_df = pd.DataFrame.from_records( data.T, columns=["calendar month", "open accounts"], ) dataframe = project.log_experiment().log_dataframe(data_df, name="open accounts") if DISPLAY_DFS: print(f"dataframe {dataframe.id}") display(data_df.head())DataframePlot( experiments=project.experiments(), dataframe_name="open accounts", x="calendar month", y="open accounts", plotting_func=px.line, ).show()

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/viz/metric-correlation-plot.ipynb

import random from rubicon_ml import Rubicon from rubicon_ml.viz import MetricCorrelationPlotrubicon = Rubicon(persistence="memory", auto_git_enabled=True) project = rubicon.get_or_create_project("metric correlation plot") for i in range(0, 100): experiment = project.log_experiment() experiment.log_parameter( name="is_standardized", value=random.choice([True, False]), ) experiment.log_parameter(name="n_estimators", value=random.randrange(2, 10, 2)) experiment.log_parameter( name="sample", value=random.choice(["A", "B", "C", "D", "E"]), ) experiment.log_metric(name="accuracy", value=random.random()) experiment.log_metric(name="confidence", value=random.random())MetricCorrelationPlot( experiments=project.experiments(), selected_metric="accuracy", ).show()

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/viz/experiments-table.ipynb

import random from rubicon_ml import Rubicon from rubicon_ml.viz import ExperimentsTablerubicon = Rubicon(persistence="memory", auto_git_enabled=True) project = rubicon.get_or_create_project("experiment table") for i in range(0, 24): experiment = project.log_experiment() experiment.log_parameter(name="max_depth", value=random.randrange(5, 25, 5)) experiment.log_parameter(name="n_estimators", value=random.randrange(2, 12, 2)) experiment.log_metric(name="accuracy", value=random.random())ExperimentsTable( experiments=project.experiments(), ).show()

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/viz/dashboard.ipynb

import random import numpy as np import pandas as pd from rubicon_ml import Rubicon from rubicon_ml.viz import ( DataframePlot, ExperimentsTable, MetricCorrelationPlot, MetricListsComparison, ) from rubicon_ml.viz.dashboard import Dashboarddates = pd.date_range(start="1/1/2010", end="12/1/2020", freq="MS") rubicon = Rubicon(persistence="memory", auto_git_enabled=True) project = rubicon.get_or_create_project("dashboard composition") for i in range(0, 10): experiment = project.log_experiment() experiment.log_parameter( name="is_standardized", value=random.choice([True, False]), ) experiment.log_parameter(name="n_estimators", value=random.randrange(2, 10, 2)) experiment.log_parameter( name="sample", value=random.choice(["A", "B", "C", "D", "E"]), ) experiment.log_metric(name="accuracy", value=random.random()) experiment.log_metric(name="confidence", value=random.random()) experiment.log_metric( name="coefficients", value=[random.random() for _ in range(0, 5)], ) experiment.log_metric( name="stderr", value=[random.random() for _ in range(0, 5)], ) data = np.array( [ list(dates), np.linspace(random.randint(0, 15000), random.randint(0, 15000), len(dates)) ] ) data_df = pd.DataFrame.from_records( data.T, columns=["calendar month", "open accounts"], ) experiment.log_dataframe(data_df, name="open accounts")default_dashbaord = Dashboard(experiments=project.experiments()) default_dashbaord.show()Dashboard( experiments=project.experiments(), widgets=[ [ ExperimentsTable(is_selectable=True), MetricCorrelationPlot(selected_metric="accuracy"), ], [ MetricListsComparison(column_names=[f"var_00{i}" for i in range(0, 5)]), DataframePlot(dataframe_name="open accounts"), ], ], ).show()

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/viz/metric-lists-comparisons.ipynb

import random from rubicon_ml import Rubicon from rubicon_ml.viz import MetricListsComparisonrubicon = Rubicon(persistence="memory", auto_git_enabled=True) project = rubicon.get_or_create_project("list metric comparison") for i in range(0, 10): experiment = project.log_experiment() experiment.log_metric( name="coefficients", value=[random.random() for _ in range(0, 25)], ) experiment.log_metric( name="p-values", value=[random.random() for _ in range(0, 25)], ) experiment.log_metric( name="stderr", value=[random.random() for _ in range(0, 25)], )MetricListsComparison( experiments=project.experiments(), column_names=["intercept"] + [f"var_{i:03}" for i in range(1, 25)], ).show()

capitalone_repos/rubicon-ml/notebooks

capitalone_repos/rubicon-ml/notebooks/tutorials/failure-modes.ipynb

from rubicon_ml import Rubicon rb = Rubicon(persistence="memory") rb.get_project(name="failure modes")from rubicon_ml import set_failure_mode set_failure_mode("warn") rb.get_project(name="failure modes")set_failure_mode("log") rb.get_project(name="failure modes")set_failure_mode("log", traceback_limit=0) rb.get_project(name="failure modes")set_failure_mode("log", traceback_chain=True) rb.get_project(name="failure modes")rb.create_project(name="failure modes") project = rb.get_project(name="failure modes") print(project)print(project.id)set_failure_mode("log") project = rb.get_project(name="failure modes v2") print(project)if project is not None: print(project.id)project = rb.create_project(name="failure modes v3") experiment = project.log_experiment() experimentclass BrokenFilesystem: pass rb.config.repository.filesystem = BrokenFilesystem() set_failure_mode("raise")from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors=1) X_train, y_train, X_test, y_test = [[0, 1, 2, 3]], [0], [[0, 1, 2, 3]], [0] knn.fit(X_train, y_train) experiment.log_parameter(name="n_neighbors", value=1) score = knn.score(X_test, y_test) experiment.log_metric(name="score", value=score) scoreset_failure_mode("log") knn = KNeighborsClassifier(n_neighbors=1) X_train, y_train, X_test, y_test = [[0, 1, 2, 3]], [0], [[0, 1, 2, 3]], [0] knn.fit(X_train, y_train) experiment.log_parameter(name="n_neighbors", value=1) score = knn.score(X_test, y_test) experiment.log_metric(name="score", value=score) score

capitalone_repos/rubicon-ml

capitalone_repos/rubicon-ml/tests/fixtures.py

import os import random import uuid import dask.array as da import dask.dataframe as dd import numpy as np import pandas as pd import pytest from dask.distributed import Client from sklearn.datasets import make_classification from rubicon_ml import Rubicon from rubicon_ml.repository import MemoryRepository class _AnotherObject: """Another object to log for schema testing.""" def __init__(self): self.another_parameter = 100 self.another_metric = 100 class _ObjectToLog: """An object to log for schema testing.""" def __init__(self): """Initialize an object to log.""" self.object_ = _AnotherObject() self.feature_names_ = ["var_001", "var_002"] self.other_feature_names_ = ["var_003", "var_004"] self.feature_importances_ = [0.75, 0.25] self.feature_name_ = "var_005" self.other_feature_name_ = "var_006" self.feature_importance_ = 1.0 self.dataframe = pd.DataFrame([[100, 0], [0, 100]], columns=["x", "y"]) self.parameter = 100 self.metric = 100 def metric_function(self): return self.metric def artifact_function(self): return self def dataframe_function(self): return pd.DataFrame([[100, 0], [0, 100]], columns=["x", "y"]) def erroring_function(self): raise RuntimeError("raised from `_ObjectToLog.erroring_function`") class _MockCompletedProcess: """Use to mock a CompletedProcess result from `subprocess.run()`.""" def __init__(self, stdout="", returncode=0): self.stdout = stdout self.returncode = returncode @pytest.fixture def mock_completed_process_empty(): return _MockCompletedProcess(stdout=b"\n") @pytest.fixture def mock_completed_process_git(): return _MockCompletedProcess(stdout=b"origin github.com (fetch)\n") @pytest.fixture def rubicon_client(): """Setup an instance of rubicon configured to log to memory and clean it up afterwards. """ from rubicon_ml import Rubicon rubicon = Rubicon(persistence="memory", root_dir="./") # teardown after yield yield rubicon rubicon.repository.filesystem.rm(rubicon.config.root_dir, recursive=True) @pytest.fixture def rubicon_composite_client(): """Setup an instance of rubicon configured to log to two memory backends and clean it up afterwards. """ from rubicon_ml import Rubicon rubicon = Rubicon( composite_config=[ {"persistence": "memory", "root_dir": "a"}, {"persistence": "memory", "root_dir": "b"}, ], ) # teardown after yield yield rubicon for i, repository in enumerate(rubicon.repositories): repository.filesystem.rm( rubicon.config.storage_options["composite_config"][i]["root_dir"], recursive=True, ) @pytest.fixture def rubicon_local_filesystem_client(): """Setup an instance of rubicon configured to log to the filesystem and clean it up afterwards. """ from rubicon_ml import Rubicon rubicon = Rubicon( persistence="filesystem", root_dir=os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubicon"), ) # teardown after yield yield rubicon rubicon.repository.filesystem.rm(rubicon.config.root_dir, recursive=True) @pytest.fixture def rubicon_local_filesystem_client_with_project(rubicon_local_filesystem_client): rubicon = rubicon_local_filesystem_client project_name = "Test Project" project = rubicon.get_or_create_project(project_name, description="testing") return rubicon, project @pytest.fixture def project_client(rubicon_client): """Setup an instance of rubicon configured to log to memory with a default project and clean it up afterwards. """ rubicon = rubicon_client project_name = "Test Project" project = rubicon.get_or_create_project( project_name, description="In memory project for testing." ) return project @pytest.fixture def project_composite_client(rubicon_composite_client): """Setup an instance of rubicon configured to log to two memory backends with a default project and clean it up afterwards. """ rubicon = rubicon_composite_client project_name = "Test Project" project = rubicon.get_or_create_project( project_name, description="In memory project for testing." ) return project @pytest.fixture def rubicon_and_project_client(rubicon_client): """Setup an instance of rubicon configured to log to memory with a default project and clean it up afterwards. Expose both the rubicon instance and the project. """ rubicon = rubicon_client project_name = "Test Project" project = rubicon.get_or_create_project( project_name, description="In memory project for testing.", github_url="test.github.url.git", ) return (rubicon, project) @pytest.fixture def rubicon_and_project_client_with_experiments(rubicon_and_project_client): """Setup an instance of rubicon configured to log to memory with a default project with experiments and clean it up afterwards. Expose both the rubicon instance and the project. """ rubicon, project = rubicon_and_project_client for e in range(0, 10): experiment = project.log_experiment( tags=["testing"], commit_hash=str(int(e / 3)), training_metadata=("training", "metadata"), ) experiment.log_parameter("n_estimators", e + 1) experiment.log_feature("year") experiment.log_metric("accuracy", (80 + e)) return (rubicon, project) @pytest.fixture def test_dataframe(): """Create a test dataframe which can be logged to a project or experiment.""" import pandas as pd from dask import dataframe as dd return dd.from_pandas( pd.DataFrame.from_records([[0, 1]], columns=["a", "b"]), npartitions=1, ) @pytest.fixture def memory_repository(): """Setup an in-memory repository and clean it up afterwards.""" root_dir = "/in-memory-root" repository = MemoryRepository(root_dir) yield repository repository.filesystem.rm(root_dir, recursive=True) @pytest.fixture def fake_estimator_cls(): """A fake estimator that exposes the same API as a sklearn estimator so we can test without relying on sklearn. """ class FakeEstimator: def __init__(self, params=None): if params is None: params = {"max_df": 0.75, "lowercase": True, "ngram_range": (1, 2)} self.params = params def get_params(self): return self.params def fit(self): pass def transform(self): pass return FakeEstimator @pytest.fixture def viz_experiments(rubicon_and_project_client): """Returns a list of experiments with the parameters, metrics, and dataframes required to test the `viz` module. """ rubicon, project = rubicon_and_project_client dates = pd.date_range(start="1/1/2010", end="12/1/2020", freq="MS") for i in range(0, 10): experiment = project.log_experiment( commit_hash="1234567", model_name="test model name", name="test name", tags=["test tag"], ) experiment.log_parameter(name="test param 0", value=random.choice([True, False])) experiment.log_parameter(name="test param 1", value=random.randrange(2, 10, 2)) experiment.log_parameter( name="test param 2", value=random.choice(["A", "B", "C", "D", "E"]) ) experiment.log_metric(name="test metric 0", value=random.random()) experiment.log_metric(name="test metric 1", value=random.random()) experiment.log_metric(name="test metric 2", value=[random.random() for _ in range(0, 5)]) experiment.log_metric(name="test metric 3", value=[random.random() for _ in range(0, 5)]) data = np.array( [ list(dates), np.linspace(random.randint(0, 15000), random.randint(0, 15000), len(dates)), ] ) data_df = pd.DataFrame.from_records(data.T, columns=["test x", "test y"]) experiment.log_dataframe(data_df, name="test dataframe") return project.experiments() @pytest.fixture def objects_to_log(): """Returns objects for testing.""" return _ObjectToLog(), _AnotherObject() @pytest.fixture def another_object_schema(): """Returns a schema representing ``_AnotherObject``.""" return { "parameters": [{"name": "another_parameter", "value_attr": "another_parameter"}], "metrics": [{"name": "another_metric", "value_attr": "another_metric"}], } @pytest.fixture def artifact_schema(): """Returns a schema for testing artifacts.""" return { "artifacts": [ "self", {"name": "object_", "data_object_attr": "object_"}, {"name": "object_b", "data_object_func": "artifact_function"}, ] } @pytest.fixture def dataframe_schema(): """Returns a schema for testing dataframes.""" return { "dataframes": [ {"name": "dataframe", "df_attr": "dataframe"}, {"name": "dataframe_b", "df_func": "dataframe_function"}, ] } @pytest.fixture def feature_schema(): """Returns a schema for testing features.""" return { "features": [ { "names_attr": "feature_names_", "importances_attr": "feature_importances_", }, {"names_attr": "other_feature_names_"}, {"name_attr": "feature_name_", "importance_attr": "feature_importance_"}, {"name_attr": "other_feature_name_"}, ] } @pytest.fixture def metric_schema(): """Returns a schema for testing metrics.""" return { "metrics": [ {"name": "metric_a", "value_attr": "metric"}, {"name": "metric_b", "value_env": "METRIC"}, {"name": "metric_c", "value_func": "metric_function"}, ], } @pytest.fixture def parameter_schema(): """Returns a schema for testing parameters.""" return { "parameters": [ {"name": "parameter_a", "value_attr": "parameter"}, {"name": "parameter_b", "value_env": "PARAMETER"}, ], } @pytest.fixture def nested_schema(): """Returns a schema for testing nested schema.""" return {"schema": [{"name": "AnotherObject", "attr": "object_"}]} @pytest.fixture def optional_schema(): """Returns a schema for testing optional attributes.""" return { "artifacts": [ { "name": "object", "data_object_attr": "missing_object", "optional": "true", }, { "name": "object_b", "data_object_func": "missing_object_func", "optional": "true", }, ], "dataframes": [ {"name": "dataframe", "df_attr": "missing_dataframe", "optional": "true"}, { "name": "dataframe_b", "df_func": "missing_dataframe_func", "optional": "true", }, ], "features": [ {"names_attr": "missing_feature_names", "optional": "true"}, {"name_attr": "missing_feature_name", "optional": "true"}, ], "metrics": [ {"name": "metric_a", "value_attr": "missing_metric", "optional": "true"}, {"name": "metric_b", "value_env": "MISSING_METRIC", "optional": "true"}, { "name": "metric_c", "value_func": "missing_metric_func", "optional": "true", }, ], "parameters": [ { "name": "parameter_a", "value_attr": "missing_parameter", "optional": "true", }, { "name": "parameter_b", "value_env": "MISSING_PARAMETER", "optional": "true", }, ], "schema": [ { "name": "MissingObject", "attr": "another_missing_object", "optional": "true", } ], } @pytest.fixture def hierarchical_schema(): """Returns a schema for testing hierarchical schema.""" return {"children": [{"name": "AnotherObject", "attr": "children"}]} @pytest.fixture def rubicon_project(): """Returns an in-memory rubicon project for testing.""" rubicon = Rubicon(persistence="memory", root_dir="/tmp") random_name = str(uuid.uuid4()) return rubicon.create_project(name=random_name) @pytest.fixture def make_classification_array(): """Returns classification data generated by scikit-learn as an array.""" X, y = make_classification( n_samples=1000, n_features=10, n_informative=5, n_redundant=5, n_classes=2, class_sep=1, random_state=3211, ) return X, y @pytest.fixture def make_classification_df(make_classification_array): """Returns classification data generated by scikit-learn as dataframes.""" X, y = make_classification_array X_df = pd.DataFrame(X, columns=[f"var_{i}" for i in range(10)]) return X_df, y @pytest.fixture def dask_client(): """Returns a dask client and shuts it down upon test completion.""" client = Client() yield client client.shutdown() @pytest.fixture def make_classification_dask_array(make_classification_array): """Returns classification data generated by scikit-learn as a dask array.""" X, y = make_classification_array X_da, y_da = da.from_array(X), da.from_array(y) return X_da, y_da @pytest.fixture def make_classification_dask_df(make_classification_df): """Returns classification data generated by scikit-learn as dataframes.""" X, y = make_classification_df X_df, y_da = dd.from_pandas(X, npartitions=1), da.from_array(y) return X_df, y_da

capitalone_repos/rubicon-ml/tests

capitalone_repos/rubicon-ml/tests/notebooks/test_notebooks.py

import os from unittest import mock import fsspec import pytest from nbconvert.preprocessors import ExecutePreprocessor from tests.notebooks.utils import get_notebook_filenames, read_notebook_file NOTEBOOK_FILENAMES = get_notebook_filenames( os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "notebooks") ) BAD_NOTEBOOK_FILENAMES = get_notebook_filenames( os.path.join(os.path.dirname(__file__), "bad-notebooks") ) BAD_NOTEBOOK_XFAIL_MARKS = [ pytest.param( n, marks=pytest.mark.xfail( reason=f"`test_notebook_is_executed_in_order` for notebook {n} is expected to fail" ), ) for n in BAD_NOTEBOOK_FILENAMES ] @pytest.mark.run_notebooks @pytest.mark.parametrize("notebook_filename", NOTEBOOK_FILENAMES + BAD_NOTEBOOK_XFAIL_MARKS) def test_notebook_is_executed_in_order(notebook_filename): notebook = read_notebook_file(notebook_filename) execution_counts = [ cell.get("execution_count") for cell in notebook.cells if cell.get("cell_type") == "code" ] is_all_cells_executed = all([ec is not None for ec in execution_counts]) if not is_all_cells_executed: failure_message = "all code cells are not executed" is_last_cell_executed = execution_counts[-1] is not None if not is_last_cell_executed: failure_message += " - there might be an empty cell at the end" pytest.fail(failure_message) is_cell_execution_ordered = all( execution_counts[i] < execution_counts[i + 1] for i in range(len(execution_counts) - 1) ) if not is_cell_execution_ordered: pytest.fail("code cells are executed out of order") IGNORE_EXECUTE_NOTEBOOK_FILENAMES = [ "classification.ipynb", "failure-modes.ipynb", "integration-prefect-workflows.ipynb", "logging-feature-plots.ipynb", "visualizing-experiments.ipynb", ] EXECUTE_NOTEBOOK_FILENAMES = [ n for n in NOTEBOOK_FILENAMES if os.path.split(n)[-1] not in IGNORE_EXECUTE_NOTEBOOK_FILENAMES ] @mock.patch.dict(os.environ, {"RUBICON_ROOT": "test-rubicon-root"}) @pytest.mark.run_notebooks @pytest.mark.parametrize("notebook_filename", EXECUTE_NOTEBOOK_FILENAMES) def test_notebooks_execute_without_error(notebook_filename): notebook = read_notebook_file(notebook_filename) resources = {"metadata": {"path": os.path.dirname(notebook_filename)}} preprocessor = ExecutePreprocessor(kernel_name="python3", timeout=60) preprocessor.preprocess(notebook, resources=resources) repo_root = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) notebook_output_dir = os.path.join(repo_root, "notebooks", "test-rubicon-root") if "logging-experiments.ipynb" not in notebook_filename: fs = fsspec.filesystem("file") try: fs.rm(notebook_output_dir, recursive=True) except FileNotFoundError: pass # some notebooks don't write output

capitalone_repos/rubicon-ml/tests

capitalone_repos/rubicon-ml/tests/notebooks/utils.py

import json import os import fsspec import nbformat DEFAULT_NBFORMAT_VERSION = 4 def get_notebook_filenames(root_path): fs = fsspec.filesystem("file") notebook_glob = os.path.join(root_path, "*.ipynb") nested_notebook_glob = os.path.join(root_path, "**", "*.ipynb") notebook_filenames = fs.glob(notebook_glob) + fs.glob(nested_notebook_glob) return [n for n in notebook_filenames if ".ipynb_checkpoints" not in n] def read_notebook_file(notebook_filename): fs = fsspec.filesystem("file") with fs.open(notebook_filename, "r") as notebook_file: notebook = notebook_file.read() notebook_json = json.loads(notebook) notebook_version = notebook_json.get("nbformat", DEFAULT_NBFORMAT_VERSION) notebook = nbformat.reads(notebook, as_version=notebook_version) return notebook

capitalone_repos/rubicon-ml/tests/notebooks

capitalone_repos/rubicon-ml/tests/notebooks/bad-notebooks/not-executed.ipynb

x = 1y = 2z = 3

capitalone_repos/rubicon-ml/tests/notebooks

capitalone_repos/rubicon-ml/tests/notebooks/bad-notebooks/empty-last-cell.ipynb

x = 1y = 2z = 3

capitalone_repos/rubicon-ml/tests/notebooks

capitalone_repos/rubicon-ml/tests/notebooks/bad-notebooks/not-executed-in-order.ipynb

x = 1y = 2z = 3

capitalone_repos/rubicon-ml/tests/notebooks

capitalone_repos/rubicon-ml/tests/notebooks/bad-notebooks/not-all-executed.ipynb

x = 1y = 2z = 3

capitalone_repos/rubicon-ml/tests

capitalone_repos/rubicon-ml/tests/integration/test_misc_dotfiles.py

import os import warnings def test_rubicon_with_misc_folders_at_project_level(rubicon_local_filesystem_client_with_project): rubicon, project = rubicon_local_filesystem_client_with_project os.makedirs(os.path.join(rubicon.config.root_dir, ".ipynb_checkpoints")) with warnings.catch_warnings(record=True) as w: projects = rubicon.projects() assert len(projects) == 1 assert "not found" in str(w[-1].message) def test_rubicon_with_misc_folders_at_sublevel_level(rubicon_local_filesystem_client_with_project): rubicon, project = rubicon_local_filesystem_client_with_project project.log_experiment("exp1") project.log_experiment("exp2") os.makedirs( os.path.join(rubicon.config.root_dir, "test-project", "experiments", ".ipynb_checkpoints") ) with warnings.catch_warnings(record=True) as w: experiments = project.experiments() assert len(experiments) == 2 assert "not found" in str(w[-1].message) def test_rubicon_with_misc_folders_at_deeper_sublevel_level( rubicon_local_filesystem_client_with_project, ): rubicon, project = rubicon_local_filesystem_client_with_project exp = project.log_experiment("exp1") exp.log_parameter("a", 1) os.makedirs( os.path.join( rubicon.config.root_dir, "test-project", "experiments", exp.id, "parameters", ".ipynb_checkpoints", ) ) with warnings.catch_warnings(record=True) as w: parameters = exp.parameters() assert len(parameters) == 1 assert "not found" in str(w[-1].message)

capitalone_repos/rubicon-ml/tests

capitalone_repos/rubicon-ml/tests/integration/test_dataframe_logging.py

import pandas as pd import pytest from dask import dataframe as dd from rubicon_ml.exceptions import RubiconException def test_pandas_df(rubicon_local_filesystem_client): rubicon = rubicon_local_filesystem_client project = rubicon.create_project("Dataframe Test Project") multi_index_df = pd.DataFrame( [[0, 1, "a"], [1, 1, "b"], [2, 2, "c"], [3, 2, "d"]], columns=["a", "b", "c"] ) multi_index_df = multi_index_df.set_index(["b", "a"]) written_dataframe = project.log_dataframe(multi_index_df) read_dataframes = project.dataframes() read_dataframe = read_dataframes[0] assert len(read_dataframes) == 1 assert read_dataframe.id == written_dataframe.id assert read_dataframe.get_data().equals(multi_index_df) def test_dask_df(rubicon_local_filesystem_client): rubicon = rubicon_local_filesystem_client project = rubicon.create_project("Dataframe Test Project") ddf = dd.from_pandas(pd.DataFrame([0, 1], columns=["a"]), npartitions=1) written_dataframe = project.log_dataframe(ddf) read_dataframes = project.dataframes() read_dataframe = read_dataframes[0] assert len(read_dataframes) == 1 assert read_dataframe.id == written_dataframe.id assert read_dataframe.get_data(df_type="dask").compute().equals(ddf.compute()) def test_df_read_error(rubicon_local_filesystem_client): rubicon = rubicon_local_filesystem_client project = rubicon.create_project("Dataframe Test Project") ddf = dd.from_pandas(pd.DataFrame([0, 1], columns=["a"]), npartitions=1) written_dataframe = project.log_dataframe(ddf) read_dataframes = project.dataframes() read_dataframe = read_dataframes[0] assert len(read_dataframes) == 1 assert read_dataframe.id == written_dataframe.id # simulate user forgetting to set `df_type` to `dask` when reading a logged dask df with pytest.raises(RubiconException) as e: read_dataframe.get_data() assert ( "This might have happened if you forgot to set `df_type='dask'` when trying to read a `dask` dataframe." in str(e) )

capitalone_repos/rubicon-ml/tests

capitalone_repos/rubicon-ml/tests/integration/test_prefect_flow.py

import numpy as np import pandas as pd from prefect import Flow from rubicon_ml import Rubicon from rubicon_ml.client import ( Artifact, Dataframe, Experiment, Feature, Metric, Parameter, Project, ) from rubicon_ml.workflow.prefect import ( create_experiment_task, get_or_create_project_task, log_artifact_task, log_dataframe_task, log_feature_task, log_metric_task, log_parameter_task, ) def test_flow(): persistence = "memory" root_dir = "./" project_name = "Prefect Integration Test" df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=["a", "b", "c"]) artifact = b"byte artifact" with Flow("testing-rubicon-tasks") as flow: project_t = get_or_create_project_task(persistence, root_dir, project_name) experiment_t = create_experiment_task(project_t) feature_t = log_feature_task(experiment_t, "test feature") parameter_t = log_parameter_task(experiment_t, "test parameter", 0) metric_t = log_metric_task(experiment_t, "test metric", 0) dataframe_t = log_dataframe_task(experiment_t, df, description="a test df") artifact_t = log_artifact_task(experiment_t, data_bytes=artifact, name="my artifact") assert len(flow.tasks) == 7 state = flow.run() assert state.is_successful() # outside of the flow, the objects are Task references assert state.result[project_t].is_successful() assert state.result[experiment_t].is_successful() assert state.result[feature_t].is_successful() assert state.result[parameter_t].is_successful() assert state.result[metric_t].is_successful() assert state.result[dataframe_t].is_successful() assert state.result[artifact_t].is_successful() assert isinstance(state.result[project_t].result, Project) assert isinstance(state.result[experiment_t].result, Experiment) assert isinstance(state.result[feature_t].result, Feature) assert isinstance(state.result[parameter_t].result, Parameter) assert isinstance(state.result[metric_t].result, Metric) assert isinstance(state.result[dataframe_t].result, Dataframe) assert isinstance(state.result[artifact_t].result, Artifact) # use Rubicon to grab the logged data rubicon = Rubicon(persistence, root_dir) project = rubicon.get_project(project_name) assert project.name == project_name experiments = project.experiments() assert len(experiments) == 1 experiment = experiments[0] # features features = experiment.features() assert len(features) == 1 assert features[0].name == "test feature" # metrics metrics = experiment.metrics() assert len(metrics) == 1 assert metrics[0].name == "test metric" assert metrics[0].value == 0 # parameters parameters = experiment.parameters() assert len(parameters) == 1 assert parameters[0].name == "test parameter" assert parameters[0].value == 0 # dataframes dataframes = experiment.dataframes() assert len(dataframes) == 1 assert dataframes[0].description == "a test df" assert df.equals(dataframes[0].get_data()) # artifacts artifacts = experiment.artifacts() assert len(artifacts) == 1 assert artifacts[0].name == "my artifact" assert artifacts[0].data == artifact

capitalone_repos/rubicon-ml/tests

capitalone_repos/rubicon-ml/tests/integration/test_schema.py

import pytest from lightgbm import LGBMClassifier, LGBMRegressor from sklearn.ensemble import RandomForestClassifier from xgboost import XGBClassifier, XGBRegressor from xgboost.dask import DaskXGBClassifier, DaskXGBRegressor PANDAS_SCHEMA_CLS = [ LGBMClassifier, LGBMRegressor, RandomForestClassifier, XGBClassifier, XGBRegressor, ] DASK_SCHEMA_CLS = [DaskXGBClassifier, DaskXGBRegressor] def _fit_and_log(X, y, schema_cls, rubicon_project): model = schema_cls() model.fit(X, y) rubicon_project.log_with_schema(model) @pytest.mark.integration @pytest.mark.parametrize("schema_cls", PANDAS_SCHEMA_CLS) def test_estimator_schema_fit_array(schema_cls, make_classification_array, rubicon_project): X, y = make_classification_array _fit_and_log(X, y, schema_cls, rubicon_project) @pytest.mark.integration @pytest.mark.parametrize("schema_cls", PANDAS_SCHEMA_CLS) def test_estimator_schema_fit_df(schema_cls, make_classification_df, rubicon_project): X, y = make_classification_df _fit_and_log(X, y, schema_cls, rubicon_project) @pytest.mark.integration @pytest.mark.parametrize("schema_cls", DASK_SCHEMA_CLS) def test_estimator_schema_fit_dask_array( schema_cls, make_classification_dask_array, rubicon_project, dask_client, ): X_da, y_da = make_classification_dask_array _fit_and_log(X_da, y_da, schema_cls, rubicon_project) @pytest.mark.integration @pytest.mark.parametrize("schema_cls", DASK_SCHEMA_CLS) def test_estimator_schema_fit_dask_df( schema_cls, make_classification_dask_df, rubicon_project, dask_client ): X_df, y_da = make_classification_dask_df _fit_and_log(X_df, y_da, schema_cls, rubicon_project)

capitalone_repos/rubicon-ml/tests

capitalone_repos/rubicon-ml/tests/integration/test_concurrency.py

import multiprocessing import pandas as pd from dask import dataframe as dd from rubicon_ml.domain.utils import uuid def _log_all_to_experiment(experiment): ddf = dd.from_pandas(pd.DataFrame([0, 1], columns=["a"]), npartitions=1) for _ in range(0, 4): experiment.log_metric(uuid.uuid4(), 0) experiment.log_feature(uuid.uuid4()) experiment.log_parameter(uuid.uuid4(), 1) experiment.log_artifact(data_bytes=b"artifact bytes", name=uuid.uuid4()) experiment.log_dataframe(ddf) experiment.add_tags([uuid.uuid4()]) def _read_all_from_experiment(experiment): for _ in range(0, 4): experiment.metrics() experiment.features() experiment.parameters() experiment.artifacts() experiment.dataframes() experiment.tags def test_filesystem_concurrency(rubicon_local_filesystem_client): rubicon = rubicon_local_filesystem_client project = rubicon.create_project("Test Concurrency") experiment = project.log_experiment() processes = [] for i in range(0, 4): process = multiprocessing.Process(target=_read_all_from_experiment, args=[experiment]) process.start() processes.append(process) for i in range(0, 4): process = multiprocessing.Process(target=_log_all_to_experiment, args=[experiment]) process.start() processes.append(process) for process in processes: process.join() assert len(experiment.metrics()) == 16 assert len(experiment.features()) == 16 assert len(experiment.parameters()) == 16 assert len(experiment.artifacts()) == 16 assert len(experiment.dataframes()) == 16 assert len(experiment.tags) == 16

capitalone_repos/rubicon-ml/tests

capitalone_repos/rubicon-ml/tests/integration/test_rubicon.py

import uuid import pandas as pd import pytest from rubicon_ml import Rubicon filesystems = [ pytest.param(Rubicon(persistence="memory")), pytest.param( Rubicon(persistence="filesystem", root_dir="./test-rubicon"), marks=pytest.mark.write_files, ), pytest.param( Rubicon(persistence="filesystem", root_dir="s3://change-me"), marks=pytest.mark.write_files, ), pytest.param( Rubicon( composite_config=[ {"persistence": "memory", "root_dir": "./memory/root"}, {"persistence": "filesystem", "root_dir": "./test-rubicon"}, ] ), marks=pytest.mark.write_files, ), ] @pytest.mark.parametrize("rubicon", filesystems) def test_rubicon(rubicon, request): for repository in rubicon.repositories: if "change-me" in repository.root_dir: root_dir = request.config.getoption("s3-path") if root_dir is None: pytest.fail("`root_dir` cannot be None. Run `pytest` with `--s3-path`.") repository.root_dir = root_dir written_project = rubicon.create_project(name=f"Test Project {uuid.uuid4()}") written_experiment = written_project.log_experiment(name=f"Test Experiment {uuid.uuid4()}") written_experiment.add_tags(["x", "y"]) written_experiment.remove_tags(["x"]) written_feature = written_experiment.log_feature(name=f"Test Feature {uuid.uuid4()}") written_parameter = written_experiment.log_parameter( name=f"Test Parameter {uuid.uuid4()}", value=8 ) written_metric = written_experiment.log_metric(name=f"Test Feature {uuid.uuid4()}", value=24) written_project_artifact = written_project.log_artifact( name=f"Test Artifact {uuid.uuid4()}", data_bytes=b"test artifact data" ) written_experiment_artifact = written_experiment.log_artifact( name=f"Test Artifact {uuid.uuid4()}", data_bytes=b"test artifact data" ) written_project_dataframe = written_project.log_dataframe( df=pd.DataFrame([[0, 1], [1, 0]], columns=["a", "b"]) ) json_dict = {"hello": "world", "numbers": [1, 2, 3]} written_project_json = written_project.log_json( name=f"Test JSON {uuid.uuid4()}.json", json_object=json_dict ) written_experiment_json = written_experiment.log_json( name=f"Test JSON {uuid.uuid4()}.json", json_object=json_dict ) written_project_dataframe.add_tags(["x", "y"]) written_project_dataframe.remove_tags(["x"]) read_project = rubicon.get_project(name=written_project.name) assert written_project.id == read_project.id read_experiments = read_project.experiments() assert len(read_experiments) == 1 assert written_experiment.id == read_experiments[0].id read_experiment = read_experiments[0] assert written_experiment.tags == read_experiment.tags read_features = read_experiment.features() assert len(read_features) == 1 assert written_feature.id == read_features[0].id read_parameters = read_experiment.parameters() assert len(read_parameters) == 1 assert written_parameter.id == read_parameters[0].id assert written_parameter.value == read_parameters[0].value read_metrics = read_experiment.metrics() assert len(read_metrics) == 1 assert written_metric.id == read_metrics[0].id assert written_metric.value == read_metrics[0].value read_project_artifacts = read_project.artifacts() assert len(read_project_artifacts) == 2 assert written_project_artifact.id == read_project_artifacts[0].id assert written_project_artifact.data == read_project_artifacts[0].data assert written_project_json.id == read_project_artifacts[1].id assert written_project_json.data == read_project_artifacts[1].data read_project.delete_artifacts([artifact.id for artifact in read_project_artifacts]) assert len(read_project.artifacts()) == 0 read_experiment_artifacts = read_experiment.artifacts() assert len(read_experiment_artifacts) == 2 assert written_experiment_artifact.id == read_experiment_artifacts[0].id assert written_experiment_artifact.data == read_experiment_artifacts[0].data assert written_experiment_json.id == read_experiment_artifacts[1].id assert written_experiment_json.data == read_experiment_artifacts[1].data assert json_dict == read_experiment_artifacts[1].get_json() read_project_dataframes = read_project.dataframes() assert len(read_project_dataframes) == 1 assert written_project_dataframe.id == read_project_dataframes[0].id assert written_project_dataframe.get_data().equals(read_project_dataframes[0].get_data()) assert written_project_dataframe.tags == read_project_dataframes[0].tags read_project.delete_dataframes([read_project_dataframes[0].id]) assert len(read_project.dataframes()) == 0 for repository in rubicon.repositories: repository.filesystem.rm(repository.root_dir, recursive=True)

capitalone_repos/rubicon-ml/tests

capitalone_repos/rubicon-ml/tests/unit/test_cli.py

import warnings from unittest.mock import patch import click from click.testing import CliRunner from rubicon_ml.cli import cli def mock_click_output(**server_args): click.echo("Running the mock server") @patch("rubicon_ml.viz.dashboard.Dashboard.serve") @patch("rubicon_ml.client.rubicon.Rubicon.projects") @patch("rubicon_ml.client.rubicon.Rubicon.get_project") def test_cli(mock_get_project, mock_projects, mock_run_server, project_client): mock_get_project.return_value = project_client mock_projects.return_value = [project_client] mock_run_server.side_effect = mock_click_output with warnings.catch_warnings(record=True) as caught_warnings: runner = CliRunner() result = runner.invoke( cli, ["ui", "--root-dir", "/path/to/root", "--page-size", 100], ) assert result.exit_code == 0 assert "Running the mock server" in result.output assert len(caught_warnings) == 2 assert "`--page-size` option will be deprecated" in str(caught_warnings[0]) assert "`--project-name` will be a required option" in str(caught_warnings[1]) def _set_up_rubicon_project(project): NUM_EXPERIMENTS = 4 for _ in range(NUM_EXPERIMENTS): tags = ["a", "b", "c"] ex = project.log_experiment(tags=tags) for feature in ["f", "g", "h", "i"]: ex.log_feature(name=feature) for parameter in [("d", 100), ("e", 1000), ("f", 1000)]: name, value = parameter ex.log_parameter(name=name, value=value) for metric in ["j", "k"]: value = 1 tags = ["l", "m", "n"] ex.log_metric(name=metric, value=value, tags=tags) ex.log_artifact(name="o", data_bytes=b"o") project.log_artifact(name="p", data_bytes=b"p") return project def test_search_cli_base(rubicon_local_filesystem_client_with_project): _, project = rubicon_local_filesystem_client_with_project QUERY = "$..experiment[*].metric" project = _set_up_rubicon_project(project=project) runner = CliRunner() result_a = runner.invoke( cli, [ "search", "--root-dir", project.repository.root_dir, "--project-name", project.name, QUERY, ], env={"RUBICON_PROJECT_NAME": None, "RUBICON_ROOT_DIR": None}, ) assert result_a.exit_code == 0 def test_search_cli_exp_fail(rubicon_local_filesystem_client_with_project): _, project = rubicon_local_filesystem_client_with_project QUERY = "$..experiment[*].metric" project = _set_up_rubicon_project(project=project) runner = CliRunner() result_a = runner.invoke( cli, ["search", QUERY], env={"RUBICON_PROJECT_NAME": None, "RUBICON_ROOT_DIR": None} ) result_b = runner.invoke( cli, [ "search", "--root-dir", project.repository.root_dir, "--project-name", "NON-EXISTENT-PROJECT", QUERY, ], env={"RUBICON_PROJECT_NAME": None, "RUBICON_ROOT_DIR": None}, ) assert "No --root-dir or --project-name provided. Exiting..." in result_a.output assert "No project with name 'NON-EXISTENT-PROJECT' found." in result_b.output assert result_a.exit_code == 1 assert result_b.exit_code == 1

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/domain/test_mixin.py

from rubicon_ml.domain.mixin import TagMixin class Taggable(TagMixin): def __init__(self, tags=[]): self.tags = tags def test_add_tags(): taggable = Taggable() taggable.add_tags(["x"]) assert taggable.tags == ["x"] def test_add_duplicate_tags(): taggable = Taggable(tags=["x"]) taggable.add_tags(["x"]) assert taggable.tags == ["x"] def test_remove_tags(): taggable = Taggable(tags=["x", "y"]) taggable.remove_tags(["x"]) assert taggable.tags == ["y"]

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/domain/test_metric.py

import pytest from rubicon_ml.domain import Metric def test_metric_throws_error_if_invalid_directionality(): with pytest.raises(ValueError) as e: Metric("accuracy", 99, directionality="invalid") assert str(["score", "loss"]) in str(e)

capitalone_repos/rubicon-ml/tests/unit/domain

capitalone_repos/rubicon-ml/tests/unit/domain/utils/test_training_metadata.py

import pytest from rubicon_ml.domain.utils import TrainingMetadata from rubicon_ml.exceptions import RubiconException def test_init_throws_validation_error(): with pytest.raises(RubiconException) as e: training_metadata = ["not", "a", "list", "of", "tuples"] TrainingMetadata(training_metadata) assert "must be a list of tuples" in str(e) def test_init_convert_to_list(): training_metadata = TrainingMetadata(("test/path", "SELECT * FROM test")) assert isinstance(training_metadata.training_metadata, list) def test_repr(): training_metadata = TrainingMetadata(("test/path", "SELECT * FROM test")) assert str(training_metadata) == str(training_metadata.training_metadata)

capitalone_repos/rubicon-ml/tests/unit/domain

capitalone_repos/rubicon-ml/tests/unit/domain/utils/test_uuid.py

from uuid import UUID from rubicon_ml.domain.utils import uuid def test_returns_valid_uuid(): uuid_str = uuid.uuid4() try: UUID(uuid_str) except ValueError as e: assert f"`{uuid_str}` is not a valid UUID" in e

capitalone_repos/rubicon-ml/tests/unit/workflow

capitalone_repos/rubicon-ml/tests/unit/workflow/prefect/test_prefect.py

import sys from unittest.mock import patch import pytest from rubicon_ml.workflow.prefect import _check_for_prefect_extras def test_missing_prefect_extra_raises_error(): install_prefect_message = "Install `prefect` with `pip install rubicon[prefect]`." with patch.dict(sys.modules, {"prefect": None}): with pytest.raises(ImportError) as e: _check_for_prefect_extras() assert str(e.value) in install_prefect_message

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/generators.py

import os import sys import holoviews as hv import pandas as pd from rubicon_ml import Rubicon from rubicon_ml.exceptions import RubiconException def get_or_create_project(rubicon, name): try: project = rubicon.create_project(name) except RubiconException: project = rubicon.get_project(name) return project def log_rubicon(path): project_name = "intake-rubicon unit testing" if os.path.exists(os.path.join(path, "projects", project_name)): return rubicon = Rubicon(persistence="filesystem", root_dir=path) project = get_or_create_project(rubicon, project_name) experiment_a = project.log_experiment(name="experiment_a", tags=["model-a", "y"]) experiment_a.log_feature("year") experiment_a.log_feature("credit score") experiment_b = project.log_experiment(name="experiment_b", tags=["model-b", "y"]) experiment_b.log_feature("year") experiment_b.log_feature("credit score") experiment_a.log_parameter("random state", 13243546) experiment_a.log_parameter("test size", "10 GB") experiment_a.log_parameter("n_estimators", 20) experiment_a.log_metric("Accuracy", "99") experiment_a.log_metric("AUC", "0.825") df = pd.DataFrame([[1, 2, 3], [2, 1, 2], [3, 2, 1]], columns=["x", "y", "z"]) dataframe = experiment_a.log_dataframe(df, tags=["x", "y"]) plot = dataframe.plot(kind="bar") plot_path = f"{path}/plot.png" hv.save(plot, plot_path, fmt="png") project.log_artifact(data_path=plot_path, description="bar plot logged with path") with open(plot_path, "rb") as f: source_data = f.read() project.log_artifact( data_bytes=source_data, name="plot.png", description="bar plot logged with bytes", ) with open(plot_path, "rb") as f: project.log_artifact(data_file=f, name="plot.png", description="bar plot logged with file") if __name__ == "__main__": here = os.path.dirname(__file__) path = os.path.join(here, "data") sys.exit(log_rubicon(path))

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/test_publish.py

import fsspec import yaml from rubicon_ml import Rubicon, publish def test_publish(project_client): project = project_client experiment = project.log_experiment() catalog_yaml = publish(project.experiments()) catalog = yaml.safe_load(catalog_yaml) assert f"experiment_{experiment.id.replace('-', '_')}" in catalog["sources"] assert ( "rubicon_ml_experiment" == catalog["sources"][f"experiment_{experiment.id.replace('-', '_')}"]["driver"] ) assert ( experiment.repository.root_dir == catalog["sources"][f"experiment_{experiment.id.replace('-', '_')}"]["args"]["urlpath"] ) assert ( experiment.id == catalog["sources"][f"experiment_{experiment.id.replace('-', '_')}"]["args"][ "experiment_id" ] ) assert ( project.name == catalog["sources"][f"experiment_{experiment.id.replace('-', '_')}"]["args"][ "project_name" ] ) def test_publish_from_multiple_sources(): rubicon_a = Rubicon(persistence="memory", root_dir="path/a") rubicon_b = Rubicon(persistence="memory", root_dir="path/b") experiment_a = rubicon_a.create_project("test").log_experiment() experiment_b = rubicon_b.create_project("test").log_experiment() catalog_yaml = publish([experiment_a, experiment_b]) catalog = yaml.safe_load(catalog_yaml) assert ( rubicon_a.repository.root_dir == catalog["sources"][f"experiment_{experiment_a.id.replace('-', '_')}"]["args"]["urlpath"] ) assert ( rubicon_b.repository.root_dir == catalog["sources"][f"experiment_{experiment_b.id.replace('-', '_')}"]["args"]["urlpath"] ) assert ( catalog["sources"][f"experiment_{experiment_a.id.replace('-', '_')}"]["args"]["urlpath"] != catalog["sources"][f"experiment_{experiment_b.id.replace('-', '_')}"]["args"]["urlpath"] ) def test_publish_to_file(project_client): project = project_client project.log_experiment() project.log_experiment() catalog_yaml = publish(project.experiments(), output_filepath="memory://catalog.yml") with fsspec.open("memory://catalog.yml", "r") as f: written_catalog = f.read() assert catalog_yaml == written_catalog def test_update_catalog(project_client): project = project_client project.log_experiment() project.log_experiment() publish(project.experiments(), output_filepath="memory://catalog.yml") # add new experiments to project experiment_c = project.log_experiment() experiment_d = project.log_experiment() new_experiments = [experiment_c, experiment_d] # publish new experiments into the exisiting catalog updated_catalog = publish( base_catalog_filepath="memory://catalog.yml", experiments=new_experiments ) with fsspec.open("memory://catalog.yml", "r") as f: written_catalog = f.read() assert updated_catalog == written_catalog

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/catalog.yml

sources: experiment_a: driver: rubicon_ml_experiment args: urlpath: '{{ CATALOG_DIR }}/data' project_name: 'intake-rubicon unit testing' experiment_id: '5f6ca6a1-563f-4f34-a34b-b45997bc4a1f'

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/test_experiment.py

import os import intake from rubicon_ml.intake_rubicon.experiment import ExperimentSource root = os.path.dirname(__file__) project_name = "intake-rubicon unit testing" experiment_id = "5f6ca6a1-563f-4f34-a34b-b45997bc4a1f" def test_source(): source = ExperimentSource(os.path.join(root, "data"), project_name, experiment_id) assert source is not None source.discover() experiment = source.read() assert experiment is not None assert experiment.id == experiment_id metadata = source._schema.extra_metadata assert metadata["experiment"]["name"] == experiment.name source.close() def test_catalog(): cat = intake.open_catalog(os.path.join(root, "catalog.yml")) source = cat.experiment_a() source.discover() experiment = source.read() assert experiment is not None assert experiment.id == experiment_id

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/metadata.json

{"name": "intake-rubicon unit testing", "id": "84c3cab2-29e1-4b55-98a3-035516bbf01a", "description": null, "training_metadata": null, "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.375744"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/5f6ca6a1-563f-4f34-a34b-b45997bc4a1f/metadata.json

{"project_name": "intake-rubicon unit testing", "id": "5f6ca6a1-563f-4f34-a34b-b45997bc4a1f", "name": "experiment_b", "description": null, "model_name": null, "commit_hash": null, "training_metadata": null, "tags": ["model-b", "y"], "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.386372"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/5f6ca6a1-563f-4f34-a34b-b45997bc4a1f/features

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/5f6ca6a1-563f-4f34-a34b-b45997bc4a1f/features/year/metadata.json

{"name": "year", "id": "a25d0c07-968e-41d2-8f48-86e4aeddba2a", "description": null, "importance": null, "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.388006"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/5f6ca6a1-563f-4f34-a34b-b45997bc4a1f/features

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/5f6ca6a1-563f-4f34-a34b-b45997bc4a1f/features/credit-score/metadata.json

{"name": "credit score", "id": "00140fd9-e96a-40ca-bbf4-0cdb9c6ec5e3", "description": null, "importance": null, "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.388985"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/metadata.json

{"project_name": "intake-rubicon unit testing", "id": "8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1", "name": "experiment_a", "description": null, "model_name": null, "commit_hash": null, "training_metadata": null, "tags": ["model-a", "y"], "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.377333"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/features

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/features/year/metadata.json

{"name": "year", "id": "cce8917c-1382-4720-8927-005ecd9d73cc", "description": null, "importance": null, "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.383581"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/features

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/features/credit-score/metadata.json

{"name": "credit score", "id": "d9022b6d-928f-450f-ad39-e6d9340acc10", "description": null, "importance": null, "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.385203"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/metrics

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/metrics/auc/metadata.json

{"name": "AUC", "value": "0.825", "id": "b41397b5-28f8-4e07-8ca2-a41342adafd5", "description": null, "directionality": "score", "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.394936"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/metrics

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/metrics/accuracy/metadata.json

{"name": "Accuracy", "value": "99", "id": "25ef306a-4798-4959-bdb6-3be80e779892", "description": null, "directionality": "score", "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.393400"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/parameters

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/parameters/nestimators/metadata.json

{"name": "n_estimators", "id": "64537e08-7a34-44d4-9906-e498acf3a434", "value": 20, "description": null, "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.391705"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/parameters

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/parameters/random-state/metadata.json

{"name": "random state", "id": "7665c636-f5fe-4a4a-bcd8-a58f7324c7b5", "value": 13243546, "description": null, "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.389866"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/parameters

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/parameters/test-size/metadata.json

{"name": "test size", "id": "5087c08c-abfc-40e7-bfff-9a64c8cefee9", "value": "10 GB", "description": null, "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.390857"}}

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/dataframes

capitalone_repos/rubicon-ml/tests/unit/intake_rubicon/data/intakerubicon-unit-testing/experiments/8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1/dataframes/aa8d5354-3e39-48fc-ace7-6939c9c79590/metadata.json

{"id": "aa8d5354-3e39-48fc-ace7-6939c9c79590", "description": null, "tags": ["x", "y"], "created_at": {"_type": "datetime", "value": "2020-07-13 15:03:21.397566"}, "parent_id": "8e5b4ad9-6afc-43ce-9e19-aaa1d80831f1"}

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_project_client.py

import os import time import warnings from unittest import mock from unittest.mock import patch import pytest from rubicon_ml import domain from rubicon_ml.client import Project, Rubicon from rubicon_ml.exceptions import RubiconException from rubicon_ml.repository.utils import slugify def _raise_error(): raise RubiconException() class MockCompletedProcess: def __init__(self, stdout="", returncode=0): self.stdout = stdout self.returncode = returncode def test_properties(): domain_project = domain.Project( "Test Project", description="a test project", github_url="github.com", training_metadata=domain.utils.TrainingMetadata([("test/path", "SELECT * FROM test")]), ) project = Project(domain_project) assert project.name == "Test Project" assert project.description == "a test project" assert project.github_url == "github.com" assert project.training_metadata == domain_project.training_metadata.training_metadata[0] assert project.created_at == domain_project.created_at assert project.id == domain_project.id def test_get_branch_name(project_client): project = project_client with mock.patch("subprocess.run") as mock_run: mock_run.return_value = MockCompletedProcess(stdout=b"branch-name\n") expected = [ mock.call(["git", "rev-parse", "--abbrev-ref", "HEAD"], capture_output=True), ] assert project._get_branch_name() == "branch-name" assert mock_run.mock_calls == expected def test_get_commit_hash(project_client): project = project_client with mock.patch("subprocess.run") as mock_run: mock_run.return_value = MockCompletedProcess(stdout=b"abcd0000\n") expected = [ mock.call(["git", "rev-parse", "HEAD"], capture_output=True), ] assert project._get_commit_hash() == "abcd0000" assert mock_run.mock_calls == expected def test_get_identifiers(project_client): project = project_client project_name, experiment_id = project._get_identifiers() assert project_name == project.name assert experiment_id is None def test_create_experiment_with_auto_git(): with mock.patch("subprocess.run") as mock_run: mock_run.return_value = MockCompletedProcess(stdout=b"test", returncode=0) rubicon = Rubicon("memory", "test-root", auto_git_enabled=True) project = rubicon.create_project("Test Project A") project.log_experiment() expected = [ mock.call(["git", "rev-parse", "--git-dir"], capture_output=True), mock.call(["git", "remote", "-v"], capture_output=True), mock.call(["git", "rev-parse", "--abbrev-ref", "HEAD"], capture_output=True), mock.call(["git", "rev-parse", "HEAD"], capture_output=True), ] assert mock_run.mock_calls == expected rubicon.repository.filesystem.store = {} def test_experiments_log_and_retrieval(project_client): project = project_client experiment1 = project.log_experiment( name="exp1", training_metadata=[("test/path", "SELECT * FROM test")] ) experiment2 = project.log_experiment(name="exp2") assert experiment1._domain.project_name == project.name assert len(project.experiments()) == 2 assert experiment1.id in [e.id for e in project.experiments()] assert experiment2.id in [e.id for e in project.experiments()] @mock.patch("rubicon_ml.repository.BaseRepository.get_experiments") def test_get_experiments_multiple_backend_error(mock_get_experiments, project_composite_client): project = project_composite_client mock_get_experiments.side_effect = _raise_error with pytest.raises(RubiconException) as e: project.experiments() assert "all configured storage backends failed" in str(e) def test_experiment_by_id(rubicon_and_project_client): project = rubicon_and_project_client[1] _experiment = project.log_experiment(tags=["x"]) project.log_experiment(tags=["y"]) experiment = project.experiment(id=_experiment.id) assert experiment.id == _experiment.id def test_experiment_by_name(project_client): project = project_client project.log_experiment(name="exp1") experiment = project.experiment(name="exp1") assert experiment.name == "exp1" def test_get_experiment_fails_both_set(project_client): project = project_client project.log_experiment(name="exp1") with pytest.raises(ValueError) as e: project.experiment(name="foo", id=123) assert "`name` OR `id` required." in str(e.value) def test_get_experiment_fails_neither_set(project_client): project = project_client project.log_experiment(name="exp1") with pytest.raises(ValueError) as e: project.experiment(name=None, id=None) assert "`name` OR `id` required." in str(e.value) @mock.patch("rubicon_ml.repository.BaseRepository.get_experiment") def test_get_experiment_multiple_backend_error(mock_get_experiment, project_composite_client): project = project_composite_client mock_get_experiment.side_effect = _raise_error with pytest.raises(RubiconException) as e: project.experiment("exp1") assert "all configured storage backends failed" in str(e) def test_experiment_warning(project_client, test_dataframe): project = project_client experiment_a = project.log_experiment(name="exp1") experiment_b = project.log_experiment(name="exp1") with warnings.catch_warnings(record=True) as w: experiment_c = project.experiment(name="exp1") assert ( "Multiple experiments found with name 'exp1'. Returning most recently logged" ) in str(w[0].message) assert experiment_c.id != experiment_a.id assert experiment_c.id == experiment_b.id def test_experiment_name_not_found_error(project_client): project = project_client with pytest.raises(RubiconException) as e: project.experiment(name="exp1") assert "No experiment found with name 'exp1'." in str(e) def test_experiments_tagged_and(project_client): project = project_client experiment = project.log_experiment(tags=["x", "y"]) project.log_experiment(tags=["x"]) project.log_experiment(tags=["y"]) experiments = project.experiments(tags=["x", "y"], qtype="and") assert len(experiments) == 1 assert experiment.id in [e.id for e in experiments] def test_experiments_tagged_or(project_client): project = project_client experiment_a = project.log_experiment(tags=["x"]) experiment_b = project.log_experiment(tags=["y"]) project.log_experiment(tags=["z"]) experiments = project.experiments(tags=["x", "y"], qtype="or") assert len(experiments) == 2 assert experiment_a.id in [e.id for e in experiments] assert experiment_b.id in [e.id for e in experiments] def test_dataframes_recursive(project_client, test_dataframe): project = project_client experiment = project.log_experiment() df = test_dataframe dataframe_a = project.log_dataframe(df) dataframe_b = experiment.log_dataframe(df) dataframes = project.dataframes(recursive=True) assert len(dataframes) == 2 assert dataframe_a.id in [d.id for d in dataframes] assert dataframe_b.id in [d.id for d in dataframes] def test_to_dask_df(rubicon_and_project_client_with_experiments): project = rubicon_and_project_client_with_experiments[1] ddf = project.to_df(df_type="dask") # compute to pandas df so we can use iloc for easier testing df = ddf.compute() # check that all experiments made it into df assert len(df) == 10 # check the cols within the df exp_details = ["id", "name", "description", "model_name", "commit_hash", "tags", "created_at"] for detail in exp_details: assert detail in df.columns def test_to_pandas_df(rubicon_and_project_client_with_experiments): project = rubicon_and_project_client_with_experiments[1] df = project.to_df(df_type="pandas") # check that all experiments made it into df assert len(df) == 10 # check the cols within the df exp_details = ["id", "name", "description", "model_name", "commit_hash", "tags", "created_at"] for detail in exp_details: assert detail in df.columns def test_to_dask_df_grouped_by_commit_hash(rubicon_and_project_client_with_experiments): project = rubicon_and_project_client_with_experiments[1] ddfs = project.to_df(df_type="dask", group_by="commit_hash") # compute to pandas df so we can use iloc for easier testing dfs = [ddf.compute() for ddf in ddfs.values()] # check df was broken into 4 groups assert len(dfs) == 4 for df in dfs: # check the cols within the df exp_details = [ "id", "name", "description", "model_name", "commit_hash", "tags", "created_at", ] for detail in exp_details: assert detail in df.columns def test_to_pandas_df_grouped_by_commit_hash(rubicon_and_project_client_with_experiments): project = rubicon_and_project_client_with_experiments[1] dfs = project.to_df(df_type="pandas", group_by="commit_hash") # check df was broken into 4 groups assert len(dfs) == 4 for df in dfs.values(): # check the cols within the df exp_details = [ "id", "name", "description", "model_name", "commit_hash", "tags", "created_at", ] for detail in exp_details: assert detail in df.columns def test_to_df_grouped_by_invalid_group(rubicon_and_project_client_with_experiments): project = rubicon_and_project_client_with_experiments[1] with pytest.raises(ValueError) as e: project.to_df(group_by="INVALID") assert "`group_by` must be one of" in str(e) def test_archive_no_experiments(project_client): project = project_client with pytest.raises(ValueError): project.archive() def test_archive_bad_experiments_argument(project_client): project = project_client experiment1 = project.log_experiment(name="experiment1") with pytest.raises(ValueError): project.archive(experiment1) project.log_experiment(name="experiment2") with pytest.raises(ValueError): project.archive(["experiment1", "experiment2"]) def test_archive_all_experiments(rubicon_local_filesystem_client_with_project): project = rubicon_local_filesystem_client_with_project[1] project.log_experiment(name="experiment1") project.log_experiment(name="experiment2") project.log_experiment(name="experiment3") archive_path = project.archive() assert project.repository._exists(archive_path) def test_archive_select_experiments(rubicon_local_filesystem_client_with_project): project = rubicon_local_filesystem_client_with_project[1] experiment1 = project.log_experiment(name="experiment1") experiment2 = project.log_experiment(name="experiment2") project.log_experiment(name="experiment3") zip_archive_filename = project.archive([experiment1, experiment2]) assert project.repository._exists(zip_archive_filename) def test_archive_bad_remote_rubicon(): rubiconA = Rubicon( persistence="filesystem", root_dir=os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconA"), ) rubiconB = Rubicon( persistence="filesystem", root_dir=os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconB"), ) rubiconA.get_or_create_project("ArchiveTesting") projectB = rubiconB.get_or_create_project("ArchiveTesting") projectB.log_experiment(name="experiment1") projectB.log_experiment(name="experiment2") with pytest.raises(ValueError): projectB.archive(remote_rubicon=os.path.dirname(os.path.realpath(__file__))) rubiconA.repository.filesystem.rm(rubiconA.config.root_dir, recursive=True) rubiconB.repository.filesystem.rm(rubiconB.config.root_dir, recursive=True) def test_archive_remote_rubicon(): rubiconA = Rubicon( persistence="filesystem", root_dir=os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconA"), ) rubiconB = Rubicon( persistence="filesystem", root_dir=os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconB"), ) projectA = rubiconA.get_or_create_project("ArchiveTesting") projectB = rubiconB.get_or_create_project("ArchiveTesting") projectB.log_experiment(name="experiment1") projectB.log_experiment(name="experiment2") zip_archive_filename = projectB.archive(remote_rubicon=rubiconA) assert projectA.repository._exists(zip_archive_filename) rubiconA.repository.filesystem.rm(rubiconA.config.root_dir, recursive=True) rubiconB.repository.filesystem.rm(rubiconB.config.root_dir, recursive=True) def test_experiments_from_archive_bad_argument(): rubiconA = Rubicon( persistence="filesystem", root_dir=os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconA"), ) rubiconB = Rubicon( persistence="filesystem", root_dir=os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconB"), ) projectA = rubiconA.get_or_create_project("ArchiveTesting") projectA.log_experiment(name="experiment1") projectA.log_experiment(name="experiment2") # no archive created projectB = rubiconB.get_or_create_project("ArchiveTesting") with pytest.raises(ValueError): projectB.experiments_from_archive( remote_rubicon=os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconA") ) rubiconA.repository.filesystem.rm(rubiconA.config.root_dir, recursive=True) rubiconB.repository.filesystem.rm(rubiconB.config.root_dir, recursive=True) def test_experiments_from_archive(): root_dirA = os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconA") rubiconA = Rubicon( persistence="filesystem", root_dir=root_dirA, ) root_dirB = os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconB") rubiconB = Rubicon( persistence="filesystem", root_dir=root_dirB, ) projectA = rubiconA.get_or_create_project("ArchiveTesting") projectA.log_experiment(name="experiment1") projectA.log_experiment(name="experiment2") projectA.archive() projectB = rubiconB.get_or_create_project("ArchiveTesting") projectB.log_experiment(name="experiment3") projectB.log_experiment(name="experiment4") experiments_dirB = os.path.join(root_dirB, slugify(projectB.name), "experiments") og_num_exps_B = len(projectB.repository._ls(experiments_dirB)) assert og_num_exps_B == 2 projectB.experiments_from_archive(remote_rubicon=rubiconA) new_num_expsB = len(projectB.repository._ls(experiments_dirB)) assert new_num_expsB == 4 rubiconA.repository.filesystem.rm(rubiconA.config.root_dir, recursive=True) rubiconB.repository.filesystem.rm(rubiconB.config.root_dir, recursive=True) def test_experiments_from_archive_latest_only(): root_dirA = os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconA") rubiconA = Rubicon( persistence="filesystem", root_dir=root_dirA, ) root_dirB = os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconB") rubiconB = Rubicon( persistence="filesystem", root_dir=root_dirB, ) projectA = rubiconA.get_or_create_project("ArchiveTesting") projectA.log_experiment(name="experiment1") projectA.log_experiment(name="experiment2") projectA.log_experiment(name="experiment3") projectA.archive() projectB = rubiconB.get_or_create_project("ArchiveTesting") projectB.log_experiment(name="experiment4") projectB.log_experiment(name="experiment5") experiment6 = projectA.log_experiment(name="experiment6") experiment7 = projectA.log_experiment(name="experiment7") time.sleep(1) projectA.archive([experiment6, experiment7]) experiments_dirB = os.path.join(root_dirB, slugify(projectB.name), "experiments") og_num_exps_B = len(projectB.repository._ls(experiments_dirB)) assert og_num_exps_B == 2 projectB.experiments_from_archive(remote_rubicon=rubiconA, latest_only=True) new_num_expsB = len(projectB.repository._ls(experiments_dirB)) assert new_num_expsB == 4 rubiconA.repository.filesystem.rm(rubiconA.config.root_dir, recursive=True) rubiconB.repository.filesystem.rm(rubiconB.config.root_dir, recursive=True) @patch("fsspec.open") def test_archive_remote_rubicon_s3(mock_open): print("buffer") rubicon_a = Rubicon( persistence="filesystem", root_dir=os.path.join(os.path.dirname(os.path.realpath(__file__)), "rubiconA"), ) s3_repo = "s3://bucket/root/path/to/data" rubicon_b = Rubicon(persistence="filesystem", root_dir=s3_repo) projectA = rubicon_a.get_or_create_project("ArchiveTesting") projectA.log_experiment(name="experiment1") projectA.log_experiment(name="experiment2") zip_archive_filename = projectA.archive(remote_rubicon=rubicon_b) mock_open.assert_called_once_with(zip_archive_filename, "wb") rubicon_a.repository.filesystem.rm(rubicon_a.config.root_dir, recursive=True)

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_metric_client.py

from unittest.mock import MagicMock from rubicon_ml import domain from rubicon_ml.client import Metric def test_properties(): domain_metric = domain.Metric("Accuracy", 99, description="some description") metric = Metric(domain_metric, MagicMock()) assert metric.name == "Accuracy" assert metric.value == 99 assert metric.directionality == "score" assert metric.description == "some description" assert metric.id == domain_metric.id assert metric.created_at == domain_metric.created_at assert hasattr(metric, "parent")

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_base_client.py

def test_str(project_client): project = project_client assert project.__str__() == project._domain.__str__() def test_get_repository(rubicon_client): rubicon = rubicon_client assert rubicon.repository == rubicon.config.repository

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_experiment_client.py

from unittest import mock import pytest from rubicon_ml import domain from rubicon_ml.client import Experiment from rubicon_ml.exceptions import RubiconException def _raise_error(): raise RubiconException() def test_properties(project_client): project = project_client domain_experiment = domain.Experiment( project_name=project.name, description="some description", name="exp-1", model_name="ModelOne model", branch_name="branch", commit_hash="a-commit-hash", training_metadata=domain.utils.TrainingMetadata([("test/path", "SELECT * FROM test")]), tags=["x"], ) experiment = Experiment(domain_experiment, project) assert experiment.name == "exp-1" assert experiment.description == "some description" assert experiment.model_name == "ModelOne model" assert experiment.branch_name == "branch" assert experiment.commit_hash == "a-commit-hash" assert experiment.name == domain_experiment.name assert experiment.commit_hash == domain_experiment.commit_hash assert experiment.training_metadata == domain_experiment.training_metadata.training_metadata[0] assert experiment.tags == domain_experiment.tags assert experiment.created_at == domain_experiment.created_at assert experiment.id == domain_experiment.id assert experiment.project == project def test_get_identifiers(project_client): project = project_client experiment = project.log_experiment() project_name, experiment_id = experiment._get_identifiers() assert project_name == project.name assert experiment_id == experiment.id def test_log_metric(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_metric("Accuracy", 99) experiment.log_metric("AUC", 0.825) assert "Accuracy" in [m.name for m in experiment.metrics()] assert "AUC" in [m.name for m in experiment.metrics()] def test_get_metrics(project_client): project = project_client experiment = project.log_experiment(name="exp1") metric = {"name": "Accuracy", "value": 99} experiment.log_metric(metric["name"], metric["value"]) metrics = experiment.metrics() assert len(metrics) == 1 assert metrics[0].name == metric["name"] assert metrics[0].value == metric["value"] @mock.patch("rubicon_ml.repository.BaseRepository.get_metrics") def test_get_metrics_multiple_backend_error(mock_get_metrics, project_composite_client): project = project_composite_client experiment = project.log_experiment(name="exp1") mock_get_metrics.side_effect = _raise_error with pytest.raises(RubiconException) as e: experiment.metrics() assert "all configured storage backends failed" in str(e) def test_get_metric_by_name(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_metric("accuracy", 100) metric = experiment.metric(name="accuracy").name assert metric == "accuracy" def test_get_metric_fails_neither_set(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_metric("accuracy", 100) with pytest.raises(ValueError) as e: experiment.metric(name=None, id=None) assert "`name` OR `id` required." in str(e) def test_get_metric_fails_both_set(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_metric("accuracy", 100) with pytest.raises(ValueError) as e: experiment.metric(name="foo", id=123) assert "`name` OR `id` required." in str(e) def test_metrics_tagged_and(project_client): project = project_client experiment = project.log_experiment(name="exp1") metric = experiment.log_metric(name="name", value=0, tags=["x", "y"]) experiment.log_metric(name="name_a", value=0, tags=["x"]) experiment.log_metric(name="name_b", value=0, tags=["y"]) metrics = experiment.metrics(tags=["x", "y"], qtype="and") assert len(metrics) == 1 assert metric.id in [d.id for d in metrics] def test_metrics_tagged_or(project_client): project = project_client experiment = project.log_experiment(name="exp1") metric_a = experiment.log_metric(name="name_a", value=0, tags=["x"]) metric_b = experiment.log_metric(name="name_b", value=0, tags=["y"]) experiment.log_metric(name="name_c", value=0, tags=["z"]) metrics = experiment.metrics(tags=["x", "y"], qtype="or") assert len(metrics) == 2 assert metric_a.id in [d.id for d in metrics] assert metric_b.id in [d.id for d in metrics] def test_get_metric_by_id(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_metric("accuracy", 100) metric_id = experiment.metric("accuracy").id metric = experiment.metric(id=metric_id).name assert metric == "accuracy" @mock.patch("rubicon_ml.repository.BaseRepository.get_metric") def test_get_metric_multiple_backend_error(mock_get_metric, project_composite_client): project = project_composite_client experiment = project.log_experiment(name="exp1") mock_get_metric.side_effect = _raise_error with pytest.raises(RubiconException) as e: experiment.metric("accuracy") assert "all configured storage backends failed" in str(e) def test_log_feature(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_feature("year") assert "year" in [f.name for f in experiment.features()] def test_get_features(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_feature("year") experiment.log_feature("credit score") features = experiment.features() assert len(features) == 2 assert features[0].name == "year" assert features[1].name == "credit score" @mock.patch("rubicon_ml.repository.BaseRepository.get_features") def test_get_features_multiple_backend_error(mock_get_features, project_composite_client): project = project_composite_client experiment = project.log_experiment(name="exp1") mock_get_features.side_effect = _raise_error with pytest.raises(RubiconException) as e: experiment.features() assert "all configured storage backends failed" in str(e) def test_get_feature_by_name(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_feature("year") feature = experiment.feature(name="year").name assert feature == "year" def test_get_feature_by_id(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_feature("year") feature_id = experiment.feature("year").id feature = experiment.feature(id=feature_id).name assert feature == "year" def test_get_feature_fails_neither_set(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_feature("year") with pytest.raises(ValueError) as e: experiment.feature(name=None, id=None) assert "`name` OR `id` required." in str(e) def test_get_feature_fails_both_set(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_feature("year") with pytest.raises(ValueError) as e: experiment.feature(name="foo", id=123) assert "`name` OR `id` required." in str(e) @mock.patch("rubicon_ml.repository.BaseRepository.get_feature") def test_get_feature_multiple_backend_error(mock_get_feature, project_composite_client): project = project_composite_client experiment = project.log_experiment(name="exp1") mock_get_feature.side_effect = _raise_error with pytest.raises(RubiconException) as e: experiment.feature("year") assert "all configured storage backends failed" in str(e) def test_features_tagged_and(project_client): project = project_client experiment = project.log_experiment(name="exp1") feature = experiment.log_feature(name="name", tags=["x", "y"]) experiment.log_feature(name="name_a", tags=["x"]) experiment.log_feature(name="name_b", tags=["y"]) features = experiment.features(tags=["x", "y"], qtype="and") assert len(features) == 1 assert feature.id in [d.id for d in features] def test_features_tagged_or(project_client): project = project_client experiment = project.log_experiment(name="exp1") feature_a = experiment.log_feature(name="name_a", tags=["x"]) feature_b = experiment.log_feature(name="name_b", tags=["y"]) experiment.log_feature(name="name_c", tags=["z"]) features = experiment.features(tags=["x", "y"], qtype="or") assert len(features) == 2 assert feature_a.id in [d.id for d in features] assert feature_b.id in [d.id for d in features] def test_log_parameter(project_client): project = project_client experiment = project.log_experiment() experiment.log_parameter("test", value="value") assert "test" in [p.name for p in experiment.parameters()] assert "value" in [p.value for p in experiment.parameters()] def test_parameters(project_client): project = project_client experiment = project.log_experiment() parameter_a = experiment.log_parameter("test_a", value="value_a") parameter_b = experiment.log_parameter("test_b", value="value_b") parameters = experiment.parameters() assert len(parameters) == 2 assert parameter_a.id in [p.id for p in parameters] assert parameter_b.id in [p.id for p in parameters] @mock.patch("rubicon_ml.repository.BaseRepository.get_parameters") def test_parameters_multiple_backend_error(mock_get_parameters, project_composite_client): project = project_composite_client experiment = project.log_experiment(name="exp1") mock_get_parameters.side_effect = _raise_error with pytest.raises(RubiconException) as e: experiment.parameters() assert "all configured storage backends failed" in str(e) def test_get_parameter_by_name(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_parameter("n_estimators", "estimator") parameter = experiment.parameter(name="n_estimators").name assert parameter == "n_estimators" def test_get_parameter_by_id(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_parameter("n_estimators", "estimator") parameter_id = experiment.parameter("n_estimators").id parameter = experiment.parameter(id=parameter_id).name assert parameter == "n_estimators" def test_get_parameter_fails_neither_set(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_parameter("n_estimators", "estimator") with pytest.raises(ValueError) as e: experiment.parameter(name=None, id=None) assert "`name` OR `id` required." in str(e) def test_get_parameter_fails_both_set(project_client): project = project_client experiment = project.log_experiment(name="exp1") experiment.log_parameter("n_estimators", "estimator") with pytest.raises(ValueError) as e: experiment.parameter(name="foo", id=123) assert "`name` OR `id` required." in str(e) @mock.patch("rubicon_ml.repository.BaseRepository.get_parameter") def test_get_parameter_multiple_backend_error(mock_get_parameter, project_composite_client): project = project_composite_client experiment = project.log_experiment(name="exp1") mock_get_parameter.side_effect = _raise_error with pytest.raises(RubiconException) as e: experiment.parameter("n_estimators") assert "all configured storage backends failed" in str(e) def test_parameters_tagged_and(project_client): project = project_client experiment = project.log_experiment(name="exp1") parameter = experiment.log_parameter(name="param_1", tags=["x", "y"]) experiment.log_parameter(name="param_2", tags=["x"]) experiment.log_parameter(name="param_3", tags=["y"]) parameters = experiment.parameters(tags=["x", "y"], qtype="and") assert len(parameters) == 1 assert parameter.id in [d.id for d in parameters] def test_parameters_tagged_or(project_client): project = project_client experiment = project.log_experiment(name="exp1") param_a = experiment.log_parameter(name="param_a", tags=["x"]) param_b = experiment.log_parameter(name="param_b", tags=["y"]) experiment.log_parameter(name="param_c", tags=["z"]) parameters = experiment.parameters(tags=["x", "y"], qtype="or") assert len(parameters) == 2 assert param_a.id in [d.id for d in parameters] assert param_b.id in [d.id for d in parameters]

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_parameter_client.py

from unittest.mock import MagicMock from rubicon_ml import domain from rubicon_ml.client import Parameter def test_properties(): domain_parameter = domain.Parameter("name", value="value", description="description") parameter = Parameter(domain_parameter, MagicMock()) assert parameter.id == domain_parameter.id assert parameter.name == domain_parameter.name assert parameter.value == domain_parameter.value assert parameter.description == domain_parameter.description assert parameter.created_at == domain_parameter.created_at assert hasattr(parameter, "parent")

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_feature_client.py

from unittest.mock import MagicMock from rubicon_ml import domain from rubicon_ml.client import Feature def test_properties(): domain_feature = domain.Feature("year", description="year feature", importance=0.5) feature = Feature(domain_feature, MagicMock()) assert feature.name == "year" assert feature.description == "year feature" assert feature.importance == 0.5 assert feature.id == domain_feature.id assert feature.created_at == domain_feature.created_at assert hasattr(feature, "parent")

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_mixin_client.py

import subprocess import warnings from unittest import mock from unittest.mock import MagicMock, patch import pytest from rubicon_ml.client.mixin import ArtifactMixin, DataframeMixin, TagMixin from rubicon_ml.exceptions import RubiconException def _raise_error(): raise RubiconException() # ArtifactMixin def test_log_artifact_from_bytes(project_client): project = project_client artifact = ArtifactMixin.log_artifact(project, data_bytes=b"content", name="test.txt") assert artifact.id in [a.id for a in project.artifacts()] assert artifact.name == "test.txt" assert artifact.data == b"content" def test_log_artifact_from_file(project_client): project = project_client mock_file = MagicMock() mock_file.__enter__().read.side_effect = [b"content"] artifact = ArtifactMixin.log_artifact(project, data_file=mock_file, name="test.txt") assert artifact.id in [a.id for a in project.artifacts()] assert artifact.name == "test.txt" assert artifact.data == b"content" @patch("fsspec.implementations.local.LocalFileSystem.open") def test_log_artifact_from_path(mock_open, project_client): project = project_client mock_file = MagicMock() mock_file().read.side_effect = [b"content"] mock_open.side_effect = mock_file artifact = ArtifactMixin.log_artifact(project, data_path="/path/to/test.txt") assert artifact.id in [a.id for a in project.artifacts()] assert artifact.name == "test.txt" assert artifact.data == b"content" def test_log_artifact_throws_error_if_data_missing(project_client): project = project_client with pytest.raises(RubiconException) as e: ArtifactMixin.log_artifact(project, name="test.txt") assert ( "One of `data_bytes`, `data_file`, `data_object` or `data_path` must be provided." in str(e) ) def test_log_artifact_throws_error_if_name_missing_data_bytes(project_client): project = project_client with pytest.raises(RubiconException) as e: ArtifactMixin.log_artifact(project, data_bytes=b"content") assert "`name` must be provided if not using `data_path`." in str(e) def test_log_artifact_throws_error_if_name_missing_data_file(project_client): project = project_client mock_file = MagicMock() mock_file.__enter__().read.side_effect = [b"content"] with pytest.raises(RubiconException) as e: ArtifactMixin.log_artifact(project, data_file=mock_file) assert "`name` must be provided if not using `data_path`." in str(e) def test_log_artifact_throws_error_if_name_missing_data_object(project_client): project = project_client class TestObject: value = "test" test_object = TestObject() with pytest.raises(RubiconException) as e: ArtifactMixin.log_artifact(project, data_object=test_object) assert "`name` must be provided if not using `data_path`." in str(e) def test_get_environment_bytes(project_client, mock_completed_process_empty): project = project_client with patch("subprocess.run") as mock_run: mock_run.return_value = mock_completed_process_empty env_bytes = project._get_environment_bytes(["conda", "env", "export"]) assert env_bytes == b"\n" def test_get_environment_bytes_error(project_client): project = project_client with pytest.raises(RubiconException) as e: with patch("subprocess.run") as mock_run: mock_run.side_effect = subprocess.CalledProcessError( returncode=-1, cmd=b"\n", stderr="yikes" ) project._get_environment_bytes(["conda", "env", "export"]) assert "yikes" in str(e) def test_log_conda_env(project_client, mock_completed_process_empty): project = project_client with patch("subprocess.run") as mock_run: mock_run.return_value = mock_completed_process_empty artifact = project.log_conda_environment() assert artifact.id in [a.id for a in project.artifacts()] assert ".yml" in artifact.name assert artifact.data == b"\n" def test_log_pip_requirements(project_client, mock_completed_process_empty): project = project_client with patch("subprocess.run") as mock_run: mock_run.return_value = mock_completed_process_empty artifact = project.log_pip_requirements() assert artifact.id in [a.id for a in project.artifacts()] assert ".txt" in artifact.name assert artifact.data == b"\n" def test_log_json(project_client): project = project_client data = {"hello": "world", "foo": [1, 2, 3]} artifact_a = ArtifactMixin.log_json(project, data, name="test.json") artifact_b = ArtifactMixin.log_json(project, data, name="test.txt") artifacts = ArtifactMixin.artifacts(project) assert len(artifacts) == 2 assert artifact_a.id in [a.id for a in artifacts] assert artifact_b.id in [a.id for a in artifacts] def test_artifacts(project_client): project = project_client data = b"content" artifact_a = ArtifactMixin.log_artifact(project, data_bytes=data, name="test.txt") artifact_b = ArtifactMixin.log_artifact(project, data_bytes=data, name="test.txt") artifacts = ArtifactMixin.artifacts(project) assert len(artifacts) == 2 assert artifact_a.id in [a.id for a in artifacts] assert artifact_b.id in [a.id for a in artifacts] @mock.patch("rubicon_ml.repository.BaseRepository.get_artifacts_metadata") def test_artifacts_multiple_backend_error(mock_get_artifacts_metadata, project_composite_client): project = project_composite_client mock_get_artifacts_metadata.side_effect = _raise_error with pytest.raises(RubiconException) as e: ArtifactMixin.artifacts(project) assert "all configured storage backends failed" in str(e) def test_artifacts_by_name(project_client): project = project_client data = b"content" artifact_a = ArtifactMixin.log_artifact(project, data_bytes=data, name="test.txt") artifact_b = ArtifactMixin.log_artifact(project, data_bytes=data, name="test.txt") ArtifactMixin.log_artifact(project, data_bytes=data, name="test2.txt") artifacts = ArtifactMixin.artifacts(project, name="test.txt") assert len(artifacts) == 2 assert artifact_a.id in [a.id for a in artifacts] assert artifact_b.id in [a.id for a in artifacts] def test_artifacts_tagged_and(project_client): project = project_client artifact = ArtifactMixin.log_artifact(project, name="name", data_bytes=b"test", tags=["x", "y"]) ArtifactMixin.log_artifact(project, name="name", data_bytes=b"test", tags=["x"]) ArtifactMixin.log_artifact(project, name="name", data_bytes=b"test", tags=["y"]) artifacts = ArtifactMixin.artifacts(project, tags=["x", "y"], qtype="and") assert len(artifacts) == 1 assert artifact.id in [d.id for d in artifacts] def test_artifacts_tagged_or(project_client): project = project_client artifact_a = ArtifactMixin.log_artifact(project, name="name", data_bytes=b"test", tags=["x"]) artifact_b = ArtifactMixin.log_artifact(project, name="name", data_bytes=b"test", tags=["y"]) ArtifactMixin.log_artifact(project, name="name", data_bytes=b"test", tags=["z"]) artifacts = ArtifactMixin.artifacts(project, tags=["x", "y"], qtype="or") assert len(artifacts) == 2 assert artifact_a.id in [d.id for d in artifacts] assert artifact_b.id in [d.id for d in artifacts] def test_artifact_warning(project_client): project = project_client data = b"content" artifact_a = ArtifactMixin.log_artifact(project, data_bytes=data, name="test.txt") artifact_b = ArtifactMixin.log_artifact(project, data_bytes=data, name="test.txt") with warnings.catch_warnings(record=True) as w: artifact_c = ArtifactMixin.artifact(project, name="test.txt") assert ( "Multiple artifacts found with name 'test.txt'. Returning most recently logged" ) in str(w[0].message) assert artifact_c.id != artifact_a.id assert artifact_c.id == artifact_b.id def test_artifact_name_not_found_error(project_client): project = project_client with pytest.raises(RubiconException) as e: ArtifactMixin.artifact(project, name="test.txt") assert "No artifact found with name 'test.txt'." in str(e) def test_artifacts_name_not_found_error(project_client): project = project_client artifacts = ArtifactMixin.artifacts(project, name="test.txt") assert artifacts == [] def test_artifact_by_name(project_client): project = project_client data = b"content" ArtifactMixin.log_artifact(project, data_bytes=data, name="test.txt") artifact = ArtifactMixin.artifact(project, name="test.txt") assert artifact.name == "test.txt" def test_artifact_by_id(project_client): project = project_client data = b"content" ArtifactMixin.log_artifact(project, data_bytes=data, name="test.txt") artifact = ArtifactMixin.artifact(project, name="test.txt") artifact_name = ArtifactMixin.artifact(project, id=artifact.id).name assert artifact_name == "test.txt" @mock.patch("rubicon_ml.repository.BaseRepository.get_artifact_metadata") def test_artifact_multiple_backend_error(mock_get_artifact_metadata, project_composite_client): project = project_composite_client data = b"content" artifact = ArtifactMixin.log_artifact(project, data_bytes=data, name="test.txt") mock_get_artifact_metadata.side_effect = _raise_error with pytest.raises(RubiconException) as e: ArtifactMixin.artifact(project, id=artifact.id) assert "all configured storage backends failed" in str(e) def test_delete_artifacts(project_client): project = project_client artifact = ArtifactMixin.log_artifact(project, data_bytes=b"content", name="test.txt") ArtifactMixin.delete_artifacts(project, [artifact.id]) assert artifact.id not in [a.id for a in project.artifacts()] # DataframeMixin def test_log_dataframe(project_client, test_dataframe): project = project_client df = test_dataframe test_df_name = "test_df" dataframe = DataframeMixin.log_dataframe(project, df, name=test_df_name, tags=["x"]) DataframeMixin.log_dataframe(project, df, name="secondary test df", tags=["x"]) assert dataframe.name == test_df_name assert dataframe.id in [df.id for df in project.dataframes()] def test_dataframes(project_client, test_dataframe): project = project_client df = test_dataframe dataframe_a = DataframeMixin.log_dataframe(project, df) dataframe_b = DataframeMixin.log_dataframe(project, df) dataframes = DataframeMixin.dataframes(project) assert len(dataframes) == 2 assert dataframe_a.id in [d.id for d in dataframes] assert dataframe_b.id in [d.id for d in dataframes] @mock.patch("rubicon_ml.repository.BaseRepository.get_dataframes_metadata") def test_dataframes_multiple_backend_error(mock_get_dataframes_metadata, project_composite_client): project = project_composite_client mock_get_dataframes_metadata.side_effect = _raise_error with pytest.raises(RubiconException) as e: DataframeMixin.dataframes(project) assert "all configured storage backends failed" in str(e) def test_dataframes_by_name(project_client, test_dataframe): project = project_client df = test_dataframe test_df_name = "test_df" dataframe_a = DataframeMixin.log_dataframe(project, df, name=test_df_name) dataframe_b = DataframeMixin.log_dataframe(project, df, name=test_df_name) dataframes = DataframeMixin.dataframes(project, name=test_df_name) assert len(dataframes) == 2 assert dataframe_a.id in [d.id for d in dataframes] assert dataframe_b.id in [d.id for d in dataframes] def test_dataframe_by_name(project_client, test_dataframe): project = project_client df = test_dataframe test_df_name = "test_df" dataframe_a = DataframeMixin.log_dataframe(project, df, name=test_df_name) dataframe_b = DataframeMixin.dataframe(project, name=test_df_name) assert dataframe_a.id == dataframe_b.id def test_dataframe_by_id(project_client, test_dataframe): project = project_client df = test_dataframe dataframe_a = DataframeMixin.log_dataframe(project, df) id = dataframe_a.id dataframe_b = DataframeMixin.dataframe(project, id=id) assert dataframe_a.id == dataframe_b.id @mock.patch("rubicon_ml.repository.BaseRepository.get_dataframe_metadata") def test_dataframe_multiple_backend_error( mock_get_dataframe_metadata, project_composite_client, test_dataframe ): project = project_composite_client dataframe = DataframeMixin.log_dataframe(project, test_dataframe) mock_get_dataframe_metadata.side_effect = _raise_error with pytest.raises(RubiconException) as e: DataframeMixin.dataframe(project, id=dataframe.id) assert "all configured storage backends failed" in str(e) def test_dataframe_warning(project_client, test_dataframe): project = project_client df = test_dataframe test_df_name = "test_df" dataframe_a = DataframeMixin.log_dataframe(project, df, name=test_df_name) dataframe_b = DataframeMixin.log_dataframe(project, df, name=test_df_name) with warnings.catch_warnings(record=True) as w: dataframe_c = DataframeMixin.dataframe(project, name=test_df_name) assert ( "Multiple dataframes found with name 'test_df'. Returning most recently logged" ) in str(w[0].message) assert dataframe_c.id != dataframe_a.id assert dataframe_c.id == dataframe_b.id def test_dataframe_by_name_not_found(project_client, test_dataframe): project = project_client test_df_name = "test_df" with pytest.raises(RubiconException) as e: DataframeMixin.dataframe(project, name=test_df_name) assert "No dataframe found with name 'test_df'." in str(e.value) def test_dataframes_by_name_not_found(project_client, test_dataframe): project = project_client test_df_name = "test_df" dataframes = DataframeMixin.dataframes(project, name=test_df_name) assert dataframes == [] def test_get_dataframe_fails_both_set(project_client, test_dataframe): project = project_client with pytest.raises(ValueError) as e: DataframeMixin.dataframe(project, name="foo", id=123) assert "`name` OR `id` required." in str(e.value) def test_get_dataframe_fails_neither_set(project_client, test_dataframe): project = project_client with pytest.raises(ValueError) as e: DataframeMixin.dataframe(project, name=None, id=None) assert "`name` OR `id` required." in str(e.value) def test_dataframes_tagged_and(project_client, test_dataframe): project = project_client df = test_dataframe dataframe = DataframeMixin.log_dataframe(project, df, tags=["x", "y"]) DataframeMixin.log_dataframe(project, df, tags=["x"]) DataframeMixin.log_dataframe(project, df, tags=["y"]) dataframes = DataframeMixin.dataframes(project, tags=["x", "y"], qtype="and") assert len(dataframes) == 1 assert dataframe.id in [d.id for d in dataframes] def test_dataframes_tagged_or(project_client, test_dataframe): project = project_client df = test_dataframe dataframe_a = DataframeMixin.log_dataframe(project, df, tags=["x"]) dataframe_b = DataframeMixin.log_dataframe(project, df, tags=["y"]) DataframeMixin.log_dataframe(project, df, tags=["z"]) dataframes = DataframeMixin.dataframes(project, tags=["x", "y"], qtype="or") assert len(dataframes) == 2 assert dataframe_a.id in [d.id for d in dataframes] assert dataframe_b.id in [d.id for d in dataframes] def test_delete_dataframes(project_client, test_dataframe): project = project_client df = test_dataframe dataframe = DataframeMixin.log_dataframe(project, df, tags=["x"]) DataframeMixin.delete_dataframes(project, [dataframe.id]) assert dataframe.id not in [df.id for df in project.dataframes()] # TagMixin def test_get_taggable_experiment_identifiers(project_client): project = project_client experiment = project.log_experiment() project_name, experiment_id, dataframe_id = TagMixin._get_taggable_identifiers(experiment) assert project_name == project.name assert experiment_id == experiment.id assert dataframe_id is None def test_get_taggable_dataframe_identifiers(project_client, test_dataframe): project = project_client experiment = project.log_experiment() df = test_dataframe project_df = project.log_dataframe(df) experiment_df = experiment.log_dataframe(df) project_name, experiment_id, dataframe_id = TagMixin._get_taggable_identifiers(project_df) assert project_name == project.name assert experiment_id is None assert dataframe_id == project_df.id project_name, experiment_id, dataframe_id = TagMixin._get_taggable_identifiers(experiment_df) assert project_name == project.name assert experiment_id is experiment.id assert dataframe_id == experiment_df.id def test_get_taggable_artifact_identifiers(project_client): project = project_client experiment = project.log_experiment() project_artifact = project.log_artifact(data_bytes=b"test", name="test") experiment_artifact = experiment.log_artifact(data_bytes=b"test", name="test") project_name, experiment_id, artifact_id = TagMixin._get_taggable_identifiers(project_artifact) assert project_name == project.name assert experiment_id is None assert artifact_id == project_artifact.id project_name, experiment_id, artifact_id = TagMixin._get_taggable_identifiers( experiment_artifact ) assert project_name == project.name assert experiment_id is experiment.id assert artifact_id == experiment_artifact.id def test_add_tags(project_client): project = project_client experiment = project.log_experiment() TagMixin.add_tags(experiment, ["x"]) assert experiment.tags == ["x"] def test_remove_tags(project_client): project = project_client experiment = project.log_experiment(tags=["x", "y"]) TagMixin.remove_tags(experiment, ["x", "y"]) assert experiment.tags == [] @mock.patch("rubicon_ml.repository.BaseRepository.get_tags") def test_tags_multiple_backend_error(mock_get_tags, project_composite_client): project = project_composite_client experiment = project.log_experiment(tags=["x", "y"]) def raise_error(): raise RubiconException() mock_get_tags.side_effect = _raise_error with pytest.raises(RubiconException) as e: experiment.tags() assert "all configured storage backends failed" in str(e)

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_rubicon_client.py

import subprocess from unittest import mock import dask.dataframe as dd import pandas as pd import pytest from rubicon_ml import client, domain from rubicon_ml.client import Rubicon from rubicon_ml.exceptions import RubiconException class TestRepository: root_dir = "" @property def filesystem(self): class TestFilesystem: def rm(self, path, recursive): pass return TestFilesystem() def _raise_error(): raise RubiconException() def test_get_repository(rubicon_client): rubicon = rubicon_client assert rubicon.repository == rubicon.config.repository def test_set_repository(rubicon_client): rubicon = rubicon_client test_repo = TestRepository() rubicon.repository = test_repo assert rubicon.config.repository == test_repo def test_repository_storage_options(): storage_options = {"key": "secret"} rubicon_memory = Rubicon(persistence="memory", root_dir="./", **storage_options) rubicon_s3 = Rubicon(persistence="filesystem", root_dir="s3://nothing", **storage_options) assert rubicon_memory.config.repository.filesystem.storage_options["key"] == "secret" assert rubicon_s3.config.repository.filesystem.storage_options["key"] == "secret" def test_get_github_url(rubicon_client, mock_completed_process_git): rubicon = rubicon_client with mock.patch("subprocess.run") as mock_run: mock_run.return_value = mock_completed_process_git expected = [ mock.call(["git", "remote", "-v"], capture_output=True), ] assert rubicon._get_github_url() == "github.com" assert mock_run.mock_calls == expected def test_get_github_url_no_remotes(rubicon_client, mock_completed_process_empty): rubicon = rubicon_client with mock.patch("subprocess.run") as mock_run: mock_run.return_value = mock_completed_process_empty assert rubicon._get_github_url() is None def test_create_project(rubicon_client): rubicon = rubicon_client project = rubicon.create_project( "Test Project A", training_metadata=[("test/path", "SELECT * FROM test")] ) assert project._domain.name == "Test Project A" def test_create_project_with_auto_git(mock_completed_process_git): with mock.patch("subprocess.run") as mock_run: mock_run.return_value = mock_completed_process_git rubicon = Rubicon("memory", "test-root", auto_git_enabled=True) rubicon.create_project("Test Project A") expected = [ mock.call(["git", "rev-parse", "--git-dir"], capture_output=True), mock.call(["git", "remote", "-v"], capture_output=True), ] assert mock_run.mock_calls == expected rubicon.repository.filesystem.store = {} def test_get_project_by_name(rubicon_and_project_client): rubicon, project = rubicon_and_project_client assert "Test Project" == rubicon.get_project("Test Project").name def test_get_project_by_id(rubicon_and_project_client): rubicon, project = rubicon_and_project_client project_id = project.id assert project_id == rubicon.get_project(id=project_id).id def test_get_project_fails_both_set(rubicon_and_project_client): rubicon, project = rubicon_and_project_client with pytest.raises(ValueError) as e: rubicon.get_project(name="foo", id=123) assert "`name` OR `id` required." in str(e.value) def test_get_project_fails_neither_set(rubicon_and_project_client): rubicon, project = rubicon_and_project_client with pytest.raises(ValueError) as e: rubicon.get_project(name=None, id=None) assert "`name` OR `id` required." in str(e.value) @mock.patch("rubicon_ml.repository.BaseRepository.get_project") def test_get_project_multiple_backend_error(mock_get_project, rubicon_composite_client): rubicon = rubicon_composite_client mock_get_project.side_effect = _raise_error with pytest.raises(RubiconException) as e: rubicon.get_project(name="Test Project") assert "all configured storage backends failed" in str(e) def test_get_projects(rubicon_client): rubicon = rubicon_client rubicon.create_project("Project A") rubicon.create_project("Project B") projects = rubicon.projects() assert len(projects) == 2 assert projects[0].name == "Project A" assert projects[1].name == "Project B" @mock.patch("rubicon_ml.repository.BaseRepository.get_projects") def test_get_projects_multiple_backend_error(mock_get_projects, rubicon_composite_client): rubicon = rubicon_composite_client mock_get_projects.side_effect = _raise_error with pytest.raises(RubiconException) as e: rubicon.projects() assert "all configured storage backends failed" in str(e) def test_get_or_create_project(rubicon_client): rubicon = rubicon_client created_project = rubicon.get_or_create_project("Test Project A") assert created_project._domain.name == "Test Project A" fetched_project = rubicon.get_or_create_project("Test Project A") assert fetched_project._domain.name == "Test Project A" assert created_project.id == fetched_project.id def test_sync_from_memory(rubicon_and_project_client): rubicon, project = rubicon_and_project_client with pytest.raises(RubiconException) as e: rubicon.sync("Test Project", "s3://test/path") assert "can't sync projects written to memory" in str(e) @mock.patch("subprocess.run") @mock.patch("rubicon_ml.client.Rubicon.get_project") def test_sync_from_local(mock_get_project, mock_run): rubicon = Rubicon(persistence="filesystem", root_dir="./local/path") project_name = "Sync Test Project" mock_get_project.return_value = client.Project(domain.Project(project_name)) rubicon.sync(project_name, "s3://test/path") assert "aws s3 sync ./local/path/sync-test-project s3://test/path" in str( mock_run._mock_call_args_list ) @mock.patch("subprocess.run") @mock.patch("rubicon_ml.client.Rubicon.get_project") def test_sync_from_local_error(mock_get_project, mock_run): rubicon = Rubicon(persistence="filesystem", root_dir="./local/path") project_name = "Sync Test Project" mock_get_project.return_value = client.Project(domain.Project(project_name)) mock_run.side_effect = subprocess.CalledProcessError( cmd="aws cli sync", stderr="Some error. I bet it was proxy tho.", returncode=1 ) with pytest.raises(RubiconException) as e: rubicon.sync(project_name, "s3://test/path") assert "Some error. I bet it was proxy tho." in str(e) def test_get_project_as_dask_df(rubicon_and_project_client_with_experiments): rubicon, project = rubicon_and_project_client_with_experiments ddf = rubicon.get_project_as_df(name="Test Project", df_type="dask") assert isinstance(ddf, dd.core.DataFrame) def test_get_project_as_pandas_df(rubicon_and_project_client_with_experiments): rubicon, project = rubicon_and_project_client_with_experiments ddf = rubicon.get_project_as_df(name="Test Project", df_type="pandas") assert isinstance(ddf, pd.DataFrame)

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_rubicon_json_client.py

import pandas as pd import pytest from rubicon_ml.client import Rubicon, RubiconJSON def test_experiment_to_json_single_experiment(rubicon_and_project_client): _, project = rubicon_and_project_client experiment = project.log_experiment("first experiment", tags=["a", "b"]) experiment.log_parameter("n estimators", 3) experiment.log_feature("year") experiment.log_metric("accuracy", 0.87) experiment.log_metric("runtime(s)", 45) experiment.log_metric("kernel", "linear") experiment.log_artifact(name="example artifact", data_bytes=b"a") experiment.log_dataframe(pd.DataFrame([[0, 1], [1, 0]])) experiment_as_json = RubiconJSON(experiments=experiment) json = experiment_as_json.json assert isinstance(json, dict) assert isinstance(json["experiment"], list) assert len(json["experiment"]) == 1 assert isinstance(json["experiment"][0], dict) assert json["experiment"][0]["project_name"] == "Test Project" assert isinstance(json["experiment"][0]["tags"], list) assert isinstance(json["experiment"][0]["feature"], list) assert isinstance(json["experiment"][0]["parameter"], list) assert isinstance(json["experiment"][0]["metric"], list) assert isinstance(json["experiment"][0]["artifact"], list) assert isinstance(json["experiment"][0]["dataframe"], list) assert isinstance(json["experiment"][0]["metric"][0]["value"], float) assert isinstance(json["experiment"][0]["metric"][1]["value"], int) assert isinstance(json["experiment"][0]["metric"][2]["value"], str) assert json["experiment"][0]["tags"] == ["a", "b"] assert len(json["experiment"][0]["metric"]) == 3 json_numeric = experiment_as_json.json_numeric assert isinstance(json_numeric["experiment"][0]["metric"][0]["value"], float) assert isinstance(json_numeric["experiment"][0]["metric"][1]["value"], int) assert len(json_numeric["experiment"][0]["metric"]) == 2 def test_experiment_to_json_multiple_experiments(rubicon_and_project_client_with_experiments): _, project = rubicon_and_project_client_with_experiments experiments_as_json = RubiconJSON(experiments=project.experiments()) json = experiments_as_json.json assert isinstance(json, dict) assert isinstance(json["experiment"], list) assert len(json["experiment"]) == 10 assert [isinstance(experiment, dict) for experiment in json["experiment"]] assert [isinstance(experiment["tags"], list) for experiment in json["experiment"]] assert [isinstance(experiment["feature"], list) for experiment in json["experiment"]] assert [isinstance(experiment["parameter"], list) for experiment in json["experiment"]] assert [isinstance(experiment["metric"], list) for experiment in json["experiment"]] def test_project_to_json_single_project(rubicon_and_project_client_with_experiments): _, project = rubicon_and_project_client_with_experiments project_as_json = RubiconJSON(projects=project) json = project_as_json.json assert isinstance(json, dict) assert isinstance(json["project"], list) assert len(json["project"]) == 1 assert json["project"][0]["name"] == "Test Project" def test_project_to_json_multiple_projects(rubicon_and_project_client_with_experiments): rubicon, _ = rubicon_and_project_client_with_experiments project = rubicon.get_or_create_project(name="Second Test Project") project.log_artifact(name="example artifact", data_bytes=b"a") project.log_dataframe(pd.DataFrame([[0, 1], [1, 0]])) projects_as_json = RubiconJSON(projects=rubicon.projects()) json = projects_as_json.json assert isinstance(json, dict) assert isinstance(json["project"], list) assert len(json["project"]) == 2 assert json["project"][0]["name"] == "Test Project" assert json["project"][1]["name"] == "Second Test Project" def test_rubicon_to_json_single_rubicon(rubicon_and_project_client_with_experiments): rubicon, _ = rubicon_and_project_client_with_experiments rubicon_as_json = RubiconJSON(rubicon_objects=rubicon) json = rubicon_as_json.json assert isinstance(json, dict) assert isinstance(json["project"], list) assert len(json["project"]) == 1 assert json["project"][0]["name"] == "Test Project" def test_rubicon_to_json_multiple_rubicons(rubicon_and_project_client_with_experiments): rubicon, _ = rubicon_and_project_client_with_experiments rubicon2 = Rubicon(persistence="memory") rubicon2.get_or_create_project(name="Test Project for Second Rubicon") rubicon_as_json = RubiconJSON(rubicon_objects=[rubicon, rubicon2]) json = rubicon_as_json.json assert isinstance(json, dict) assert isinstance(json["project"], list) assert len(json["project"]) == 2 assert json["project"][0]["name"] == "Test Project" assert json["project"][1]["name"] == "Test Project for Second Rubicon" def test_convert_to_json_invalid_rubicon_objects_argument(rubicon_client): with pytest.raises(ValueError): RubiconJSON(rubicon_objects=5) with pytest.raises(ValueError): RubiconJSON(rubicon_objects=[rubicon_client, 5]) def test_convert_to_json_invalid_projects_argument(project_client): with pytest.raises(ValueError): RubiconJSON(projects=5) with pytest.raises(ValueError): RubiconJSON(rubicon_objects=[project_client, 5]) def test_convert_to_json_invalid_experiments_argument(project_client): with pytest.raises(ValueError): RubiconJSON(experiments=5) experiment = project_client.log_experiment(name="Test Experiment") with pytest.raises(ValueError): RubiconJSON(experiments=[experiment, 5]) def test_convert_to_json_rubicon_and_projects_input(rubicon_and_project_client_with_experiments): rubicon, _ = rubicon_and_project_client_with_experiments rubicon2 = Rubicon(persistence="memory") additional_project = rubicon2.get_or_create_project(name="Second Test Project") conversion_to_json = RubiconJSON(rubicon_objects=rubicon, projects=additional_project) json = conversion_to_json.json assert isinstance(json, dict) assert isinstance(json["project"], list) assert len(json["project"]) == 2 assert json["project"][0]["name"] == "Test Project" assert json["project"][1]["name"] == "Second Test Project" def test_convert_to_json_projects_and_experiments_input( rubicon_and_project_client_with_experiments, ): _, project = rubicon_and_project_client_with_experiments rubicon2 = Rubicon(persistence="memory") additional_project = rubicon2.get_or_create_project(name="Second Test Project") experiment1 = additional_project.log_experiment(name="additional experiment 1") experiment2 = additional_project.log_experiment(name="additional experiment 2") conversion_to_json = RubiconJSON(projects=project, experiments=[experiment1, experiment2]) json = conversion_to_json.json assert isinstance(json, dict) assert isinstance(json["project"], list) assert len(json["experiment"]) == 2 @pytest.mark.parametrize( ["query", "expected_results"], [ ("$..experiment[*].metric[?(@.value>0.0)].name", ["accuracy", "runtime(s)"]), ("$..experiment[*].metric[?(@.name='kernel')].value", ["linear"]), ], ) def test_search(query, expected_results, rubicon_and_project_client): _, project = rubicon_and_project_client experiment = project.log_experiment("search experiment", tags=["a", "b"]) experiment.log_metric("accuracy", 0.87) experiment.log_metric("runtime(s)", 45) experiment.log_metric("kernel", "linear") experiment_as_json = RubiconJSON(experiments=experiment) results = experiment_as_json.search(query) for result, expected_result in zip(results, expected_results): assert result.value == expected_result

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_dataframe_client.py

import pytest from rubicon_ml import domain from rubicon_ml.client import Dataframe, Rubicon from rubicon_ml.exceptions import RubiconException def test_properties(project_client): parent = project_client domain_dataframe = domain.Dataframe( description="some description", tags=["x"], name="test title" ) dataframe = Dataframe(domain_dataframe, parent) assert dataframe.id == domain_dataframe.id assert dataframe.name == domain_dataframe.name assert dataframe.description == domain_dataframe.description assert dataframe.tags == domain_dataframe.tags assert dataframe.created_at == domain_dataframe.created_at assert dataframe.parent == parent def test_get_data(project_client, test_dataframe): parent = project_client df = test_dataframe logged_df = parent.log_dataframe(df) assert logged_df.get_data().compute().equals(df.compute()) def test_get_data_multiple_backend_error(test_dataframe): rb = Rubicon( composite_config=[ {"persistence": "memory", "root_dir": "./memory/rootA"}, {"persistence": "memory", "root_dir": "./memory/rootB"}, ] ) project = rb.create_project("test") df = test_dataframe logged_df = project.log_dataframe(df) for repo in rb.repositories: repo.delete_dataframe(project.name, logged_df.id) with pytest.raises(RubiconException) as e: logged_df.get_data() assert f"No data for dataframe with id `{logged_df.id}`" in str(e)

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_config.py

from unittest.mock import patch import pytest from rubicon_ml.client import Config from rubicon_ml.exceptions import RubiconException from rubicon_ml.repository import LocalRepository, MemoryRepository, S3Repository def test_parameters(): config = Config("memory", "./rubicon-file-system") assert config.persistence == "memory" assert config.root_dir == "./rubicon-file-system" def test_enviroment(): with patch.dict("os.environ", {"PERSISTENCE": "memory", "ROOT_DIR": "./rubicon-file-system"}): config = Config() assert config.persistence == "memory" assert config.root_dir == "./rubicon-file-system" def test_init_local_repository(): config = Config("filesystem", "/local/rubicon-file-system") assert isinstance(config.repository, LocalRepository) assert config.root_dir == config.repository.root_dir def test_init_s3_repository(): config = Config("filesystem", "s3://rubicon-file-system") assert isinstance(config.repository, S3Repository) assert config.root_dir == config.repository.root_dir def test_init_memory_repository(): config = Config("memory", "/memory/rubicon-file-system") assert isinstance(config.repository, MemoryRepository) assert config.root_dir == config.repository.root_dir def test_init_multiple_backend(): config = Config( composite_config=[ {"persistence": "filesystem", "root_dir": "./local/root", "is_auto_git_enabled": False}, { "persistence": "filesystem", "root_dir": "s3://remote/bucket/root", "is_auto_git_enabled": False, }, {"persistence": "memory", "root_dir": "./memory/root", "is_auto_git_enabled": False}, ] ) assert hasattr(config, "repositories") assert isinstance(config.repositories[0], LocalRepository) assert isinstance(config.repositories[1], S3Repository) assert isinstance(config.repositories[2], MemoryRepository) def test_invalid_persistence(): with pytest.raises(ValueError) as e: Config("invalid") assert str(Config.PERSISTENCE_TYPES) in str(e) def test_invalid_root_dir(): with pytest.raises(ValueError) as e: Config("filesystem") assert "root_dir cannot be None" in str(e) def test_not_in_git_repo(): with patch("subprocess.run") as mock_run: class MockCompletedProcess: returncode = 1 mock_run.return_value = MockCompletedProcess() with pytest.raises(RubiconException) as e: Config("memory", is_auto_git_enabled=True) assert "Not a `git` repo" in str(e)

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/client/test_artifact_client.py

import os from unittest.mock import MagicMock, patch import pytest from rubicon_ml import domain from rubicon_ml.client import Artifact, Rubicon from rubicon_ml.exceptions import RubiconException def test_properties(project_client): parent = project_client domain_artifact = domain.Artifact(name="test.txt") artifact = Artifact(domain_artifact, parent) assert artifact.id == domain_artifact.id assert artifact.name == domain_artifact.name assert artifact.description == domain_artifact.description assert artifact.created_at == domain_artifact.created_at assert artifact.parent == parent def test_get_data(project_client): project = project_client data = b"content" artifact = project.log_artifact(name="test.txt", data_bytes=data) artifact._get_data() assert artifact.data == data def test_get_json(project_client): project = project_client data = {"hello": "world", "numbers": [1, 2, 3]} artifact = project.log_json(json_object=data, name="test.json") assert artifact.get_json() == data def test_internal_get_data_multiple_backend_error(): rb = Rubicon( composite_config=[ {"persistence": "memory", "root_dir": "./memory/rootA"}, {"persistence": "memory", "root_dir": "./memory/rootB"}, ] ) project = rb.create_project("test") data = b"content" artifact = project.log_artifact(name="test.txt", data_bytes=data) for repo in rb.repositories: repo.delete_artifact(project.name, artifact.id) with pytest.raises(RubiconException) as e: artifact._get_data() assert "all configured storage backends failed" in str(e) def test_get_data_unpickle_false(project_client): project = project_client data = b"content" artifact = project.log_artifact(name="test.txt", data_bytes=data) assert artifact.get_data(unpickle=False) == data def test_get_data_unpickle_true(project_client): """Unpickle=True intended for retrieving python objects that were logged as artifacts, hence dummy object is needed. """ project = project_client global TestObject # cannot pickle local variable class TestObject: value = 1 test_object = TestObject() artifact = project.log_artifact(name="test object", data_object=test_object) assert artifact.get_data(unpickle=True).value == test_object.value def test_get_data_multiple_backend_error(): rb = Rubicon( composite_config=[ {"persistence": "memory", "root_dir": "./memory/rootA"}, {"persistence": "memory", "root_dir": "./memory/rootB"}, ] ) project = rb.create_project("test") data = b"content" artifact = project.log_artifact(name="test.txt", data_bytes=data) for repo in rb.repositories: repo.delete_artifact(project.name, artifact.id) with pytest.raises(RubiconException) as e: artifact.get_data() assert "all configured storage backends failed" in str(e) @patch("fsspec.implementations.local.LocalFileSystem.open") def test_download_cwd(mock_open, project_client): project = project_client data = b"content" artifact = project.log_artifact(name="test.txt", data_bytes=data) artifact.data mock_file = MagicMock() mock_open.side_effect = mock_file artifact.download() mock_open.assert_called_once_with(os.path.join(os.getcwd(), artifact.name), mode="wb") mock_file().write.assert_called_once_with(data) @patch("fsspec.implementations.local.LocalFileSystem.open") def test_download_location(mock_open, project_client): project = project_client data = b"content" artifact = project.log_artifact(name="test.txt", data_bytes=data) artifact.data mock_file = MagicMock() mock_open.side_effect = mock_file artifact.download(location="/path/to/tests", name="new_name.txt") mock_open.assert_called_once_with("/path/to/tests/new_name.txt", mode="wb") mock_file().write.assert_called_once_with(data)

capitalone_repos/rubicon-ml/tests/unit/client

capitalone_repos/rubicon-ml/tests/unit/client/utils/test_exception_handling.py

from unittest.mock import patch import pytest from rubicon_ml.client.utils.exception_handling import FAILURE_MODES, set_failure_mode from rubicon_ml.exceptions import RubiconException @pytest.mark.parametrize("failure_mode", FAILURE_MODES) def test_set_failure_mode(failure_mode): set_failure_mode(failure_mode=failure_mode) from rubicon_ml.client.utils.exception_handling import FAILURE_MODE assert FAILURE_MODE == failure_mode # cleanup: reset to default set_failure_mode("raise") def test_set_failure_mode_traceback_options(): traceback_chain = True traceback_limit = 1 set_failure_mode("raise", traceback_chain=traceback_chain, traceback_limit=traceback_limit) from rubicon_ml.client.utils.exception_handling import ( TRACEBACK_CHAIN, TRACEBACK_LIMIT, ) assert TRACEBACK_CHAIN == traceback_chain assert TRACEBACK_LIMIT == traceback_limit def test_set_failure_mode_error(): with pytest.raises(ValueError) as e: set_failure_mode("invalid mode") assert f"`failure_mode` must be one of {FAILURE_MODES}" in repr(e) def test_failure_mode_raise(rubicon_client): set_failure_mode("raise") with pytest.raises(RubiconException) as e: rubicon_client.get_project(name="does not exist") assert "No project with name 'does not exist' found." in repr(e) @patch("warnings.warn") def test_failure_mode_log(mock_warn, rubicon_client): set_failure_mode("warn") rubicon_client.get_project(name="does not exist") mock_warn.assert_called_once() # cleanup: reset to default set_failure_mode("raise") @patch("logging.error") def test_failure_mode_warn(mock_logger, rubicon_client): set_failure_mode("log") rubicon_client.get_project(name="does not exist") mock_logger.assert_called_once() # cleanup: reset to default set_failure_mode("raise")

capitalone_repos/rubicon-ml/tests/unit/client

capitalone_repos/rubicon-ml/tests/unit/client/utils/test_tags.py

from rubicon_ml.client.utils.tags import has_tag_requirements def test_or_single_success(): assert has_tag_requirements(["x", "y", "z"], ["y"], "or") def test_or_multiple_success(): assert has_tag_requirements(["x", "y", "z"], ["a", "y"], "or") def test_or_single_failure(): assert not has_tag_requirements(["x", "y", "z"], ["a"], "or") def test_and_single_success(): assert has_tag_requirements(["x", "y", "z"], ["y"], "and") def test_and_multiple_success(): assert has_tag_requirements(["x", "y", "z"], ["y", "z"], "and") def test_and_single_failure(): assert not has_tag_requirements(["x", "y", "z"], ["a"], "and") def test_and_multiple_failure(): assert not has_tag_requirements(["x", "y", "z"], ["a", "z"], "and")

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/sklearn/test_estimator_logger.py

from unittest.mock import patch import pytest from rubicon_ml import domain from rubicon_ml.client.experiment import Experiment from rubicon_ml.sklearn.estimator_logger import EstimatorLogger def test_log_parameters_triggers_experiment_log_parameter(project_client, fake_estimator_cls): project = project_client experiment = Experiment(domain.Experiment(project_name=project.name), project) estimator = fake_estimator_cls() base_logger = EstimatorLogger(estimator=estimator, experiment=experiment, step_name="vect") with patch.object(Experiment, "log_parameter", return_value=None) as mock_log_parameter: base_logger.log_parameters() assert mock_log_parameter.call_count == 3 # the step name gets prepended to each param mock_log_parameter.assert_called_with(name="vect__ngram_range", value=(1, 2)) def test_log_unserializable_param_triggers_exception(project_client, fake_estimator_cls): project = project_client experiment = Experiment(domain.Experiment(project_name=project.name), project) estimator = fake_estimator_cls(params={"unserializable": b"not serializable"}) base_logger = EstimatorLogger(estimator=estimator, experiment=experiment, step_name="vect") with patch.object(Experiment, "log_parameter", side_effect=Exception("test")): with pytest.warns(Warning): base_logger.log_parameters()

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/sklearn/test_filter_estimator_logger.py

from unittest.mock import patch import pytest from rubicon_ml import domain from rubicon_ml.client.experiment import Experiment from rubicon_ml.exceptions import RubiconException from rubicon_ml.sklearn.filter_estimator_logger import FilterEstimatorLogger def test_select_and_ignore_exception(): with pytest.raises(RubiconException) as e: FilterEstimatorLogger(select=["this", "should"], ignore=["fail"]) assert "provide either `select` OR `ignore`" in str(e) def test_ignore_all(fake_estimator_cls): logger = FilterEstimatorLogger(estimator=fake_estimator_cls(), ignore_all=True) with patch.object(Experiment, "log_parameter", return_value=None) as mock_log_parameter: logger.log_parameters() mock_log_parameter.assert_not_called() def test_ignore_parameters(project_client, fake_estimator_cls): project = project_client experiment = Experiment(domain.Experiment(project_name=project.name), project) estimator = fake_estimator_cls() logger = FilterEstimatorLogger( estimator=estimator, experiment=experiment, step_name="vect", select=["max_df"] ) with patch.object(Experiment, "log_parameter", return_value=None) as mock_log_parameter: logger.log_parameters() assert mock_log_parameter.call_count == 1 # the step name gets prepended to each param mock_log_parameter.assert_called_with(name="vect__max_df", value=0.75) def test_select_parameters(project_client, fake_estimator_cls): project = project_client experiment = Experiment(domain.Experiment(project_name=project.name), project) estimator = fake_estimator_cls() logger = FilterEstimatorLogger( estimator=estimator, experiment=experiment, step_name="vect", ignore=["ngram_range"] ) with patch.object(Experiment, "log_parameter", return_value=None) as mock_log_parameter: logger.log_parameters() assert mock_log_parameter.call_count == 2 # the step name gets prepended to each param mock_log_parameter.assert_called_with(name="vect__lowercase", value=True)

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/sklearn/test_pipeline.py

from tempfile import mkdtemp from unittest.mock import patch from pytest import raises from rubicon_ml.sklearn import RubiconPipeline from rubicon_ml.sklearn.estimator_logger import EstimatorLogger from rubicon_ml.sklearn.filter_estimator_logger import FilterEstimatorLogger from rubicon_ml.sklearn.pipeline import Pipeline, make_pipeline def test_get_default_estimator_logger(project_client, fake_estimator_cls): project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] pipeline = RubiconPipeline(project, steps) logger = pipeline.get_estimator_logger() assert isinstance(logger, EstimatorLogger) def test_get_user_defined_estimator_logger(project_client, fake_estimator_cls): project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] user_defined_logger = {"est": FilterEstimatorLogger(ignore_all=True)} pipeline = RubiconPipeline(project, steps, user_defined_loggers=user_defined_logger) logger = pipeline.get_estimator_logger("est") assert isinstance(logger, FilterEstimatorLogger) def test_fit_logs_parameters(project_client, fake_estimator_cls): project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] user_defined_logger = {"est": FilterEstimatorLogger(ignore_all=True)} pipeline = RubiconPipeline(project, steps, user_defined_logger) with patch.object(Pipeline, "fit", return_value=None): with patch.object( FilterEstimatorLogger, "log_parameters", return_value=None ) as mock_log_parameters: pipeline.fit(["fake data"]) mock_log_parameters.assert_called_once() def test_fit_logs_fit_parameters(project_client, fake_estimator_cls): project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] user_defined_logger = {"est": FilterEstimatorLogger(ignore_all=True)} pipeline = RubiconPipeline(project, steps, user_defined_logger) with patch.object(Pipeline, "fit", return_value=None): with patch.object(FilterEstimatorLogger, "log_parameters", return_value=None): pipeline.fit(["fake data"], est__test_fit_param="test_value") parameters = pipeline.experiment.parameters() assert len(parameters) == 1 parameter = parameters[0] assert parameter.name == "est__test_fit_param" assert parameter.value == "test_value" def test_score_logs_metric(project_client, fake_estimator_cls): project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] pipeline = RubiconPipeline(project, steps) with patch.object(Pipeline, "score", return_value=None): with patch.object(EstimatorLogger, "log_metric", return_value=None) as mock_log_metric: pipeline.score(["fake data"]) mock_log_metric.assert_called_once() def test_make_pipeline(project_client, fake_estimator_cls): project = project_client clf = fake_estimator_cls() clf1 = fake_estimator_cls() pipe = make_pipeline( project, clf, clf1, experiment_kwargs={"name": "RubiconPipeline experiment"} ) assert len(pipe.steps) == 2 assert pipe.steps[0][1] == clf assert pipe.steps[1][1] == clf1 assert len(pipe.user_defined_loggers) == 0 def test_make_pipeline_with_loggers(project_client, fake_estimator_cls): project = project_client clf = fake_estimator_cls() clf1 = fake_estimator_cls() user_defined_logger = FilterEstimatorLogger() user_defined_logger1 = FilterEstimatorLogger() pipe = make_pipeline( project, (clf, user_defined_logger), (clf1, user_defined_logger1), experiment_kwargs={"name": "RubiconPipeline experiment"}, ) assert len(pipe.steps) == 2 assert pipe.steps[0][1] == clf assert pipe.steps[1][1] == clf1 assert len(pipe.user_defined_loggers) == 2 assert pipe.user_defined_loggers[pipe.steps[0][0]] == user_defined_logger assert pipe.user_defined_loggers[pipe.steps[1][0]] == user_defined_logger1 pipe = make_pipeline( project, clf, (clf1, user_defined_logger1), experiment_kwargs={"name": "RubiconPipeline experiment"}, ) assert len(pipe.steps) == 2 assert pipe.steps[0][1] == clf assert pipe.steps[1][1] == clf1 assert len(pipe.user_defined_loggers) == 1 assert pipe.user_defined_loggers[pipe.steps[1][0]] == user_defined_logger1 def test_make_pipeline_without_project(fake_estimator_cls): estimator = fake_estimator_cls() steps = [("est", estimator)] with raises(ValueError) as e: make_pipeline(steps) assert "project" + str(steps) + " must be of type rubicon_ml.client.project.Project" == str( e.value ) def test_pipeline_memory_verbose(project_client, fake_estimator_cls): project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] cachedir = mkdtemp() user_defined_logger = FilterEstimatorLogger() pipeline = RubiconPipeline(project, steps, {"est": user_defined_logger}, memory=cachedir) assert pipeline.memory == cachedir assert pipeline.verbose is False pipeline = RubiconPipeline(project, steps, {"est": user_defined_logger}, verbose=True) assert pipeline.memory is None assert pipeline.verbose is True pipeline = RubiconPipeline(project, steps, {"est": user_defined_logger}) assert pipeline.memory is None assert pipeline.verbose is False def test_multiple_fit_multiple_scores(project_client, fake_estimator_cls): project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] user_defined_logger = {"est": FilterEstimatorLogger(ignore_all=True)} pipeline = RubiconPipeline(project, steps, user_defined_logger) with patch.object(Pipeline, "fit", return_value=None): with patch.object( FilterEstimatorLogger, "log_parameters", return_value=None ) as mock_log_parameters: pipeline.fit(["fake data"]) pipeline.fit("additional fake data") assert mock_log_parameters._mock_call_count == 2 assert len(project.experiments()) == 2 with patch.object(Pipeline, "score", return_value=None): with patch.object(EstimatorLogger, "log_metric", return_value=None) as mock_log_metric: pipeline.score(["fake data"]) assert pipeline.experiment is None pipeline.score(["additional fake data"]) assert mock_log_metric._mock_call_count == 2 assert len(project.experiments()) == 3 def test_multiple_scores(project_client, fake_estimator_cls): project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] pipeline = RubiconPipeline(project, steps) with patch.object(Pipeline, "score", return_value=None) as mock_log_metric: with patch.object(EstimatorLogger, "log_metric", return_value=None): # first score gets its own explicitly declared experiment experiment = project.log_experiment(name="fake experiment") pipeline.score(["fake data"], experiment=experiment) pipeline.score(["additional fake data"]) experiments = project.experiments() assert len(experiments) == 2 assert mock_log_metric._mock_call_count == 2 assert experiments[0].name == "fake experiment" assert experiments[1].name == "RubiconPipeline experiment" def test_multiple_fits(project_client, fake_estimator_cls): project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] user_defined_logger = {"est": FilterEstimatorLogger(ignore_all=True)} pipeline = RubiconPipeline(project, steps, user_defined_logger) with patch.object(Pipeline, "fit", return_value=None): with patch.object( FilterEstimatorLogger, "log_parameters", return_value=None ) as mock_log_parameters: # first fit gets its own explicitly declared experiment experiment = project.log_experiment(name="fake experiment") pipeline.fit(["fake data"], experiment=experiment) pipeline.fit("additional fake data") experiments = project.experiments() assert len(experiments) == 2 assert mock_log_parameters._mock_call_count == 2 assert experiments[0].name == "fake experiment" assert experiments[1].name == "RubiconPipeline experiment" def test_score_samples(project_client, fake_estimator_cls): project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] user_defined_logger = {"est": FilterEstimatorLogger(ignore_all=True)} pipeline = RubiconPipeline(project, steps, user_defined_logger) project = project_client estimator = fake_estimator_cls() steps = [("est", estimator)] user_defined_logger = {"est": FilterEstimatorLogger(ignore_all=True)} pipeline = RubiconPipeline(project, steps, user_defined_logger) with patch.object(Pipeline, "fit", return_value=None): with patch.object(FilterEstimatorLogger, "log_parameters", return_value=None): pipeline.fit(["fake data"]) assert len(project.experiments()) == 1 with patch.object(Pipeline, "score_samples", return_value=None): with patch.object(EstimatorLogger, "log_metric", return_value=None) as mock_log_metric: pipeline.score_samples(["fake data"]) pipeline.score_samples(["additional fake data"]) experiment = project.log_experiment(name="fake experiment") pipeline.score_samples(["additional fake data"], experiment=experiment) assert mock_log_metric._mock_call_count == 3 assert len(project.experiments()) == 3 assert project.experiments()[2].name == "fake experiment" def test_pipeline_slices(project_client, fake_estimator_cls): project = project_client steps = [ ("est", fake_estimator_cls()), ("est1", fake_estimator_cls()), ("est2", fake_estimator_cls()), ] cachedir = mkdtemp() est_logger = FilterEstimatorLogger() est1_logger = FilterEstimatorLogger user_defined_loggers = {"est": est_logger, "est1": est1_logger} pipeline = RubiconPipeline(project, steps, user_defined_loggers, memory=cachedir) assert pipeline[1:].steps == steps[1:] assert pipeline[1:].user_defined_loggers == {"est1": est1_logger} assert pipeline[:-1].steps == steps[:-1] with raises(ValueError) as e: pipeline[::-1] assert "Pipeline slicing only supports a step of 1" == str(e.value) def test_sklearn_pipeline_invalid_step_count(project_client, fake_estimator_cls): project = project_client steps = [ ("est", fake_estimator_cls()), ("est1", fake_estimator_cls()), ("est2", fake_estimator_cls()), ] cachedir = mkdtemp() est_logger = FilterEstimatorLogger() est1_logger = FilterEstimatorLogger user_defined_loggers = {"est": est_logger, "est1": est1_logger} pipeline = RubiconPipeline(project, steps, user_defined_loggers, memory=cachedir) with raises(ValueError) as e: pipeline[::-1] assert "Pipeline slicing only supports a step of 1" == str(e.value)

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/viz/test_experiments_table.py

from dash import Dash from rubicon_ml.viz import ExperimentsTable def test_experiments_table(viz_experiments): experiments_table = ExperimentsTable(experiments=viz_experiments, is_selectable=True) expected_experiment_ids = [e.id for e in viz_experiments] for experiment in experiments_table.experiments: assert experiment.id in expected_experiment_ids expected_experiment_ids.remove(experiment.id) assert len(expected_experiment_ids) == 0 assert experiments_table.is_selectable is True def test_experiments_table_load_data(viz_experiments): experiments_table = ExperimentsTable(experiments=viz_experiments) experiments_table.load_experiment_data() expected_experiment_ids = [e.id for e in viz_experiments] expected_metric_names = [m.name for m in viz_experiments[0].metrics()] expected_parameter_names = [p.name for p in viz_experiments[0].parameters()] for record in experiments_table.experiment_records: assert record["id"] in expected_experiment_ids assert all([record.get(name) is not None for name in expected_metric_names]) assert all([record.get(name) is not None for name in expected_parameter_names]) assert experiments_table.commit_hash == viz_experiments[0].commit_hash assert experiments_table.github_url == f"test.github.url/tree/{viz_experiments[0].commit_hash}" def test_experiments_table_layout(viz_experiments): experiments_table = ExperimentsTable(experiments=viz_experiments) experiments_table.load_experiment_data() layout = experiments_table.layout assert len(layout.children) == 4 assert layout.children[-1].children.id == "experiment-table" assert layout.children[-2].id == "publish-modal" def test_experiments_table_layout_not_selectable(viz_experiments): experiments_table = ExperimentsTable(experiments=viz_experiments, is_selectable=False) experiments_table.load_experiment_data() layout = experiments_table.layout assert len(layout.children) == 3 assert layout.children[-1].children.id == "experiment-table" assert layout.children[-2].id == "publish-modal" def test_experiments_table_register_callbacks(viz_experiments): experiments_table = ExperimentsTable(experiments=viz_experiments) experiments_table.app = Dash(__name__, title="test callbacks") experiments_table.register_callbacks() callback_values = list(experiments_table.app.callback_map.values()) assert len(callback_values) == 4 registered_callback_names = [callback["callback"].__name__ for callback in callback_values] assert "update_selected_column_checkboxes" in registered_callback_names assert "update_hidden_experiment_table_cols" in registered_callback_names assert "update_selected_experiment_table_rows" in registered_callback_names assert "toggle_publish_modal" in registered_callback_names

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/viz/test_common.py

import pytest from rubicon_ml.viz.common.colors import get_rubicon_colorscale from rubicon_ml.viz.common.dropdown_header import dropdown_header @pytest.mark.parametrize("num_colors,expected", [(1, 2), (4, 4)]) def test_get_rubicon_colorscale(num_colors, expected): colors = get_rubicon_colorscale(num_colors) assert len(colors) == expected def test_dropdown_header(): layout = dropdown_header(["A", "B", "C"], "A", "test left", "test right", "test-id") assert layout.id.startswith("test-id") assert layout.children[1].children.label == "A" assert [child.children for child in layout.children[1].children.children] == ["A", "B", "C"] assert layout.children[0].children.children == "test left" assert layout.children[2].children.children == "test right"

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/viz/test_base.py

from dash import Dash, html from rubicon_ml.viz.base import VizBase class VizBaseTest(VizBase): app = Dash(__name__, title="test base") @property def layout(self): return html.Div(id="test-layout") def test_base_build_layout(): base = VizBaseTest() base.build_layout() layout = base.app.layout assert len(layout) == 8 assert layout.children.children[-1].children.id == "test-layout"

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/viz/test_metric_correlation_plot.py

import pytest from dash import Dash from rubicon_ml.viz import MetricCorrelationPlot def test_metric_correlation_plot(viz_experiments): metric_plot = MetricCorrelationPlot( experiments=viz_experiments, metric_names=["test metric 0", "test metric 1"], parameter_names=["test param 1", "test param 2"], selected_metric="test metric 1", ) expected_experiment_ids = [e.id for e in viz_experiments] for experiment in metric_plot.experiments: assert experiment.id in expected_experiment_ids expected_experiment_ids.remove(experiment.id) assert len(expected_experiment_ids) == 0 assert metric_plot.metric_names == ["test metric 0", "test metric 1"] assert metric_plot.parameter_names == ["test param 1", "test param 2"] assert metric_plot.selected_metric == "test metric 1" def test_metric_correlation_plot_load_data(viz_experiments): metric_plot = MetricCorrelationPlot( experiments=viz_experiments, metric_names=["test metric 0", "test metric 1"], ) metric_plot.load_experiment_data() expected_experiment_ids = [e.id for e in viz_experiments] expected_metric_names = ["test metric 0", "test metric 1"] expected_parameter_names = [p.name for p in viz_experiments[0].parameters()] for experiment_id, record in metric_plot.experiment_records.items(): assert experiment_id in expected_experiment_ids for metric_name in record["metrics"].keys(): assert metric_name in expected_metric_names for parameter_name in record["parameters"].keys(): assert parameter_name in expected_parameter_names assert metric_plot.visible_parameter_names.sort() == expected_parameter_names.sort() assert metric_plot.visible_metric_names.sort() == expected_metric_names.sort() assert metric_plot.selected_metric == expected_metric_names[0] def test_metric_correlation_plot_load_data_throws_error(viz_experiments): metric_plot = MetricCorrelationPlot( experiments=viz_experiments, metric_names=["test metric 0", "test metric 1"], selected_metric="nonexistant metric", ) with pytest.raises(ValueError) as e: metric_plot.load_experiment_data() assert "no metric named `selected_metric` 'nonexistant metric'" in str(e.value) def test_metric_correlation_plot_layout(viz_experiments): metric_plot = MetricCorrelationPlot( experiments=viz_experiments, metric_names=["test metric 0", "test metric 1"], selected_metric="test metric 1", ) metric_plot.load_experiment_data() layout = metric_plot.layout assert len(layout.children) == 2 assert layout.children[-1].children.id == "metric-correlation-plot-container" assert layout.children[-1].children.children.id == "metric-correlation-plot" @pytest.mark.parametrize("is_linked,expected", [(False, 1), (True, 2)]) def test_metric_correlation_plot_register_callbacks(viz_experiments, is_linked, expected): metric_plot = MetricCorrelationPlot( experiments=viz_experiments, metric_names=["test metric 0", "test metric 1"], ) metric_plot.app = Dash(__name__, title="test callbacks") metric_plot.register_callbacks(link_experiment_table=is_linked) callback_values = list(metric_plot.app.callback_map.values()) assert len(callback_values) == 1 registered_callback_name = callback_values[0]["callback"].__name__ registered_callback_len_input = len(callback_values[0]["inputs"]) assert registered_callback_name == "update_metric_plot" assert registered_callback_len_input == expected def test_metric_correlation_plot_get_dimensions(viz_experiments): metric_plot = MetricCorrelationPlot( experiments=viz_experiments, metric_names=["test metric 0", "test metric 1"], selected_metric="test metric 1", ) metric_plot.load_experiment_data() dimension = metric_plot._get_dimension("test dimension", [1, 2, 3, 4]) assert list(dimension["values"]) == [1, 2, 3, 4] def test_metric_correlation_plot_get_string_dimensions(viz_experiments): metric_plot = MetricCorrelationPlot( experiments=viz_experiments, metric_names=["test metric 0", "test metric 1"], selected_metric="test metric 1", ) metric_plot.load_experiment_data() dimension = metric_plot._get_dimension("test dimension", ["A", "B", "A", "B", "C"]) assert dimension["tickvals"] == [0, 1, 2] assert list(dimension["ticktext"]) == ["A", "B", "C"] assert list(dimension["values"]) == [0, 1, 0, 1, 2] def test_metric_correlation_plot_get_boolean_dimensions(viz_experiments): metric_plot = MetricCorrelationPlot( experiments=viz_experiments, metric_names=["test metric 0", "test metric 1"], selected_metric="test metric 1", ) metric_plot.load_experiment_data() dimension = metric_plot._get_dimension("test dimension", [True, False, True]) assert dimension["tickvals"] == [0, 1] assert list(dimension["ticktext"]) == ["False", "True"] assert list(dimension["values"]) == [1, 0, 1]

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/viz/test_dataframe_plot.py

import pytest from dash import Dash from rubicon_ml.viz import DataframePlot def test_dataframe_plot(viz_experiments): def test_plotting_func(*args, **kwargs): return dataframe_plot = DataframePlot( "test dataframe", experiments=viz_experiments, plotting_func=test_plotting_func, plotting_func_kwargs={"test": "test"}, x="test x", y="test y", ) expected_experiment_ids = [e.id for e in viz_experiments] for experiment in dataframe_plot.experiments: assert experiment.id in expected_experiment_ids expected_experiment_ids.remove(experiment.id) assert len(expected_experiment_ids) == 0 assert dataframe_plot.dataframe_name == "test dataframe" assert dataframe_plot.plotting_func == test_plotting_func assert dataframe_plot.plotting_func_kwargs == {"test": "test"} assert dataframe_plot.x == "test x" assert dataframe_plot.y == "test y" def test_dataframe_plot_load_data(viz_experiments): dataframe_plot = DataframePlot("test dataframe", experiments=viz_experiments) dataframe_plot.load_experiment_data() expected_experiment_ids = [e.id for e in viz_experiments] for eeid in expected_experiment_ids: assert eeid in list(dataframe_plot.data_df["experiment_id"]) expected_len_df = 0 for experiment in viz_experiments: for dataframe in experiment.dataframes(): expected_len_df = expected_len_df + len(dataframe.get_data()) assert len(dataframe_plot.data_df) == expected_len_df def test_dataframe_plot_layout(viz_experiments): dataframe_plot = DataframePlot("test dataframe", experiments=viz_experiments) dataframe_plot.load_experiment_data() layout = dataframe_plot.layout assert len(layout.children) == 3 assert layout.children[0].id == "dummy-callback-trigger" assert layout.children[-1].children.id == "dataframe-plot" @pytest.mark.parametrize("is_linked,expected", [(False, 1), (True, 2)]) def test_dataframe_plot_register_callbacks_link(viz_experiments, is_linked, expected): dataframe_plot = DataframePlot("test dataframe", experiments=viz_experiments) dataframe_plot.app = Dash(__name__, title="test callbacks") dataframe_plot.register_callbacks(link_experiment_table=is_linked) callback_values = list(dataframe_plot.app.callback_map.values()) assert len(callback_values) == 1 registered_callback_name = callback_values[0]["callback"].__name__ registered_callback_len_input = len(callback_values[0]["inputs"]) assert registered_callback_name == "update_dataframe_plot" assert registered_callback_len_input == expected

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/viz/test_dashboard.py

from dash import Dash, html from rubicon_ml.viz import Dashboard, ExperimentsTable, MetricCorrelationPlot from rubicon_ml.viz.base import VizBase class WidgetTest(VizBase): def __init__(self, layout_id): self.layout_id = layout_id self.load_experiment_data_call_count = 0 self.register_callbacks_call_count = 0 @property def layout(self): return html.Div(id=self.layout_id) def load_experiment_data(self): self.load_experiment_data_call_count += 1 def register_callbacks(self, link_experiment_table=False): self.register_callbacks_call_count += 1 def test_dashboard(viz_experiments): widget_a = WidgetTest(layout_id="a") widget_b = WidgetTest(layout_id="b") dashboard = Dashboard(viz_experiments, widgets=[[widget_a, widget_b]]) expected_experiment_ids = [e.id for e in viz_experiments] for experiment in dashboard.experiments: assert experiment.id in expected_experiment_ids expected_experiment_ids.remove(experiment.id) assert dashboard.widgets == [[widget_a, widget_b]] def test_dashboard_load_data(viz_experiments): widget_a = WidgetTest(layout_id="a") widget_b = WidgetTest(layout_id="b") assert widget_a.load_experiment_data_call_count == 0 assert widget_b.load_experiment_data_call_count == 0 dashboard = Dashboard(viz_experiments, widgets=[[widget_a, widget_b]]) dashboard.load_experiment_data() assert widget_a.load_experiment_data_call_count == 1 assert widget_b.load_experiment_data_call_count == 1 def test_dashboard_layout_horizontal(viz_experiments): widget_a = WidgetTest(layout_id="a") widget_b = WidgetTest(layout_id="b") dashboard = Dashboard(viz_experiments, widgets=[[widget_a, widget_b]]) layout = dashboard.layout layout.children[0].children[0].children == widget_a.layout layout.children[0].children[1].children == widget_b.layout def test_dashboard_layout_veritcal(viz_experiments): widget_a = WidgetTest(layout_id="a") widget_b = WidgetTest(layout_id="b") dashboard = Dashboard(viz_experiments, widgets=[[widget_a], [widget_b]]) layout = dashboard.layout layout.children[0].children[0].children == widget_a.layout layout.children[1].children[0].children == widget_b.layout def test_dashboard_register_callbacks(viz_experiments): widget_a = WidgetTest(layout_id="a") widget_b = WidgetTest(layout_id="b") assert widget_a.register_callbacks_call_count == 0 assert widget_b.register_callbacks_call_count == 0 dashboard = Dashboard(viz_experiments, widgets=[[widget_a, widget_b]]) dashboard.app = Dash(__name__, title="test dashboard") dashboard.register_callbacks() assert widget_a.register_callbacks_call_count == 1 assert widget_b.register_callbacks_call_count == 1 def test_default_dashbaord(viz_experiments): dashboard = Dashboard(viz_experiments) assert isinstance(dashboard.widgets[0][0], ExperimentsTable) assert isinstance(dashboard.widgets[1][0], MetricCorrelationPlot)

capitalone_repos/rubicon-ml/tests/unit

capitalone_repos/rubicon-ml/tests/unit/viz/test_metric_lists_comparison.py

import pytest from dash import Dash from rubicon_ml.viz import MetricListsComparison def test_metric_lists_comparison(viz_experiments): metric_comparison = MetricListsComparison( column_names=["var_0", "var_1", "var_2", "var_3", "var_4"], experiments=viz_experiments, selected_metric="test metric 2", ) expected_experiment_ids = [e.id for e in viz_experiments] for experiment in metric_comparison.experiments: assert experiment.id in expected_experiment_ids expected_experiment_ids.remove(experiment.id) assert len(expected_experiment_ids) == 0 assert metric_comparison.column_names == ["var_0", "var_1", "var_2", "var_3", "var_4"] assert metric_comparison.selected_metric == "test metric 2" def test_metric_lists_comparison_load_data(viz_experiments): metric_comparison = MetricListsComparison(experiments=viz_experiments) metric_comparison.load_experiment_data() expected_experiment_ids = [e.id for e in viz_experiments] expected_metric_names = ["test metric 2", "test metric 3"] for experiment_id, record in metric_comparison.experiment_records.items(): assert experiment_id in expected_experiment_ids for metric_name in expected_metric_names: assert metric_name in record assert metric_comparison.selected_metric == expected_metric_names[0] def test_metric_lists_comparison_load_data_throws_error(viz_experiments): metric_comparison = MetricListsComparison( experiments=viz_experiments, selected_metric="nonexistant metric", ) with pytest.raises(ValueError) as e: metric_comparison.load_experiment_data() assert "no metric named `selected_metric` 'nonexistant metric'" in str(e.value) def test_metric_list_comparison_layout(viz_experiments): metric_comparison = MetricListsComparison(experiments=viz_experiments) metric_comparison.load_experiment_data() layout = metric_comparison.layout assert len(layout.children) == 2 assert layout.children[-1].children.id == "metric-heatmap-container" assert layout.children[-1].children.children.id == "metric-heatmap" @pytest.mark.parametrize("is_linked,expected", [(False, 1), (True, 2)]) def test_metric_correlation_plot_register_callbacks(viz_experiments, is_linked, expected): metric_comparison = MetricListsComparison(experiments=viz_experiments) metric_comparison.app = Dash(__name__, title="test callbacks") metric_comparison.register_callbacks(link_experiment_table=is_linked) callback_values = list(metric_comparison.app.callback_map.values()) assert len(callback_values) == 1 registered_callback_name = callback_values[0]["callback"].__name__ registered_callback_len_input = len(callback_values[0]["inputs"]) assert registered_callback_name == "update_metric_heatmap" assert registered_callback_len_input == expected