Diego Hurtado
GitHub
parent
24bc71eb2e
commit
5a7935ff1f
|
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@ -66,7 +66,6 @@ jobs:
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- "redis"
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- "remoulade"
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- "requests"
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- "sklearn"
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- "sqlalchemy"
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- "sqlite3"
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- "starlette"
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@ -75,14 +74,6 @@ jobs:
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- "tortoiseorm"
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os: [ubuntu-20.04]
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exclude:
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- python-version: py39
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package: "sklearn"
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- python-version: py310
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package: "sklearn"
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- python-version: py311
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package: "sklearn"
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- python-version: py312
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package: "sklearn"
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- python-version: py312
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package: "boto"
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- python-version: py312
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@ -103,8 +94,6 @@ jobs:
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package: "remoulade"
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- python-version: pypy3
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package: "requests"
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- python-version: pypy3
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package: "sklearn"
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- python-version: pypy3
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package: "confluent-kafka"
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- python-version: pypy3
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|
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@ -93,31 +93,3 @@ jobs:
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key: v7-build-tox-cache-${{ matrix.package }}-${{ hashFiles('tox.ini', 'gen-requirements.txt', 'dev-requirements.txt') }}
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- name: run tox
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run: tox -e lint-${{ matrix.package }}
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lint-3_8:
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strategy:
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fail-fast: false # ensures the entire test matrix is run, even if one permutation fails
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matrix:
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package:
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- "instrumentation-sklearn"
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os: [ubuntu-20.04]
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runs-on: ubuntu-20.04
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steps:
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- name: Checkout Contrib Repo @ SHA - ${{ github.sha }}
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uses: actions/checkout@v4
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- name: Set up Python 3.8
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uses: actions/setup-python@v5
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with:
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python-version: 3.8
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- name: Install tox
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run: pip install tox
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- name: Cache tox environment
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# Preserves .tox directory between runs for faster installs
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uses: actions/cache@v4
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with:
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path: |
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.tox
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~/.cache/pip
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key: v7-build-tox-cache-${{ matrix.package }}-${{ hashFiles('tox.ini', 'gen-requirements.txt', 'dev-requirements.txt') }}
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- name: run tox
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run: tox -e lint-${{ matrix.package }}
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|
|
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@ -9,6 +9,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Added
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- `opentelemetry-instrumentation-sklearn` Deprecated the sklearn instrumentation
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([#2708](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2708))
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- `opentelemetry-instrumentation-pyramid` Record exceptions raised when serving a request
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([#2622](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2622))
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- `opentelemetry-sdk-extension-aws` Add AwsXrayLambdaPropagator
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|
|
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@ -54,7 +54,6 @@ packages=
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[lintroots]
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extraroots=examples/*,scripts/
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subglob=*.py,tests/,test/,src/*,examples/*
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ignore=sklearn
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[testroots]
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extraroots=examples/*,tests/
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|
|
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@ -38,7 +38,6 @@
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| [opentelemetry-instrumentation-redis](./opentelemetry-instrumentation-redis) | redis >= 2.6 | No | experimental
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| [opentelemetry-instrumentation-remoulade](./opentelemetry-instrumentation-remoulade) | remoulade >= 0.50 | No | experimental
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| [opentelemetry-instrumentation-requests](./opentelemetry-instrumentation-requests) | requests ~= 2.0 | Yes | migration
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| [opentelemetry-instrumentation-sklearn](./opentelemetry-instrumentation-sklearn) | scikit-learn ~= 0.24.0 | No | experimental
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| [opentelemetry-instrumentation-sqlalchemy](./opentelemetry-instrumentation-sqlalchemy) | sqlalchemy | Yes | experimental
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| [opentelemetry-instrumentation-sqlite3](./opentelemetry-instrumentation-sqlite3) | sqlite3 | No | experimental
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| [opentelemetry-instrumentation-starlette](./opentelemetry-instrumentation-starlette) | starlette ~= 0.13.0 | Yes | experimental
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|
|
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|
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@ -1,201 +0,0 @@
|
|||
Apache License
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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APPENDIX: How to apply the Apache License to your work.
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|
@ -1,24 +0,0 @@
|
|||
OpenTelemetry Scikit-Learn Instrumentation
|
||||
==========================================
|
||||
|
||||
|pypi|
|
||||
|
||||
.. |pypi| image:: https://badge.fury.io/py/opentelemetry-instrumentation-sklearn.svg
|
||||
:target: https://pypi.org/project/opentelemetry-instrumentation-sklearn/
|
||||
|
||||
This library allows tracing HTTP requests made by the
|
||||
`scikit-learn <https://scikit-learn.org/stable/>`_ library.
|
||||
|
||||
Installation
|
||||
------------
|
||||
|
||||
::
|
||||
|
||||
pip install opentelemetry-instrumentation-sklearn
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
* `OpenTelemetry sklearn Instrumentation <https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/sklearn/sklearn.html>`_
|
||||
* `OpenTelemetry Project <https://opentelemetry.io/>`_
|
||||
* `OpenTelemetry Python Examples <https://github.com/open-telemetry/opentelemetry-python/tree/main/docs/examples>`_
|
||||
|
|
@ -1,49 +0,0 @@
|
|||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[project]
|
||||
name = "opentelemetry-instrumentation-sklearn"
|
||||
dynamic = ["version"]
|
||||
description = "OpenTelemetry sklearn instrumentation"
|
||||
readme = "README.rst"
|
||||
license = "Apache-2.0"
|
||||
requires-python = ">=3.8"
|
||||
authors = [
|
||||
{ name = "OpenTelemetry Authors", email = "cncf-opentelemetry-contributors@lists.cncf.io" },
|
||||
]
|
||||
classifiers = [
|
||||
"Development Status :: 4 - Beta",
|
||||
"Intended Audience :: Developers",
|
||||
"License :: OSI Approved :: Apache Software License",
|
||||
"Programming Language :: Python",
|
||||
"Programming Language :: Python :: 3",
|
||||
"Programming Language :: Python :: 3.8",
|
||||
]
|
||||
dependencies = [
|
||||
"opentelemetry-api ~= 1.12",
|
||||
"opentelemetry-instrumentation == 0.47b0.dev",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
instruments = [
|
||||
"scikit-learn ~= 0.24.0",
|
||||
]
|
||||
|
||||
[project.entry-points.opentelemetry_instrumentor]
|
||||
sklearn = "opentelemetry.instrumentation.sklearn:SklearnInstrumentor"
|
||||
|
||||
[project.urls]
|
||||
Homepage = "https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation/opentelemetry-instrumentation-sklearn"
|
||||
|
||||
[tool.hatch.version]
|
||||
path = "src/opentelemetry/instrumentation/sklearn/version.py"
|
||||
|
||||
[tool.hatch.build.targets.sdist]
|
||||
include = [
|
||||
"/src",
|
||||
"/tests",
|
||||
]
|
||||
|
||||
[tool.hatch.build.targets.wheel]
|
||||
packages = ["src/opentelemetry"]
|
||||
|
|
@ -1,792 +0,0 @@
|
|||
# Copyright 2020, OpenTelemetry Authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
The integration with sklearn supports the scikit-learn compatible libraries,
|
||||
it can be enabled by using ``SklearnInstrumentor``.
|
||||
|
||||
.. sklearn: https://github.com/scikit-learn/scikit-learn
|
||||
|
||||
Usage
|
||||
-----
|
||||
|
||||
Package instrumentation example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from opentelemetry.instrumentation.sklearn import SklearnInstrumentor
|
||||
|
||||
# instrument the sklearn library
|
||||
SklearnInstrumentor().instrument()
|
||||
|
||||
# instrument sklearn and other libraries
|
||||
SklearnInstrumentor(
|
||||
packages=["sklearn", "lightgbm", "xgboost"]
|
||||
).instrument()
|
||||
|
||||
|
||||
Model instrumentation example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from opentelemetry.instrumentation.sklearn import SklearnInstrumentor
|
||||
from sklearn.datasets import load_iris
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.pipeline import Pipeline
|
||||
|
||||
X, y = load_iris(return_X_y=True)
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y)
|
||||
|
||||
model = Pipeline(
|
||||
[
|
||||
("class", RandomForestClassifier(n_estimators=10)),
|
||||
]
|
||||
)
|
||||
|
||||
model.fit(X_train, y_train)
|
||||
|
||||
SklearnInstrumentor().instrument_estimator(model)
|
||||
|
||||
"""
|
||||
import logging
|
||||
import os
|
||||
from functools import wraps
|
||||
from importlib import import_module
|
||||
from inspect import isclass
|
||||
from pkgutil import iter_modules
|
||||
from typing import (
|
||||
Callable,
|
||||
Collection,
|
||||
Dict,
|
||||
List,
|
||||
MutableMapping,
|
||||
Sequence,
|
||||
Type,
|
||||
Union,
|
||||
)
|
||||
|
||||
from sklearn.base import BaseEstimator
|
||||
from sklearn.pipeline import FeatureUnion, Pipeline
|
||||
from sklearn.tree import BaseDecisionTree
|
||||
from sklearn.utils.metaestimators import _IffHasAttrDescriptor
|
||||
|
||||
from opentelemetry.instrumentation.instrumentor import BaseInstrumentor
|
||||
|
||||
# pylint: disable=no-name-in-module
|
||||
from opentelemetry.instrumentation.sklearn.package import _instruments
|
||||
from opentelemetry.instrumentation.sklearn.version import __version__
|
||||
from opentelemetry.trace import get_tracer
|
||||
from opentelemetry.util.types import Attributes
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def implement_span_estimator(
|
||||
func: Callable,
|
||||
estimator: Union[BaseEstimator, Type[BaseEstimator]],
|
||||
attributes: Attributes = None,
|
||||
):
|
||||
"""Wrap the method call with a span.
|
||||
|
||||
Args:
|
||||
func: A callable to be wrapped in a span
|
||||
estimator: An instance or class of an estimator
|
||||
attributes: Attributes to apply to the span
|
||||
|
||||
Returns:
|
||||
The passed function wrapped in a span.
|
||||
"""
|
||||
if isclass(estimator):
|
||||
name = estimator.__name__
|
||||
else:
|
||||
name = estimator.__class__.__name__
|
||||
logger.debug("Instrumenting: %s.%s", name, func.__name__)
|
||||
attributes = attributes or {}
|
||||
name = f"{name}.{func.__name__}"
|
||||
return implement_span_function(func, name, attributes)
|
||||
|
||||
|
||||
def implement_span_function(func: Callable, name: str, attributes: Attributes):
|
||||
"""Wrap the function with a span.
|
||||
|
||||
Args:
|
||||
func: A callable to be wrapped in a span
|
||||
name: The name of the span
|
||||
attributes: Attributes to apply to the span
|
||||
|
||||
Returns:
|
||||
The passed function wrapped in a span.
|
||||
"""
|
||||
|
||||
@wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
with get_tracer(
|
||||
__name__,
|
||||
__version__,
|
||||
schema_url="https://opentelemetry.io/schemas/1.11.0",
|
||||
).start_as_current_span(name=name) as span:
|
||||
if span.is_recording():
|
||||
for key, val in attributes.items():
|
||||
span.set_attribute(key, val)
|
||||
return func(*args, **kwargs)
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
def implement_span_delegator(
|
||||
obj: _IffHasAttrDescriptor, attributes: Attributes = None
|
||||
):
|
||||
"""Wrap the descriptor's fn with a span.
|
||||
|
||||
Args:
|
||||
obj: An instance of _IffHasAttrDescriptor
|
||||
attributes: Attributes to apply to the span
|
||||
"""
|
||||
# Don't instrument inherited delegators
|
||||
if hasattr(obj, "_otel_original_fn"):
|
||||
logger.debug("Already instrumented: %s", obj.fn.__qualname__)
|
||||
return
|
||||
logger.debug("Instrumenting: %s", obj.fn.__qualname__)
|
||||
attributes = attributes or {}
|
||||
setattr(obj, "_otel_original_fn", getattr(obj, "fn"))
|
||||
setattr(
|
||||
obj,
|
||||
"fn",
|
||||
implement_span_function(obj.fn, obj.fn.__qualname__, attributes),
|
||||
)
|
||||
|
||||
|
||||
def get_delegator(
|
||||
estimator: Type[BaseEstimator], method_name: str
|
||||
) -> Union[_IffHasAttrDescriptor, None]:
|
||||
"""Get the delegator from a class method or None.
|
||||
|
||||
Args:
|
||||
estimator: A class derived from ``sklearn``'s ``BaseEstimator``.
|
||||
method_name (str): The method name of the estimator on which to
|
||||
check for delegation.
|
||||
|
||||
Returns:
|
||||
The delegator, if one exists, otherwise None.
|
||||
"""
|
||||
class_attr = getattr(estimator, method_name)
|
||||
if getattr(class_attr, "__closure__", None) is not None:
|
||||
for cell in class_attr.__closure__:
|
||||
if isinstance(cell.cell_contents, _IffHasAttrDescriptor):
|
||||
return cell.cell_contents
|
||||
return None
|
||||
|
||||
|
||||
def get_base_estimators(packages: List[str]) -> Dict[str, Type[BaseEstimator]]:
|
||||
"""Walk package hierarchies to get BaseEstimator-derived classes.
|
||||
|
||||
Args:
|
||||
packages (list(str)): A list of package names to instrument.
|
||||
|
||||
Returns:
|
||||
A dictionary of qualnames and classes inheriting from
|
||||
``BaseEstimator``.
|
||||
"""
|
||||
klasses = {}
|
||||
for package_name in packages:
|
||||
lib = import_module(package_name)
|
||||
package_dir = os.path.dirname(lib.__file__)
|
||||
for _, module_name, _ in iter_modules([package_dir]):
|
||||
# import the module and iterate through its attributes
|
||||
try:
|
||||
module = import_module(package_name + "." + module_name)
|
||||
except ImportError:
|
||||
logger.warning(
|
||||
"Unable to import %s.%s", package_name, module_name
|
||||
)
|
||||
continue
|
||||
for attribute_name in dir(module):
|
||||
attrib = getattr(module, attribute_name)
|
||||
if isclass(attrib) and issubclass(attrib, BaseEstimator):
|
||||
klasses[
|
||||
".".join([package_name, module_name, attribute_name])
|
||||
] = attrib
|
||||
return klasses
|
||||
|
||||
|
||||
# Methods on which spans should be applied.
|
||||
DEFAULT_METHODS = [
|
||||
"fit",
|
||||
"transform",
|
||||
"predict",
|
||||
"predict_proba",
|
||||
"_fit",
|
||||
"_transform",
|
||||
"_predict",
|
||||
"_predict_proba",
|
||||
]
|
||||
|
||||
# Classes and their attributes which contain a list of tupled estimators
|
||||
# through which we should walk recursively for estimators.
|
||||
DEFAULT_NAMEDTUPLE_ATTRIBS = {
|
||||
Pipeline: ["steps"],
|
||||
FeatureUnion: ["transformer_list"],
|
||||
}
|
||||
|
||||
# Classes and their attributes which contain an estimator or sequence of
|
||||
# estimators through which we should walk recursively for estimators.
|
||||
DEFAULT_ATTRIBS = {}
|
||||
|
||||
# Classes (including children) explicitly excluded from autoinstrumentation
|
||||
DEFAULT_EXCLUDE_CLASSES = [BaseDecisionTree]
|
||||
|
||||
# Default packages for autoinstrumentation
|
||||
DEFAULT_PACKAGES = ["sklearn"]
|
||||
|
||||
|
||||
class SklearnInstrumentor(BaseInstrumentor):
|
||||
"""Instrument a fitted sklearn model with opentelemetry spans.
|
||||
|
||||
Instrument methods of ``BaseEstimator``-derived components in a sklearn
|
||||
model. The assumption is that a machine learning model ``Pipeline`` (or
|
||||
class descendent) is being instrumented with opentelemetry. Within a
|
||||
``Pipeline`` is some hierarchy of estimators and transformers.
|
||||
|
||||
The ``instrument_estimator`` method walks this hierarchy of estimators,
|
||||
implementing each of the defined methods with its own span.
|
||||
|
||||
Certain estimators in the sklearn ecosystem contain other estimators as
|
||||
instance attributes. Support for walking this embedded sub-hierarchy is
|
||||
supported with ``recurse_attribs``. This argument is a dictionary
|
||||
with classes as keys, and a list of attributes representing embedded
|
||||
estimators as values. By default, ``recurse_attribs`` is empty.
|
||||
|
||||
Similar to Pipelines, there are also estimators which have class attributes
|
||||
as a list of 2-tuples; for instance, the ``FeatureUnion`` and its attribute
|
||||
``transformer_list``. Instrumenting estimators like this is also
|
||||
supported through the ``recurse_namedtuple_attribs`` argument. This
|
||||
argument is a dictionary with classes as keys, and a list of attribute
|
||||
names representing the namedtuple list(s). By default, the
|
||||
``recurse_namedtuple_attribs`` dictionary supports
|
||||
``Pipeline`` with ``steps``, and ``FeatureUnion`` with
|
||||
``transformer_list``.
|
||||
|
||||
Note that spans will not be generated for any child transformer whose
|
||||
parent transformer has ``n_jobs`` parameter set to anything besides
|
||||
``None`` or ``1``.
|
||||
|
||||
Package instrumentation example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from opentelemetry.instrumentation.sklearn import SklearnInstrumentor
|
||||
|
||||
# instrument the sklearn library
|
||||
SklearnInstrumentor().instrument()
|
||||
|
||||
# instrument several sklearn-compatible libraries
|
||||
packages = ["sklearn", "lightgbm", "xgboost"]
|
||||
SklearnInstrumentor(packages=packages).instrument()
|
||||
|
||||
|
||||
Model instrumentation example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from opentelemetry.instrumentation.sklearn import SklearnInstrumentor
|
||||
from sklearn.datasets import load_iris
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.pipeline import Pipeline
|
||||
|
||||
X, y = load_iris(return_X_y=True)
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y)
|
||||
|
||||
model = Pipeline(
|
||||
[
|
||||
("class", RandomForestClassifier(n_estimators=10)),
|
||||
]
|
||||
)
|
||||
|
||||
model.fit(X_train, y_train)
|
||||
|
||||
SklearnInstrumentor().instrument_estimator(model)
|
||||
|
||||
Args:
|
||||
methods (list): A list of method names on which to instrument a span.
|
||||
This list of methods will be checked on all estimators in the model
|
||||
hierarchy. Used in package and model instrumentation
|
||||
recurse_attribs (dict): A dictionary of ``BaseEstimator``-derived
|
||||
sklearn classes as keys, with values being a list of attributes. Each
|
||||
attribute represents either an estimator or list of estimators on
|
||||
which to also implement spans. An example is
|
||||
``RandomForestClassifier`` and its attribute ``estimators_``. Used
|
||||
in model instrumentation only.
|
||||
recurse_namedtuple_attribs (dict): A dictionary of ``BaseEstimator``-
|
||||
derived sklearn types as keys, with values being a list of
|
||||
attribute names. Each attribute represents a list of 2-tuples in
|
||||
which the first element is the estimator name, and the second
|
||||
element is the estimator. Defaults include sklearn's ``Pipeline``
|
||||
and its attribute ``steps``, and the ``FeatureUnion`` and its
|
||||
attribute ``transformer_list``. Used in model instrumentation only.
|
||||
packages: A list of sklearn-compatible packages to
|
||||
instrument. Used with package instrumentation only.
|
||||
exclude_classes: A list of classes to exclude from instrumentation.
|
||||
Child classes are also excluded. Default is sklearn's
|
||||
``[BaseDecisionTree]``.
|
||||
"""
|
||||
|
||||
def __new__(cls, *args, **kwargs):
|
||||
"""Override new.
|
||||
|
||||
The base class' new method passes args and kwargs. We override because
|
||||
we init the class with configuration and Python raises TypeError when
|
||||
additional arguments are passed to the object.__new__() method.
|
||||
"""
|
||||
if cls._instance is None:
|
||||
cls._instance = object.__new__(cls)
|
||||
|
||||
return cls._instance
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
methods: List[str] = None,
|
||||
recurse_attribs: Dict[Type[BaseEstimator], List[str]] = None,
|
||||
recurse_namedtuple_attribs: Dict[
|
||||
Type[BaseEstimator], List[str]
|
||||
] = None,
|
||||
packages: List[str] = None,
|
||||
exclude_classes: List[Type] = None,
|
||||
):
|
||||
self.methods = methods or DEFAULT_METHODS
|
||||
self.recurse_attribs = recurse_attribs or DEFAULT_ATTRIBS
|
||||
self.recurse_namedtuple_attribs = (
|
||||
recurse_namedtuple_attribs or DEFAULT_NAMEDTUPLE_ATTRIBS
|
||||
)
|
||||
self.packages = packages or DEFAULT_PACKAGES
|
||||
if exclude_classes is None:
|
||||
self.exclude_classes = tuple(DEFAULT_EXCLUDE_CLASSES)
|
||||
else:
|
||||
self.exclude_classes = tuple(exclude_classes)
|
||||
|
||||
def instrumentation_dependencies(self) -> Collection[str]:
|
||||
return _instruments
|
||||
|
||||
def _instrument(self, **kwargs):
|
||||
"""Instrument the library, and any additional specified on init."""
|
||||
klasses = get_base_estimators(packages=self.packages)
|
||||
attributes = kwargs.get("attributes")
|
||||
for _, klass in klasses.items():
|
||||
if issubclass(klass, self.exclude_classes):
|
||||
logger.debug("Not instrumenting (excluded): %s", str(klass))
|
||||
else:
|
||||
logger.debug("Instrumenting: %s", str(klass))
|
||||
for method_name in self.methods:
|
||||
if hasattr(klass, method_name):
|
||||
self._instrument_class_method(
|
||||
estimator=klass,
|
||||
method_name=method_name,
|
||||
attributes=attributes,
|
||||
)
|
||||
|
||||
def _uninstrument(self, **kwargs):
|
||||
"""Uninstrument the library"""
|
||||
klasses = get_base_estimators(packages=self.packages)
|
||||
for _, klass in klasses.items():
|
||||
logger.debug("Uninstrumenting: %s", str(klass))
|
||||
for method_name in self.methods:
|
||||
if hasattr(klass, method_name):
|
||||
self._uninstrument_class_method(
|
||||
estimator=klass, method_name=method_name
|
||||
)
|
||||
|
||||
def instrument_estimator(
|
||||
self, estimator: BaseEstimator, attributes: Attributes = None
|
||||
):
|
||||
"""Instrument a fitted estimator and its hierarchy where configured.
|
||||
|
||||
Args:
|
||||
estimator (sklearn.base.BaseEstimator): A fitted ``sklearn``
|
||||
estimator, typically a ``Pipeline`` instance.
|
||||
attributes (dict): Attributes to attach to the spans.
|
||||
"""
|
||||
if isinstance(estimator, self.exclude_classes):
|
||||
logger.debug(
|
||||
"Not instrumenting (excluded): %s",
|
||||
estimator.__class__.__name__,
|
||||
)
|
||||
return
|
||||
|
||||
if isinstance(
|
||||
estimator, tuple(self.recurse_namedtuple_attribs.keys())
|
||||
):
|
||||
self._instrument_estimator_namedtuple(
|
||||
estimator=estimator, attributes=attributes
|
||||
)
|
||||
|
||||
if isinstance(estimator, tuple(self.recurse_attribs.keys())):
|
||||
self._instrument_estimator_attribute(
|
||||
estimator=estimator, attributes=attributes
|
||||
)
|
||||
|
||||
for method_name in self.methods:
|
||||
if hasattr(estimator, method_name):
|
||||
self._instrument_instance_method(
|
||||
estimator=estimator,
|
||||
method_name=method_name,
|
||||
attributes=attributes,
|
||||
)
|
||||
|
||||
def uninstrument_estimator(self, estimator: BaseEstimator):
|
||||
"""Uninstrument a fitted estimator and its hierarchy where configured.
|
||||
|
||||
Args:
|
||||
estimator (sklearn.base.BaseEstimator): A fitted ``sklearn``
|
||||
estimator, typically a ``Pipeline`` instance.
|
||||
"""
|
||||
if isinstance(estimator, self.exclude_classes):
|
||||
logger.debug(
|
||||
"Not uninstrumenting (excluded): %s",
|
||||
estimator.__class__.__name__,
|
||||
)
|
||||
return
|
||||
|
||||
if isinstance(
|
||||
estimator, tuple(self.recurse_namedtuple_attribs.keys())
|
||||
):
|
||||
self._uninstrument_estimator_namedtuple(estimator=estimator)
|
||||
|
||||
if isinstance(estimator, tuple(self.recurse_attribs.keys())):
|
||||
self._uninstrument_estimator_attribute(estimator=estimator)
|
||||
|
||||
for method_name in self.methods:
|
||||
if hasattr(estimator, method_name):
|
||||
self._uninstrument_instance_method(
|
||||
estimator=estimator, method_name=method_name
|
||||
)
|
||||
|
||||
def _check_instrumented(
|
||||
self,
|
||||
estimator: Union[BaseEstimator, Type[BaseEstimator]],
|
||||
method_name: str,
|
||||
) -> bool:
|
||||
"""Check an estimator-method is instrumented.
|
||||
|
||||
Args:
|
||||
estimator (BaseEstimator): A class or instance of an ``sklearn``
|
||||
estimator.
|
||||
method_name (str): The method name of the estimator on which to
|
||||
check for instrumentation.
|
||||
"""
|
||||
orig_method_name = "_otel_original_" + method_name
|
||||
has_original = hasattr(estimator, orig_method_name)
|
||||
orig_class, orig_method = getattr(
|
||||
estimator, orig_method_name, (None, None)
|
||||
)
|
||||
same_class = orig_class == estimator
|
||||
if has_original and same_class:
|
||||
class_method = self._unwrap_function(
|
||||
getattr(estimator, method_name)
|
||||
)
|
||||
# if they match then the subclass doesn't override
|
||||
# if they don't then the overridden method needs instrumentation
|
||||
if class_method.__name__ == orig_method.__name__:
|
||||
return True
|
||||
return False
|
||||
|
||||
def _uninstrument_class_method(
|
||||
self, estimator: Type[BaseEstimator], method_name: str
|
||||
):
|
||||
"""Uninstrument a class method.
|
||||
|
||||
Replaces the patched method with the original, and deletes the
|
||||
attribute which stored the original method.
|
||||
|
||||
Args:
|
||||
estimator (BaseEstimator): A class or instance of an ``sklearn``
|
||||
estimator.
|
||||
method_name (str): The method name of the estimator on which to
|
||||
apply a span.
|
||||
"""
|
||||
orig_method_name = "_otel_original_" + method_name
|
||||
if isclass(estimator):
|
||||
qualname = estimator.__qualname__
|
||||
else:
|
||||
qualname = estimator.__class__.__qualname__
|
||||
delegator = get_delegator(estimator, method_name)
|
||||
if self._check_instrumented(estimator, method_name):
|
||||
logger.debug(
|
||||
"Uninstrumenting: %s.%s",
|
||||
qualname,
|
||||
method_name,
|
||||
)
|
||||
_, orig_method = getattr(estimator, orig_method_name)
|
||||
setattr(
|
||||
estimator,
|
||||
method_name,
|
||||
orig_method,
|
||||
)
|
||||
delattr(estimator, orig_method_name)
|
||||
elif delegator is not None:
|
||||
if not hasattr(delegator, "_otel_original_fn"):
|
||||
logger.debug(
|
||||
"Already uninstrumented: %s.%s",
|
||||
qualname,
|
||||
method_name,
|
||||
)
|
||||
return
|
||||
setattr(
|
||||
delegator,
|
||||
"fn",
|
||||
getattr(delegator, "_otel_original_fn"),
|
||||
)
|
||||
delattr(delegator, "_otel_original_fn")
|
||||
else:
|
||||
logger.debug(
|
||||
"Already uninstrumented: %s.%s",
|
||||
qualname,
|
||||
method_name,
|
||||
)
|
||||
|
||||
def _uninstrument_instance_method(
|
||||
self, estimator: BaseEstimator, method_name: str
|
||||
):
|
||||
"""Uninstrument an instance method.
|
||||
|
||||
Replaces the patched method with the original, and deletes the
|
||||
attribute which stored the original method.
|
||||
|
||||
Args:
|
||||
estimator (BaseEstimator): A class or instance of an ``sklearn``
|
||||
estimator.
|
||||
method_name (str): The method name of the estimator on which to
|
||||
apply a span.
|
||||
"""
|
||||
orig_method_name = "_otel_original_" + method_name
|
||||
if isclass(estimator):
|
||||
qualname = estimator.__qualname__
|
||||
else:
|
||||
qualname = estimator.__class__.__qualname__
|
||||
if self._check_instrumented(estimator, method_name):
|
||||
logger.debug(
|
||||
"Uninstrumenting: %s.%s",
|
||||
qualname,
|
||||
method_name,
|
||||
)
|
||||
_, orig_method = getattr(estimator, orig_method_name)
|
||||
setattr(
|
||||
estimator,
|
||||
method_name,
|
||||
orig_method,
|
||||
)
|
||||
delattr(estimator, orig_method_name)
|
||||
else:
|
||||
logger.debug(
|
||||
"Already uninstrumented: %s.%s",
|
||||
qualname,
|
||||
method_name,
|
||||
)
|
||||
|
||||
def _instrument_class_method(
|
||||
self,
|
||||
estimator: Type[BaseEstimator],
|
||||
method_name: str,
|
||||
attributes: Attributes = None,
|
||||
):
|
||||
"""Instrument an estimator method with a span.
|
||||
|
||||
When instrumenting we attach a tuple of (Class, method) to the
|
||||
attribute ``_otel_original_<method_name>`` for each method. This allows
|
||||
us to replace the patched with the original in uninstrumentation, but
|
||||
also allows proper instrumentation of child classes without
|
||||
instrumenting inherited methods twice.
|
||||
|
||||
Args:
|
||||
estimator (BaseEstimator): A ``BaseEstimator``-derived
|
||||
class
|
||||
method_name (str): The method name of the estimator on which to
|
||||
apply a span.
|
||||
attributes (dict): Attributes to attach to the spans.
|
||||
"""
|
||||
if self._check_instrumented(estimator, method_name):
|
||||
logger.debug(
|
||||
"Already instrumented: %s.%s",
|
||||
estimator.__qualname__,
|
||||
method_name,
|
||||
)
|
||||
return
|
||||
class_attr = getattr(estimator, method_name)
|
||||
delegator = get_delegator(estimator, method_name)
|
||||
if isinstance(class_attr, property):
|
||||
logger.debug(
|
||||
"Not instrumenting found property: %s.%s",
|
||||
estimator.__qualname__,
|
||||
method_name,
|
||||
)
|
||||
elif delegator is not None:
|
||||
implement_span_delegator(delegator)
|
||||
else:
|
||||
setattr(
|
||||
estimator,
|
||||
"_otel_original_" + method_name,
|
||||
(estimator, class_attr),
|
||||
)
|
||||
setattr(
|
||||
estimator,
|
||||
method_name,
|
||||
implement_span_estimator(class_attr, estimator, attributes),
|
||||
)
|
||||
|
||||
def _unwrap_function(self, function):
|
||||
"""Fetch the function underlying any decorators"""
|
||||
if hasattr(function, "__wrapped__"):
|
||||
return self._unwrap_function(function.__wrapped__)
|
||||
return function
|
||||
|
||||
def _instrument_instance_method(
|
||||
self,
|
||||
estimator: BaseEstimator,
|
||||
method_name: str,
|
||||
attributes: Attributes = None,
|
||||
):
|
||||
"""Instrument an estimator instance method with a span.
|
||||
|
||||
When instrumenting we attach a tuple of (Class, method) to the
|
||||
attribute ``_otel_original_<method_name>`` for each method. This allows
|
||||
us to replace the patched with the original in unstrumentation.
|
||||
|
||||
Args:
|
||||
estimator (BaseEstimator): A fitted ``sklearn`` estimator.
|
||||
method_name (str): The method name of the estimator on which to
|
||||
apply a span.
|
||||
attributes (dict): Attributes to attach to the spans.
|
||||
"""
|
||||
if self._check_instrumented(estimator, method_name):
|
||||
logger.debug(
|
||||
"Already instrumented: %s.%s",
|
||||
estimator.__class__.__qualname__,
|
||||
method_name,
|
||||
)
|
||||
return
|
||||
|
||||
class_attr = getattr(type(estimator), method_name, None)
|
||||
if isinstance(class_attr, property):
|
||||
logger.debug(
|
||||
"Not instrumenting found property: %s.%s",
|
||||
estimator.__class__.__qualname__,
|
||||
method_name,
|
||||
)
|
||||
else:
|
||||
method = getattr(estimator, method_name)
|
||||
setattr(
|
||||
estimator, "_otel_original_" + method_name, (estimator, method)
|
||||
)
|
||||
setattr(
|
||||
estimator,
|
||||
method_name,
|
||||
implement_span_estimator(method, estimator, attributes),
|
||||
)
|
||||
|
||||
def _instrument_estimator_attribute(
|
||||
self, estimator: BaseEstimator, attributes: Attributes = None
|
||||
):
|
||||
"""Instrument instance attributes which also contain estimators.
|
||||
|
||||
Handle instance attributes which are also estimators, are a list
|
||||
(Sequence) of estimators, or are mappings (dictionary) in which
|
||||
the values are estimators.
|
||||
|
||||
Examples include ``RandomForestClassifier`` and
|
||||
``MultiOutputRegressor`` instances which have attributes
|
||||
``estimators_`` attributes.
|
||||
|
||||
Args:
|
||||
estimator (BaseEstimator): A fitted ``sklearn`` estimator, with an
|
||||
attribute which also contains an estimator or collection of
|
||||
estimators.
|
||||
attributes (dict): Attributes to attach to the spans.
|
||||
"""
|
||||
attribs = self.recurse_attribs.get(estimator.__class__, [])
|
||||
for attrib in attribs:
|
||||
attrib_value = getattr(estimator, attrib)
|
||||
if isinstance(attrib_value, Sequence):
|
||||
for value in attrib_value:
|
||||
self.instrument_estimator(
|
||||
estimator=value, attributes=attributes
|
||||
)
|
||||
elif isinstance(attrib_value, MutableMapping):
|
||||
for value in attrib_value.values():
|
||||
self.instrument_estimator(
|
||||
estimator=value, attributes=attributes
|
||||
)
|
||||
else:
|
||||
self.instrument_estimator(
|
||||
estimator=attrib_value, attributes=attributes
|
||||
)
|
||||
|
||||
def _instrument_estimator_namedtuple(
|
||||
self, estimator: BaseEstimator, attributes: Attributes = None
|
||||
):
|
||||
"""Instrument attributes with (name, estimator) tupled components.
|
||||
|
||||
Examples include Pipeline and FeatureUnion instances which
|
||||
have attributes steps and transformer_list, respectively.
|
||||
|
||||
Args:
|
||||
estimator: A fitted sklearn estimator, with an attribute which also
|
||||
contains an estimator or collection of estimators.
|
||||
attributes (dict): Attributes to attach to the spans.
|
||||
"""
|
||||
attribs = self.recurse_namedtuple_attribs.get(estimator.__class__, [])
|
||||
for attrib in attribs:
|
||||
for _, est in getattr(estimator, attrib):
|
||||
self.instrument_estimator(estimator=est, attributes=attributes)
|
||||
|
||||
def _uninstrument_estimator_attribute(self, estimator: BaseEstimator):
|
||||
"""Uninstrument instance attributes which also contain estimators.
|
||||
|
||||
Handle instance attributes which are also estimators, are a list
|
||||
(Sequence) of estimators, or are mappings (dictionary) in which
|
||||
the values are estimators.
|
||||
|
||||
Examples include ``RandomForestClassifier`` and
|
||||
``MultiOutputRegressor`` instances which have attributes
|
||||
``estimators_`` attributes.
|
||||
|
||||
Args:
|
||||
estimator (BaseEstimator): A fitted ``sklearn`` estimator, with an
|
||||
attribute which also contains an estimator or collection of
|
||||
estimators.
|
||||
"""
|
||||
attribs = self.recurse_attribs.get(estimator.__class__, [])
|
||||
for attrib in attribs:
|
||||
attrib_value = getattr(estimator, attrib)
|
||||
if isinstance(attrib_value, Sequence):
|
||||
for value in attrib_value:
|
||||
self.uninstrument_estimator(estimator=value)
|
||||
elif isinstance(attrib_value, MutableMapping):
|
||||
for value in attrib_value.values():
|
||||
self.uninstrument_estimator(estimator=value)
|
||||
else:
|
||||
self.uninstrument_estimator(estimator=attrib_value)
|
||||
|
||||
def _uninstrument_estimator_namedtuple(self, estimator: BaseEstimator):
|
||||
"""Uninstrument attributes with (name, estimator) tupled components.
|
||||
|
||||
Examples include Pipeline and FeatureUnion instances which
|
||||
have attributes steps and transformer_list, respectively.
|
||||
|
||||
Args:
|
||||
estimator: A fitted sklearn estimator, with an attribute which also
|
||||
contains an estimator or collection of estimators.
|
||||
"""
|
||||
attribs = self.recurse_namedtuple_attribs.get(estimator.__class__, [])
|
||||
for attrib in attribs:
|
||||
for _, est in getattr(estimator, attrib):
|
||||
self.uninstrument_estimator(estimator=est)
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
# Copyright 2020, OpenTelemetry Authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
_instruments = ("scikit-learn ~= 0.24.0",)
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
# Copyright 2020, OpenTelemetry Authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
__version__ = "0.47b0.dev"
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
asgiref==3.7.2
|
||||
Deprecated==1.2.14
|
||||
importlib-metadata==6.11.0
|
||||
iniconfig==2.0.0
|
||||
joblib==1.3.2
|
||||
numpy==1.24.4
|
||||
packaging==24.0
|
||||
pluggy==1.5.0
|
||||
py-cpuinfo==9.0.0
|
||||
pytest==7.4.4
|
||||
scikit-learn==0.24.2
|
||||
scipy==1.10.1
|
||||
threadpoolctl==3.3.0
|
||||
tomli==2.0.1
|
||||
typing_extensions==4.10.0
|
||||
wrapt==1.16.0
|
||||
zipp==3.19.2
|
||||
-e opentelemetry-instrumentation
|
||||
-e instrumentation/opentelemetry-instrumentation-sklearn
|
||||
|
|
@ -1,54 +0,0 @@
|
|||
# Copyright 2020, OpenTelemetry Authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import numpy as np
|
||||
from sklearn.datasets import load_iris
|
||||
from sklearn.decomposition import PCA, TruncatedSVD
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.pipeline import FeatureUnion, Pipeline
|
||||
from sklearn.preprocessing import Normalizer, StandardScaler
|
||||
|
||||
X, y = load_iris(return_X_y=True)
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y)
|
||||
|
||||
|
||||
def pipeline():
|
||||
"""A dummy model that has a bunch of components that we can test."""
|
||||
model = Pipeline(
|
||||
[
|
||||
("scaler", StandardScaler()),
|
||||
("normal", Normalizer()),
|
||||
(
|
||||
"union",
|
||||
FeatureUnion(
|
||||
[
|
||||
("pca", PCA(n_components=1)),
|
||||
("svd", TruncatedSVD(n_components=2)),
|
||||
],
|
||||
n_jobs=1, # parallelized components won't generate spans
|
||||
),
|
||||
),
|
||||
("class", RandomForestClassifier(n_estimators=10)),
|
||||
]
|
||||
)
|
||||
model.fit(X_train, y_train)
|
||||
return model
|
||||
|
||||
|
||||
def random_input():
|
||||
"""A random record from the feature set."""
|
||||
rows = X.shape[0]
|
||||
random_row = np.random.choice(rows, size=1)
|
||||
return X[random_row, :]
|
||||
|
|
@ -1,190 +0,0 @@
|
|||
# Copyright 2020, OpenTelemetry Authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
|
||||
# pylint: disable=no-name-in-module
|
||||
from opentelemetry.instrumentation.sklearn import (
|
||||
DEFAULT_EXCLUDE_CLASSES,
|
||||
DEFAULT_METHODS,
|
||||
SklearnInstrumentor,
|
||||
get_base_estimators,
|
||||
get_delegator,
|
||||
)
|
||||
from opentelemetry.test.test_base import TestBase
|
||||
from opentelemetry.trace import SpanKind
|
||||
|
||||
from .fixtures import pipeline, random_input
|
||||
|
||||
|
||||
def assert_instrumented(base_estimators):
|
||||
for _, estimator in base_estimators.items():
|
||||
for method_name in DEFAULT_METHODS:
|
||||
original_method_name = "_otel_original_" + method_name
|
||||
if issubclass(estimator, tuple(DEFAULT_EXCLUDE_CLASSES)):
|
||||
assert not hasattr(estimator, original_method_name)
|
||||
continue
|
||||
class_attr = getattr(estimator, method_name, None)
|
||||
if isinstance(class_attr, property):
|
||||
assert not hasattr(estimator, original_method_name)
|
||||
continue
|
||||
delegator = None
|
||||
if hasattr(estimator, method_name):
|
||||
delegator = get_delegator(estimator, method_name)
|
||||
if delegator is not None:
|
||||
assert hasattr(delegator, "_otel_original_fn")
|
||||
elif hasattr(estimator, method_name):
|
||||
assert hasattr(estimator, original_method_name)
|
||||
|
||||
|
||||
def assert_uninstrumented(base_estimators):
|
||||
for _, estimator in base_estimators.items():
|
||||
for method_name in DEFAULT_METHODS:
|
||||
original_method_name = "_otel_original_" + method_name
|
||||
if issubclass(estimator, tuple(DEFAULT_EXCLUDE_CLASSES)):
|
||||
assert not hasattr(estimator, original_method_name)
|
||||
continue
|
||||
class_attr = getattr(estimator, method_name, None)
|
||||
if isinstance(class_attr, property):
|
||||
assert not hasattr(estimator, original_method_name)
|
||||
continue
|
||||
delegator = None
|
||||
if hasattr(estimator, method_name):
|
||||
delegator = get_delegator(estimator, method_name)
|
||||
if delegator is not None:
|
||||
assert not hasattr(delegator, "_otel_original_fn")
|
||||
elif hasattr(estimator, method_name):
|
||||
assert not hasattr(estimator, original_method_name)
|
||||
|
||||
|
||||
class TestSklearn(TestBase):
|
||||
def test_package_instrumentation(self):
|
||||
ski = SklearnInstrumentor()
|
||||
|
||||
base_estimators = get_base_estimators(packages=["sklearn"])
|
||||
|
||||
model = pipeline()
|
||||
|
||||
ski.instrument()
|
||||
assert_instrumented(base_estimators)
|
||||
|
||||
x_test = random_input()
|
||||
|
||||
model.predict(x_test)
|
||||
|
||||
spans = self.memory_exporter.get_finished_spans()
|
||||
self.assertEqual(len(spans), 8)
|
||||
self.memory_exporter.clear()
|
||||
|
||||
ski.uninstrument()
|
||||
assert_uninstrumented(base_estimators)
|
||||
|
||||
model = pipeline()
|
||||
x_test = random_input()
|
||||
|
||||
model.predict(x_test)
|
||||
|
||||
spans = self.memory_exporter.get_finished_spans()
|
||||
self.assertEqual(len(spans), 0)
|
||||
|
||||
def test_span_properties(self):
|
||||
"""Test that we get all of the spans we expect."""
|
||||
model = pipeline()
|
||||
ski = SklearnInstrumentor()
|
||||
ski.instrument_estimator(estimator=model)
|
||||
|
||||
x_test = random_input()
|
||||
|
||||
model.predict(x_test)
|
||||
|
||||
spans = self.memory_exporter.get_finished_spans()
|
||||
self.assertEqual(len(spans), 8)
|
||||
span = spans[0]
|
||||
self.assertEqual(span.name, "StandardScaler.transform")
|
||||
self.assertEqual(span.kind, SpanKind.INTERNAL)
|
||||
self.assertEqual(span.parent.span_id, spans[-1].context.span_id)
|
||||
span = spans[1]
|
||||
self.assertEqual(span.name, "Normalizer.transform")
|
||||
self.assertEqual(span.kind, SpanKind.INTERNAL)
|
||||
self.assertEqual(span.parent.span_id, spans[-1].context.span_id)
|
||||
span = spans[2]
|
||||
self.assertEqual(span.name, "PCA.transform")
|
||||
self.assertEqual(span.kind, SpanKind.INTERNAL)
|
||||
self.assertEqual(span.parent.span_id, spans[4].context.span_id)
|
||||
span = spans[3]
|
||||
self.assertEqual(span.name, "TruncatedSVD.transform")
|
||||
self.assertEqual(span.kind, SpanKind.INTERNAL)
|
||||
self.assertEqual(span.parent.span_id, spans[4].context.span_id)
|
||||
span = spans[4]
|
||||
self.assertEqual(span.name, "FeatureUnion.transform")
|
||||
self.assertEqual(span.kind, SpanKind.INTERNAL)
|
||||
self.assertEqual(span.parent.span_id, spans[-1].context.span_id)
|
||||
span = spans[5]
|
||||
self.assertEqual(span.name, "RandomForestClassifier.predict_proba")
|
||||
self.assertEqual(span.kind, SpanKind.INTERNAL)
|
||||
self.assertEqual(span.parent.span_id, spans[6].context.span_id)
|
||||
span = spans[6]
|
||||
self.assertEqual(span.name, "RandomForestClassifier.predict")
|
||||
self.assertEqual(span.kind, SpanKind.INTERNAL)
|
||||
self.assertEqual(span.parent.span_id, spans[-1].context.span_id)
|
||||
span = spans[7]
|
||||
self.assertEqual(span.name, "Pipeline.predict")
|
||||
self.assertEqual(span.kind, SpanKind.INTERNAL)
|
||||
|
||||
self.memory_exporter.clear()
|
||||
|
||||
# uninstrument
|
||||
ski.uninstrument_estimator(estimator=model)
|
||||
x_test = random_input()
|
||||
model.predict(x_test)
|
||||
spans = self.memory_exporter.get_finished_spans()
|
||||
self.assertEqual(len(spans), 0)
|
||||
|
||||
def test_attrib_config(self):
|
||||
"""Test that the attribute config makes spans on the decision trees."""
|
||||
model = pipeline()
|
||||
attrib_config = {RandomForestClassifier: ["estimators_"]}
|
||||
ski = SklearnInstrumentor(
|
||||
recurse_attribs=attrib_config,
|
||||
exclude_classes=[], # decision trees excluded by default
|
||||
)
|
||||
ski.instrument_estimator(estimator=model)
|
||||
|
||||
x_test = random_input()
|
||||
model.predict(x_test)
|
||||
|
||||
spans = self.memory_exporter.get_finished_spans()
|
||||
self.assertEqual(len(spans), 8 + model.steps[-1][-1].n_estimators)
|
||||
|
||||
self.memory_exporter.clear()
|
||||
|
||||
ski.uninstrument_estimator(estimator=model)
|
||||
x_test = random_input()
|
||||
model.predict(x_test)
|
||||
spans = self.memory_exporter.get_finished_spans()
|
||||
self.assertEqual(len(spans), 0)
|
||||
|
||||
def test_span_attributes(self):
|
||||
model = pipeline()
|
||||
attributes = {"model_name": "random_forest_model"}
|
||||
ski = SklearnInstrumentor()
|
||||
ski.instrument_estimator(estimator=model, attributes=attributes)
|
||||
|
||||
x_test = random_input()
|
||||
|
||||
model.predict(x_test)
|
||||
|
||||
spans = self.memory_exporter.get_finished_spans()
|
||||
for span in spans:
|
||||
assert span.attributes["model_name"] == "random_forest_model"
|
||||
|
|
@ -66,7 +66,6 @@ dependencies = [
|
|||
"opentelemetry-instrumentation-redis==0.47b0.dev",
|
||||
"opentelemetry-instrumentation-remoulade==0.47b0.dev",
|
||||
"opentelemetry-instrumentation-requests==0.47b0.dev",
|
||||
"opentelemetry-instrumentation-sklearn==0.47b0.dev",
|
||||
"opentelemetry-instrumentation-sqlalchemy==0.47b0.dev",
|
||||
"opentelemetry-instrumentation-sqlite3==0.47b0.dev",
|
||||
"opentelemetry-instrumentation-starlette==0.47b0.dev",
|
||||
|
|
|
|||
|
|
@ -152,10 +152,6 @@ libraries = [
|
|||
"library": "requests ~= 2.0",
|
||||
"instrumentation": "opentelemetry-instrumentation-requests==0.47b0.dev",
|
||||
},
|
||||
{
|
||||
"library": "scikit-learn ~= 0.24.0",
|
||||
"instrumentation": "opentelemetry-instrumentation-sklearn==0.47b0.dev",
|
||||
},
|
||||
{
|
||||
"library": "sqlalchemy",
|
||||
"instrumentation": "opentelemetry-instrumentation-sqlalchemy==0.47b0.dev",
|
||||
|
|
|
|||
16
tox.ini
16
tox.ini
|
|
@ -273,10 +273,6 @@ envlist =
|
|||
pypy3-test-instrumentation-celery
|
||||
lint-instrumentation-celery
|
||||
|
||||
; opentelemetry-instrumentation-sklearn
|
||||
py3{8}-test-instrumentation-sklearn
|
||||
lint-instrumentation-sklearn
|
||||
|
||||
; opentelemetry-instrumentation-system-metrics
|
||||
py3{8,9,10,11,12}-test-instrumentation-system-metrics
|
||||
pypy3-test-instrumentation-system-metrics
|
||||
|
|
@ -701,12 +697,6 @@ commands_pre =
|
|||
prometheus: pip install opentelemetry-test-utils@{env:CORE_REPO}\#egg=opentelemetry-test-utils&subdirectory=tests/opentelemetry-test-utils
|
||||
prometheus: pip install -r {toxinidir}/exporter/opentelemetry-exporter-prometheus-remote-write/test-requirements.txt
|
||||
|
||||
sklearn: pip install opentelemetry-api@{env:CORE_REPO}\#egg=opentelemetry-api&subdirectory=opentelemetry-api
|
||||
sklearn: pip install opentelemetry-semantic-conventions@{env:CORE_REPO}\#egg=opentelemetry-semantic-conventions&subdirectory=opentelemetry-semantic-conventions
|
||||
sklearn: pip install opentelemetry-sdk@{env:CORE_REPO}\#egg=opentelemetry-sdk&subdirectory=opentelemetry-sdk
|
||||
sklearn: pip install opentelemetry-test-utils@{env:CORE_REPO}\#egg=opentelemetry-test-utils&subdirectory=tests/opentelemetry-test-utils
|
||||
sklearn: pip install -r {toxinidir}/instrumentation/opentelemetry-instrumentation-sklearn/test-requirements.txt
|
||||
|
||||
sqlalchemy: pip install opentelemetry-api@{env:CORE_REPO}\#egg=opentelemetry-api&subdirectory=opentelemetry-api
|
||||
sqlalchemy: pip install opentelemetry-semantic-conventions@{env:CORE_REPO}\#egg=opentelemetry-semantic-conventions&subdirectory=opentelemetry-semantic-conventions
|
||||
sqlalchemy: pip install opentelemetry-sdk@{env:CORE_REPO}\#egg=opentelemetry-sdk&subdirectory=opentelemetry-sdk
|
||||
|
|
@ -1021,12 +1011,6 @@ commands =
|
|||
lint-instrumentation-requests: flake8 --config {toxinidir}/.flake8 {toxinidir}/instrumentation/opentelemetry-instrumentation-requests
|
||||
lint-instrumentation-requests: sh -c "cd instrumentation && pylint --rcfile ../.pylintrc opentelemetry-instrumentation-requests"
|
||||
|
||||
test-instrumentation-sklearn: pytest {toxinidir}/instrumentation/opentelemetry-instrumentation-sklearn/tests {posargs}
|
||||
lint-instrumentation-sklearn: black --diff --check --config {toxinidir}/pyproject.toml {toxinidir}/instrumentation/opentelemetry-instrumentation-sklearn
|
||||
lint-instrumentation-sklearn: isort --diff --check-only --settings-path {toxinidir}/.isort.cfg {toxinidir}/instrumentation/opentelemetry-instrumentation-sklearn
|
||||
lint-instrumentation-sklearn: flake8 --config {toxinidir}/.flake8 {toxinidir}/instrumentation/opentelemetry-instrumentation-sklearn
|
||||
lint-instrumentation-sklearn: sh -c "cd instrumentation && pylint --rcfile ../.pylintrc opentelemetry-instrumentation-sklearn"
|
||||
|
||||
test-instrumentation-sqlalchemy: pytest {toxinidir}/instrumentation/opentelemetry-instrumentation-sqlalchemy/tests {posargs}
|
||||
lint-instrumentation-sqlalchemy: black --diff --check --config {toxinidir}/pyproject.toml {toxinidir}/instrumentation/opentelemetry-instrumentation-sqlalchemy
|
||||
lint-instrumentation-sqlalchemy: isort --diff --check-only --settings-path {toxinidir}/.isort.cfg {toxinidir}/instrumentation/opentelemetry-instrumentation-sqlalchemy
|
||||
|
|
|
|||
Loading…
Reference in New Issue