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Autologging functionality for scikit-learn integration with XGBoost (Part 2)#5055

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Autologging functionality for scikit-learn integration with XGBoost (Part 2)#5055
jwyyy wants to merge 7 commits into
mlflow:masterfrom
jwyyy:xgb_sklearn_autolog

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@jwyyy jwyyy commented Nov 12, 2021

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What changes are proposed in this pull request?

This is the second PR to add autologging for XGBoost sklearn models using mlflow.sklearn autologging routine.

(Previous PR: #4954)

(Draft + discussion: #4885)

How is this patch tested?

A new example is provided. Tests will be added later.

Does this PR change the documentation?

  • No. You can skip the rest of this section.
  • Yes. Make sure the changed pages / sections render correctly by following the steps below.
  1. Check the status of the ci/circleci: build_doc check. If it's successful, proceed to the
    next step, otherwise fix it.
  2. Click Details on the right to open the job page of CircleCI.
  3. Click the Artifacts tab.
  4. Click docs/build/html/index.html.
  5. Find the changed pages / sections and make sure they render correctly.

Release Notes

Is this a user-facing change?

  • No. You can skip the rest of this section.
  • Yes. Give a description of this change to be included in the release notes for MLflow users.

Success merge of this PR will enable autologging for XGBoost scikit-learn models.

What component(s), interfaces, languages, and integrations does this PR affect?

Components

  • area/artifacts: Artifact stores and artifact logging
  • area/build: Build and test infrastructure for MLflow
  • area/docs: MLflow documentation pages
  • area/examples: Example code
  • area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
  • area/models: MLmodel format, model serialization/deserialization, flavors
  • area/projects: MLproject format, project running backends
  • area/scoring: MLflow Model server, model deployment tools, Spark UDFs
  • area/server-infra: MLflow Tracking server backend
  • area/tracking: Tracking Service, tracking client APIs, autologging

Interface

  • area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
  • area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
  • area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
  • area/windows: Windows support

Language

  • language/r: R APIs and clients
  • language/java: Java APIs and clients
  • language/new: Proposals for new client languages

Integrations

  • integrations/azure: Azure and Azure ML integrations
  • integrations/sagemaker: SageMaker integrations
  • integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

  • rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
  • rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
  • rn/feature - A new user-facing feature worth mentioning in the release notes
  • rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
  • rn/documentation - A user-facing documentation change worth mentioning in the release notes

jwyyy added 3 commits November 11, 2021 08:19
@jwyyy
init commit
1dd5af7 
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
@jwyyy
add examples
f8f162a 
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
@jwyyy
remove example mlruns folder
dd14016 
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
@github-actions github-actions Bot added area/examples Example code area/tracking Tracking service, tracking client APIs, autologging rn/documentation Mention under Documentation Changes in Changelogs. labels Nov 12, 2021
jwyyy added 2 commits November 11, 2021 18:13
@jwyyy
fix lint + err
3acb252 
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
@jwyyy
fix err caused by flavor conflict
48645c5 
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
jwyyy
jwyyy commented Nov 12, 2021
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Comment thread mlflow/sklearn/__init__.py
mlflow.sklearn.log_model(sk_model, "sk_models")
"""
return Model.log(
Model.log(

@jwyyy jwyyy Nov 12, 2021

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It seems Model.log() doesn't return any value. Maybe we can remove return.

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jwyyy commented Nov 12, 2021
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Comment thread mlflow/sklearn/__init__.py


def _autolog(
flavor_name=FLAVOR_NAME,

@jwyyy jwyyy Nov 12, 2021

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Internal API for sklearn autologging. The flavor_name field allows mlflow.xgboost to specify the xgboost_sklearn flavor, preventing flavor conflict with mlflow.sklearn.

Comment thread mlflow/xgboost.py Outdated
Comment thread mlflow/xgboost.py

def _mlflow_xgboost_logging(
importance_types, autologging_client, logger, original, sklearn_estimator, *args, **kwargs,
):

@jwyyy jwyyy Nov 12, 2021

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Re-organize early stopping call backs and feature importance plot. This function is re-used in mlflow.sklearn for logging XGBoost sklearn estimators.

Comment thread tests/autologging/test_autologging_behaviors_integration.py

safe_patch_call_count = (
safe_patch_mock.call_count + xgb_sklearn_safe_patch_mock.call_count
)

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Since mlflow.sklearn._autolog() is called inside mlflow.xgboost, we need to count safe_patch called due to enabling sklearn autologging.

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jwyyy commented Nov 12, 2021

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Hi @harupy @dbczumar, I made a new PR to complete the autologging functionality for XGBoost sklearn estimators. It is based on our previous discussion #4885. I left a few comments in the PR to highlight the changes:

  1. Early stopping call backs and feature importance plots are re-organized inside the internal function _mlflow_xgboost_logging(). This function stays in mlflow.xgboost and is not moved to a new utils file.
  2. _autolog() is the internal API for sklearn autologging. It is called by mlflow.sklearn.autolog() and mlflow.xgboost.autolog(). However, it seems using the sklearn flavor name for both cases will cause some flavor name conflicts. So a new flavor name xgboost_sklearn is introduced to resolve the conflict.
  3. A short example is provided. Will add test cases later and revise doc once we finalize the PR.

Please correct me if I missed anything. Also please let me know your feedback and suggestions! Thanks a lot!

jwyyy
jwyyy commented Nov 13, 2021
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Comment thread mlflow/sklearn/__init__.py Outdated
@jwyyy
update
5ef5a7d 
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
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jwyyy commented Nov 13, 2021

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Regarding the tests, I was trying to integrate XGBoost sklearn estimators tests with the existing tests: change

# current 
expected_params = {"num_boost_round": 20, "early_stopping_rounds": 5, "verbose_eval": False}
xgb.train(bst_params, dtrain, evals=[(dtrain, "train")], **expected_params)

to something like

# new
def xgb_train(mode, bst_params, data, other_kwargs):
    if mode == "xgboost_sklearn": 
        # return XGBoost sklearn model using bst_params and other_kwargs
    else:
        # mode == "xgboost"
        # return xgb.train(...)

# insider a test function
xgb_train(mode, bst_params, data, other_kwargs)

but the integration could be messy in this way.

Not all parameters passed to xgboost.train() are used to initialized XGBoost sklearn models. In fact, some are passed to the fit() method, i.e., with parameters bst_params and kwargs, we can do

xgboost.train(bst_params, dtrain, **kwargs)

but

xgb_sklearn_model = xgboost.XGBClassifier(**bst_params, **kwargs)
xgb_sklearn_model.fit(X, y) # X, y from dtrain

generally is not error proof.

Here is an example:

  1. eval_metric is passed to fit() for XGBoost sklearn models in xgboost < 1.6.0.
  2. In xgboost.train(), we can set num_boost_round, but it becomes n_estimator in XGBoost sklearn models.
  3. evals and eval_results are not acceptable arguments for XGBoost sklearn estimators.
    (In fact, evals is constructed inside fit() using the eval_set argument.)
  4. The correct configuration for a xgboost.XGBClassifier (<1.6.0) in this case is:
xgb_classifier = xgb.XGBClassifier(objective="multi:softprob", num_class=3, n_estimators=20)
xgb_classifier.fit(X, y, eval_metric=["merror", "mlogloss"], eval_set=[(X1,y1),(X2,y2)])

@harupy @dbczumar Should we keep doing the integration approach? Or is it a better idea to create new separate tests for XGBoost sklearn models? What are your opinions / suggestions? Thanks!

@jwyyy
resolve conflict
f3d162f 
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
@jwyyy jwyyy closed this Nov 17, 2021
@jwyyy jwyyy deleted the xgb_sklearn_autolog branch November 17, 2021 19:26
@jwyyy jwyyy mentioned this pull request Nov 17, 2021
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