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H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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Are SVM MOJOs and POJOs supported? #8208

Open exalate-issue-sync[bot] opened 1 year ago

exalate-issue-sync[bot] commented 1 year ago

From user [~accountid:557058:168c187c-bd44-429d-8971-7a1f7e2ce2f6]

The documentation states support for SVM but in my testing it doesn’t seem to have_mojo or have_pojo:

{code:python}import h2o from h2o.estimators import H2OSupportVectorMachineEstimator splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm") svm = H2OSupportVectorMachineEstimator(gamma=0.01, rank_ratio=0.1, disable_training_metrics=False) svm.train(y="C1", training_frame=splice) print print(svm.have_pojo) print(svm.have_mojo) print(h2o.version)

False False '3.28.1.2'{code}

I noticed similarly with {{H2ONaiveBayesEstimator}} POJO is supported but MOJO isn’t.

I’m looking here [https://github.com/h2oai/h2o-3/blob/master/h2o-py/h2o/estimators/estimator_base.py#L382-L392|https://github.com/h2oai/h2o-3/blob/master/h2o-py/h2o/estimators/estimator_base.py#L382-L392|smart-link]

{noformat}if (model_json["algo"]=="glm") and self.HGLM: m._have_pojo = False m._have_mojo = False else: m._have_pojo = model_json.get('have_pojo', True) m._have_mojo = model_json.get('have_mojo', True){noformat}

But not yet seeing where the have_mojo and have_pojo attributes are being set.

Is this a bug in documentation or code implementation? Thank you!

h2o-ops commented 1 year ago

JIRA Issue Migration Info

Jira Issue: PUBDEV-7429 Assignee: New H2O Bugs Reporter: Angela Bartz State: Open Fix Version: Backlog Attachments: N/A Development PRs: N/A