Fix DataFrame feature name warnings in sklearn wrapper models (#540)

[ugbotueferhire]

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Closes #540

What does this PR do?

Adds X = check_array(X) to decision_function() in four sklearn-wrapper models (IForest, OCSVM, LOF, GMM) that were missing input validation. This prevents the UserWarning: X has feature names, but <Model> was fitted without feature names warning when using pandas DataFrames.

Root cause: fit() already calls check_array(X), which strips DataFrame column names before fitting the underlying sklearn estimator. But decision_function() was passing the raw DataFrame directly to sklearn at predict time, causing a feature-name mismatch warning.

Three other wrapper models (MCD, PCA, HDBSCAN) already had check_array(X) in their decision_function() and were unaffected.

Changes

• pyod/models/iforest.py — add X = check_array(X) in decision_function()
• pyod/models/ocsvm.py — same
• pyod/models/lof.py — same
• pyod/models/gmm.py — same
• pyod/test/test_iforest.py — add regression test test_dataframe_no_feature_name_warning

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[ugbotueferhire]

Hi @yzhao062, I’ve addressed the Codex review feedback on this PR and pushed the fixes.

The latest commit preserves sparse input support in LOF.decision_function and makes the pandas DataFrame regression test skip cleanly when pandas is unavailable.

Could you please take another look and merge when you have a chance? Thank you!