ENH Add dtype preservation to LocalOutlierFactor
#22665
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test_lof.py to test implementations on 32bit datasetsCo-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Tests currently fails because X can be a list of list of numbers and thus not have a `dtype` attribute. See this RFC for discussions: scikit-learn#24745
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We should be adding the |
| @@ -71,32 +78,32 @@ def test_lof_performance(): | |||
| assert roc_auc_score(y_test, y_pred) > 0.99 | |||
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We should be testing somewhere that y_pred is preserving dtype also.
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we do it later for decision_function and score_samples
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I understand your last message as indicating that we are not needing and assertion for dtype preservation because this is done in test_score_samples. Is this what you meant?
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Otherwise, I think that the other tests are fine. |
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
LocalOutlierFactor
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I've changed the scope of this PR from |
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I am thinking that we could isolate the check of the attribute dtype in a separate test. It would be more readable. We could also add a test to check the consistency 32/64 bits.
In some way this is what we try to do in other preservation dtype PRs: https://github.com/scikit-learn/scikit-learn/pull/24714/files#diff-1f2d3a6511fee4ecd34ef77089119895929c26b5f1193493eacdfd4e745cded7R253
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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I would still propose adding a test for the equivalence 32/64 bits regarding the prediction/score functions. I also find it cleaner to delay the check for the I would propose the following patch: diff --git a/sklearn/neighbors/tests/test_lof.py b/sklearn/neighbors/tests/test_lof.py
index 6ef5e145d4..3ccb3bc3ea 100644
--- a/sklearn/neighbors/tests/test_lof.py
+++ b/sklearn/neighbors/tests/test_lof.py
@@ -149,12 +149,6 @@ def test_score_samples(global_dtype):
clf2_scores = clf2.score_samples(X_test)
clf2_decisions = clf2.decision_function(X_test)
- assert clf1_scores.dtype == global_dtype
- assert clf1_decisions.dtype == global_dtype
-
- assert clf2_scores.dtype == global_dtype
- assert clf2_decisions.dtype == global_dtype
-
assert_allclose(
clf1_scores,
clf1_decisions + clf1.offset_,
@@ -201,7 +195,6 @@ def test_novelty_training_scores(global_dtype):
scores_2 = clf_2.negative_outlier_factor_
assert_allclose(scores_1, scores_2)
- assert scores_1.dtype == scores_2.dtype == global_dtype
def test_hasattr_prediction():
@@ -278,3 +271,42 @@ def test_lof_input_dtype_preservation(global_dtype, algorithm, contamination, no
iso.fit(X)
assert iso.negative_outlier_factor_.dtype == global_dtype
+
+ for method in ("score_samples", "decision_function"):
+ if hasattr(iso, method):
+ y_pred = getattr(iso, method)(X)
+ assert y_pred.dtype == global_dtype
+
+
+@pytest.mark.parametrize("algorithm", ["auto", "ball_tree", "kd_tree", "brute"])
+@pytest.mark.parametrize("novelty", [True, False])
+@pytest.mark.parametrize("contamination", [0.5, "auto"])
+def test_lof_dtype_equivalence(algorithm, novelty, contamination):
+ """Check the equivalence of the results with 32 and 64 bits input."""
+
+ inliers = iris.data[:50] # setosa iris are really distinct from others
+ outliers = iris.data[-5:] # virginica will be considered as outliers
+ # lower the precision of the input data to check that we have an equivalence when
+ # making the computation in 32 and 64 bits.
+ X = np.concatenate([inliers, outliers], axis=0).astype(np.float32)
+
+ lof_32 = neighbors.LocalOutlierFactor(
+ algorithm=algorithm, novelty=novelty, contamination=contamination
+ )
+ X_32 = X.astype(np.float32, copy=True)
+ lof_32.fit(X_32)
+
+ lof_64 = neighbors.LocalOutlierFactor(
+ algorithm=algorithm, novelty=novelty, contamination=contamination
+ )
+ X_64 = X.astype(np.float64, copy=True)
+ lof_64.fit(X_64)
+
+ assert_allclose(lof_32.negative_outlier_factor_, lof_64.negative_outlier_factor_)
+
+ for method in ("score_samples", "decision_function", "predict", "fit_predict"):
+ if hasattr(lof_32, method):
+ y_pred_32 = getattr(lof_32, method)(X_32)
+ y_pred_64 = getattr(lof_64, method)(X_64)
+ assert_allclose(y_pred_32, y_pred_64)
+ |
Authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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Reference Issues/PRs
Partially addresses #22881
Precedes #22590
What does this implement/fix? Explain your changes.
This makes
LocalOutlierFactorpreserves inputs dtype, in particularnp.float32dtyped inputs.This also parametrizes tests from
test_lof.pyto run on np.float32 datasets.