8000 [MRG] scorer: add sample_weight support (+test) by vene · Pull Request #3401 · scikit-learn/scikit-learn · GitHub
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[MRG] scorer: add sample_weight support (+test) #3401

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Merged
merged 4 commits into from
Jul 19, 2014
Merged

[MRG] scorer: add sample_weight support (+test) #3401

merged 4 commits into from
Jul 19, 2014

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vene
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@vene vene commented Jul 16, 2014

Wraps up #3098 (a part of #1574), ready for review.

Initial description by @ndawe:

This is part of the larger #1574 and adds support for sample weights in the scorer interface.

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vene commented Jul 16, 2014

The test is simple but the change is equally simple, just passing the sample_weight parameter through.

@vene vene mentioned this pull request Jul 16, 2014
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amueller
amueller reviewed Jul 16, 2014
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sklearn/metrics/tests/test_score_objects.py

for name, scorer in SCORERS.items():
try:
weighted = scorer(estimator[name], X_test, y_test,
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I would rather have a more explicit test. For example having for the regressor [0, 0, 1, 1, 2, 2] as output and then using weights=[0, 0, 1, 1, 0, 0] and once 1-weights and check that the expected thing happens.

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@amueller The difficulty is that, unlike when testing the metrics directly, for testing the scorer it's tricky to anticipate the output of the classifier / regressor.

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not if you use the dummys ;) What is wrong with my suggestion?

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The 'expected thing' would need to be manually computed for each different scorer, right?

That would be just like coming up with a different hand-crafted test for each scorer, which I could do, if you insist.

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You are right, if you are testing all score_functions, that doesn't make
any sense.

On 07/16/2014 05:23 PM, Vlad Niculae wrote:

In sklearn/metrics/tests/test_score_objects.py:

  • sample_weight = np.ones_like(y_test)
  • sample_weight[:10] = 0

  • get sensible estimators for each metric

  • sensible_regr = DummyRegressor(strategy='median')
  • sensible_regr.fit(X_train, y_train)
  • sensible_clf = DecisionTreeClassifier()
  • sensible_clf.fit(X_train, y_train)
  • estimator = dict([(name, sensible_regr)
  •                  for name in REGRESSION_SCORER_NAMES] +

  •                 [(name, sensible_clf)

  •                  for name in CLF_SCORER_NAMES])
  • for name, scorer in SCORERS.items():
  •    try:

  •        weighted = scorer(estimator[name], X_test, y_test,

The 'expected thing' would need to be manually computed for each
different scorer, right?

That would be just like coming up with a different hand-crafted test
for each scorer, which I could do, if you insist.

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https://github.com/scikit-learn/scikit-learn/pull/3401/files#r15004137.

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arjoly commented Jul 16, 2014

LGTM when travis is green.

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lgtm

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ndawe commented Jul 16, 2014

Thanks a lot for taking this over @vene! I've had no time to work on this lately.

@ndawe ndawe mentioned this pull request Jul 16, 2014
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jnothman
jnothman reviewed Jul 17, 2014
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sklearn/metrics/tests/test_score_objects.py
ignored = scorer(estimator[name], X_test[10:], y_test[10:])
unweighted = scorer(estimator[name], X_test, y_test)
assert_not_equal(weighted, unweighted,
"scorer {} behaves identically when called with "
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support for Py2.6 means we can't use {}. Use {0}, {1}, {2}, ...

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arjoly commented Jul 19, 2014

Not much to do to get that merged.

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Coverage Status

Coverage increased (+0.0%) when pulling 6a4aa1d on vene:scorer_weights into 4ec8630 on scikit-learn:master.

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arjoly commented Jul 19, 2014

Travis is happy ! merging

arjoly added a commit that referenced this pull request Jul 19, 2014
@arjoly
Merge pull request #3401 from vene/scorer_weights
70f4d47 
 [MRG] scorer: add sample_weight support (+test)
@arjoly arjoly merged commit 70f4d47 into scikit-learn:master Jul 19, 2014
@ndawe
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ndawe commented Jul 19, 2014

Thanks! 🍻

@arjoly arjoly mentioned this pull request Jul 19, 2014
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@jnothman
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I'm glad to see this moving through. Thanks Noel and Vlad. I look forward
to the cross-validation support.

On 20 July 2014 07:47, Noel Dawe notifications@github.com wrote:

Thanks! [image: 🍻]

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#3401 (comment)
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@vene @arjoly @amueller @ndawe @coveralls @jnothman
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