8000 [MRG] Separated regression metrics from other metrics in test_sample_weight_invariance in metrics/tests/test_common.py by nikitasingh981 · Pull Request #8537 · scikit-learn/scikit-learn · GitHub
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[MRG] Separated regression metrics from other metrics in test_sample_weight_invariance in metrics/tests/test_common.py #8537

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Merged
merged 6 commits into from
Mar 5, 2017
Merged

[MRG] Separated regression metrics from other metrics in test_sample_weight_invariance in metrics/tests/test_common.py #8537

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17a59e8
Separated tests for regression features in test_sample_weight_invariance
Mar 4, 2017
9f8e306
Fixed pep8
Mar 5, 2017
e3a2102
Removed unecessary check for regression
Mar 5, 2017
9e240fa
Updated regression metrics
Mar 5, 2017
13e49fb
Merge branch 'feature' of https://github.com/nikitasingh981/scikit-le…
Mar 5, 2017
5e19b9a
Joel's suggestions
Mar 5, 2017
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15 changes: 15 additions & 0 deletions sklearn/metrics/tests/test_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1013,13 +1013,26 @@ def check_sample_weight_invariance(name, metric, y1, y2):

def test_sample_weight_invariance(n_samples=50):
random_state = check_random_state(0)
# regression
y_true = random_state.random_sample(size=(n_samples,))
y_pred = random_state.random_sample(size=(n_samples,))
for name in ALL_METRICS:
if name not in REGRESSION_METRICS:
continue
if name in METRICS_WITHOUT_SAMPLE_WEIGHT:
continue
metric = ALL_METRICS[name]
yield _named_check(check_sample_weight_invariance, name), name,\
metric, y_true, y_pred

# binary
random_state = check_random_state(0)
y_true = random_state.randint(0, 2, size=(n_samples, ))
y_pred = random_state.randint(0, 2, size=(n_samples, ))
y_score = random_state.random_sample(size=(n_samples,))
for name in ALL_METRICS:
if name in REGRESSION_METRICS:
continue
if (name in METRICS_WITHOUT_SAMPLE_WEIGHT or
name in METRIC_UNDEFINED_BINARY):
continue
Expand All @@ -1037,6 +1050,8 @@ def test_sample_weight_invariance(n_samples=50):
y_pred = random_state.randint(0, 5, size=(n_samples, ))
y_score = random_state.random_sample(size=(n_samples, 5))
for name in ALL_METRICS:
if name in REGRESSION_METRICS:
continue
if (name in METRICS_WITHOUT_SAMPLE_WEIGHT or
name in METRIC_UNDEFINED_BINARY_MULTICLASS):
continue
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