Merge e276bca into d9df1c8

qiime2 · Jun 26, 2020 · 831df61 · 831df61
2 parents d9df1c8 + e276bca
commit 831df61
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Showing 3 changed files with 11 additions and 10 deletions.
diff --git a/q2_sample_classifier/classify.py b/q2_sample_classifier/classify.py
@@ -469,12 +469,12 @@ def detect_outliers(table: biom.Table,
         X_train = features
 
     # fit isolation tree
-    estimator = Pipeline(
-        [('dv', DictVectorizer()),
-         ('est', IsolationForest(n_jobs=n_jobs, n_estimators=n_estimators,
-                                 contamination=contamination,
-                                 random_state=random_state,
-                                ))])
+    estimator = Pipeline([('dv', DictVectorizer()),
+                          ('est', IsolationForest(n_jobs=n_jobs,
+                                                  n_estimators=n_estimators,
+                                                  contamination=contamination,
+                                                  random_state=random_state,
+                                                  ))])
     estimator.fit(X_train)
 
     # predict outlier status

diff --git a/q2_sample_classifier/tests/test_classifier.py b/q2_sample_classifier/tests/test_classifier.py
@@ -45,11 +45,12 @@ def setUp(self):
             index=pd.Index(range(1, 21)), name='Accuracy')
         self.exp2 = pd.Series([
             0.5000000000000001, 0.52, 0.48, 0.5599999999999998, 0.5,
-            0.5799999999999998, 0.54, 0.4600000000000001, 0.6, 
+            0.5799999999999998, 0.54, 0.4600000000000001, 0.6,
             0.45999999999999996, 0.45999999999999996],
             index=pd.Index([1] + [i for i in range(2, 21, 2)]),
             name='Accuracy')
-        self.exp3 = pd.Series({1: 0.4600000000000001, 20: 0.45999999999999996}, name='Accuracy')
+        self.exp3 = pd.Series({1: 0.4600000000000001, 20: 0.45999999999999996},
+                              name='Accuracy')
 
     def extract_rfe_scores_template(self, steps, expected):
         selector = RFECV(RandomForestClassifier(

diff --git a/q2_sample_classifier/tests/test_estimators.py b/q2_sample_classifier/tests/test_estimators.py
@@ -291,8 +291,8 @@ def test_classify_samples_ncv_accuracy(self):
             [0.595111111111111, 0.23155555555555551, 0.17333333333333334],
             index=pd.Index(['o3', 'o1', 'o2']), columns=['importance'])
         exp_probabilities = pd.DataFrame(
-            [[0.5, 0.5], [0., 1.], [0., 1.], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5],
-             [0.5, 0.5], [0., 1.], [1., 0.], [1., 0.]],
+            [[0.5, 0.5], [0., 1.], [0., 1.], [0.5, 0.5], [0.5, 0.5],
+             [0.5, 0.5], [0.5, 0.5], [0., 1.], [1., 0.], [1., 0.]],
             index=pd.Index(['s4', 's6', 's1', 's10', 's5', 's8', 's2', 's9',
                             's3', 's7'], name='SampleID'),
             columns=['blue', 'red'])