From 0efbd3769a53f3d92d431e1f05baa8a4611e2a5a Mon Sep 17 00:00:00 2001
From: Liz Gehret <elizabeth.gehret@gmail.com>
Date: Tue, 30 Apr 2024 11:27:36 -0700
Subject: [PATCH] maint: changes for test failures from python 3.9 update

---
 q2_feature_classifier/_skl.py                  | 2 +-
 q2_feature_classifier/classifier.py            | 4 ++++
 q2_feature_classifier/tests/test_classifier.py | 4 ++--
 q2_feature_classifier/tests/test_custom.py     | 4 ++--
 4 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/q2_feature_classifier/_skl.py b/q2_feature_classifier/_skl.py
index 0ffb834..6021327 100644
--- a/q2_feature_classifier/_skl.py
+++ b/q2_feature_classifier/_skl.py
@@ -61,7 +61,7 @@ def num_leaf_nodes(self) -> int:
            {'__type__': 'feature_extraction.text.HashingVectorizer',
             'analyzer': 'char_wb',
             'n_features': 8192,
-            'ngram_range': [7, 7],
+            'ngram_range': (7, 7),
             'alternate_sign': False}],
           ['classify',
            {'__type__': 'custom.LowMemoryMultinomialNB',
diff --git a/q2_feature_classifier/classifier.py b/q2_feature_classifier/classifier.py
index 9f5bbe0..9ec107f 100644
--- a/q2_feature_classifier/classifier.py
+++ b/q2_feature_classifier/classifier.py
@@ -86,6 +86,8 @@ def default(self, obj):
 
 def pipeline_from_spec(spec):
     def as_steps(obj):
+        if 'ngram_range' in obj:
+            obj['ngram_range'] = tuple(obj['ngram_range'])
         if '__type__' in obj:
             klass = _load_class(obj['__type__'])
             return klass(**{k: v for k, v in obj.items() if k != '__type__'})
@@ -332,6 +334,8 @@ def generic_fitter(reference_reads: DNAIterator,
                 kwargs[param] = json.loads(kwargs[param])
             except (json.JSONDecodeError, TypeError):
                 pass
+            if param == 'feat_ext__ngram_range':
+                kwargs[param] = tuple(kwargs[param])
         pipeline = pipeline_from_spec(spec)
         pipeline.set_params(**kwargs)
         if class_weight is not None:
diff --git a/q2_feature_classifier/tests/test_classifier.py b/q2_feature_classifier/tests/test_classifier.py
index a3814bc..ad08289 100644
--- a/q2_feature_classifier/tests/test_classifier.py
+++ b/q2_feature_classifier/tests/test_classifier.py
@@ -66,7 +66,7 @@ def test_populate_class_weight(self):
                      {'__type__': 'feature_extraction.text.HashingVectorizer',
                       'analyzer': 'char_wb',
                       'n_features': 8192,
-                      'ngram_range': [8, 8],
+                      'ngram_range': (8, 8),
                       'alternate_sign': False}],
                     ['classify',
                      {'__type__': 'naive_bayes.GaussianNB'}]]
@@ -117,7 +117,7 @@ def test_class_weight(self):
                      {'__type__': 'feature_extraction.text.HashingVectorizer',
                       'analyzer': 'char_wb',
                       'n_features': 8192,
-                      'ngram_range': [8, 8],
+                      'ngram_range': (8, 8),
                       'alternate_sign': False}],
                     ['classify',
                      {'__type__': 'linear_model.LogisticRegression'}]]
diff --git a/q2_feature_classifier/tests/test_custom.py b/q2_feature_classifier/tests/test_custom.py
index e38ab74..b2779c0 100644
--- a/q2_feature_classifier/tests/test_custom.py
+++ b/q2_feature_classifier/tests/test_custom.py
@@ -39,7 +39,7 @@ def test_low_memory_multinomial_nb(self):
                 {'__type__': 'feature_extraction.text.HashingVectorizer',
                  'analyzer': 'char',
                  'n_features': 8192,
-                 'ngram_range': [8, 8],
+                 'ngram_range': (8, 8),
                  'alternate_sign': False}],
                 ['classify',
                  {'__type__': 'custom.LowMemoryMultinomialNB',
@@ -68,7 +68,7 @@ def test_chunked_hashing_vectorizer(self):
 
         params = {'analyzer': 'char',
                   'n_features': 8192,
-                  'ngram_range': [8, 8],
+                  'ngram_range': (8, 8),
                   'alternate_sign': False}
         hv = HashingVectorizer(**params)
         unchunked = hv.fit_transform(X)