Merge pull request #59 from chakki-works/enhancement/checkConsistentL…

…ength

Add length check to classification_report v1

seqeval/metrics/v1.py

@@ -92,8 +92,11 @@ def check_consistent_length(y_true: List[List[str]], y_pred: List[List[str]]):
     """
     len_true = list(map(len, y_true))
     len_pred = list(map(len, y_pred))
-    is_list = set(map(type, y_true + y_pred))
-    if len(y_true) != len(y_pred) or len_true != len_pred or not is_list == {list}:
+    is_list = set(map(type, y_true)) | set(map(type, y_pred))
+    if not is_list == {list}:
+        raise TypeError('Found input variables without list of list.')
+
+    if len(y_true) != len(y_pred) or len_true != len_pred:
         message = 'Found input variables with inconsistent numbers of samples:\n{}\n{}'.format(len_true, len_pred)
         raise ValueError(message)
 
@@ -338,6 +341,8 @@ def classification_report(y_true: List[List[str]],
        weighted avg       0.50      0.50      0.50         2
         <BLANKLINE>
     """
+    check_consistent_length(y_true, y_pred)
+
     if scheme is None or not issubclass(scheme, Token):
         scheme = auto_detect(y_true, suffix)
     target_names = unique_labels(y_true, y_pred, scheme, suffix)

tests/test_v1.py

@@ -4,7 +4,7 @@
 from sklearn.utils._testing import (assert_array_almost_equal,
                                     assert_array_equal)
 
-from seqeval.metrics.v1 import (classification_report,
+from seqeval.metrics.v1 import (check_consistent_length, classification_report,
                                 precision_recall_fscore_support, unique_labels)
 from seqeval.scheme import IOB2
 
@@ -26,6 +26,43 @@ def test_unique_labels(y_true, y_pred, expected):
     assert labels == expected
 
 
+class TestCheckConsistentLength:
+
+    @pytest.mark.parametrize(
+        'y_true, y_pred',
+        [
+            ([[]], [[]]),
+            ([['B']], [['B']])
+        ]
+    )
+    def test_check_valid_list(self, y_true, y_pred):
+        check_consistent_length(y_true, y_pred)
+
+    @pytest.mark.parametrize(
+        'y_true, y_pred',
+        [
+            ([()], [()]),
+            (np.array([[]]), np.array([[]])),
+            (np.array([[]]), [[]])
+        ]
+    )
+    def test_check_invalid_type(self, y_true, y_pred):
+        with pytest.raises(TypeError):
+            check_consistent_length(y_true, y_pred)
+
+    @pytest.mark.parametrize(
+        'y_true, y_pred',
+        [
+            ([[]], [['B']]),
+            ([['B'], []], [['B']]),
+            ([['B'], []], [['B'], ['I']])
+        ]
+    )
+    def test_invalid_length(self, y_true, y_pred):
+        with pytest.raises(ValueError):
+            check_consistent_length(y_true, y_pred)
+
+
 class TestPrecisionRecallFscoreSupport:
 
     def test_bad_beta(self):
@@ -101,6 +138,30 @@ def test_average_scores_beta_inf(self, average, expected):
 
 class TestClassificationReport:
 
+    @pytest.mark.parametrize(
+        'y_true, y_pred',
+        [
+            ([()], [()]),
+            (np.array([[]]), np.array([[]])),
+            (np.array([[]]), [[]])
+        ]
+    )
+    def test_check_invalid_type(self, y_true, y_pred):
+        with pytest.raises(TypeError):
+            check_consistent_length(y_true, y_pred)
+
+    @pytest.mark.parametrize(
+        'y_true, y_pred',
+        [
+            ([[]], [['B']]),
+            ([['B'], []], [['B']]),
+            ([['B'], []], [['B'], ['I']])
+        ]
+    )
+    def test_invalid_length(self, y_true, y_pred):
+        with pytest.raises(ValueError):
+            check_consistent_length(y_true, y_pred)
+
     def test_output_dict(self):
         y_true = [['B-A', 'B-B', 'O', 'B-A']]
         y_pred = [['O', 'B-B', 'B-C', 'B-A']]