Add support for allowing multi-dimensional inputs (ndim > 2) #minor

hiclass/HierarchicalClassifier.py

@@ -136,7 +136,7 @@ def _pre_fit(self, X, y, sample_weight):
 
         if not self.bert:
             self.X_, self.y_ = self._validate_data(
-                X, y, multi_output=True, accept_sparse="csr"
+                X, y, multi_output=True, accept_sparse="csr", allow_nd=True
             )
         else:
             self.X_ = np.array(X)

hiclass/LocalClassifierPerLevel.py

@@ -140,7 +140,7 @@ def predict(self, X):
 
         # Input validation
         if not self.bert:
-            X = check_array(X, accept_sparse="csr")
+            X = check_array(X, accept_sparse="csr", allow_nd=True, ensure_2d=False)
         else:
             X = np.array(X)
 

hiclass/LocalClassifierPerNode.py

@@ -150,7 +150,7 @@ def predict(self, X):
 
         # Input validation
         if not self.bert:
-            X = check_array(X, accept_sparse="csr")
+            X = check_array(X, accept_sparse="csr", allow_nd=True, ensure_2d=False)
         else:
             X = np.array(X)
 

hiclass/LocalClassifierPerParentNode.py

@@ -133,7 +133,7 @@ def predict(self, X):
 
         # Input validation
         if not self.bert:
-            X = check_array(X, accept_sparse="csr")
+            X = check_array(X, accept_sparse="csr", allow_nd=True, ensure_2d=False)
         else:
             X = np.array(X)
 

tests/test_LocalClassifierPerLevel.py

@@ -215,3 +215,20 @@ def test_knn():
     check_is_fitted(lcpl)
     # predictions = lcpl.predict(X)
     # assert_array_equal(y, predictions)
+
+
+def test_fit_multiple_dim_input():
+    lcpl = LocalClassifierPerLevel()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcpl.fit(X, y)
+    check_is_fitted(lcpl)
+
+
+def test_predict_multiple_dim_input():
+    lcpl = LocalClassifierPerLevel()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcpl.fit(X, y)
+    predictions = lcpl.predict(X)
+    assert predictions is not None

tests/test_LocalClassifierPerNode.py

@@ -276,3 +276,20 @@ def test_knn():
     check_is_fitted(lcpn)
     # predictions = lcpn.predict(X)
     # assert_array_equal(y, predictions)
+
+
+def test_fit_multiple_dim_input():
+    lcpn = LocalClassifierPerNode()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcpn.fit(X, y)
+    check_is_fitted(lcpn)
+
+
+def test_predict_multiple_dim_input():
+    lcpn = LocalClassifierPerNode()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcpn.fit(X, y)
+    predictions = lcpn.predict(X)
+    assert predictions is not None

tests/test_LocalClassifierPerParentNode.py

@@ -267,3 +267,20 @@ def test_knn():
     check_is_fitted(lcppn)
     # predictions = lcppn.predict(X)
     # assert_array_equal(y, predictions)
+
+
+def test_fit_multiple_dim_input():
+    lcppn = LocalClassifierPerParentNode()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcppn.fit(X, y)
+    check_is_fitted(lcppn)
+
+
+def test_predict_multiple_dim_input():
+    lcppn = LocalClassifierPerParentNode()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcppn.fit(X, y)
+    predictions = lcppn.predict(X)
+    assert predictions is not None