AttributeError: 'Flags' object has no attribute 'c_contiguous'

### Describe the bug

This is the error I am getting when I run my knn classifier. Everything was fine until last night and I am very puzzled to see this issue this morning. I am new to ML so please help.

```pytb
AttributeError                            Traceback (most recent call last)
Cell In[39], line 4
      2 knn = KNeighborsClassifier(n_neighbors=3)
      3 knn = knn.fit(X_train, y_train)
----> 4 y_pred = knn.predict(X_test)
      6 # Preciision, recall, f-score from the multi-class support function
      7 print("Classification Report")

File ~/anaconda3/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:246, in KNeighborsClassifier.predict(self, X)
    244 check_is_fitted(self, "_fit_method")
    245 if self.weights == "uniform":
--> 246     if self._fit_method == "brute" and ArgKminClassMode.is_usable_for(
    247         X, self._fit_X, self.metric
    248     ):
    249         probabilities = self.predict_proba(X)
    250         if self.outputs_2d_:

File ~/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:471, in ArgKminClassMode.is_usable_for(cls, X, Y, metric)
    448 @classmethod
    449 def is_usable_for(cls, X, Y, metric) -> bool:
    450     """Return True if the dispatcher can be used for the given parameters.
    451 
    452     Parameters
   (...)
    468     True if the PairwiseDistancesReduction can be used, else False.
    469     """
    470     return (
--> 471         ArgKmin.is_usable_for(X, Y, metric)
    472         # TODO: Support CSR matrices.
    473         and not issparse(X)
    474         and not issparse(Y)
    475         # TODO: implement Euclidean specialization with GEMM.
    476         and metric not in ("euclidean", "sqeuclidean")
    477     )

File ~/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:115, in BaseDistancesReductionDispatcher.is_usable_for(cls, X, Y, metric)
    101 def is_valid_sparse_matrix(X):
    102     return (
    103         isspmatrix_csr(X)
    104         and
   (...)
    110         X.indices.dtype == X.indptr.dtype == np.int32
    111     )
    113 is_usable = (
    114     get_config().get("enable_cython_pairwise_dist", True)
--> 115     and (is_numpy_c_ordered(X) or is_valid_sparse_matrix(X))
    116     and (is_numpy_c_ordered(Y) or is_valid_sparse_matrix(Y))
    117     and X.dtype == Y.dtype
    118     and X.dtype in (np.float32, np.float64)
    119     and metric in cls.valid_metrics()
    120 )
    122 return is_usable

File ~/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:99, in BaseDistancesReductionDispatcher.is_usable_for.<locals>.is_numpy_c_ordered(X)
     98 def is_numpy_c_ordered(X):
---> 99     return hasattr(X, "flags") and X.flags.c_contiguous

AttributeError: 'Flags' object has no attribute 'c_contiguous'
```

### Steps/Code to Reproduce

```python
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier

X = pd.DataFrame(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]), columns=['A, 'B'])
y = np.array([5, 6, 7, 8])

knn3 = KNeighborsClassifier(n_neighbors=3)

knn3.fit(X, y)

knn3.predict(X)
```

### Expected Results

I should not be getting any error message after this. 

### Actual Results

```pytb
AttributeError                            Traceback (most recent call last)
Cell In[39], line 4
      2 knn = KNeighborsClassifier(n_neighbors=3)
      3 knn = knn.fit(X_train, y_train)
----> 4 y_pred = knn.predict(X_test)
      6 # Preciision, recall, f-score from the multi-class support function
      7 print("Classification Report")

File ~/anaconda3/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:246, in KNeighborsClassifier.predict(self, X)
    244 check_is_fitted(self, "_fit_method")
    245 if self.weights == "uniform":
--> 246     if self._fit_method == "brute" and ArgKminClassMode.is_usable_for(
    247         X, self._fit_X, self.metric
    248     ):
    249         probabilities = self.predict_proba(X)
    250         if self.outputs_2d_:

File ~/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:471, in ArgKminClassMode.is_usable_for(cls, X, Y, metric)
    448 @classmethod
    449 def is_usable_for(cls, X, Y, metric) -> bool:
    450     """Return True if the dispatcher can be used for the given parameters.
    451 
    452     Parameters
   (...)
    468     True if the PairwiseDistancesReduction can be used, else False.
    469     """
    470     return (
--> 471         ArgKmin.is_usable_for(X, Y, metric)
    472         # TODO: Support CSR matrices.
    473         and not issparse(X)
    474         and not issparse(Y)
    475         # TODO: implement Euclidean specialization with GEMM.
    476         and metric not in ("euclidean", "sqeuclidean")
    477     )

File ~/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:115, in BaseDistancesReductionDispatcher.is_usable_for(cls, X, Y, metric)
    101 def is_valid_sparse_matrix(X):
    102     return (
    103         isspmatrix_csr(X)
    104         and
   (...)
    110         X.indices.dtype == X.indptr.dtype == np.int32
    111     )
    113 is_usable = (
    114     get_config().get("enable_cython_pairwise_dist", True)
--> 115     and (is_numpy_c_ordered(X) or is_valid_sparse_matrix(X))
    116     and (is_numpy_c_ordered(Y) or is_valid_sparse_matrix(Y))
    117     and X.dtype == Y.dtype
    118     and X.dtype in (np.float32, np.float64)
    119     and metric in cls.valid_metrics()
    120 )
    122 return is_usable

File ~/anaconda3/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:99, in BaseDistancesReductionDispatcher.is_usable_for.<locals>.is_numpy_c_ordered(X)
     98 def is_numpy_c_ordered(X):
---> 99     return hasattr(X, "flags") and X.flags.c_contiguous

AttributeError: 'Flags' object has no attribute 'c_contiguous'
```

### Versions

```shell
System:
    python: 3.10.12 (main, Jul  5 2023, 15:34:07) [Clang 14.0.6 ]
executable: /Users/rtax/anaconda3/bin/python
   machine: macOS-10.16-x86_64-i386-64bit

Python dependencies:
      sklearn: 1.3.0
          pip: 22.3.1
   setuptools: 65.6.3
        numpy: 1.25.2
        scipy: 1.11.1
       Cython: None
       pandas: 2.0.3
   matplotlib: 3.7.1
       joblib: 1.2.0
threadpoolctl: 2.2.0

Built with OpenMP: True

threadpoolctl info:
       filepath: /Users/rtax/anaconda3/lib/libopenblasp-r0.3.21.dylib
         prefix: libopenblas
       user_api: blas
   internal_api: openblas
        version: 0.3.21
    num_threads: 4
threading_layer: pthreads
   architecture: Haswell

       filepath: /Users/rtax/anaconda3/lib/libomp.dylib
         prefix: libomp
       user_api: openmp
   internal_api: openmp
        version: None
    num_threads: 8
```

COMMENT:
@rtax9  The issue was related to passing a Pandas DataFrame to the predict method of the KNeighborsClassifier, which expected a NumPy array. So, instead of `knn3.predict(X)` use this `knn3.predict(X.values)`. This change should help resolve the AttributeError you were experiencing.

Labels: Bug, Needs Triage