KNN classifier gives different results when using predict and kneighbors from sklearn.neighbors.KNeighborsClassifier

Question

I want to classify the extracted features from a CNN with k-nearest neighbors classifier from sklearn.neighbors.KNeighborsClassifier. But when I used predict() function on test data it gives a class different than the majority votes that can be found by kneighbors(). I am using the following Resnet50 pretrained model to extract the features which is a branch of a siamese network. Details of the siamese network can be found here.

def embedding_model():
    
    baseModel = ResNet50(weights="imagenet", include_top=False,input_tensor=Input(shape=(IMAGE_SIZE, IMAGE_SIZE, 3)))
    for layer in baseModel.layers[:165]:
        layer.trainable = False
    
    headModel = baseModel.output
    headModel = GlobalAveragePooling2D()(headModel)
    model = Model(inputs=baseModel.input, outputs=headModel, name = 'embedding_model')

    return model

#get embedding model weights from saved weights
embeddings_weights = siamese_test.get_layer('embedding_model').get_weights()
embeddings_branch = siamese_test.get_layer('embedding_model')

input_shape = (224,224,3)

input = Input(shape=input_shape)
x = embeddings_branch(input)

model = Model(input, x)
model.set_weights(embeddings_weights )
out_shape = model.layers[-1].output_shape

Model summary can be found here. I used the following function to extract the features using the model.

def create_features(dataset, pre_model,out_shape,batchSize=16):
    features = pre_model.predict(dataset, batchSize)
    features_flatten = features.reshape((features.shape[0], out_shape[1] ))
    return features, features_flatten
train_features, train_features_flatten = create_features(x_train,model,out_shape, batchSize)
test_features, test_features_flatten = create_features(x_test,model,out_shape, batchSize)

Then I used KNN classifier to predict on test features

from sklearn.neighbors import KNeighborsClassifier

KNN_classifier = KNeighborsClassifier(n_neighbors=3)
KNN_classifier.fit(train_features_flatten, y_train)

y_pred = KNN_classifier.predict(test_features_flatten)

I used keighbors() function to find the nearest neighbors distance and their corresponding index. But it gives me different results than the predicted one.

neighbors_dist, neighbors_index = KNN_classifier.kneighbors(test_features_flatten)

#replace the index with actual class 
data2 = np.zeros(neighbors_index.shape, dtype=object)
for i in range(neighbors_index.shape[0]):
  for j in range(neighbors_index.shape[1]):
    data2[i,j] = str(y_test[neighbors_index[i][j]])

#get the majority class 
from collections import Counter
majority_class = np.array([Counter(sorted(row, reverse=True)).most_common(1)[0][0] for row in data2])

As you can see the predicted class is not same as the majority class for first 10 samples

for i, pred in enumerate(y_pred):
  print(i,pred)

for i, c in enumerate(majority_class):
  print(i,c)

Predicted output for first 10 samples: 0 corduroy 1 wool 2 wool 3 brown_bread 4 wood 5 corduroy 6 corduroy 7 corduroy 8 wool 9 wood 10 corduroy

Majority class for first 10 samples: 0 corduroy 1 cork 2 cork 3 lettuce_leaf 4 linen 5 corduroy 6 wool 7 corduroy 8 brown_bread 9 linen 10 wool

Is there anything I am doing wrong ? Any help would be appreciated. Thank you.

Answer 1

This is incorrect:

    data2[i,j] = str(y_test[neighbors_index[i][j]])

The kneighbors method (and also predict) finds the nearest training points to the inputs, so you should reference y_train here.