Amazon SageMaker
Developer Guide

K-Nearest Neighbors Hyperparameters

Parameter Name Description
feature_dim

The number of features in the input data.

Required

Valid values: positive integer.

k

The number of nearest neighbors.

Required

Valid values: positive integer

predictor_type

The type of inference to use on the data labels.

Required

Valid values: classifier for classification or regressor for regression.

sample_size

The number of data points to be sampled from the training data set.

Required

Valid values: positive integer

dimension_reduction_target

The target dimension to reduce to.

Required when you specify the dimension_reduction_type parameter.

Valid values: positive integer greater than 0 and lesss than feature_dim.

dimension_reduction_type

The type of dimension reduction method.

Optional

Valid values: sign for random projection or fjlt for the fast Johnson-Lindenstrauss transform.

Default value: No dimension reduction

faiss_index_ivf_nlists

The number of centroids to construct in the index when index_type is faiss.IVFFlat or faiss.IVFPQ.

Optional

Valid values: positive integer

Default value: auto, which resolves to sqrt(sample_size).

faiss_index_pq_m

The number of vector sub-components to construct in the index when index_type is set to faiss.IVFPQ.

The Facebook AI Similarity Search (FAISS) library requires that the value of faiss_index_pq_m is a divisor of the data dimension. If faiss_index_pq_m is not a divisor of the data dimension, we increase the data dimension to smallest integer divisible by faiss_index_pq_m. If no dimension reduction is applied, the algorithm adds a padding of zeros. If dimension reduction is applied, the algorithm increase the value of the dimension_reduction_target hyper-parameter.

Optional

Valid values: One of the following positive integers: 1, 2, 3, 4, 8, 12, 16, 20, 24, 28, 32, 40, 48, 56, 64, 96

index_metric

The metric to measure the distance between points when finding nearest neighbors. When training with index_type set to faiss.IVFPQ, the INNER_PRODUCT distance and COSINE similarity are not supported.

Optional

Valid values: L2 for Euclidean-distance, INNER_PRODUCT for inner-product distance, COSINE for cosine similarity.

Default value: L2

index_type

The type of index.

Optional

Valid values: faiss.Flat, faiss.IVFFlat, faiss.IVFPQ.

Default values: faiss.Flat

mini_batch_size

The number of observations per mini-batch for the data iterator.

Optional

Valid values: positive integer

Default value: 5000