Amazon SageMaker
Developer Guide

How It Works

Step 1: Sampling

To specify the total number of data points to be sampled from the training dataset, use the sample_sizeparameter. For example, if the initial dataset has 1,000 data points and the sample_size is set to 100, where the total number of instances is 2, each worker would sample 50 points. A total set of 100 data points would be collected. Sampling runs in linear time with respect to the number of data points.

Step 2: Dimension Reduction

The current implementation of k-NN has two methods of dimension reduction. You specify the method in the dimension_reduction_type hyperparameter. The sign method specifies a random projection, which uses a linear projection using a matrix of random signs, and the fjlt method specifies a fast Johnson-Lindenstrauss transform, a method based on the Fourier transform. Both methods preserve the L2 and inner product distances. The fjlt method should be used when the target dimension is large and has better performance with CPU inference. The methods differ in their computational complexity. The sign method requires O(ndk) time to reduce the dimension of a batch of n points of dimension d into a target dimension k. The fjlt method requires O(nd log(d)) time, but the constants involved are larger. Using dimension reduction introduces noise into the data and this noise can reduce prediction accuracy.

Step 3: Building an Index

During inference, the algorithm queries the index for the k-nearest-neighbors of a sample point. Based on the references to the points, the algorithm makes the classification or regression prediction. It makes the prediction based on the class labels or values provided. k-NN provides three different types of indexes: a flat index, an inverted index, and an inverted index with product quantization. You specify the type with the index_type parameter.

Model Serialization

When the k-NN algorithm finishes training, it serializes three files to prepare for inference.

  • model_algo-1: Contains the serialized index for computing the nearest neighbors.

  • model_algo-1.labels: Contains serialized labels (np.float32 binary format) for computing the predicted label based on the query result from the index.

  • model_algo-1.json: Contains the JSON-formatted model metadata which stores the k and predictor_type hyper-parameters from training for inference along with other relevant state.

With the current implementation of k-NN, you can modify the metadata file to change the way predictions are computed. For example, you can change k to 10 or change predictor_type to regressor.

{ "k": 5, "predictor_type": "classifier", "dimension_reduction": {"type": "sign", "seed": 3, "target_dim": 10, "input_dim": 20}, "normalize": False, "version": "1.0" }