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The Essential k-NN Algorithm

The Essential k-NN Algorithm

Learn about the k-NN algorithm and its implementation using different modules.

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Overview

We can summarize the kk-NN algorithm as having the following steps:

  1. Create a list of all (distance, training sample) pairs.
  2. Sort these in ascending order.
  3. Pick to the first kk, which will be the k nearest neighbors.
  4. Chose the mode (the highest frequency) label for the kk nearest neighbors.

The implementation would look like this:

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class Measured(NamedTuple):
    distance: float
    sample: TrainingKnownSample
def k_nn_1(
  k: int, dist: DistanceFunc, training_data: TrainingList,
  unknown: AnySample) -> str:
  distances = sorted(
    map(
      lambda t: Measured(dist(t, unknown), t), training_data) )
  k_nearest = distances[:k]
  k_frequencies: Counter[str] = collections.Counter(
      s.sample.sample.species for s in k_nearest
  )
  mode, fq = k_frequencies.most_common(1)[0]
  return mode

While clear, this does accumulate a large number of distance values in the distances list object, when only kk are actually needed. The sorted() function consumes the source generator and creates a (potentially large) list of intermediate values.

One of the high-cost parts of this specific kk-NN algorithm is sorting the entire set of training data after the distances have been computed. We summarize the complexity with the description as an O(nlog⁡n)O(n \log n) operation. A way to avoid cost is to avoid sorting the entire set of distance computations.

Steps ...