87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
import numpy as np
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import numba
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from sklearn.neighbors import KDTree
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from umap.distances import named_distances
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@numba.njit()
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def trustworthiness_vector_bulk(
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indices_source, indices_embedded, max_k
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): # pragma: no cover
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n_samples = indices_embedded.shape[0]
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trustworthiness = np.zeros(max_k + 1, dtype=np.float64)
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for i in range(n_samples):
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for j in range(max_k):
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rank = 0
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while indices_source[i, rank] != indices_embedded[i, j]:
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rank += 1
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for k in range(j + 1, max_k + 1):
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if rank > k:
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trustworthiness[k] += rank - k
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for k in range(1, max_k + 1):
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trustworthiness[k] = 1.0 - trustworthiness[k] * (
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2.0 / (n_samples * k * (2.0 * n_samples - 3.0 * k - 1.0))
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)
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return trustworthiness
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def make_trustworthiness_calculator(metric): # pragma: no cover
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@numba.njit(parallel=True)
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def trustworthiness_vector_lowmem(source, indices_embedded, max_k):
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n_samples = indices_embedded.shape[0]
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trustworthiness = np.zeros(max_k + 1, dtype=np.float64)
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dist_vector = np.zeros(n_samples, dtype=np.float64)
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for i in range(n_samples):
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for j in numba.prange(n_samples):
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dist_vector[j] = metric(source[i], source[j])
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indices_source = np.argsort(dist_vector)
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for j in range(max_k):
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rank = 0
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while indices_source[rank] != indices_embedded[i, j]:
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rank += 1
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for k in range(j + 1, max_k + 1):
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if rank > k:
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trustworthiness[k] += rank - k
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for k in range(1, max_k + 1):
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trustworthiness[k] = 1.0 - trustworthiness[k] * (
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2.0 / (n_samples * k * (2.0 * n_samples - 3.0 * k - 1.0))
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)
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trustworthiness[0] = 1.0
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return trustworthiness
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return trustworthiness_vector_lowmem
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def trustworthiness_vector(
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source, embedding, max_k, metric="euclidean"
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): # pragma: no cover
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tree = KDTree(embedding, metric=metric)
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indices_embedded = tree.query(embedding, k=max_k, return_distance=False)
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# Drop the actual point itself
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indices_embedded = indices_embedded[:, 1:]
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dist = named_distances[metric]
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vec_calculator = make_trustworthiness_calculator(dist)
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result = vec_calculator(source, indices_embedded, max_k)
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return result
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