/
distances.py
829 lines (680 loc) · 20.9 KB
/
distances.py
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# Author: Leland McInnes <leland.mcinnes@gmail.com>
#
# License: BSD 3 clause
import numpy as np
import numba
from pynndescent.optimal_transport import (
allocate_graph_structures,
initialize_graph_structures,
initialize_supply,
initialize_cost,
network_simplex_core,
total_cost,
ProblemStatus,
)
_mock_identity = np.eye(2, dtype=np.float32)
_mock_ones = np.ones(2, dtype=np.float32)
_dummy_cost = np.zeros((2, 2), dtype=np.float64)
FLOAT32_EPS = np.finfo(np.float32).eps
FLOAT32_MAX = np.finfo(np.float32).max
@numba.njit(fastmath=True, cache=True)
def euclidean(x, y):
r"""Standard euclidean distance.
.. math::
D(x, y) = \\sqrt{\sum_i (x_i - y_i)^2}
"""
result = 0.0
for i in range(x.shape[0]):
result += (x[i] - y[i]) ** 2
return np.sqrt(result)
@numba.njit(
[
"f4(f4[::1],f4[::1])",
numba.types.float32(
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
),
],
fastmath=True,
locals={
"result": numba.types.float32,
"diff": numba.types.float32,
"dim": numba.types.intp,
"i": numba.types.uint16,
},
)
def squared_euclidean(x, y):
r"""Squared euclidean distance.
.. math::
D(x, y) = \sum_i (x_i - y_i)^2
"""
result = 0.0
dim = x.shape[0]
for i in range(dim):
diff = x[i] - y[i]
result += diff * diff
return result
@numba.njit(fastmath=True, cache=True)
def standardised_euclidean(x, y, sigma=_mock_ones):
r"""Euclidean distance standardised against a vector of standard
deviations per coordinate.
.. math::
D(x, y) = \sqrt{\sum_i \frac{(x_i - y_i)**2}{v_i}}
"""
result = 0.0
for i in range(x.shape[0]):
result += ((x[i] - y[i]) ** 2) / sigma[i]
return np.sqrt(result)
@numba.njit(fastmath=True, cache=True)
def manhattan(x, y):
r"""Manhattan, taxicab, or l1 distance.
.. math::
D(x, y) = \sum_i |x_i - y_i|
"""
result = 0.0
for i in range(x.shape[0]):
result += np.abs(x[i] - y[i])
return result
@numba.njit(fastmath=True, cache=True)
def chebyshev(x, y):
r"""Chebyshev or l-infinity distance.
.. math::
D(x, y) = \max_i |x_i - y_i|
"""
result = 0.0
for i in range(x.shape[0]):
result = max(result, np.abs(x[i] - y[i]))
return result
@numba.njit(fastmath=True, cache=True)
def minkowski(x, y, p=2):
r"""Minkowski distance.
.. math::
D(x, y) = \left(\sum_i |x_i - y_i|^p\right)^{\frac{1}{p}}
This is a general distance. For p=1 it is equivalent to
manhattan distance, for p=2 it is Euclidean distance, and
for p=infinity it is Chebyshev distance. In general it is better
to use the more specialised functions for those distances.
"""
result = 0.0
for i in range(x.shape[0]):
result += (np.abs(x[i] - y[i])) ** p
return result ** (1.0 / p)
@numba.njit(fastmath=True, cache=True)
def weighted_minkowski(x, y, w=_mock_ones, p=2):
r"""A weighted version of Minkowski distance.
.. math::
D(x, y) = \left(\sum_i w_i |x_i - y_i|^p\right)^{\frac{1}{p}}
If weights w_i are inverse standard deviations of graph_data in each dimension
then this represented a standardised Minkowski distance (and is
equivalent to standardised Euclidean distance for p=1).
"""
result = 0.0
for i in range(x.shape[0]):
result += (w[i] * np.abs(x[i] - y[i])) ** p
return result ** (1.0 / p)
@numba.njit(fastmath=True, cache=True)
def mahalanobis(x, y, vinv=_mock_identity):
result = 0.0
diff = np.empty(x.shape[0], dtype=np.float32)
for i in range(x.shape[0]):
diff[i] = x[i] - y[i]
for i in range(x.shape[0]):
tmp = 0.0
for j in range(x.shape[0]):
tmp += vinv[i, j] * diff[j]
result += tmp * diff[i]
return np.sqrt(result)
@numba.njit(fastmath=True, cache=True)
def hamming(x, y):
result = 0.0
for i in range(x.shape[0]):
if x[i] != y[i]:
result += 1.0
return float(result) / x.shape[0]
@numba.njit(fastmath=True, cache=True)
def canberra(x, y):
result = 0.0
for i in range(x.shape[0]):
denominator = np.abs(x[i]) + np.abs(y[i])
if denominator > 0:
result += np.abs(x[i] - y[i]) / denominator
return result
@numba.njit(fastmath=True, cache=True)
def bray_curtis(x, y):
numerator = 0.0
denominator = 0.0
for i in range(x.shape[0]):
numerator += np.abs(x[i] - y[i])
denominator += np.abs(x[i] + y[i])
if denominator > 0.0:
return float(numerator) / denominator
else:
return 0.0
@numba.njit(fastmath=True, cache=True)
def jaccard(x, y):
num_non_zero = 0.0
num_equal = 0.0
for i in range(x.shape[0]):
x_true = x[i] != 0
y_true = y[i] != 0
num_non_zero += x_true or y_true
num_equal += x_true and y_true
if num_non_zero == 0.0:
return 0.0
else:
return float(num_non_zero - num_equal) / num_non_zero
@numba.njit(
[
"f4(f4[::1],f4[::1])",
numba.types.float32(
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
),
],
fastmath=True,
locals={
"result": numba.types.float32,
"num_non_zero": numba.types.float32,
"num_equal": numba.types.float32,
"x_true": numba.types.uint8,
"y_true": numba.types.uint8,
"dim": numba.types.intp,
"i": numba.types.uint16,
},
)
def alternative_jaccard(x, y):
num_non_zero = 0.0
num_equal = 0.0
dim = x.shape[0]
for i in range(dim):
x_true = x[i] != 0
y_true = y[i] != 0
num_non_zero += x_true or y_true
num_equal += x_true and y_true
if num_non_zero == 0.0:
return 0.0
else:
return -np.log2(num_equal / num_non_zero)
@numba.vectorize(fastmath=True)
def correct_alternative_jaccard(v):
return 1.0 - pow(2.0, -v)
@numba.njit(fastmath=True, cache=True)
def matching(x, y):
num_not_equal = 0.0
for i in range(x.shape[0]):
x_true = x[i] != 0
y_true = y[i] != 0
num_not_equal += x_true != y_true
return float(num_not_equal) / x.shape[0]
@numba.njit(fastmath=True, cache=True)
def dice(x, y):
num_true_true = 0.0
num_not_equal = 0.0
for i in range(x.shape[0]):
x_true = x[i] != 0
y_true = y[i] != 0
num_true_true += x_true and y_true
num_not_equal += x_true != y_true
if num_not_equal == 0.0:
return 0.0
else:
return num_not_equal / (2.0 * num_true_true + num_not_equal)
@numba.njit(fastmath=True, cache=True)
def kulsinski(x, y):
num_true_true = 0.0
num_not_equal = 0.0
for i in range(x.shape[0]):
x_true = x[i] != 0
y_true = y[i] != 0
num_true_true += x_true and y_true
num_not_equal += x_true != y_true
if num_not_equal == 0:
return 0.0
else:
return float(num_not_equal - num_true_true + x.shape[0]) / (
num_not_equal + x.shape[0]
)
@numba.njit(fastmath=True, cache=True)
def rogers_tanimoto(x, y):
num_not_equal = 0.0
for i in range(x.shape[0]):
x_true = x[i] != 0
y_true = y[i] != 0
num_not_equal += x_true != y_true
return (2.0 * num_not_equal) / (x.shape[0] + num_not_equal)
@numba.njit(fastmath=True, cache=True)
def russellrao(x, y):
num_true_true = 0.0
for i in range(x.shape[0]):
x_true = x[i] != 0
y_true = y[i] != 0
num_true_true += x_true and y_true
if num_true_true == np.sum(x != 0) and num_true_true == np.sum(y != 0):
return 0.0
else:
return float(x.shape[0] - num_true_true) / (x.shape[0])
@numba.njit(fastmath=True, cache=True)
def sokal_michener(x, y):
num_not_equal = 0.0
for i in range(x.shape[0]):
x_true = x[i] != 0
y_true = y[i] != 0
num_not_equal += x_true != y_true
return (2.0 * num_not_equal) / (x.shape[0] + num_not_equal)
@numba.njit(fastmath=True, cache=True)
def sokal_sneath(x, y):
num_true_true = 0.0
num_not_equal = 0.0
for i in range(x.shape[0]):
x_true = x[i] != 0
y_true = y[i] != 0
num_true_true += x_true and y_true
num_not_equal += x_true != y_true
if num_not_equal == 0.0:
return 0.0
else:
return num_not_equal / (0.5 * num_true_true + num_not_equal)
@numba.njit(fastmath=True, cache=True)
def haversine(x, y):
if x.shape[0] != 2:
raise ValueError("haversine is only defined for 2 dimensional graph_data")
sin_lat = np.sin(0.5 * (x[0] - y[0]))
sin_long = np.sin(0.5 * (x[1] - y[1]))
result = np.sqrt(sin_lat ** 2 + np.cos(x[0]) * np.cos(y[0]) * sin_long ** 2)
return 2.0 * np.arcsin(result)
@numba.njit(fastmath=True, cache=True)
def yule(x, y):
num_true_true = 0.0
num_true_false = 0.0
num_false_true = 0.0
for i in range(x.shape[0]):
x_true = x[i] != 0
y_true = y[i] != 0
num_true_true += x_true and y_true
num_true_false += x_true and (not y_true)
num_false_true += (not x_true) and y_true
num_false_false = x.shape[0] - num_true_true - num_true_false - num_false_true
if num_true_false == 0.0 or num_false_true == 0.0:
return 0.0
else:
return (2.0 * num_true_false * num_false_true) / (
num_true_true * num_false_false + num_true_false * num_false_true
)
@numba.njit(fastmath=True, cache=True)
def cosine(x, y):
result = 0.0
norm_x = 0.0
norm_y = 0.0
for i in range(x.shape[0]):
result += x[i] * y[i]
norm_x += x[i] ** 2
norm_y += y[i] ** 2
if norm_x == 0.0 and norm_y == 0.0:
return 0.0
elif norm_x == 0.0 or norm_y == 0.0:
return 1.0
else:
return 1.0 - (result / np.sqrt(norm_x * norm_y))
@numba.njit(
[
"f4(f4[::1],f4[::1])",
numba.types.float32(
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
),
],
fastmath=True,
locals={
"result": numba.types.float32,
"norm_x": numba.types.float32,
"norm_y": numba.types.float32,
"dim": numba.types.intp,
"i": numba.types.uint16,
},
)
def alternative_cosine(x, y):
result = 0.0
norm_x = 0.0
norm_y = 0.0
dim = x.shape[0]
for i in range(dim):
result += x[i] * y[i]
norm_x += x[i] * x[i]
norm_y += y[i] * y[i]
if norm_x == 0.0 and norm_y == 0.0:
return 0.0
elif norm_x == 0.0 or norm_y == 0.0:
return FLOAT32_MAX
elif result <= 0.0:
return FLOAT32_MAX
else:
result = np.sqrt(norm_x * norm_y) / result
return np.log2(result)
@numba.njit(
"f4(f4[::1],f4[::1])",
fastmath=True,
locals={
"result": numba.types.float32,
"dim": numba.types.intp,
"i": numba.types.uint16,
},
)
def dot(x, y):
result = 0.0
dim = x.shape[0]
for i in range(dim):
result += x[i] * y[i]
if result <= 0.0:
return 1.0
else:
return 1.0 - result
@numba.njit(
[
"f4(f4[::1],f4[::1])",
numba.types.float32(
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
),
],
fastmath=True,
locals={
"result": numba.types.float32,
"dim": numba.types.intp,
"i": numba.types.uint16,
},
)
def alternative_dot(x, y):
result = 0.0
dim = x.shape[0]
for i in range(dim):
result += x[i] * y[i]
if result <= 0.0:
return FLOAT32_MAX
else:
return -np.log2(result)
@numba.vectorize(fastmath=True)
def correct_alternative_cosine(d):
return 1.0 - pow(2.0, -d)
@numba.njit(fastmath=True)
def tsss(x, y):
d_euc_squared = 0.0
d_cos = 0.0
norm_x = 0.0
norm_y = 0.0
dim = x.shape[0]
for i in range(dim):
diff = x[i] - y[i]
d_euc_squared += diff * diff
d_cos += x[i] * y[i]
norm_x += x[i] * x[i]
norm_y += y[i] * y[i]
norm_x = np.sqrt(norm_x)
norm_y = np.sqrt(norm_y)
magnitude_difference = np.abs(norm_x - norm_y)
d_cos /= norm_x * norm_y
theta = np.arccos(d_cos) + np.radians(10) # Add 10 degrees as an "epsilon" to
# avoid problems
sector = ((np.sqrt(d_euc_squared) + magnitude_difference) ** 2) * theta
triangle = norm_x * norm_y * np.sin(theta) / 2.0
return triangle * sector
@numba.njit(fastmath=True)
def true_angular(x, y):
result = 0.0
norm_x = 0.0
norm_y = 0.0
dim = x.shape[0]
for i in range(dim):
result += x[i] * y[i]
norm_x += x[i] * x[i]
norm_y += y[i] * y[i]
if norm_x == 0.0 and norm_y == 0.0:
return 0.0
elif norm_x == 0.0 or norm_y == 0.0:
return FLOAT32_MAX
elif result <= 0.0:
return FLOAT32_MAX
else:
result = result / np.sqrt(norm_x * norm_y)
return 1.0 - (np.arccos(result) / np.pi)
@numba.vectorize(fastmath=True)
def true_angular_from_alt_cosine(d):
return 1.0 - (np.arccos(pow(2.0, -d)) / np.pi)
@numba.njit(fastmath=True, cache=True)
def correlation(x, y):
mu_x = 0.0
mu_y = 0.0
norm_x = 0.0
norm_y = 0.0
dot_product = 0.0
for i in range(x.shape[0]):
mu_x += x[i]
mu_y += y[i]
mu_x /= x.shape[0]
mu_y /= x.shape[0]
for i in range(x.shape[0]):
shifted_x = x[i] - mu_x
shifted_y = y[i] - mu_y
norm_x += shifted_x ** 2
norm_y += shifted_y ** 2
dot_product += shifted_x * shifted_y
if norm_x == 0.0 and norm_y == 0.0:
return 0.0
elif dot_product == 0.0:
return 1.0
else:
return 1.0 - (dot_product / np.sqrt(norm_x * norm_y))
@numba.njit(
[
"f4(f4[::1],f4[::1])",
numba.types.float32(
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
),
],
fastmath=True,
locals={
"result": numba.types.float32,
"l1_norm_x": numba.types.float32,
"l1_norm_y": numba.types.float32,
"dim": numba.types.intp,
"i": numba.types.uint16,
},
)
def hellinger(x, y):
result = 0.0
l1_norm_x = 0.0
l1_norm_y = 0.0
dim = x.shape[0]
for i in range(dim):
result += np.sqrt(x[i] * y[i])
l1_norm_x += x[i]
l1_norm_y += y[i]
if l1_norm_x == 0 and l1_norm_y == 0:
return 0.0
elif l1_norm_x == 0 or l1_norm_y == 0:
return 1.0
else:
return np.sqrt(1 - result / np.sqrt(l1_norm_x * l1_norm_y))
@numba.njit(
[
"f4(f4[::1],f4[::1])",
numba.types.float32(
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
numba.types.Array(numba.types.float32, 1, "C", readonly=True),
),
],
fastmath=True,
locals={
"result": numba.types.float32,
"l1_norm_x": numba.types.float32,
"l1_norm_y": numba.types.float32,
"dim": numba.types.intp,
"i": numba.types.uint16,
},
)
def alternative_hellinger(x, y):
result = 0.0
l1_norm_x = 0.0
l1_norm_y = 0.0
dim = x.shape[0]
for i in range(dim):
result += np.sqrt(x[i] * y[i])
l1_norm_x += x[i]
l1_norm_y += y[i]
if l1_norm_x == 0 and l1_norm_y == 0:
return 0.0
elif l1_norm_x == 0 or l1_norm_y == 0:
return FLOAT32_MAX
elif result <= 0:
return FLOAT32_MAX
else:
result = np.sqrt(l1_norm_x * l1_norm_y) / result
return np.log2(result)
@numba.vectorize(fastmath=True)
def correct_alternative_hellinger(d):
return np.sqrt(1.0 - pow(2.0, -d))
@numba.njit()
def rankdata(a, method="average"):
arr = np.ravel(np.asarray(a))
if method == "ordinal":
sorter = arr.argsort(kind="mergesort")
else:
sorter = arr.argsort(kind="quicksort")
inv = np.empty(sorter.size, dtype=np.intp)
inv[sorter] = np.arange(sorter.size)
if method == "ordinal":
return (inv + 1).astype(np.float64)
arr = arr[sorter]
obs = np.ones(arr.size, np.bool_)
obs[1:] = arr[1:] != arr[:-1]
dense = obs.cumsum()[inv]
if method == "dense":
return dense.astype(np.float64)
# cumulative counts of each unique value
nonzero = np.nonzero(obs)[0]
count = np.concatenate((nonzero, np.array([len(obs)], nonzero.dtype)))
if method == "max":
return count[dense].astype(np.float64)
if method == "min":
return (count[dense - 1] + 1).astype(np.float64)
# average method
return 0.5 * (count[dense] + count[dense - 1] + 1)
@numba.njit(fastmath=True)
def spearmanr(x, y):
a = np.column_stack((x, y))
n_vars = a.shape[1]
for i in range(n_vars):
a[:, i] = rankdata(a[:, i])
rs = np.corrcoef(a, rowvar=0)
return rs[1, 0]
@numba.njit(nogil=True)
def kantorovich(x, y, cost=_dummy_cost, max_iter=100000):
row_mask = x != 0
col_mask = y != 0
a = x[row_mask].astype(np.float64)
b = y[col_mask].astype(np.float64)
a_sum = a.sum()
b_sum = b.sum()
# if not isclose(a_sum, b_sum):
# raise ValueError(
# "Kantorovich distance inputs must be valid probability distributions."
# )
a /= a_sum
b /= b_sum
sub_cost = cost[row_mask, :][:, col_mask]
node_arc_data, spanning_tree, graph = allocate_graph_structures(
a.shape[0],
b.shape[0],
False,
)
initialize_supply(a, -b, graph, node_arc_data.supply)
initialize_cost(sub_cost, graph, node_arc_data.cost)
# initialize_cost(cost, graph, node_arc_data.cost)
init_status = initialize_graph_structures(graph, node_arc_data, spanning_tree)
if init_status == False:
raise ValueError(
"Kantorovich distance inputs must be valid probability distributions."
)
solve_status = network_simplex_core(
node_arc_data,
spanning_tree,
graph,
max_iter,
)
# if solve_status == ProblemStatus.MAX_ITER_REACHED:
# print("WARNING: RESULT MIGHT BE INACCURATE\nMax number of iteration reached!")
if solve_status == ProblemStatus.INFEASIBLE:
raise ValueError(
"Optimal transport problem was INFEASIBLE. Please check " "inputs."
)
elif solve_status == ProblemStatus.UNBOUNDED:
raise ValueError(
"Optimal transport problem was UNBOUNDED. Please check " "inputs."
)
result = total_cost(node_arc_data.flow, node_arc_data.cost)
return result
named_distances = {
# general minkowski distances
"euclidean": euclidean,
"l2": euclidean,
"sqeuclidean": squared_euclidean,
"manhattan": manhattan,
"taxicab": manhattan,
"l1": manhattan,
"chebyshev": chebyshev,
"linfinity": chebyshev,
"linfty": chebyshev,
"linf": chebyshev,
"minkowski": minkowski,
# Standardised/weighted distances
"seuclidean": standardised_euclidean,
"standardised_euclidean": standardised_euclidean,
"wminkowski": weighted_minkowski,
"weighted_minkowski": weighted_minkowski,
"mahalanobis": mahalanobis,
# Other distances
"canberra": canberra,
"cosine": cosine,
"dot": dot,
"correlation": correlation,
"hellinger": hellinger,
"haversine": haversine,
"braycurtis": bray_curtis,
"spearmanr": spearmanr,
"kantorovich": kantorovich,
"wasserstein": kantorovich,
"tsss": tsss,
"true_angular": true_angular,
# Binary distances
"hamming": hamming,
"jaccard": jaccard,
"dice": dice,
"matching": matching,
"kulsinski": kulsinski,
"rogerstanimoto": rogers_tanimoto,
"russellrao": russellrao,
"sokalsneath": sokal_sneath,
"sokalmichener": sokal_michener,
"yule": yule,
}
# Some distances have a faster to compute alternative that
# retains the same ordering of distances. We can compute with
# this instead, and then correct the final distances when complete.
# This provides a list of distances that have such an alternative
# along with the alternative distance function and the correction
# function to be applied.
fast_distance_alternatives = {
"euclidean": {"dist": squared_euclidean, "correction": np.sqrt},
"l2": {"dist": squared_euclidean, "correction": np.sqrt},
"cosine": {"dist": alternative_cosine, "correction": correct_alternative_cosine},
"dot": {"dist": alternative_dot, "correction": correct_alternative_cosine},
"true_angular": {
"dist": alternative_cosine,
"correction": true_angular_from_alt_cosine,
},
"hellinger": {
"dist": alternative_hellinger,
"correction": correct_alternative_hellinger,
},
"jaccard": {"dist": alternative_jaccard, "correction": correct_alternative_jaccard},
}