423 lines
14 KiB
Python
423 lines
14 KiB
Python
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import copy
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import itertools
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import pickle
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import numpy as np
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import pytest
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from scipy.spatial.distance import cdist
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from sklearn.metrics import DistanceMetric
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from sklearn.metrics._dist_metrics import (
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BOOL_METRICS,
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DistanceMetric32,
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DistanceMetric64,
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)
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from sklearn.utils import check_random_state
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from sklearn.utils._testing import assert_allclose, create_memmap_backed_data
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from sklearn.utils.fixes import CSR_CONTAINERS, parse_version, sp_version
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def dist_func(x1, x2, p):
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return np.sum((x1 - x2) ** p) ** (1.0 / p)
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rng = check_random_state(0)
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d = 4
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n1 = 20
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n2 = 25
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X64 = rng.random_sample((n1, d))
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Y64 = rng.random_sample((n2, d))
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X32 = X64.astype("float32")
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Y32 = Y64.astype("float32")
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[X_mmap, Y_mmap] = create_memmap_backed_data([X64, Y64])
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# make boolean arrays: ones and zeros
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X_bool = (X64 < 0.3).astype(np.float64) # quite sparse
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Y_bool = (Y64 < 0.7).astype(np.float64) # not too sparse
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[X_bool_mmap, Y_bool_mmap] = create_memmap_backed_data([X_bool, Y_bool])
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V = rng.random_sample((d, d))
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VI = np.dot(V, V.T)
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METRICS_DEFAULT_PARAMS = [
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("euclidean", {}),
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("cityblock", {}),
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("minkowski", dict(p=(0.5, 1, 1.5, 2, 3))),
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("chebyshev", {}),
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("seuclidean", dict(V=(rng.random_sample(d),))),
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("mahalanobis", dict(VI=(VI,))),
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("hamming", {}),
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("canberra", {}),
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("braycurtis", {}),
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("minkowski", dict(p=(0.5, 1, 1.5, 3), w=(rng.random_sample(d),))),
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]
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@pytest.mark.parametrize(
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"metric_param_grid", METRICS_DEFAULT_PARAMS, ids=lambda params: params[0]
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)
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@pytest.mark.parametrize("X, Y", [(X64, Y64), (X32, Y32), (X_mmap, Y_mmap)])
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_cdist(metric_param_grid, X, Y, csr_container):
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metric, param_grid = metric_param_grid
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keys = param_grid.keys()
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X_csr, Y_csr = csr_container(X), csr_container(Y)
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for vals in itertools.product(*param_grid.values()):
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kwargs = dict(zip(keys, vals))
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rtol_dict = {}
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if metric == "mahalanobis" and X.dtype == np.float32:
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# Computation of mahalanobis differs between
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# the scipy and scikit-learn implementation.
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# Hence, we increase the relative tolerance.
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# TODO: Inspect slight numerical discrepancy
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# with scipy
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rtol_dict = {"rtol": 1e-6}
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# TODO: Remove when scipy minimum version >= 1.7.0
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# scipy supports 0<p<1 for minkowski metric >= 1.7.0
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if metric == "minkowski":
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p = kwargs["p"]
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if sp_version < parse_version("1.7.0") and p < 1:
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pytest.skip("scipy does not support 0<p<1 for minkowski metric < 1.7.0")
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D_scipy_cdist = cdist(X, Y, metric, **kwargs)
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dm = DistanceMetric.get_metric(metric, X.dtype, **kwargs)
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# DistanceMetric.pairwise must be consistent for all
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# combinations of formats in {sparse, dense}.
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D_sklearn = dm.pairwise(X, Y)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_scipy_cdist, **rtol_dict)
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D_sklearn = dm.pairwise(X_csr, Y_csr)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_scipy_cdist, **rtol_dict)
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D_sklearn = dm.pairwise(X_csr, Y)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_scipy_cdist, **rtol_dict)
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D_sklearn = dm.pairwise(X, Y_csr)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_scipy_cdist, **rtol_dict)
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@pytest.mark.parametrize("metric", BOOL_METRICS)
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@pytest.mark.parametrize(
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"X_bool, Y_bool", [(X_bool, Y_bool), (X_bool_mmap, Y_bool_mmap)]
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)
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_cdist_bool_metric(metric, X_bool, Y_bool, csr_container):
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D_scipy_cdist = cdist(X_bool, Y_bool, metric)
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dm = DistanceMetric.get_metric(metric)
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D_sklearn = dm.pairwise(X_bool, Y_bool)
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assert_allclose(D_sklearn, D_scipy_cdist)
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# DistanceMetric.pairwise must be consistent
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# on all combinations of format in {sparse, dense}².
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X_bool_csr, Y_bool_csr = csr_container(X_bool), csr_container(Y_bool)
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D_sklearn = dm.pairwise(X_bool, Y_bool)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_scipy_cdist)
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D_sklearn = dm.pairwise(X_bool_csr, Y_bool_csr)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_scipy_cdist)
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D_sklearn = dm.pairwise(X_bool, Y_bool_csr)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_scipy_cdist)
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D_sklearn = dm.pairwise(X_bool_csr, Y_bool)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_scipy_cdist)
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@pytest.mark.parametrize(
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"metric_param_grid", METRICS_DEFAULT_PARAMS, ids=lambda params: params[0]
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)
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@pytest.mark.parametrize("X", [X64, X32, X_mmap])
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_pdist(metric_param_grid, X, csr_container):
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metric, param_grid = metric_param_grid
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keys = param_grid.keys()
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X_csr = csr_container(X)
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for vals in itertools.product(*param_grid.values()):
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kwargs = dict(zip(keys, vals))
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rtol_dict = {}
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if metric == "mahalanobis" and X.dtype == np.float32:
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# Computation of mahalanobis differs between
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# the scipy and scikit-learn implementation.
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# Hence, we increase the relative tolerance.
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# TODO: Inspect slight numerical discrepancy
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# with scipy
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rtol_dict = {"rtol": 1e-6}
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# TODO: Remove when scipy minimum version >= 1.7.0
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# scipy supports 0<p<1 for minkowski metric >= 1.7.0
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if metric == "minkowski":
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p = kwargs["p"]
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if sp_version < parse_version("1.7.0") and p < 1:
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pytest.skip("scipy does not support 0<p<1 for minkowski metric < 1.7.0")
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D_scipy_pdist = cdist(X, X, metric, **kwargs)
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dm = DistanceMetric.get_metric(metric, X.dtype, **kwargs)
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D_sklearn = dm.pairwise(X)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_scipy_pdist, **rtol_dict)
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D_sklearn_csr = dm.pairwise(X_csr)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn_csr, D_scipy_pdist, **rtol_dict)
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D_sklearn_csr = dm.pairwise(X_csr, X_csr)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn_csr, D_scipy_pdist, **rtol_dict)
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@pytest.mark.parametrize(
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"metric_param_grid", METRICS_DEFAULT_PARAMS, ids=lambda params: params[0]
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)
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def test_distance_metrics_dtype_consistency(metric_param_grid):
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# DistanceMetric must return similar distances for both float32 and float64
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# input data.
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metric, param_grid = metric_param_grid
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keys = param_grid.keys()
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# Choose rtol to make sure that this test is robust to changes in the random
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# seed in the module-level test data generation code.
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rtol = 1e-5
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for vals in itertools.product(*param_grid.values()):
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kwargs = dict(zip(keys, vals))
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dm64 = DistanceMetric.get_metric(metric, np.float64, **kwargs)
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dm32 = DistanceMetric.get_metric(metric, np.float32, **kwargs)
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D64 = dm64.pairwise(X64)
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D32 = dm32.pairwise(X32)
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assert D64.dtype == np.float64
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assert D32.dtype == np.float32
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# assert_allclose introspects the dtype of the input arrays to decide
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# which rtol value to use by default but in this case we know that D32
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# is not computed with the same precision so we set rtol manually.
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assert_allclose(D64, D32, rtol=rtol)
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D64 = dm64.pairwise(X64, Y64)
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D32 = dm32.pairwise(X32, Y32)
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assert_allclose(D64, D32, rtol=rtol)
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@pytest.mark.parametrize("metric", BOOL_METRICS)
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@pytest.mark.parametrize("X_bool", [X_bool, X_bool_mmap])
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_pdist_bool_metrics(metric, X_bool, csr_container):
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D_scipy_pdist = cdist(X_bool, X_bool, metric)
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dm = DistanceMetric.get_metric(metric)
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D_sklearn = dm.pairwise(X_bool)
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assert_allclose(D_sklearn, D_scipy_pdist)
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X_bool_csr = csr_container(X_bool)
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D_sklearn = dm.pairwise(X_bool_csr)
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assert_allclose(D_sklearn, D_scipy_pdist)
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@pytest.mark.parametrize("writable_kwargs", [True, False])
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@pytest.mark.parametrize(
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"metric_param_grid", METRICS_DEFAULT_PARAMS, ids=lambda params: params[0]
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)
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@pytest.mark.parametrize("X", [X64, X32])
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def test_pickle(writable_kwargs, metric_param_grid, X):
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metric, param_grid = metric_param_grid
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keys = param_grid.keys()
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for vals in itertools.product(*param_grid.values()):
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if any(isinstance(val, np.ndarray) for val in vals):
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vals = copy.deepcopy(vals)
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for val in vals:
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if isinstance(val, np.ndarray):
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val.setflags(write=writable_kwargs)
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kwargs = dict(zip(keys, vals))
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dm = DistanceMetric.get_metric(metric, X.dtype, **kwargs)
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D1 = dm.pairwise(X)
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dm2 = pickle.loads(pickle.dumps(dm))
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D2 = dm2.pairwise(X)
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assert_allclose(D1, D2)
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@pytest.mark.parametrize("metric", BOOL_METRICS)
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@pytest.mark.parametrize("X_bool", [X_bool, X_bool_mmap])
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def test_pickle_bool_metrics(metric, X_bool):
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dm = DistanceMetric.get_metric(metric)
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D1 = dm.pairwise(X_bool)
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dm2 = pickle.loads(pickle.dumps(dm))
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D2 = dm2.pairwise(X_bool)
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assert_allclose(D1, D2)
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@pytest.mark.parametrize("X, Y", [(X64, Y64), (X32, Y32), (X_mmap, Y_mmap)])
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_haversine_metric(X, Y, csr_container):
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# The Haversine DistanceMetric only works on 2 features.
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X = np.asarray(X[:, :2])
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Y = np.asarray(Y[:, :2])
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X_csr, Y_csr = csr_container(X), csr_container(Y)
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# Haversine is not supported by scipy.special.distance.{cdist,pdist}
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# So we reimplement it to have a reference.
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def haversine_slow(x1, x2):
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return 2 * np.arcsin(
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np.sqrt(
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np.sin(0.5 * (x1[0] - x2[0])) ** 2
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+ np.cos(x1[0]) * np.cos(x2[0]) * np.sin(0.5 * (x1[1] - x2[1])) ** 2
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)
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)
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D_reference = np.zeros((X_csr.shape[0], Y_csr.shape[0]))
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for i, xi in enumerate(X):
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for j, yj in enumerate(Y):
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D_reference[i, j] = haversine_slow(xi, yj)
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haversine = DistanceMetric.get_metric("haversine", X.dtype)
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D_sklearn = haversine.pairwise(X, Y)
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assert_allclose(
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haversine.dist_to_rdist(D_sklearn), np.sin(0.5 * D_reference) ** 2, rtol=1e-6
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)
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assert_allclose(D_sklearn, D_reference)
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D_sklearn = haversine.pairwise(X_csr, Y_csr)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_reference)
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D_sklearn = haversine.pairwise(X_csr, Y)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_reference)
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D_sklearn = haversine.pairwise(X, Y_csr)
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assert D_sklearn.flags.c_contiguous
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assert_allclose(D_sklearn, D_reference)
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def test_pyfunc_metric():
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X = np.random.random((10, 3))
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euclidean = DistanceMetric.get_metric("euclidean")
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pyfunc = DistanceMetric.get_metric("pyfunc", func=dist_func, p=2)
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# Check if both callable metric and predefined metric initialized
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# DistanceMetric object is picklable
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euclidean_pkl = pickle.loads(pickle.dumps(euclidean))
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pyfunc_pkl = pickle.loads(pickle.dumps(pyfunc))
|
||
|
|
|
||
|
|
D1 = euclidean.pairwise(X)
|
||
|
|
D2 = pyfunc.pairwise(X)
|
||
|
|
|
||
|
|
D1_pkl = euclidean_pkl.pairwise(X)
|
||
|
|
D2_pkl = pyfunc_pkl.pairwise(X)
|
||
|
|
|
||
|
|
assert_allclose(D1, D2)
|
||
|
|
assert_allclose(D1_pkl, D2_pkl)
|
||
|
|
|
||
|
|
|
||
|
|
def test_input_data_size():
|
||
|
|
# Regression test for #6288
|
||
|
|
# Previously, a metric requiring a particular input dimension would fail
|
||
|
|
def custom_metric(x, y):
|
||
|
|
assert x.shape[0] == 3
|
||
|
|
return np.sum((x - y) ** 2)
|
||
|
|
|
||
|
|
rng = check_random_state(0)
|
||
|
|
X = rng.rand(10, 3)
|
||
|
|
|
||
|
|
pyfunc = DistanceMetric.get_metric("pyfunc", func=custom_metric)
|
||
|
|
eucl = DistanceMetric.get_metric("euclidean")
|
||
|
|
assert_allclose(pyfunc.pairwise(X), eucl.pairwise(X) ** 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_readonly_kwargs():
|
||
|
|
# Non-regression test for:
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/issues/21685
|
||
|
|
|
||
|
|
rng = check_random_state(0)
|
||
|
|
|
||
|
|
weights = rng.rand(100)
|
||
|
|
VI = rng.rand(10, 10)
|
||
|
|
weights.setflags(write=False)
|
||
|
|
VI.setflags(write=False)
|
||
|
|
|
||
|
|
# Those distances metrics have to support readonly buffers.
|
||
|
|
DistanceMetric.get_metric("seuclidean", V=weights)
|
||
|
|
DistanceMetric.get_metric("mahalanobis", VI=VI)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"w, err_type, err_msg",
|
||
|
|
[
|
||
|
|
(np.array([1, 1.5, -13]), ValueError, "w cannot contain negative weights"),
|
||
|
|
(np.array([1, 1.5, np.nan]), ValueError, "w contains NaN"),
|
||
|
|
*[
|
||
|
|
(
|
||
|
|
csr_container([[1, 1.5, 1]]),
|
||
|
|
TypeError,
|
||
|
|
"Sparse data was passed for w, but dense data is required",
|
||
|
|
)
|
||
|
|
for csr_container in CSR_CONTAINERS
|
||
|
|
],
|
||
|
|
(np.array(["a", "b", "c"]), ValueError, "could not convert string to float"),
|
||
|
|
(np.array([]), ValueError, "a minimum of 1 is required"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_minkowski_metric_validate_weights_values(w, err_type, err_msg):
|
||
|
|
with pytest.raises(err_type, match=err_msg):
|
||
|
|
DistanceMetric.get_metric("minkowski", p=3, w=w)
|
||
|
|
|
||
|
|
|
||
|
|
def test_minkowski_metric_validate_weights_size():
|
||
|
|
w2 = rng.random_sample(d + 1)
|
||
|
|
dm = DistanceMetric.get_metric("minkowski", p=3, w=w2)
|
||
|
|
msg = (
|
||
|
|
"MinkowskiDistance: the size of w must match "
|
||
|
|
f"the number of features \\({X64.shape[1]}\\). "
|
||
|
|
f"Currently len\\(w\\)={w2.shape[0]}."
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
dm.pairwise(X64, Y64)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("metric, metric_kwargs", METRICS_DEFAULT_PARAMS)
|
||
|
|
@pytest.mark.parametrize("dtype", (np.float32, np.float64))
|
||
|
|
def test_get_metric_dtype(metric, metric_kwargs, dtype):
|
||
|
|
specialized_cls = {
|
||
|
|
np.float32: DistanceMetric32,
|
||
|
|
np.float64: DistanceMetric64,
|
||
|
|
}[dtype]
|
||
|
|
|
||
|
|
# We don't need the entire grid, just one for a sanity check
|
||
|
|
metric_kwargs = {k: v[0] for k, v in metric_kwargs.items()}
|
||
|
|
generic_type = type(DistanceMetric.get_metric(metric, dtype, **metric_kwargs))
|
||
|
|
specialized_type = type(specialized_cls.get_metric(metric, **metric_kwargs))
|
||
|
|
|
||
|
|
assert generic_type is specialized_type
|
||
|
|
|
||
|
|
|
||
|
|
def test_get_metric_bad_dtype():
|
||
|
|
dtype = np.int32
|
||
|
|
msg = r"Unexpected dtype .* provided. Please select a dtype from"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
DistanceMetric.get_metric("manhattan", dtype)
|
||
|
|
|
||
|
|
|
||
|
|
def test_minkowski_metric_validate_bad_p_parameter():
|
||
|
|
msg = "p must be greater than 0"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
DistanceMetric.get_metric("minkowski", p=0)
|