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Fix UMAP and simplicial set functions metric #5490

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merged 14 commits into from
Aug 3, 2023
Merged

Fix UMAP and simplicial set functions metric #5490

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0bd5c77
Fix UMAP metric
viclafargue Jul 4, 2023
1641209
Merge branch 'branch-23.08' into fix-umap-metric
dantegd Jul 13, 2023
77e207c
fix metrics for simpl_set functions
viclafargue Jul 13, 2023
f8bfcdd
removing jaccard metric from tests
viclafargue Jul 13, 2023
9f37c8f
addressing review
viclafargue Jul 14, 2023
90d9151
small fix
viclafargue Jul 14, 2023
8f2d6ff
fix issue
viclafargue Jul 14, 2023
84490c4
adressing review
viclafargue Jul 24, 2023
71dd03b
Merge branch 'branch-23.08' into fix-umap-metric
viclafargue Jul 25, 2023
a96bb61
use cast
viclafargue Jul 31, 2023
a7d5614
Merge branch 'branch-23.08' into fix-umap-metric
viclafargue Jul 31, 2023
caeeb87
Merge branch 'branch-23.08' into fix-umap-metric
csadorf Aug 1, 2023
b23af4b
Distinguish between metrics supported for dense and sparse inputs.
csadorf Aug 2, 2023
fc42da9
Revise doc-strings to include jaccard.
csadorf Aug 2, 2023
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3 changes: 3 additions & 0 deletions cpp/src/umap/knn_graph/algo.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -71,6 +71,9 @@ inline void launcher(const raft::handle_t& handle,
out.knn_indices,
out.knn_dists,
n_neighbors,
true,
true,
static_cast<std::vector<int64_t>*>(nullptr),
params->metric,
params->p);
}
Expand Down
49 changes: 40 additions & 9 deletions python/cuml/manifold/simpl_set.pyx
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Expand Up @@ -22,7 +22,8 @@ from cuml.internals.safe_imports import gpu_only_import
cp = gpu_only_import('cupy')

from cuml.manifold.umap_utils cimport *
from cuml.manifold.umap_utils import GraphHolder, find_ab_params
from cuml.manifold.umap_utils import GraphHolder, find_ab_params, \
metric_parsing

from cuml.internals.input_utils import input_to_cuml_array
from cuml.internals.array import CumlArray
Expand Down Expand Up @@ -82,10 +83,17 @@ def fuzzy_simplicial_set(X,
structure to the detriment of the larger picture.
random_state: numpy RandomState or equivalent
A state capable being used as a numpy random state.
metric: string or function (optional, default 'euclidean')
unused
metric_kwds: dict (optional, default {})
unused
metric: string (default='euclidean').
Distance metric to use. Supported distances are ['l1, 'cityblock',
'taxicab', 'manhattan', 'euclidean', 'l2', 'sqeuclidean', 'canberra',
'minkowski', 'chebyshev', 'linf', 'cosine', 'correlation', 'hellinger',
'hamming', 'jaccard']
Metrics that take arguments (such as minkowski) can have arguments
passed via the metric_kwds dictionary.
Note: The 'jaccard' distance metric is only supported for sparse
inputs.
metric_kwds: dict (optional, default=None)
Metric argument
knn_indices: array of shape (n_samples, n_neighbors) (optional)
If the k-nearest neighbors of each point has already been calculated
you can pass them in here to save computation time. This should be
Expand Down Expand Up @@ -138,6 +146,14 @@ def fuzzy_simplicial_set(X,
umap_params.deterministic = <bool> deterministic
umap_params.set_op_mix_ratio = <float> set_op_mix_ratio
umap_params.local_connectivity = <float> local_connectivity
try:
umap_params.metric = metric_parsing[metric.lower()]
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except KeyError:
raise ValueError(f"Invalid value for metric: {metric}")
if metric_kwds is None:
umap_params.p = <float> 2.0
else:
umap_params.p = <float> metric_kwds.get("p", 2.0)
umap_params.verbosity = <int> verbose

X_m, _, _, _ = \
Expand Down Expand Up @@ -245,10 +261,17 @@ def simplicial_set_embedding(
* A numpy array of initial embedding positions.
random_state: numpy RandomState or equivalent
A state capable being used as a numpy random state.
metric: string or callable
unused
metric_kwds: dict
unused
metric: string (default='euclidean').
Distance metric to use. Supported distances are ['l1, 'cityblock',
'taxicab', 'manhattan', 'euclidean', 'l2', 'sqeuclidean', 'canberra',
'minkowski', 'chebyshev', 'linf', 'cosine', 'correlation', 'hellinger',
'hamming', 'jaccard']
Metrics that take arguments (such as minkowski) can have arguments
passed via the metric_kwds dictionary.
Note: The 'jaccard' distance metric is only supported for sparse
inputs.
metric_kwds: dict (optional, default=None)
Metric argument
output_metric: function
Function returning the distance between two points in embedding space
and the gradient of the distance wrt the first argument.
Expand Down Expand Up @@ -306,6 +329,14 @@ def simplicial_set_embedding(
umap_params.init = <int> 0
umap_params.random_state = <int> random_state
umap_params.deterministic = <bool> deterministic
try:
umap_params.metric = metric_parsing[metric.lower()]
except KeyError:
raise ValueError(f"Invalid value for metric: {metric}")
if metric_kwds is None:
umap_params.p = <float> 2.0
else:
umap_params.p = <float> metric_kwds.get("p", 2.0)
if output_metric == 'euclidean':
umap_params.target_metric = MetricType.EUCLIDEAN
else: # output_metric == 'categorical'
Expand Down
53 changes: 20 additions & 33 deletions python/cuml/manifold/umap.pyx
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Expand Up @@ -29,7 +29,8 @@ cupyx = gpu_only_import('cupyx')
cuda = gpu_only_import('numba.cuda')

from cuml.manifold.umap_utils cimport *
from cuml.manifold.umap_utils import GraphHolder, find_ab_params
from cuml.manifold.umap_utils import GraphHolder, find_ab_params, \
metric_parsing, DENSE_SUPPORTED_METRICS, SPARSE_SUPPORTED_METRICS

from cuml.common.sparsefuncs import extract_knn_infos
from cuml.internals.safe_imports import gpu_only_import_from
Expand All @@ -47,7 +48,6 @@ from cuml.internals.array import CumlArray
from cuml.internals.array_sparse import SparseCumlArray
from cuml.internals.mixins import CMajorInputTagMixin
from cuml.common.sparse_utils import is_sparse
from cuml.metrics.distance_type cimport DistanceType

from cuml.manifold.simpl_set import fuzzy_simplicial_set # no-cython-lint
from cuml.manifold.simpl_set import simplicial_set_embedding # no-cython-lint
Expand Down Expand Up @@ -152,13 +152,17 @@ class UMAP(UniversalBase,
n_components: int (optional, default 2)
The dimension of the space to embed into. This defaults to 2 to
provide easy visualization, but can reasonably be set to any
metric : string (default='euclidean').
metric: string (default='euclidean').
Distance metric to use. Supported distances are ['l1, 'cityblock',
'taxicab', 'manhattan', 'euclidean', 'l2', 'sqeuclidean', 'canberra',
'minkowski', 'chebyshev', 'linf', 'cosine', 'correlation', 'hellinger',
'hamming', 'jaccard']
Metrics that take arguments (such as minkowski) can have arguments
passed via the metric_kwds dictionary.
Note: The 'jaccard' distance metric is only supported for sparse
inputs.
metric_kwds: dict (optional, default=None)
Metric argument
n_epochs: int (optional, default None)
The number of training epochs to be used in optimizing the
low dimensional embedding. Larger values result in more accurate
Expand Down Expand Up @@ -419,7 +423,7 @@ class UMAP(UniversalBase,
raise ValueError("min_dist should be <= spread")

@staticmethod
def _build_umap_params(cls):
def _build_umap_params(cls, sparse):
cdef UMAPParams* umap_params = new UMAPParams()
umap_params.n_neighbors = <int> cls.n_neighbors
umap_params.n_components = <int> cls.n_components
Expand Down Expand Up @@ -448,37 +452,20 @@ class UMAP(UniversalBase,
umap_params.random_state = <uint64_t> cls.random_state
umap_params.deterministic = <bool> cls.deterministic

# metric
metric_parsing = {
"l2": DistanceType.L2SqrtUnexpanded,
"euclidean": DistanceType.L2SqrtUnexpanded,
"sqeuclidean": DistanceType.L2Unexpanded,
"cityblock": DistanceType.L1,
"l1": DistanceType.L1,
"manhattan": DistanceType.L1,
"taxicab": DistanceType.L1,
"minkowski": DistanceType.LpUnexpanded,
"chebyshev": DistanceType.Linf,
"linf": DistanceType.Linf,
"cosine": DistanceType.CosineExpanded,
"correlation": DistanceType.CorrelationExpanded,
"hellinger": DistanceType.HellingerExpanded,
"hamming": DistanceType.HammingUnexpanded,
"jaccard": DistanceType.JaccardExpanded,
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Jaccard is supported in the sparse distances- is there any reason we're not separating the sparse from dense supported metrics? I can't see why we'd want to remove jaccard from being executed on sparse metrics just because it's not yet provided for dense.

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@cjnolet This should be addressed now.

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My bad, didn't saw it was used in the sparse case. Thanks for fixing this @csadorf.

"canberra": DistanceType.Canberra
}

if cls.metric.lower() in metric_parsing:
try:
umap_params.metric = metric_parsing[cls.metric.lower()]
else:
raise ValueError("Invalid value for metric: {}"
.format(cls.metric))

if sparse:
if umap_params.metric not in SPARSE_SUPPORTED_METRICS:
raise NotImplementedError(f"Metric '{cls.metric}' not supported for sparse inputs.")
elif umap_params.metric not in DENSE_SUPPORTED_METRICS:
raise NotImplementedError(f"Metric '{cls.metric}' not supported for dense inputs.")

except KeyError:
raise ValueError(f"Invalid value for metric: {cls.metric}")
if cls.metric_kwds is None:
umap_params.p = <float> 2.0
else:
umap_params.p = <float>cls.metric_kwds.get('p')

umap_params.p = <float> cls.metric_kwds.get("p", 2.0)
cdef uintptr_t callback_ptr = 0
if cls.callback:
callback_ptr = cls.callback.get_native_callback()
Expand Down Expand Up @@ -576,7 +563,7 @@ class UMAP(UniversalBase,
cdef uintptr_t embed_raw = self.embedding_.ptr

cdef UMAPParams* umap_params = \
<UMAPParams*> <size_t> UMAP._build_umap_params(self)
<UMAPParams*> <size_t> UMAP._build_umap_params(self, self.sparse_fit)

cdef uintptr_t y_raw = 0

Expand Down Expand Up @@ -742,7 +729,7 @@ class UMAP(UniversalBase,
cdef uintptr_t embed_ptr = self.embedding_.ptr

cdef UMAPParams* umap_params = \
<UMAPParams*> <size_t> UMAP._build_umap_params(self)
<UMAPParams*> <size_t> UMAP._build_umap_params(self, self.sparse_fit)

if self.sparse_fit:
transform_sparse(handle_[0],
Expand Down
51 changes: 51 additions & 0 deletions python/cuml/manifold/umap_utils.pyx
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Expand Up @@ -19,6 +19,7 @@
from rmm._lib.memory_resource cimport get_current_device_resource
from pylibraft.common.handle cimport handle_t
from cuml.manifold.umap_utils cimport *
from cuml.metrics.distance_type cimport DistanceType
from libcpp.utility cimport move
from cuml.internals.safe_imports import cpu_only_import
np = cpu_only_import('numpy')
Expand Down Expand Up @@ -130,3 +131,53 @@ def find_ab_params(spread, min_dist):
yv[xv >= min_dist] = np.exp(-(xv[xv >= min_dist] - min_dist) / spread)
params, _ = curve_fit(curve, xv, yv)
return params[0], params[1]


metric_parsing = {
"l2": DistanceType.L2SqrtUnexpanded,
"euclidean": DistanceType.L2SqrtUnexpanded,
"sqeuclidean": DistanceType.L2Unexpanded,
"cityblock": DistanceType.L1,
"l1": DistanceType.L1,
"manhattan": DistanceType.L1,
"taxicab": DistanceType.L1,
"minkowski": DistanceType.LpUnexpanded,
"chebyshev": DistanceType.Linf,
"linf": DistanceType.Linf,
"cosine": DistanceType.CosineExpanded,
"correlation": DistanceType.CorrelationExpanded,
"hellinger": DistanceType.HellingerExpanded,
"hamming": DistanceType.HammingUnexpanded,
"jaccard": DistanceType.JaccardExpanded,
"canberra": DistanceType.Canberra
}


DENSE_SUPPORTED_METRICS = [
DistanceType.Canberra,
DistanceType.CorrelationExpanded,
DistanceType.CosineExpanded,
DistanceType.HammingUnexpanded,
DistanceType.HellingerExpanded,
# DistanceType.JaccardExpanded, # not supported
DistanceType.L1,
DistanceType.L2SqrtUnexpanded,
DistanceType.L2Unexpanded,
DistanceType.Linf,
DistanceType.LpUnexpanded,
]


SPARSE_SUPPORTED_METRICS = [
DistanceType.Canberra,
DistanceType.CorrelationExpanded,
DistanceType.CosineExpanded,
DistanceType.HammingUnexpanded,
DistanceType.HellingerExpanded,
DistanceType.JaccardExpanded,
DistanceType.L1,
DistanceType.L2SqrtUnexpanded,
DistanceType.L2Unexpanded,
DistanceType.Linf,
DistanceType.LpUnexpanded,
]
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