[FEA] Allow cosine distance metric in dbscan #4776
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tarang-jain
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cpp/src/dbscan/dbscan.cuh
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| int algo_vd; | ||
| if (metric == raft::distance::Precomputed) { | ||
| algo_vd = 2; | ||
| } else if (metric == raft::distance::CosineExpanded) { |
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Rather than duplicating the call to the epsilon neighborhood primitive for the cosine case, I'd prefer to pass the metric through directly when metric != precomputed and normalize the input conditionally in the case where metric == cosine.
cpp/src/dbscan/vertexdeg/cosine.cuh
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| value_t eps2 = 2 * data.eps; | ||
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| rmm::device_uvector<value_t> rowNorms(m, stream); | ||
| rmm::device_uvector<value_t> l2Normalized(m * n, stream); |
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We have two options for supporting cosine- either we normalize the input or we perform the normalization in the computation. If we normalize the input, we should do so directly to the input and then revert the values back afterwords because this is very expensive.
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There is a problem with attempting to modify the input: the output array address in raft::linalg::matrixVectorOp cannot be a const float *. Note that data.x is of the type const float *.
cpp/src/dbscan/vertexdeg/runner.cuh
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| @@ -47,6 +48,9 @@ void run(const raft::handle_t& handle, | |||
| case 2: | |||
| Precomputed::launcher<Type_f, Index_>(handle, data, start_vertex_id, batch_size, stream); | |||
| break; | |||
| case 3: | |||
| Cosine::launcher<Type_f, Index_>(handle, data, start_vertex_id, batch_size, stream); | |||
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As mentioned above, I prefer to use the same "launcher" as the other metrics and pass the metric in directly. This will also make it much easier to support other metrics in the future, rather than having to duplicate the launcher each time.
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So should we also pass algo_vd as an argument to the launcher?
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I don't think so. Ideally Algo::launcher would just accept the distance type.
python/cuml/cluster/dbscan.pyx
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| @@ -147,7 +147,7 @@ class DBSCAN(Base, | |||
| min_samples : int (default = 5) | |||
| The number of samples in a neighborhood such that this group can be | |||
| considered as an important core point (including the point itself). | |||
| metric: {'euclidean', 'precomputed'}, default = 'euclidean' | |||
| metric: {'euclidean', 'precomputed', 'cosine'}, default = 'euclidean' | |||
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Just a little nitpick: I would prefer if we kept precomputed either before or after the actual distance metrics for clarity. We should also add a little note to the docs here that the input will be modified temporarily when cosine distance is used (and might not match completely afterwards due to numerical rounding).
python/cuml/tests/test_dbscan.py
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| @@ -107,6 +107,41 @@ def test_dbscan_precomputed(datatype, nrows, max_mbytes_per_batch, out_dtype): | |||
| algorithm="brute") | |||
| sk_labels = sk_dbscan.fit_predict(X_dist) | |||
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| print("cu_labels:", cu_labels) | |||
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Reminder to remove debug prints from tests
python/cuml/cluster/dbscan.pyx
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| @@ -267,6 +267,7 @@ class DBSCAN(Base, | |||
| "L2": DistanceType.L2SqrtUnexpanded, | |||
| "euclidean": DistanceType.L2SqrtUnexpanded, | |||
| "precomputed": DistanceType.Precomputed, | |||
| "cosine": DistanceType.CosineExpanded | |||
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Also move this, maybe after euclidean, for readability.
cpp/src/dbscan/vertexdeg/cosine.cuh
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| @@ -0,0 +1,90 @@ | |||
| /* | |||
| * Copyright (c) 2018-2022, NVIDIA CORPORATION. | |||
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We should only use the current year for new files.
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So should this be:
Copyright (c) 2021-2022, NVIDIA CORPORATION ?
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No, for new files it would just be 2022.
cjnolet
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improvement
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@tarang-jain I think this PR is waiting on a style fix: https://gpuci.gpuopenanalytics.com/blue/organizations/jenkins/rapidsai%2Fgpuci%2Fcuml%2Fprb%2Fcuml-style/detail/cuml-style/10390/pipeline#log-119 |
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@tarang-jain can you merge the current upstream (22.08) into your PR branch? The CI failures here have since been fixed. |
…cosine-distance-metric-DBSCAN
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rerun tests |
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@gpucibot merge |
closes rapidsai#4210 Added cosine distance metric for computing epsilon neighborhood in DBSCAN. The cosine distance computed as L2 norm of L2 normalized vectors and the epsilon value is adjusted accordingly. Authors: - Tarang Jain (https://github.com/tarang-jain) Approvers: - Corey J. Nolet (https://github.com/cjnolet) URL: rapidsai#4776
closes #4210
Added cosine distance metric for computing epsilon neighborhood in DBSCAN. The cosine distance computed as L2 norm of L2 normalized vectors and the epsilon value is adjusted accordingly.