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approximate_predict function for HDBSCAN (#4872)
PR for HDBSCAN approximate_predict - [x] Building cluster_map - [x] Modifying PredictionData class - [x] Obtaining nearest neighbor in MR space - [x] Computing probability - [x] Tests Closes #4877 Closes #4448 Authors: - Tarang Jain (https://github.com/tarang-jain) - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Corey J. Nolet (https://github.com/cjnolet) URL: #4872
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| /* | ||
| * Copyright (c) 2022, NVIDIA CORPORATION. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| #pragma once | ||
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| namespace ML { | ||
| namespace HDBSCAN { | ||
| namespace detail { | ||
| namespace Predict { | ||
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| template <typename value_idx, typename value_t> | ||
| __global__ void min_mutual_reachability_kernel(value_t* input_core_dists, | ||
| value_t* prediction_core_dists, | ||
| value_t* pairwise_dists, | ||
| value_idx* neighbor_indices, | ||
| size_t n_prediction_points, | ||
| value_idx neighborhood, | ||
| value_t* min_mr_dists, | ||
| value_idx* min_mr_indices) | ||
| { | ||
| value_idx idx = blockDim.x * blockIdx.x + threadIdx.x; | ||
| if (idx < value_idx(n_prediction_points)) { | ||
| value_t min_mr_dist = std::numeric_limits<value_t>::max(); | ||
| value_idx min_mr_ind = -1; | ||
| for (int i = 0; i < neighborhood; i++) { | ||
| value_t mr_dist = prediction_core_dists[idx]; | ||
| if (input_core_dists[neighbor_indices[idx * neighborhood + i]] > mr_dist) { | ||
| mr_dist = input_core_dists[neighbor_indices[idx * neighborhood + i]]; | ||
| } | ||
| if (pairwise_dists[idx * neighborhood + i] > mr_dist) { | ||
| mr_dist = pairwise_dists[idx * neighborhood + i]; | ||
| } | ||
| if (min_mr_dist > mr_dist) { | ||
| min_mr_dist = mr_dist; | ||
| min_mr_ind = neighbor_indices[idx * neighborhood + i]; | ||
| } | ||
| } | ||
| min_mr_dists[idx] = min_mr_dist; | ||
| min_mr_indices[idx] = min_mr_ind; | ||
| } | ||
| return; | ||
| } | ||
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| template <typename value_idx, typename value_t> | ||
| __global__ void cluster_probability_kernel(value_idx* min_mr_indices, | ||
| value_t* prediction_lambdas, | ||
| value_idx* index_into_children, | ||
| value_idx* labels, | ||
| value_t* deaths, | ||
| value_idx* selected_clusters, | ||
| value_idx* parents, | ||
| value_t* lambdas, | ||
| value_idx n_leaves, | ||
| size_t n_prediction_points, | ||
| value_idx* predicted_labels, | ||
| value_t* cluster_probabilities) | ||
| { | ||
| value_idx idx = blockDim.x * blockIdx.x + threadIdx.x; | ||
| if (idx < value_idx(n_prediction_points)) { | ||
| value_idx cluster_label = labels[min_mr_indices[idx]]; | ||
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| if (cluster_label >= 0 && selected_clusters[cluster_label] > n_leaves && | ||
| lambdas[index_into_children[selected_clusters[cluster_label]]] < prediction_lambdas[idx]) { | ||
| predicted_labels[idx] = cluster_label; | ||
| } else if (cluster_label >= 0 && selected_clusters[cluster_label] == n_leaves) { | ||
| predicted_labels[idx] = cluster_label; | ||
| } else { | ||
| predicted_labels[idx] = -1; | ||
| } | ||
| if (predicted_labels[idx] >= 0) { | ||
| value_t max_lambda = deaths[selected_clusters[cluster_label] - n_leaves]; | ||
| if (max_lambda > 0) { | ||
| cluster_probabilities[idx] = | ||
| (max_lambda < prediction_lambdas[idx] ? max_lambda : prediction_lambdas[idx]) / | ||
| max_lambda; | ||
| } else { | ||
| cluster_probabilities[idx] = 1.0; | ||
| } | ||
| } else { | ||
| cluster_probabilities[idx] = 0.0; | ||
| } | ||
| } | ||
| return; | ||
| } | ||
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| }; // namespace Predict | ||
| }; // namespace detail | ||
| }; // namespace HDBSCAN | ||
| }; // namespace ML |
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