From 0fe729d0f6fa48016a01dda381dc0547237ff569 Mon Sep 17 00:00:00 2001 From: "Corey J. Nolet" Date: Thu, 8 Apr 2021 15:44:43 -0400 Subject: [PATCH] Updating docs, readme, and umap param tests for 0.19 (#3731) Authors: - Corey J. Nolet (https://github.com/cjnolet) Approvers: - John Zedlewski (https://github.com/JohnZed) URL: https://github.com/rapidsai/cuml/pull/3731 --- README.md | 7 +++++-- cpp/test/sg/umap_parametrizable_test.cu | 4 ++-- docs/source/api.rst | 6 ++++++ 3 files changed, 13 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 219aa94176..6737b54a06 100644 --- a/README.md +++ b/README.md @@ -87,6 +87,7 @@ repo](https://github.com/rapidsai/notebooks-contrib). | --- | --- | --- | | **Clustering** | Density-Based Spatial Clustering of Applications with Noise (DBSCAN) | Multi-node multi-GPU via Dask | | | K-Means | Multi-node multi-GPU via Dask | +| | Single-Linkage Agglomerative Clustering | | | **Dimensionality Reduction** | Principal Components Analysis (PCA) | Multi-node multi-GPU via Dask| | | Incremental PCA | | | | Truncated Singular Value Decomposition (tSVD) | Multi-node multi-GPU via Dask | @@ -110,8 +111,10 @@ repo](https://github.com/rapidsai/notebooks-contrib). | **Preprocessing** | Standardization, or mean removal and variance scaling / Normalization / Encoding categorical features / Discretization / Imputation of missing values / Polynomial features generation / and coming soon custom transformers and non-linear transformation | Based on Scikit-Learn preprocessing | **Time Series** | Holt-Winters Exponential Smoothing | | | | Auto-regressive Integrated Moving Average (ARIMA) | Supports seasonality (SARIMA) | -| **Model Explanation** | SHAP Kernel Explainer | [Based on SHAP](https://shap.readthedocs.io/en/latest/) (experimental) | -| | SHAP Permutation Explainer | [Based on SHAP](https://shap.readthedocs.io/en/latest/) (experimental) | +| **Model Explanation** | SHAP Kernel Explainer +| [Based on SHAP](https://shap.readthedocs.io/en/latest/) | +| | SHAP Permutation Explainer +| [Based on SHAP](https://shap.readthedocs.io/en/latest/) | | **Other** | K-Nearest Neighbors (KNN) Search | Multi-node multi-GPU via Dask+[UCX](https://github.com/rapidsai/ucx-py), uses [Faiss](https://github.com/facebookresearch/faiss) for Nearest Neighbors Query. | --- diff --git a/cpp/test/sg/umap_parametrizable_test.cu b/cpp/test/sg/umap_parametrizable_test.cu index d952465ecd..6ad7edf104 100644 --- a/cpp/test/sg/umap_parametrizable_test.cu +++ b/cpp/test/sg/umap_parametrizable_test.cu @@ -309,9 +309,9 @@ class UMAPParametrizableTest : public ::testing::Test { umap_params_vec[3].n_components = 25; umap_params_vec[3].random_state = 43; #if CUDART_VERSION < 110200 - umap_params_vec[2].init = 1; + umap_params_vec[3].init = 1; #else - umap_params_vec[2].init = 0; + umap_params_vec[3].init = 0; #endif umap_params_vec[3].multicore_implem = false; umap_params_vec[3].optim_batch_size = 0; // use default value diff --git a/docs/source/api.rst b/docs/source/api.rst index 68adcbc340..4de1484f4e 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -333,6 +333,12 @@ DBSCAN .. autoclass:: cuml.DBSCAN :members: +Agglomerative Clustering +------------------------ + +.. autoclass:: cuml.AgglomerativeClustering + :members: + Dimensionality Reduction and Manifold Learning ==============================================