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: #3731
rapidsai · Apr 8, 2021 · 0fe729d · 0fe729d
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Showing 3 changed files with 13 additions and 4 deletions.
diff --git 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
@@ -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
@@ -333,6 +333,12 @@ DBSCAN
 .. autoclass:: cuml.DBSCAN
     :members:
 
+Agglomerative Clustering
+------------------------
+
+.. autoclass:: cuml.AgglomerativeClustering
+    :members:
+
 Dimensionality Reduction and Manifold Learning
 ==============================================