Merge pull request #18 from rapidsai/branch-0.17

sync with upstream

.gitignore

@@ -18,6 +18,7 @@ __pycache__
 htmlcov
 build/
 build_prims/
+cmake-build*
 cuml.egg-info/
 dist/
 python/cuml/**/*.cpp
@@ -29,6 +30,9 @@ log
 dask-worker-space/
 tmp/
 
+## files pickled in notebook when ran during python docstring generation
+docs/source/*.model
+
 ## eclipse
 .project
 .cproject

BUILD.md

@@ -15,10 +15,17 @@ To install cuML from source, ensure the following dependencies are met:
 9. NCCL (>=2.4)
 10. UCX [optional] (>= 1.7) - enables point-to-point messaging in the cuML standard communicator. This is necessary for many multi-node multi-GPU cuML algorithms to function.
 
-It is recommended to use conda for environment/package management. If doing so, a convenience environment .yml file is located in `conda/environments/cuml_dec_cudax.y.yml` (replace x.y for your CUDA version). This file contains most of the dependencies mentioned above (notable exceptions are `gcc` and `zlib`). To use it, for example to create an environment named `cuml_dev` for CUDA 10.0 and Python 3.7, you can use the follow command:
+It is recommended to use conda for environment/package management. If doing so, a convenience environment .yml file is located in `conda/environments/cuml_dec_cudax.y.yml` (replace x.y for your CUDA version). This file contains most of the dependencies mentioned above (notable exceptions are `gcc` and `zlib`). To use it, for example to create an environment named `cuml_dev` for CUDA 10.2 and Python 3.7, you can use the follow command:
 
+```bash
+conda create -n cuml_dev python=3.7
+conda env update -n cuml_dev --file=conda/environments/cuml_dev_cuda10.2.yml
 ```
-conda env create -n cuml_dev python=3.7 --file=conda/environments/cuml_dev_cuda10.0.yml
+
+These conda environments are based on the general RAPIDS meta packages that install common dependencies for RAPIDS projects. To install different versions of packages contained in those meta packages after creating the environment, it is recommended to remove those meta packages (without removing the actual packages contained in the environment) with the following command (having the environment active):
+
+```bash
+conda remove --force rapids-build-env rapids-notebook-env rapids-doc-env
 ```
 
 ## Installing from Source:

CHANGELOG.md

@@ -1,6 +1,145 @@
+# cuML 0.17.0 (Date TBD)
+
+## New Features
+
+## Improvements
+- PR #3070: Speed up dask/test_datasets tests
+- PR #3075: Speed up test_linear_model tests
+- PR #3078: Speed up test_incremental_pca tests
+- PR #2902: `matrix/matrix.cuh` in RAFT namespacing
+- PR #2903: Moving linalg's gemm, gemv, transpose to RAFT namespaces
+- PR #2905: `stats` prims `mean_center`, `sum` to RAFT namespaces
+- PR #2904: Moving `linalg` basic math ops to RAFT namespaces
+- PR #3000: Pin cmake policies to cmake 3.17 version, bump project version to 0.17
+- PR #3083: Improving test_make_blobs testing time
+- PR #2906: Moving `linalg` decomp to RAFT namespaces
+- PR #2996: Removing the max_depth restriction for switching to the batched backend
+- PR #3004: Remove Single Process Multi GPU (SPMG) code
+- PR #3044: Move leftover `linalg` and `stats` to RAFT namespaces
+- PR #3067: Deleting prims moved to RAFT and updating header paths
+- PR #3074: Reducing dask coordinate descent test runtime
+
+## Bug Fixes
+- PR #3072: Fusing metrics and score directories in src_prims
+- PR #3037: Avoid logging deadlock in multi-threaded C code
+- PR #2983: Fix seeding of KISS99 RNG
+- PR #3011: Fix unused initialize_embeddings parameter in Barnes-Hut t-SNE
+- PR #3008: Check number of columns in check_array validator
+- PR #3012: Increasing learning rate for SGD log loss and invscaling pytests
+- PR #3021: Fix a hang in cuML RF experimental backend
+- PR #3039: Update RF and decision tree parameter initializations in benchmark codes
+- PR #3061: Handle C++ exception thrown from FIL predict
+- PR #3073: Update mathjax CDN URL for documentation
+- PR #3062: Bumping xgboost version to match cuml version
+- PR #3086: Reverting FIL Notebook Testing
+
+# cuML 0.16.0 (Date TBD)
+
+## New Features
+- PR #2922: Install RAFT headers with cuML
+- PR #2909: Update allgatherv for compatibility with latest RAFT
+- PR #2677: Ability to export RF trees as JSON
+- PR #2698: Distributed TF-IDF transformer
+- PR #2476: Porter Stemmer
+- PR #2789: Dask LabelEncoder
+- PR #2152: add FIL C++ benchmark
+- PR #2638: Improve cython build with custom `build_ext`
+- PR #2866: Support XGBoost-style multiclass models (gradient boosted decision trees) in FIL C++
+- PR #2874: Issue warning for degraded accuracy with float64 models in Treelite
+- PR #2881: Introduces experimental batched backend for random forest
+- PR #2916: Add SKLearn multi-class GBDT model support in FIL
+
+## Improvements
+- PR #2947: Add more warnings for accuracy degradation with 64-bit models
+- PR #2873: Remove empty marker kernel code for NVTX markers
+- PR #2796: Remove tokens of length 1 by default for text vectorizers
+- PR #2741: Use rapids build packages in conda environments
+- PR #2735: Update seed to random_state in random forest and associated tests
+- PR #2739: Use cusparse_wrappers.h from RAFT
+- PR #2729: Replace `cupy.sparse` with `cupyx.scipy.sparse`
+- PR #2749: Correct docs for python version used in cuml_dev conda environment
+- PR #2747: Adopting raft::handle_t and raft::comms::comms_t in cuML
+- PR #2762: Fix broken links and provide minor edits to docs
+- PR #2723: Support and enable convert_dtype in estimator predict
+- PR #2758: Match sklearn's default n_components behavior for PCA
+- PR #2770: Fix doxygen version during cmake
+- PR #2766: Update default RandomForestRegressor score function to use r2
+- PR #2775: Enablinbg mg gtests w/ raft mpi comms
+- PR #2783: Add pytest that will fail when GPU IDs in Dask cluster are not unique
+- PR #2784: Add SparseCumlArray container for sparse index/data arrays
+- PR #2785: Add in cuML-specific dev conda dependencies
+- PR #2778: Add README for FIL
+- PR #2799: Reenable lightgbm test with lower (1%) proba accuracy
+- PR #2800: Align cuML's spdlog version with RMM's
+- PR #2824: Make data conversions warnings be debug level
+- PR #2835: Rng prims, utils, and dependencies in RAFT
+- PR #2541: Improve Documentation Examples and Source Linking
+- PR #2837: Make the FIL node reorder loop more obvious
+- PR #2849: make num_classes significant in FLOAT_SCALAR case
+- PR #2792: Project flash (new build process) script changes
+- PR #2850: Clean up unused params in paramsPCA
+- PR #2871: Add timing function to utils
+- PR #2863: in FIL, rename leaf_value_t enums to more descriptive
+- PR #2867: improve stability of FIL benchmark measurements
+- PR #2798: Add python tests for FIL multiclass classification of lightgbm models
+- PR #2892: Update ci/local/README.md
+- PR #2910: Adding Support for CuPy 8.x
+- PR #2914: Add tests for XGBoost multi-class models in FIL
+- PR #2622: Simplify tSNE perplexity search
+- PR #2930: Pin libfaiss to <=1.6.3
+- PR #2928: Updating Estimators Derived from Base for Consistency
+- PR #2942: Adding `cuml.experimental` to the Docs
+- PR #3010: Improve gpuCI Scripts
+
+## Bug Fixes
+- PR #2973: Allow data imputation for nan values
+- PR #2982: Adjust kneighbors classifier test threshold to avoid intermittent failure
+- PR #2885: Changing test target for NVTX wrapper test
+- PR #2882: Allow import on machines without GPUs
+- PR #2875: Bug fix to enable colorful NVTX markers
+- PR #2744: Supporting larger number of classes in KNeighborsClassifier
+- PR #2769: Remove outdated doxygen options for 1.8.20
+- PR #2787: Skip lightgbm test for version 3 and above temporarily
+- PR #2805: Retain index in stratified splitting for dataframes
+- PR #2781: Use Python print to correctly redirect spdlogs when sys.stdout is changed
+- PR #2787: Skip lightgbm test for version 3 and above temporarily
+- PR #2813: Fix memory access in generation of non-row-major random blobs
+- PR #2810: Update Rf MNMG threshold to prevent sporadic test failure
+- PR #2808: Relax Doxygen version required in CMake to coincide with integration repo
+- PR #2818: Fix parsing of singlegpu option in build command
+- PR #2827: Force use of whole dataset when sample bootstrapping is disabled
+- PR #2829: Fixing description for labels in docs and removing row number constraint from PCA xform/inverse_xform
+- PR #2832: Updating stress tests that fail with OOM
+- PR #2831: Removing repeated capture and parameter in lambda function
+- PR #2847: Workaround for TSNE lockup, change caching preference.
+- PR #2842: KNN index preprocessors were using incorrect n_samples
+- PR #2848: Fix typo in Python docstring for UMAP
+- PR #2856: Fix LabelEncoder for filtered input
+- PR #2855: Updates for RMM being header only
+- PR #2844: Fix for OPG KNN Classifier & Regressor
+- PR #2880: Fix bugs in Auto-ARIMA when s==None
+- PR #2877: TSNE exception for n_components > 2
+- PR #2879: Update unit test for LabelEncoder on filtered input
+- PR #2932: Marking KBinsDiscretizer pytests as xfail
+- PR #2925: Fixing Owner Bug When Slicing CumlArray Objects
+- PR #2931: Fix notebook error handling in gpuCI
+- PR #2941: Fixing dask tsvd stress test failure
+- PR #2943: Remove unused shuffle_features parameter
+- PR #2940: Correcting labels meta dtype for `cuml.dask.make_classification`
+- PR #2965: Notebooks update
+- PR #2955: Fix for conftest for singlegpu build
+- PR #2968: Remove shuffle_features from RF param names
+- PR #2957: Fix ols test size for stability
+- PR #2972: Upgrade Treelite to 0.93
+- PR #2981: Prevent unguarded import of sklearn in SVC
+- PR #2984: Fix GPU test scripts gcov error
+- PR #2990: Reduce MNMG kneighbors regressor test threshold
+- PR #2997: Changing ARIMA `get/set_params` to `get/set_fit_params`
+
 # cuML 0.15.0 (Date TBD)
 
 ## New Features
+- PR #2581: Added model persistence via joblib in each section of estimator_intro.ipynb
 - PR #2554: Hashing Vectorizer and general vectorizer improvements
 - PR #2240: Making Dask models pickleable
 - PR #2267: CountVectorizer estimator
@@ -12,11 +151,23 @@
 - PR #2394: Adding cosine & correlation distance for KNN
 - PR #2392: PCA can accept sparse inputs, and sparse prim for computing covariance
 - PR #2465: Support pandas 1.0+
+- PR #2550: Single GPU Target Encoder
 - PR #2519: Precision recall curve using cupy
 - PR #2500: Replace UMAP functionality dependency on nvgraph with RAFT Spectral Clustering
+- PR #2502: cuML Implementation of `sklearn.metrics.pairwise_distances`
 - PR #2520: TfidfVectorizer estimator
 - PR #2211: MNMG KNN Classifier & Regressor
 - PR #2461: Add KNN Sparse Output Functionality
+- PR #2615: Incremental PCA
+- PR #2594: Confidence intervals for ARIMA forecasts
+- PR #2607: Add support for probability estimates in SVC
+- PR #2618: SVM class and sample weights
+- PR #2635: Decorator to generate docstrings with autodetection of parameters
+- PR #2270: Multi class MNMG RF
+- PR #2661: CUDA-11 support for single-gpu code
+- PR #2322: Sparse FIL forests with 8-byte nodes
+- PR #2675: Update conda recipes to support CUDA 11
+- PR #2645: Add experimental, sklearn-based preprocessing
 
 ## Improvements
 - PR #2336: Eliminate `rmm.device_array` usage
@@ -46,6 +197,7 @@
 - PR #2403: Support for input and output type consistency in logistic regression predict_proba
 - PR #2473: Add metrics.roc_auc_score to API docs. Additional readability and minor docs bug fixes
 - PR #2468: Add `_n_features_in_` attribute to all single GPU estimators that implement fit
+- PR #2489: Removing explicit FAISS build and adding dependency on libfaiss conda package
 - PR #2480: Moving MNMG glm and solvers to cuml
 - PR #2490: Moving MNMG KMeans to cuml
 - PR #2483: Moving MNMG KNN to cuml
@@ -55,6 +207,7 @@
 - PR #2237: Refactor RF cython code
 - PR #2513: Fixing LGTM Analysis Issues
 - PR #2099: Raise an error when float64 data is used with dask RF
+- PR #2522: Renaming a few arguments in KNeighbors* to be more readable
 - PR #2499: Provide access to `cuml.DBSCAN` core samples
 - PR #2526: Removing PCA TSQR as a solver due to scalability issues
 - PR #2536: Update conda upload versions for new supported CUDA/Python
@@ -69,8 +222,25 @@
 - PR #2591: Generate benchmark datsets using `cuml.datasets`
 - PR #2548: Fix limitation on number of rows usable with tSNE and refactor memory allocation
 - PR #2589: including cuda-11 build fixes into raft
+- PR #2599: Add Stratified train_test_split
 - PR #2487: Set classes_ attribute during classifier fit
 - PR #2605: Reduce memory usage in tSNE
+- PR #2611: Adding building doxygen docs to gpu ci
+- PR #2631: Enabling use of gtest conda package for build
+- PR #2623: Fixing kmeans score() API to be compatible with Scikit-learn
+- PR #2629: Add naive_bayes api docs
+- PR #2643: 'dense' and 'sparse' values of `storage_type` for FIL
+- PR #2691: Generic Base class attribute setter
+- PR #2666: Update MBSGD documentation to mention that the model is experimental
+- PR #2687: Update xgboost version to 1.2.0dev.rapidsai0.15
+- PR #2684: CUDA 11 conda development environment yml and faiss patch
+- PR #2648: Replace CNMeM with `rmm::mr::pool_memory_resource`.
+- PR #2686: Improve SVM tests
+- PR #2692: Changin LBFGS log level
+- PR #2705: Add sum operator and base operator overloader functions to cumlarray
+- PR #2701: Updating README + Adding ref to UMAP paper
+- PR #2721: Update API docs
+- PR #2730: Unpin cumlprims in conda recipes for release
 
 ## Bug Fixes
 - PR #2369: Update RF code to fix set_params memory leak
@@ -94,6 +264,8 @@
 - PR #2497: Changes to accomodate cuDF unsigned categorical changes
 - PR #2209: Fix FIL benchmark for gpuarray-c input
 - PR #2507: Import `treelite.sklearn`
+- PR #2521: Fixing invalid smem calculation in KNeighborsCLassifier
+- PR #2515: Increase tolerance for LogisticRegression test
 - PR #2532: Updating doxygen in new MG headers
 - PR #2521: Fixing invalid smem calculation in KNeighborsCLassifier
 - PR #2515: Increase tolerance for LogisticRegression test
@@ -105,12 +277,41 @@
 - PR #2535: Fix issue with incorrect docker image being used in local build script
 - PR #2542: Fix small memory leak in TSNE
 - PR #2552: Fixed the length argument of updateDevice calls in RF test
+- PR #2565: Fix cell allocation code to avoid loops in quad-tree. Prevent NaNs causing infinite descent
 - PR #2563: Update scipy call for arima gradient test
 - PR #2569: Fix for cuDF update
 - PR #2508: Use keyword parameters in sklearn.datasets.make_* functions
+- PR #2587: Attributes for estimators relying on solvers
 - PR #2586: Fix SVC decision function data type
 - PR #2573: Considering managed memory as device type on checking for KMeans
 - PR #2574: Fixing include path in `tsvd_mg.pyx`
+- PR #2506: Fix usage of CumlArray attributes on `cuml.common.base.Base`
+- PR #2593: Fix inconsistency in train_test_split
+- PR #2609: Fix small doxygen issues
+- PR #2610: Remove cuDF tolist call
+- PR #2613: Removing thresholds from kmeans score tests (SG+MG)
+- PR #2616: Small test code fix for pandas dtype tests
+- PR #2617: Fix floating point precision error in tSNE
+- PR #2625: Update Estimator notebook to resolve errors
+- PR #2634: singlegpu build option fixes
+- PR #2641: [Breaking] Make `max_depth` in RF compatible with scikit-learn
+- PR #2650: Make max_depth behave consistently for max_depth > 14
+- PR #2651: AutoARIMA Python bug fix
+- PR #2654: Fix for vectorizer concatenations
+- PR #2655: Fix C++ RF predict function access of rows/samples array
+- PR #2649: Cleanup sphinx doc warnings for 0.15
+- PR #2668: Order conversion improvements to account for cupy behavior changes
+- PR #2669: Revert PR 2655 Revert "Fixes C++ RF predict function"
+- PR #2683: Fix incorrect "Bad CumlArray Use" error messages on test failures
+- PR #2695: Fix debug build issue due to incorrect host/device method setup
+- PR #2709: Fixing OneHotEncoder Overflow Error
+- PR #2710: Fix SVC doc statement about predic_proba
+- PR #2726: Return correct output type in QN
+- PR #2711: Fix Dask RF failure intermittently
+- PR #2718: Fix temp directory for py.test
+- PR #2719: Set KNeighborsRegressor output dtype according to training target dtype
+- PR #2720: Updates to outdated links
+- PR #2722: Getting cuML covariance test passing w/ Cupy 7.8 & CUDA 11
 
 # cuML 0.14.0 (03 Jun 2020)
 
@@ -132,6 +333,7 @@
 - PR #2256: Add a `make_arima` generator
 - PR #2245: ElasticNet, Lasso and Coordinate Descent MNMG
 - PR #2242: Pandas input support with output as NumPy arrays by default
+- PR #2551: Add cuML RF multiclass prediction using FIL from python
 - PR #1728: Added notebook testing to gpuCI gpu build
 
 ## Improvements
@@ -283,6 +485,8 @@
 - PR #2295: Fix convert_to_dtype copy even with same dtype
 - PR #2305: Fixed race condition in DBScan
 - PR #2354: Fix broken links in README
+- PR #2619: Explicitly skip raft test folder for pytest 6.0.0
+- PR #2788: Set the minimum number of columns that can be sampled to 1 to fix 0 mem allocation error
 
 # cuML 0.13.0 (31 Mar 2020)
 

CONTRIBUTING.md

@@ -24,7 +24,7 @@ into three categories:
 
 ### Your first issue
 
-1. Read the project's [README.md](https://github.com/rapidsai/cuml/blob/master/README.md)
+1. Read the project's [README.md](https://github.com/rapidsai/cuml/blob/main/README.md)
     to learn how to setup the development environment.
 2. Find an issue to work on. The best way is to look for the [good first issue](https://github.com/rapidsai/cuml/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
     or [help wanted](https://github.com/rapidsai/cuml/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) labels
@@ -62,12 +62,12 @@ implementation of the issue, ask them in the issue instead of the PR.
 
 The cuML repository has two main branches: 
 
-1. `master` branch: it contains the last released version. Only hotfixes are targeted and merged into it.  
+1. `main` branch: it contains the last released version. Only hotfixes are targeted and merged into it.  
 2. `branch-x.y`: it is the development branch which contains the upcoming release. All the new features should be based on this branch and Merge/Pull request should target this branch (with the exception of hotfixes).
 
 ### Additional details
 
-For every new version `x.y` of cuML there is a corresponding branch called `branch-x.y`, from where new feature development starts and PRs will be targeted and merged before its release. The exceptions to this are the 'hotfixes' that target the `master` branch, which target critical issues raised by Github users and are directly merged to `master` branch, and create a new subversion of the project. While trying to patch an issue which requires a 'hotfix', please state the intent in the PR. 
+For every new version `x.y` of cuML there is a corresponding branch called `branch-x.y`, from where new feature development starts and PRs will be targeted and merged before its release. The exceptions to this are the 'hotfixes' that target the `main` branch, which target critical issues raised by Github users and are directly merged to `main` branch, and create a new subversion of the project. While trying to patch an issue which requires a 'hotfix', please state the intent in the PR. 
 
 For all development, your changes should be pushed into a branch (created using the naming instructions below) in your own fork of cuML and then create a pull request when the code is ready. 
 

Dockerfile

@@ -1,4 +1,4 @@
-# From: https://github.com/rapidsai/cudf/blob/master/Dockerfile
+# From: https://github.com/rapidsai/cudf/blob/main/Dockerfile
 FROM cudf
 
 ENV CONDA_ENV=cudf