Package: cubrif
DescriptionBuild random forests for large data sets using CUDA.
This is the GPU-enabled version ofbrif.
The same program is available onCRANfor R users.Build from sourcePrerequisitesAn Nvidia graphics / compute card must be present and theCUDA Toolkitmust be installed.For Windows, Microsoft Visual StudioBuild Tools for C++must be installed. For Linux and MacOS, some C++ build tool chain (e.g., gcc) is required.Pythonbuildis required, can be installed viapipinstallbuildThe pandas and numpy packages are required, can be installed viapipinstallpandasnumpyBuild and install on WindowsClone (or download as zip and extract) this project to a local directory.Search in the Windows search bar and run as administrator the "x64 Native Tools Command Prompt for VS 2022".
In the command window thus opened, cd into the project root directory, and runmkdirbuildcdbuild
cmake../If successful, the file cubrif.sln (among other files) will be generated, then runMSBuild.execubrif.sln/p:Configuration=ReleaseIf successful, several files will be created in the Release subfolder. Important ones include cubrif.lib, cubrif.dll and cubrif_main.exe. cubrif.lib will be used in building python package, cubrif.dll will be used in runtime, and cubrif_main.exe is a standalone executable.Copy cubrif.lib to the project root directory:copyRelease\cubrif.lib..\Now go back to the project root and build the Python package, as followscd..
python-mbuildIf successful, the package, e.g., cubrif-1.4.0.tar.gz, will be create in the dist subfolder.Install the package bypipinstalldist/cubrif-1.4.0.tar.gzTo use the package, the cubrif.dll must be visible to python, for example:importosos.add_dll_directory("C:/path/to/project/build/Release")fromcubrifimportcubrifBuild and install on UbuntuThe build process is similar, but use 'make' instead of MSBuild.exe, and the dynamic library file generated will be libcubrif.so instead of cubrif.dll.mkdirbuildcdbuild
cmake../
make
cplibcubrif.so../cd..
python3-mbuild
pipinstalldist/cubrif-1.4.0.tar.gzIn the above step, if "python3 -m build" does not work, use the equivalent commandpython3setup.pysdistbdist_wheelTo use the package, thelibcubrif.somust be visible to python. Either copy libcubrif.so to usr/lib or use os.add_dll_directory() as described above. For example,sudocplibcubrif.so/usr/libor in python,importosos.add_dll_directory("C:/path/to/project/build/Release")Usage Examplesfromcubrifimportcubrifimportpandasaspd# Create a brif object with default parameters.bf=cubrif.cubrif()# Display the current parameter values.bf.get_param()# To change certain parameter values, e.g.:bf.set_param({'ntrees':10,'nthreads':2,'GPU':1})# Or simply:bf.ntrees=50# Load input data frame. Data must be a pandas data frame with appropriate headers.df=pd.read_csv("auto.csv")# Train the modelbf.fit(df,'origin')# specify the target column name# Or equivalentlybf.fit(df,7)# specify the target column index# Make predictions# The target variable column must be excluded, and all other columns should appear in the same order as in training# Here, predict the first 10 rows of dfpred_labels=bf.predict(df.iloc[0:10,0:7],type='class')# return a list containing the predicted class labelspred_scores=bf.predict(df.iloc[0:10,0:7],type='score')# return a data frame containing predicted probabilities by class# Note: for a regression problem (i.e., when the response variable is numeric type), the predict function will always return a list containing the predicted valuesParameterstmp_preddataa character string specifying a filename to save the temporary scoring data. Default is "tmp_brif_preddata.txt".n_numeric_cutsan integer value indicating the maximum number of split points to generate for each numeric variable.n_integer_cutsan integer value indicating the maximum number of split points to generate for each integer variable.max_integer_classesan integer value. If the target variable is integer and has more than max_integer_classes unique values in the training data, then the target variable will be grouped into max_integer_classes bins. If the target variable is numeric, then the smaller of max_integer_classes and the number of unique values number of bins will be created on the target variables and the regression problem will be solved as a classification problem.max_depthan integer specifying the maximum depth of each tree. Maximum is 40.min_node_sizean integer specifying the minimum number of training cases a leaf node must contain.ntreesan integer specifying the number of trees in the forest.psan integer indicating the number of predictors to sample at each node split. Default is 0, meaning to use sqrt(p), where p is the number of predictors in the input.max_factor_levelsan integer. If any factor variables has more than max_factor_levels, the program stops and prompts the user to increase the value of this parameter if the too-many-level factor is indeed intended.seeda positive integer, random number generator seed.nthreadsan integer specifying the number of threads used by the program. This parameter takes effect only on systems supporting OpenMP.blocksizean integer specifying the CUDA thread block size. Must be a multiple of 64, and no more than 1024.GPUan integer (0, 1 or 2). 0: Do not use the GPU (for small datasets, e.g., less than 100,000 rows, using GPU is slower). 1: Force use the GPU. 2: Use GPU to evaluate splits only when the node size is greater than or equal to n_lb_GPU.n_lb_GPUan integer specifying the threshold number of rows in the training data to use GPU for training. This parameter takes effect only when GPU = 2.vote_methodan integer (0 or 1) specifying the voting method in prediction. 0: each leaf contributes the raw count and an average is taken on the sum over all leaves; 1: each leaf contributes an intra-node fraction which is then averaged over all leaves with equal weight.na_numerica numeric value, substitute for 'nan' in numeric variables.na_integeran integer value, substitute for 'nan' in integer variables.na_factora character string, substitute for missing values in factor variables.typea character string indicating the return content of the predict function. For a classification problem, "score" means the by-class probabilities and "class" means the class labels (i.e., the target variable levels). For regression, the predicted values are returned. This is a parameter for the predict function, not an attribute of the brif object.