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        "author": "Mehmet Basbug",
        "author_email": "mbasbug@princeton.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Framework :: Buildout",
            "Intended Audience :: Information Technology",
            "License :: Free for non-commercial use",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "*******************************************************************************\nPBOOST : Hybrid Parallel Implementation of Boosted Trees\n\nMehmet Basbug\nPhD Candidate\nPrinceton University\n\nFebruary 2013\n*******************************************************************************\n\nA.QUICK INSTALL\n-------------------------------------------------------------------------------\nFirst make sure that you have OpenMPI and HDF5 installed. Then run the following command to install pboost with all the dependencies:\n\npip install pboost\n\nOR\n\neasy_install pboost\n\nThis will also create a directory named 'pboost' in your home folder.\n\nThis should work fine for many users; however, you may be missing some required libraries. On Debian/Ubuntu run the following command first (add sudo if necessary)\n\napt-get install build-essential python-dev python-setuptools python-pip python-numpy python-scipy python-matplotlib openmpi-bin openmpi-doc libopenmpi-dev libhdf5-serial-dev\n\n\nB.RUNNING DEMO\n-------------------------------------------------------------------------------\nTo run the demo, execute the following command\n\nmpirun -np 2 python ~/pboost/run.py 0\n\nYou should see something similar to the following output\n\nInfo : Confusion matrix for combined validation error with zero threshold :\n{'FP': 108, 'TN': 392, 'FN': 115, 'TP': 153}\nInfo : Training Error of the final classifier : 0.0\nInfo : Validation Error of the final classifier : 0.29296875\n2015-10-22 12:25:46.468247 Rank 0 Info : Reporter finished running.\n2015-10-22 12:25:46.468293 Rank 0 Info : Total runtime : 25.71 seconds\n\nA quick note, if you installed pboost with sudo privileges, you may need to change the ownership of pboost folder and the files in it. In linux, run the following command\n\nsudo chown -R {USERNAME} ~/pboost\n\n\nC.CREATING YOUR CONFIGURATION FILE\n-------------------------------------------------------------------------------\nThe recommended way is appending your new configurations to configurations.cfg file in pboost folder. Alternatively you can create an empty text file with the extension of cfg. A shorter explanation of options can be found in configurations.cfg file. For the detailed explanations see below:\n\n# train_file    = [HDF5 File]\n#                 Raw file of examples and their attributes for training\n#                 (Must be specified)\n#\n# test_file     = [HDF5 File]\n#                 Raw file of examples and their attributes for testing\n#                 (Optional)\n\nData files (train_file and test_file) should be in HDF5 format and the raw data must be named 'data' and label must be named 'label'. Alternatively one can use the last column of 'data' dataset as the label.\n\nIf a separate 'label' dataset is not given, the program will assume the last column as labels. At this point only binary labeling is supported; therefore, all the labels should be zero or one.\n\nIf there is no separate test dataset, please leave test_data option blank.\n\n# factory_files = [.py File(s), separated by comma]\n#                 File(s) containing the concrete implementations of\n#                 the BaseFeatureFactory class\n#                 (Leaving blank will include default implementation)\n\nfactory_files option refers to the files containing user defined feature factories. See examples of Feature Factory classes in pima_feature_factory.py file in demo folder.\n\nIn order to use each column of your raw data as a feature, leave this option blank or use the keyword default.\n\nMore than one options can be specified, for instance the following is perfectly okay.\n\tfactory_files = pima_feature_factory.py,my_feature_factory.py\n\nEach user defined Feature Factory class should inherit from BaseFeatureFactory and override blueprint and make methods. The  blueprint method refers to the functionality of features. The produce method is about giving arguments to actually create features. One should call make method for every single feature creation within that method. All Feature Factory Files should be in the working directory.\n\n# working_dir \t= [string]\n#                 Path to a shared disk location containing data files, factory files\n#                 (Leaving blank will set it to the same directory as this file)\n\nWorking directory should be on a shared disk space. Data files and feature factory files should be put in that directory.\n\n# algorithm \t= [string]\n#                 Boosting algorithm to use.\n#                 Current options are \"conf-rated\", \"adaboost\" and \"adaboost-fast\"\n#                 (Leaving blank will set this field to \"conf-rated\")\n\nCurrently only \"adaboost\", \"adaboost-fast\" and \"conf-rated\" are supported. The second one is recommended as it is likely to converge faster.\n\n# rounds \t\t= [integer]\n#                 Number of rounds that the boosting algorithm should iterate for\n#                 (Leaving blank will set this number to 100)\n\nThe runtime of the program is directly proportional to the number of rounds. It is recommended not give a very large number; training error of boosting usually converges with a moderate number of rounds and a large number might cause over fitting.\n\n# xval_no \t\t= [integer]\n#                 Number of cross-validation folds\n#                 (Leaving blank will set it to 1, that is disabling x validation)\n\nIn order to have k-fold cross validation, set this number to k. Cross validation is useful when you do not have a separate testing dataset. In this case, validation error might be a good estimate of testing error. A large number should be avoided, as it will increase the runtime. For k-fold cross validation pboost will train k+1 algorithms, meaning that it will take k times longer compared to not using cross validation.\n\n# max_memory \t= [integer/float]\n#                 Amount of available RAM to each core, in GigaBytes\n#                 (Leaving blank will set this number to 2, that is 2GB per core)\n\nSince MPI does not treat differently to cores on the same node, this option should be specified in per core basis.\n\n# show_plots    = [y/n]\n#                 Enable to show the plots before the program terminates\n#                 (Leaving blank will set this field to y)\n\nIn both cases plots will be saved in the output directory\n\n# deduplication = [y/n]\n#\t\t\t\t  Enable deduplication for extracted features\n#\t\t\t      (Leaving blank will set this field to n)\n\n# omp_threads   = [integer]\n#\t\t\t\t  Number of OMP threads per MPI process. Only available for adaboost-fast\n#\t\t\t      (Leaving blank will set this field to 1)\n\nThe recommended parallelization is to set omp_thread to the number of cores in each node and create mpi threads per node with -bynode flag of mpirun\n\n# tree_depth    = [integer]\n#\t\t\t\t  The height of the decision tree\n#\t\t\t      (Leaving blank will set this field to 1)\n\nFixed height decision tree is used as weak learner. Height of 1 corresponds to Decision Stumps. The type of boosting algorithm determines which objection function is minimized when a node is created.\n\nAll the necessary output data is always stored in an output folder and plots can be generated later on.\n\nD. RUNNING YOUR PROGRAM\n-------------------------------------------------------------------------------\nrun.py takes two arguments cp and cn\n  cn : Configuration numbers to process\n  cp (optional) : Path to the configuration file\n\nA typical command to run pboost looks like\n\nmpirun -np NUMBER_OF_MPI_THREADS python run.py -cp CONF_PATH CONF_NUM_1 CONF_NUM2\n\nThe default setting for cp is '~/pboost/configurations.cfg'\n\nThe output of the program will be in the folder out_{CONF_NUM} in working directory. The output folder contains the following files in it:\n\nfeature factory files : All Feature Factory Files necessary to make necessary features\nfinal_classifier.py : A script to classify any given raw data\nhypotheses.npy : Data file containing hypotheses found by pboost\ndump.npz : Data file containing predictions and other information about the job\n\nOne can generate plots using plot.py and giving dump.npz as input to that script\n\nE. 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