ExecClassifMonoView.py

#!/usr/bin/env python

""" Execution: Script to perform a MonoView classification """

# Import built-in modules
import argparse  # for command line arguments
import datetime  # for TimeStamp in CSVFile
import os  # to geth path of the running script
import time  # for time calculations
import operator
import errno

# Import 3rd party modules
import numpy as np  # for reading CSV-files and Series
import logging  # To create Log-Files
from sklearn import metrics  # For stastics on classification
import h5py

# Import own modules
import MonoviewUtils  # Functions for classification
import ExportResults  # Functions to render results
import MonoviewClassifiers
import Metrics
from analyzeResult import execute
from utils.Dataset import getV, getValue, extractSubset

# Author-Info
__author__ = "Nikolas Huelsmann, Baptiste BAUVIN"
__status__ = "Prototype"  # Production, Development, Prototype
__date__ = 2016 - 03 - 25


def ExecMonoview_multicore(directory, name, labelsNames, classificationIndices, KFolds, datasetFileIndex, databaseType,
                           path, randomState, hyperParamSearch="randomizedSearch",
                           metrics=[["accuracy_score", None]], nIter=30, **args):
    DATASET = h5py.File(path + name + str(datasetFileIndex) + ".hdf5", "r")
    kwargs = args["args"]
    views = [DATASET.get("View" + str(viewIndex)).attrs["name"] for viewIndex in
             range(DATASET.get("Metadata").attrs["nbView"])]
    neededViewIndex = views.index(kwargs["feat"])
    X = DATASET.get("View" + str(neededViewIndex))
    Y = DATASET.get("Labels").value
    return ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFolds, 1, databaseType, path,
                        randomState, hyperParamSearch=hyperParamSearch,
                        metrics=metrics, nIter=nIter, **args)


def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFolds, nbCores, databaseType, path,
                 randomState, hyperParamSearch="randomizedSearch",
                 metrics=[["accuracy_score", None]], nIter=30, **args):
    logging.debug("Start:\t Loading data")
    try:
        kwargs = args["args"]
    except:
        kwargs = args
    t_start = time.time()
    feat = X.attrs["name"]
    CL_type = kwargs["CL_type"]
    X = getValue(X)
    learningRate = len(classificationIndices[0]) / (len(classificationIndices[0]) + len(classificationIndices[1]))
    labelsString = "-".join(labelsNames)
    timestr = time.strftime("%Y%m%d-%H%M%S")
    CL_type_string = CL_type
    outputFileName = directory + "/" + CL_type_string + "/" + "/" + feat + "/" + timestr + "Results-" + CL_type_string + "-" + labelsString + \
                     '-learnRate' + str(learningRate) + '-' + name + "-" + feat + "-"
    if not os.path.exists(os.path.dirname(outputFileName)):
        try:
            os.makedirs(os.path.dirname(outputFileName))
        except OSError as exc:
            if exc.errno != errno.EEXIST:
                raise
    logging.debug("Done:\t Loading data")
    # Determine the Database to extract features
    logging.debug("Info:\t Classification - Database:" + str(name) + " Feature:" + str(feat) + " train ratio:"
                  + str(learningRate) + ", CrossValidation k-folds: " + str(KFolds.n_splits) + ", cores:"
                  + str(nbCores) + ", algorithm : " + CL_type)

    trainIndices, testIndices = classificationIndices
    # Calculate Train/Test data
    logging.debug("Start:\t Determine Train/Test split")
    X_train = extractSubset(X, trainIndices)
    X_test = extractSubset(X, testIndices)
    y_train = Y[trainIndices]
    y_test = Y[testIndices]

    logging.debug("Info:\t Shape X_train:" + str(X_train.shape) + ", Length of y_train:" + str(len(y_train)))
    logging.debug("Info:\t Shape X_test:" + str(X_test.shape) + ", Length of y_test:" + str(len(y_test)))
    logging.debug("Done:\t Determine Train/Test split")

    classifierModule = getattr(MonoviewClassifiers, CL_type)

    if hyperParamSearch != "None":
        classifierHPSearch = getattr(classifierModule, hyperParamSearch)
        logging.debug("Start:\t RandomSearch best settings with " + str(nIter) + " iterations for " + CL_type)
        cl_desc = classifierHPSearch(X_train, y_train, randomState, outputFileName, KFolds=KFolds, nbCores=nbCores,
                                     metric=metrics[0], nIter=nIter)
        clKWARGS = dict((str(index), desc) for index, desc in enumerate(cl_desc))
        logging.debug("Done:\t RandomSearch best settings")
    else:
        clKWARGS = kwargs[kwargs["CL_type"] + "KWARGS"]
    logging.debug("Start:\t Training")
    cl_res = classifierModule.fit(X_train, y_train, randomState, NB_CORES=nbCores, **clKWARGS)
    logging.debug("Done:\t Training")

    logging.debug("Start:\t Predicting")
    # Stats Result
    y_train_pred = cl_res.predict(X_train)
    y_test_pred = cl_res.predict(X_test)

    full_labels = cl_res.predict(X)
    logging.debug("Done:\t Predicting")
    t_end = time.time() - t_start
    logging.debug("Info:\t Time for training and predicting: " + str(t_end) + "[s]")

    logging.debug("Start:\t Getting Results")

    stringAnalysis, imagesAnalysis, metricsScores = execute(name, classificationIndices, KFolds, nbCores,
                                                            hyperParamSearch, metrics, nIter, feat, CL_type,
                                                            clKWARGS, labelsNames, X.shape,
                                                            y_train, y_train_pred, y_test, y_test_pred, t_end,
                                                            randomState)
    cl_desc = [value for key, value in sorted(clKWARGS.iteritems())]
    logging.debug("Done:\t Getting Results")
    logging.info(stringAnalysis)
    # labelsString = "-".join(labelsNames)
    # timestr = time.strftime("%Y%m%d-%H%M%S")
    # CL_type_string = CL_type
    # outputFileName = directory + "/"+CL_type_string+"/"+"/"+feat+"/"+timestr +"Results-" + CL_type_string + "-" + labelsString + \
    #                  '-learnRate' + str(learningRate) + '-' + name + "-" + feat + "-"


    outputTextFile = open(outputFileName + '.txt', 'w')
    outputTextFile.write(stringAnalysis)
    outputTextFile.close()
    np.savetxt(outputFileName + "full_pred.csv", full_labels.astype(np.int16), delimiter=",")
    np.savetxt(outputFileName + "train_pred.csv", y_train_pred.astype(np.int16), delimiter=",")
    np.savetxt(outputFileName + "train_labels.csv", y_train.astype(np.int16), delimiter=",")

    if imagesAnalysis is not None:
        for imageName in imagesAnalysis:
            if os.path.isfile(outputFileName + imageName + ".png"):
                for i in range(1, 20):
                    testFileName = outputFileName + imageName + "-" + str(i) + ".png"
                    if not os.path.isfile(testFileName):
                        imagesAnalysis[imageName].savefig(testFileName)
                        break

            imagesAnalysis[imageName].savefig(outputFileName + imageName + '.png')

    logging.info("Done:\t Result Analysis")
    viewIndex = args["viewIndex"]
    return viewIndex, [CL_type, cl_desc + [feat], metricsScores, full_labels, clKWARGS]


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description='This methods permits to execute a multiclass classification with one single view. At this point the used classifier is a RandomForest. The GridSearch permits to vary the number of trees and CrossValidation with k-folds. The result will be a plot of the score per class and a CSV with the best classifier found by the GridSearch.',
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)

    groupStandard = parser.add_argument_group('Standard arguments')
    groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
    groupStandard.add_argument('--type', metavar='STRING', action='store', help='Type of Dataset', default=".hdf5")
    groupStandard.add_argument('--name', metavar='STRING', action='store',
                               help='Name of Database (default: %(default)s)', default='DB')
    groupStandard.add_argument('--feat', metavar='STRING', action='store',
                               help='Name of Feature for Classification (default: %(default)s)', default='RGB')
    groupStandard.add_argument('--pathF', metavar='STRING', action='store',
                               help='Path to the views (default: %(default)s)', default='Results-FeatExtr/')
    groupStandard.add_argument('--fileCL', metavar='STRING', action='store',
                               help='Name of classLabels CSV-file  (default: %(default)s)', default='classLabels.csv')
    groupStandard.add_argument('--fileCLD', metavar='STRING', action='store',
                               help='Name of classLabels-Description CSV-file  (default: %(default)s)',
                               default='classLabels-Description.csv')
    groupStandard.add_argument('--fileFeat', metavar='STRING', action='store',
                               help='Name of feature CSV-file  (default: %(default)s)', default='feature.csv')

    groupClass = parser.add_argument_group('Classification arguments')
    groupClass.add_argument('--CL_type', metavar='STRING', action='store', help='Classifier to use',
                            default="RandomForest")
    groupClass.add_argument('--CL_CV', metavar='INT', action='store', help='Number of k-folds for CV', type=int,
                            default=10)
    groupClass.add_argument('--CL_Cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
                            default=1)
    groupClass.add_argument('--CL_split', metavar='FLOAT', action='store', help='Split ratio for train and test',
                            type=float, default=0.9)
    groupClass.add_argument('--CL_metrics', metavar='STRING', action='store',
                            help='Determine which metrics to use, separate with ":" if multiple, if empty, considering all',
                            default='')

    groupClassifier = parser.add_argument_group('Classifier Config')
    groupClassifier.add_argument('--CL_config', metavar='STRING', nargs="+", action='store',
                                 help='GridSearch: Determine the trees', default=['25:75:125:175'])

    args = parser.parse_args()

    classifierKWARGS = dict((key, value) for key, value in enumerate([arg.split(":") for arg in args.CL_config]))
    ### Main Programm


    # Configure Logger
    directory = os.path.dirname(os.path.abspath(__file__)) + "/Results-ClassMonoView/"
    logfilename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + args.name + "-" + args.feat + "-LOG"
    logfile = directory + logfilename
    if os.path.isfile(logfile + ".log"):
        for i in range(1, 20):
            testFileName = logfilename + "-" + str(i) + ".log"
            if not os.path.isfile(directory + testFileName):
                logfile = directory + testFileName
                break
    else:
        logfile += ".log"

    logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', filename=logfile, level=logging.DEBUG,
                        filemode='w')

    if args.log:
        logging.getLogger().addHandler(logging.StreamHandler())

    # Read the features
    logging.debug("Start:\t Read " + args.type + " Files")

    if args.type == ".csv":
        X = np.genfromtxt(args.pathF + args.fileFeat, delimiter=';')
        Y = np.genfromtxt(args.pathF + args.fileCL, delimiter=';')
    elif args.type == ".hdf5":
        dataset = h5py.File(args.pathF + args.name + ".hdf5", "r")
        viewsDict = dict((dataset.get("View" + str(viewIndex)).attrs["name"], viewIndex) for viewIndex in
                         range(dataset.get("Metadata").attrs["nbView"]))
        X = dataset["View" + str(viewsDict[args.feat])][...]
        Y = dataset["Labels"][...]

    logging.debug("Info:\t Shape of Feature:" + str(X.shape) + ", Length of classLabels vector:" + str(Y.shape))
    logging.debug("Done:\t Read CSV Files")

    arguments = {args.CL_type + "KWARGS": classifierKWARGS, "feat": args.feat, "fileFeat": args.fileFeat,
                 "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": args.CL_type}
    ExecMonoview(X, Y, args.name, args.CL_split, args.CL_CV, args.CL_Cores, args.type, args.pathF,
                 metrics=args.CL_metrics, **arguments)