gpEMOCreg/helperFunctions.py

import numpy
import ConfigParser
import os
import sys
import subprocess
import time
import sklearn.covariance


def getSingleRMSEs(yTest, pred):

    rmses = numpy.asarray(numpy.empty([1,yTest.shape[1]]), dtype=numpy.float)
    rmses[:,:] = numpy.sqrt(numpy.mean(numpy.square(yTest - pred), axis=0))

    return rmses


def getMeanRMSE(yTest, pred):

    return numpy.mean(getSingleRMSEs(yTest, pred))


def normLP(x, norm, axis=1):

    if norm == 1:
        return numpy.sum(numpy.abs(x), axis=axis)
    elif norm == 2:
        return numpy.sqrt(numpy.sum(numpy.square(x), axis=axis))
    else:
        raise Exception('Yet unsupported norm %d!'%norm)


def normalizeLP(x, norm):

    return numpy.divide(x, normLP(x, norm))


def normalizeUCI(x):

    normX = x

    # TODO: vectorize
    for i in range(x.shape[0]):
        for j in range(x.shape[1]):

            normX[i,j] = (x[i,j] - numpy.min(x[:,j])) / (numpy.max(x[:,j]) - numpy.min(x[:,j]))

    return normX


def estTimeLeft(t0, t1, curRun, totalRuns):

    timePast = float(t1 - t0)
    avgTimePerPass = timePast / float(curRun)
    estTimeOva = avgTimePerPass * totalRuns
    estTimeLeft = estTimeOva - timePast

    return [timePast , avgTimePerPass , estTimeLeft , estTimeOva]


def getConfig(pathtoConfig, section, option, default=None, dtype='str', verbose=False):

    # set default
    value = default
    defaultUsed = True

    # check if file is available
    if pathtoConfig is not None and os.path.isfile(pathtoConfig):

        # init
        config = ConfigParser.ConfigParser()
        configFile = open(pathtoConfig)
        config.readfp(configFile)
        configFile.close()

        # check if section and option is available
        if config.has_section(section) and config.has_option(section, option):

            # get requested type
            if dtype == 'str':
                value = config.get(section, option)
            elif dtype == 'int':
                value = config.getint(section, option)
            elif dtype == 'float':
                value = config.getfloat(section, option)
            elif dtype == 'bool':
                value = config.getboolean(section, option)
            elif dtype == 'strList':
                value = config.get(section, option).split(',')
            elif dtype == 'intList':
                value = [int(entry) for entry in config.get(section, option).split(',')]
            elif dtype == 'floatList':
                value = [float(entry) for entry in config.get(section, option).split(',')]
            elif dtype == 'boolList':
                value = [bool(entry) for entry in config.get(section, option).split(',')]
            else:
                raise Exception('Unknown dtype!')

            defaultUsed = False

    # print config
    if verbose:
        aux = ''
        if 'List' in dtype and value is not None and len(value) > 0:
            aux = '| entryDtype:' + str(type(value[0]))
        print 'default:', defaultUsed, '| section:', section, '| option:', option, '| value:', value, '| dtype:', type(value), aux

    # return
    return value


def getGitHash(gitPath=os.path.dirname(os.path.abspath(__file__))):
    curDir = os.getcwd()
    os.chdir(gitPath)
    gitHash = subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], stderr=subprocess.STDOUT).strip()
    os.chdir(curDir)
    return gitHash


def showProgressBarTerminal(current, total, pre):

    sys.stdout.write('\r%s %0.2f %%'%(pre,(float(current)/float(total))*100.0))
    sys.stdout.flush()