def checkClfPerformanceByCV():
data, testData, fieldMaps, sampleWeights, testSampleWeights = readData(outputDir=rootdir)
random_state = 0
trans = Pipeline([('filler', MissingValueFiller()), ('normer', Normalizer())])
X = trans.fit_transform(data.X, data.Y)
y = data.Y
verb = 1
gbjjn_jobs = 10
maxNumIt = 300
clfs = [
GradientBoost_JJ(learners=[svc_f, svc_m, rf_f, rf_m,gb], verbosity=verb, subsample=0.75, learningRate=0.5,
numIterations=maxNumIt, lossFunction=LeastSquaresError(1), stoppingError=0.1, numDeemedStuck=15, n_jobs=gbjjn_jobs),
GradientBoost_JJ(learners=[svc_f, svc_m, rf_f, rf_m,gb], verbosity=verb, subsample=0.5, learningRate=0.5,
numIterations=maxNumIt, lossFunction=LeastSquaresError(1), stoppingError=0.1, numDeemedStuck=15, n_jobs=gbjjn_jobs),
GradientBoost_JJ(learners=[svc_f, svc_m, rf_f, rf_m,gb], verbosity=verb, subsample=0.75, learningRate=0.05,
numIterations=maxNumIt, lossFunction=LeastSquaresError(1), stoppingError=0.1, numDeemedStuck=15, n_jobs=gbjjn_jobs),
GradientBoost_JJ(learners=[svc_f, svc_m, rf_f, rf_m,gb], verbosity=verb, subsample=0.5, learningRate=0.05,
numIterations=maxNumIt, lossFunction=LeastSquaresError(1), stoppingError=0.1, numDeemedStuck=15, n_jobs=gbjjn_jobs),
GradientBoost_JJ(learners=[svc_f, svc_m, rf_f, rf_m,gb], verbosity=verb, subsample=0.5, learningRate=0.01,
numIterations=maxNumIt, lossFunction=LeastSquaresError(1), stoppingError=0.1, numDeemedStuck=15, n_jobs=gbjjn_jobs),
GradientBoost_JJ(learners=[svc_f, svc_m, rf_f, rf_m,gb], verbosity=verb, subsample=0.5, learningRate=0.01,
numIterations=maxNumIt, lossFunction=LeastSquaresError(1), stoppingError=0.07, numDeemedStuck=15, n_jobs=gbjjn_jobs)]
clfs = clfs[2:]
res = [cvScores(clf, X, y, n_jobs=24, scoreFuncsToUse='accuracy_score', numCVs=10, doesPrint=False) for clf in clfs]
# pool = MyPool(processes=10, initializer=init, initargs=(X, y))
# temp = pool.map_async(cvScoreInnerLoop, clfs)
# temp.wait()
# res = temp.get()
print res
for i in range(len(clfs)):
print '=' * 10
print clfs[i]
print res[i]
pool.close()
pool.join()
print(result)
def func1(**kwargs):
print 'in func 1'
func2(**kwargs)
def func2(a):
print 'in func 2'
print 'a =', a
if __name__ == '__main__':
data, testData, fieldMaps, _, _ = readData(outputDir=rootdir)
random_states = range(10)
useJJ = True
# make pipe and allParamsDict
# name = 'svc'
# step1 = fillertoTry
# step2 = normalizerToTry
# step3 = (name,classifiersToTry[name])
# pipe, allParamsDict = makePipe([step1, step2, step3])
# pprint(allParamsDict)
# print '>'*20
# p = loadObject("H:/allparamsdict")
# pprint(getParamsFromIndices([2, 2, 1, 0, 2], p))
numIterations=maxNumIt, lossFunction=LeastSquaresError(1), stoppingError=0.1, numDeemedStuck=15, n_jobs=gbjjn_jobs),
GradientBoost_JJ(learners=[svc_f, svc_m, rf_f, rf_m,gb], verbosity=verb, subsample=0.5, learningRate=0.05,
numIterations=maxNumIt, lossFunction=LeastSquaresError(1), stoppingError=0.1, numDeemedStuck=15, n_jobs=gbjjn_jobs),
GradientBoost_JJ(learners=[svc_f, svc_m, rf_f, rf_m,gb], verbosity=verb, subsample=0.5, learningRate=0.01,
numIterations=maxNumIt, lossFunction=LeastSquaresError(1), stoppingError=0.1, numDeemedStuck=15, n_jobs=gbjjn_jobs),
GradientBoost_JJ(learners=[svc_f, svc_m, rf_f, rf_m,gb], verbosity=verb, subsample=0.5, learningRate=0.01,
numIterations=maxNumIt, lossFunction=LeastSquaresError(1), stoppingError=0.07, numDeemedStuck=15, n_jobs=gbjjn_jobs)]
clfs = clfs[2:]
res = [cvScores(clf, X, y, n_jobs=24, scoreFuncsToUse='accuracy_score', numCVs=10, doesPrint=False) for clf in clfs]
# pool = MyPool(processes=10, initializer=init, initargs=(X, y))
# temp = pool.map_async(cvScoreInnerLoop, clfs)
# temp.wait()
# res = temp.get()
print res
for i in range(len(clfs)):
print '=' * 10
print clfs[i]
print res[i]
if __name__ == "__main__":
checkClfPerformanceByCV()
data, testData, fieldMaps, sampleWeights, testSampleWeights = readData(outputDir=rootdir)
# random_state = 0
#
# buildModel(data, testData, fieldMaps, selectedClfs=['JJGradientBoostsimple'], n_jobs=4, writeResults=True,
# colNames='all', cvNumSplits=10, random_state=random_state)
print '\n>>>>>>>>>>> Done <<<<<<<<<<<<<<'
