def main():
finalcv = []
finalPredict = []
for stub in listOfDirectories:
newList = []
directory = "/Users/BaZ/Desktop/KaggleTest/Data/" + stub + "/"
trainingList = eT.readDirectoryAndReturnTransformedTrainingList(
directory, stub, transforms.HillTFWTFC)
# Initialize classifier:
firstForest = RandomForestClassifier(n_estimators=3000,
min_samples_split=1,
bootstrap=False,
n_jobs=4,
random_state=0)
thisScore, this_y_cv, this_y_predict = em.kfold_evaluate_roc_auc(
trainingList, firstForest)
print "KF- Doing RandomForest on transforms.fft on", stub, " yields a roc_auc of:", thisScore
finalcv += this_y_cv.tolist()
finalPredict += this_y_predict.tolist()
print "KF- Over all the runs in the list, using RF_3000_mss1_bsF_nj4_rs0 using transforms.fft, roc_auc = ", metrics.roc_auc_score(
finalcv, finalPredict)
print "Accuracy = ", em.accuracy(finalcv, finalPredict)
return
def main():
finalcv = []
finalPredict = []
for stub in listOfDirectories:
newList = []
directory = "/Users/BaZ/Desktop/KaggleTest/Data/" + stub + "/"
trainingList = eT.readDirectoryAndReturnTransformedTrainingList(directory, stub, transforms.fft)
# Initialize classifier:
firstForest = RandomForestClassifier(n_estimators=3000, min_samples_split=1, bootstrap=False, n_jobs=4, random_state=0)
thisScore, this_y_cv, this_y_predict = em.kfold_evaluate_roc_auc(trainingList, firstForest)
print "KF- Doing RandomForest on transforms.fft on", stub, " yields a roc_auc of:", thisScore
finalcv += this_y_cv.tolist()
finalPredict += this_y_predict.tolist()
print "KF- Over all the runs in the list, using RF_3000_mss1_bsF_nj4_rs0 using transforms.fft, roc_auc = ", metrics.roc_auc_score(finalcv, finalPredict)
print "Accuracy = ", em.accuracy(finalcv, finalPredict)
return
def main():
finalcv = []
finalPredict = []
for stub in listOfDirectories:
newList = []
directory = "/Users/BaZ/Desktop/KaggleTest/Data/" + stub + "/"
trainingList = eT.readDirectoryAndReturnTransformedTrainingList(directory, stub, transforms.thisIsRidiculous, fiveSpawn = True) #HillTFWTFC
# print trainingList
# Initialize classifier:
firstForest = RandomForestClassifier(n_estimators=3000, min_samples_split=1, bootstrap=False, n_jobs=4, random_state=0) # Where have I recorded what Random State does?
thisScore, this_y_cv, this_y_predict = em.kfold_evaluate_roc_auc(trainingList, firstForest)
print "KF- Doing RandomForest on transforms.HILLFWTFC5Spawn on", stub, " without rebuilding yields a roc_auc of:", thisScore
finalcv += this_y_cv.tolist()
finalPredict += this_y_predict.tolist()
print "KF- Over all the runs in the list, using RF_3000_mss1_bsF_nj4_rs0 using transforms.HILLFWTFC5Spawn, no rebuild, roc_auc = ", metrics.roc_auc_score(finalcv, finalPredict)
print "Accuracy = ", em.accuracy(finalcv, finalPredict)
return