'''

a playground for evaluating classifiers

'''

import kddutil

import cPickle as pickle

import sys

import sklearn

from sklearn import svm, tree, lda, qda, metrics, cross_validation, grid_search, datasets, ensemble, linear_model, naive_bayes

if len(sys.argv) <= 1:

print "so what pkl do you want me to read, hm?"

sys.exit()

randomForest = ensemble.RandomForestClassifier(verbose=False

)

randomForestRegress = ensemble.RandomForestClassifier(verbose=True

)

gradBoost = ensemble.GradientBoostingClassifier(verbose=True

)

with open(sys.argv[1]) as infile:

train, _ = pickle.load(infile)

if type(train[1][0]) == list:

ids, info, labels = kddutil.notrash(*train)

info = kddutil.bound(info, max=10000, min=-10000)

else:

ids, info, labels = train

print "assuming compacted data; skip preprocessing"

print "Random Forest"

print kddutil.evaluate_k(randomForest, ids, info, labels, fold=3)#, postprocess=kddutil.disambiguate)

#print "Gradient Boosting"

#print kddutil.evaluate(gradBoost, ids, info, labels)