def evalFold(arg):
train_index, test_index = arg
SS = StrategyScheduler()
SS.fit(X[train_index], ys[train_index], yNames)
success_count = 0
for i in test_index:
success, score = SSS.eval_schedule('%s#%s' % (XNames[i], SS.predict(X[i])))
if success:
success_count += 1
print 'Current: %i / %i' % (success_count, len(test_index))
return (success_count, len(test_index))
from StrategyScheduler import StrategyScheduler
from MLiP_eval import StrategyScheduleScore
if __name__ == '__main__':
trainFile = 'orig/MLiP_train'
X, ys, XNames, yNames = StrategyScheduler.read(trainFile)
SS = StrategyScheduler()
SS.fit(X, ys, yNames)
SS.analyze(X, ys)
