## alHSVM = ActiveLearnerHintSVM(dtst, nEstimators, 'hint-SVM', model, batchSize, K, 0.1, 0.1, .5, None, 'linear', 'rbf', 3, 0.1, 0., 1e-3, 1, 100., 0)
alMinExp = ActiveLearnerMinExpError(dtst, 'minExpError', model, batchSize)
# als = [alR, alU, alC, alBC100, alBC10, alBC1, alBC01, alBC001, alQ, alLALindepend, alLALiterative, alMinExp]
als = [alMinExp]
#
exp = Experiment(nIterations, quality_metrics, dtst, als, maxVoteCount, 'here we can put a comment about the current experiments')
# # the Results class helps to add, save and plot results of the experiments
res = Results(exp, nExperiments)
print("Running AL experiments...")
for i in range(nExperiments):
print('\n experiment #'+str(i+1))
# run an experiment
performance = exp.run()
res.addPerformance(performance)
# reset the experiment (including sampling a new starting state for the dataset)
exp.reset()
print("Done!")
res.saveResults('rte_combined_minExp')
resplot = Results()
resplot.readResult('rte_combined_minExp')
resplot.plotResults(metrics = ['accuracy', 'f-measure', 'f1', 'f3'])
# res.mergeResults('emotion_combined2', 'emotion_k001', 'emotion_combined')
#
# resplot = Results()
# resplot.readResult('emotion_combined')
# resplot.plotResults(metrics = ['f1'])
