## 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'])