mcmc.MH(per_loop) b = time.time() print ("finished iteration: " + str((c + 1) * per_loop) + " in " + str(int(b - a)) + " seconds") end = time.time() print ("Finished burnin and " + str(num_iterations) + " iterations in " + str(int(end - start)) + " seconds.") folder = "plots_" + fname # plotting samples will also load the MAP estimates # mcmc.plot_samples(folder + "/", str(num_iterations) + '_iterations') # load up test data and run predictions model.load_test_split(Xtest, Ptest, True) pred = model.get_predictions() num = model.get_num_predictions() mast = model.get_mastery() err = pred - Xtest rmse = np.sqrt(np.sum(err ** 2) / num) errl = np.zeros(num) predl = np.zeros(num) mastl = np.zeros(num) xtestl = np.zeros(num) i = 0 for n in range(pred.shape[0]): for t in range(pred.shape[1]): if pred[n][t] == -1: break
def run_learned_model(skill, diff_params = None): intermediate_states = 0 fname = skill.replace(" ","_") fname = fname.replace("\"","") X = np.loadtxt(open("dump/observations_" + fname + ".csv", "rb"), delimiter=",") P = np.loadtxt(open("dump/problems_" + fname + ".csv","rb"),delimiter=",") k = 5 #split 1/kth into test set N = X.shape[0] Xtest = [] Xnew = [] Ptest = [] Pnew = [] for c in range(N): if c % k == 0:#random.random() < 1 / (k+0.0): Xtest.append(X[c,:]) Ptest.append(P[c,:]) else: Xnew.append(X[c,:]) Pnew.append(P[c,:]) X = Xnew Xtest = np.array(Xtest) P = Pnew Ptest = np.array(Ptest) model = MLFKTModel(X, P, 0, 0.1) predl = [] errl = [] for c in range(3): param_dict = json.load(open("feb20_exps/PARAMS_"+skill+"_2states_500iter.json","r")) param_dict = param_dict[c] params = model.get_parameters() for k, v in param_dict.iteritems(): #print k, v if k == "Pi": val = np.array(v) params["L"].set(val) params["L"].save() elif k == "Trans": val = np.array(v) params["T"].set(val) params["T"].save() elif k == "Emit": G = scipy.special.logit(v[0][1]) S = scipy.special.logit(v[1][0]) params["G_0"].set(G) params["S"].set(S) params["G_0"].save() params["S"].save() else: if diff_params is None: params[k].set(v) params[k].save() else: params[k].set(diff_params[k]) params[k].save() params['Dsigma'].save() model.load_test_split(Xtest, Ptest) preds = model.get_predictions() err = preds - Xtest predl.append(preds) errl.append(err) return Xtest, Ptest, np.mean(predl,0), np.mean(errl,0)