model.gamma = gamma model.fit(list(data), list(unlab)) score = model.score(list(testData), list(testLabels)) print(score, ' gamma = ', gamma, ' max_iter = ', max_iter, ' alpha = ', alpha) if (score in scoreDict): scoreDict[score].append( 'gamma = ' + str(gamma) + ' max_iter = ' + str(max_iter) + ' alpha = ' + str(alpha)) else: scoreDict[score] = [ 'gamma = ' + str(gamma) + ' max_iter = ' + str(max_iter) + ' alpha = ' + str(alpha)] knnModel = LabelSpreading(kernel='knn') knnParams = {'n_neighbors': [1, 4, 9, 16], 'max_iter': [10, 100, 1000], 'alpha': [0.2, 0.4, 0.6, 0.8]} for max_iter in knnParams['max_iter']: model.max_iter = max_iter for alpha in knnParams['alpha']: model.alpha = alpha for n_neighbors in knnParams['n_neighbors']: model.n_neighbors = n_neighbors model.fit(list(data), list(unlab)) score = model.score(list(testData), list(testLabels)) print(score, ' n_neighbors = ', n_neighbors, ' max_iter = ', max_iter, ' alpha = ', alpha) if (score in scoreDict): scoreDict[score].append( 'n_neighbors = ' + str(n_neighbors) + ' max_iter = ' + str(max_iter) + ' alpha = ' + str(alpha)) else: scoreDict[score] = [ 'n_neighbors = ' + str(n_neighbors) + ' max_iter = ' + str(max_iter) + ' alpha = ' + str(alpha)] best = sorted(list(scoreDict.keys()))[-1] print('Best accuracy: ', best, scoreDict[best])