def run_training(): data = load_data() # KNN model selection k_values = range(1, 201, 2) print( '\n------------- Selekcja liczby sasiadow dla modelu dla KNN -------------' ) print( '-------------------- Wartosci k: 1, 3, ..., 200 -----------------------' ) print( '--------------------- To moze potrwac ok. 1 min ------------------------' ) error_best, best_k, errors = model_selection_knn(data['Xval'], data['Xtrain'], data['yval'], data['ytrain'], k_values) print('Najlepsze k: {num1} i najlepszy blad: {num2:.4f}'.format( num1=best_k, num2=error_best)) print('\n--- Wcisnij klawisz, aby kontynuowac ---') classification_KNN_vs_no_neighbours(k_values, errors) a_values = [1, 3, 10, 30, 100, 300, 1000] b_values = [1, 3, 10, 30, 100, 300, 1000] print( '\n----------------- Selekcja parametrow a i b dla NB --------------------' ) print( '--------- Wartosci a i b: 1, 3, 10, 30, 100, 300, 1000 -----------------' ) print( '--------------------- To moze potrwac ok. 1 min ------------------------' ) # NB model selection error_best, best_a, best_b, errors = model_selection_nb( data['Xtrain'], data['Xval'], data['ytrain'], data['yval'], a_values, b_values) print('Najlepsze a: {}, b: {} i najlepszy blad: {:.4f}'.format( best_a, best_b, error_best)) print('\n--- Wcisnij klawisz, aby kontynuowac ---') plot_a_b_errors(errors, a_values, b_values) p_x_y = estimate_p_x_y_nb(data['Xtrain'], data['ytrain'], best_a, best_b) classes_no = p_x_y.shape[0] print( '\n------Wizualizacja najbardziej popularnych slow dla poszczegolnych klas------' ) print( '--Sa to slowa o najwyzszym prawdopodobienstwie w danej klasie dla modelu NB--' ) groupnames = data['groupnames'] words = {} for x in range(classes_no): indices = np.argsort(p_x_y[x, :])[::-1][:50] words[groupnames[x]] = { word: prob for word, prob in zip(data['wordlist'][indices], p_x_y[x, indices]) } try: word_clouds(words.values(), words.keys()) except Exception: print('---Wystapil problem z biblioteka wordcloud--- ') print('\n--- Wcisnij klawisz, aby kontynuowac ---') print( '\n----------------Porownanie bledow dla KNN i NB---------------------' ) Dist = hamming_distance(data['Xtest'], data['Xtrain']) y_sorted = sort_train_labels_knn(Dist, data['ytrain']) p_y_x = p_y_x_knn(y_sorted, best_k) error_KNN = classification_error(p_y_x, data['ytest']) p_y = estimate_a_priori_nb(data['ytrain']) p_y_x = p_y_x_nb(p_y, p_x_y, data['Xtest']) error_NB = classification_error(p_y_x, data['ytest']) plot_error_NB_KNN(error_NB, error_KNN) print('\n--- Wcisnij klawisz, aby kontynuowac ---')
def test_model_selection_knn_best_error(self): data = test_data['model_selection_KNN'] out = model_selection_knn(data['Xval'], data['Xtrain'], data['yval'], data['ytrain'], data['K_values']) self.assertAlmostEquals(out[0], data['error_best'], 8)
def test_model_selection_knn_best_k(self): data = test_data['model_selection_KNN'] out = model_selection_knn(data['Xval'], data['Xtrain'], data['yval'], data['ytrain'], data['K_values']) self.assertEquals(out[1], data['best_K'])