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'])