def test_k_nearest_neighbours(self):
X, y = compute_moves_v2_without_duplicates()
X, y = eliminate_duplicates(X, y)
# split training and test data (test size 20%)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1234)
acc = k_nearest_neighbours(X_train, X_test, y_train, y_test)
def test_multilayer_perceptron(self):
X, y = compute_moves_v2_without_duplicates()
X, y = eliminate_duplicates(X, y)
# split training and test data (test size 20%)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1234)
acc = multilayer_perceptron(X_train, X_test, y_train, y_test)
def test_logistic_regression(self):
X, y = compute_moves_v2_without_duplicates()
X, y = eliminate_duplicates(X, y)
# split training and test data (test size 20%)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1234)
acc = logistic_regression(X_train, X_test, y_train, y_test)
def test_accuracies(self):
accuracies = []
classifiers = [
'lin_svm', 'k_n_neighb', 'dec_tree', 'log_regr', 'naive_bayes',
'mlp'
]
X, y = compute_moves_v2_without_duplicates()
X, y = eliminate_duplicates(X, y)
# split training and test data (test size 20%)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1234)
acc = linear_svm(X_train, X_test, y_train, y_test)
accuracies.append(acc)
acc = k_nearest_neighbours(X_train, X_test, y_train, y_test)
accuracies.append(acc)
acc = decision_tree(X_train, X_test, y_train, y_test)
accuracies.append(acc)
acc = logistic_regression(X_train, X_test, y_train, y_test)
accuracies.append(acc)
acc = naive_bayes(X_train, X_test, y_train, y_test)
accuracies.append(acc)
acc = multilayer_perceptron(X_train, X_test, y_train, y_test)
accuracies.append(acc)
plt.bar(classifiers, accuracies)
# Namimg the x and y axis
plt.xlabel('Classifiers')
plt.ylabel('Accuracies')
# Giving the tilte for the plot
plt.title('Accuracies of our Classifiers')
# Saving the plot as a 'png'
# plt.savefig('ACCPlot.png')
plt.show()
print(accuracies)
print(classifiers)
assert True
def test_eliminate_duplicates(self):
X, y = compute_moves_v2_without_duplicates()
X, y = eliminate_duplicates(X, y)
print('X: ', X, 'y: ', y)
assert X.shape[0] == y.shape[0]