def multi_voters_example():
""" Example of using a combination of many types of voters, which may be seen as multi-kernel learning (MKL).
This particular dataset is easy to solve and combining voters degrades performance. However, it might be a good
idea for more complex datasets.
"""
# MinCq parameters, fixed to a given value as this is a simple example.
mu = 0.001
# We load iris dataset, We convert the labels to be -1 or 1, and we split it in two parts: train and test.
dataset = load_iris()
dataset.target[dataset.target == 0] = -1
dataset.target[dataset.target == 2] = -1
X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, random_state=42)
# We create a set of voters of different kind.
voters = voter.KernelVotersGenerator(rbf_kernel, gamma=0.01).generate(X_train)
voters = np.append(voters, voter.KernelVotersGenerator(rbf_kernel, gamma=0.1).generate(X_train))
voters = np.append(voters, voter.KernelVotersGenerator(rbf_kernel, gamma=1).generate(X_train))
voters = np.append(voters, voter.KernelVotersGenerator(rbf_kernel, gamma=10).generate(X_train))
voters = np.append(voters, voter.KernelVotersGenerator(rbf_kernel, gamma=100).generate(X_train))
voters = np.append(voters, voter.KernelVotersGenerator(polynomial_kernel, degree=2).generate(X_train))
voters = np.append(voters, voter.KernelVotersGenerator(polynomial_kernel, degree=3).generate(X_train))
voters = np.append(voters, voter.KernelVotersGenerator(linear_kernel).generate(X_train))
# We train MinCq using these voters, on the training set.
learner = MinCqLearner(mu, voters_type='manual')
learner.fit(X_train, y_train, voters)
# We predict the train and test labels and print the risk.
predictions_train = learner.predict(X_train)
predictions_test = learner.predict(X_test)
print("\nMultiVotersMinCq")
print("-----------")
print("Training set risk: {:.4f}".format(zero_one_loss(y_train, predictions_train)))
print("Testing set risk: {:.4f}\n".format(zero_one_loss(y_test, predictions_test)))
def simple_classification_example():
""" Simple example : with fixed hyperparameters, run four versions of MinCq on a single dataset.
"""
# MinCq parameters, fixed to a given value as this is a simple example.
mu = 0.001
# We load iris dataset, We convert the labels to be -1 or 1, and we split it in two parts: train and test.
dataset = load_iris()
dataset.target[dataset.target == 0] = -1
dataset.target[dataset.target == 2] = -1
X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, random_state=42)
# We train MinCq using decision stumps as voters, on the training set.
learner = MinCqLearner(mu, voters_type='stumps')
learner.fit(X_train, y_train)
# We predict the train and test labels and print the risk.
predictions_train = learner.predict(X_train)
predictions_test = learner.predict(X_test)
print("\nStumpsMinCq")
print("-----------")
print("Training set risk: {:.4f}".format(zero_one_loss(y_train, predictions_train)))
print("Testing set risk: {:.4f}\n".format(zero_one_loss(y_test, predictions_test)))
# We do the same again, now with a linear kernel.
learner = MinCqLearner(mu, voters_type='kernel', kernel='linear')
learner.fit(X_train, y_train)
predictions_train = learner.predict(X_train)
predictions_test = learner.predict(X_test)
print("\nLinearMinCq")
print("-----------")
print("Training set risk: {:.4f}".format(zero_one_loss(y_train, predictions_train)))
print("Testing set risk: {:.4f}\n".format(zero_one_loss(y_test, predictions_test)))
# We do the same again, now with a polynomial kernel.
learner = MinCqLearner(mu, voters_type='kernel', kernel='poly')
learner.fit(X_train, y_train)
predictions_train = learner.predict(X_train)
predictions_test = learner.predict(X_test)
print("\nPolyMinCq")
print("-----------")
print("Training set risk: {:.4f}".format(zero_one_loss(y_train, predictions_train)))
print("Testing set risk: {:.4f}\n".format(zero_one_loss(y_test, predictions_test)))
# We do the same again, now with an RBF kernel.
learner = MinCqLearner(mu, voters_type='kernel', kernel='rbf', gamma=0.0)
learner.fit(X_train, y_train)
predictions_train = learner.predict(X_train)
predictions_test = learner.predict(X_test)
print("\nRbfMinCq")
print("--------")
print("Training set risk: {:.4f}".format(zero_one_loss(y_train, predictions_train)))
print("Testing set risk: {:.4f}\n".format(zero_one_loss(y_test, predictions_test)))
