def experiment(x_train, x_test, y_train, y_test):
"""Perform experiment.
Args:
x_train (ndarray): training data.
x_test (ndarray): test data.
y_train (ndarray): training labels.
y_test (ndarray): test labels.
Returns:
None.
"""
# Array of training sizes to plot the learning curves over.
training_sizes = np.arange(20, int(len(x_train) * 0.9), 10)
# K-Nearest Neighbor
print('\n--------------------------')
knn = KNN(k=1, weights='uniform', p=2)
knn.experiment(x_train,
x_test,
y_train,
y_test,
cv=10,
y_lim=0.3,
n_neighbors_range=np.arange(1, 50, 2),
p_range=np.arange(1, 20),
weight_functions=['uniform', 'distance'],
train_sizes=training_sizes)
# Support Vector Machines
print('\n--------------------------')
svm = SVM(c=1., kernel='rbf', degree=3, gamma=0.001, random_state=42)
svm.experiment(x_train,
x_test,
y_train,
y_test,
cv=10,
y_lim=0.2,
C_range=[1, 5] + list(range(10, 100, 20)) +
list(range(100, 1000, 50)),
kernels=['linear', 'poly', 'rbf'],
gamma_range=np.logspace(-7, 0, 50),
poly_degrees=[2, 3, 4],
train_sizes=training_sizes)
# Decision Trees
print('\n--------------------------')
dt = DecisionTree(max_depth=1, min_samples_leaf=1, random_state=42)
dt.experiment(x_train,
x_test,
y_train,
y_test,
cv=10,
y_lim=0.1,
max_depth_range=list(range(1, 50)),
min_samples_leaf_range=list(range(1, 30)),
train_sizes=training_sizes)
# AdaBoost
print('\n--------------------------')
boosted_dt = AdaBoost(n_estimators=50,
learning_rate=1.,
max_depth=3,
random_state=42)
boosted_dt.experiment(x_train,
x_test,
y_train,
y_test,
cv=10,
y_lim=0.2,
max_depth_range=list(range(1, 30)),
n_estimators_range=[1, 3, 5, 8] +
list(range(10, 100, 5)) + list(range(100, 1000, 50)),
learning_rate_range=np.logspace(-6, 1, 50),
train_sizes=training_sizes)
# Neural Networks
print('\n--------------------------')
nn = NeuralNetwork(alpha=0.01,
layer1_nodes=50,
layer2_nodes=30,
learning_rate=0.001,
max_iter=100)
nn.experiment(x_train,
x_test,
y_train,
y_test,
cv=10,
y_lim=0.1,
alpha_range=np.logspace(-5, 1, 30),
learning_rate_range=np.logspace(-4, 0, 50),
train_sizes=training_sizes)
