def testLinearRegressionOptimizer(self):
algorithm = LinearRegression(
optimizer=NumericGradientChecker(GradientDescent(
learning_rate=0.1)))
# Expect only some minor fp inaccuracy
self.runSingleLinearRegression(algorithm, max_mse=1e-8)
def main(num_samples=50, points_per_dimension=20):
X, y = datasets.make_classification(n_samples=num_samples,
n_features=2,
n_informative=2,
n_redundant=0,
n_clusters_per_class=2,
flip_y=0.1)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
logistic_reg = LogisticRegression(optimizer=GradientDescent(
num_iterations=20000))
logistic_reg.fit(X_train, y_train)
decision_boundary_graph(X_test,
y_test,
logistic_reg,
"Logistic Regression",
points_per_dimension=points_per_dimension)
if svm_able_to_run:
logistic_reg = SVM(Kernel.linear_kernel(), C=1)
logistic_reg.fit(X_train, y_train)
decision_boundary_graph(X_test,
y_test,
logistic_reg,
"SVM - Linear Kernel",
points_per_dimension=points_per_dimension)
logistic_reg = SVM(Kernel.gaussian_kernel(sigma=2), C=1)
logistic_reg.fit(X_train, y_train)
decision_boundary_graph(X_test,
y_test,
logistic_reg,
"SVM - Gaussian Kernel",
points_per_dimension=points_per_dimension)
else:
print("WARNING: cvxopt not installed, SVM will not work.")
logistic_reg = KNN_Classification(k=1)
logistic_reg.fit(X, y)
logistic_reg2 = KNN_Classification(k=3)
logistic_reg2.fit(X, y)
decision_boundary_graph(X_test,
y_test,
logistic_reg,
"KNN K=1",
points_per_dimension=points_per_dimension)
decision_boundary_graph(X_test,
y_test,
logistic_reg2,
"KKN K=3",
points_per_dimension=points_per_dimension)
def __init__(self,
optimizer=None,
eps=1e-3,
acceptable_diff=1e-6,
print_out_diff_gradient=True):
if optimizer is None:
optimizer = GradientDescent()
self._optimizer = optimizer
self._print_out_diff_gradient = print_out_diff_gradient
self._eps = eps
self._acceptable_diff = acceptable_diff
def main(num_iterations=200, iterations_per_update=20):
# Just has one feature to make it easy to graph.
X, y = datasets.make_classification(n_samples=200,
n_features=1,
n_informative=1,
n_redundant=0,
n_clusters_per_class=1,
flip_y=0.1)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
logistic_reg = LogisticRegression(optimizer=OptimizerCostGraph(
GradientDescent(num_iterations=num_iterations),
iterations_per_update=iterations_per_update))
logistic_reg.fit(X_train, y_train)
def main():
# Just has one feature to make it easy to graph.
X, y = datasets.make_classification(n_samples=200, n_features=1, n_informative=1, n_redundant=0,
n_clusters_per_class=1, flip_y=0.1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_proportion=0.2)
logistic_reg = LogisticRegression(optimizer=GradientDescent(num_iterations=20000))
logistic_reg.fit(X_train, y_train)
y_pred_probability = logistic_reg.predict(X_test)
mse = mean_square_error(y_pred_probability, y_test)
logistic_reg.set_classification_boundary(0.5)
y_pred_classified = logistic_reg.predict(X_test)
acc = accuracy(y_pred_classified, y_test)
plt.figure()
plt.scatter(X_test, y_test, color="Black", label="Actual")
plt.scatter(X_test, y_pred_probability, color="Red", label="Classification Probability")
plt.scatter(X_test, y_pred_classified, color="Blue", label="Rounded Prediction")
plt.legend(loc='center right', fontsize=8)
plt.title("Logistic Regression %.2f MSE, %.2f%% Accuracy)" % (mse, acc*100))
plt.show()
def main():
# Just has one feature to make it easy to graph.
X, y = datasets.make_regression(n_samples=200, n_features=1,
bias=random.uniform(-10, 10), noise=5)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_proportion=0.2)
linear_reg = LinearRegression()
linear_reg.fit(X_train, y_train)
y_pred = linear_reg.predict(X_test)
mse = mean_square_error(y_pred, y_test)
linear_reg_w_grad_desc = LinearRegression(optimizer=GradientDescent(num_iterations=2500))
linear_reg_w_grad_desc.fit(X_train, y_train)
y_pred_w_grad_desc = linear_reg_w_grad_desc.predict(X_test)
mse_w_grad_desc = mean_square_error(y_pred_w_grad_desc, y_test)
plt.figure()
plt.scatter(X_test, y_test, color="Black", label="Actual")
plt.plot(X_test, y_pred, label="Estimate")
plt.plot(X_test, y_pred_w_grad_desc, label="Estimate using Optimizer")
plt.legend(loc='lower right', fontsize=8)
plt.title("Linear Regression %.2f MSE Normal Eq, %.2f MSE Gradient Descent)" % (mse, mse_w_grad_desc))
plt.show()
def CreateDefaultLogisticRegression():
return LogisticRegression(GradientDescent())
def _createLogisticRegression(self):
return LogisticRegression(NumericGradientChecker(GradientDescent()))