def initializeDataMaps(X, y, X_map, y_map):
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
X_map['train'] = X_train
y_map['train'] = y_train
X_map['test'] = X_test
y_map['test'] = y_test
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 main(should_print_tree=False):
X, y = create_1d_categorical_feature_regression()
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
# Can't run CART with regression when using categorical variables
train_and_run_dtree(DecisionTreeRegression(algorithm_to_use='ID3'),
X_train, X_test, y_train, y_test,
'Decision Tree ID3 (MSE {:.2f})', should_print_tree)
def linearly_separable():
X, y = create_linearly_separable_two_class()
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
classifier = SVM(Kernel.gaussian_kernel(sigma=1))
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
acc = accuracy(y_pred, y_test)
class_estimation_graph(
2, X_test, y_test, y_pred,
"SVM linear %.2f%% Accuracy on Linearly Separable" % (acc * 100))
def main(should_print_tree=False):
X, y = create_2d_categorical_feature_two_class()
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
train_and_run_dtree(DecisionTreeClassifier(algorithm_to_use='ID3'),
X_train, X_test, y_train, y_test,
'Decision Tree ID3 (accuracy {:.2f}%)',
should_print_tree)
train_and_run_dtree(DecisionTreeClassifier(algorithm_to_use='CART'),
X_train, X_test, y_train, y_test,
'Decision Tree CART (accuracy {:.2f}%)',
should_print_tree)
def main():
n_classes = 4
# Just has one feature to make it easy to graph.
X, y = datasets.make_classification(n_samples=200, n_features=2, n_informative=2, n_redundant=0,
n_clusters_per_class=1, flip_y=0.1, n_classes=n_classes)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_proportion=0.2)
k=4
classifier = KNN_Classification(k=k)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
acc = accuracy(y_pred, y_test)
class_estimation_graph(n_classes, X_test, y_test, y_pred,
"KNN %.2f%% Accuracy" % (acc*100))
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 with_data_error_force_accurate():
n_classes = 2
# Just has one feature to make it easy to graph.
X, y = datasets.make_classification(n_samples=200,
n_features=2,
n_informative=2,
n_redundant=0,
n_clusters_per_class=1,
flip_y=0.1,
n_classes=n_classes)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
classifier = SVM(Kernel.linear_kernel())
try:
classifier.fit(X_train, y_train)
raise RuntimeError("Should not have successfully fit the model")
except AssertionError as e:
# We expect an AssertionError since the problem is non-separaable.
pass
def with_data_error_with_slack():
n_classes = 2
# Just has one feature to make it easy to graph.
X, y = datasets.make_classification(n_samples=200,
n_features=2,
n_informative=2,
n_redundant=0,
n_clusters_per_class=2,
flip_y=0.1,
n_classes=n_classes)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
classifier = SVM(Kernel.gaussian_kernel(sigma=1), C=1)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
acc = accuracy(y_pred, y_test)
class_estimation_graph(n_classes, X_test, y_test, y_pred,
"SVM linear %.2f%% Accuracy" % (acc * 100))
def main(_=None):
# 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 = LogisticRegressionTF()
logistic_reg.fit(X_train, y_train)
y_pred_probability = logistic_reg.predict(X_test)
y_pred_probability = np.squeeze(y_pred_probability)
mse = mean_square_error(y_pred_probability, y_test)
logistic_reg.set_classification_boundary(0.5)
y_pred_classified = logistic_reg.predict(X_test)
y_pred_classified = np.squeeze(y_pred_classified)
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(_=None):
# 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 = LinearRegressionTF()
linear_reg.fit(X_train, y_train)
y_pred = linear_reg.predict(X_test)
y_pred = np.squeeze(y_pred)
mse = mean_square_error(y_pred, y_test)
plt.figure()
plt.scatter(X_test, y_test, color="Black", label="Actual")
plt.plot(X_test, y_pred, label="Estimate")
plt.legend(loc='lower right', fontsize=8)
plt.title("Linear Regression %.2f MSE)" % (mse))
plt.show()
def main():
n_classes = 4
# Just has one feature to make it easy to graph.
X, y = datasets.make_classification(n_samples=200,
n_features=2,
n_informative=2,
n_redundant=0,
n_clusters_per_class=1,
flip_y=0.1,
n_classes=n_classes)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
logistic_reg = OneVsAllClassification(CreateDefaultLogisticRegression)
logistic_reg.fit(X_train, y_train)
y_pred = logistic_reg.predict(X_test)
acc = accuracy(y_pred, y_test)
class_estimation_graph(
n_classes, X_test, y_test, y_pred,
"Logistic Regression %.2f%% Accuracy.\nShape is true class, color is estimate"
% (acc * 100))
def main():
# Just using one feature to make it graphable
X, y = datasets.make_regression(n_samples=200,
n_features=1,
bias=150,
noise=4)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
reg = KNN_Regression(k=4)
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)
mse = mean_square_error(y_pred, y_test)
plt.scatter(X_test, y_test, color="Black", label="Actual")
plt.scatter(X_test, y_pred, color="Red", label="Prediction")
plt.legend(loc='lower right', fontsize=8)
plt.title("KNN Regression (%.2f MSE)" % mse)
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()
"""
self.fit(X, y)
return self.transform(X)
if __name__ == "__main__":
# Just include one relevant feature, which we will graph upon.
# Given far too many features with not enough samples, so will often
# overfit when not pruning.
X, y = datasets.make_regression(n_samples=100,
n_features=30,
n_informative=1,
noise=5)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_proportion=0.2)
# Without any pruning
reg_orig = linear_regression.LinearRegression()
reg_orig.fit(X_train, y_train)
y_pred_orig = reg_orig.predict(X_test)
orig_mse = mean_square_error(y_pred_orig, y_test)
# Setup pruner and prune the # of feautres features down to 1
pruner = FeaturePruner(linear_regression.LinearRegression(), 1)
X_train_pruned = pruner.fit_transform(X_train, y_train)
X_test_pruned = pruner.transform(X_test)
reg_pruned = linear_regression.LinearRegression()
reg_pruned.fit(X_train_pruned, y_train)