def experiment_3(train_seasons, test_seasons):
""" Experiment #3: Use spatial, position, and shot type features """
base_positions = ['G', 'F', 'C']
base_shot_types = ['3pt','fade away', 'hook', 'layup', 'jump', 'dunk']
pos_and_shot_type = base_positions + base_shot_types
# load training data
train_features, train_labels = load_seasons(train_seasons, attributes=pos_and_shot_type, with_attributes_flag=True)
# load test data
test_features, test_labels = load_seasons(test_seasons, attributes=pos_and_shot_type, with_attributes_flag=True)
###### non-parametric models #####
# Logistic Regression
log_reg = LogisticRegression()
log_reg.train(train_features, train_labels)
log_reg.test(test_features, test_labels)
def experiment_2(train_seasons, test_seasons):
""" Experiment #2: Use spatial and position features for prediction """
positions = ['G', 'F', 'C']
for position in positions:
# load training data
made_data, missed_data = load_seasons(seasons=train_seasons,
split_flag=True,
attributes=[position])
train_features, train_labels = load_seasons(seasons=train_seasons,
attributes=[position])
# load test data
test_features, test_labels = load_seasons(seasons=test_seasons,
attributes=[position])
output_directory = "./position_graphs/%s/" % (position)
###### non-parametric models #####
# Logistic Regression
log_reg = LogisticRegression()
log_reg.train(train_features, train_labels)
log_reg.test(test_features, test_labels)
log_reg.visualize(output_dir=output_directory)
# k-Nearest Neighbors
num_of_neighbors = 5
kNN_model = kNN(num_of_neighbors)
kNN_model.train(train_features, train_labels)
kNN_model.test(test_features, test_labels)
kNN_model.visualize(output_dir=output_directory)
###### parametric models #####
# 2-D Gaussian Classifier
gauss_model = Gaussian2DClassifier()
gauss_model.train(made_data, missed_data)
gauss_model.test(test_features, test_labels)
gauss_model.visualize(output_dir=output_directory)
# Mixture of 2-D Gaussians Classifier
num_of_mixtures = 3
gauss_mixture_model = GaussianMixtureClassifier(num_of_mixtures)
gauss_mixture_model.train(made_data, missed_data, 10)
gauss_mixture_model.test(test_features, test_labels)
gauss_mixture_model.visualize(output_dir=output_directory)
def experiment_3(train_seasons, test_seasons):
""" Experiment #3: Use spatial, position, and shot type features """
base_positions = ['G', 'F', 'C']
base_shot_types = ['3pt', 'fade away', 'hook', 'layup', 'jump', 'dunk']
pos_and_shot_type = base_positions + base_shot_types
# load training data
train_features, train_labels = load_seasons(train_seasons,
attributes=pos_and_shot_type,
with_attributes_flag=True)
# load test data
test_features, test_labels = load_seasons(test_seasons,
attributes=pos_and_shot_type,
with_attributes_flag=True)
###### non-parametric models #####
# Logistic Regression
log_reg = LogisticRegression()
log_reg.train(train_features, train_labels)
log_reg.test(test_features, test_labels)
def experiment_2(train_seasons, test_seasons):
""" Experiment #2: Use spatial and position features for prediction """
positions = ['G', 'F', 'C']
for position in positions:
# load training data
made_data, missed_data = load_seasons(seasons=train_seasons, split_flag=True, attributes=[position])
train_features, train_labels = load_seasons(seasons=train_seasons, attributes=[position])
# load test data
test_features, test_labels = load_seasons(seasons=test_seasons, attributes=[position])
output_directory = "./position_graphs/%s/"%(position)
###### non-parametric models #####
# Logistic Regression
log_reg = LogisticRegression()
log_reg.train(train_features, train_labels)
log_reg.test(test_features, test_labels)
log_reg.visualize(output_dir=output_directory)
# k-Nearest Neighbors
num_of_neighbors = 5
kNN_model = kNN(num_of_neighbors)
kNN_model.train(train_features, train_labels)
kNN_model.test(test_features, test_labels)
kNN_model.visualize(output_dir=output_directory)
###### parametric models #####
# 2-D Gaussian Classifier
gauss_model = Gaussian2DClassifier()
gauss_model.train(made_data, missed_data)
gauss_model.test(test_features, test_labels)
gauss_model.visualize(output_dir=output_directory)
# Mixture of 2-D Gaussians Classifier
num_of_mixtures = 3
gauss_mixture_model = GaussianMixtureClassifier(num_of_mixtures)
gauss_mixture_model.train(made_data, missed_data, 10)
gauss_mixture_model.test(test_features, test_labels)
gauss_mixture_model.visualize(output_dir=output_directory)
def experiment_1(train_seasons, test_seasons):
""" Experiment #1: Use only spatial features for prediction """
# load training data
made_data, missed_data = load_seasons(train_seasons, split_flag=True)
train_features, train_labels = load_seasons(train_seasons)
# load test data
test_features, test_labels = load_seasons(test_seasons)
###### non-parametric models #####
# Logistic Regression
log_reg = LogisticRegression()
log_reg.train(train_features, train_labels)
log_reg.test(test_features, test_labels)
log_reg.visualize()
# k-Nearest Neighbors
num_of_neighbors = 5
kNN_model = kNN(num_of_neighbors)
kNN_model.train(train_features, train_labels)
kNN_model.test(test_features, test_labels)
kNN_model.visualize()
###### parametric models #####
# 2-D Gaussian Classifier
gauss_model = Gaussian2DClassifier()
gauss_model.train(made_data, missed_data)
gauss_model.test(test_features, test_labels)
gauss_model.visualize()
# Mixture of 2-D Gaussians Classifier
num_of_mixtures = 3
gauss_mixture_model = GaussianMixtureClassifier(num_of_mixtures)
gauss_mixture_model.train(made_data, missed_data, 10)
gauss_mixture_model.test(test_features, test_labels)
gauss_mixture_model.visualize()
def experiment_1(train_seasons, test_seasons):
""" Experiment #1: Use only spatial features for prediction """
# load training data
made_data, missed_data = load_seasons(train_seasons, split_flag=True)
train_features, train_labels = load_seasons(train_seasons)
# load test data
test_features, test_labels = load_seasons(test_seasons)
###### non-parametric models #####
# Logistic Regression
log_reg = LogisticRegression()
log_reg.train(train_features, train_labels)
log_reg.test(test_features, test_labels)
log_reg.visualize()
# k-Nearest Neighbors
num_of_neighbors = 5
kNN_model = kNN(num_of_neighbors)
kNN_model.train(train_features, train_labels)
kNN_model.test(test_features, test_labels)
kNN_model.visualize()
###### parametric models #####
# 2-D Gaussian Classifier
gauss_model = Gaussian2DClassifier()
gauss_model.train(made_data, missed_data)
gauss_model.test(test_features, test_labels)
gauss_model.visualize()
# Mixture of 2-D Gaussians Classifier
num_of_mixtures = 3
gauss_mixture_model = GaussianMixtureClassifier(num_of_mixtures)
gauss_mixture_model.train(made_data, missed_data, 10)
gauss_mixture_model.test(test_features, test_labels)
gauss_mixture_model.visualize()
