def run(*args):
config = copy(CLASSIFIER_CONFIG)
for conf in args:
config[conf[0]] = conf[1]
data_set = data_manager.load_data_set(config.data_set)
classifier_scores = []
for nrun in range(config.runs):
X = data_set.data
y = data_set.target
class_indices = list(set(y))
if config.training_test_split:
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33)
else:
X_train = X_test = X
y_train = y_test = y
clf = acoc.PolyACO(X_train.shape[1], class_indices, config)
clf.train(X_train, y_train)
predictions = clf.evaluate(X_test)
classifier_scores.append(acoc.compute_score(predictions, y_test))
print("\nRun {}/{} score: {:.4f}".format(nrun + 1, config.runs,
classifier_scores[-1]))
return classifier_scores
def run(**kwargs):
conf = copy(CLASSIFIER_CONFIG)
for k, v in kwargs.items():
conf[k] = v
data_set = data_manager.load_data_set(conf.data_set)
X = data_set.data
y = data_set.target
class_indices = list(set(y))
# Split data into training and testing set
if conf.training_test_split:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
else:
X_train = X_test = X
y_train = y_test = y
clf = acoc.PolyACO(X.shape[1], class_indices, save_folder=SAVE_FOLDER)
clf.train(X_train, y_train, start_time=conf.start_time)
predictions = clf.evaluate(X_test)
return acoc.compute_score(predictions, y_test)
]
if len(vertices_in_column) > 0:
next_north = min(vertices_in_column, key=lambda pos: pos.y)
edge = Edge(vertex, next_north, tau_initial)
edges.append(edge)
return edges
if __name__ == "__main__":
from acoc.acoc_plotter import plot_pheromones, plot_matrix_and_data
from utils import generate_folder_name
plot = True
save = True
show = False
data_set = data_manager.load_data_set('breast_cancer')
data = data_set.data[:, :2]
target = np.array([data_set.target]).T
np.place(target, target == 4, 1)
np.place(target, target == 2, 0)
dt = np.concatenate((data, target), axis=1)
# dt = np.array([[0, 0.02, 0],
# [0.05, 0, 1],
# [1, 1, 1]])
mtrx = AcocMatrix(dt)
pol = mtrx.edges[:4]
mtrx.edges[1].pheromone_strength = 5
mtrx.edges[0].pheromone_strength = 3
save_folder = generate_folder_name()
vertices_in_column = [p for p in vertices if p.y > vertex.y and p.x == vertex.x]
if len(vertices_in_column) > 0:
next_north = min(vertices_in_column, key=lambda pos: pos.y)
edge = Edge(vertex, next_north, tau_initial)
edges.append(edge)
return edges
if __name__ == "__main__":
from acoc.acoc_plotter import plot_pheromones, plot_matrix_and_data
from utils import generate_folder_name
plot = True
save = True
show = False
data_set = data_manager.load_data_set('breast_cancer')
data = data_set.data[:, :2]
target = np.array([data_set.target]).T
np.place(target, target == 4, 1)
np.place(target, target == 2, 0)
dt = np.concatenate((data, target), axis=1)
# dt = np.array([[0, 0.02, 0],
# [0.05, 0, 1],
# [1, 1, 1]])
mtrx = AcocMatrix(dt)
pol = mtrx.edges[:4]
mtrx.edges[1].pheromone_strength = 5
mtrx.edges[0].pheromone_strength = 3
save_folder = generate_folder_name()