"""A standard machine learning task using sacred's magic."""
from sacred import Experiment
from sacred.observers import FileStorageObserver
from sklearn import svm, datasets, model_selection
ex = Experiment("svm")
ex.observers.append(FileStorageObserver("my_runs"))
@ex.config # Configuration is defined through local variables.
def cfg():
C = 1.0
gamma = 0.7
kernel = "rbf"
seed = 42
@ex.capture
def get_model(C, gamma, kernel):
return svm.SVC(C=C, kernel=kernel, gamma=gamma)
@ex.automain # Using automain to enable command line integration.
def run():
X, y = datasets.load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.2
)
clf = get_model() # Parameters are injected automatically.
clf.fit(X_train, y_train)
return clf.score(X_test, y_test)
