dataset = "scene"
# dataset = "yeast"
if dataset == "yeast":
yeast = fetch_mldata("yeast")
X = yeast.data
X = np.hstack([X, np.ones((X.shape[0], 1))])
y = yeast.target.toarray().astype(np.int).T
X_train, X_test = X[:1500], X[1500:]
y_train, y_test = y[:1500], y[1500:]
else:
scene = load_scene()
X_train, X_test = scene['X_train'], scene['X_test']
y_train, y_test = scene['y_train'], scene['y_test']
n_labels = y_train.shape[1]
full = np.vstack([x for x in itertools.combinations(range(n_labels), 2)])
tree = chow_liu_tree(y_train)
full_model = MultiLabelClf(edges=full, inference_method='qpbo')
independent_model = MultiLabelClf(inference_method='unary')
tree_model = MultiLabelClf(edges=tree, inference_method="max-product")
full_ssvm = OneSlackSSVM(full_model, inference_cache=50, C=.1, tol=0.01)
tree_ssvm = OneSlackSSVM(tree_model, inference_cache=50, C=.1, tol=0.01)
dataset = "scene"
# dataset = "yeast"
if dataset == "yeast":
yeast = fetch_mldata("yeast")
X = yeast.data
X = np.hstack([X, np.ones((X.shape[0], 1))])
y = yeast.target.toarray().astype(np.int).T
X_train, X_test = X[:1500], X[1500:]
y_train, y_test = y[:1500], y[1500:]
else:
scene = load_scene()
X_train, X_test = scene['X_train'], scene['X_test']
y_train, y_test = scene['y_train'], scene['y_test']
n_labels = y_train.shape[1]
full = np.vstack([x for x in itertools.combinations(range(n_labels), 2)])
tree = chow_liu_tree(y_train)
#tree_model = MultiLabelClf(edges=tree, inference_method=('ogm', {'alg': 'dyn'}))
tree_model = MultiLabelClf(edges=tree, inference_method='max-product')
tree_ssvm = OneSlackSSVM(tree_model, inference_cache=50, C=.1, tol=0.01)
print("fitting tree model...")
tree_ssvm.fit(X_train, y_train)
def test_dataset_loading():
# test that we can read the datasets.
load_scene()
load_letters()
load_snakes()
