def test_plot_estimator_and_lightgbm(tmpdir):
pytest.importorskip('graphviz')
lightgbm = pytest.importorskip('lightgbm')
from pygbm.plotting import plot_tree
n_classes = 3
X, y = make_classification(n_samples=150,
n_classes=n_classes,
n_features=5,
n_informative=3,
n_redundant=0,
random_state=0)
n_trees = 3
est_pygbm = GradientBoostingClassifier(max_iter=n_trees,
n_iter_no_change=None)
est_pygbm.fit(X, y)
est_lightgbm = lightgbm.LGBMClassifier(n_estimators=n_trees)
est_lightgbm.fit(X, y)
n_total_trees = n_trees * n_classes
for i in range(n_total_trees):
filename = tmpdir.join('plot_mixed_predictors.pdf')
plot_tree(est_pygbm,
est_lightgbm=est_lightgbm,
tree_index=i,
view=False,
filename=filename)
assert filename.exists()
def test_plot_grower(tmpdir, classification_data):
pytest.importorskip('graphviz')
from pygbm.plotting import plot_tree
dataset = Dataset(classification_data[0], classification_data[1])
n_trees_per_iteration = 1
loss = BinaryCrossEntropy()
clf = GradientBoostingClassifier()
gradients, hessians = loss.init_gradients_and_hessians(
n_samples=dataset.shape[0], prediction_dim=n_trees_per_iteration)
y = clf._encode_y(dataset.y)
baseline_prediction_ = loss.get_baseline_prediction(y, 1)
raw_predictions = np.zeros(shape=(dataset.shape[0], n_trees_per_iteration),
dtype=baseline_prediction_.dtype)
raw_predictions += baseline_prediction_
loss.update_gradients_and_hessians(gradients, hessians, y, raw_predictions)
options = OptionSet(clf.parameter_dict)
options['max_leaf_nodes'] = 5
grower = TreeGrower(dataset, gradients, hessians, options)
grower.grow()
filename = tmpdir.join('plot_grower.pdf')
plot_tree(grower, view=False, filename=filename)
assert filename.exists()
def test_plot_estimator(tmpdir):
pytest.importorskip('graphviz')
from pygbm.plotting import plot_tree
n_trees = 3
est = GradientBoostingRegressor(max_iter=n_trees)
est.fit(X, y)
for i in range(n_trees):
filename = tmpdir.join('plot_predictor.pdf')
plot_tree(est, tree_index=i, view=False, filename=filename)
assert filename.exists()
def test_plot_grower(tmpdir):
pytest.importorskip('graphviz')
from pygbm.plotting import plot_tree
X_binned = BinMapper().fit_transform(X)
gradients = np.asarray(y, dtype=np.float32).copy()
hessians = np.ones(1, dtype=np.float32)
grower = TreeGrower(X_binned, gradients, hessians, max_leaf_nodes=5)
grower.grow()
filename = tmpdir.join('plot_grower.pdf')
plot_tree(grower, view=False, filename=filename)
assert filename.exists()
def test_plot_estimator_and_lightgbm(tmpdir):
pytest.importorskip('graphviz')
lightgbm = pytest.importorskip('lightgbm')
from pygbm.plotting import plot_tree
n_trees = 3
est_pygbm = GradientBoostingMachine(max_iter=n_trees)
est_pygbm.fit(X, y)
est_lightgbm = lightgbm.LGBMRegressor(n_estimators=n_trees)
est_lightgbm.fit(X, y)
for i in range(n_trees):
filename = tmpdir.join('plot_mixed_predictors.pdf')
plot_tree(est_pygbm,
est_lightgbm=est_lightgbm,
tree_index=i,
view=False,
filename=filename)
assert filename.exists()
from pygbm.binning import BinMapper
from pygbm.grower import TreeGrower
from pygbm import plotting
rng = np.random.RandomState(0)
n_samples = int(1e7)
n_leaf_nodes = 5
X, y = make_classification(n_samples=n_samples,
n_classes=2,
n_features=5,
n_informative=3,
n_redundant=0,
random_state=rng)
bin_mapper_ = BinMapper(random_state=rng)
X_binned = bin_mapper_.fit_transform(X)
gradients = np.asarray(y, dtype=np.float32).copy()
hessians = np.ones(1, dtype=np.float32)
# First run to trigger the compilation of numba jit methods to avoid recording
# the compiler overhead in the profile report.
TreeGrower(X_binned, gradients, hessians, max_leaf_nodes=n_leaf_nodes).grow()
# New run with to collect timing statistics that will be included in the plot.
grower = TreeGrower(X_binned, gradients, hessians, max_leaf_nodes=n_leaf_nodes)
grower.grow()
plotting.plot_tree(grower)