def test_early_stopping_classification(data, scoring, validation_split,
n_iter_no_change, tol):
max_iter = 500
X, y = data
if validation_split is not None:
X, X_test, y, y_test = train_test_split(X,
y,
test_size=validation_split,
random_state=42)
eval_set = (X_test, y_test)
else:
eval_set = None
gb = GradientBoostingClassifier(
verbose=True, # just for coverage
scoring=scoring,
tol=tol,
max_iter=max_iter,
n_iter_no_change=n_iter_no_change,
random_state=0)
gb.fit(X, y, eval_set=eval_set)
if n_iter_no_change != -1:
assert n_iter_no_change <= gb.n_iter_ < max_iter
else:
assert gb.n_iter_ == max_iter
def test_early_stopping_loss(n_samples, max_iter, n_iter_no_change, tree_type):
# Make sure that when scoring is None, the early stopping is done w.r.t to
# the loss. Using scoring='neg_log_loss' and scoring=None should be
# equivalent since the loss is precisely the negative log likelihood
X, y = make_classification(n_samples, random_state=0)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1)
clf_scoring = GradientBoostingClassifier(max_iter=max_iter,
scoring='neg_log_loss',
n_iter_no_change=n_iter_no_change,
tol=1e-4,
verbose=True,
random_state=0,
tree_type=tree_type)
clf_scoring.fit(X, y, eval_set=(X_val, y_val))
clf_loss = GradientBoostingClassifier(max_iter=max_iter,
scoring=None,
n_iter_no_change=n_iter_no_change,
tol=1e-4,
verbose=True,
random_state=0,
tree_type=tree_type)
clf_loss.fit(X, y, eval_set=(X_val, y_val))
assert n_iter_no_change < clf_loss.n_iter_ < max_iter
assert clf_loss.n_iter_ == clf_scoring.n_iter_
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_early_stopping_loss():
# Make sure that when scoring is None, the early stopping is done w.r.t to
# the loss. Using scoring='neg_log_loss' and scoring=None should be
# equivalent since the loss is precisely the negative log likelihood
n_samples = int(1e3)
max_iter = 100
n_iter_no_change = 5
X, y = make_classification(n_samples, random_state=0)
clf_scoring = GradientBoostingClassifier(max_iter=max_iter,
scoring='neg_log_loss',
validation_split=.1,
n_iter_no_change=n_iter_no_change,
tol=1e-4,
verbose=1,
random_state=0)
clf_scoring.fit(X, y)
clf_loss = GradientBoostingClassifier(max_iter=max_iter,
scoring=None,
validation_split=.1,
n_iter_no_change=n_iter_no_change,
tol=1e-4,
verbose=1,
random_state=0)
clf_loss.fit(X, y)
assert n_iter_no_change < clf_loss.n_iter_ < max_iter
assert clf_loss.n_iter_ == clf_scoring.n_iter_
def test_same_predictions_classification(seed, min_samples_leaf, n_samples,
max_leaf_nodes):
# Same as test_same_predictions_regression but for classification
rng = np.random.RandomState(seed=seed)
n_samples = n_samples
max_iter = 1
max_bins = 256
X, y = make_classification(n_samples=n_samples,
n_classes=2,
n_features=5,
n_informative=5,
n_redundant=0,
random_state=0)
if n_samples > 255:
# bin data and convert it to float32 so that the estimator doesn't
# treat it as pre-binned
X = BinMapper(max_bins=max_bins).fit_transform(X).astype(np.float32)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
est_pygbm = GradientBoostingClassifier(loss='binary_crossentropy',
max_iter=max_iter,
max_bins=max_bins,
learning_rate=1,
n_iter_no_change=None,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes)
est_lightgbm = get_lightgbm_estimator(est_pygbm)
est_lightgbm.fit(X_train, y_train)
est_pygbm.fit(X_train, y_train)
# We need X to be treated an numerical data, not pre-binned data.
X_train, X_test = X_train.astype(np.float32), X_test.astype(np.float32)
pred_lightgbm = est_lightgbm.predict(X_train)
pred_pygbm = est_pygbm.predict(X_train)
assert np.mean(pred_pygbm == pred_lightgbm) > .89
acc_lgbm = accuracy_score(y_train, pred_lightgbm)
acc_pygbm = accuracy_score(y_train, pred_pygbm)
np.testing.assert_almost_equal(acc_lgbm, acc_pygbm)
if max_leaf_nodes < 10 and n_samples >= 1000:
pred_lightgbm = est_lightgbm.predict(X_test)
pred_pygbm = est_pygbm.predict(X_test)
assert np.mean(pred_pygbm == pred_lightgbm) > .89
acc_lgbm = accuracy_score(y_test, pred_lightgbm)
acc_pygbm = accuracy_score(y_test, pred_pygbm)
np.testing.assert_almost_equal(acc_lgbm, acc_pygbm, decimal=2)
def test_same_predictions_classification(seed, min_samples_leaf, n_samples,
max_leaf_nodes):
# Same as test_same_predictions_regression but for classification
rng = np.random.RandomState(seed=seed)
n_samples = n_samples
max_iter = 1
max_bins = 256
X, y = make_classification(n_samples=n_samples,
n_classes=2,
n_features=5,
n_informative=5,
n_redundant=0,
random_state=0)
if n_samples > 255:
X = BinMapper(max_bins=max_bins).fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
est_pygbm = GradientBoostingClassifier(loss='binary_crossentropy',
max_iter=max_iter,
max_bins=max_bins,
learning_rate=1,
validation_split=None,
scoring=None,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes)
est_lightgbm = get_lightgbm_estimator(est_pygbm)
est_lightgbm.fit(X_train, y_train)
est_pygbm.fit(X_train, y_train)
pred_lightgbm = est_lightgbm.predict(X_train)
pred_pygbm = est_pygbm.predict(X_train)
assert np.mean(pred_pygbm == pred_lightgbm) > .89
acc_lgbm = accuracy_score(y_train, pred_lightgbm)
acc_pygbm = accuracy_score(y_train, pred_pygbm)
np.testing.assert_almost_equal(acc_lgbm, acc_pygbm)
if max_leaf_nodes < 10 and n_samples >= 1000:
pred_lightgbm = est_lightgbm.predict(X_test)
pred_pygbm = est_pygbm.predict(X_test)
assert np.mean(pred_pygbm == pred_lightgbm) > .89
acc_lgbm = accuracy_score(y_test, pred_lightgbm)
acc_pygbm = accuracy_score(y_test, pred_pygbm)
np.testing.assert_almost_equal(acc_lgbm, acc_pygbm, decimal=2)
def test_early_stopping_classification(data, scoring, validation_split, tol):
max_iter = 500
n_iter_no_change = 5
X, y = data
gb = GradientBoostingClassifier(
verbose=1, # just for coverage
scoring=scoring,
tol=tol,
validation_split=validation_split,
max_iter=max_iter,
n_iter_no_change=n_iter_no_change,
random_state=0)
gb.fit(X, y)
if scoring is not None:
assert n_iter_no_change <= gb.n_iter_ < max_iter
else:
assert gb.n_iter_ == max_iter
data_train, target_train = data_train[:subsample], target_train[:subsample]
n_samples, n_features = data_train.shape
print(f"Training set with {n_samples} records with {n_features} features.")
print("JIT compiling code for the pygbm model...")
tic = time()
pygbm_model = GradientBoostingClassifier(learning_rate=lr,
max_iter=1,
max_bins=max_bins,
max_leaf_nodes=n_leaf_nodes,
random_state=0,
scoring=None,
verbose=0,
validation_split=None)
pygbm_model.fit(data_train[:100], target_train[:100])
pygbm_model.predict(data_train[:100]) # prediction code is also jitted
toc = time()
print(f"done in {toc - tic:.3f}s")
print("Fitting a pygbm model...")
tic = time()
pygbm_model = GradientBoostingClassifier(loss='binary_crossentropy',
learning_rate=lr,
max_iter=n_trees,
max_bins=max_bins,
max_leaf_nodes=n_leaf_nodes,
random_state=0,
scoring=None,
verbose=1,
validation_split=None)
def test_same_predictions_multiclass_classification(seed, min_samples_leaf,
n_samples, max_leaf_nodes):
# Same as test_same_predictions_regression but for classification
rng = np.random.RandomState(seed=seed)
n_samples = n_samples
max_iter = 1
max_bins = 256
lr = 1
X, y = make_classification(n_samples=n_samples,
n_classes=3,
n_features=5,
n_informative=5,
n_redundant=0,
n_clusters_per_class=1,
random_state=0)
if n_samples > 255:
X = BinMapper(max_bins=max_bins).fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
est_pygbm = GradientBoostingClassifier(loss='categorical_crossentropy',
max_iter=max_iter,
max_bins=max_bins,
learning_rate=lr,
validation_split=None,
scoring=None,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes)
est_lightgbm = get_lightgbm_estimator(est_pygbm)
est_lightgbm.fit(X_train, y_train)
est_pygbm.fit(X_train, y_train)
pred_lightgbm = est_lightgbm.predict(X_train)
pred_pygbm = est_pygbm.predict(X_train)
assert np.mean(pred_pygbm == pred_lightgbm) > .89
proba_lightgbm = est_lightgbm.predict_proba(X_train)
proba_pygbm = est_pygbm.predict_proba(X_train)
# assert more than 75% of the predicted probabilities are the same up to
# the second decimal
assert np.mean(np.abs(proba_lightgbm - proba_pygbm) < 1e-2) > .75
acc_lgbm = accuracy_score(y_train, pred_lightgbm)
acc_pygbm = accuracy_score(y_train, pred_pygbm)
np.testing.assert_almost_equal(acc_lgbm, acc_pygbm, decimal=2)
if max_leaf_nodes < 10 and n_samples >= 1000:
pred_lightgbm = est_lightgbm.predict(X_test)
pred_pygbm = est_pygbm.predict(X_test)
assert np.mean(pred_pygbm == pred_lightgbm) > .89
proba_lightgbm = est_lightgbm.predict_proba(X_train)
proba_pygbm = est_pygbm.predict_proba(X_train)
# assert more than 75% of the predicted probabilities are the same up
# to the second decimal
assert np.mean(np.abs(proba_lightgbm - proba_pygbm) < 1e-2) > .75
acc_lgbm = accuracy_score(y_test, pred_lightgbm)
acc_pygbm = accuracy_score(y_test, pred_pygbm)
np.testing.assert_almost_equal(acc_lgbm, acc_pygbm, decimal=2)
rng = np.random.RandomState(0)
n_samples = int(1e6)
X, y = make_classification(n_samples, random_state=rng)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
print('Early stopping on held-out validation data')
clf = GradientBoostingClassifier(max_iter=100,
scoring='neg_log_loss',
validation_split=.1,
n_iter_no_change=5,
tol=1e-4,
verbose=1,
random_state=rng)
clf.fit(X_train, y_train)
print(f'Early stopped at iteration {clf.n_iter_}')
print(f'Mean accuracy: {clf.score(X_test, y_test)}')
print('Early stopping on training data')
clf = GradientBoostingClassifier(max_iter=100,
scoring='neg_log_loss',
validation_split=None,
n_iter_no_change=5,
tol=1e-4,
verbose=1,
random_state=rng)
clf.fit(X_train, y_train)
print(f'Early stopped at iteration {clf.n_iter_}')
print(f'Mean accuracy: {clf.score(X_test, y_test)}')