def test_boston_dataset(max_bins):
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(
boston.data, boston.target, random_state=42)
mapper = BinMapper(max_bins=max_bins, random_state=42)
X_train_binned = mapper.fit_transform(X_train)
X_test_binned = mapper.transform(X_test)
# Init gradients and hessians to that of least squares loss
gradients = -y_train.astype(np.float32)
hessians = np.ones(1, dtype=np.float32)
min_samples_leaf = 8
max_leaf_nodes = 31
grower = TreeGrower(X_train_binned, gradients, hessians,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes, max_bins=max_bins,
n_bins_per_feature=mapper.n_bins_per_feature_)
grower.grow()
predictor = grower.make_predictor(bin_thresholds=mapper.bin_thresholds_)
assert r2_score(y_train, predictor.predict_binned(X_train_binned)) > 0.85
assert r2_score(y_test, predictor.predict_binned(X_test_binned)) > 0.70
assert_allclose(predictor.predict(X_train),
predictor.predict_binned(X_train_binned))
assert_allclose(predictor.predict(X_test),
predictor.predict_binned(X_test_binned))
assert r2_score(y_train, predictor.predict(X_train)) > 0.85
assert r2_score(y_test, predictor.predict(X_test)) > 0.70
def test_min_samples_leaf(n_samples, min_samples_leaf, n_bins,
constant_hessian, noise):
rng = np.random.RandomState(seed=0)
# data = linear target, 3 features, 1 irrelevant.
X = rng.normal(size=(n_samples, 3))
y = X[:, 0] - X[:, 1]
if noise:
y_scale = y.std()
y += rng.normal(scale=noise, size=n_samples) * y_scale
mapper = BinMapper(max_bins=n_bins)
X = mapper.fit_transform(X)
all_gradients = y.astype(np.float32)
if constant_hessian:
all_hessians = np.ones(shape=1, dtype=np.float32)
else:
all_hessians = np.ones_like(all_gradients)
grower = TreeGrower(X,
all_gradients,
all_hessians,
max_bins=n_bins,
shrinkage=1.,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=n_samples)
grower.grow()
predictor = grower.make_predictor(bin_thresholds=mapper.bin_thresholds_)
if n_samples >= min_samples_leaf:
for node in predictor.nodes:
if node['is_leaf']:
assert node['count'] >= min_samples_leaf
else:
assert predictor.nodes.shape[0] == 1
assert predictor.nodes[0]['is_leaf']
assert predictor.nodes[0]['count'] == n_samples
def test_boston_dataset():
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data,
boston.target,
random_state=42)
mapper = BinMapper(random_state=42)
X_train_binned = mapper.fit_transform(X_train)
X_test_binned = mapper.transform(X_test)
gradients = y_train.astype(np.float32)
hessians = np.ones(1, dtype=np.float32)
min_samples_leaf = 8
max_leaf_nodes = 31
grower = TreeGrower(X_train_binned,
gradients,
hessians,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes)
grower.grow()
predictor = grower.make_predictor(bin_thresholds=mapper.bin_thresholds_)
assert r2_score(y_train, predictor.predict_binned(X_train_binned)) > 0.75
assert r2_score(y_test, predictor.predict_binned(X_test_binned)) > 0.65
assert_allclose(predictor.predict(X_train),
predictor.predict_binned(X_train_binned))
assert_allclose(predictor.predict(X_test),
predictor.predict_binned(X_test_binned))
assert r2_score(y_train, predictor.predict(X_train)) > 0.75
assert r2_score(y_test, predictor.predict(X_test)) > 0.65
def test_min_samples_leaf_root(n_samples, min_samples_leaf):
# Make sure root node isn't split if n_samples is not at least twice
# min_samples_leaf
rng = np.random.RandomState(seed=0)
max_bins = 255
# data = linear target, 3 features, 1 irrelevant.
X = rng.normal(size=(n_samples, 3))
y = X[:, 0] - X[:, 1]
mapper = BinMapper(max_bins=max_bins)
X = mapper.fit_transform(X)
all_gradients = y.astype(np.float32)
all_hessians = np.ones(shape=1, dtype=np.float32)
grower = TreeGrower(X,
all_gradients,
all_hessians,
max_bins=max_bins,
shrinkage=1.,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=n_samples)
grower.grow()
if n_samples >= min_samples_leaf * 2:
assert len(grower.finalized_leaves) >= 2
else:
assert len(grower.finalized_leaves) == 1
def test_pre_binned_data():
# Make sure ValueError is raised when predictor.predict() is called while
# the predictor does not have any numerical thresholds.
X, y = make_regression()
# Init gradients and hessians to that of least squares loss
gradients = -y.astype(np.float32)
hessians = np.ones(1, dtype=np.float32)
mapper = BinMapper(random_state=0)
X_binned = mapper.fit_transform(X)
grower = TreeGrower(X_binned,
gradients,
hessians,
n_bins_per_feature=mapper.n_bins_per_feature_)
grower.grow()
predictor = grower.make_predictor(numerical_thresholds=None)
assert_raises_regex(ValueError,
'This predictor does not have numerical thresholds',
predictor.predict, X)
assert_raises_regex(ValueError, 'binned_data dtype should be uint8',
predictor.predict_binned, X)
predictor.predict_binned(X_binned) # No error
predictor = grower.make_predictor(
numerical_thresholds=mapper.numerical_thresholds_)
assert_raises_regex(ValueError, 'X has uint8 dtype', predictor.predict,
X_binned)
def test_bin_mapper_small_random_data(n_samples, n_bins):
data = np.random.RandomState(42).normal(size=n_samples).reshape(-1, 1)
assert len(np.unique(data)) == n_samples
mapper = BinMapper(max_bins=n_bins, random_state=42)
binned = mapper.fit_transform(data)
assert binned.shape == data.shape
assert binned.dtype == np.uint8
assert_array_equal(binned.ravel()[np.argsort(data.ravel())],
np.arange(n_samples))
def test_subsample():
# Make sure bin thresholds are different when applying subsampling
mapper_no_subsample = BinMapper(subsample=None, random_state=0).fit(DATA)
mapper_subsample = BinMapper(subsample=256, random_state=0).fit(DATA)
for feature in range(DATA.shape[1]):
with pytest.raises(AssertionError):
np.testing.assert_array_almost_equal(
mapper_no_subsample.numerical_thresholds_[feature],
mapper_subsample.numerical_thresholds_[feature],
decimal=3)
def test_same_predictions_regression(seed, min_samples_leaf, n_samples,
max_leaf_nodes):
# Make sure pygbm has the same predictions as LGBM for easy targets.
#
# In particular when the size of the trees are bound and the number of
# samples is large enough, the structure of the prediction trees found by
# LightGBM and PyGBM should be exactly identical.
#
# Notes:
# - Several candidate splits may have equal gains when the number of
# samples in a node is low (and because of float errors). Therefore the
# predictions on the test set might differ if the structure of the tree
# is not exactly the same. To avoid this issue we only compare the
# predictions on the test set when the number of samples is large enough
# and max_leaf_nodes is low enough.
# - To ignore discrepancies caused by small differences the binning
# strategy, data is pre-binned if n_samples > 255.
rng = np.random.RandomState(seed=seed)
n_samples = n_samples
max_iter = 1
max_bins = 256
X, y = make_regression(n_samples=n_samples,
n_features=5,
n_informative=5,
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 = GradientBoostingRegressor(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_lgbm = est_lightgbm.predict(X_train)
pred_pygbm = est_pygbm.predict(X_train)
# less than 1% of the predictions are different up to the 3rd decimal
assert np.mean(abs(pred_lgbm - pred_pygbm) > 1e-3) < .011
if max_leaf_nodes < 10 and n_samples >= 1000:
pred_lgbm = est_lightgbm.predict(X_test)
pred_pygbm = est_pygbm.predict(X_test)
# less than 1% of the predictions are different up to the 4th decimal
assert np.mean(abs(pred_lgbm - pred_pygbm) > 1e-4) < .01
def test_n_bins_per_feature(max_bins, diff):
# Check that n_bins_per_feature is n_unique_values when
# n_unique_values <= max_bins, else max_bins.
n_unique_values = max_bins + diff
X = list(range(n_unique_values)) * 2
X = np.array(X).reshape(-1, 1)
mapper = BinMapper(max_bins=max_bins).fit(X)
assert np.all(mapper.n_bins_per_feature_ == min(max_bins, n_unique_values))
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_easy_target(seed, n_samples, max_leaf_nodes):
# Make sure pygbm has the same predictions as LGBM for very easy targets.
#
# In particular when the size of the trees are bound and the number of
# samples is large enough, the structure of the prediction trees found by
# LightGBM and PyGBM should be exactly identical.
#
# Notes:
# - Several candidate splits may have equal gains when the number of
# samples in a node is low (and because of float errors). Therefore the
# predictions on the test set might differ if the structure of the tree
# is not exactly the same. To avoid this issue we only compare the
# predictions on the test set when the number of samples is large enough
# and max_leaf_nodes is low enough.
# - To ignore discrepancies caused by small differences the binning
# strategy, data is pre-binned if n_samples > 255.
lb = pytest.importorskip("lightgbm")
rng = np.random.RandomState(seed=seed)
n_samples = n_samples
min_samples_leaf = 1 # XXX: changing this breaks the test
max_iter = 1
# data = linear target, 5 features, 3 irrelevant.
X = rng.normal(size=(n_samples, 5))
y = X[:, 0] - X[:, 1]
if n_samples > 255:
X = BinMapper().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
est_lightgbm = lb.LGBMRegressor(n_estimators=max_iter,
min_data_in_bin=1,
learning_rate=1,
min_data_in_leaf=min_samples_leaf,
num_leaves=max_leaf_nodes)
est_pygbm = GradientBoostingMachine(max_iter=max_iter,
learning_rate=1,
validation_split=None,
scoring=None,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes)
est_lightgbm.fit(X_train, y_train)
est_pygbm.fit(X_train, y_train)
pred_lgbm = est_lightgbm.predict(X_train)
pred_pygbm = est_pygbm.predict(X_train)
np.testing.assert_array_almost_equal(pred_lgbm, pred_pygbm, decimal=3)
if max_leaf_nodes < 10 and n_samples > 1000:
pred_lgbm = est_lightgbm.predict(X_test)
pred_pygbm = est_pygbm.predict(X_test)
np.testing.assert_array_almost_equal(pred_lgbm, pred_pygbm, decimal=3)
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_bin_mapper_idempotence(n_bins_small, n_bins_large):
assert n_bins_large >= n_bins_small
data = np.random.RandomState(42).normal(size=30000).reshape(-1, 1)
mapper_small = BinMapper(max_bins=n_bins_small)
mapper_large = BinMapper(max_bins=n_bins_large)
binned_small = mapper_small.fit_transform(data)
binned_large = mapper_large.fit_transform(binned_small)
assert_array_equal(binned_small, binned_large)
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_pre_binned_data():
# Make sure that:
# - training on numerical data and predicting on numerical data is the
# same as training on binned data and predicting on binned data
# - training on numerical data and predicting on numerical data is the
# same as training on numerical data and predicting on binned data
# - training on binned data and predicting on numerical data is not
# possible.
X, y = make_regression(random_state=0)
gbdt = GradientBoostingRegressor(scoring=None, random_state=0)
mapper = BinMapper(random_state=0)
X_binned = mapper.fit_transform(X)
fit_num_pred_num = gbdt.fit(X, y).predict(X)
fit_binned_pred_binned = gbdt.fit(X_binned, y).predict(X_binned)
fit_num_pred_binned = gbdt.fit(X, y).predict(X_binned)
assert_allclose(fit_num_pred_num, fit_binned_pred_binned)
assert_allclose(fit_num_pred_num, fit_num_pred_binned)
assert_raises_regex(ValueError,
'This estimator was fitted with pre-binned data ',
gbdt.fit(X_binned, y).predict, X)
def test_bin_mapper_random_data(n_bins):
n_samples, n_features = DATA.shape
expected_count_per_bin = n_samples // n_bins
tol = int(0.05 * expected_count_per_bin)
mapper = BinMapper(max_bins=n_bins, random_state=42).fit(DATA)
binned = mapper.transform(DATA)
assert binned.shape == (n_samples, n_features)
assert binned.dtype == np.uint8
assert_array_equal(binned.min(axis=0), np.array([0, 0]))
assert_array_equal(binned.max(axis=0), np.array([n_bins - 1, n_bins - 1]))
assert len(mapper.numerical_thresholds_) == n_features
for i in range(len(mapper.numerical_thresholds_)):
assert mapper.numerical_thresholds_[i].shape == (n_bins - 1, )
assert mapper.numerical_thresholds_[i].dtype == DATA.dtype
assert np.all(mapper.n_bins_per_feature_ == n_bins)
# Check that the binned data is approximately balanced across bins.
for feature_idx in range(n_features):
for bin_idx in range(n_bins):
count = (binned[:, feature_idx] == bin_idx).sum()
assert abs(count - expected_count_per_bin) < tol
def test_bin_mapper_repeated_values_invariance(n_distinct):
rng = np.random.RandomState(42)
distinct_values = rng.normal(size=n_distinct)
assert len(np.unique(distinct_values)) == n_distinct
repeated_indices = rng.randint(low=0, high=n_distinct, size=1000)
data = distinct_values[repeated_indices]
rng.shuffle(data)
assert_array_equal(np.unique(data), np.sort(distinct_values))
data = data.reshape(-1, 1)
mapper_1 = BinMapper(max_bins=n_distinct)
binned_1 = mapper_1.fit_transform(data)
assert_array_equal(np.unique(binned_1[:, 0]), np.arange(n_distinct))
# Adding more bins to the mapper yields the same results (same thresholds)
mapper_2 = BinMapper(max_bins=min(256, n_distinct * 3))
binned_2 = mapper_2.fit_transform(data)
assert_allclose(mapper_1.bin_thresholds_[0], mapper_2.bin_thresholds_[0])
assert_array_equal(binned_1, binned_2)
class GradientBoostingMachine(BaseEstimator, RegressorMixin):
def __init__(self,
learning_rate=0.1,
max_iter=100,
max_leaf_nodes=31,
max_depth=None,
min_samples_leaf=20,
l2_regularization=0.,
max_bins=255,
max_no_improvement=5,
validation_split=0.1,
scoring='neg_mean_squared_error',
tol=1e-7,
verbose=0,
random_state=None):
self.learning_rate = learning_rate
self.max_iter = max_iter
self.max_leaf_nodes = max_leaf_nodes
self.max_depth = max_depth
self.min_samples_leaf = min_samples_leaf
self.l2_regularization = l2_regularization
self.max_bins = max_bins
self.max_no_improvement = max_no_improvement
self.validation_split = validation_split
self.scoring = scoring
self.tol = tol
self.verbose = verbose
self.random_state = random_state
def fit(self, X, y):
fit_start_time = time()
acc_find_split_time = 0. # time spent finding the best splits
acc_apply_split_time = 0. # time spent splitting nodes
# time spent predicting X for gradient and hessians update
acc_prediction_time = 0.
# TODO: add support for mixed-typed (numerical + categorical) data
# TODO: add support for missing data
# TODO: add support for pre-binned data (pass-through)?
X, y = check_X_y(X, y, dtype=[np.float32, np.float64])
y = y.astype(np.float32, copy=False)
rng = check_random_state(self.random_state)
if self.verbose:
print(f"Binning {X.nbytes / 1e9:.3f} GB of data: ",
end="",
flush=True)
tic = time()
self.bin_mapper_ = BinMapper(max_bins=self.max_bins, random_state=rng)
X_binned = self.bin_mapper_.fit_transform(X)
toc = time()
if self.verbose:
duration = toc - tic
troughput = X.nbytes / duration
print(f"{duration:.3f} s ({troughput / 1e6:.3f} MB/s)")
if self.validation_split is not None:
X_binned_train, X_binned_val, y_train, y_val = train_test_split(
X_binned,
y,
test_size=self.validation_split,
stratify=y,
random_state=rng)
# Histogram computation is faster on feature-aligned data.
X_binned_train = np.asfortranarray(X_binned_train)
else:
X_binned_train, y_train = X_binned, y
X_binned_val, y_val = None, None
# Subsample the training set for score-based monitoring.
subsample_size = 10000
if X_binned_train.shape[0] < subsample_size:
X_binned_small_train = np.ascontiguousarray(X_binned_train)
y_small_train = y_train
else:
indices = rng.choice(np.arange(X_binned_train.shape[0]),
subsample_size)
X_binned_small_train = X_binned_train[indices]
y_small_train = y_train[indices]
if self.verbose:
print("Fitting gradient boosted rounds:")
# TODO: plug custom loss functions
y_pred = np.zeros_like(y_train, dtype=np.float32)
gradients = np.asarray(y_train, dtype=np.float32).copy()
hessians = np.ones(1, dtype=np.float32)
self.predictors_ = predictors = []
self.train_scores_ = []
if self.validation_split is not None:
self.validation_scores_ = []
scorer = check_scoring(self, self.scoring)
gb_start_time = time()
# TODO: compute training loss and use it for early stopping if no
# validation data is provided?
self.n_iter_ = 0
while True:
should_stop = self._stopping_criterion(gb_start_time, scorer,
X_binned_small_train,
y_small_train, X_binned_val,
y_val)
if should_stop or self.n_iter_ == self.max_iter:
break
shrinkage = 1. if self.n_iter_ == 0 else self.learning_rate
grower = TreeGrower(X_binned_train,
gradients,
hessians,
n_bins=self.max_bins,
max_leaf_nodes=self.max_leaf_nodes,
max_depth=self.max_depth,
min_samples_leaf=self.min_samples_leaf,
shrinkage=shrinkage)
grower.grow()
predictor = grower.make_predictor(
bin_thresholds=self.bin_mapper_.bin_thresholds_)
predictors.append(predictor)
self.n_iter_ += 1
tic_pred = time()
leaves_data = [(l.value, l.sample_indices)
for l in grower.finalized_leaves]
_update_y_pred(leaves_data, y_pred)
gradients = y_train - y_pred
toc_pred = time()
acc_prediction_time += toc_pred - tic_pred
acc_apply_split_time += grower.total_apply_split_time
acc_find_split_time += grower.total_find_split_time
if self.verbose:
duration = time() - fit_start_time
n_leaf_nodes = sum(p.get_n_leaf_nodes() for p in self.predictors_)
print(f"Fit {len(self.predictors_)} trees in {duration:.3f} s, "
f"({n_leaf_nodes} total leaf nodes)")
print('{:<32} {:.3f}s'.format('Time spent finding best splits:',
acc_find_split_time))
print('{:<32} {:.3f}s'.format('Time spent applying splits:',
acc_apply_split_time))
print('{:<32} {:.3f}s'.format('Time spent predicting:',
acc_prediction_time))
self.train_scores_ = np.asarray(self.train_scores_)
if self.validation_split is not None:
self.validation_scores_ = np.asarray(self.validation_scores_)
return self
def predict(self, X):
# TODO: check input / check_fitted
# TODO: make predictor behave correctly on pre-binned data
# TODO: handle classification and output class labels in this case
predicted = np.zeros(X.shape[0], dtype=np.float32)
for predictor in self.predictors_:
predicted += predictor.predict(X)
return predicted
def _predict_binned(self, X_binned):
predicted = np.zeros(X_binned.shape[0], dtype=np.float32)
for predictor in self.predictors_:
predicted += predictor.predict_binned(X_binned)
return predicted
def _stopping_criterion(self, start_time, scorer, X_binned_train, y_train,
X_binned_val, y_val):
log_msg = f"[{self.n_iter_}/{self.max_iter}]"
if self.scoring is not None:
# TODO: make sure that self.predict can work on binned data and
# then only use the public scorer.__call__.
predicted_train = self._predict_binned(X_binned_train)
score_train = scorer._score_func(y_train, predicted_train)
self.train_scores_.append(score_train)
log_msg += f" {self.scoring} train: {score_train:.5f},"
if self.validation_split is not None:
predicted_val = self._predict_binned(X_binned_val)
score_val = scorer._score_func(y_val, predicted_val)
self.validation_scores_.append(score_val)
log_msg += f", {self.scoring} val: {score_val:.5f},"
if self.n_iter_ > 0:
iteration_time = (time() - start_time) / self.n_iter_
predictor_nodes = self.predictors_[-1].nodes
max_depth = predictor_nodes['depth'].max()
n_leaf_nodes = predictor_nodes['is_leaf'].sum()
log_msg += (f" {n_leaf_nodes} leaf nodes, max depth {max_depth}"
f" in {iteration_time:0.3f}s")
if self.verbose:
print(log_msg)
if self.validation_split is not None:
return self._should_stop(self.validation_scores_)
else:
return self._should_stop(self.train_scores_)
def _should_stop(self, scores):
if (len(scores) == 0 or (self.max_no_improvement
and len(scores) < self.max_no_improvement)):
return False
context_scores = scores[-self.max_no_improvement:]
candidate = scores[-self.max_no_improvement]
tol = 0. if self.tol is None else self.tol
# sklearn scores: higher is always better.
best_with_tol = max(context_scores) * (1 - tol)
return candidate >= best_with_tol
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)
from numpy.testing import assert_allclose
from sklearn.datasets import make_regression
from pygbm.binning import BinMapper
from pygbm import GradientBoostingRegressor
n_samples = int(5e6)
X, y = make_regression(n_samples=n_samples, n_features=5)
est = GradientBoostingRegressor(max_iter=1,
scoring=None,
validation_split=None,
random_state=0)
est.fit(X, y)
predictor = est.predictors_[0][0]
bin_mapper = BinMapper(random_state=0)
X_binned = bin_mapper.fit_transform(X)
X_binned_c = np.ascontiguousarray(X_binned)
print("Compiling predictor code...")
tic = time()
predictor.predict_binned(np.asfortranarray(X_binned[:100]))
predictor.predict_binned(X_binned_c[:100])
predictor.predict(np.asfortranarray(X[:100]))
predictor.predict(X[:100])
toc = time()
print(f"done in {toc - tic:0.3f}s")
data_size = X_binned.nbytes
print("Computing predictions (F-contiguous binned data)...")
tic = time()
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)
class BaseGradientBoostingMachine(BaseEstimator, ABC):
"""Base class for gradient boosting estimators."""
multi_output = False
prediction_dim = 1
@abstractmethod
def __init__(self, loss, learning_rate, max_iter, max_leaf_nodes,
max_depth, min_samples_leaf, l2_regularization, max_bins,
scoring, validation_split, n_iter_no_change, tol, verbose,
random_state):
self.loss = loss
self.learning_rate = learning_rate
self.max_iter = max_iter
self.max_leaf_nodes = max_leaf_nodes
self.max_depth = max_depth
self.min_samples_leaf = min_samples_leaf
self.l2_regularization = l2_regularization
self.max_bins = max_bins
self.n_iter_no_change = n_iter_no_change
self.validation_split = validation_split
self.scoring = scoring
self.tol = tol
self.verbose = verbose
self.random_state = random_state
def _validate_parameters(self, X):
"""Validate parameters passed to __init__.
The parameters that are directly passed to the grower are checked in
TreeGrower."""
if self.loss not in self._VALID_LOSSES:
raise ValueError("Loss {} is not supported for {}. Accepted losses"
"are {}.".format(self.loss,
self.__class__.__name__,
', '.join(self._VALID_LOSSES)))
if self.learning_rate <= 0:
raise ValueError(f'learning_rate={self.learning_rate} must '
f'be strictly positive')
if self.max_iter < 1:
raise ValueError(f'max_iter={self.max_iter} must '
f'not be smaller than 1.')
if self.n_iter_no_change is not None and self.n_iter_no_change < 0:
raise ValueError(f'n_iter_no_change={self.n_iter_no_change} '
f'must be positive.')
if self.validation_split is not None and self.validation_split <= 0:
raise ValueError(f'validation_split={self.validation_split} '
f'must be strictly positive, or None.')
if self.tol is not None and self.tol < 0:
raise ValueError(f'tol={self.tol} ' f'must not be smaller than 0.')
if X.dtype == np.uint8: # pre-binned data
max_bin_index = X.max()
if self.max_bins < max_bin_index + 1:
raise ValueError(
f'max_bins is set to {self.max_bins} but the data is '
f'pre-binned with {max_bin_index + 1} bins.')
def fit(self, X, y):
"""Fit the gradient boosting model.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
The input samples. If ``X.dtype == np.uint8``, the data is
assumed to be pre-binned and the prediction methods
(``predict``, ``predict_proba``) will only accept pre-binned
data as well.
y : array-like, shape=(n_samples,)
Target values.
Returns
-------
self : object
"""
fit_start_time = time()
acc_find_split_time = 0. # time spent finding the best splits
acc_apply_split_time = 0. # time spent splitting nodes
# time spent predicting X for gradient and hessians update
acc_prediction_time = 0.
# TODO: add support for mixed-typed (numerical + categorical) data
# TODO: add support for missing data
self.multi_output = len(y.ravel()) != len(y)
if self.multi_output:
self.prediction_dim = y.shape[1]
else:
self.prediction_dim = 1
X, y = check_X_y(X,
y,
dtype=[np.float32, np.float64, np.uint8],
multi_output=self.multi_output)
y = self._encode_y(y)
if X.shape[0] == 1 or X.shape[1] == 1:
raise ValueError(
'Passing only one sample or one feature is not supported yet. '
'See numba issue #3569.')
rng = check_random_state(self.random_state)
self._validate_parameters(X)
self.n_features_ = X.shape[1] # used for validation in predict()
if X.dtype == np.uint8: # data is pre-binned
if self.verbose:
print("X is pre-binned.")
X_binned = X
self.bin_mapper_ = None
numerical_thresholds = None
n_bins_per_feature = X.max(axis=0).astype(np.uint32)
else:
if self.verbose:
print(f"Binning {X.nbytes / 1e9:.3f} GB of data: ",
end="",
flush=True)
tic = time()
self.bin_mapper_ = BinMapper(max_bins=self.max_bins,
random_state=rng)
X_binned = self.bin_mapper_.fit_transform(X)
numerical_thresholds = self.bin_mapper_.numerical_thresholds_
n_bins_per_feature = self.bin_mapper_.n_bins_per_feature_
toc = time()
if self.verbose:
duration = toc - tic
throughput = X.nbytes / duration
print(f"{duration:.3f} s ({throughput / 1e6:.3f} MB/s)")
self.loss_ = self._get_loss()
do_early_stopping = (self.n_iter_no_change is not None
and self.n_iter_no_change > 0)
if do_early_stopping and self.validation_split is not None:
# stratify for classification
stratify = y if hasattr(self.loss_, 'predict_proba') else None
X_binned_train, X_binned_val, y_train, y_val = train_test_split(
X_binned,
y,
test_size=self.validation_split,
stratify=stratify,
random_state=rng)
if X_binned_train.size == 0 or X_binned_val.size == 0:
raise ValueError(
f'Not enough data (n_samples={X_binned.shape[0]}) to '
f'perform early stopping with validation_split='
f'{self.validation_split}. Use more training data or '
f'adjust validation_split.')
# Predicting is faster of C-contiguous arrays, training is faster
# on Fortran arrays.
X_binned_val = np.ascontiguousarray(X_binned_val)
X_binned_train = np.asfortranarray(X_binned_train)
else:
X_binned_train, y_train = X_binned, y
X_binned_val, y_val = None, None
# Subsample the training set for score-based monitoring.
if do_early_stopping:
subsample_size = 10000
n_samples_train = X_binned_train.shape[0]
if n_samples_train > subsample_size:
indices = rng.choice(X_binned_train.shape[0], subsample_size)
X_binned_small_train = X_binned_train[indices]
y_small_train = y_train[indices]
else:
X_binned_small_train = X_binned_train
y_small_train = y_train
# Predicting is faster of C-contiguous arrays.
X_binned_small_train = np.ascontiguousarray(X_binned_small_train)
if self.verbose:
print("Fitting gradient boosted rounds:")
n_samples = X_binned_train.shape[0]
self.baseline_prediction_ = self.loss_.get_baseline_prediction(
y_train, self.prediction_dim)
# raw_predictions are the accumulated values predicted by the trees
# for the training data.
raw_predictions = np.zeros(shape=(n_samples, self.prediction_dim),
dtype=self.baseline_prediction_.dtype)
if not self.multi_output:
raw_predictions = raw_predictions.ravel()
raw_predictions += self.baseline_prediction_
# gradients and hessians are 1D arrays of size
# n_samples * n_trees_per_iteration
gradients, hessians = self.loss_.init_gradients_and_hessians(
n_samples=n_samples, prediction_dim=self.prediction_dim)
if not self.multi_output:
gradients = gradients.ravel()
# predictors_ is a matrix of TreePredictor objects with shape
# (n_iter_, n_trees_per_iteration)
self.predictors_ = predictors = []
# scorer_ is a callable with signature (est, X, y) and calls
# est.predict() or est.predict_proba() depending on its nature.
self.scorer_ = check_scoring(self, self.scoring)
self.train_scores_ = []
self.validation_scores_ = []
if do_early_stopping:
# Add predictions of the initial model (before the first tree)
self.train_scores_.append(self._get_scores(X_binned_train,
y_train))
if self.validation_split is not None:
self.validation_scores_.append(
self._get_scores(X_binned_val, y_val))
for iteration in range(self.max_iter):
if self.verbose:
iteration_start_time = time()
print(f"[{iteration + 1}/{self.max_iter}] ",
end='',
flush=True)
# Update gradients and hessians, inplace
self.loss_.update_gradients_and_hessians(gradients, hessians,
y_train, raw_predictions)
predictors.append([])
if self.multi_output:
proj_gradients, proj_hessians = self.randomly_project_gradients_and_hessians(
gradients, hessians)
else:
proj_gradients, proj_hessians = gradients.ravel(
), hessians.ravel()
# Build `n_trees_per_iteration` trees.
for k, (gradients_at_k, hessians_at_k) in enumerate(
zip(
np.array_split(proj_gradients,
self.n_trees_per_iteration_),
np.array_split(proj_hessians,
self.n_trees_per_iteration_))):
# the xxxx_at_k arrays are **views** on the original arrays.
# Note that for binary classif and regressions,
# n_trees_per_iteration is 1 and xxxx_at_k is equivalent to the
# whole array.
grower = TreeGrower(X_binned_train,
gradients_at_k,
hessians_at_k,
max_bins=self.max_bins,
n_bins_per_feature=n_bins_per_feature,
max_leaf_nodes=self.max_leaf_nodes,
max_depth=self.max_depth,
min_samples_leaf=self.min_samples_leaf,
l2_regularization=self.l2_regularization,
shrinkage=self.learning_rate)
grower.grow()
if self.multi_output:
for l in grower.finalized_leaves:
l.residual = (
-self.learning_rate *
np.sum(a=gradients[l.sample_indices, :], axis=0) /
(l.sum_hessians + self.l2_regularization +
np.finfo(np.float64).eps))
leaves_data = [(l.residual, l.sample_indices)
for l in grower.finalized_leaves]
else:
leaves_data = [(l.value, l.sample_indices)
for l in grower.finalized_leaves]
acc_apply_split_time += grower.total_apply_split_time
acc_find_split_time += grower.total_find_split_time
predictor = grower.make_predictor(numerical_thresholds)
predictors[-1].append(predictor)
tic_pred = time()
# prepare leaves_data so that _update_raw_predictions can be
# @njitted
_update_raw_predictions(leaves_data, raw_predictions)
toc_pred = time()
acc_prediction_time += toc_pred - tic_pred
should_early_stop = False
if do_early_stopping:
should_early_stop = self._check_early_stopping(
X_binned_small_train, y_small_train, X_binned_val, y_val)
if self.verbose:
self._print_iteration_stats(iteration_start_time,
do_early_stopping)
if should_early_stop:
break
if self.verbose:
duration = time() - fit_start_time
n_total_leaves = sum(
predictor.get_n_leaf_nodes()
for predictors_at_ith_iteration in self.predictors_
for predictor in predictors_at_ith_iteration)
n_predictors = sum(
len(predictors_at_ith_iteration)
for predictors_at_ith_iteration in self.predictors_)
print(f"Fit {n_predictors} trees in {duration:.3f} s, "
f"({n_total_leaves} total leaves)")
print(f"{'Time spent finding best splits:':<32} "
f"{acc_find_split_time:.3f}s")
print(f"{'Time spent applying splits:':<32} "
f"{acc_apply_split_time:.3f}s")
print(f"{'Time spent predicting:':<32} "
f"{acc_prediction_time:.3f}s")
self.train_scores_ = np.asarray(self.train_scores_)
self.validation_scores_ = np.asarray(self.validation_scores_)
return self
def _check_early_stopping(self, X_binned_train, y_train, X_binned_val,
y_val):
"""Check if fitting should be early-stopped.
Scores are computed on validation data or on training data.
"""
self.train_scores_.append(self._get_scores(X_binned_train, y_train))
if self.validation_split is not None:
self.validation_scores_.append(
self._get_scores(X_binned_val, y_val))
return self._should_stop(self.validation_scores_)
return self._should_stop(self.train_scores_)
def _should_stop(self, scores):
"""
Return True (do early stopping) if the last n scores aren't better
than the (n-1)th-to-last score, up to some tolerance.
"""
reference_position = self.n_iter_no_change + 1
if len(scores) < reference_position:
return False
# A higher score is always better. Higher tol means that it will be
# harder for subsequent iteration to be considered an improvement upon
# the reference score, and therefore it is more likely to early stop
# because of the lack of significant improvement.
tol = 0 if self.tol is None else self.tol
reference_score = scores[-reference_position] + tol
recent_scores = scores[-reference_position + 1:]
recent_improvements = [
score > reference_score for score in recent_scores
]
return not any(recent_improvements)
def _get_scores(self, X, y):
"""Compute scores on data X with target y.
Scores are either computed with a scorer if scoring parameter is not
None, else with the loss. As higher is always better, we return
-loss_value.
"""
if self.scoring is not None:
return self.scorer_(self, X, y)
# Else, use the negative loss as score.
if self.multi_output:
raw_predictions = self._raw_predict_multi(X)
else:
raw_predictions = self._raw_predict(X)
return -self.loss_(y, raw_predictions)
def _print_iteration_stats(self, iteration_start_time, do_early_stopping):
"""Print info about the current fitting iteration."""
log_msg = ''
predictors_of_ith_iteration = [
predictors_list for predictors_list in self.predictors_[-1]
if predictors_list
]
n_trees = len(predictors_of_ith_iteration)
max_depth = max(predictor.get_max_depth()
for predictor in predictors_of_ith_iteration)
n_leaves = sum(predictor.get_n_leaf_nodes()
for predictor in predictors_of_ith_iteration)
if n_trees == 1:
log_msg += (f"{n_trees} tree, {n_leaves} leaves, ")
else:
log_msg += (f"{n_trees} trees, {n_leaves} leaves ")
log_msg += (f"({int(n_leaves / n_trees)} on avg), ")
log_msg += f"max depth = {max_depth}, "
if do_early_stopping:
log_msg += f"{self.scoring} train: {self.train_scores_[-1]:.5f}, "
if self.validation_split is not None:
log_msg += (f"{self.scoring} val: "
f"{self.validation_scores_[-1]:.5f}, ")
iteration_time = time() - iteration_start_time
log_msg += f"in {iteration_time:0.3f}s"
print(log_msg)
def _raw_predict(self, X):
"""Return the sum of the leaves values over all predictors.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
The input samples. If ``X.dtype == np.uint8``, the data is assumed
to be pre-binned and the estimator must have been fitted with
pre-binned data.
Returns
-------
raw_predictions : array, shape (n_samples * n_trees_per_iteration,)
The raw predicted values.
"""
X = check_array(X)
check_is_fitted(self, 'predictors_')
if X.shape[1] != self.n_features_:
raise ValueError(
f'X has {X.shape[1]} features but this estimator was '
f'trained with {self.n_features_} features.')
is_binned = X.dtype == np.uint8
if not is_binned and self.bin_mapper_ is None:
raise ValueError(
'This estimator was fitted with pre-binned data and '
'can only predict pre-binned data as well. If your data *is* '
'already pre-binnned, convert it to uint8 using e.g. '
'X.astype(np.uint8). If the data passed to fit() was *not* '
'pre-binned, convert it to float32 and call fit() again.')
n_samples = X.shape[0]
raw_predictions = np.zeros(shape=(n_samples,
self.n_trees_per_iteration_),
dtype=self.baseline_prediction_.dtype)
raw_predictions += self.baseline_prediction_
# Should we parallelize this?
for predictors_of_ith_iteration in self.predictors_:
for k, predictor in enumerate(predictors_of_ith_iteration):
predict = (predictor.predict_binned
if is_binned else predictor.predict)
raw_predictions[:, k] += predict(X)
return raw_predictions
def _raw_predict_multi(self, X):
"""Return the sum of the leaves values over all predictors.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
The input samples. If ``X.dtype == np.uint8``, the data is assumed
to be pre-binned and the estimator must have been fitted with
pre-binned data.
Returns
-------
raw_predictions : array, shape (n_samples * n_trees_per_iteration,)
The raw predicted values.
"""
X = check_array(X)
check_is_fitted(self, 'predictors_')
if X.shape[1] != self.n_features_:
raise ValueError(
f'X has {X.shape[1]} features but this estimator was '
f'trained with {self.n_features_} features.')
is_binned = X.dtype == np.uint8
if not is_binned and self.bin_mapper_ is None:
raise ValueError(
'This estimator was fitted with pre-binned data and '
'can only predict pre-binned data as well. If your data *is* '
'already pre-binnned, convert it to uint8 using e.g. '
'X.astype(np.uint8). If the data passed to fit() was *not* '
'pre-binned, convert it to float32 and call fit() again.')
n_samples = X.shape[0]
raw_predictions = np.zeros(shape=(n_samples, self.prediction_dim),
dtype=self.baseline_prediction_.dtype)
raw_predictions += self.baseline_prediction_
# Should we parallelize this?
for predictors_of_ith_iteration in self.predictors_:
for k, predictor in enumerate(predictors_of_ith_iteration):
predict = (predictor.predict_binned_multi
if is_binned else predictor.predict_multi)
tmp = predict(X, self.prediction_dim)
if tmp.dtype != 'float32':
print(tmp)
raw_predictions = np.add(raw_predictions,
predict(X, self.prediction_dim))
return raw_predictions
def randomly_project_gradients_and_hessians(self, gradients, hessians):
proj_g = SparseRandomProjection(
n_components=1,
random_state=self.random_state).fit_transform(X=gradients)
proj_h = hessians #SparseRandomProjection(n_components=1, random_state=self.random_state).fit_transform(X=hessians)
return proj_g.ravel().astype(np.float32), proj_h.astype(np.float32)
@abstractmethod
def _get_loss(self):
pass
@abstractmethod
def _encode_y(self, y=None):
pass
@property
def n_iter_(self):
check_is_fitted(self, 'predictors_')
return len(self.predictors_)
def test_bin_mapper_identity_small(n_bins, scale, offset):
data = np.arange(n_bins).reshape(-1, 1) * scale + offset
binned = BinMapper(max_bins=n_bins).fit_transform(data)
assert_array_equal(binned, np.arange(n_bins).reshape(-1, 1))
def test_bin_mapper_identity_repeated_values(n_bins, n_distinct, multiplier):
data = np.array(list(range(n_distinct)) * multiplier).reshape(-1, 1)
binned = BinMapper(max_bins=n_bins).fit_transform(data)
assert_array_equal(data, binned)
def fit(self, X, y):
"""Fit the gradient boosting model.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
The input samples. If ``X.dtype == np.uint8``, the data is
assumed to be pre-binned and the prediction methods
(``predict``, ``predict_proba``) will only accept pre-binned
data as well.
y : array-like, shape=(n_samples,)
Target values.
Returns
-------
self : object
"""
fit_start_time = time()
acc_find_split_time = 0. # time spent finding the best splits
acc_apply_split_time = 0. # time spent splitting nodes
# time spent predicting X for gradient and hessians update
acc_prediction_time = 0.
# TODO: add support for mixed-typed (numerical + categorical) data
# TODO: add support for missing data
self.multi_output = len(y.ravel()) != len(y)
if self.multi_output:
self.prediction_dim = y.shape[1]
else:
self.prediction_dim = 1
X, y = check_X_y(X,
y,
dtype=[np.float32, np.float64, np.uint8],
multi_output=self.multi_output)
y = self._encode_y(y)
if X.shape[0] == 1 or X.shape[1] == 1:
raise ValueError(
'Passing only one sample or one feature is not supported yet. '
'See numba issue #3569.')
rng = check_random_state(self.random_state)
self._validate_parameters(X)
self.n_features_ = X.shape[1] # used for validation in predict()
if X.dtype == np.uint8: # data is pre-binned
if self.verbose:
print("X is pre-binned.")
X_binned = X
self.bin_mapper_ = None
numerical_thresholds = None
n_bins_per_feature = X.max(axis=0).astype(np.uint32)
else:
if self.verbose:
print(f"Binning {X.nbytes / 1e9:.3f} GB of data: ",
end="",
flush=True)
tic = time()
self.bin_mapper_ = BinMapper(max_bins=self.max_bins,
random_state=rng)
X_binned = self.bin_mapper_.fit_transform(X)
numerical_thresholds = self.bin_mapper_.numerical_thresholds_
n_bins_per_feature = self.bin_mapper_.n_bins_per_feature_
toc = time()
if self.verbose:
duration = toc - tic
throughput = X.nbytes / duration
print(f"{duration:.3f} s ({throughput / 1e6:.3f} MB/s)")
self.loss_ = self._get_loss()
do_early_stopping = (self.n_iter_no_change is not None
and self.n_iter_no_change > 0)
if do_early_stopping and self.validation_split is not None:
# stratify for classification
stratify = y if hasattr(self.loss_, 'predict_proba') else None
X_binned_train, X_binned_val, y_train, y_val = train_test_split(
X_binned,
y,
test_size=self.validation_split,
stratify=stratify,
random_state=rng)
if X_binned_train.size == 0 or X_binned_val.size == 0:
raise ValueError(
f'Not enough data (n_samples={X_binned.shape[0]}) to '
f'perform early stopping with validation_split='
f'{self.validation_split}. Use more training data or '
f'adjust validation_split.')
# Predicting is faster of C-contiguous arrays, training is faster
# on Fortran arrays.
X_binned_val = np.ascontiguousarray(X_binned_val)
X_binned_train = np.asfortranarray(X_binned_train)
else:
X_binned_train, y_train = X_binned, y
X_binned_val, y_val = None, None
# Subsample the training set for score-based monitoring.
if do_early_stopping:
subsample_size = 10000
n_samples_train = X_binned_train.shape[0]
if n_samples_train > subsample_size:
indices = rng.choice(X_binned_train.shape[0], subsample_size)
X_binned_small_train = X_binned_train[indices]
y_small_train = y_train[indices]
else:
X_binned_small_train = X_binned_train
y_small_train = y_train
# Predicting is faster of C-contiguous arrays.
X_binned_small_train = np.ascontiguousarray(X_binned_small_train)
if self.verbose:
print("Fitting gradient boosted rounds:")
n_samples = X_binned_train.shape[0]
self.baseline_prediction_ = self.loss_.get_baseline_prediction(
y_train, self.prediction_dim)
# raw_predictions are the accumulated values predicted by the trees
# for the training data.
raw_predictions = np.zeros(shape=(n_samples, self.prediction_dim),
dtype=self.baseline_prediction_.dtype)
if not self.multi_output:
raw_predictions = raw_predictions.ravel()
raw_predictions += self.baseline_prediction_
# gradients and hessians are 1D arrays of size
# n_samples * n_trees_per_iteration
gradients, hessians = self.loss_.init_gradients_and_hessians(
n_samples=n_samples, prediction_dim=self.prediction_dim)
if not self.multi_output:
gradients = gradients.ravel()
# predictors_ is a matrix of TreePredictor objects with shape
# (n_iter_, n_trees_per_iteration)
self.predictors_ = predictors = []
# scorer_ is a callable with signature (est, X, y) and calls
# est.predict() or est.predict_proba() depending on its nature.
self.scorer_ = check_scoring(self, self.scoring)
self.train_scores_ = []
self.validation_scores_ = []
if do_early_stopping:
# Add predictions of the initial model (before the first tree)
self.train_scores_.append(self._get_scores(X_binned_train,
y_train))
if self.validation_split is not None:
self.validation_scores_.append(
self._get_scores(X_binned_val, y_val))
for iteration in range(self.max_iter):
if self.verbose:
iteration_start_time = time()
print(f"[{iteration + 1}/{self.max_iter}] ",
end='',
flush=True)
# Update gradients and hessians, inplace
self.loss_.update_gradients_and_hessians(gradients, hessians,
y_train, raw_predictions)
predictors.append([])
if self.multi_output:
proj_gradients, proj_hessians = self.randomly_project_gradients_and_hessians(
gradients, hessians)
else:
proj_gradients, proj_hessians = gradients.ravel(
), hessians.ravel()
# Build `n_trees_per_iteration` trees.
for k, (gradients_at_k, hessians_at_k) in enumerate(
zip(
np.array_split(proj_gradients,
self.n_trees_per_iteration_),
np.array_split(proj_hessians,
self.n_trees_per_iteration_))):
# the xxxx_at_k arrays are **views** on the original arrays.
# Note that for binary classif and regressions,
# n_trees_per_iteration is 1 and xxxx_at_k is equivalent to the
# whole array.
grower = TreeGrower(X_binned_train,
gradients_at_k,
hessians_at_k,
max_bins=self.max_bins,
n_bins_per_feature=n_bins_per_feature,
max_leaf_nodes=self.max_leaf_nodes,
max_depth=self.max_depth,
min_samples_leaf=self.min_samples_leaf,
l2_regularization=self.l2_regularization,
shrinkage=self.learning_rate)
grower.grow()
if self.multi_output:
for l in grower.finalized_leaves:
l.residual = (
-self.learning_rate *
np.sum(a=gradients[l.sample_indices, :], axis=0) /
(l.sum_hessians + self.l2_regularization +
np.finfo(np.float64).eps))
leaves_data = [(l.residual, l.sample_indices)
for l in grower.finalized_leaves]
else:
leaves_data = [(l.value, l.sample_indices)
for l in grower.finalized_leaves]
acc_apply_split_time += grower.total_apply_split_time
acc_find_split_time += grower.total_find_split_time
predictor = grower.make_predictor(numerical_thresholds)
predictors[-1].append(predictor)
tic_pred = time()
# prepare leaves_data so that _update_raw_predictions can be
# @njitted
_update_raw_predictions(leaves_data, raw_predictions)
toc_pred = time()
acc_prediction_time += toc_pred - tic_pred
should_early_stop = False
if do_early_stopping:
should_early_stop = self._check_early_stopping(
X_binned_small_train, y_small_train, X_binned_val, y_val)
if self.verbose:
self._print_iteration_stats(iteration_start_time,
do_early_stopping)
if should_early_stop:
break
if self.verbose:
duration = time() - fit_start_time
n_total_leaves = sum(
predictor.get_n_leaf_nodes()
for predictors_at_ith_iteration in self.predictors_
for predictor in predictors_at_ith_iteration)
n_predictors = sum(
len(predictors_at_ith_iteration)
for predictors_at_ith_iteration in self.predictors_)
print(f"Fit {n_predictors} trees in {duration:.3f} s, "
f"({n_total_leaves} total leaves)")
print(f"{'Time spent finding best splits:':<32} "
f"{acc_find_split_time:.3f}s")
print(f"{'Time spent applying splits:':<32} "
f"{acc_apply_split_time:.3f}s")
print(f"{'Time spent predicting:':<32} "
f"{acc_prediction_time:.3f}s")
self.train_scores_ = np.asarray(self.train_scores_)
self.validation_scores_ = np.asarray(self.validation_scores_)
return self
def fit(self, X, y):
fit_start_time = time()
acc_find_split_time = 0. # time spent finding the best splits
acc_apply_split_time = 0. # time spent splitting nodes
# time spent predicting X for gradient and hessians update
acc_prediction_time = 0.
# TODO: add support for mixed-typed (numerical + categorical) data
# TODO: add support for missing data
# TODO: add support for pre-binned data (pass-through)?
X, y = check_X_y(X, y, dtype=[np.float32, np.float64])
y = y.astype(np.float32, copy=False)
rng = check_random_state(self.random_state)
if self.verbose:
print(f"Binning {X.nbytes / 1e9:.3f} GB of data: ",
end="",
flush=True)
tic = time()
self.bin_mapper_ = BinMapper(max_bins=self.max_bins, random_state=rng)
X_binned = self.bin_mapper_.fit_transform(X)
toc = time()
if self.verbose:
duration = toc - tic
troughput = X.nbytes / duration
print(f"{duration:.3f} s ({troughput / 1e6:.3f} MB/s)")
if self.validation_split is not None:
X_binned_train, X_binned_val, y_train, y_val = train_test_split(
X_binned,
y,
test_size=self.validation_split,
stratify=y,
random_state=rng)
# Histogram computation is faster on feature-aligned data.
X_binned_train = np.asfortranarray(X_binned_train)
else:
X_binned_train, y_train = X_binned, y
X_binned_val, y_val = None, None
# Subsample the training set for score-based monitoring.
subsample_size = 10000
if X_binned_train.shape[0] < subsample_size:
X_binned_small_train = np.ascontiguousarray(X_binned_train)
y_small_train = y_train
else:
indices = rng.choice(np.arange(X_binned_train.shape[0]),
subsample_size)
X_binned_small_train = X_binned_train[indices]
y_small_train = y_train[indices]
if self.verbose:
print("Fitting gradient boosted rounds:")
# TODO: plug custom loss functions
y_pred = np.zeros_like(y_train, dtype=np.float32)
gradients = np.asarray(y_train, dtype=np.float32).copy()
hessians = np.ones(1, dtype=np.float32)
self.predictors_ = predictors = []
self.train_scores_ = []
if self.validation_split is not None:
self.validation_scores_ = []
scorer = check_scoring(self, self.scoring)
gb_start_time = time()
# TODO: compute training loss and use it for early stopping if no
# validation data is provided?
self.n_iter_ = 0
while True:
should_stop = self._stopping_criterion(gb_start_time, scorer,
X_binned_small_train,
y_small_train, X_binned_val,
y_val)
if should_stop or self.n_iter_ == self.max_iter:
break
shrinkage = 1. if self.n_iter_ == 0 else self.learning_rate
grower = TreeGrower(X_binned_train,
gradients,
hessians,
n_bins=self.max_bins,
max_leaf_nodes=self.max_leaf_nodes,
max_depth=self.max_depth,
min_samples_leaf=self.min_samples_leaf,
shrinkage=shrinkage)
grower.grow()
predictor = grower.make_predictor(
bin_thresholds=self.bin_mapper_.bin_thresholds_)
predictors.append(predictor)
self.n_iter_ += 1
tic_pred = time()
leaves_data = [(l.value, l.sample_indices)
for l in grower.finalized_leaves]
_update_y_pred(leaves_data, y_pred)
gradients = y_train - y_pred
toc_pred = time()
acc_prediction_time += toc_pred - tic_pred
acc_apply_split_time += grower.total_apply_split_time
acc_find_split_time += grower.total_find_split_time
if self.verbose:
duration = time() - fit_start_time
n_leaf_nodes = sum(p.get_n_leaf_nodes() for p in self.predictors_)
print(f"Fit {len(self.predictors_)} trees in {duration:.3f} s, "
f"({n_leaf_nodes} total leaf nodes)")
print('{:<32} {:.3f}s'.format('Time spent finding best splits:',
acc_find_split_time))
print('{:<32} {:.3f}s'.format('Time spent applying splits:',
acc_apply_split_time))
print('{:<32} {:.3f}s'.format('Time spent predicting:',
acc_prediction_time))
self.train_scores_ = np.asarray(self.train_scores_)
if self.validation_split is not None:
self.validation_scores_ = np.asarray(self.validation_scores_)
return self
def fit(self, X, y):
"""Fit the gradient boosting model.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
The input samples.
y : array-like, shape=(n_samples,)
Target values.
Returns
-------
self : object
"""
fit_start_time = time()
acc_find_split_time = 0. # time spent finding the best splits
acc_apply_split_time = 0. # time spent splitting nodes
# time spent predicting X for gradient and hessians update
acc_prediction_time = 0.
# TODO: add support for mixed-typed (numerical + categorical) data
# TODO: add support for missing data
# TODO: add support for pre-binned data (pass-through)?
# TODO: test input checking
X, y = check_X_y(X, y, dtype=[np.float32, np.float64])
y = self._encode_y(y)
if X.shape[0] == 1 or X.shape[1] == 1:
raise ValueError(
'Passing only one sample or one feature is not supported yet. '
'See numba issue #3569.'
)
rng = check_random_state(self.random_state)
self._validate_parameters()
self.n_features_ = X.shape[1] # used for validation in predict()
if self.verbose:
print(f"Binning {X.nbytes / 1e9:.3f} GB of data: ", end="",
flush=True)
tic = time()
self.bin_mapper_ = BinMapper(max_bins=self.max_bins, random_state=rng)
X_binned = self.bin_mapper_.fit_transform(X)
toc = time()
if self.verbose:
duration = toc - tic
troughput = X.nbytes / duration
print(f"{duration:.3f} s ({troughput / 1e6:.3f} MB/s)")
self.loss_ = self._get_loss()
if self.scoring is not None and self.validation_split is not None:
# stratify for classification
stratify = y if hasattr(self.loss_, 'predict_proba') else None
X_binned_train, X_binned_val, y_train, y_val = train_test_split(
X_binned, y, test_size=self.validation_split,
stratify=stratify, random_state=rng)
if X_binned_train.size == 0 or X_binned_val.size == 0:
raise ValueError(
f'Not enough data (n_samples={X_binned.shape[0]}) to '
f'perform early stopping with validation_split='
f'{self.validation_split}. Use more training data or '
f'adjust validation_split.'
)
# Histogram computation is faster on feature-aligned data.
X_binned_train = np.asfortranarray(X_binned_train)
else:
X_binned_train, y_train = X_binned, y
X_binned_val, y_val = None, None
# Subsample the training set for score-based monitoring.
subsample_size = 10000
if X_binned_train.shape[0] < subsample_size:
X_binned_small_train = np.ascontiguousarray(X_binned_train)
y_small_train = y_train
else:
indices = rng.choice(
np.arange(X_binned_train.shape[0]), subsample_size)
X_binned_small_train = X_binned_train[indices]
y_small_train = y_train[indices]
if self.verbose:
print("Fitting gradient boosted rounds:")
n_samples = X_binned_train.shape[0]
# values predicted by the trees. Used as-is in regression, and
# transformed into probas and / or classes for classification
raw_predictions = np.zeros(
shape=(n_samples, self.n_trees_per_iteration_),
dtype=y_train.dtype
)
# gradients and hessians are 1D arrays of size
# n_samples * n_trees_per_iteration
gradients, hessians = self.loss_.init_gradients_and_hessians(
n_samples=n_samples,
n_trees_per_iteration=self.n_trees_per_iteration_
)
# predictors_ is a matrix of TreePredictor objects with shape
# (n_iter_, n_trees_per_iteration)
self.predictors_ = predictors = []
scorer = check_scoring(self, self.scoring)
self.train_scores_ = []
if self.scoring is not None:
# Add predictions of the initial model (before the first tree)
predicted_train = self._predict_binned(X_binned_train)
score_train = scorer._sign * scorer._score_func(y_train,
predicted_train)
self.train_scores_.append(score_train)
if self.validation_split is not None:
self.validation_scores_ = []
predicted_val = self._predict_binned(X_binned_val)
score_val = scorer._sign * scorer._score_func(y_val,
predicted_val)
self.validation_scores_.append(score_val)
for iteration in range(self.max_iter):
if self.verbose:
iteration_start_time = time()
print(f"[{iteration + 1}/{self.max_iter}] ", end='',
flush=True)
# Update gradients and hessians, inplace
self.loss_.update_gradients_and_hessians(gradients, hessians,
y_train, raw_predictions)
predictors.append([])
# Build `n_trees_per_iteration` trees.
for k, (gradients_at_k, hessians_at_k) in enumerate(zip(
np.array_split(gradients, self.n_trees_per_iteration_),
np.array_split(hessians, self.n_trees_per_iteration_))):
# the xxxx_at_k arrays are **views** on the original arrays.
# Note that for binary classif and regressions,
# n_trees_per_iteration is 1 and xxxx_at_k is equivalent to the
# whole array.
grower = TreeGrower(
X_binned_train, gradients_at_k, hessians_at_k,
max_bins=self.max_bins,
n_bins_per_feature=self.bin_mapper_.n_bins_per_feature_,
max_leaf_nodes=self.max_leaf_nodes,
max_depth=self.max_depth,
min_samples_leaf=self.min_samples_leaf,
l2_regularization=self.l2_regularization,
shrinkage=self.learning_rate)
grower.grow()
acc_apply_split_time += grower.total_apply_split_time
acc_find_split_time += grower.total_find_split_time
predictor = grower.make_predictor(
bin_thresholds=self.bin_mapper_.bin_thresholds_)
predictors[-1].append(predictor)
tic_pred = time()
# prepare leaves_data so that _update_raw_predictions can be
# @njitted
leaves_data = [(l.value, l.sample_indices)
for l in grower.finalized_leaves]
_update_raw_predictions(leaves_data, raw_predictions[:, k])
toc_pred = time()
acc_prediction_time += toc_pred - tic_pred
should_stop = self._check_early_stopping(
scorer, X_binned_small_train, y_small_train,
X_binned_val, y_val)
if self.verbose:
self._print_iteration_stats(iteration_start_time)
if should_stop:
break
if self.verbose:
duration = time() - fit_start_time
n_total_leaves = sum(
predictor.get_n_leaf_nodes()
for predictors_at_ith_iteration in self.predictors_
for predictor in predictors_at_ith_iteration)
n_predictors = sum(
len(predictors_at_ith_iteration)
for predictors_at_ith_iteration in self.predictors_)
print(f"Fit {n_predictors} trees in {duration:.3f} s, "
f"({n_total_leaves} total leaves)")
print(f"{'Time spent finding best splits:':<32} "
f"{acc_find_split_time:.3f}s")
print(f"{'Time spent applying splits:':<32} "
f"{acc_apply_split_time:.3f}s")
print(f"{'Time spent predicting:':<32} "
f"{acc_prediction_time:.3f}s")
self.train_scores_ = np.asarray(self.train_scores_)
if self.scoring is not None and self.validation_split is not None:
self.validation_scores_ = np.asarray(self.validation_scores_)
return self
class BaseGradientBoostingMachine(BaseEstimator, ABC):
"""Base class for gradient boosting estimators."""
@abstractmethod
def __init__(self, loss, learning_rate, max_iter, max_leaf_nodes,
max_depth, min_samples_leaf, l2_regularization, max_bins,
scoring, validation_split, n_iter_no_change, tol, verbose,
random_state):
self.loss = loss
self.learning_rate = learning_rate
self.max_iter = max_iter
self.max_leaf_nodes = max_leaf_nodes
self.max_depth = max_depth
self.min_samples_leaf = min_samples_leaf
self.l2_regularization = l2_regularization
self.max_bins = max_bins
self.n_iter_no_change = n_iter_no_change
self.validation_split = validation_split
self.scoring = scoring
self.tol = tol
self.verbose = verbose
self.random_state = random_state
def _validate_parameters(self):
"""Validate parameters passed to __init__.
The parameters that are directly passed to the grower are checked in
TreeGrower."""
if self.loss not in self._VALID_LOSSES:
raise ValueError(
"Loss {} is not supported for {}. Accepted losses"
"are {}.".format(self.loss, self.__class__.__name__,
', '.join(self._VALID_LOSSES)))
if self.learning_rate <= 0:
raise ValueError(f'learning_rate={self.learning_rate} must '
f'be strictly positive')
if self.max_iter < 1:
raise ValueError(f'max_iter={self.max_iter} must '
f'not be smaller than 1.')
if self.n_iter_no_change < 2:
raise ValueError(f'n_iter_no_change={self.n_iter_no_change} '
f'must not be smaller than 2.')
if self.validation_split is not None and self.validation_split <= 0:
raise ValueError(f'validation_split={self.validation_split} '
f'must be strictly positive, or None.')
if self.tol is not None and self.tol < 0:
raise ValueError(f'tol={self.tol} '
f'must not be smaller than 0.')
def fit(self, X, y):
"""Fit the gradient boosting model.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
The input samples.
y : array-like, shape=(n_samples,)
Target values.
Returns
-------
self : object
"""
fit_start_time = time()
acc_find_split_time = 0. # time spent finding the best splits
acc_apply_split_time = 0. # time spent splitting nodes
# time spent predicting X for gradient and hessians update
acc_prediction_time = 0.
# TODO: add support for mixed-typed (numerical + categorical) data
# TODO: add support for missing data
# TODO: add support for pre-binned data (pass-through)?
# TODO: test input checking
X, y = check_X_y(X, y, dtype=[np.float32, np.float64])
y = self._encode_y(y)
if X.shape[0] == 1 or X.shape[1] == 1:
raise ValueError(
'Passing only one sample or one feature is not supported yet. '
'See numba issue #3569.'
)
rng = check_random_state(self.random_state)
self._validate_parameters()
self.n_features_ = X.shape[1] # used for validation in predict()
if self.verbose:
print(f"Binning {X.nbytes / 1e9:.3f} GB of data: ", end="",
flush=True)
tic = time()
self.bin_mapper_ = BinMapper(max_bins=self.max_bins, random_state=rng)
X_binned = self.bin_mapper_.fit_transform(X)
toc = time()
if self.verbose:
duration = toc - tic
troughput = X.nbytes / duration
print(f"{duration:.3f} s ({troughput / 1e6:.3f} MB/s)")
self.loss_ = self._get_loss()
if self.scoring is not None and self.validation_split is not None:
# stratify for classification
stratify = y if hasattr(self.loss_, 'predict_proba') else None
X_binned_train, X_binned_val, y_train, y_val = train_test_split(
X_binned, y, test_size=self.validation_split,
stratify=stratify, random_state=rng)
if X_binned_train.size == 0 or X_binned_val.size == 0:
raise ValueError(
f'Not enough data (n_samples={X_binned.shape[0]}) to '
f'perform early stopping with validation_split='
f'{self.validation_split}. Use more training data or '
f'adjust validation_split.'
)
# Histogram computation is faster on feature-aligned data.
X_binned_train = np.asfortranarray(X_binned_train)
else:
X_binned_train, y_train = X_binned, y
X_binned_val, y_val = None, None
# Subsample the training set for score-based monitoring.
subsample_size = 10000
if X_binned_train.shape[0] < subsample_size:
X_binned_small_train = np.ascontiguousarray(X_binned_train)
y_small_train = y_train
else:
indices = rng.choice(
np.arange(X_binned_train.shape[0]), subsample_size)
X_binned_small_train = X_binned_train[indices]
y_small_train = y_train[indices]
if self.verbose:
print("Fitting gradient boosted rounds:")
n_samples = X_binned_train.shape[0]
# values predicted by the trees. Used as-is in regression, and
# transformed into probas and / or classes for classification
raw_predictions = np.zeros(
shape=(n_samples, self.n_trees_per_iteration_),
dtype=y_train.dtype
)
# gradients and hessians are 1D arrays of size
# n_samples * n_trees_per_iteration
gradients, hessians = self.loss_.init_gradients_and_hessians(
n_samples=n_samples,
n_trees_per_iteration=self.n_trees_per_iteration_
)
# predictors_ is a matrix of TreePredictor objects with shape
# (n_iter_, n_trees_per_iteration)
self.predictors_ = predictors = []
scorer = check_scoring(self, self.scoring)
self.train_scores_ = []
if self.scoring is not None:
# Add predictions of the initial model (before the first tree)
predicted_train = self._predict_binned(X_binned_train)
score_train = scorer._sign * scorer._score_func(y_train,
predicted_train)
self.train_scores_.append(score_train)
if self.validation_split is not None:
self.validation_scores_ = []
predicted_val = self._predict_binned(X_binned_val)
score_val = scorer._sign * scorer._score_func(y_val,
predicted_val)
self.validation_scores_.append(score_val)
for iteration in range(self.max_iter):
if self.verbose:
iteration_start_time = time()
print(f"[{iteration + 1}/{self.max_iter}] ", end='',
flush=True)
# Update gradients and hessians, inplace
self.loss_.update_gradients_and_hessians(gradients, hessians,
y_train, raw_predictions)
predictors.append([])
# Build `n_trees_per_iteration` trees.
for k, (gradients_at_k, hessians_at_k) in enumerate(zip(
np.array_split(gradients, self.n_trees_per_iteration_),
np.array_split(hessians, self.n_trees_per_iteration_))):
# the xxxx_at_k arrays are **views** on the original arrays.
# Note that for binary classif and regressions,
# n_trees_per_iteration is 1 and xxxx_at_k is equivalent to the
# whole array.
grower = TreeGrower(
X_binned_train, gradients_at_k, hessians_at_k,
max_bins=self.max_bins,
n_bins_per_feature=self.bin_mapper_.n_bins_per_feature_,
max_leaf_nodes=self.max_leaf_nodes,
max_depth=self.max_depth,
min_samples_leaf=self.min_samples_leaf,
l2_regularization=self.l2_regularization,
shrinkage=self.learning_rate)
grower.grow()
acc_apply_split_time += grower.total_apply_split_time
acc_find_split_time += grower.total_find_split_time
predictor = grower.make_predictor(
bin_thresholds=self.bin_mapper_.bin_thresholds_)
predictors[-1].append(predictor)
tic_pred = time()
# prepare leaves_data so that _update_raw_predictions can be
# @njitted
leaves_data = [(l.value, l.sample_indices)
for l in grower.finalized_leaves]
_update_raw_predictions(leaves_data, raw_predictions[:, k])
toc_pred = time()
acc_prediction_time += toc_pred - tic_pred
should_stop = self._check_early_stopping(
scorer, X_binned_small_train, y_small_train,
X_binned_val, y_val)
if self.verbose:
self._print_iteration_stats(iteration_start_time)
if should_stop:
break
if self.verbose:
duration = time() - fit_start_time
n_total_leaves = sum(
predictor.get_n_leaf_nodes()
for predictors_at_ith_iteration in self.predictors_
for predictor in predictors_at_ith_iteration)
n_predictors = sum(
len(predictors_at_ith_iteration)
for predictors_at_ith_iteration in self.predictors_)
print(f"Fit {n_predictors} trees in {duration:.3f} s, "
f"({n_total_leaves} total leaves)")
print(f"{'Time spent finding best splits:':<32} "
f"{acc_find_split_time:.3f}s")
print(f"{'Time spent applying splits:':<32} "
f"{acc_apply_split_time:.3f}s")
print(f"{'Time spent predicting:':<32} "
f"{acc_prediction_time:.3f}s")
self.train_scores_ = np.asarray(self.train_scores_)
if self.scoring is not None and self.validation_split is not None:
self.validation_scores_ = np.asarray(self.validation_scores_)
return self
def _check_early_stopping(self, scorer, X_binned_train, y_train,
X_binned_val, y_val):
"""Check if fitting should be early-stopped.
Return True (do early stopping) if the score at iteration i hasn't
improved any of the last n_iter_no_change scores by at least tol
percent.
Scores are computed on validation data or on training data.
"""
if self.scoring is None:
# no early stopping.
# In sklearn early stopping is not done if n_iter_no_change is None
return False
def _should_stop(scores):
if len(scores) - 1 < self.n_iter_no_change:
# - 1 because scores[0] is for the init model before the first
# tree.
return False
current_score = scores[-1] # score at current iteration
previous_scores = scores[-self.n_iter_no_change:-1]
return all(
current_score < prev_score * (1 + self.tol * scorer._sign)
for prev_score in previous_scores
)
# TODO: make sure that self.predict can work on binned data and
# then only use the public scorer.__call__.
predicted_train = self._predict_binned(X_binned_train)
score_train = scorer._sign * scorer._score_func(y_train,
predicted_train)
self.train_scores_.append(score_train)
if self.validation_split is not None:
predicted_val = self._predict_binned(X_binned_val)
score_val = scorer._sign * scorer._score_func(y_val,
predicted_val)
self.validation_scores_.append(score_val)
return _should_stop(self.validation_scores_)
return _should_stop(self.train_scores_)
def _print_iteration_stats(self, iteration_start_time):
"""Print info about the current fitting iteration."""
log_msg = ''
predictors_of_ith_iteration = [
predictors_list for predictors_list in self.predictors_[-1]
if predictors_list
]
n_trees = len(predictors_of_ith_iteration)
max_depth = max(predictor.get_max_depth()
for predictor in predictors_of_ith_iteration)
n_leaves = sum(predictor.get_n_leaf_nodes()
for predictor in predictors_of_ith_iteration)
if n_trees == 1:
log_msg += (f"{n_trees} tree, {n_leaves} leaves, ")
else:
log_msg += (f"{n_trees} trees, {n_leaves} leaves ")
log_msg += (f"({int(n_leaves / n_trees)} on avg), ")
log_msg += f"max depth = {max_depth}, "
if self.scoring is not None:
log_msg += f"{self.scoring} train: {self.train_scores_[-1]:.5f}, "
if self.validation_split is not None:
log_msg += (f"{self.scoring} val: "
f"{self.validation_scores_[-1]:.5f}, ")
iteration_time = time() - iteration_start_time
log_msg += f"in {iteration_time:0.3f}s"
print(log_msg)
def _raw_predict(self, X, binned=False):
"""Return the sum of the leaves values over all predictors.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
The input samples.
binned : bool, optional (default=False)
If True, X is considered to be already binned.
Returns
-------
raw_predictions : array, shape (n_samples * n_trees_per_iteration,)
The raw predicted values.
"""
X = check_array(X)
check_is_fitted(self, 'predictors_')
if X.shape[1] != self.n_features_:
raise ValueError(
f'X has {X.shape[1]} features but this estimator was '
f'trained with {self.n_features_} features.'
)
n_samples = X.shape[0]
raw_predictions = np.zeros(
shape=(n_samples, self.n_trees_per_iteration_),
dtype=np.float32
)
# Should we parallelize this?
for predictors_of_ith_iteration in self.predictors_:
for k, predictor in enumerate(predictors_of_ith_iteration):
predict = (predictor.predict_binned if binned
else predictor.predict)
raw_predictions[:, k] += predict(X)
return raw_predictions
@abstractmethod
def _get_loss(self):
pass
@abstractmethod
def _encode_y(self, y=None):
pass
@property
def n_iter_(self):
check_is_fitted(self, 'predictors_')
return len(self.predictors_)
@abstractmethod
def _predict_binned(self, X_binned):
pass
