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Really slow tests on aarch64
See original GitHub issuelogs_conda-forge_scikit-learn-feedstock_2_2.log Man, I can understand if these will be “won’t fix” but when building on drone, some tests timeout after 5 mins, but only sometimes.
They are the following
- test_least_absolute_deviation
- test_missing_values_minmax_imputation
- test_warm_start_clear
- test_recursion_decision_function
- test_estimators
- test_early_stopping_regression
- test_missing_values_resilience
- test_warm_start_yields_identical_results
Logs
________________________ test_least_absolute_deviation _________________________
[gw7] linux -- Python 3.7.3 $PREFIX/bin/python
def test_least_absolute_deviation():
# For coverage only.
X, y = make_regression(n_samples=500, random_state=0)
gbdt = HistGradientBoostingRegressor(loss='least_absolute_deviation',
random_state=0)
> gbdt.fit(X, y)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py:163:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:319: in fit
grower.grow()
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/grower.py:252: in grow
self.split_next()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <sklearn.ensemble._hist_gradient_boosting.grower.TreeGrower object at 0xffff8ace4470>
def split_next(self):
"""Split the node with highest potential gain.
Returns
-------
left : TreeNode
The resulting left child.
right : TreeNode
The resulting right child.
"""
# Consider the node with the highest loss reduction (a.k.a. gain)
node = heappop(self.splittable_nodes)
tic = time()
(sample_indices_left,
sample_indices_right,
right_child_pos) = self.splitter.split_indices(node.split_info,
> node.sample_indices)
E Failed: Timeout >300.0s
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/grower.py:320: Failed
____________________ test_missing_values_minmax_imputation _____________________
[gw5] linux -- Python 3.7.3 $PREFIX/bin/python
def test_missing_values_minmax_imputation():
# Compare the buit-in missing value handling of Histogram GBC with an
# a-priori missing value imputation strategy that should yield the same
# results in terms of decision function.
#
# Each feature (containing NaNs) is replaced by 2 features:
# - one where the nans are replaced by min(feature) - 1
# - one where the nans are replaced by max(feature) + 1
# A split where nans go to the left has an equivalent split in the
# first (min) feature, and a split where nans go to the right has an
# equivalent split in the second (max) feature.
#
# Assuming the data is such that there is never a tie to select the best
# feature to split on during training, the learned decision trees should be
# strictly equivalent (learn a sequence of splits that encode the same
# decision function).
#
# The MinMaxImputer transformer is meant to be a toy implementation of the
# "Missing In Attributes" (MIA) missing value handling for decision trees
# https://www.sciencedirect.com/science/article/abs/pii/S0167865508000305
# The implementation of MIA as an imputation transformer was suggested by
# "Remark 3" in https://arxiv.org/abs/1902.06931
class MinMaxImputer(BaseEstimator, TransformerMixin):
def fit(self, X, y=None):
mm = MinMaxScaler().fit(X)
self.data_min_ = mm.data_min_
self.data_max_ = mm.data_max_
return self
def transform(self, X):
X_min, X_max = X.copy(), X.copy()
for feature_idx in range(X.shape[1]):
nan_mask = np.isnan(X[:, feature_idx])
X_min[nan_mask, feature_idx] = self.data_min_[feature_idx] - 1
X_max[nan_mask, feature_idx] = self.data_max_[feature_idx] + 1
return np.concatenate([X_min, X_max], axis=1)
def make_missing_value_data(n_samples=int(1e4), seed=0):
rng = np.random.RandomState(seed)
X, y = make_regression(n_samples=n_samples, n_features=4,
random_state=rng)
# Pre-bin the data to ensure a deterministic handling by the 2
# strategies and also make it easier to insert np.nan in a structured
# way:
X = KBinsDiscretizer(n_bins=42, encode="ordinal").fit_transform(X)
# First feature has missing values completely at random:
rnd_mask = rng.rand(X.shape[0]) > 0.9
X[rnd_mask, 0] = np.nan
# Second and third features have missing values for extreme values
# (censoring missingness):
low_mask = X[:, 1] == 0
X[low_mask, 1] = np.nan
high_mask = X[:, 2] == X[:, 2].max()
X[high_mask, 2] = np.nan
# Make the last feature nan pattern very informative:
y_max = np.percentile(y, 70)
y_max_mask = y >= y_max
y[y_max_mask] = y_max
X[y_max_mask, 3] = np.nan
# Check that there is at least one missing value in each feature:
for feature_idx in range(X.shape[1]):
assert any(np.isnan(X[:, feature_idx]))
# Let's use a test set to check that the learned decision function is
# the same as evaluated on unseen data. Otherwise it could just be the
# case that we find two independent ways to overfit the training set.
return train_test_split(X, y, random_state=rng)
# n_samples need to be large enough to minimize the likelihood of having
# several candidate splits with the same gain value in a given tree.
X_train, X_test, y_train, y_test = make_missing_value_data(
n_samples=int(1e4), seed=0)
# Use a small number of leaf nodes and iterations so as to keep
# under-fitting models to minimize the likelihood of ties when training the
# model.
gbm1 = HistGradientBoostingRegressor(max_iter=100,
max_leaf_nodes=5,
random_state=0)
gbm1.fit(X_train, y_train)
gbm2 = make_pipeline(MinMaxImputer(), clone(gbm1))
> gbm2.fit(X_train, y_train)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py:386:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/pipeline.py:352: in fit
self._final_estimator.fit(Xt, y, **fit_params)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:301: in fit
y_train, raw_predictions)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <sklearn.ensemble._hist_gradient_boosting.loss.LeastSquares object at 0xffff9580a828>
gradients = array([ 4.6511135, 5.4659395, -74.38958 , ..., 28.82124 ,
3.4787138, 6.115484 ], dtype=float32)
hessians = array([[1.]], dtype=float32)
y_true = array([-123.57738749, 74.6151337 , 53.44932862, ..., -66.93498585,
74.6151337 , 74.6151337 ])
raw_predictions = array([-118.92627394, 80.081073 , -20.94025014, ..., -38.11374701,
78.09384736, 80.73061789])
def update_gradients_and_hessians(self, gradients, hessians, y_true,
raw_predictions):
# shape (1, n_samples) --> (n_samples,). reshape(-1) is more likely to
# return a view.
raw_predictions = raw_predictions.reshape(-1)
gradients = gradients.reshape(-1)
> _update_gradients_least_squares(gradients, y_true, raw_predictions)
E Failed: Timeout >300.0s
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/loss.py:153: Failed
__________ test_warm_start_clear[HistGradientBoostingRegressor-X1-y1] __________
[gw1] linux -- Python 3.7.3 $PREFIX/bin/python
GradientBoosting = <class 'sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingRegressor'>
X = array([[-1.5415874 , 0.22739278, -1.35338886, ..., 0.86727663,
0.40520408, -0.06964158],
[-1.0212540...985, -0.67832984],
[ 1.83708069, -0.00296475, 1.80144921, ..., 0.11230769,
0.33109242, 1.51848293]])
y = array([ 5.89981499e+01, -1.51472301e+02, 3.92774675e+00, 1.30275835e+02,
2.04728060e+00, 1.01587138e+02, -1...4499e+02, 1.17344962e+02, 5.81688314e+01,
-7.85896400e+01, 1.59876618e+01, -2.50470392e+02, -2.19074129e+02])
@pytest.mark.parametrize('GradientBoosting, X, y', [
(HistGradientBoostingClassifier, X_classification, y_classification),
(HistGradientBoostingRegressor, X_regression, y_regression)
])
def test_warm_start_clear(GradientBoosting, X, y):
# Test if fit clears state.
gb_1 = GradientBoosting(n_iter_no_change=5, random_state=42)
gb_1.fit(X, y)
gb_2 = GradientBoosting(n_iter_no_change=5, random_state=42,
warm_start=True)
gb_2.fit(X, y) # inits state
gb_2.set_params(warm_start=False)
> gb_2.fit(X, y) # clears old state and equals est
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py:143:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:362: in fit
X_binned_val, y_val,
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:443: in _check_early_stopping_scorer
self.scorer_(self, X_binned_val, y_val)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/metrics/_scorer.py:371: in _passthrough_scorer
return estimator.score(*args, **kwargs)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/base.py:422: in score
y_pred = self.predict(X)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:813: in predict
return self._raw_predict(X).ravel()
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:594: in _raw_predict
raw_predictions[k, :] += predict(X)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <sklearn.ensemble._hist_gradient_boosting.predictor.TreePredictor object at 0xffffad5fdeb8>
X = array([[49, 61, 6, 31, 62, 42, 3, 3, 80, 6, 27, 33, 89, 71, 20, 86,
59, 19, 86, 13, 79, 59, 56, 45, 47, 60... 57, 56,
36, 54, 49, 47, 75, 72, 46, 68, 14, 8, 74, 23, 44, 30, 74, 74,
87, 75, 9, 78]], dtype=uint8)
missing_values_bin_idx = 255
def predict_binned(self, X, missing_values_bin_idx):
"""Predict raw values for binned data.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
The input samples.
missing_values_bin_idx : uint8
Index of the bin that is used for missing values. This is the
index of the last bin and is always equal to max_bins (as passed
to the GBDT classes), or equivalently to n_bins - 1.
Returns
-------
y : ndarray, shape (n_samples,)
The raw predicted values.
"""
out = np.empty(X.shape[0], dtype=Y_DTYPE)
> _predict_from_binned_data(self.nodes, X, missing_values_bin_idx, out)
E Failed: Timeout >300.0s
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/predictor.py:68: Failed
___________________ test_recursion_decision_function[0-est1] ___________________
[gw5] linux -- Python 3.7.3 $PREFIX/bin/python
est = HistGradientBoostingClassifier(l2_regularization=0.0, learning_rate=0.1,
loss='auto', m...07,
validation_fraction=0.1, verbose=0,
warm_start=False)
target_feature = 0
@pytest.mark.parametrize('est', (
GradientBoostingClassifier(random_state=0),
HistGradientBoostingClassifier(random_state=0),
))
@pytest.mark.parametrize('target_feature', (0, 1, 2, 3, 4, 5))
def test_recursion_decision_function(est, target_feature):
# Make sure the recursion method (implicitly uses decision_function) has
# the same result as using brute method with
# response_method=decision_function
X, y = make_classification(n_classes=2, n_clusters_per_class=1,
random_state=1)
assert np.mean(y) == .5 # make sure the init estimator predicts 0 anyway
est.fit(X, y)
preds_1, _ = partial_dependence(est, X, [target_feature],
response_method='decision_function',
method='recursion')
preds_2, _ = partial_dependence(est, X, [target_feature],
response_method='decision_function',
> method='brute')
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/tests/test_partial_dependence.py:230:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:401: in partial_dependence
estimator, grid, features_indices, X, response_method
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:147: in _partial_dependence_brute
predictions = prediction_method(X_eval)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1030: in decision_function
decision = self._raw_predict(X)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:594: in _raw_predict
raw_predictions[k, :] += predict(X)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <sklearn.ensemble._hist_gradient_boosting.predictor.TreePredictor object at 0xffff94286c18>
X = array([[ 0.51731301, 0.26686086, 1.6169496 , ..., 0.19915097,
-1.23685338, 1.24328724],
[ 0.5173130...673, 1.17899425],
[ 0.51731301, 2.27449013, 1.37333246, ..., 0.71939066,
-2.17071106, -1.6845077 ]])
def predict(self, X):
"""Predict raw values for non-binned data.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
The input samples.
Returns
-------
y : ndarray, shape (n_samples,)
The raw predicted values.
"""
out = np.empty(X.shape[0], dtype=Y_DTYPE)
> _predict_from_numeric_data(self.nodes, X, out)
E Failed: Timeout >300.0s
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/predictor.py:47: Failed
----------------------------- Captured stderr call -----------------------------
___________________ test_recursion_decision_function[1-est1] ___________________
[gw7] linux -- Python 3.7.3 $PREFIX/bin/python
est = HistGradientBoostingClassifier(l2_regularization=0.0, learning_rate=0.1,
loss='auto', m...07,
validation_fraction=0.1, verbose=0,
warm_start=False)
target_feature = 1
@pytest.mark.parametrize('est', (
GradientBoostingClassifier(random_state=0),
HistGradientBoostingClassifier(random_state=0),
))
@pytest.mark.parametrize('target_feature', (0, 1, 2, 3, 4, 5))
def test_recursion_decision_function(est, target_feature):
# Make sure the recursion method (implicitly uses decision_function) has
# the same result as using brute method with
# response_method=decision_function
X, y = make_classification(n_classes=2, n_clusters_per_class=1,
random_state=1)
assert np.mean(y) == .5 # make sure the init estimator predicts 0 anyway
est.fit(X, y)
preds_1, _ = partial_dependence(est, X, [target_feature],
response_method='decision_function',
method='recursion')
preds_2, _ = partial_dependence(est, X, [target_feature],
response_method='decision_function',
> method='brute')
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/tests/test_partial_dependence.py:230:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:401: in partial_dependence
estimator, grid, features_indices, X, response_method
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:147: in _partial_dependence_brute
predictions = prediction_method(X_eval)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1030: in decision_function
decision = self._raw_predict(X)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:594: in _raw_predict
raw_predictions[k, :] += predict(X)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <sklearn.ensemble._hist_gradient_boosting.predictor.TreePredictor object at 0xffff88236f60>
X = array([[-0.58652394, 0.88056426, 1.6169496 , ..., 0.19915097,
-1.23685338, 1.24328724],
[ 0.1855356...673, 1.17899425],
[ 0.94926809, 0.88056426, 1.37333246, ..., 0.71939066,
-2.17071106, -1.6845077 ]])
def predict(self, X):
"""Predict raw values for non-binned data.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
The input samples.
Returns
-------
y : ndarray, shape (n_samples,)
The raw predicted values.
"""
out = np.empty(X.shape[0], dtype=Y_DTYPE)
> _predict_from_numeric_data(self.nodes, X, out)
E Failed: Timeout >300.0s
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/predictor.py:47: Failed
___________________ test_recursion_decision_function[4-est1] ___________________
[gw5] linux -- Python 3.7.3 $PREFIX/bin/python
est = HistGradientBoostingClassifier(l2_regularization=0.0, learning_rate=0.1,
loss='auto', m...07,
validation_fraction=0.1, verbose=0,
warm_start=False)
target_feature = 4
@pytest.mark.parametrize('est', (
GradientBoostingClassifier(random_state=0),
HistGradientBoostingClassifier(random_state=0),
))
@pytest.mark.parametrize('target_feature', (0, 1, 2, 3, 4, 5))
def test_recursion_decision_function(est, target_feature):
# Make sure the recursion method (implicitly uses decision_function) has
# the same result as using brute method with
# response_method=decision_function
X, y = make_classification(n_classes=2, n_clusters_per_class=1,
random_state=1)
assert np.mean(y) == .5 # make sure the init estimator predicts 0 anyway
est.fit(X, y)
preds_1, _ = partial_dependence(est, X, [target_feature],
response_method='decision_function',
method='recursion')
preds_2, _ = partial_dependence(est, X, [target_feature],
response_method='decision_function',
> method='brute')
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/tests/test_partial_dependence.py:230:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:401: in partial_dependence
estimator, grid, features_indices, X, response_method
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:147: in _partial_dependence_brute
predictions = prediction_method(X_eval)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1030: in decision_function
decision = self._raw_predict(X)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:594: in _raw_predict
raw_predictions[k, :] += predict(X)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <sklearn.ensemble._hist_gradient_boosting.predictor.TreePredictor object at 0xffff942860b8>
X = array([[-0.58652394, 0.26686086, 1.6169496 , ..., 0.19915097,
-1.23685338, 1.24328724],
[ 0.1855356...673, 1.17899425],
[ 0.94926809, 2.27449013, 1.37333246, ..., 0.71939066,
-2.17071106, -1.6845077 ]])
def predict(self, X):
"""Predict raw values for non-binned data.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
The input samples.
Returns
-------
y : ndarray, shape (n_samples,)
The raw predicted values.
"""
out = np.empty(X.shape[0], dtype=Y_DTYPE)
> _predict_from_numeric_data(self.nodes, X, out)
E Failed: Timeout >300.0s
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/predictor.py:47: Failed
___________________ test_recursion_decision_function[3-est1] ___________________
[gw3] linux -- Python 3.7.3 $PREFIX/bin/python
est = HistGradientBoostingClassifier(l2_regularization=0.0, learning_rate=0.1,
loss='auto', m...07,
validation_fraction=0.1, verbose=0,
warm_start=False)
target_feature = 3
@pytest.mark.parametrize('est', (
GradientBoostingClassifier(random_state=0),
HistGradientBoostingClassifier(random_state=0),
))
@pytest.mark.parametrize('target_feature', (0, 1, 2, 3, 4, 5))
def test_recursion_decision_function(est, target_feature):
# Make sure the recursion method (implicitly uses decision_function) has
# the same result as using brute method with
# response_method=decision_function
X, y = make_classification(n_classes=2, n_clusters_per_class=1,
random_state=1)
assert np.mean(y) == .5 # make sure the init estimator predicts 0 anyway
est.fit(X, y)
preds_1, _ = partial_dependence(est, X, [target_feature],
response_method='decision_function',
method='recursion')
preds_2, _ = partial_dependence(est, X, [target_feature],
response_method='decision_function',
> method='brute')
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/tests/test_partial_dependence.py:230:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:401: in partial_dependence
estimator, grid, features_indices, X, response_method
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:147: in _partial_dependence_brute
predictions = prediction_method(X_eval)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1030: in decision_function
decision = self._raw_predict(X)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:594: in _raw_predict
raw_predictions[k, :] += predict(X)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <sklearn.ensemble._hist_gradient_boosting.predictor.TreePredictor object at 0xffff96ebf9e8>
X = array([[-0.58652394, 0.26686086, 1.6169496 , ..., 0.19915097,
-1.23685338, 1.24328724],
[ 0.1855356...673, 1.17899425],
[ 0.94926809, 2.27449013, 1.37333246, ..., 0.71939066,
-2.17071106, -1.6845077 ]])
def predict(self, X):
"""Predict raw values for non-binned data.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
The input samples.
Returns
-------
y : ndarray, shape (n_samples,)
The raw predicted values.
"""
out = np.empty(X.shape[0], dtype=Y_DTYPE)
> _predict_from_numeric_data(self.nodes, X, out)
E Failed: Timeout >300.0s
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/predictor.py:47: Failed
_______________ test_estimators[TSNE()-check_estimators_dtypes] ________________
[gw2] linux -- Python 3.7.3 $PREFIX/bin/python
estimator = TSNE(angle=0.5, early_exaggeration=12.0, init='random', learning_rate=200.0,
method='barnes_hut', metric='euclide...omponents=2, n_iter=1000, n_iter_without_progress=300, n_jobs=None,
perplexity=30.0, random_state=None, verbose=0)
check = functools.partial(<function check_estimators_dtypes at 0xffffa3d99950>, 'TSNE')
@parametrize_with_checks(_tested_estimators())
def test_estimators(estimator, check):
# Common tests for estimator instances
with ignore_warnings(category=(FutureWarning,
ConvergenceWarning,
UserWarning, FutureWarning)):
_set_checking_parameters(estimator)
> check(estimator)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/tests/test_common.py:98:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/utils/_testing.py:327: in wrapper
return fn(*args, **kwargs)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/utils/estimator_checks.py:1338: in check_estimators_dtypes
estimator.fit(X_train, y)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/manifold/_t_sne.py:904: in fit
self.fit_transform(X)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/manifold/_t_sne.py:886: in fit_transform
embedding = self._fit(X)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/manifold/_t_sne.py:798: in _fit
skip_num_points=skip_num_points)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/manifold/_t_sne.py:852: in _tsne
**opt_args)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/manifold/_t_sne.py:358: in _gradient_descent
error, grad = objective(p, *args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
params = array([-155.24246 , -50.656303 , 56.69138 , 169.73177 ,
-110.365 , -132.27484 , 77.20271 , -149.4...546 , -28.501383 , 15.549845 ,
-189.69225 , 40.249886 , 127.85508 , -69.97335 ],
dtype=float32)
P = <20x20 sparse matrix of type '<class 'numpy.float64'>'
with 380 stored elements in Compressed Sparse Row format>
degrees_of_freedom = 1, n_samples = 20, n_components = 2, angle = 0.5
skip_num_points = 0, verbose = 0, compute_error = False, num_threads = 96
def _kl_divergence_bh(params, P, degrees_of_freedom, n_samples, n_components,
angle=0.5, skip_num_points=0, verbose=False,
compute_error=True, num_threads=1):
"""t-SNE objective function: KL divergence of p_ijs and q_ijs.
Uses Barnes-Hut tree methods to calculate the gradient that
runs in O(NlogN) instead of O(N^2)
Parameters
----------
params : array, shape (n_params,)
Unraveled embedding.
P : csr sparse matrix, shape (n_samples, n_sample)
Sparse approximate joint probability matrix, computed only for the
k nearest-neighbors and symmetrized.
degrees_of_freedom : int
Degrees of freedom of the Student's-t distribution.
n_samples : int
Number of samples.
n_components : int
Dimension of the embedded space.
angle : float (default: 0.5)
This is the trade-off between speed and accuracy for Barnes-Hut T-SNE.
'angle' is the angular size (referred to as theta in [3]) of a distant
node as measured from a point. If this size is below 'angle' then it is
used as a summary node of all points contained within it.
This method is not very sensitive to changes in this parameter
in the range of 0.2 - 0.8. Angle less than 0.2 has quickly increasing
computation time and angle greater 0.8 has quickly increasing error.
skip_num_points : int (optional, default:0)
This does not compute the gradient for points with indices below
`skip_num_points`. This is useful when computing transforms of new
data where you'd like to keep the old data fixed.
verbose : int
Verbosity level.
compute_error: bool (optional, default:True)
If False, the kl_divergence is not computed and returns NaN.
num_threads : int (optional, default:1)
Number of threads used to compute the gradient. This is set here to
avoid calling _openmp_effective_n_threads for each gradient step.
Returns
-------
kl_divergence : float
Kullback-Leibler divergence of p_ij and q_ij.
grad : array, shape (n_params,)
Unraveled gradient of the Kullback-Leibler divergence with respect to
the embedding.
"""
params = params.astype(np.float32, copy=False)
X_embedded = params.reshape(n_samples, n_components)
val_P = P.data.astype(np.float32, copy=False)
neighbors = P.indices.astype(np.int64, copy=False)
indptr = P.indptr.astype(np.int64, copy=False)
grad = np.zeros(X_embedded.shape, dtype=np.float32)
error = _barnes_hut_tsne.gradient(val_P, X_embedded, neighbors, indptr,
grad, angle, n_components, verbose,
dof=degrees_of_freedom,
compute_error=compute_error,
> num_threads=num_threads)
E Failed: Timeout >300.0s
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/manifold/_t_sne.py:262: Failed
----------------------------- Captured stderr call -----------------------------
___________________ test_recursion_decision_function[5-est1] ___________________
[gw7] linux -- Python 3.7.3 $PREFIX/bin/python
est = HistGradientBoostingClassifier(l2_regularization=0.0, learning_rate=0.1,
loss='auto', m...07,
validation_fraction=0.1, verbose=0,
warm_start=False)
target_feature = 5
@pytest.mark.parametrize('est', (
GradientBoostingClassifier(random_state=0),
HistGradientBoostingClassifier(random_state=0),
))
@pytest.mark.parametrize('target_feature', (0, 1, 2, 3, 4, 5))
def test_recursion_decision_function(est, target_feature):
# Make sure the recursion method (implicitly uses decision_function) has
# the same result as using brute method with
# response_method=decision_function
X, y = make_classification(n_classes=2, n_clusters_per_class=1,
random_state=1)
assert np.mean(y) == .5 # make sure the init estimator predicts 0 anyway
est.fit(X, y)
preds_1, _ = partial_dependence(est, X, [target_feature],
response_method='decision_function',
method='recursion')
preds_2, _ = partial_dependence(est, X, [target_feature],
response_method='decision_function',
> method='brute')
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/tests/test_partial_dependence.py:230:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:401: in partial_dependence
estimator, grid, features_indices, X, response_method
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:147: in _partial_dependence_brute
predictions = prediction_method(X_eval)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1030: in decision_function
decision = self._raw_predict(X)
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:594: in _raw_predict
raw_predictions[k, :] += predict(X)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <sklearn.ensemble._hist_gradient_boosting.predictor.TreePredictor object at 0xffff880f7160>
X = array([[-0.58652394, 0.26686086, 1.6169496 , ..., 0.19915097,
-1.23685338, 1.24328724],
[ 0.1855356...673, 1.17899425],
[ 0.94926809, 2.27449013, 1.37333246, ..., 0.71939066,
-2.17071106, -1.6845077 ]])
def predict(self, X):
"""Predict raw values for non-binned data.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
The input samples.
Returns
-------
y : ndarray, shape (n_samples,)
The raw predicted values.
"""
out = np.empty(X.shape[0], dtype=Y_DTYPE)
> _predict_from_numeric_data(self.nodes, X, out)
E Failed: Timeout >300.0s
../_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.7/site-packages/sklearn/ensemble/_hist_gradient_boosting/predictor.py:47: Failed
Issue Analytics
- State:
- Created 4 years ago
- Comments:10 (6 by maintainers)
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Thanks, actually setting OMP_NUM_THREADS really helped things. Thanks for that pointer!
Thanks for the report and for trying to build aarch64 and ppc on conda-forge.
A fair amount of these use
HistGradientBoostingClassifier.HistGradientBoostingClassifieruses OpenMP and if you are usingpytest-xdistyou should definitely restrict the number of threads per worker in tests withOMP_NUM_THREADS(currently onlyOPENBLAS_NUM_THREADSis set in the recipe I think). OtherwiseHistGradientBoostingClassifiercan be very slow in case of CPU oversubscription particularly when used together withpytest-xdisthttps://github.com/scikit-learn/scikit-learn/issues/15078 we haven’t really found the root cause of it.