1405 lines
51 KiB
Python
1405 lines
51 KiB
Python
"""
|
|
Testing for the gradient boosting module (sklearn.ensemble.gradient_boosting).
|
|
"""
|
|
import warnings
|
|
import numpy as np
|
|
|
|
from scipy.sparse import csr_matrix
|
|
from scipy.sparse import csc_matrix
|
|
from scipy.sparse import coo_matrix
|
|
from scipy.special import expit
|
|
|
|
import pytest
|
|
|
|
from sklearn import datasets
|
|
from sklearn.base import clone
|
|
from sklearn.datasets import (make_classification, fetch_california_housing,
|
|
make_regression)
|
|
from sklearn.ensemble import GradientBoostingClassifier
|
|
from sklearn.ensemble import GradientBoostingRegressor
|
|
from sklearn.ensemble._gradient_boosting import predict_stages
|
|
from sklearn.preprocessing import OneHotEncoder
|
|
from sklearn.svm import LinearSVC
|
|
from sklearn.metrics import mean_squared_error
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.utils import check_random_state, tosequence
|
|
from sklearn.utils._mocking import NoSampleWeightWrapper
|
|
from sklearn.utils._testing import assert_almost_equal
|
|
from sklearn.utils._testing import assert_array_almost_equal
|
|
from sklearn.utils._testing import assert_array_equal
|
|
from sklearn.utils._testing import assert_raises
|
|
from sklearn.utils._testing import assert_raise_message
|
|
from sklearn.utils._testing import assert_warns
|
|
from sklearn.utils._testing import assert_warns_message
|
|
from sklearn.utils._testing import skip_if_32bit
|
|
from sklearn.utils._testing import ignore_warnings
|
|
from sklearn.exceptions import DataConversionWarning
|
|
from sklearn.exceptions import NotFittedError
|
|
from sklearn.dummy import DummyClassifier, DummyRegressor
|
|
from sklearn.pipeline import make_pipeline
|
|
from sklearn.linear_model import LinearRegression
|
|
from sklearn.svm import NuSVR
|
|
|
|
|
|
GRADIENT_BOOSTING_ESTIMATORS = [GradientBoostingClassifier,
|
|
GradientBoostingRegressor]
|
|
|
|
# toy sample
|
|
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
|
|
y = [-1, -1, -1, 1, 1, 1]
|
|
T = [[-1, -1], [2, 2], [3, 2]]
|
|
true_result = [-1, 1, 1]
|
|
|
|
rng = np.random.RandomState(0)
|
|
# also load the boston dataset
|
|
# and randomly permute it
|
|
boston = datasets.load_boston()
|
|
perm = rng.permutation(boston.target.size)
|
|
boston.data = boston.data[perm]
|
|
boston.target = boston.target[perm]
|
|
|
|
# also load the iris dataset
|
|
# and randomly permute it
|
|
iris = datasets.load_iris()
|
|
perm = rng.permutation(iris.target.size)
|
|
iris.data = iris.data[perm]
|
|
iris.target = iris.target[perm]
|
|
|
|
|
|
def check_classification_toy(loss):
|
|
# Check classification on a toy dataset.
|
|
clf = GradientBoostingClassifier(loss=loss, n_estimators=10,
|
|
random_state=1)
|
|
|
|
assert_raises(ValueError, clf.predict, T)
|
|
|
|
clf.fit(X, y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert 10 == len(clf.estimators_)
|
|
|
|
deviance_decrease = (clf.train_score_[:-1] - clf.train_score_[1:])
|
|
assert np.any(deviance_decrease >= 0.0)
|
|
|
|
leaves = clf.apply(X)
|
|
assert leaves.shape == (6, 10, 1)
|
|
|
|
|
|
@pytest.mark.parametrize('loss', ('deviance', 'exponential'))
|
|
def test_classification_toy(loss):
|
|
check_classification_toy(loss)
|
|
|
|
|
|
def test_classifier_parameter_checks():
|
|
# Check input parameter validation for GradientBoostingClassifier.
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(n_estimators=0).fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(n_estimators=-1).fit, X, y)
|
|
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(learning_rate=0.0).fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(learning_rate=-1.0).fit, X, y)
|
|
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(loss='foobar').fit, X, y)
|
|
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(min_samples_split=0.0).fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(min_samples_split=-1.0).fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(min_samples_split=1.1).fit, X, y)
|
|
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(min_samples_leaf=0).fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(min_samples_leaf=-1.0).fit, X, y)
|
|
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(min_weight_fraction_leaf=-1.).fit,
|
|
X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(min_weight_fraction_leaf=0.6).fit,
|
|
X, y)
|
|
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(subsample=0.0).fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(subsample=1.1).fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(subsample=-0.1).fit, X, y)
|
|
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(max_depth=-0.1).fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(max_depth=0).fit, X, y)
|
|
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(init={}).fit, X, y)
|
|
|
|
# test fit before feature importance
|
|
assert_raises(ValueError,
|
|
lambda: GradientBoostingClassifier().feature_importances_)
|
|
|
|
# deviance requires ``n_classes >= 2``.
|
|
assert_raises(ValueError,
|
|
lambda X, y: GradientBoostingClassifier(
|
|
loss='deviance').fit(X, y),
|
|
X, [0, 0, 0, 0])
|
|
|
|
|
|
def test_regressor_parameter_checks():
|
|
# Check input parameter validation for GradientBoostingRegressor
|
|
assert_raise_message(ValueError, "alpha must be in (0.0, 1.0) but was 1.2",
|
|
GradientBoostingRegressor(loss='huber', alpha=1.2)
|
|
.fit, X, y)
|
|
assert_raise_message(ValueError, "alpha must be in (0.0, 1.0) but was 1.2",
|
|
GradientBoostingRegressor(loss='quantile', alpha=1.2)
|
|
.fit, X, y)
|
|
assert_raise_message(ValueError, "Invalid value for max_features: "
|
|
"'invalid'. Allowed string values are 'auto', 'sqrt'"
|
|
" or 'log2'.",
|
|
GradientBoostingRegressor(max_features='invalid').fit,
|
|
X, y)
|
|
assert_raise_message(ValueError, "n_iter_no_change should either be None"
|
|
" or an integer. 'invalid' was passed",
|
|
GradientBoostingRegressor(n_iter_no_change='invalid')
|
|
.fit, X, y)
|
|
|
|
|
|
def test_loss_function():
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(loss='ls').fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(loss='lad').fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(loss='quantile').fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingClassifier(loss='huber').fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingRegressor(loss='deviance').fit, X, y)
|
|
assert_raises(ValueError,
|
|
GradientBoostingRegressor(loss='exponential').fit, X, y)
|
|
|
|
|
|
def check_classification_synthetic(loss):
|
|
# Test GradientBoostingClassifier on synthetic dataset used by
|
|
# Hastie et al. in ESLII Example 12.7.
|
|
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
|
|
|
|
X_train, X_test = X[:2000], X[2000:]
|
|
y_train, y_test = y[:2000], y[2000:]
|
|
|
|
gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=2,
|
|
max_depth=1, loss=loss,
|
|
learning_rate=1.0, random_state=0)
|
|
gbrt.fit(X_train, y_train)
|
|
error_rate = (1.0 - gbrt.score(X_test, y_test))
|
|
assert error_rate < 0.09
|
|
|
|
gbrt = GradientBoostingClassifier(n_estimators=200, min_samples_split=2,
|
|
max_depth=1, loss=loss,
|
|
learning_rate=1.0, subsample=0.5,
|
|
random_state=0)
|
|
gbrt.fit(X_train, y_train)
|
|
error_rate = (1.0 - gbrt.score(X_test, y_test))
|
|
assert error_rate < 0.08
|
|
|
|
|
|
@pytest.mark.parametrize('loss', ('deviance', 'exponential'))
|
|
def test_classification_synthetic(loss):
|
|
check_classification_synthetic(loss)
|
|
|
|
|
|
def check_boston(loss, subsample):
|
|
# Check consistency on dataset boston house prices with least squares
|
|
# and least absolute deviation.
|
|
ones = np.ones(len(boston.target))
|
|
last_y_pred = None
|
|
for sample_weight in None, ones, 2 * ones:
|
|
clf = GradientBoostingRegressor(n_estimators=100,
|
|
loss=loss,
|
|
max_depth=4,
|
|
subsample=subsample,
|
|
min_samples_split=2,
|
|
random_state=1)
|
|
|
|
assert_raises(ValueError, clf.predict, boston.data)
|
|
clf.fit(boston.data, boston.target,
|
|
sample_weight=sample_weight)
|
|
leaves = clf.apply(boston.data)
|
|
assert leaves.shape == (506, 100)
|
|
|
|
y_pred = clf.predict(boston.data)
|
|
mse = mean_squared_error(boston.target, y_pred)
|
|
assert mse < 6.0
|
|
|
|
if last_y_pred is not None:
|
|
assert_array_almost_equal(last_y_pred, y_pred)
|
|
|
|
last_y_pred = y_pred
|
|
|
|
|
|
@pytest.mark.parametrize('loss', ('ls', 'lad', 'huber'))
|
|
@pytest.mark.parametrize('subsample', (1.0, 0.5))
|
|
def test_boston(loss, subsample):
|
|
check_boston(loss, subsample)
|
|
|
|
|
|
def check_iris(subsample, sample_weight):
|
|
# Check consistency on dataset iris.
|
|
clf = GradientBoostingClassifier(n_estimators=100,
|
|
loss='deviance',
|
|
random_state=1,
|
|
subsample=subsample)
|
|
clf.fit(iris.data, iris.target, sample_weight=sample_weight)
|
|
score = clf.score(iris.data, iris.target)
|
|
assert score > 0.9
|
|
|
|
leaves = clf.apply(iris.data)
|
|
assert leaves.shape == (150, 100, 3)
|
|
|
|
|
|
@pytest.mark.parametrize('subsample', (1.0, 0.5))
|
|
@pytest.mark.parametrize('sample_weight', (None, 1))
|
|
def test_iris(subsample, sample_weight):
|
|
if sample_weight == 1:
|
|
sample_weight = np.ones(len(iris.target))
|
|
check_iris(subsample, sample_weight)
|
|
|
|
|
|
def test_regression_synthetic():
|
|
# Test on synthetic regression datasets used in Leo Breiman,
|
|
# `Bagging Predictors?. Machine Learning 24(2): 123-140 (1996).
|
|
random_state = check_random_state(1)
|
|
regression_params = {'n_estimators': 100, 'max_depth': 4,
|
|
'min_samples_split': 2, 'learning_rate': 0.1,
|
|
'loss': 'ls'}
|
|
|
|
# Friedman1
|
|
X, y = datasets.make_friedman1(n_samples=1200,
|
|
random_state=random_state,
|
|
noise=1.0)
|
|
X_train, y_train = X[:200], y[:200]
|
|
X_test, y_test = X[200:], y[200:]
|
|
|
|
clf = GradientBoostingRegressor()
|
|
clf.fit(X_train, y_train)
|
|
mse = mean_squared_error(y_test, clf.predict(X_test))
|
|
assert mse < 5.0
|
|
|
|
# Friedman2
|
|
X, y = datasets.make_friedman2(n_samples=1200, random_state=random_state)
|
|
X_train, y_train = X[:200], y[:200]
|
|
X_test, y_test = X[200:], y[200:]
|
|
|
|
clf = GradientBoostingRegressor(**regression_params)
|
|
clf.fit(X_train, y_train)
|
|
mse = mean_squared_error(y_test, clf.predict(X_test))
|
|
assert mse < 1700.0
|
|
|
|
# Friedman3
|
|
X, y = datasets.make_friedman3(n_samples=1200, random_state=random_state)
|
|
X_train, y_train = X[:200], y[:200]
|
|
X_test, y_test = X[200:], y[200:]
|
|
|
|
clf = GradientBoostingRegressor(**regression_params)
|
|
clf.fit(X_train, y_train)
|
|
mse = mean_squared_error(y_test, clf.predict(X_test))
|
|
assert mse < 0.015
|
|
|
|
|
|
def test_feature_importances():
|
|
X = np.array(boston.data, dtype=np.float32)
|
|
y = np.array(boston.target, dtype=np.float32)
|
|
|
|
clf = GradientBoostingRegressor(n_estimators=100, max_depth=5,
|
|
min_samples_split=2, random_state=1)
|
|
clf.fit(X, y)
|
|
assert hasattr(clf, 'feature_importances_')
|
|
|
|
|
|
def test_probability_log():
|
|
# Predict probabilities.
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
assert_raises(ValueError, clf.predict_proba, T)
|
|
|
|
clf.fit(X, y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
|
|
# check if probabilities are in [0, 1].
|
|
y_proba = clf.predict_proba(T)
|
|
assert np.all(y_proba >= 0.0)
|
|
assert np.all(y_proba <= 1.0)
|
|
|
|
# derive predictions from probabilities
|
|
y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0)
|
|
assert_array_equal(y_pred, true_result)
|
|
|
|
|
|
def test_check_inputs():
|
|
# Test input checks (shape and type of X and y).
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
assert_raises(ValueError, clf.fit, X, y + [0, 1])
|
|
|
|
weight = [0, 0, 0, 1, 1, 1]
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
msg = ("y contains 1 class after sample_weight trimmed classes with "
|
|
"zero weights, while a minimum of 2 classes are required.")
|
|
assert_raise_message(ValueError, msg, clf.fit, X, y, sample_weight=weight)
|
|
|
|
|
|
def test_check_inputs_predict():
|
|
# X has wrong shape
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
clf.fit(X, y)
|
|
|
|
x = np.array([1.0, 2.0])[:, np.newaxis]
|
|
assert_raises(ValueError, clf.predict, x)
|
|
|
|
x = np.array([[]])
|
|
assert_raises(ValueError, clf.predict, x)
|
|
|
|
x = np.array([1.0, 2.0, 3.0])[:, np.newaxis]
|
|
assert_raises(ValueError, clf.predict, x)
|
|
|
|
clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
|
|
clf.fit(X, rng.rand(len(X)))
|
|
|
|
x = np.array([1.0, 2.0])[:, np.newaxis]
|
|
assert_raises(ValueError, clf.predict, x)
|
|
|
|
x = np.array([[]])
|
|
assert_raises(ValueError, clf.predict, x)
|
|
|
|
x = np.array([1.0, 2.0, 3.0])[:, np.newaxis]
|
|
assert_raises(ValueError, clf.predict, x)
|
|
|
|
|
|
def test_check_inputs_predict_stages():
|
|
# check that predict_stages through an error if the type of X is not
|
|
# supported
|
|
x, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
x_sparse_csc = csc_matrix(x)
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
clf.fit(x, y)
|
|
score = np.zeros((y.shape)).reshape(-1, 1)
|
|
assert_raise_message(ValueError,
|
|
"When X is a sparse matrix, a CSR format is expected",
|
|
predict_stages, clf.estimators_, x_sparse_csc,
|
|
clf.learning_rate, score)
|
|
x_fortran = np.asfortranarray(x)
|
|
assert_raise_message(ValueError,
|
|
"X should be C-ordered np.ndarray",
|
|
predict_stages, clf.estimators_, x_fortran,
|
|
clf.learning_rate, score)
|
|
|
|
|
|
def test_check_max_features():
|
|
# test if max_features is valid.
|
|
clf = GradientBoostingRegressor(n_estimators=100, random_state=1,
|
|
max_features=0)
|
|
assert_raises(ValueError, clf.fit, X, y)
|
|
|
|
clf = GradientBoostingRegressor(n_estimators=100, random_state=1,
|
|
max_features=(len(X[0]) + 1))
|
|
assert_raises(ValueError, clf.fit, X, y)
|
|
|
|
clf = GradientBoostingRegressor(n_estimators=100, random_state=1,
|
|
max_features=-0.1)
|
|
assert_raises(ValueError, clf.fit, X, y)
|
|
|
|
|
|
def test_max_feature_regression():
|
|
# Test to make sure random state is set properly.
|
|
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
|
|
|
|
X_train, X_test = X[:2000], X[2000:]
|
|
y_train, y_test = y[:2000], y[2000:]
|
|
|
|
gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=5,
|
|
max_depth=2, learning_rate=.1,
|
|
max_features=2, random_state=1)
|
|
gbrt.fit(X_train, y_train)
|
|
deviance = gbrt.loss_(y_test, gbrt.decision_function(X_test))
|
|
assert deviance < 0.5, "GB failed with deviance %.4f" % deviance
|
|
|
|
|
|
@pytest.mark.network
|
|
def test_feature_importance_regression():
|
|
"""Test that Gini importance is calculated correctly.
|
|
|
|
This test follows the example from [1]_ (pg. 373).
|
|
|
|
.. [1] Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements
|
|
of statistical learning. New York: Springer series in statistics.
|
|
"""
|
|
california = fetch_california_housing()
|
|
X, y = california.data, california.target
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
|
|
|
|
reg = GradientBoostingRegressor(loss='huber', learning_rate=0.1,
|
|
max_leaf_nodes=6, n_estimators=100,
|
|
random_state=0)
|
|
reg.fit(X_train, y_train)
|
|
sorted_idx = np.argsort(reg.feature_importances_)[::-1]
|
|
sorted_features = [california.feature_names[s] for s in sorted_idx]
|
|
|
|
# The most important feature is the median income by far.
|
|
assert sorted_features[0] == 'MedInc'
|
|
|
|
# The three subsequent features are the following. Their relative ordering
|
|
# might change a bit depending on the randomness of the trees and the
|
|
# train / test split.
|
|
assert set(sorted_features[1:4]) == {'Longitude', 'AveOccup', 'Latitude'}
|
|
|
|
|
|
def test_max_feature_auto():
|
|
# Test if max features is set properly for floats and str.
|
|
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
|
|
_, n_features = X.shape
|
|
|
|
X_train = X[:2000]
|
|
y_train = y[:2000]
|
|
|
|
gbrt = GradientBoostingClassifier(n_estimators=1, max_features='auto')
|
|
gbrt.fit(X_train, y_train)
|
|
assert gbrt.max_features_ == int(np.sqrt(n_features))
|
|
|
|
gbrt = GradientBoostingRegressor(n_estimators=1, max_features='auto')
|
|
gbrt.fit(X_train, y_train)
|
|
assert gbrt.max_features_ == n_features
|
|
|
|
gbrt = GradientBoostingRegressor(n_estimators=1, max_features=0.3)
|
|
gbrt.fit(X_train, y_train)
|
|
assert gbrt.max_features_ == int(n_features * 0.3)
|
|
|
|
gbrt = GradientBoostingRegressor(n_estimators=1, max_features='sqrt')
|
|
gbrt.fit(X_train, y_train)
|
|
assert gbrt.max_features_ == int(np.sqrt(n_features))
|
|
|
|
gbrt = GradientBoostingRegressor(n_estimators=1, max_features='log2')
|
|
gbrt.fit(X_train, y_train)
|
|
assert gbrt.max_features_ == int(np.log2(n_features))
|
|
|
|
gbrt = GradientBoostingRegressor(n_estimators=1,
|
|
max_features=0.01 / X.shape[1])
|
|
gbrt.fit(X_train, y_train)
|
|
assert gbrt.max_features_ == 1
|
|
|
|
|
|
def test_staged_predict():
|
|
# Test whether staged decision function eventually gives
|
|
# the same prediction.
|
|
X, y = datasets.make_friedman1(n_samples=1200,
|
|
random_state=1, noise=1.0)
|
|
X_train, y_train = X[:200], y[:200]
|
|
X_test = X[200:]
|
|
clf = GradientBoostingRegressor()
|
|
# test raise ValueError if not fitted
|
|
assert_raises(ValueError, lambda X: np.fromiter(
|
|
clf.staged_predict(X), dtype=np.float64), X_test)
|
|
|
|
clf.fit(X_train, y_train)
|
|
y_pred = clf.predict(X_test)
|
|
|
|
# test if prediction for last stage equals ``predict``
|
|
for y in clf.staged_predict(X_test):
|
|
assert y.shape == y_pred.shape
|
|
|
|
assert_array_almost_equal(y_pred, y)
|
|
|
|
|
|
def test_staged_predict_proba():
|
|
# Test whether staged predict proba eventually gives
|
|
# the same prediction.
|
|
X, y = datasets.make_hastie_10_2(n_samples=1200,
|
|
random_state=1)
|
|
X_train, y_train = X[:200], y[:200]
|
|
X_test, y_test = X[200:], y[200:]
|
|
clf = GradientBoostingClassifier(n_estimators=20)
|
|
# test raise NotFittedError if not fitted
|
|
assert_raises(NotFittedError, lambda X: np.fromiter(
|
|
clf.staged_predict_proba(X), dtype=np.float64), X_test)
|
|
|
|
clf.fit(X_train, y_train)
|
|
|
|
# test if prediction for last stage equals ``predict``
|
|
for y_pred in clf.staged_predict(X_test):
|
|
assert y_test.shape == y_pred.shape
|
|
|
|
assert_array_equal(clf.predict(X_test), y_pred)
|
|
|
|
# test if prediction for last stage equals ``predict_proba``
|
|
for staged_proba in clf.staged_predict_proba(X_test):
|
|
assert y_test.shape[0] == staged_proba.shape[0]
|
|
assert 2 == staged_proba.shape[1]
|
|
|
|
assert_array_almost_equal(clf.predict_proba(X_test), staged_proba)
|
|
|
|
|
|
@pytest.mark.parametrize('Estimator', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_staged_functions_defensive(Estimator):
|
|
# test that staged_functions make defensive copies
|
|
rng = np.random.RandomState(0)
|
|
X = rng.uniform(size=(10, 3))
|
|
y = (4 * X[:, 0]).astype(np.int) + 1 # don't predict zeros
|
|
estimator = Estimator()
|
|
estimator.fit(X, y)
|
|
for func in ['predict', 'decision_function', 'predict_proba']:
|
|
staged_func = getattr(estimator, "staged_" + func, None)
|
|
if staged_func is None:
|
|
# regressor has no staged_predict_proba
|
|
continue
|
|
with warnings.catch_warnings(record=True):
|
|
staged_result = list(staged_func(X))
|
|
staged_result[1][:] = 0
|
|
assert np.all(staged_result[0] != 0)
|
|
|
|
|
|
def test_serialization():
|
|
# Check model serialization.
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
clf.fit(X, y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert 100 == len(clf.estimators_)
|
|
|
|
try:
|
|
import cPickle as pickle
|
|
except ImportError:
|
|
import pickle
|
|
|
|
serialized_clf = pickle.dumps(clf, protocol=pickle.HIGHEST_PROTOCOL)
|
|
clf = None
|
|
clf = pickle.loads(serialized_clf)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert 100 == len(clf.estimators_)
|
|
|
|
|
|
def test_degenerate_targets():
|
|
# Check if we can fit even though all targets are equal.
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
# classifier should raise exception
|
|
assert_raises(ValueError, clf.fit, X, np.ones(len(X)))
|
|
|
|
clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
|
|
clf.fit(X, np.ones(len(X)))
|
|
clf.predict([rng.rand(2)])
|
|
assert_array_equal(np.ones((1,), dtype=np.float64),
|
|
clf.predict([rng.rand(2)]))
|
|
|
|
|
|
def test_quantile_loss():
|
|
# Check if quantile loss with alpha=0.5 equals lad.
|
|
clf_quantile = GradientBoostingRegressor(n_estimators=100, loss='quantile',
|
|
max_depth=4, alpha=0.5,
|
|
random_state=7)
|
|
|
|
clf_quantile.fit(boston.data, boston.target)
|
|
y_quantile = clf_quantile.predict(boston.data)
|
|
|
|
clf_lad = GradientBoostingRegressor(n_estimators=100, loss='lad',
|
|
max_depth=4, random_state=7)
|
|
|
|
clf_lad.fit(boston.data, boston.target)
|
|
y_lad = clf_lad.predict(boston.data)
|
|
assert_array_almost_equal(y_quantile, y_lad, decimal=4)
|
|
|
|
|
|
def test_symbol_labels():
|
|
# Test with non-integer class labels.
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
symbol_y = tosequence(map(str, y))
|
|
|
|
clf.fit(X, symbol_y)
|
|
assert_array_equal(clf.predict(T), tosequence(map(str, true_result)))
|
|
assert 100 == len(clf.estimators_)
|
|
|
|
|
|
def test_float_class_labels():
|
|
# Test with float class labels.
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
float_y = np.asarray(y, dtype=np.float32)
|
|
|
|
clf.fit(X, float_y)
|
|
assert_array_equal(clf.predict(T),
|
|
np.asarray(true_result, dtype=np.float32))
|
|
assert 100 == len(clf.estimators_)
|
|
|
|
|
|
def test_shape_y():
|
|
# Test with float class labels.
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
|
|
y_ = np.asarray(y, dtype=np.int32)
|
|
y_ = y_[:, np.newaxis]
|
|
|
|
# This will raise a DataConversionWarning that we want to
|
|
# "always" raise, elsewhere the warnings gets ignored in the
|
|
# later tests, and the tests that check for this warning fail
|
|
assert_warns(DataConversionWarning, clf.fit, X, y_)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert 100 == len(clf.estimators_)
|
|
|
|
|
|
def test_mem_layout():
|
|
# Test with different memory layouts of X and y
|
|
X_ = np.asfortranarray(X)
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
clf.fit(X_, y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert 100 == len(clf.estimators_)
|
|
|
|
X_ = np.ascontiguousarray(X)
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
clf.fit(X_, y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert 100 == len(clf.estimators_)
|
|
|
|
y_ = np.asarray(y, dtype=np.int32)
|
|
y_ = np.ascontiguousarray(y_)
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
clf.fit(X, y_)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert 100 == len(clf.estimators_)
|
|
|
|
y_ = np.asarray(y, dtype=np.int32)
|
|
y_ = np.asfortranarray(y_)
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
|
|
clf.fit(X, y_)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
assert 100 == len(clf.estimators_)
|
|
|
|
|
|
def test_oob_improvement():
|
|
# Test if oob improvement has correct shape and regression test.
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
|
|
subsample=0.5)
|
|
clf.fit(X, y)
|
|
assert clf.oob_improvement_.shape[0] == 100
|
|
# hard-coded regression test - change if modification in OOB computation
|
|
assert_array_almost_equal(clf.oob_improvement_[:5],
|
|
np.array([0.19, 0.15, 0.12, -0.12, -0.11]),
|
|
decimal=2)
|
|
|
|
|
|
def test_oob_improvement_raise():
|
|
# Test if oob improvement has correct shape.
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
|
|
subsample=1.0)
|
|
clf.fit(X, y)
|
|
assert_raises(AttributeError, lambda: clf.oob_improvement_)
|
|
|
|
|
|
def test_oob_multilcass_iris():
|
|
# Check OOB improvement on multi-class dataset.
|
|
clf = GradientBoostingClassifier(n_estimators=100, loss='deviance',
|
|
random_state=1, subsample=0.5)
|
|
clf.fit(iris.data, iris.target)
|
|
score = clf.score(iris.data, iris.target)
|
|
assert score > 0.9
|
|
assert clf.oob_improvement_.shape[0] == clf.n_estimators
|
|
# hard-coded regression test - change if modification in OOB computation
|
|
# FIXME: the following snippet does not yield the same results on 32 bits
|
|
# assert_array_almost_equal(clf.oob_improvement_[:5],
|
|
# np.array([12.68, 10.45, 8.18, 6.43, 5.13]),
|
|
# decimal=2)
|
|
|
|
|
|
def test_verbose_output():
|
|
# Check verbose=1 does not cause error.
|
|
from io import StringIO
|
|
|
|
import sys
|
|
old_stdout = sys.stdout
|
|
sys.stdout = StringIO()
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
|
|
verbose=1, subsample=0.8)
|
|
clf.fit(X, y)
|
|
verbose_output = sys.stdout
|
|
sys.stdout = old_stdout
|
|
|
|
# check output
|
|
verbose_output.seek(0)
|
|
header = verbose_output.readline().rstrip()
|
|
# with OOB
|
|
true_header = ' '.join(['%10s'] + ['%16s'] * 3) % (
|
|
'Iter', 'Train Loss', 'OOB Improve', 'Remaining Time')
|
|
assert true_header == header
|
|
|
|
n_lines = sum(1 for l in verbose_output.readlines())
|
|
# one for 1-10 and then 9 for 20-100
|
|
assert 10 + 9 == n_lines
|
|
|
|
|
|
def test_more_verbose_output():
|
|
# Check verbose=2 does not cause error.
|
|
from io import StringIO
|
|
import sys
|
|
old_stdout = sys.stdout
|
|
sys.stdout = StringIO()
|
|
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
|
|
verbose=2)
|
|
clf.fit(X, y)
|
|
verbose_output = sys.stdout
|
|
sys.stdout = old_stdout
|
|
|
|
# check output
|
|
verbose_output.seek(0)
|
|
header = verbose_output.readline().rstrip()
|
|
# no OOB
|
|
true_header = ' '.join(['%10s'] + ['%16s'] * 2) % (
|
|
'Iter', 'Train Loss', 'Remaining Time')
|
|
assert true_header == header
|
|
|
|
n_lines = sum(1 for l in verbose_output.readlines())
|
|
# 100 lines for n_estimators==100
|
|
assert 100 == n_lines
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start(Cls):
|
|
# Test if warm start equals fit.
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
est = Cls(n_estimators=200, max_depth=1)
|
|
est.fit(X, y)
|
|
|
|
est_ws = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est_ws.fit(X, y)
|
|
est_ws.set_params(n_estimators=200)
|
|
est_ws.fit(X, y)
|
|
|
|
if Cls is GradientBoostingRegressor:
|
|
assert_array_almost_equal(est_ws.predict(X), est.predict(X))
|
|
else:
|
|
# Random state is preserved and hence predict_proba must also be
|
|
# same
|
|
assert_array_equal(est_ws.predict(X), est.predict(X))
|
|
assert_array_almost_equal(est_ws.predict_proba(X),
|
|
est.predict_proba(X))
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start_n_estimators(Cls):
|
|
# Test if warm start equals fit - set n_estimators.
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
est = Cls(n_estimators=300, max_depth=1)
|
|
est.fit(X, y)
|
|
|
|
est_ws = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est_ws.fit(X, y)
|
|
est_ws.set_params(n_estimators=300)
|
|
est_ws.fit(X, y)
|
|
|
|
assert_array_almost_equal(est_ws.predict(X), est.predict(X))
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start_max_depth(Cls):
|
|
# Test if possible to fit trees of different depth in ensemble.
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est.fit(X, y)
|
|
est.set_params(n_estimators=110, max_depth=2)
|
|
est.fit(X, y)
|
|
|
|
# last 10 trees have different depth
|
|
assert est.estimators_[0, 0].max_depth == 1
|
|
for i in range(1, 11):
|
|
assert est.estimators_[-i, 0].max_depth == 2
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start_clear(Cls):
|
|
# Test if fit clears state.
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
est = Cls(n_estimators=100, max_depth=1)
|
|
est.fit(X, y)
|
|
|
|
est_2 = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est_2.fit(X, y) # inits state
|
|
est_2.set_params(warm_start=False)
|
|
est_2.fit(X, y) # clears old state and equals est
|
|
|
|
assert_array_almost_equal(est_2.predict(X), est.predict(X))
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start_zero_n_estimators(Cls):
|
|
# Test if warm start with zero n_estimators raises error
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est.fit(X, y)
|
|
est.set_params(n_estimators=0)
|
|
assert_raises(ValueError, est.fit, X, y)
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start_smaller_n_estimators(Cls):
|
|
# Test if warm start with smaller n_estimators raises error
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est.fit(X, y)
|
|
est.set_params(n_estimators=99)
|
|
assert_raises(ValueError, est.fit, X, y)
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start_equal_n_estimators(Cls):
|
|
# Test if warm start with equal n_estimators does nothing
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
est = Cls(n_estimators=100, max_depth=1)
|
|
est.fit(X, y)
|
|
|
|
est2 = clone(est)
|
|
est2.set_params(n_estimators=est.n_estimators, warm_start=True)
|
|
est2.fit(X, y)
|
|
|
|
assert_array_almost_equal(est2.predict(X), est.predict(X))
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start_oob_switch(Cls):
|
|
# Test if oob can be turned on during warm start.
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
|
|
est.fit(X, y)
|
|
est.set_params(n_estimators=110, subsample=0.5)
|
|
est.fit(X, y)
|
|
|
|
assert_array_equal(est.oob_improvement_[:100], np.zeros(100))
|
|
# the last 10 are not zeros
|
|
assert_array_equal(est.oob_improvement_[-10:] == 0.0,
|
|
np.zeros(10, dtype=np.bool))
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start_oob(Cls):
|
|
# Test if warm start OOB equals fit.
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
est = Cls(n_estimators=200, max_depth=1, subsample=0.5,
|
|
random_state=1)
|
|
est.fit(X, y)
|
|
|
|
est_ws = Cls(n_estimators=100, max_depth=1, subsample=0.5,
|
|
random_state=1, warm_start=True)
|
|
est_ws.fit(X, y)
|
|
est_ws.set_params(n_estimators=200)
|
|
est_ws.fit(X, y)
|
|
|
|
assert_array_almost_equal(est_ws.oob_improvement_[:100],
|
|
est.oob_improvement_[:100])
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start_sparse(Cls):
|
|
# Test that all sparse matrix types are supported
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
sparse_matrix_type = [csr_matrix, csc_matrix, coo_matrix]
|
|
est_dense = Cls(n_estimators=100, max_depth=1, subsample=0.5,
|
|
random_state=1, warm_start=True)
|
|
est_dense.fit(X, y)
|
|
est_dense.predict(X)
|
|
est_dense.set_params(n_estimators=200)
|
|
est_dense.fit(X, y)
|
|
y_pred_dense = est_dense.predict(X)
|
|
|
|
for sparse_constructor in sparse_matrix_type:
|
|
X_sparse = sparse_constructor(X)
|
|
|
|
est_sparse = Cls(n_estimators=100, max_depth=1, subsample=0.5,
|
|
random_state=1, warm_start=True)
|
|
est_sparse.fit(X_sparse, y)
|
|
est_sparse.predict(X)
|
|
est_sparse.set_params(n_estimators=200)
|
|
est_sparse.fit(X_sparse, y)
|
|
y_pred_sparse = est_sparse.predict(X)
|
|
|
|
assert_array_almost_equal(est_dense.oob_improvement_[:100],
|
|
est_sparse.oob_improvement_[:100])
|
|
assert_array_almost_equal(y_pred_dense, y_pred_sparse)
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_warm_start_fortran(Cls):
|
|
# Test that feeding a X in Fortran-ordered is giving the same results as
|
|
# in C-ordered
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
est_c = Cls(n_estimators=1, random_state=1, warm_start=True)
|
|
est_fortran = Cls(n_estimators=1, random_state=1, warm_start=True)
|
|
|
|
est_c.fit(X, y)
|
|
est_c.set_params(n_estimators=11)
|
|
est_c.fit(X, y)
|
|
|
|
X_fortran = np.asfortranarray(X)
|
|
est_fortran.fit(X_fortran, y)
|
|
est_fortran.set_params(n_estimators=11)
|
|
est_fortran.fit(X_fortran, y)
|
|
|
|
assert_array_almost_equal(est_c.predict(X), est_fortran.predict(X))
|
|
|
|
|
|
def early_stopping_monitor(i, est, locals):
|
|
"""Returns True on the 10th iteration. """
|
|
if i == 9:
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_monitor_early_stopping(Cls):
|
|
# Test if monitor return value works.
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
|
|
est = Cls(n_estimators=20, max_depth=1, random_state=1, subsample=0.5)
|
|
est.fit(X, y, monitor=early_stopping_monitor)
|
|
assert est.n_estimators == 20 # this is not altered
|
|
assert est.estimators_.shape[0] == 10
|
|
assert est.train_score_.shape[0] == 10
|
|
assert est.oob_improvement_.shape[0] == 10
|
|
|
|
# try refit
|
|
est.set_params(n_estimators=30)
|
|
est.fit(X, y)
|
|
assert est.n_estimators == 30
|
|
assert est.estimators_.shape[0] == 30
|
|
assert est.train_score_.shape[0] == 30
|
|
|
|
est = Cls(n_estimators=20, max_depth=1, random_state=1, subsample=0.5,
|
|
warm_start=True)
|
|
est.fit(X, y, monitor=early_stopping_monitor)
|
|
assert est.n_estimators == 20
|
|
assert est.estimators_.shape[0] == 10
|
|
assert est.train_score_.shape[0] == 10
|
|
assert est.oob_improvement_.shape[0] == 10
|
|
|
|
# try refit
|
|
est.set_params(n_estimators=30, warm_start=False)
|
|
est.fit(X, y)
|
|
assert est.n_estimators == 30
|
|
assert est.train_score_.shape[0] == 30
|
|
assert est.estimators_.shape[0] == 30
|
|
assert est.oob_improvement_.shape[0] == 30
|
|
|
|
|
|
def test_complete_classification():
|
|
# Test greedy trees with max_depth + 1 leafs.
|
|
from sklearn.tree._tree import TREE_LEAF
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
k = 4
|
|
|
|
est = GradientBoostingClassifier(n_estimators=20, max_depth=None,
|
|
random_state=1, max_leaf_nodes=k + 1)
|
|
est.fit(X, y)
|
|
|
|
tree = est.estimators_[0, 0].tree_
|
|
assert tree.max_depth == k
|
|
assert (tree.children_left[tree.children_left == TREE_LEAF].shape[0] ==
|
|
k + 1)
|
|
|
|
|
|
def test_complete_regression():
|
|
# Test greedy trees with max_depth + 1 leafs.
|
|
from sklearn.tree._tree import TREE_LEAF
|
|
k = 4
|
|
|
|
est = GradientBoostingRegressor(n_estimators=20, max_depth=None,
|
|
random_state=1, max_leaf_nodes=k + 1)
|
|
est.fit(boston.data, boston.target)
|
|
|
|
tree = est.estimators_[-1, 0].tree_
|
|
assert (tree.children_left[tree.children_left == TREE_LEAF].shape[0] ==
|
|
k + 1)
|
|
|
|
|
|
def test_zero_estimator_reg():
|
|
# Test if init='zero' works for regression.
|
|
|
|
est = GradientBoostingRegressor(n_estimators=20, max_depth=1,
|
|
random_state=1, init='zero')
|
|
est.fit(boston.data, boston.target)
|
|
y_pred = est.predict(boston.data)
|
|
mse = mean_squared_error(boston.target, y_pred)
|
|
assert_almost_equal(mse, 33.0, decimal=0)
|
|
|
|
est = GradientBoostingRegressor(n_estimators=20, max_depth=1,
|
|
random_state=1, init='foobar')
|
|
assert_raises(ValueError, est.fit, boston.data, boston.target)
|
|
|
|
|
|
def test_zero_estimator_clf():
|
|
# Test if init='zero' works for classification.
|
|
X = iris.data
|
|
y = np.array(iris.target)
|
|
|
|
est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
|
|
random_state=1, init='zero')
|
|
est.fit(X, y)
|
|
|
|
assert est.score(X, y) > 0.96
|
|
|
|
# binary clf
|
|
mask = y != 0
|
|
y[mask] = 1
|
|
y[~mask] = 0
|
|
est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
|
|
random_state=1, init='zero')
|
|
est.fit(X, y)
|
|
assert est.score(X, y) > 0.96
|
|
|
|
est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
|
|
random_state=1, init='foobar')
|
|
assert_raises(ValueError, est.fit, X, y)
|
|
|
|
|
|
@pytest.mark.parametrize('GBEstimator', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_max_leaf_nodes_max_depth(GBEstimator):
|
|
# Test precedence of max_leaf_nodes over max_depth.
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
|
|
k = 4
|
|
|
|
est = GBEstimator(max_depth=1, max_leaf_nodes=k).fit(X, y)
|
|
tree = est.estimators_[0, 0].tree_
|
|
assert tree.max_depth == 1
|
|
|
|
est = GBEstimator(max_depth=1).fit(X, y)
|
|
tree = est.estimators_[0, 0].tree_
|
|
assert tree.max_depth == 1
|
|
|
|
|
|
@pytest.mark.parametrize('GBEstimator', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_min_impurity_split(GBEstimator):
|
|
# Test if min_impurity_split of base estimators is set
|
|
# Regression test for #8006
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
|
|
est = GBEstimator(min_impurity_split=0.1)
|
|
est = assert_warns_message(FutureWarning,
|
|
"min_impurity_decrease",
|
|
est.fit, X, y)
|
|
for tree in est.estimators_.flat:
|
|
assert tree.min_impurity_split == 0.1
|
|
|
|
|
|
@pytest.mark.parametrize('GBEstimator', GRADIENT_BOOSTING_ESTIMATORS)
|
|
def test_min_impurity_decrease(GBEstimator):
|
|
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
|
|
|
|
est = GBEstimator(min_impurity_decrease=0.1)
|
|
est.fit(X, y)
|
|
for tree in est.estimators_.flat:
|
|
# Simply check if the parameter is passed on correctly. Tree tests
|
|
# will suffice for the actual working of this param
|
|
assert tree.min_impurity_decrease == 0.1
|
|
|
|
|
|
def test_warm_start_wo_nestimators_change():
|
|
# Test if warm_start does nothing if n_estimators is not changed.
|
|
# Regression test for #3513.
|
|
clf = GradientBoostingClassifier(n_estimators=10, warm_start=True)
|
|
clf.fit([[0, 1], [2, 3]], [0, 1])
|
|
assert clf.estimators_.shape[0] == 10
|
|
clf.fit([[0, 1], [2, 3]], [0, 1])
|
|
assert clf.estimators_.shape[0] == 10
|
|
|
|
|
|
def test_probability_exponential():
|
|
# Predict probabilities.
|
|
clf = GradientBoostingClassifier(loss='exponential',
|
|
n_estimators=100, random_state=1)
|
|
|
|
assert_raises(ValueError, clf.predict_proba, T)
|
|
|
|
clf.fit(X, y)
|
|
assert_array_equal(clf.predict(T), true_result)
|
|
|
|
# check if probabilities are in [0, 1].
|
|
y_proba = clf.predict_proba(T)
|
|
assert np.all(y_proba >= 0.0)
|
|
assert np.all(y_proba <= 1.0)
|
|
score = clf.decision_function(T).ravel()
|
|
assert_array_almost_equal(y_proba[:, 1], expit(2 * score))
|
|
|
|
# derive predictions from probabilities
|
|
y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0)
|
|
assert_array_equal(y_pred, true_result)
|
|
|
|
|
|
def test_non_uniform_weights_toy_edge_case_reg():
|
|
X = [[1, 0],
|
|
[1, 0],
|
|
[1, 0],
|
|
[0, 1]]
|
|
y = [0, 0, 1, 0]
|
|
# ignore the first 2 training samples by setting their weight to 0
|
|
sample_weight = [0, 0, 1, 1]
|
|
for loss in ('huber', 'ls', 'lad', 'quantile'):
|
|
gb = GradientBoostingRegressor(learning_rate=1.0, n_estimators=2,
|
|
loss=loss)
|
|
gb.fit(X, y, sample_weight=sample_weight)
|
|
assert gb.predict([[1, 0]])[0] > 0.5
|
|
|
|
|
|
def test_non_uniform_weights_toy_edge_case_clf():
|
|
X = [[1, 0],
|
|
[1, 0],
|
|
[1, 0],
|
|
[0, 1]]
|
|
y = [0, 0, 1, 0]
|
|
# ignore the first 2 training samples by setting their weight to 0
|
|
sample_weight = [0, 0, 1, 1]
|
|
for loss in ('deviance', 'exponential'):
|
|
gb = GradientBoostingClassifier(n_estimators=5, loss=loss)
|
|
gb.fit(X, y, sample_weight=sample_weight)
|
|
assert_array_equal(gb.predict([[1, 0]]), [1])
|
|
|
|
|
|
def check_sparse_input(EstimatorClass, X, X_sparse, y):
|
|
dense = EstimatorClass(n_estimators=10, random_state=0,
|
|
max_depth=2, min_impurity_decrease=1e-7).fit(X, y)
|
|
sparse = EstimatorClass(n_estimators=10, random_state=0,
|
|
max_depth=2,
|
|
min_impurity_decrease=1e-7).fit(X_sparse, y)
|
|
|
|
assert_array_almost_equal(sparse.apply(X), dense.apply(X))
|
|
assert_array_almost_equal(sparse.predict(X), dense.predict(X))
|
|
assert_array_almost_equal(sparse.feature_importances_,
|
|
dense.feature_importances_)
|
|
|
|
assert_array_almost_equal(sparse.predict(X_sparse), dense.predict(X))
|
|
assert_array_almost_equal(dense.predict(X_sparse), sparse.predict(X))
|
|
|
|
if issubclass(EstimatorClass, GradientBoostingClassifier):
|
|
assert_array_almost_equal(sparse.predict_proba(X),
|
|
dense.predict_proba(X))
|
|
assert_array_almost_equal(sparse.predict_log_proba(X),
|
|
dense.predict_log_proba(X))
|
|
|
|
assert_array_almost_equal(sparse.decision_function(X_sparse),
|
|
sparse.decision_function(X))
|
|
assert_array_almost_equal(dense.decision_function(X_sparse),
|
|
sparse.decision_function(X))
|
|
for res_sparse, res in zip(sparse.staged_decision_function(X_sparse),
|
|
sparse.staged_decision_function(X)):
|
|
assert_array_almost_equal(res_sparse, res)
|
|
|
|
|
|
@skip_if_32bit
|
|
@pytest.mark.parametrize(
|
|
'EstimatorClass',
|
|
(GradientBoostingClassifier, GradientBoostingRegressor))
|
|
@pytest.mark.parametrize('sparse_matrix', (csr_matrix, csc_matrix, coo_matrix))
|
|
def test_sparse_input(EstimatorClass, sparse_matrix):
|
|
y, X = datasets.make_multilabel_classification(random_state=0,
|
|
n_samples=50,
|
|
n_features=1,
|
|
n_classes=20)
|
|
y = y[:, 0]
|
|
|
|
check_sparse_input(EstimatorClass, X, sparse_matrix(X), y)
|
|
|
|
|
|
def test_gradient_boosting_early_stopping():
|
|
X, y = make_classification(n_samples=1000, random_state=0)
|
|
|
|
gbc = GradientBoostingClassifier(n_estimators=1000,
|
|
n_iter_no_change=10,
|
|
learning_rate=0.1, max_depth=3,
|
|
random_state=42)
|
|
|
|
gbr = GradientBoostingRegressor(n_estimators=1000, n_iter_no_change=10,
|
|
learning_rate=0.1, max_depth=3,
|
|
random_state=42)
|
|
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y,
|
|
random_state=42)
|
|
# Check if early_stopping works as expected
|
|
for est, tol, early_stop_n_estimators in ((gbc, 1e-1, 28), (gbr, 1e-1, 13),
|
|
(gbc, 1e-3, 70),
|
|
(gbr, 1e-3, 28)):
|
|
est.set_params(tol=tol)
|
|
est.fit(X_train, y_train)
|
|
assert est.n_estimators_ == early_stop_n_estimators
|
|
assert est.score(X_test, y_test) > 0.7
|
|
|
|
# Without early stopping
|
|
gbc = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1,
|
|
max_depth=3, random_state=42)
|
|
gbc.fit(X, y)
|
|
gbr = GradientBoostingRegressor(n_estimators=200, learning_rate=0.1,
|
|
max_depth=3, random_state=42)
|
|
gbr.fit(X, y)
|
|
|
|
assert gbc.n_estimators_ == 100
|
|
assert gbr.n_estimators_ == 200
|
|
|
|
|
|
def test_gradient_boosting_validation_fraction():
|
|
X, y = make_classification(n_samples=1000, random_state=0)
|
|
|
|
gbc = GradientBoostingClassifier(n_estimators=100,
|
|
n_iter_no_change=10,
|
|
validation_fraction=0.1,
|
|
learning_rate=0.1, max_depth=3,
|
|
random_state=42)
|
|
gbc2 = clone(gbc).set_params(validation_fraction=0.3)
|
|
gbc3 = clone(gbc).set_params(n_iter_no_change=20)
|
|
|
|
gbr = GradientBoostingRegressor(n_estimators=100, n_iter_no_change=10,
|
|
learning_rate=0.1, max_depth=3,
|
|
validation_fraction=0.1,
|
|
random_state=42)
|
|
gbr2 = clone(gbr).set_params(validation_fraction=0.3)
|
|
gbr3 = clone(gbr).set_params(n_iter_no_change=20)
|
|
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
|
|
# Check if validation_fraction has an effect
|
|
gbc.fit(X_train, y_train)
|
|
gbc2.fit(X_train, y_train)
|
|
assert gbc.n_estimators_ != gbc2.n_estimators_
|
|
|
|
gbr.fit(X_train, y_train)
|
|
gbr2.fit(X_train, y_train)
|
|
assert gbr.n_estimators_ != gbr2.n_estimators_
|
|
|
|
# Check if n_estimators_ increase monotonically with n_iter_no_change
|
|
# Set validation
|
|
gbc3.fit(X_train, y_train)
|
|
gbr3.fit(X_train, y_train)
|
|
assert gbr.n_estimators_ < gbr3.n_estimators_
|
|
assert gbc.n_estimators_ < gbc3.n_estimators_
|
|
|
|
|
|
def test_early_stopping_stratified():
|
|
# Make sure data splitting for early stopping is stratified
|
|
X = [[1, 2], [2, 3], [3, 4], [4, 5]]
|
|
y = [0, 0, 0, 1]
|
|
|
|
gbc = GradientBoostingClassifier(n_iter_no_change=5)
|
|
with pytest.raises(
|
|
ValueError,
|
|
match='The least populated class in y has only 1 member'):
|
|
gbc.fit(X, y)
|
|
|
|
|
|
def _make_multiclass():
|
|
return make_classification(n_classes=3, n_clusters_per_class=1)
|
|
|
|
|
|
# TODO: Remove in 0.24 when DummyClassifier's `strategy` default updates
|
|
@ignore_warnings(category=FutureWarning)
|
|
@pytest.mark.parametrize(
|
|
"gb, dataset_maker, init_estimator",
|
|
[(GradientBoostingClassifier, make_classification, DummyClassifier),
|
|
(GradientBoostingClassifier, _make_multiclass, DummyClassifier),
|
|
(GradientBoostingRegressor, make_regression, DummyRegressor)],
|
|
ids=["binary classification", "multiclass classification", "regression"])
|
|
def test_gradient_boosting_with_init(gb, dataset_maker, init_estimator):
|
|
# Check that GradientBoostingRegressor works when init is a sklearn
|
|
# estimator.
|
|
# Check that an error is raised if trying to fit with sample weight but
|
|
# initial estimator does not support sample weight
|
|
|
|
X, y = dataset_maker()
|
|
sample_weight = np.random.RandomState(42).rand(100)
|
|
|
|
# init supports sample weights
|
|
init_est = init_estimator()
|
|
gb(init=init_est).fit(X, y, sample_weight=sample_weight)
|
|
|
|
# init does not support sample weights
|
|
init_est = NoSampleWeightWrapper(init_estimator())
|
|
gb(init=init_est).fit(X, y) # ok no sample weights
|
|
with pytest.raises(ValueError,
|
|
match="estimator.*does not support sample weights"):
|
|
gb(init=init_est).fit(X, y, sample_weight=sample_weight)
|
|
|
|
|
|
def test_gradient_boosting_with_init_pipeline():
|
|
# Check that the init estimator can be a pipeline (see issue #13466)
|
|
|
|
X, y = make_regression(random_state=0)
|
|
init = make_pipeline(LinearRegression())
|
|
gb = GradientBoostingRegressor(init=init)
|
|
gb.fit(X, y) # pipeline without sample_weight works fine
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match='The initial estimator Pipeline does not support sample '
|
|
'weights'):
|
|
gb.fit(X, y, sample_weight=np.ones(X.shape[0]))
|
|
|
|
# Passing sample_weight to a pipeline raises a ValueError. This test makes
|
|
# sure we make the distinction between ValueError raised by a pipeline that
|
|
# was passed sample_weight, and a ValueError raised by a regular estimator
|
|
# whose input checking failed.
|
|
with pytest.raises(
|
|
ValueError,
|
|
match='nu <= 0 or nu > 1'):
|
|
# Note that NuSVR properly supports sample_weight
|
|
init = NuSVR(gamma='auto', nu=1.5)
|
|
gb = GradientBoostingRegressor(init=init)
|
|
gb.fit(X, y, sample_weight=np.ones(X.shape[0]))
|
|
|
|
|
|
@pytest.mark.parametrize('estimator, missing_method', [
|
|
(GradientBoostingClassifier(init=LinearSVC()), 'predict_proba'),
|
|
(GradientBoostingRegressor(init=OneHotEncoder()), 'predict')
|
|
])
|
|
def test_gradient_boosting_init_wrong_methods(estimator, missing_method):
|
|
# Make sure error is raised if init estimators don't have the required
|
|
# methods (fit, predict, predict_proba)
|
|
|
|
message = ("The init parameter must be a valid estimator and support "
|
|
"both fit and " + missing_method)
|
|
with pytest.raises(ValueError, match=message):
|
|
estimator.fit(X, y)
|
|
|
|
|
|
def test_early_stopping_n_classes():
|
|
# when doing early stopping (_, , y_train, _ = train_test_split(X, y))
|
|
# there might be classes in y that are missing in y_train. As the init
|
|
# estimator will be trained on y_train, we need to raise an error if this
|
|
# happens.
|
|
|
|
X = [[1]] * 10
|
|
y = [0, 0] + [1] * 8 # only 2 negative class over 10 samples
|
|
gb = GradientBoostingClassifier(n_iter_no_change=5, random_state=0,
|
|
validation_fraction=8)
|
|
with pytest.raises(
|
|
ValueError,
|
|
match='The training data after the early stopping split'):
|
|
gb.fit(X, y)
|
|
|
|
# No error if we let training data be big enough
|
|
gb = GradientBoostingClassifier(n_iter_no_change=5, random_state=0,
|
|
validation_fraction=4)
|
|
|
|
|
|
def test_gbr_degenerate_feature_importances():
|
|
# growing an ensemble of single node trees. See #13620
|
|
X = np.zeros((10, 10))
|
|
y = np.ones((10,))
|
|
gbr = GradientBoostingRegressor().fit(X, y)
|
|
assert_array_equal(gbr.feature_importances_,
|
|
np.zeros(10, dtype=np.float64))
|
|
|
|
|
|
@pytest.mark.parametrize('Cls', GRADIENT_BOOSTING_ESTIMATORS)
|
|
@pytest.mark.parametrize('presort', ['auto', True, False])
|
|
def test_presort_deprecated(Cls, presort):
|
|
X = np.zeros((10, 10))
|
|
y = np.r_[[0] * 5, [1] * 5]
|
|
gb = Cls(presort=presort)
|
|
with pytest.warns(FutureWarning,
|
|
match="The parameter 'presort' is deprecated "):
|
|
gb.fit(X, y)
|