def test_gbrt_with_std():
# simple test of the interface
rng = np.random.RandomState(1)
X = rng.uniform(0, 5, 500)[:, np.newaxis]
noise_level = 0.5
y = truth(X) + rng.normal(0, noise_level, len(X))
X_ = np.linspace(0, 5, 1000)[:, np.newaxis]
model = GradientBoostingQuantileRegressor()
model.fit(X, y)
# three quantiles, so three numbers per sample
assert_array_equal(
model.predict(X_, return_quantiles=True).shape, (len(X_), 3))
# "traditional" API which returns one number per sample, in this case
# just the median/mean
assert_array_equal(model.predict(X_).shape, (len(X_)))
l, c, h = model.predict(X_, return_quantiles=True).T
assert_equal(l.shape, c.shape)
assert_equal(c.shape, h.shape)
assert_equal(l.shape[0], X_.shape[0])
mean, std = model.predict(X_, return_std=True)
assert_array_equal(mean, c)
assert_array_equal(std, (h - l) / 2.0)
def test_gbrt_with_std():
# simple test of the interface
rng = np.random.RandomState(1)
X = rng.uniform(0, 5, 500)[:, np.newaxis]
noise_level = 0.5
y = truth(X) + rng.normal(0, noise_level, len(X))
X_ = np.linspace(0, 5, 1000)[:, np.newaxis]
model = GradientBoostingQuantileRegressor()
model.fit(X, y)
# three quantiles, so three numbers per sample
assert_array_equal(model.predict(X_, return_quantiles=True).shape,
(len(X_), 3))
# "traditional" API which returns one number per sample, in this case
# just the median/mean
assert_array_equal(model.predict(X_).shape, (len(X_)))
l, c, h = model.predict(X_, return_quantiles=True).T
assert_equal(l.shape, c.shape, h.shape)
assert_equal(l.shape[0], X_.shape[0])
mean, std = model.predict(X_, return_std=True)
assert_array_equal(mean, c)
assert_array_equal(std, (h - l) / 2.0)
def test_gbrt_gaussian():
# estimate quantiles of the normal distribution
rng = np.random.RandomState(1)
N = 10000
X = np.ones((N, 1))
y = rng.normal(size=N)
rgr = GradientBoostingQuantileRegressor()
rgr.fit(X, y)
estimates = rgr.predict(X)
assert_almost_equal(stats.norm.ppf(rgr.quantiles),
np.mean(estimates, axis=0),
decimal=2)
def test_gbrt_gaussian():
# estimate quantiles of the normal distribution
rng = np.random.RandomState(1)
N = 10000
X = np.ones((N, 1))
y = rng.normal(size=N)
rgr = GradientBoostingQuantileRegressor()
rgr.fit(X, y)
estimates = rgr.predict(X)
assert_almost_equal(stats.norm.ppf(rgr.quantiles),
np.mean(estimates, axis=0),
decimal=2)
def test_gbrt_in_parallel():
# check estimate quantiles with parallel
rng = np.random.RandomState(1)
N = 10000
X = np.ones((N, 1))
y = rng.normal(size=N)
rgr = GradientBoostingQuantileRegressor(
n_jobs=1, random_state=np.random.RandomState(1))
rgr.fit(X, y)
estimates = rgr.predict(X)
rgr.set_params(n_jobs=2, random_state=np.random.RandomState(1))
rgr.fit(X, y)
estimates_parallel = rgr.predict(X)
assert_array_equal(estimates, estimates_parallel)
def test_gbrt_in_parallel():
# check estimate quantiles with parallel
rng = np.random.RandomState(1)
N = 10000
X = np.ones((N, 1))
y = rng.normal(size=N)
rgr = GradientBoostingQuantileRegressor(
n_jobs=1, random_state=np.random.RandomState(1))
rgr.fit(X, y)
estimates = rgr.predict(X)
rgr.set_params(n_jobs=2, random_state=np.random.RandomState(1))
rgr.fit(X, y)
estimates_parallel = rgr.predict(X)
assert_array_equal(estimates, estimates_parallel)
def test_gbrt_with_std():
# simple test of the interface
rng = np.random.RandomState(1)
X = rng.uniform(0, 5, 500)[:, np.newaxis]
noise_level = 0.5
y = truth(X) + sample_noise(X, noise_level, random_state=rng)
X_ = np.linspace(0, 5, 1000)[:, np.newaxis]
model = GradientBoostingQuantileRegressor()
model.fit(X, y)
assert_array_equal(model.predict(X_).shape, (len(X_), 3))
l, c, h = model.predict(X_).T
assert_equal(l.shape, c.shape, h.shape)
assert_equal(l.shape[0], X_.shape[0])
def test_gbrt_with_std():
# simple test of the interface
rng = np.random.RandomState(1)
X = rng.uniform(0, 5, 500)[:, np.newaxis]
noise_level = 0.5
y = truth(X) + rng.normal(0, noise_level, len(X))
X_ = np.linspace(0, 5, 1000)[:, np.newaxis]
model = GradientBoostingQuantileRegressor()
model.fit(X, y)
assert_array_equal(model.predict(X_).shape, (len(X_), 3))
l, c, h = model.predict(X_).T
assert_equal(l.shape, c.shape, h.shape)
assert_equal(l.shape[0], X_.shape[0])
mean, std = model.predict(X_, return_std=True)
assert_array_equal(mean, c)
assert_array_equal(std, (h - l) / 2.0)
def test_gbrt_base_estimator():
rng = np.random.RandomState(1)
N = 10000
X = np.ones((N, 1))
y = rng.normal(size=N)
base = RandomForestRegressor()
rgr = GradientBoostingQuantileRegressor(base_estimator=base)
with pytest.raises(ValueError):
# 'type GradientBoostingRegressor',
rgr.fit(X, y)
base = GradientBoostingRegressor()
rgr = GradientBoostingQuantileRegressor(base_estimator=base)
with pytest.raises(ValueError):
# 'quantile loss'
rgr.fit(X, y)
base = GradientBoostingRegressor(loss='quantile', n_estimators=20)
rgr = GradientBoostingQuantileRegressor(base_estimator=base)
rgr.fit(X, y)
estimates = rgr.predict(X, return_quantiles=True)
assert_almost_equal(stats.norm.ppf(rgr.quantiles),
np.mean(estimates, axis=0),
decimal=2)
def test_gbrt_with_std():
# simple test of the interface
rng = np.random.RandomState(1)
X = rng.uniform(0, 5, 500)[:, np.newaxis]
noise_level = 0.5
y = truth(X) + sample_noise(X, noise_level, random_state=rng)
X_ = np.linspace(0, 5, 1000)[:, np.newaxis]
model = GradientBoostingQuantileRegressor()
model.fit(X, y)
assert_array_equal(model.predict(X_).shape, (len(X_), 3))
l, c, h = model.predict(X_).T
assert_equal(l.shape, c.shape, h.shape)
assert_equal(l.shape[0], X_.shape[0])
mean, std = model.predict(X_, return_std=True)
assert_array_equal(mean, c)
assert_array_equal(std, (h - l) / 2.0)
def test_gbrt_base_estimator():
rng = np.random.RandomState(1)
N = 10000
X = np.ones((N, 1))
y = rng.normal(size=N)
base = RandomForestRegressor()
rgr = GradientBoostingQuantileRegressor(base_estimator=base)
assert_raise_message(ValueError, 'type GradientBoostingRegressor',
rgr.fit, X, y)
base = GradientBoostingRegressor()
rgr = GradientBoostingQuantileRegressor(base_estimator=base)
assert_raise_message(ValueError, 'quantile loss', rgr.fit, X, y)
base = GradientBoostingRegressor(loss='quantile', n_estimators=20)
rgr = GradientBoostingQuantileRegressor(base_estimator=base)
rgr.fit(X, y)
estimates = rgr.predict(X)
assert_almost_equal(stats.norm.ppf(rgr.quantiles),
np.mean(estimates, axis=0),
decimal=2)
