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Replace boston in ensemble test_forest #16927

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merged 11 commits into from May 20, 2020
Merged

Replace boston in ensemble test_forest #16927

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4868141
test_diabetes
lucyleeow Apr 15, 2020
c466853
diabetes
lucyleeow Apr 15, 2020
fc19cb7
california
lucyleeow Apr 15, 2020
42f5bd2
comment about subset
lucyleeow Apr 15, 2020
566afca
use diabetes
lucyleeow Apr 16, 2020
f6f8cc8
use diabetes in missed tests
lucyleeow Apr 16, 2020
679afa7
amend score
lucyleeow Apr 16, 2020
110d8bd
try make regression
lucyleeow Apr 22, 2020
d04619b
test
lucyleeow Apr 23, 2020
3684063
use make regression
lucyleeow May 19, 2020
5680948
merge master
lucyleeow May 19, 2020
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53 changes: 26 additions & 27 deletions sklearn/ensemble/tests/test_forest.py
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Expand Up @@ -72,12 +72,9 @@
iris.data = iris.data[perm]
iris.target = iris.target[perm]

# 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]
# Make regression dataset
X_reg, y_reg = datasets.make_regression(n_samples=500, n_features=10,
random_state=1)

# also make a hastie_10_2 dataset
hastie_X, hastie_y = datasets.make_hastie_10_2(n_samples=20, random_state=1)
Expand Down Expand Up @@ -159,29 +156,29 @@ def test_iris(name, criterion):
check_iris_criterion(name, criterion)


def check_boston_criterion(name, criterion):
# Check consistency on dataset boston house prices.
def check_regression_criterion(name, criterion):
# Check consistency on regression dataset.
ForestRegressor = FOREST_REGRESSORS[name]

reg = ForestRegressor(n_estimators=5, criterion=criterion,
random_state=1)
reg.fit(boston.data, boston.target)
score = reg.score(boston.data, boston.target)
assert score > 0.94, ("Failed with max_features=None, criterion %s "
reg.fit(X_reg, y_reg)
score = reg.score(X_reg, y_reg)
assert score > 0.93, ("Failed with max_features=None, criterion %s "
"and score = %f" % (criterion, score))

reg = ForestRegressor(n_estimators=5, criterion=criterion,
max_features=6, random_state=1)
reg.fit(boston.data, boston.target)
score = reg.score(boston.data, boston.target)
assert score > 0.95, ("Failed with max_features=6, criterion %s "
reg.fit(X_reg, y_reg)
score = reg.score(X_reg, y_reg)
assert score > 0.92, ("Failed with max_features=6, criterion %s "
"and score = %f" % (criterion, score))


@pytest.mark.parametrize('name', FOREST_REGRESSORS)
@pytest.mark.parametrize('criterion', ("mse", "mae", "friedman_mse"))
def test_boston(name, criterion):
check_boston_criterion(name, criterion)
def test_regression(name, criterion):
check_regression_criterion(name, criterion)


def check_regressor_attributes(name):
Expand Down Expand Up @@ -384,12 +381,10 @@ def check_oob_score(name, X, y, n_estimators=20):
n_samples = X.shape[0]
est.fit(X[:n_samples // 2, :], y[:n_samples // 2])
test_score = est.score(X[n_samples // 2:, :], y[n_samples // 2:])

if name in FOREST_CLASSIFIERS:
assert abs(test_score - est.oob_score_) < 0.1
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Indeed we are already doing this for the classification. I think it makes sense to do that for the regression.
We only require a comment to mention that in the first case, this is a diff between accuracies and in the second one a diff between R2.

else:
assert test_score > est.oob_score_
assert est.oob_score_ > .8
assert abs(test_score - est.oob_score_) < 0.1

# Check warning if not enough estimators
with np.errstate(divide="ignore", invalid="ignore"):
Expand All @@ -411,10 +406,10 @@ def test_oob_score_classifiers(name):

@pytest.mark.parametrize('name', FOREST_REGRESSORS)
def test_oob_score_regressors(name):
check_oob_score(name, boston.data, boston.target, 50)
check_oob_score(name, X_reg, y_reg, 50)

# csc matrix
check_oob_score(name, csc_matrix(boston.data), boston.target, 50)
check_oob_score(name, csc_matrix(X_reg), y_reg, 50)


def check_oob_score_raise_error(name):
Expand Down Expand Up @@ -475,11 +470,13 @@ def check_parallel(name, X, y):
@pytest.mark.parametrize('name', FOREST_CLASSIFIERS_REGRESSORS)
def test_parallel(name):
if name in FOREST_CLASSIFIERS:
ds = iris
X = iris.data
y = iris.target
elif name in FOREST_REGRESSORS:
ds = boston
X = X_reg
y = y_reg

check_parallel(name, ds.data, ds.target)
check_parallel(name, X, y)


def check_pickle(name, X, y):
Expand All @@ -500,11 +497,13 @@ def check_pickle(name, X, y):
@pytest.mark.parametrize('name', FOREST_CLASSIFIERS_REGRESSORS)
def test_pickle(name):
if name in FOREST_CLASSIFIERS:
ds = iris
X = iris.data
y = iris.target
elif name in FOREST_REGRESSORS:
ds = boston
X = X_reg
y = y_reg

check_pickle(name, ds.data[::2], ds.target[::2])
check_pickle(name, X[::2], y[::2])


def check_multioutput(name):
Expand Down