def test_sample_weight_warning(self):
clf = LinearRegression(data_norm=5.5, bounds_X=(0, 5), bounds_y=(0, 1))
X = np.array([
0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 1.75, 2.00, 2.25, 2.50, 2.75,
3.00, 3.25, 3.50, 4.00, 4.25, 4.50, 4.75, 5.00, 5.50
])
y = np.array(
[0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1])
X = X[:, np.newaxis]
with self.assertWarns(DiffprivlibCompatibilityWarning):
clf.fit(X, y, sample_weight=np.ones_like(y))
def test_no_params(self):
clf = LinearRegression()
X = np.array([
0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 1.75, 2.00, 2.25, 2.50, 2.75,
3.00, 3.25, 3.50, 4.00, 4.25, 4.50, 4.75, 5.00, 5.50
])
y = np.array(
[0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1])
X = X[:, np.newaxis]
with self.assertWarns(PrivacyLeakWarning):
clf.fit(X, y)
def test_large_norm(self):
X = np.array([
0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 1.75, 2.00, 2.25, 2.50, 2.75,
3.00, 3.25, 3.50, 4.00, 4.25, 4.50, 4.75, 5.00, 5.50
])
y = np.array(
[0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1])
X = X[:, np.newaxis]
clf = LinearRegression(data_norm=1.0)
with self.assertWarns(PrivacyLeakWarning):
clf.fit(X, y)
def test_simple(self):
X = np.linspace(-1, 1, 1000)
y = X.copy()
X = X[:, np.newaxis]
clf = LinearRegression(epsilon=2,
fit_intercept=False,
bounds_X=(-1, 1),
bounds_y=(-1, 1))
clf.fit(X, y)
print(clf.predict(np.array([0.5]).reshape(-1, 1)))
self.assertIsNotNone(clf)
self.assertAlmostEqual(clf.predict(np.array([0.5]).reshape(-1, 1))[0],
0.5,
delta=.05)
def test_simple(self):
X = np.linspace(-1, 1, 1000)
y = X.copy()
X = X[:, np.newaxis]
clf = LinearRegression(epsilon=2,
data_norm=1,
range_X=[1],
range_y=1,
fit_intercept=False)
clf.fit(X, y)
self.assertIsNotNone(clf)
self.assertAlmostEqual(clf.predict(np.array([0.5]).reshape(-1, 1))[0],
0.5,
places=3)
def test_copy(self):
X = np.linspace(-1, 1, 1000)
y = X.copy()
X = X[:, np.newaxis]
clf = LinearRegression(epsilon=2,
fit_intercept=False,
bounds_X=(-1, 1),
bounds_y=(-1, 1),
copy_X=True)
clf.fit(X, y)
self.assertIsNotNone(clf)
self.assertIsNotNone(
clf._preprocess_data(X,
y,
fit_intercept=True,
bounds_X=(-1, 1),
bounds_y=(-1, 1),
check_input=False,
copy=True))
def test_large_norm(self):
X = np.array([
0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 1.75, 2.00, 2.25, 2.50, 2.75,
3.00, 3.25, 3.50, 4.00, 4.25, 4.50, 4.75, 5.00, 5.50
])
y = np.array(
[0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1])
X = X[:, np.newaxis]
clf = LinearRegression(data_norm=1.0, fit_intercept=False)
self.assertIsNotNone(clf.fit(X, y))
def test_different_results(self):
from sklearn import datasets
from sklearn import linear_model
from sklearn.model_selection import train_test_split
dataset = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(dataset.data,
dataset.target,
test_size=0.2)
clf = LinearRegression(bounds_X=([4.3, 2.0, 1.1,
0.1], [7.9, 4.4, 6.9, 2.5]),
bounds_y=(0, 2))
clf.fit(X_train, y_train)
predict1 = clf.predict(X_test)
clf = LinearRegression(bounds_X=([4.3, 2.0, 1.1,
0.1], [7.9, 4.4, 6.9, 2.5]),
bounds_y=(0, 2))
clf.fit(X_train, y_train)
predict2 = clf.predict(X_test)
clf = linear_model.LinearRegression()
clf.fit(X_train, y_train)
predict3 = clf.predict(X_test)
self.assertFalse(np.all(predict1 == predict2))
self.assertFalse(
np.all(predict3 == predict1) and np.all(predict3 == predict2))
def test_accountant(self):
from diffprivlib.accountant import BudgetAccountant
acc = BudgetAccountant()
X = np.linspace(-1, 1, 100)
y = X.copy()
X = X[:, np.newaxis]
clf = LinearRegression(epsilon=2,
fit_intercept=False,
bounds_X=(-1, 1),
bounds_y=(-1, 1),
accountant=acc)
clf.fit(X, y)
self.assertEqual((2, 0), acc.total())
with BudgetAccountant(3, 0) as acc2:
clf = LinearRegression(epsilon=2,
fit_intercept=False,
bounds_X=(-1, 1),
bounds_y=(-1, 1))
clf.fit(X, y)
self.assertEqual((2, 0), acc2.total())
with self.assertRaises(BudgetError):
clf.fit(X, y)
def test_different_results(self):
from sklearn import datasets
from sklearn import linear_model
from sklearn.model_selection import train_test_split
dataset = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(dataset.data,
dataset.target,
test_size=0.2)
clf = LinearRegression(data_norm=12,
range_X=[3.6, 2.2, 5.9, 2.4],
range_y=2)
clf.fit(X_train, y_train)
predict1 = clf.predict(X_test)
clf = LinearRegression(data_norm=12,
range_X=[3.6, 2.2, 5.9, 2.4],
range_y=2)
clf.fit(X_train, y_train)
predict2 = clf.predict(X_test)
clf = linear_model.LinearRegression()
clf.fit(X_train, y_train)
predict3 = clf.predict(X_test)
self.assertFalse(np.all(predict1 == predict2))
self.assertFalse(
np.all(predict3 == predict1) and np.all(predict3 == predict2))
def test_same_results(self):
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import linear_model
dataset = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(dataset.data,
dataset.target,
test_size=0.2)
clf = LinearRegression(data_norm=12,
epsilon=float("inf"),
range_X=[3.6, 2.2, 5.9, 2.4],
range_y=2)
clf.fit(X_train, y_train)
predict1 = clf.predict(X_test)
clf = linear_model.LinearRegression(normalize=False)
clf.fit(X_train, y_train)
predict2 = clf.predict(X_test)
self.assertTrue(np.allclose(predict1, predict2))