def test_piecewise_regressor_issue(self):
X, y = make_regression(10000, n_features=1, n_informative=1, # pylint: disable=W0632
n_targets=1)
y = y.reshape((-1, 1))
model = PiecewiseRegressor(
binner=DecisionTreeRegressor(min_samples_leaf=300))
model.fit(X, y)
vvc = model.predict(X)
self.assertEqual(vvc.shape, (X.shape[0], ))
def test_piecewise_regressor_pandas(self):
X = pandas.DataFrame(numpy.array([[0.1, 0.2], [0.2, 0.3]]))
Y = numpy.array([1., 1.1])
clr = LinearRegression(fit_intercept=False)
clr.fit(X, Y)
clq = PiecewiseRegressor()
clq.fit(X, Y)
pred1 = clr.predict(X)
pred2 = clq.predict(X)
self.assertEqual(pred1, pred2)
def test_piecewise_regressor_intercept_weights3(self):
X = numpy.array([[0.1, 0.2], [0.2, 0.3], [0.3, 0.3]])
Y = numpy.array([1., 1.1, 1.2])
W = numpy.array([1., 1., 1.])
clr = LinearRegression(fit_intercept=True)
clr.fit(X, Y, W)
clq = PiecewiseRegressor(verbose=False)
clq.fit(X, Y, W)
pred1 = clr.predict(X)
pred2 = clq.predict(X)
self.assertNotEqual(pred2.min(), pred2.max())
self.assertEqual(pred1, pred2)
def test_piecewise_regressor_grid_search(self):
X = random(100)
eps1 = (random(90) - 0.5) * 0.1
eps2 = random(10) * 2
eps = numpy.hstack([eps1, eps2])
X = X.reshape((100, 1)) # pylint: disable=E1101
Y = X.ravel() * 3.4 + 5.6 + eps
self.assertRaise(lambda: test_sklearn_grid_search_cv(
lambda: PiecewiseRegressor(), X, Y), ValueError)
res = test_sklearn_grid_search_cv(lambda: PiecewiseRegressor(),
X, Y, binner__max_depth=[2, 3])
self.assertIn('model', res)
self.assertIn('score', res)
self.assertGreater(res['score'], 0)
self.assertLesser(res['score'], 1)
def test_piecewise_regressor_pickle(self):
X = numpy.random.random(100)
eps1 = (numpy.random.random(90) - 0.5) * 0.1
eps2 = numpy.random.random(10) * 2
eps = numpy.hstack([eps1, eps2])
X = X.reshape((100, 1)) # pylint: disable=E1101
Y = X.ravel() * 3.4 + 5.6 + eps
test_sklearn_pickle(lambda: LinearRegression(), X, Y)
test_sklearn_pickle(lambda: PiecewiseRegressor(), X, Y)
def test_piecewise_regressor_diff(self):
X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
Y = numpy.array([1., 1.1, 1.2, 10, 1.4])
clr = LinearRegression()
clr.fit(X, Y)
clq = PiecewiseRegressor(verbose=False)
clq.fit(X, Y)
pred1 = clr.predict(X)
self.assertNotEmpty(pred1)
pred2 = clq.predict(X)
self.assertEqual(len(clq.estimators_), 2)
p1 = clq.estimators_[0].predict(X[:3, :])
p2 = clq.estimators_[1].predict(X[3:, :])
self.assertEqual(pred2[:3], p1)
self.assertEqual(pred2[-2:], p2)
sc = clq.score(X, Y)
self.assertEqual(sc, 1)
def test_piecewise_regressor_no_intercept_bins(self):
X = numpy.array([[0.1, 0.2], [0.2, 0.3], [0.2, 0.35], [0.2, 0.36]])
Y = numpy.array([1., 1.1, 1.15, 1.2])
clr = LinearRegression(fit_intercept=False)
clr.fit(X, Y)
clq = PiecewiseRegressor(binner="bins")
clq.fit(X, Y)
pred1 = clr.predict(X)
pred2 = clq.predict(X)
self.assertEqual(pred1.shape, (4, ))
self.assertEqual(pred2.shape, (4, ))
sc1 = clr.score(X, Y)
sc2 = clq.score(X, Y)
self.assertIsInstance(sc1, float)
self.assertIsInstance(sc2, float)
paths = clq.binner_.transform(X)
self.assertIn(paths.shape, ((4, 7), (4, 8), (4, 9), (4, 10)))
self.assertNotEqual(pred2.min(), pred2.max())
def test_piecewise_regressor_no_intercept(self):
X = numpy.array([[0.1, 0.2], [0.2, 0.3], [0.2, 0.35], [0.2, 0.36]])
Y = numpy.array([1., 1.1, 1.15, 1.2])
clr = LinearRegression(fit_intercept=False)
clr.fit(X, Y)
clq = PiecewiseRegressor()
clq.fit(X, Y)
pred1 = clr.predict(X)
pred2 = clq.predict(X)
self.assertEqual(pred1.shape, (4, ))
self.assertEqual(pred2.shape, (4, ))
sc1 = clr.score(X, Y)
sc2 = clq.score(X, Y)
sc3 = clq.binner_.score(X, Y)
self.assertIsInstance(sc1, float)
self.assertIsInstance(sc2, float)
self.assertIsInstance(sc3, float)
paths = clq.binner_.decision_path(X)
s = paths.sum()
self.assertEqual(s, 8)
self.assertNotEqual(pred2.min(), pred2.max())
self.assertGreater(clq.n_estimators_, 1)
def test_piecewise_regressor_clone(self):
test_sklearn_clone(lambda: PiecewiseRegressor(verbose=True))
def test_piecewise_regressor_list(self):
X = [[0.1, 0.2], [0.2, 0.3]]
Y = numpy.array([1., 1.1])
clq = PiecewiseRegressor()
self.assertRaise(lambda: clq.fit(X, Y), TypeError)
def test_piecewise_regressor_raise(self):
X, y = make_regression(10000, n_features=2, n_informative=2, # pylint: disable=W0632
n_targets=2)
model = PiecewiseRegressor(
binner=DecisionTreeRegressor(min_samples_leaf=300))
self.assertRaise(lambda: model.fit(X, y), RuntimeError)
for method, parameters in [
("pmi", ["Batchsize", "Alpha"]),
("lexstat", ["LWeight", "GOP"] + onehot(df, "Alignment")),
]:
data = df[df["Method"] == method]
if linearmodeltree:
model = PiecewiseRegressor(
DecisionTreeRegressor(criterion='mse',
max_depth=None,
max_features=None,
max_leaf_nodes=20,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=300,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
presort=False,
random_state=None,
splitter='best'),
estimator=LinearRegression(copy_X=True,
fit_intercept=True,
n_jobs=None,
normalize=False),
n_jobs=None,
verbose=True)
else:
model = DecisionTreeRegressor(splitter="best",
max_leaf_nodes=40,
criterion="friedman_mse")
# model = LinearRegression()
dataset_features = [f for f in features if f in data.columns]