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fixed unit tests
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@lennybronner
lennybronner committed Sep 25, 2024
1 parent 585ae38 commit d752cd7
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Showing 3 changed files with 31 additions and 33 deletions.
2 changes: 0 additions & 2 deletions src/elexsolver/QuantileRegressionSolver.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,11 +118,9 @@ def fit(
taus = [taus]
else:
assert y.shape[1] == 1 # you can either have multiple taus or multiple ys

coefficients_array = []
for tau in taus:
for y_arr in y.T:
y_arr = y_arr.reshape(-1,1)
if lambda_ > 0:
coefficients = self._fit_with_regularization(
x, y_arr, weights, tau, lambda_, regularize_intercept, n_feat_ignore_reg
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14 changes: 7 additions & 7 deletions tests/test_ols.py
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Original file line number Diff line number Diff line change
Expand Up @@ -211,17 +211,17 @@ def test_residuals_no_weights(random_data_no_weights):
lm.fit(x, y, fit_intercept=False)
predictions = lm.predict(x)

residuals = lm.residuals(y, predictions, loo=False, center=False)

residuals = lm.residuals(x, y, K=None, center=False)
assert residuals[0] == pytest.approx(0.885973530)
assert residuals[-1] == pytest.approx(0.841996302)

residuals = lm.residuals(y, predictions, loo=True, center=False)
# equivalent of leave-one-out residuals
residuals = lm.residuals(x, y, K=100, center=False)

assert residuals[0] == pytest.approx(0.920112164)
assert residuals[-1] == pytest.approx(0.875896477)

residuals = lm.residuals(y, predictions, loo=True, center=True)
residuals = lm.residuals(x, y, K=100, center=True)
assert np.sum(residuals) == pytest.approx(0)


Expand All @@ -234,17 +234,17 @@ def test_residuals_weights(random_data_weights):
lm.fit(x, y, weights=weights, fit_intercept=False)
predictions = lm.predict(x)

residuals = lm.residuals(y, predictions, loo=False, center=False)
residuals = lm.residuals(x, y, weights=weights, K=None, center=False)

assert residuals[0] == pytest.approx(-1.971798590)
assert residuals[-1] == pytest.approx(-1.373951578)

residuals = lm.residuals(y, predictions, loo=True, center=False)
residuals = lm.residuals(x, y, weights=weights, K=100, center=False)

assert residuals[0] == pytest.approx(-1.999718445)
assert residuals[-1] == pytest.approx(-1.438563033)

residuals = lm.residuals(y, predictions, loo=True, center=True)
residuals = lm.residuals(x, y, k=100, center=True)
assert np.sum(residuals) == pytest.approx(0)


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48 changes: 24 additions & 24 deletions tests/test_quantile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ def test_basic_median_1():
preds = quantreg.predict(x)
# you'd think it would be 8 instead of 7.5, but run quantreg in R to confirm
# has to do with missing intercept
np.testing.assert_array_equal(preds, [[7.5, 7.5, 7.5, 15]])
np.testing.assert_array_equal(preds, [[7.5], [7.5], [7.5], [15]])


def test_basic_median_2():
Expand All @@ -33,7 +33,7 @@ def test_basic_median_2():
y = np.asarray([3, 8, 9, 15])
quantreg.fit(x, y, tau)
preds = quantreg.predict(x)
np.testing.assert_array_equal(preds, [[8, 8, 8, 15]])
np.testing.assert_array_equal(preds, [[8], [8], [8], [15]])


def test_basic_lower():
Expand All @@ -43,7 +43,7 @@ def test_basic_lower():
y = np.asarray([3, 8, 9, 15])
quantreg.fit(x, y, tau)
preds = quantreg.predict(x)
np.testing.assert_array_equal(preds, [[3, 3, 3, 15]])
np.testing.assert_array_equal(preds, [[3], [3], [3], [15]])


def test_basic_upper():
Expand All @@ -53,7 +53,7 @@ def test_basic_upper():
y = np.asarray([3, 8, 9, 15])
quantreg.fit(x, y, tau)
preds = quantreg.predict(x)
np.testing.assert_array_equal(preds, [[9, 9, 9, 15]])
np.testing.assert_array_equal(preds, [[9], [9], [9], [15]])


######################
Expand All @@ -68,7 +68,7 @@ def test_random_median(random_data_no_weights):
y = random_data_no_weights["y"].values
quantreg.fit(x, y, tau, fit_intercept=False)
quantreg.predict(x)
np.testing.assert_allclose(quantreg.coefficients, [[1.57699, 6.74906, 4.40175, 4.85346, 4.51814]], rtol=TOL)
np.testing.assert_allclose(quantreg.coefficients, [[1.57699], [6.74906], [4.40175], [4.85346], [4.51814]], rtol=TOL)


def test_random_lower(random_data_no_weights):
Expand All @@ -78,7 +78,7 @@ def test_random_lower(random_data_no_weights):
y = random_data_no_weights["y"].values
quantreg.fit(x, y, tau, fit_intercept=False)
quantreg.predict(x)
np.testing.assert_allclose(quantreg.coefficients, [[0.17759, 6.99588, 4.18896, 4.83906, 3.22546]], rtol=TOL)
np.testing.assert_allclose(quantreg.coefficients, [[0.17759], [6.99588], [4.18896], [4.83906], [3.22546]], rtol=TOL)


def test_random_upper(random_data_no_weights):
Expand All @@ -88,7 +88,7 @@ def test_random_upper(random_data_no_weights):
y = random_data_no_weights["y"].values
quantreg.fit(x, y, tau, fit_intercept=False)
quantreg.predict(x)
np.testing.assert_allclose(quantreg.coefficients, [[1.85617, 6.81286, 6.05586, 5.51965, 4.19864]], rtol=TOL)
np.testing.assert_allclose(quantreg.coefficients, [[1.85617], [6.81286], [6.05586], [5.51965], [4.19864]], rtol=TOL)


#################
Expand All @@ -103,10 +103,10 @@ def test_multiple(random_data_no_weights):
y = random_data_no_weights["y"].values
quantreg.fit(x, y, taus, fit_intercept=False)
quantreg.predict(x)
assert len(quantreg.coefficients) == 3
np.testing.assert_allclose(quantreg.coefficients[0], [0.17759, 6.99588, 4.18896, 4.83906, 3.22546], rtol=TOL)
np.testing.assert_allclose(quantreg.coefficients[1], [1.57699, 6.74906, 4.40175, 4.85346, 4.51814], rtol=TOL)
np.testing.assert_allclose(quantreg.coefficients[2], [1.85617, 6.81286, 6.05586, 5.51965, 4.19864], rtol=TOL)
assert quantreg.coefficients.shape == (5, 3)
np.testing.assert_allclose(quantreg.coefficients[:,0], [0.17759, 6.99588, 4.18896, 4.83906, 3.22546], rtol=TOL)
np.testing.assert_allclose(quantreg.coefficients[:,1], [1.57699, 6.74906, 4.40175, 4.85346, 4.51814], rtol=TOL)
np.testing.assert_allclose(quantreg.coefficients[:,2], [1.85617, 6.81286, 6.05586, 5.51965, 4.19864], rtol=TOL)


######################
Expand All @@ -122,7 +122,7 @@ def test_basic_median_weights():
weights = np.asarray([1, 1, 100, 3])
quantreg.fit(x, y, tau, weights)
preds = quantreg.predict(x)
np.testing.assert_array_equal(preds, [[9, 9, 9, 15]])
np.testing.assert_array_equal(preds, [[9], [9], [9], [15]])


def test_random_median_weights(random_data_weights):
Expand All @@ -133,7 +133,7 @@ def test_random_median_weights(random_data_weights):
weights = random_data_weights["weights"].values
quantreg.fit(x, y, tau, weights=weights, fit_intercept=False)
quantreg.predict(x)
np.testing.assert_allclose(quantreg.coefficients, [[1.59521, 2.17864, 4.68050, 3.10920, 9.63739]], rtol=TOL)
np.testing.assert_allclose(quantreg.coefficients, [[1.59521], [2.17864], [4.68050], [3.10920], [9.63739]], rtol=TOL)


def test_random_lower_weights(random_data_weights):
Expand All @@ -144,7 +144,7 @@ def test_random_lower_weights(random_data_weights):
weights = random_data_weights["weights"].values
quantreg.fit(x, y, tau, weights=weights, fit_intercept=False)
quantreg.predict(x)
np.testing.assert_allclose(quantreg.coefficients, [[0.63670, 1.27028, 4.81500, 3.08055, 8.69929]], rtol=TOL)
np.testing.assert_allclose(quantreg.coefficients, [[0.63670], [1.27028], [4.81500], [3.08055], [8.69929]], rtol=TOL)


def test_random_upper_weights(random_data_weights):
Expand All @@ -155,7 +155,7 @@ def test_random_upper_weights(random_data_weights):
weights = random_data_weights["weights"].values
quantreg.fit(x, y, tau, weights=weights, fit_intercept=False)
quantreg.predict(x)
np.testing.assert_allclose(quantreg.coefficients, [[3.47742, 2.07360, 4.51754, 4.15237, 9.58856]], rtol=TOL)
np.testing.assert_allclose(quantreg.coefficients, [[3.47742], [2.07360], [4.51754], [4.15237], [9.58856]], rtol=TOL)


#############################
Expand Down Expand Up @@ -189,12 +189,12 @@ def test_weight_normalization_same_fit(random_data_weights):
quantreg = QuantileRegressionSolver()
quantreg.fit(x, y, tau, weights, normalize_weights=True)
preds = quantreg.predict(x)
np.testing.assert_allclose(preds, [[9, 9, 9, 15]], rtol=TOL)
np.testing.assert_allclose(preds, [[9], [9], [9], [15]], rtol=TOL)

quantreg = QuantileRegressionSolver()
quantreg.fit(x, y, tau, weights, normalize_weights=False)
preds = quantreg.predict(x)
np.testing.assert_allclose(preds, [[9, 9, 9, 15]], rtol=TOL)
np.testing.assert_allclose(preds, [[9], [9], [9], [15]], rtol=TOL)


########################
Expand All @@ -205,13 +205,13 @@ def test_weight_normalization_same_fit(random_data_weights):
def test_regularization_without_intercept(random_data_no_weights):
tau = 0.5
x = random_data_no_weights[["x0", "x1", "x2", "x3", "x4"]].values
y = random_data_no_weights["y"].values
y = random_data_no_weights["y"].values.flatten()

quantreg = QuantileRegressionSolver()
lambda_ = 1e6
quantreg.fit(x, y, tau, lambda_=lambda_, fit_intercept=False, regularize_intercept=True)
np.testing.assert_allclose(
quantreg.coefficients, [[0, 0, 0, 0, 0]], atol=TOL
quantreg.coefficients, [[0], [0], [0], [0], [0]], atol=TOL
) # using absolute tolerance since comparing to zero


Expand All @@ -225,7 +225,7 @@ def test_regularization_with_intercept(random_data_no_weights):
lambda_ = 1e6
quantreg.fit(x, y, tau, lambda_=lambda_, fit_intercept=True, regularize_intercept=False)
coefficients_w_reg = quantreg.coefficients
np.testing.assert_allclose(quantreg.coefficients[0][1:], [0, 0, 0, 0], atol=TOL)
np.testing.assert_allclose(quantreg.coefficients[1:], [[0], [0], [0], [0]], atol=TOL)
assert np.abs(coefficients_w_reg[0][0]) > TOL


Expand All @@ -239,10 +239,10 @@ def test_regularization_with_intercept_and_features(random_data_no_weights):
lambda_ = 1e6
quantreg.fit(x, y, tau, lambda_=lambda_, fit_intercept=True, regularize_intercept=False, n_feat_ignore_reg=2)
coefficients_w_reg = quantreg.coefficients
np.testing.assert_allclose(quantreg.coefficients[0][3:], [0, 0], atol=TOL)
assert np.abs(coefficients_w_reg[0][0]) > TOL
assert np.abs(coefficients_w_reg[0][1]) > TOL
assert np.abs(coefficients_w_reg[0][2]) > TOL
np.testing.assert_allclose(quantreg.coefficients[3:], [[0], [0]], atol=TOL)
assert np.abs(coefficients_w_reg[0]) > TOL
assert np.abs(coefficients_w_reg[1]) > TOL
assert np.abs(coefficients_w_reg[2]) > TOL


########################
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