updated tests

tests/conftest.py

@@ -4,6 +4,7 @@
 import sys
 
 import pandas as pd
+import numpy as np
 import pytest
 
 _TEST_FOLDER = os.path.dirname(__file__)
@@ -40,3 +41,8 @@ def random_data_no_weights(get_fixture):
 @pytest.fixture(scope="session")
 def random_data_weights(get_fixture):
     return get_fixture("random_data_n100_p5_12549_weights.csv")
+
+@pytest.fixture(scope="session")
+def rng():
+    seed = 8232
+    return np.random.default_rng(seed=seed)

tests/test_linear_solver.py

@@ -2,9 +2,27 @@
 import pytest
 
 from elexsolver.LinearSolver import LinearSolver
-
+from elexsolver.QuantileRegressionSolver import QuantileRegressionSolver
 
 def test_fit():
     solver = LinearSolver()
     with pytest.raises(NotImplementedError):
         solver.fit(np.ndarray((5, 3)), np.ndarray((1, 3)))
+
+
+##################
+# Test residuals #
+##################
+def test_residuals_without_weights(rng):
+    x = rng.normal(size=(100, 5))
+    beta = rng.normal(size=(5, 1))
+    y = x @ beta
+
+    # we need an a subclass of LinearSolver to actually run a fit
+    reg = QuantileRegressionSolver()
+    reg.fit(x, y, fit_intercept=False)
+    reg.predict(x)
+
+    residuals_train = reg.residuals(x, y, K=None, center=False)
+    residuals_K = reg.residuals(x, y, K=10, center=False)
+    import pdb; pdb.set_trace()

tests/test_ols.py

@@ -30,6 +30,20 @@ def test_basic2():
     preds = lm.predict(x)
     assert all(np.abs(preds - [6.666667, 6.666667, 6.666667, 15]) <= TOL)
 
+def test_cache():
+    lm = OLSRegressionSolver()
+    x = np.asarray([[1, 1], [1, 1], [1, 1], [1, 2]])
+    y = np.asarray([3, 8, 9, 15])
+    lm.fit(x, y, fit_intercept=True, cache=False)
+
+    assert lm.normal_eqs is None
+    assert lm.hat_vals is None
+
+    lm.fit(x, y, fit_intercept=True, cache=True)
+
+    assert lm.normal_eqs is not None
+    assert lm.hat_vals is not None
+    assert lm.coefficients is not None
 
 ######################
 # Intermediate tests #

tests/test_quantile.py

@@ -55,6 +55,17 @@ def test_basic_upper():
     preds = quantreg.predict(x)
     np.testing.assert_array_equal(preds, [[9], [9], [9], [15]])
 
+def test_cache():
+    quantreg = QuantileRegressionSolver()
+    tau = 0.9
+    x = np.asarray([[1, 1], [1, 1], [1, 1], [1, 2]])
+    y = np.asarray([3, 8, 9, 15])
+    quantreg.fit(x, y, tau, cache=False)
+
+    assert quantreg.coefficients == []
+
+    quantreg.fit(x, y, tau, cache=True)
+    assert len(quantreg.coefficients) > 0
 
 ######################
 # Intermediate tests #