enhance tests

.github/workflows/codecov.yml

@@ -27,8 +27,7 @@ jobs:
                   pip install -r requirements.txt
             - name: Generate coverage report
               run: |
-                pip install pytest
-                pip install pytest-cov
+                pip install pytest pytest-cov
                 pytest --cov=./ --cov-report=xml
             - name: Upload coverage to Codecov
               uses: codecov/codecov-action@v3

.github/workflows/main.yml

@@ -30,5 +30,5 @@ jobs:
                   pip install -r requirements.txt
             - name: Test with pytest
               run: |
-                  pip install pytest pytest-cov
-                  pytest -s ./tests --doctest-modules --cov-report=html
+                  pip install pytest
+                  pytest ./tests

pypfopt/discrete_allocation.py

@@ -3,9 +3,11 @@
 offers multiple methods to generate a discrete portfolio allocation from continuous weights.
 """
 import collections
+
+import cvxpy as cp
 import numpy as np
 import pandas as pd
-import cvxpy as cp
+
 from . import exceptions
 
 
@@ -249,7 +251,7 @@ def greedy_portfolio(self, reinvest=False, verbose=False):
             self._allocation_rmse_error(verbose)
         return self.allocation, available_funds
 
-    def lp_portfolio(self, reinvest=False, verbose=False, solver="ECOS_BB"):
+    def lp_portfolio(self, reinvest=False, verbose=False, solver=None):
         """
         Convert continuous weights into a discrete portfolio allocation
         using integer programming.
@@ -319,9 +321,6 @@ def lp_portfolio(self, reinvest=False, verbose=False, solver="ECOS_BB"):
         objective = cp.sum(u) + r
 
         opt = cp.Problem(cp.Minimize(objective), constraints)
-
-        if solver is not None and solver not in cp.installed_solvers():
-            raise NameError("Solver {} is not installed. ".format(solver))
         opt.solve(solver=solver)
 
         if opt.status not in {"optimal", "optimal_inaccurate"}:  # pragma: no cover

tests/test_discrete_allocation.py

@@ -1,11 +1,9 @@
 import numpy as np
 import pandas as pd
 import pytest
+from cvxpy.error import SolverError
 
 from pypfopt.discrete_allocation import get_latest_prices, DiscreteAllocation
-from pypfopt.efficient_frontier import EfficientFrontier
-from pypfopt.expected_returns import mean_historical_return
-from pypfopt.risk_models import sample_cov
 from tests.utilities_for_tests import get_data, setup_efficient_frontier
 
 
@@ -71,7 +69,7 @@ def test_greedy_allocation_rmse_error():
     da.greedy_portfolio()
 
     np.testing.assert_almost_equal(
-        da._allocation_rmse_error(verbose=False), 0.017086185150415774
+        da._allocation_rmse_error(verbose=True), 0.017086185150415774
     )
 
 
@@ -132,7 +130,7 @@ def test_greedy_allocation_rmse_error_short():
     da.greedy_portfolio()
 
     np.testing.assert_almost_equal(
-        da._allocation_rmse_error(verbose=False), 0.06063511265243106
+        da._allocation_rmse_error(verbose=True), 0.06063511265243106
     )
 
 
@@ -275,7 +273,7 @@ def test_lp_allocation_rmse_error():
     latest_prices = get_latest_prices(df)
     da = DiscreteAllocation(w, latest_prices, short_ratio=0.3)
     da.lp_portfolio()
-    assert da._allocation_rmse_error(verbose=False) < 0.02
+    assert da._allocation_rmse_error(verbose=True) < 0.02
 
 
 def test_lp_portfolio_allocation_short():
@@ -378,7 +376,7 @@ def test_lp_allocation_rmse_error_short():
     latest_prices = get_latest_prices(df)
     da = DiscreteAllocation(w, latest_prices, short_ratio=0.3)
     da.lp_portfolio()
-    assert da._allocation_rmse_error(verbose=False) < 0.1
+    assert da._allocation_rmse_error(verbose=True) < 0.1
 
 
 def test_lp_portfolio_allocation_different_params():
@@ -420,11 +418,11 @@ def test_rmse_decreases_with_value():
 
     da1 = DiscreteAllocation(w, latest_prices, total_portfolio_value=10000)
     da1.greedy_portfolio()
-    rmse1 = da1._allocation_rmse_error(verbose=False)
+    rmse1 = da1._allocation_rmse_error(verbose=True)
 
     da2 = DiscreteAllocation(w, latest_prices, total_portfolio_value=100000)
     da2.greedy_portfolio()
-    rmse2 = da2._allocation_rmse_error(verbose=False)
+    rmse2 = da2._allocation_rmse_error(verbose=True)
 
     assert rmse2 < rmse1
 
@@ -445,7 +443,7 @@ def test_allocation_errors():
         DiscreteAllocation(w, latest_prices, total_portfolio_value=0)
     with pytest.raises(ValueError):
         DiscreteAllocation(w, latest_prices, short_ratio=-0.4)
-    with pytest.raises(NameError):
+    with pytest.raises(SolverError):
         da = DiscreteAllocation(w, latest_prices)
         da.lp_portfolio(solver="ABCDEF")