[MNT] testing package without soft dependencies, isolate matplotlib (#662)

* adds a CI job, `pytest-nosoftdeps`, to test the package without soft
dependencies.
* isolates soft dependency `matplotlib` in tests and the plotting module
* isolates the `ecos` dependency almost entirely. There is one default
that remains, which is deprecated and scheduled for removal in 1.7.0.
* adds `scikit-base` soft dependency (without further dependencies) to
manage soft dependencies

This is also useful to check whether soft dependencies are properly
isolated.

Author: fkiraly

.github/workflows/main.yml

@@ -50,9 +50,50 @@ jobs:
             echo "No changed files to check."
           fi
 
-  pytest:
+  pytest-nosoftdeps:
     needs: code-quality
-    name: py${{ matrix.python-version }} on ${{ matrix.os }}
+    name: nosoftdeps (${{ matrix.python-version }}, ${{ matrix.os }})
+    runs-on: ${{ matrix.os }}
+    env:
+      MPLBACKEND: Agg # https://github.com/orgs/community/discussions/26434
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+      fail-fast: false  # to not fail all combinations if just one fails
+
+    steps:
+      - uses: actions/checkout@v5
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+        with:
+          enable-cache: true
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v6
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Display Python version
+        run: python -c "import sys; print(sys.version)"
+
+      - name: Install dependencies
+        shell: bash
+        run: uv pip install ".[dev]" --no-cache-dir
+        env:
+          UV_SYSTEM_PYTHON: 1
+
+      - name: Show dependencies
+        run: uv pip list
+
+      - name: Test with pytest
+        run: |
+          pytest ./tests
+
+  pytest:
+    needs: pytest-nosoftdeps
+    name: (${{ matrix.python-version }}, ${{ matrix.os }})
     runs-on: ${{ matrix.os }}
     env:
       MPLBACKEND: Agg # https://github.com/orgs/community/discussions/26434
@@ -92,7 +133,7 @@ jobs:
           pytest ./tests
 
   codecov:
-    name: py${{ matrix.python-version }} on ${{ matrix.os }}
+    name: coverage (${{ matrix.python-version }} on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     needs: code-quality
     env:
@@ -141,8 +182,8 @@ jobs:
           fail_ci_if_error: true
 
   notebooks:
-    runs-on: ubuntu-latest
     needs: code-quality
+    runs-on: ubuntu-latest
 
     strategy:
       matrix:

pypfopt/discrete_allocation.py

@@ -4,10 +4,12 @@
 """
 
 import collections
+from warnings import warn
 
 import cvxpy as cp
 import numpy as np
 import pandas as pd
+from skbase.utils.dependencies import _check_soft_dependencies
 
 from . import exceptions
 
@@ -252,7 +254,8 @@ 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"):
+    # todo 1.7.0: remove ECOS_BB defaulting behavior from docstring
+    def lp_portfolio(self, reinvest=False, verbose=False, solver=None):
         """
         Convert continuous weights into a discrete portfolio allocation
         using integer programming.
@@ -262,11 +265,23 @@ def lp_portfolio(self, reinvest=False, verbose=False, solver="ECOS_BB"):
         :param verbose: print error analysis?
         :type verbose: bool
         :param solver: the CVXPY solver to use (must support mixed-integer programs)
-        :type solver: str, defaults to "ECOS_BB"
+        :type solver: str, defaults to "ECOS_BB" if ecos is installed, else None
         :return: the number of shares of each ticker that should be purchased, along with the amount
                 of funds leftover.
         :rtype: (dict, float)
         """
+        # todo 1.7.0: remove this defaulting behavior
+        if solver is None and _check_soft_dependencies("ecos", severity="none"):
+            solver = "ECOS_BB"
+            warn(
+                "The default solver for lp_portfolio will change from ECOS_BB to"
+                "None, the cvxpy default solver, in release 1.7.0."
+                "To continue using ECOS_BB as the solver, "
+                "please set solver='ECOS_BB' explicitly.",
+                FutureWarning,
+            )
+        # end todo
+
         if any([w < 0 for _, w in self.weights]):
             longs = {t: w for t, w in self.weights if w >= 0}
             shorts = {t: -w for t, w in self.weights if w < 0}

pypfopt/plotting.py

@@ -16,10 +16,15 @@
 
 from . import CLA, EfficientFrontier, exceptions, risk_models
 
-try:
-    import matplotlib.pyplot as plt
-except (ModuleNotFoundError, ImportError):  # pragma: no cover
-    raise ImportError("Please install matplotlib via pip or poetry")
+
+def _import_matplotlib():
+    """Helper function to import matplotlib only when needed"""
+    try:
+        import matplotlib.pyplot as plt
+
+        return plt
+    except (ModuleNotFoundError, ImportError):  # pragma: no cover
+        raise ImportError("Please install matplotlib via pip or poetry")
 
 
 def _get_plotly():
@@ -46,6 +51,8 @@ def _plot_io(**kwargs):
     :param showfig: whether to plt.show() the figure, defaults to False
     :type showfig: bool, optional
     """
+    plt = _import_matplotlib()
+
     filename = kwargs.get("filename", None)
     showfig = kwargs.get("showfig", False)
     dpi = kwargs.get("dpi", 300)
@@ -73,6 +80,8 @@ def plot_covariance(cov_matrix, plot_correlation=False, show_tickers=True, **kwa
     :return: matplotlib axis
     :rtype: matplotlib.axes object
     """
+    plt = _import_matplotlib()
+
     if plot_correlation:
         matrix = risk_models.cov_to_corr(cov_matrix)
     else:
@@ -110,6 +119,8 @@ def plot_dendrogram(hrp, ax=None, show_tickers=True, **kwargs):
     :return: matplotlib axis
     :rtype: matplotlib.axes object
     """
+    plt = _import_matplotlib()
+
     ax = ax or plt.gca()
 
     if hrp.clusters is None:
@@ -337,6 +348,8 @@ def plot_efficient_frontier(
     :return: matplotlib axis
     :rtype: matplotlib.axes object
     """
+    plt = _import_matplotlib()
+
     if interactive:
         go, _ = _get_plotly()
         ax = go.Figure()
@@ -393,6 +406,8 @@ def plot_weights(weights, ax=None, **kwargs):
     :return: matplotlib axis
     :rtype: matplotlib.axes
     """
+    plt = _import_matplotlib()
+
     ax = ax or plt.gca()
 
     desc = sorted(weights.items(), key=lambda x: x[1], reverse=True)

pyproject.toml

@@ -37,6 +37,7 @@ dependencies = [
     "pandas>=0.19",
     "scikit-learn>=0.24.1",
     "scipy>=1.3.0",
+    "scikit-base<0.14.0",
 ]
 
 [project.optional-dependencies]

tests/test_efficient_cdar.py

@@ -1,5 +1,6 @@
 import numpy as np
 import pytest
+from skbase.utils.dependencies import _check_soft_dependencies
 
 from pypfopt import EfficientCDaR, expected_returns, objective_functions
 from pypfopt.exceptions import OptimizationError
@@ -151,6 +152,10 @@ def test_min_cdar_extra_constraints():
     assert w["GOOG"] >= 0.025 and w["MA"] <= 0.035
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["ecos"], severity="none"),
+    reason="skip test if ecos is not installed in environment",
+)
 def test_min_cdar_different_solver():
     cd = setup_efficient_cdar(solver="ECOS")
     w = cd.min_cdar()
@@ -182,6 +187,10 @@ def test_min_cdar_tx_costs():
     assert np.abs(prev_w - w2).sum() < np.abs(prev_w - w1).sum()
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["ecos"], severity="none"),
+    reason="skip test if ecos is not installed in environment",
+)
 def test_min_cdar_L2_reg():
     cd = setup_efficient_cdar(solver="ECOS")
     cd.add_objective(objective_functions.L2_reg, gamma=0.1)

tests/test_efficient_cvar.py

@@ -1,5 +1,6 @@
 import numpy as np
 import pytest
+from skbase.utils.dependencies import _check_soft_dependencies
 
 from pypfopt import EfficientCVaR, expected_returns, objective_functions
 from pypfopt.exceptions import OptimizationError
@@ -156,6 +157,10 @@ def test_min_cvar_extra_constraints():
     assert w["GOOG"] >= 0.025 and w["AAPL"] <= 0.035
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["ecos"], severity="none"),
+    reason="skip test if ecos is not installed in environment",
+)
 def test_min_cvar_different_solver():
     cv = setup_efficient_cvar(solver="ECOS")
     w = cv.min_cvar()
@@ -186,6 +191,10 @@ def test_min_cvar_tx_costs():
     assert np.abs(prev_w - w2).sum() < np.abs(prev_w - w1).sum()
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["ecos"], severity="none"),
+    reason="skip test if ecos is not installed in environment",
+)
 def test_min_cvar_L2_reg():
     cv = setup_efficient_cvar(solver="ECOS")
     cv.add_objective(objective_functions.L2_reg, gamma=0.1)

tests/test_efficient_frontier.py

@@ -5,6 +5,7 @@
 import pandas as pd
 import pytest
 import scipy.optimize as sco
+from skbase.utils.dependencies import _check_soft_dependencies
 
 from pypfopt import (
     EfficientFrontier,
@@ -106,6 +107,10 @@ def test_min_volatility():
     )
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["ecos"], severity="none"),
+    reason="skip test if ecos is not installed in environment",
+)
 def test_min_volatility_different_solver():
     ef = setup_efficient_frontier(solver="ECOS")
     w = ef.min_volatility()
@@ -1042,6 +1047,10 @@ def test_efficient_risk_market_neutral_L2_reg():
     )
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["ecos"], severity="none"),
+    reason="skip test if ecos is not installed in environment",
+)
 def test_efficient_risk_market_neutral_warning():
     ef = setup_efficient_frontier(solver=cp.ECOS)
     with pytest.warns(RuntimeWarning) as w:
@@ -1088,6 +1097,10 @@ def test_efficient_frontier_error():
         EfficientFrontier(ef.expected_returns, 0.01)
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["ecos"], severity="none"),
+    reason="skip test if ecos is not installed in environment",
+)
 def test_efficient_return_many_values():
     ef = setup_efficient_frontier(solver=cp.ECOS)
     for target_return in np.arange(0.25, 0.28, 0.01):
@@ -1217,6 +1230,10 @@ def test_efficient_return_market_neutral_unbounded():
     assert long_only_sharpe < sharpe
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["ecos"], severity="none"),
+    reason="skip test if ecos is not installed in environment",
+)
 def test_efficient_return_market_neutral_warning():
     # This fails
     ef = setup_efficient_frontier(solver=cp.ECOS)

tests/test_efficient_semivariance.py

@@ -1,6 +1,7 @@
+from cvxpy.error import SolverError
 import numpy as np
 import pytest
-from cvxpy.error import SolverError
+from skbase.utils.dependencies import _check_soft_dependencies
 
 from pypfopt import (
     EfficientFrontier,
@@ -176,6 +177,10 @@ def test_min_semivariance_extra_constraints():
     assert w["GOOG"] >= 0.025 and w["AAPL"] <= 0.035
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["ecos"], severity="none"),
+    reason="skip test if ecos is not installed in environment",
+)
 def test_min_semivariance_different_solver():
     es = setup_efficient_semivariance(solver="ECOS")
     w = es.min_semivariance()
@@ -347,6 +352,10 @@ def test_max_quadratic_utility_with_shorts():
     )
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["ecos"], severity="none"),
+    reason="skip test if ecos is not installed in environment",
+)
 def test_max_quadratic_utility_market_neutral():
     es = setup_efficient_semivariance(solver="ECOS", weight_bounds=(-1, 1))
     es.max_quadratic_utility(market_neutral=True)