[ENH] move base_optimizer to a new base module

docs/GeneralEfficientFrontier.rst

@@ -203,7 +203,7 @@ For example, perhaps our objective is to construct a basket of assets that best
 particular index, in other words, to minimise the **tracking error**. This does not fit within
 a mean-variance optimization paradigm, but we can still implement it in PyPortfolioOpt::
 
-    from pypfopt.base_optimizer import BaseConvexOptimizer
+    from pypfopt.base import BaseConvexOptimizer
     from pypfopt.objective_functions import ex_post_tracking_error
 
     historic_rets = ... # dataframe of historic asset returns
@@ -231,7 +231,7 @@ or a ``nonconvex_objective``, which uses ``scipy.optimize`` as the backend and t
 different API. For more examples, check out this `cookbook recipe
 <https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/3-Advanced-Mean-Variance-Optimization.ipynb>`_.
 
-    .. class:: pypfopt.base_optimizer.BaseConvexOptimizer
+    .. class:: pypfopt.base.BaseConvexOptimizer
 
         .. automethod:: convex_objective
 

docs/MeanVariance.rst

@@ -114,9 +114,9 @@ Basic Usage
                 If you would like to use the ``portfolio_performance`` function independently of any
                 optimizer (e.g for debugging purposes), you can use::
 
-                    from pypfopt import base_optimizer
+                    from pypfopt.base import portfolio_performance
 
-                    base_optimizer.portfolio_performance(
+                    portfolio_performance(
                         weights, expected_returns, cov_matrix, verbose=True, risk_free_rate=0.02
                     )
 
@@ -135,7 +135,7 @@ EfficientFrontier inherits from the BaseConvexOptimizer class. In particular, th
 add constraints and objectives are documented below:
 
 
-.. class:: pypfopt.base_optimizer.BaseConvexOptimizer
+.. class:: pypfopt.base.BaseConvexOptimizer
     :noindex:
 
     .. automethod:: add_constraint

docs/OtherOptimizers.rst

@@ -95,11 +95,11 @@ to code up compared to custom objective functions.
 
 To implement a custom optimizer that is compatible with the rest of PyPortfolioOpt, just
 extend ``BaseOptimizer`` (or ``BaseConvexOptimizer`` if you want to use ``cvxpy``),
-both of which can be found in ``base_optimizer.py``. This gives you access to utility
+both of which can be found in ``pypfopt.base``. This gives you access to utility
 methods like ``clean_weights()``, as well as making sure that any output is compatible
 with ``portfolio_performance()`` and post-processing methods.
 
-.. automodule:: pypfopt.base_optimizer
+.. automodule:: pypfopt.base
 
     .. autoclass:: BaseOptimizer
         :members:

pypfopt/base/__init__.py

@@ -0,0 +1,9 @@
+"""Base classes."""
+
+from pypfopt.base._base_optimizer import (
+    BaseConvexOptimizer,
+    BaseOptimizer,
+    portfolio_performance,
+)
+
+__all__ = ["BaseOptimizer", "BaseConvexOptimizer", "portfolio_performance"]

pypfopt/black_litterman.py

@@ -13,7 +13,7 @@
 import numpy as np
 import pandas as pd
 
-from . import base_optimizer
+from pypfopt.base import BaseOptimizer, portfolio_performance
 
 
 def market_implied_prior_returns(
@@ -97,7 +97,7 @@ def market_implied_risk_aversion(market_prices, frequency=252, risk_free_rate=0.
     return (r - risk_free_rate) / var
 
 
-class BlackLittermanModel(base_optimizer.BaseOptimizer):
+class BlackLittermanModel(BaseOptimizer):
     """
     A BlackLittermanModel object (inheriting from BaseOptimizer) contains requires
     a specific input format, specifying the prior, the views, the uncertainty in views,
@@ -542,7 +542,7 @@ def portfolio_performance(self, verbose=False, risk_free_rate=0.0):
         """
         if self.posterior_cov is None:
             self.posterior_cov = self.bl_cov()
-        return base_optimizer.portfolio_performance(
+        return portfolio_performance(
             self.weights,
             self.posterior_rets,
             self.posterior_cov,

pypfopt/cla.py

@@ -7,10 +7,10 @@
 import numpy as np
 import pandas as pd
 
-from . import base_optimizer
+from pypfopt.base import BaseOptimizer, portfolio_performance
 
 
-class CLA(base_optimizer.BaseOptimizer):
+class CLA(BaseOptimizer):
     """
     Instance variables:
 
@@ -506,7 +506,7 @@ def portfolio_performance(self, verbose=False, risk_free_rate=0.0):
         (float, float, float)
             expected return, volatility, Sharpe ratio.
         """
-        return base_optimizer.portfolio_performance(
+        return portfolio_performance(
             self.weights,
             self.expected_returns,
             self.cov_matrix,

pypfopt/efficient_frontier/efficient_frontier.py

@@ -9,10 +9,12 @@
 import numpy as np
 import pandas as pd
 
-from .. import base_optimizer, exceptions, objective_functions
+from pypfopt.base import BaseConvexOptimizer, portfolio_performance
 
+from .. import exceptions, objective_functions
 
-class EfficientFrontier(base_optimizer.BaseConvexOptimizer):
+
+class EfficientFrontier(BaseConvexOptimizer):
     """
     An EfficientFrontier object (inheriting from BaseConvexOptimizer) contains multiple
     optimization methods that can be called (corresponding to different objective
@@ -500,7 +502,7 @@ def portfolio_performance(self, verbose=False, risk_free_rate=0.0):
                 )
             risk_free_rate = self._risk_free_rate
 
-        return base_optimizer.portfolio_performance(
+        return portfolio_performance(
             self.weights,
             self.expected_returns,
             self.cov_matrix,

pypfopt/hierarchical_portfolio.py

@@ -19,10 +19,11 @@
 import scipy.cluster.hierarchy as sch
 import scipy.spatial.distance as ssd
 
-from . import base_optimizer, risk_models
+from pypfopt.base import BaseOptimizer, portfolio_performance
+from pypfopt.risk_models import cov_to_corr
 
 
-class HRPOpt(base_optimizer.BaseOptimizer):
+class HRPOpt(BaseOptimizer):
     """
     A HRPOpt object (inheriting from BaseOptimizer) constructs a hierarchical
     risk parity portfolio.
@@ -180,7 +181,7 @@ def optimize(self, linkage_method="single"):
 
         if self.returns is None:
             cov = self.cov_matrix
-            corr = risk_models.cov_to_corr(self.cov_matrix).round(6)
+            corr = cov_to_corr(self.cov_matrix).round(6)
         else:
             corr, cov = self.returns.corr(), self.returns.cov()
 
@@ -234,6 +235,4 @@ def portfolio_performance(self, verbose=False, risk_free_rate=0.0, frequency=252
             cov = self.returns.cov() * frequency
             mu = self.returns.mean() * frequency
 
-        return base_optimizer.portfolio_performance(
-            self.weights, mu, cov, verbose, risk_free_rate
-        )
+        return portfolio_performance(self.weights, mu, cov, verbose, risk_free_rate)

tests/test_base_optimizer.py

@@ -8,7 +8,7 @@
 import pytest
 
 from pypfopt import EfficientFrontier, exceptions, objective_functions
-from pypfopt.base_optimizer import BaseOptimizer, portfolio_performance
+from pypfopt.base import BaseOptimizer, portfolio_performance
 from tests.utilities_for_tests import get_data, setup_efficient_frontier
 
 
@@ -218,7 +218,7 @@ def test_save_weights_to_file():
 
 def test_portfolio_performance():
     """
-    Cover logic in base_optimizer.portfolio_performance not covered elsewhere.
+    Cover logic in base.portfolio_performance not covered elsewhere.
     """
     ef = setup_efficient_frontier()
     ef.min_volatility()

tests/test_custom_objectives.py

@@ -9,7 +9,7 @@
     objective_functions,
     risk_models,
 )
-from pypfopt.base_optimizer import BaseConvexOptimizer
+from pypfopt.base import BaseConvexOptimizer
 from tests.utilities_for_tests import get_data, setup_efficient_frontier
 
 

tests/test_imports.py

@@ -1,6 +1,8 @@
+from pypfopt.base import BaseConvexOptimizer, BaseOptimizer, portfolio_performance
+
+
 def test_import_modules():
     from pypfopt import (
-        base_optimizer,
         black_litterman,
         cla,
         discrete_allocation,