From bdb596b84a06d0c97e39d42586a51fc36ba44186 Mon Sep 17 00:00:00 2001 From: Michael Orlitzky Date: Wed, 26 Oct 2016 14:05:20 -0400 Subject: [PATCH] Add setup.py and reorganize everything to make its "test" command happy. --- {src/dunshire => dunshire}/__init__.py | 0 {src/dunshire => dunshire}/cones.py | 0 {src/dunshire => dunshire}/errors.py | 0 {src/dunshire => dunshire}/games.py | 0 {src/dunshire => dunshire}/matrices.py | 0 {src/dunshire => dunshire}/options.py | 0 makefile | 7 +- setup.py | 14 + src/test/__init__.py | 1 - src/test/symmetric_linear_game_test.py | 528 ------------------------- src/test/suite.py => test/__init__.py | 21 +- test/__main__.py | 3 + 12 files changed, 34 insertions(+), 540 deletions(-) rename {src/dunshire => dunshire}/__init__.py (100%) rename {src/dunshire => dunshire}/cones.py (100%) rename {src/dunshire => dunshire}/errors.py (100%) rename {src/dunshire => dunshire}/games.py (100%) rename {src/dunshire => dunshire}/matrices.py (100%) rename {src/dunshire => dunshire}/options.py (100%) create mode 100644 setup.py delete mode 100644 src/test/__init__.py delete mode 100644 src/test/symmetric_linear_game_test.py rename src/test/suite.py => test/__init__.py (73%) create mode 100644 test/__main__.py diff --git a/src/dunshire/__init__.py b/dunshire/__init__.py similarity index 100% rename from src/dunshire/__init__.py rename to dunshire/__init__.py diff --git a/src/dunshire/cones.py b/dunshire/cones.py similarity index 100% rename from src/dunshire/cones.py rename to dunshire/cones.py diff --git a/src/dunshire/errors.py b/dunshire/errors.py similarity index 100% rename from src/dunshire/errors.py rename to dunshire/errors.py diff --git a/src/dunshire/games.py b/dunshire/games.py similarity index 100% rename from src/dunshire/games.py rename to dunshire/games.py diff --git a/src/dunshire/matrices.py b/dunshire/matrices.py similarity index 100% rename from src/dunshire/matrices.py rename to dunshire/matrices.py diff --git a/src/dunshire/options.py b/dunshire/options.py similarity index 100% rename from src/dunshire/options.py rename to dunshire/options.py diff --git a/makefile b/makefile index 7fe104e..0cd5182 100644 --- a/makefile +++ b/makefile @@ -1,5 +1,5 @@ PN := dunshire -SRCS := src/$(PN)/*.py src/test/*.py +SRCS := $(PN)/*.py test/*.py doc: $(SRCS) doc/source/conf.py doc/makefile cd doc && $(MAKE) html @@ -10,7 +10,7 @@ doctest: .PHONY: check check: - PYTHONPATH=src python src/test/suite.py + PYTHONPATH="." python test/ .PHONY: lint lint: @@ -18,4 +18,5 @@ lint: .PHONY: clean clean: - rm -rf src/$(PN)/__pycache__ src/test/__pycache__ doc/build + rm -rf $(PN)/__pycache__ test/__pycache__ doc/build + rm -rf $(PN).egg-info diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..8e81428 --- /dev/null +++ b/setup.py @@ -0,0 +1,14 @@ +from setuptools import setup + +setup( + name = 'dunshire', + version = '0.0.1', + author = 'Michael Orlitzky', + author_email = 'michael@orlitzky.com', + url = 'http://michael.orlitzky.com/code/dunshire.php', + packages = ['dunshire'], + description = 'A library for solving linear (cone) games', + license = 'doc/LICENSE', + install_requires = [ 'cvxopt >= 1.1.8' ], + test_suite = 'test.build_suite' +) diff --git a/src/test/__init__.py b/src/test/__init__.py deleted file mode 100644 index c5fbe98..0000000 --- a/src/test/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# <3 git. diff --git a/src/test/symmetric_linear_game_test.py b/src/test/symmetric_linear_game_test.py deleted file mode 100644 index 647d013..0000000 --- a/src/test/symmetric_linear_game_test.py +++ /dev/null @@ -1,528 +0,0 @@ -""" -Unit tests for the :class:`SymmetricLinearGame` class. -""" - -from math import sqrt -from random import randint, uniform -from unittest import TestCase - -from cvxopt import matrix -from dunshire.cones import NonnegativeOrthant, IceCream -from dunshire.games import SymmetricLinearGame -from dunshire.matrices import (append_col, append_row, eigenvalues_re, - identity, inner_product) -from dunshire import options - - -def random_matrix(dims): - """ - Generate a random square matrix. - - Parameters - ---------- - - dims : int - The number of rows/columns you want in the returned matrix. - - Returns - ------- - - matrix - A new matrix whose entries are random floats chosen uniformly from - the interval [-10, 10]. - - Examples - -------- - - >>> A = random_matrix(3) - >>> A.size - (3, 3) - - """ - return matrix([[uniform(-10, 10) for i in range(dims)] - for j in range(dims)]) - - -def random_nonnegative_matrix(dims): - """ - Generate a random square matrix with nonnegative entries. - - Parameters - ---------- - - dims : int - The number of rows/columns you want in the returned matrix. - - Returns - ------- - - matrix - A new matrix whose entries are random floats chosen uniformly from - the interval [0, 10]. - - Examples - -------- - - >>> A = random_nonnegative_matrix(3) - >>> A.size - (3, 3) - >>> all([entry >= 0 for entry in A]) - True - - """ - L = random_matrix(dims) - return matrix([abs(entry) for entry in L], (dims, dims)) - - -def random_diagonal_matrix(dims): - """ - Generate a random square matrix with zero off-diagonal entries. - - These matrices are Lyapunov-like on the nonnegative orthant, as is - fairly easy to see. - - Parameters - ---------- - - dims : int - The number of rows/columns you want in the returned matrix. - - Returns - ------- - - matrix - A new matrix whose diagonal entries are random floats chosen - uniformly from the interval [-10, 10] and whose off-diagonal - entries are zero. - - Examples - -------- - - >>> A = random_diagonal_matrix(3) - >>> A.size - (3, 3) - >>> A[0,1] == A[0,2] == A[1,0] == A[2,0] == A[1,2] == A[2,1] == 0 - True - - """ - return matrix([[uniform(-10, 10)*int(i == j) for i in range(dims)] - for j in range(dims)]) - - -def random_skew_symmetric_matrix(dims): - """ - Generate a random skew-symmetrix matrix. - - Parameters - ---------- - - dims : int - The number of rows/columns you want in the returned matrix. - - Returns - ------- - - matrix - A new skew-matrix whose strictly above-diagonal entries are - random floats chosen uniformly from the interval [-10, 10]. - - Examples - -------- - - >>> A = random_skew_symmetric_matrix(3) - >>> A.size - (3, 3) - - >>> from dunshire.matrices import norm - >>> A = random_skew_symmetric_matrix(randint(1, 10)) - >>> norm(A + A.trans()) < options.ABS_TOL - True - - """ - strict_ut = [[uniform(-10, 10)*int(i < j) for i in range(dims)] - for j in range(dims)] - - strict_ut = matrix(strict_ut, (dims, dims)) - return strict_ut - strict_ut.trans() - - -def random_lyapunov_like_icecream(dims): - r""" - Generate a random matrix Lyapunov-like on the ice-cream cone. - - The form of these matrices is cited in Gowda and Tao - [GowdaTao]_. The scalar ``a`` and the vector ``b`` (using their - notation) are easy to generate. The submatrix ``D`` is a little - trickier, but it can be found noticing that :math:`C + C^{T} = 0` - for a skew-symmetric matrix :math:`C` implying that :math:`C + C^{T} - + \left(2a\right)I = \left(2a\right)I`. Thus we can stick an - :math:`aI` with each of :math:`C,C^{T}` and let those be our - :math:`D,D^{T}`. - - Parameters - ---------- - - dims : int - The dimension of the ice-cream cone (not of the matrix you want!) - on which the returned matrix should be Lyapunov-like. - - Returns - ------- - - matrix - A new matrix, Lyapunov-like on the ice-cream cone in ``dims`` - dimensions, whose free entries are random floats chosen uniformly - from the interval [-10, 10]. - - References - ---------- - - .. [GowdaTao] M. S. Gowda and J. Tao. On the bilinearity rank of a - proper cone and Lyapunov-like transformations. Mathematical - Programming, 147:155–170, 2014. - - Examples - -------- - - >>> L = random_lyapunov_like_icecream(3) - >>> L.size - (3, 3) - >>> x = matrix([1,1,0]) - >>> s = matrix([1,-1,0]) - >>> abs(inner_product(L*x, s)) < options.ABS_TOL - True - - """ - a = matrix([uniform(-10, 10)], (1, 1)) - b = matrix([uniform(-10, 10) for idx in range(dims-1)], (dims-1, 1)) - D = random_skew_symmetric_matrix(dims-1) + a*identity(dims-1) - row1 = append_col(a, b.trans()) - row2 = append_col(b, D) - return append_row(row1, row2) - - -def random_orthant_params(): - """ - Generate the ``L``, ``K``, ``e1``, and ``e2`` parameters for a - random game over the nonnegative orthant. - """ - ambient_dim = randint(1, 10) - K = NonnegativeOrthant(ambient_dim) - e1 = [uniform(0.5, 10) for idx in range(K.dimension())] - e2 = [uniform(0.5, 10) for idx in range(K.dimension())] - L = random_matrix(K.dimension()) - return (L, K, matrix(e1), matrix(e2)) - - -def random_icecream_params(): - """ - Generate the ``L``, ``K``, ``e1``, and ``e2`` parameters for a - random game over the ice-cream cone. - """ - # Use a minimum dimension of two to avoid divide-by-zero in - # the fudge factor we make up later. - ambient_dim = randint(2, 10) - K = IceCream(ambient_dim) - e1 = [1] # Set the "height" of e1 to one - e2 = [1] # And the same for e2 - - # If we choose the rest of the components of e1,e2 randomly - # between 0 and 1, then the largest the squared norm of the - # non-height part of e1,e2 could be is the 1*(dim(K) - 1). We - # need to make it less than one (the height of the cone) so - # that the whole thing is in the cone. The norm of the - # non-height part is sqrt(dim(K) - 1), and we can divide by - # twice that. - fudge_factor = 1.0 / (2.0*sqrt(K.dimension() - 1.0)) - e1 += [fudge_factor*uniform(0, 1) for idx in range(K.dimension() - 1)] - e2 += [fudge_factor*uniform(0, 1) for idx in range(K.dimension() - 1)] - L = random_matrix(K.dimension()) - - return (L, K, matrix(e1), matrix(e2)) - - -# Tell pylint to shut up about the large number of methods. -class SymmetricLinearGameTest(TestCase): # pylint: disable=R0904 - """ - Tests for the SymmetricLinearGame and Solution classes. - """ - def assert_within_tol(self, first, second): - """ - Test that ``first`` and ``second`` are equal within our default - tolerance. - """ - self.assertTrue(abs(first - second) < options.ABS_TOL) - - - def assert_solution_exists(self, L, K, e1, e2): - """ - Given the parameters needed to construct a SymmetricLinearGame, - ensure that that game has a solution. - """ - # The matrix() constructor assumes that ``L`` is a list of - # columns, so we transpose it to agree with what - # SymmetricLinearGame() thinks. - G = SymmetricLinearGame(L.trans(), K, e1, e2) - soln = G.solution() - - expected = inner_product(L*soln.player1_optimal(), - soln.player2_optimal()) - self.assert_within_tol(soln.game_value(), expected) - - - def test_solution_exists_orthant(self): - """ - Every linear game has a solution, so we should be able to solve - every symmetric linear game over the NonnegativeOrthant. Pick - some parameters randomly and give it a shot. The resulting - optimal solutions should give us the optimal game value when we - apply the payoff operator to them. - """ - (L, K, e1, e2) = random_orthant_params() - self.assert_solution_exists(L, K, e1, e2) - - - def test_solution_exists_icecream(self): - """ - Like :meth:`test_solution_exists_nonnegative_orthant`, except - over the ice cream cone. - """ - (L, K, e1, e2) = random_icecream_params() - self.assert_solution_exists(L, K, e1, e2) - - - def test_negative_value_z_operator(self): - """ - Test the example given in Gowda/Ravindran of a Z-matrix with - negative game value on the nonnegative orthant. - """ - K = NonnegativeOrthant(2) - e1 = [1, 1] - e2 = e1 - L = [[1, -2], [-2, 1]] - G = SymmetricLinearGame(L, K, e1, e2) - self.assertTrue(G.solution().game_value() < -options.ABS_TOL) - - - def assert_scaling_works(self, L, K, e1, e2): - """ - Test that scaling ``L`` by a nonnegative number scales the value - of the game by the same number. - """ - game1 = SymmetricLinearGame(L, K, e1, e2) - value1 = game1.solution().game_value() - - alpha = uniform(0.1, 10) - game2 = SymmetricLinearGame(alpha*L, K, e1, e2) - value2 = game2.solution().game_value() - self.assert_within_tol(alpha*value1, value2) - - - def test_scaling_orthant(self): - """ - Test that scaling ``L`` by a nonnegative number scales the value - of the game by the same number over the nonnegative orthant. - """ - (L, K, e1, e2) = random_orthant_params() - self.assert_scaling_works(L, K, e1, e2) - - - def test_scaling_icecream(self): - """ - The same test as :meth:`test_nonnegative_scaling_orthant`, - except over the ice cream cone. - """ - (L, K, e1, e2) = random_icecream_params() - self.assert_scaling_works(L, K, e1, e2) - - - def assert_translation_works(self, L, K, e1, e2): - """ - Check that translating ``L`` by alpha*(e1*e2.trans()) increases - the value of the associated game by alpha. - """ - # We need to use ``L`` later, so make sure we transpose it - # before passing it in as a column-indexed matrix. - game1 = SymmetricLinearGame(L.trans(), K, e1, e2) - soln1 = game1.solution() - value1 = soln1.game_value() - x_bar = soln1.player1_optimal() - y_bar = soln1.player2_optimal() - - alpha = uniform(-10, 10) - tensor_prod = e1*e2.trans() - - # This is the "correct" representation of ``M``, but COLUMN - # indexed... - M = L + alpha*tensor_prod - - # so we have to transpose it when we feed it to the constructor. - game2 = SymmetricLinearGame(M.trans(), K, e1, e2) - value2 = game2.solution().game_value() - - self.assert_within_tol(value1 + alpha, value2) - - # Make sure the same optimal pair works. - self.assert_within_tol(value2, inner_product(M*x_bar, y_bar)) - - - def test_translation_orthant(self): - """ - Test that translation works over the nonnegative orthant. - """ - (L, K, e1, e2) = random_orthant_params() - self.assert_translation_works(L, K, e1, e2) - - - def test_translation_icecream(self): - """ - The same as :meth:`test_translation_orthant`, except over the - ice cream cone. - """ - (L, K, e1, e2) = random_icecream_params() - self.assert_translation_works(L, K, e1, e2) - - - def assert_opposite_game_works(self, L, K, e1, e2): - """ - Check the value of the "opposite" game that gives rise to a - value that is the negation of the original game. Comes from - some corollary. - """ - # We need to use ``L`` later, so make sure we transpose it - # before passing it in as a column-indexed matrix. - game1 = SymmetricLinearGame(L.trans(), K, e1, e2) - - # This is the "correct" representation of ``M``, but - # COLUMN indexed... - M = -L.trans() - - # so we have to transpose it when we feed it to the constructor. - game2 = SymmetricLinearGame(M.trans(), K, e2, e1) - - soln1 = game1.solution() - x_bar = soln1.player1_optimal() - y_bar = soln1.player2_optimal() - soln2 = game2.solution() - - self.assert_within_tol(-soln1.game_value(), soln2.game_value()) - - # Make sure the switched optimal pair works. - self.assert_within_tol(soln2.game_value(), - inner_product(M*y_bar, x_bar)) - - - def test_opposite_game_orthant(self): - """ - Test the value of the "opposite" game over the nonnegative - orthant. - """ - (L, K, e1, e2) = random_orthant_params() - self.assert_opposite_game_works(L, K, e1, e2) - - - def test_opposite_game_icecream(self): - """ - Like :meth:`test_opposite_game_orthant`, except over the - ice-cream cone. - """ - (L, K, e1, e2) = random_icecream_params() - self.assert_opposite_game_works(L, K, e1, e2) - - - def assert_orthogonality(self, L, K, e1, e2): - """ - Two orthogonality relations hold at an optimal solution, and we - check them here. - """ - # We need to use ``L`` later, so make sure we transpose it - # before passing it in as a column-indexed matrix. - game = SymmetricLinearGame(L.trans(), K, e1, e2) - soln = game.solution() - x_bar = soln.player1_optimal() - y_bar = soln.player2_optimal() - value = soln.game_value() - - ip1 = inner_product(y_bar, L*x_bar - value*e1) - self.assert_within_tol(ip1, 0) - - ip2 = inner_product(value*e2 - L.trans()*y_bar, x_bar) - self.assert_within_tol(ip2, 0) - - - def test_orthogonality_orthant(self): - """ - Check the orthgonality relationships that hold for a solution - over the nonnegative orthant. - """ - (L, K, e1, e2) = random_orthant_params() - self.assert_orthogonality(L, K, e1, e2) - - - def test_orthogonality_icecream(self): - """ - Check the orthgonality relationships that hold for a solution - over the ice-cream cone. - """ - (L, K, e1, e2) = random_icecream_params() - self.assert_orthogonality(L, K, e1, e2) - - - def test_positive_operator_value(self): - """ - Test that a positive operator on the nonnegative orthant gives - rise to a a game with a nonnegative value. - - This test theoretically applies to the ice-cream cone as well, - but we don't know how to make positive operators on that cone. - """ - (K, e1, e2) = random_orthant_params()[1:] - L = random_nonnegative_matrix(K.dimension()) - - game = SymmetricLinearGame(L, K, e1, e2) - self.assertTrue(game.solution().game_value() >= -options.ABS_TOL) - - - def assert_lyapunov_works(self, L, K, e1, e2): - """ - Check that Lyapunov games act the way we expect. - """ - game = SymmetricLinearGame(L, K, e1, e2) - soln = game.solution() - - # We only check for positive/negative stability if the game - # value is not basically zero. If the value is that close to - # zero, we just won't check any assertions. - eigs = eigenvalues_re(L) - if soln.game_value() > options.ABS_TOL: - # L should be positive stable - positive_stable = all([eig > -options.ABS_TOL for eig in eigs]) - self.assertTrue(positive_stable) - elif soln.game_value() < -options.ABS_TOL: - # L should be negative stable - negative_stable = all([eig < options.ABS_TOL for eig in eigs]) - self.assertTrue(negative_stable) - - # The dual game's value should always equal the primal's. - dualsoln = game.dual().solution() - self.assert_within_tol(dualsoln.game_value(), soln.game_value()) - - - def test_lyapunov_orthant(self): - """ - Test that a Lyapunov game on the nonnegative orthant works. - """ - (K, e1, e2) = random_orthant_params()[1:] - L = random_diagonal_matrix(K.dimension()) - - self.assert_lyapunov_works(L, K, e1, e2) - - - def test_lyapunov_icecream(self): - """ - Test that a Lyapunov game on the ice-cream cone works. - """ - (K, e1, e2) = random_icecream_params()[1:] - L = random_lyapunov_like_icecream(K.dimension()) - - self.assert_lyapunov_works(L, K, e1, e2) diff --git a/src/test/suite.py b/test/__init__.py similarity index 73% rename from src/test/suite.py rename to test/__init__.py index e10c38d..6942bf2 100644 --- a/src/test/suite.py +++ b/test/__init__.py @@ -2,7 +2,9 @@ The whole test suite. This module compiles the doctests and unittests from the rest of the -codebase into one big TestSuite() and the runs it. +codebase into one big TestSuite() and the runs it. It also provides a +function :func:`build_suite` that merely builds the suite; the result +can be used by setuptools. """ from unittest import TestLoader, TestSuite, TextTestRunner @@ -14,10 +16,9 @@ from dunshire import matrices from dunshire import games from test import symmetric_linear_game_test -def run_suite(): +def build_suite(): """ - Run all of the unit and doctests for the ``dunshire`` and ``test`` - packages. + Build our test suite, separately from running it. """ suite = TestSuite() suite.addTest(DocTestSuite(cones)) @@ -27,8 +28,12 @@ def run_suite(): suite.addTest(DocTestSuite(symmetric_linear_game_test)) slg_tests = TestLoader().loadTestsFromModule(symmetric_linear_game_test) suite.addTest(slg_tests) - runner = TextTestRunner(verbosity=1) - runner.run(suite) + return suite -if __name__ == '__main__': - run_suite() +def run_suite(s): + """ + Run all of the unit and doctests for the ``dunshire`` and ``test`` + packages. + """ + runner = TextTestRunner(verbosity=1) + runner.run(s) diff --git a/test/__main__.py b/test/__main__.py new file mode 100644 index 0000000..175c884 --- /dev/null +++ b/test/__main__.py @@ -0,0 +1,3 @@ +from test import build_suite, run_suite + +run_suite(build_suite()) -- 2.44.2