linear programming in simus done

setup.py

@@ -62,7 +62,7 @@
 
 REQUIREMENTS = [
     "numpy", "scipy", "six", "mock", "tabulate", "matplotlib", "pulp",
-    "json-tricks", "joblib"  # this is for io
+    "json-tricks", "joblib", "attrs"
 ]
 
 

skcriteria/base.py

@@ -361,7 +361,7 @@ def preprocess(self, data):
         return Data(mtx=nmtx, criteria=data.criteria, weights=nweights,
                     anames=data.anames, cnames=data.cnames)
 
-    def decide(self, data, criteria=None, weights=None):
+    def decide(self, data, criteria=None, weights=None, **kwargs):
         """Execute the Solver over the given data.
 
         Parameters
@@ -383,6 +383,8 @@ def decide(self, data, criteria=None, weights=None):
             - If data is 2d array_like and weights are 1d array_like with `m`
               elements (number of criteria); the i-nth element represent the
               importance of the i-nth criteria.
+        kwargs : optional
+            keywords arguments for the solve method
 
         Returns
         -------
@@ -401,7 +403,7 @@ def decide(self, data, criteria=None, weights=None):
                                  "provide a 'criteria' array")
             data = Data(data, criteria, weights)
         pdata = self.preprocess(data)
-        result = self.solve(pdata)
+        result = self.solve(pdata, **kwargs)
         return self.make_result(data, *result)
 
     @abc.abstractmethod

skcriteria/madm/_dmaker.py

@@ -404,7 +404,7 @@ def make_result(self, data, kernel, rank, extra):
         -------
 
         :py:class:`skcriteria.madm.Decision`
-            A convenient instance conraining all the parameters.
+            A convenient instance containing all the parameters.
 
         """
         decision = Decision(

skcriteria/madm/simus.py

@@ -0,0 +1,166 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2016-2017, Cabral, Juan; Luczywo, Nadia
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+
+# * Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+
+# =============================================================================
+# FUTURE
+# =============================================================================
+
+from __future__ import unicode_literals
+
+
+# =============================================================================
+# DOCS
+# =============================================================================
+
+__doc__ = """Simus methods"""
+
+__all__ = [
+    "SIMUS"]
+
+
+# =============================================================================
+# IMPORTS
+# =============================================================================
+
+import operator
+
+import numpy as np
+
+import joblib
+
+from ..validate import MAX, MIN
+from ..utils import lp
+from ..utils.doc_inherit import doc_inherit
+
+from ._dmaker import DecisionMaker
+
+
+# =============================================================================
+# FUNCTIONS
+# =============================================================================
+
+def make_stage(mtx, b, senses, zindex, solver):
+    # retrieve the problem class
+    problem = lp.Minimize if senses[zindex] == MIN else lp.Maximize
+
+    # create the variables
+    xs = tuple(
+        lp.Float("x{}".format(idx), low=0) for idx in range(mtx.shape[1]))
+
+    # create the objective function
+    z_coef = mtx[zindex]
+    z = sum(c * x for c, x in zip(z_coef, xs))
+
+    # the conditions
+    conditions = []
+    for idx in range(mtx.shape[0]):
+        if idx == zindex:
+            continue
+        coef = mtx[idx]
+
+        left = sum(c * x for c, x in zip(coef, xs))
+        op = operator.le if senses[idx] == MAX else operator.ge
+        right = b[idx]
+
+        condition = op(left, right)
+        conditions.append(condition)
+    stage = problem(z=z, solver=solver).sa(*conditions)
+    return stage
+
+
+def solve(stage):
+    return stage.solve()
+
+
+def simus(nmtx, ncriteria, nweights, b=None, solver="pulp", njobs=None):
+
+    t_nmtx = nmtx.T
+
+    b = np.asarray(b)
+    if None in b:
+        mins = np.min(t_nmtx, axis=1)
+        maxs = np.max(t_nmtx, axis=1)
+
+        auto_b = np.where(ncriteria == MAX, maxs, mins)
+        b = np.where(b.astype(bool), b, auto_b)
+
+    njobs = njobs or joblib.cpu_count()
+    with joblib.Parallel(n_jobs=njobs) as jobs:
+        stages = jobs(
+            joblib.delayed(make_stage)(
+                mtx=t_nmtx, b=b, senses=ncriteria, zindex=idx, solver=solver)
+            for idx in range(t_nmtx.shape[0]))
+
+        results = jobs(
+            joblib.delayed(solve)(stage) for stage in stages)
+
+        return None, results
+
+
+# =============================================================================
+# OO
+# =============================================================================
+
+class SIMUS(DecisionMaker):
+    r"""
+
+    """
+
+    def __init__(self, mnorm="none", wnorm="none", solver="pulp", njobs=None):
+        super(SIMUS, self).__init__(mnorm=mnorm, wnorm=wnorm)
+        self._solver = solver
+        self._njobs = njobs
+
+    @doc_inherit
+    def solve(self, ndata, b):
+        nmtx, ncriteria, nweights = ndata.mtx, ndata.criteria, ndata.weights
+        rank, points = simus(
+            nmtx, ncriteria, nweights,
+            b=b, solver=self._solver, njobs=self._njobs)
+        return None, rank, {"points": points}
+
+    @property
+    def solver(self):
+        return self._solver
+
+    @property
+    def njobs(self):
+        return self._njobs
+
+
+# =============================================================================
+# MAIN
+# =============================================================================
+
+if __name__ == "__main__":
+    print(__doc__)

skcriteria/tests/madm/test_simus.py

@@ -0,0 +1,84 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2016-2017-2018, Cabral, Juan; Luczywo, Nadia
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+
+# * Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+
+# =============================================================================
+# FUTURE
+# =============================================================================
+
+from __future__ import unicode_literals
+
+
+# =============================================================================
+# DOC
+# =============================================================================
+
+__doc__ = """test simple methods"""
+
+
+# =============================================================================
+# IMPORTS
+# =============================================================================
+
+from skcriteria import Data
+from ...madm import simus
+
+from ..tcore import SKCriteriaTestCase
+
+
+# =============================================================================
+# Tests
+# =============================================================================
+
+class SimusTest(SKCriteriaTestCase):
+    mnorm = "sum"
+    wnorm = "sum"
+
+    def setUp(self):
+        # Data From:
+        # Munier, N., Carignano, C., & Alberto, C.
+        # UN MÉTODO DE PROGRAMACIÓN MULTIOBJETIVO.
+        # Revista de la Escuela de Perfeccionamiento en Investigación
+        # Operativa, 24(39).
+
+        self.data = Data(
+            mtx=[[250, 120, 20, 800],
+                 [130, 200, 40, 1000],
+                 [350, 340, 15, 600]],
+            criteria=[max, max, min, max],
+            anames=["Proyecto 1", "Proyecto 2", "Proyecto 3"],
+            cnames=["Criterio 1", "Criterio 2", "Criterio 3", "Criterio 4"])
+        self.b = [None, 500, None, None]
+
+    def test_simus(self):
+        dm = simus.SIMUS()
+        dm.decide(self.data, b=self.b)

skcriteria/utils/lp.py

@@ -44,7 +44,6 @@
 # =============================================================================
 
 import operator
-from collections import Mapping
 
 import pulp
 
@@ -55,12 +54,6 @@
 # CONSTANTS
 # =============================================================================
 
-VAR_TYPE = {
-    int: pulp.LpInteger,
-    float: pulp.LpContinuous,
-    bool: pulp.LpBinary
-}
-
 SOLVERS = {
     "pulp": pulp.solvers.PULP_CBC_CMD,
 
@@ -115,12 +108,35 @@ class Result(object):
     variables = attr.ib(converter=Bunch)
 
 
-class Var(pulp.LpVariable):
+# =============================================================================
+# VARIABLES
+# =============================================================================
 
-    def __init__(self, name, low=None, up=None, type=float, *args, **kwargs):
-        super(Var, self).__init__(
+class _Var(pulp.LpVariable):
+
+    def __init__(self, name, low=None, up=None, *args, **kwargs):
+        super(_Var, self).__init__(
             name=name, lowBound=low, upBound=up,
-            cat=VAR_TYPE[type], *args, **kwargs)
+            cat=self.var_type, *args, **kwargs)
+
+
+class Float(_Var):
+    var_type = pulp.LpContinuous
+
+
+class Int(_Var):
+    var_type = pulp.LpInteger
+
+
+class Bool(_Var):
+    var_type = pulp.LpBinary
+
+
+VAR_TYPE = {
+    int: Int,
+    float: Float,
+    bool: Bool
+}
 
 
 # =============================================================================
@@ -147,15 +163,11 @@ def frommtx(cls, c, A, b, x=None, *args, **kwargs):
         else:
             x_n = len(x)
 
-        xkwds = []
-        for xi in x:
-            if xi is None:
-                xi = {"low": 0, "type": float}
-            elif xi in VAR_TYPE:
-                xi = {"low": 0, "type": xi}
-            xkwds.append(xi)
-
-        x_s = [Var("x{}".format(idx + 1), **xkwds[idx]) for idx in range(x_n)]
+        x_s = []
+        for idx, xi in enumerate(x):
+            cls = VAR_TYPE.get(type(xi), Float)
+            x = cls("x{}".format(idx + 1), low=0)
+            x_s.append(x)
 
         # Z
         z = sum([ci * xi for ci, xi in zip(c, x_s)])
@@ -185,8 +197,8 @@ def solve(self):
         super(_LP, self).solve()
         objective = pulp.value(self.objective)
         variables = {v.name: v.varValue for v in self.variables()}
-        status=pulp.LpStatus[self.status]
-        model.assignVarsVals(dict.fromkeys(variables, None))
+        status = pulp.LpStatus[self.status]
+        self.assignVarsVals(dict.fromkeys(variables, None))
         return Result(status_code=self.status,
                       status=status,
                       objective=objective,