Minor fix to define QUnfoldQUBO gurobi solver methods only if gurobipy is explicitly installed

Author: SimoneGasperini

CITATION.cff

@@ -6,7 +6,7 @@ authors:
   - family-names: "Gasperini"
     given-names: "Simone"
 title: "QUnfold"
-version: 0.3.3
+version: 0.3.4
 doi: https://zenodo.org/badge/latestdoi/652649177
-date-released: 2024-07-04
+date-released: 2024-07-07
 url: "https://github.com/JustWhit3/QUnfold"

README.md

@@ -50,6 +50,11 @@ To install the `QUnfold` latest version released on [PyPI](https://pypi.org/proj
 pip install QUnfold
 ```
 
+If you also want to enable the classical [Gurobi](https://www.gurobi.com/) solver ([Python API](https://www.gurobi.com/documentation/current/refman/py_python_api_details.html)) both for the integer optimization and QUBO problem, you need to install `QUnfold` including extra requirements:
+```shell
+pip install QUnfold[gurobi]
+```
+
 ### *Developer-mode*
 To create a dedicated [`conda`](https://docs.conda.io/en/latest/) environment and install `QUnfold` in developer-mode you can do:
 ```shell
@@ -58,7 +63,7 @@ conda activate qunfold-dev
 git clone https://github.com/JustWhit3/QUnfold.git
 cd QUnfold
 pip install -r requirements-dev.txt
-pip install -e .
+pip install -e .[gurobi]
 ```
 
 ## Usage example

docs/source/conf.py

@@ -9,7 +9,7 @@
 project = "QUnfold"
 copyright = "2024, Gianluca Bianco, Simone Gasperini"
 author = "Gianluca Bianco, Simone Gasperini"
-release = "0.3.3"  # version
+release = "0.3.4"  # version
 master_doc = "index"
 
 

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = QUnfold
-version = 0.3.3
+version = 0.3.4
 url = https://github.com/JustWhit3/QUnfold
 author = Gianluca Bianco, Simone Gasperini
 author_email = biancogianluca9@gmail.com, simone.gasperini4@unibo.it

src/QUnfold/QUnfoldQUBO.py

@@ -1,18 +1,20 @@
 import os
+import sys
 import numpy as np
 import scipy as sp
 import dimod
 import minorminer
-try:
-	import gurobipy
-except ImportError:
-	pass
 from tqdm import tqdm
 from concurrent.futures import ThreadPoolExecutor
 from dwave.samplers import SimulatedAnnealingSampler
 from dwave.system import LeapHybridSampler
 from dwave.system import DWaveSampler, FixedEmbeddingComposite
 
+try:
+    import gurobipy
+except ImportError:
+    pass
+
 
 class QUnfoldQUBO:
     def __init__(self, response, measured, lam=0.0):
@@ -180,53 +182,42 @@ def compute_energy(self, x):
         energy = self.dwave_bqm.energy(sample=xbin)
         return energy
 
-    def solve_gurobi_integer(self):
-        try:
+    if "gurobipy" in sys.modules:
+
+        def solve_gurobi_integer(self):
             model = gurobipy.Model()
-        except Exception as e:    
-            import traceback
-            import sys
-            print(traceback.format_exc())
-            print("\n\nGurobi not installed in this env, to install use pip install QUnfold[guro]\n\n")
-            sys.exit(1)
-        model.setParam("OutputFlag", 0)
-        x = [
-            model.addVar(vtype=gurobipy.GRB.INTEGER, lb=0, ub=2**b - 1)
-            for b in self.num_bits
-        ]
-        a = self.linear_coeffs
-        B = self.quadratic_coeffs
-        model.setObjective(a @ x + x @ B @ x, sense=gurobipy.GRB.MINIMIZE)
-        model.optimize()
-        sol = np.array([var.x for var in x])
-        err = np.sqrt(sol)
-        cov = np.diag(sol)
-        return sol, err, cov
+            model.setParam("OutputFlag", 0)
+            x = [
+                model.addVar(vtype=gurobipy.GRB.INTEGER, lb=0, ub=2**b - 1)
+                for b in self.num_bits
+            ]
+            a = self.linear_coeffs
+            B = self.quadratic_coeffs
+            model.setObjective(a @ x + x @ B @ x, sense=gurobipy.GRB.MINIMIZE)
+            model.optimize()
+            sol = np.array([var.x for var in x])
+            err = np.sqrt(sol)
+            cov = np.diag(sol)
+            return sol, err, cov
 
-    def solve_gurobi_binary(self):
-        try:
+        def solve_gurobi_binary(self):
             model = gurobipy.Model()
-        except Exception as e:    
-            import traceback
-            import sys
-            print(traceback.format_exc())
-            print("\n\nGurobi not installed in this env, to install use pip install QUnfold[guro]\n\n")
-            sys.exit(1)
-        model.setParam("OutputFlag", 0)
-        num_bits = self.num_bits
-        x = [
-            model.addVar(vtype=gurobipy.GRB.BINARY)
-            for i in range(self.num_bins)
-            for _ in range(num_bits[i])
-        ]
-        Q = self.qubo_matrix
-        model.setObjective(x @ Q @ x, sense=gurobipy.GRB.MINIMIZE)
-        model.optimize()
-        bitstr = np.array([var.x for var in x], dtype=int)
-        arrays = np.split(bitstr, np.cumsum(num_bits[:-1]))
-        sol = np.array(
-            [int("".join(arr.astype(str))[::-1], base=2) for arr in arrays], dtype=float
-        )
-        err = np.sqrt(sol)
-        cov = np.diag(sol)
-        return sol, err, cov
+            model.setParam("OutputFlag", 0)
+            num_bits = self.num_bits
+            x = [
+                model.addVar(vtype=gurobipy.GRB.BINARY)
+                for i in range(self.num_bins)
+                for _ in range(num_bits[i])
+            ]
+            Q = self.qubo_matrix
+            model.setObjective(x @ Q @ x, sense=gurobipy.GRB.MINIMIZE)
+            model.optimize()
+            bitstr = np.array([var.x for var in x], dtype=int)
+            arrays = np.split(bitstr, np.cumsum(num_bits[:-1]))
+            sol = np.array(
+                [int("".join(arr.astype(str))[::-1], base=2) for arr in arrays],
+                dtype=float,
+            )
+            err = np.sqrt(sol)
+            cov = np.diag(sol)
+            return sol, err, cov

src/QUnfold/utils.py

@@ -1,4 +1,6 @@
 import numpy as np
+import scipy as sp
+from QUnfold import QUnfoldQUBO
 
 
 def normalize_response(response, truth_mc):
@@ -31,20 +33,25 @@ def compute_chi2(observed, expected, covariance):
     chi2 = residuals.T @ np.linalg.pinv(covariance) @ residuals
     chi2_red = chi2 / len(expected)
     return chi2_red
-    
 
-from QUnfold import QUnfoldQUBO
-from QUnfold.utils import compute_chi2
-from scipy.optimize import minimize_scalar
-from scipy.optimize import minimize_scalar
 
-def lambda_optimizer(response, measured, truth, maxlam = 1, minlam = 0):
-    def lamba_func(lam = .1):
+def lambda_optimizer(
+    response, measured, truth, min_lam=0.0, max_lam=1.0, verbose=False
+):
+    def lambda_func(lam):
         unfolder = QUnfoldQUBO(response, measured, lam=lam)
         unfolder.initialize_qubo_model()
-        sol, err, cov = unfolder.solve_gurobi_integer()
+        sol, _, cov = unfolder.solve_gurobi_integer()
         chi2 = compute_chi2(observed=sol, expected=truth, covariance=cov)
         return chi2
 
-    minimizer = minimize_scalar(lamba_func,bracket=(minlam,maxlam),method='brent' ,options={'disp':3})
+    try:
+        options = {"disp": 3 if verbose else 0}
+        minimizer = sp.optimize.minimize_scalar(
+            lambda_func, bracket=(min_lam, max_lam), method="brent", options=options
+        )
+    except AttributeError:
+        raise ModuleNotFoundError(
+            "Function 'lambda_optimizer' requires Gurobi solver: 'pip install gurobipy'"
+        )
     return minimizer.x