Algorithms and methods for specific set of variables

pycona/active_algorithms/gquacq.py

@@ -21,7 +21,7 @@ class GQuAcq(AlgorithmCAInteractive):
 
     def __init__(self, ca_env: ActiveCAEnv = None, qg_max=10):
         """
-        Initialize the PQuAcq algorithm with an optional constraint acquisition environment.
+        Initialize the GQuAcq algorithm with an optional constraint acquisition environment.
 
         :param ca_env: An instance of ActiveCAEnv, default is None.
         : param GQmax: maximum number of generalization queries
@@ -30,16 +30,21 @@ def __init__(self, ca_env: ActiveCAEnv = None, qg_max=10):
         self._negativeQ = []
         self._qg_max = qg_max
 
-    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, metrics: Metrics = None):
+    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, X=None, metrics: Metrics = None):
         """
-        Learn constraints using the QuAcq algorithm by generating queries and analyzing the results.
+        Learn constraints using the GQuAcq algorithm by generating queries and analyzing the results.
 
         :param instance: the problem instance to acquire the constraints for
         :param oracle: An instance of Oracle, default is to use the user as the oracle.
         :param verbose: Verbosity level, default is 0.
         :param metrics: statistics logger during learning
+        :param X: The set of variables to consider, default is None.
         :return: the learned instance
         """
+        if X is None:
+            X = instance.X
+        assert isinstance(X, list) and set(X).issubset(set(instance.X)), "When using .learn(), set parameter X must be a list of variables"
+
         self.env.init_state(instance, oracle, verbose, metrics)
 
         if len(self.env.instance.bias) == 0:
@@ -52,8 +57,8 @@ def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbos
                 print("Number of Queries: ", self.env.metrics.membership_queries_count)
 
             gen_start = time.time()
-            Y = self.env.run_query_generation()
-            gen_end = time.time()
+            Y = self.env.run_query_generation(X)
+            gen_end = time.time()   
 
             if len(Y) == 0:
                 # if no query can be generated it means we have (prematurely) converged to the target network -----

pycona/active_algorithms/growacq.py

@@ -26,39 +26,45 @@ def __init__(self, ca_env: ActiveCAEnv = None, inner_algorithm: AlgorithmCAInter
         super().__init__(env)
         self.inner_algorithm = inner_algorithm if inner_algorithm is not None else MQuAcq2(ca_env)
 
-    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, metrics: Metrics = None):
+    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, X=None, metrics: Metrics = None):
         """
         Learn constraints by incrementally adding variables and using the inner algorithm to learn constraints
         for each added variable.
 
         :param instance: the problem instance to acquire the constraints for
         :param oracle: An instance of Oracle, default is to use the user as the oracle.
         :param verbose: Verbosity level, default is 0.
+        :param X: The set of variables to consider, default is None.
         :param metrics: statistics logger during learning
         :return: the learned instance
         """
+        if X is None:
+            X = instance.X
+        assert isinstance(X, list) and set(X).issubset(set(instance.X)), "When using .learn(), set parameter X must be a list of variables"
+
         self.env.init_state(instance, oracle, verbose, metrics)
 
         if verbose >= 1:
             print(f"Running growacq with {self.inner_algorithm} as inner algorithm")
 
         self.inner_algorithm.env = copy.copy(self.env)
 
-        self.env.instance.X = []
+        Y = []
 
-        n_vars = len(self.env.instance.variables.flat)
-        for x in self.env.instance.variables.flat:
+        n_vars = len(X)
+        for x in X:
             # we 'grow' the inner bias by adding one extra variable at a time
-            self.env.instance.X.append(x)
+            Y.append(x)
             # add the constraints involving x and other added variables
-            self.env.instance.construct_bias_for_var(x)
+            if len(self.env.instance.bias) == 0:
+                self.env.instance.construct_bias_for_var(x, Y)
             if verbose >= 3:
                 print(f"Added variable {x} in GrowAcq")
                 print("size of B in growacq: ", len(self.env.instance.bias))
 
             if verbose >= 2:
-                print(f"\nGrowAcq: calling inner_algorithm for {len(self.env.instance.X)}/{n_vars} variables")
-            self.env.instance = self.inner_algorithm.learn(self.env.instance, oracle, verbose=verbose, metrics=self.env.metrics)
+                print(f"\nGrowAcq: calling inner_algorithm for {len(Y)}/{n_vars} variables")
+            self.env.instance = self.inner_algorithm.learn(self.env.instance, oracle, verbose=verbose, X=Y, metrics=self.env.metrics)
 
             if verbose >= 3:
                 print("C_L: ", len(self.env.instance.cl))

pycona/active_algorithms/mquacq.py

@@ -21,16 +21,21 @@ def __init__(self, ca_env: ActiveCAEnv = None):
         """
         super().__init__(ca_env)
 
-    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, metrics: Metrics = None):
+    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, X=None, metrics: Metrics = None):
         """
         Learn constraints using the modified QuAcq algorithm by generating queries and analyzing the results.
 
         :param instance: the problem instance to acquire the constraints for
         :param oracle: An instance of Oracle, default is to use the user as the oracle.
         :param verbose: Verbosity level, default is 0.
         :param metrics: statistics logger during learning
+        :param X: The set of variables to consider, default is None.
         :return: the learned instance
         """
+        if X is None:
+            X = instance.X
+        assert isinstance(X, list) and set(X).issubset(set(instance.X)), "When using .learn(), set parameter X must be a list of variables"
+
         self.env.init_state(instance, oracle, verbose, metrics)
 
         if len(self.env.instance.bias) == 0:
@@ -47,7 +52,7 @@ def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbos
 
             # generate e in D^X accepted by C_l and rejected by B
             gen_start = time.time()
-            Y = self.env.run_query_generation()
+            Y = self.env.run_query_generation(X)
             gen_end = time.time()
 
             if len(Y) == 0:

pycona/active_algorithms/mquacq2.py

@@ -31,16 +31,21 @@ def __init__(self, ca_env: ActiveCAEnv = None, *, perform_analyzeAndLearn: bool
         self.cl_neighbours = None
         self.hashX = None
 
-    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, metrics: Metrics = None):
+    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, X=None, metrics: Metrics = None):
         """
         Learn constraints using the modified QuAcq algorithm by generating queries and analyzing the results.
 
         :param instance: the problem instance to acquire the constraints for
         :param oracle: An instance of Oracle, default is to use the user as the oracle.
         :param verbose: Verbosity level, default is 0.
         :param metrics: statistics logger during learning
+        :param X: The set of variables to consider, default is None.
         :return: the learned instance
         """
+        if X is None:
+            X = instance.X
+        assert isinstance(X, list) and set(X).issubset(set(instance.X)), "When using .learn(), set parameter X must be a list of variables"
+
         self.env.init_state(instance, oracle, verbose, metrics)
 
         # Hash the variables
@@ -52,7 +57,7 @@ def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbos
 
         while True:
             gen_start = time.time()
-            Y = self.env.run_query_generation()
+            Y = self.env.run_query_generation(X)
             gen_end = time.time()
             self.env.metrics.increase_generation_time(gen_end - gen_start)
 

pycona/active_algorithms/pquacq.py

@@ -25,16 +25,21 @@ def __init__(self, ca_env: ActiveCAEnv = None):
         """
         super().__init__(ca_env)
 
-    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, metrics: Metrics = None):
+    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, X=None, metrics: Metrics = None):
         """
         Learn constraints using the QuAcq algorithm by generating queries and analyzing the results.
 
         :param instance: the problem instance to acquire the constraints for
         :param oracle: An instance of Oracle, default is to use the user as the oracle.
         :param verbose: Verbosity level, default is 0.
         :param metrics: statistics logger during learning
+        :param X: The set of variables to consider, default is None.
         :return: the learned instance
         """
+        if X is None:
+            X = instance.X
+        assert isinstance(X, list) and set(X).issubset(set(instance.X)), "When using .learn(), set parameter X must be a list of variables"
+
         self.env.init_state(instance, oracle, verbose, metrics)
 
         if len(self.env.instance.bias) == 0:
@@ -47,7 +52,7 @@ def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbos
                 print("Number of Queries: ", self.env.metrics.membership_queries_count)
 
             gen_start = time.time()
-            Y = self.env.run_query_generation()
+            Y = self.env.run_query_generation(X)
             gen_end = time.time()
 
             if len(Y) == 0:

pycona/active_algorithms/quacq.py

@@ -21,16 +21,21 @@ def __init__(self, ca_env: ActiveCAEnv = None):
         """
         super().__init__(ca_env)
 
-    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, metrics: Metrics = None):
+    def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbose=0, X=None, metrics: Metrics = None):
         """
         Learn constraints using the QuAcq algorithm by generating queries and analyzing the results.
 
         :param instance: the problem instance to acquire the constraints for
         :param oracle: An instance of Oracle, default is to use the user as the oracle.
         :param verbose: Verbosity level, default is 0.
         :param metrics: statistics logger during learning
+        :param X: The set of variables to consider, default is None.
         :return: the learned instance
         """
+        if X is None:
+            X = instance.X
+        assert isinstance(X, list) and set(X).issubset(set(instance.X)), "When using .learn(), set parameter X must be a list of variables"
+
         self.env.init_state(instance, oracle, verbose, metrics)
 
         if len(self.env.instance.bias) == 0:
@@ -43,7 +48,7 @@ def learn(self, instance: ProblemInstance, oracle: Oracle = UserOracle(), verbos
                 print("Number of Queries: ", self.env.metrics.membership_queries_count)
 
             gen_start = time.time()
-            Y = self.env.run_query_generation()
+            Y = self.env.run_query_generation(X)
             gen_end = time.time()
 
             if len(Y) == 0:

pycona/benchmarks/exam_timetabling.py

@@ -2,7 +2,7 @@
 
 from ..answering_queries.constraint_oracle import ConstraintOracle
 from ..problem_instance import ProblemInstance, absvar
-
+from cpmpy.transformations.normalize import toplevel_list
 
 def day_of_exam(course, slots_per_day):
     return course // slots_per_day
@@ -27,7 +27,7 @@ def construct_examtt_simple(nsemesters=9, courses_per_semester=6, slots_per_day=
     for row in courses:
         model += cp.AllDifferent(day_of_exam(row, slots_per_day)).decompose()
 
-    C_T = list(model.constraints)
+    C_T = list(set(toplevel_list(model.constraints)))
 
     if model.solve():
         courses.clear()

pycona/benchmarks/job_shop_scheduling.py

@@ -3,7 +3,7 @@
 import cpmpy as cp
 import numpy as np
 from cpmpy.expressions.utils import all_pairs
-
+from cpmpy.transformations.normalize import toplevel_list
 from ..answering_queries.constraint_oracle import ConstraintOracle
 from ..problem_instance import ProblemInstance, absvar
 
@@ -56,7 +56,7 @@ def construct_job_shop_scheduling_problem(n_jobs, machines, horizon, seed=0):
         for (j1, t1), (j2, t2) in all_pairs(zip(*tasks_on_mach)):
             m += (end[j1, t1] <= start[j2, t2]) | (end[j2, t2] <= start[j1, t1])
 
-    C_T = list(model.constraints)
+    C_T = list(set(toplevel_list(model.constraints)))
 
     max_duration = max(duration)
 

pycona/benchmarks/jsudoku.py

@@ -1,5 +1,5 @@
 import cpmpy as cp
-
+from cpmpy.transformations.normalize import toplevel_list
 from ..answering_queries.constraint_oracle import ConstraintOracle
 from ..problem_instance import ProblemInstance, absvar
 
@@ -49,6 +49,6 @@ def construct_jsudoku():
 
     instance = ProblemInstance(variables=grid, params=parameters, language=lang, name="jsudoku")
 
-    oracle = ConstraintOracle(C_T)
+    oracle = ConstraintOracle(list(set(toplevel_list(C_T))))
 
     return instance, oracle

pycona/benchmarks/murder.py

@@ -1,6 +1,6 @@
 
 import cpmpy as cp
-
+from cpmpy.transformations.normalize import toplevel_list
 from ..answering_queries.constraint_oracle import ConstraintOracle
 from ..problem_instance import ProblemInstance, absvar
 
@@ -45,6 +45,6 @@ def construct_murder_problem():
 
     instance = ProblemInstance(variables=grid, language=lang, name="murder")
 
-    oracle = ConstraintOracle(C_T)
+    oracle = ConstraintOracle(list(set(toplevel_list(C_T))))
 
     return instance, oracle

pycona/benchmarks/nurse_rostering.py

@@ -1,5 +1,5 @@
 import cpmpy as cp
-
+from cpmpy.transformations.normalize import toplevel_list
 from ..answering_queries.constraint_oracle import ConstraintOracle
 from ..problem_instance import ProblemInstance, absvar
 
@@ -30,7 +30,7 @@ def construct_nurse_rostering(shifts_per_day=3, num_days=5, num_nurses=8, nurses
     if not model.solve():
         raise Exception("The problem has no solution")
 
-    C_T = list(model.constraints)
+    C_T = list(set(toplevel_list(model.constraints)))
 
     # Create the language:
     AV = absvar(2)  # create abstract vars - as many as maximum arity

pycona/benchmarks/sudoku.py

@@ -1,5 +1,5 @@
 import cpmpy as cp
-
+from cpmpy.transformations.normalize import toplevel_list
 from ..answering_queries.constraint_oracle import ConstraintOracle
 from ..problem_instance import ProblemInstance, absvar
 
@@ -29,7 +29,7 @@ def construct_sudoku(block_size_row, block_size_col, grid_size):
         for j in range(0, grid_size, block_size_col):
             model += cp.AllDifferent(grid[i:i + block_size_row, j:j + block_size_col]).decompose()  # python's indexing
 
-    C_T = list(model.constraints)
+    C_T = list(set(toplevel_list(model.constraints)))
 
     # Create the language:
     AV = absvar(2)  # create abstract vars - as many as maximum arity

pycona/ca_environment/acive_ca_proba.py

@@ -55,12 +55,12 @@ def init_state(self, instance, oracle, verbose, metrics=None):
         else:
             self._bias_proba = {c: 0.01 for c in self.instance.bias}
 
-    def run_query_generation(self):
+    def run_query_generation(self, X=None):
         """ Run the query generation process. """
         if self.training_frequency > 0 and len(set(self.datasetY)) == 2:
             self._train_classifier()
         self._predict_bias_proba()
-        return super().run_query_generation()
+        return super().run_query_generation(X)
 
     def run_find_scope(self, Y):
         """ Run the find scope process. """

pycona/ca_environment/active_ca.py

@@ -50,9 +50,9 @@ def init_state(self, instance, oracle, verbose, metrics=None):
         self.find_scope.ca = self
         self.findc.ca = self
 
-    def run_query_generation(self):
+    def run_query_generation(self, Y=None):
         """ Run the query generation process. """
-        Y = self.qgen.generate()
+        Y = self.qgen.generate(Y)
         return Y
 
     def run_find_scope(self, Y):

pycona/problem_instance/problem_instance.py

@@ -241,15 +241,19 @@ def construct_bias(self):
 
         self.bias = all_cons
 
-    def construct_bias_for_var(self, v1):
+    def construct_bias_for_var(self, v1, X=None):
         """
         Construct the bias (candidate constraints) for a specific variable.
 
         :param v1: The variable for which to construct the bias.
+        :param X: The set of variables to consider, default is None.
         """
+        if X is None:
+            X = self.X
+        assert isinstance(X, list) and set(X).issubset(set(self.X)), "When using .construct_bias_for_var(), set parameter X must be a list of variables. Instead, got: " + str(X)
 
         all_cons = []
-        X = list(set(self.X) - {v1})
+        X = list(set(X) - {v1})
 
         for relation in self.language:
             abs_vars = get_scope(relation)