Feature: support for import and cardinalities analysis

Author: jmhorcas

Makefile

@@ -1,10 +1,12 @@
 .ONESHELL:
 
 lint:
-	prospector
+	echo "To lint this project, make sure that you have installed the core" >&2;
+	ruff check .
 
 mypy:
-	mypy flamapy --no-namespace-packages
+	echo "To lint this project, make sure that you have installed the core" >&2;
+	mypy -p flamapy
 
 test:
 	python -m pytest -sv

flamapy/metamodels/bdd_metamodel/models/bdd_model.py

@@ -49,7 +49,7 @@ def build_bdd(self, expression: str) -> None:
         self._bdd.declare(*self.variables_features.keys())
         self._root = self._bdd.add_expr(expression)
         _bdd.reorder(self._bdd)
-        self._levels_variables = {l: v for v, l in self._bdd.var_levels.items()}
+        self._levels_variables = {level: var for var, level in self._bdd.var_levels.items()}
 
     @property
     def formula(self) -> str:
@@ -65,7 +65,7 @@ def bdd(self, new_bdd: Union[_bdd.BDD, _dd_bdd.BDD]) -> None:
         self.features_variables = {var: var for var in self._bdd.vars}
         self.variables_features = dict(self.features_variables)
         self._root = next(iter(self._bdd.roots), None)
-        self._levels_variables = {l: v for v, l in self._bdd.var_levels.items()}
+        self._levels_variables = {level: var for var, level in self._bdd.var_levels.items()}
 
     @property
     def root(self) -> Union[_bdd.Function, int]:
@@ -76,7 +76,7 @@ def root(self, new_root: Union[_bdd.Function, int]) -> None:
         self._root = new_root
         self.features_variables = {var: var for var in self._bdd.vars}
         self.variables_features = dict(self.features_variables)
-        self._levels_variables = {l: v for v, l in self._bdd.var_levels.items()}
+        self._levels_variables = {level: var for var, level in self._bdd.var_levels.items()}
 
     # @classmethod
     # def from_logic_formula(cls, 

flamapy/metamodels/bdd_metamodel/operations/bdd_metrics.py

@@ -34,7 +34,7 @@ def __init__(self) -> None:
         self._configurations_number: int = 0
         self._fip: dict[Any, float] = {}
         self._prod_dist: list[int] = []
-        self._prod_dist_op: bdd_operations.BDDProductDistribution = None
+        self._prod_dist_op: bdd_operations.BDDProductDistribution | None = None
         self._variant_features: list[Any] = []
 
     @property
@@ -233,10 +233,13 @@ def descriptive_statistics(self) -> dict[str, Any]:
         if self.model is None:
             raise FlamaException('Model not initialized.')
         name = "Descriptive statistics"
-        _desc_stats = self._prod_dist_op.descriptive_statistics()
-        return self.construct_result(name=name,
-                                     doc=self.descriptive_statistics.__doc__,
-                                     result=_desc_stats)
+        if self._prod_dist_op is not None:
+            _desc_stats = self._prod_dist_op.descriptive_statistics()
+            return self.construct_result(name=name,
+                                        doc=self.descriptive_statistics.__doc__,
+                                        result=_desc_stats)
+        else:
+            return {}
 
     @metric_method
     def feature_inclusion_probabilities(self) -> dict[str, Any]:

flamapy/metamodels/bdd_metamodel/operations/bdd_product_distribution.py

@@ -76,11 +76,14 @@ def get_prod_dist(bdd_model: BDDModel,
             get_prod_dist(bdd_model, low, dist, mark, complemented ^ bdd_model.negated(low))
 
         # compute low_dist to account for the removed nodes through low
-        removed_nodes = bdd_model.index(low) - bdd_model.index(node) - 1
-        low_dist = [0] * (removed_nodes + len(dist[id_low]))
-        for i in range(removed_nodes + 1):
-            for j in range(len(dist[id_low])):
-                low_dist[i + j] = low_dist[i + j] + dist[id_low][j] * math.comb(removed_nodes, i)
+        low_index = bdd_model.index(low)
+        node_index = bdd_model.index(node)
+        if low_index is not None and node_index is not None:
+            removed_nodes = low_index - node_index - 1
+            low_dist = [0] * (removed_nodes + len(dist[id_low]))
+            for i in range(removed_nodes + 1):
+                for j in range(len(dist[id_low])):
+                    low_dist[i + j] = low_dist[i + j] + dist[id_low][j] * math.comb(removed_nodes, i)
 
         # traverse
         high = bdd_model.get_high_node(node)
@@ -89,12 +92,15 @@ def get_prod_dist(bdd_model: BDDModel,
             get_prod_dist(bdd_model, high, dist, mark, complemented)
 
         # compute high_dist to account for the removed nodes through high
-        removed_nodes = bdd_model.index(high) - bdd_model.index(node) - 1
-        high_dist = [0] * (removed_nodes + len(dist[id_high]))
-        for i in range(removed_nodes + 1):
-            for j in range(len(dist[id_high])):
-                high_dist[i + j] = high_dist[i + j] + dist[id_high][j] * (
-                    math.comb(removed_nodes, i))
+        high_index = bdd_model.index(high)
+        node_index = bdd_model.index(node)
+        if high_index is not None and node_index is not None:
+            removed_nodes = high_index - node_index - 1
+            high_dist = [0] * (removed_nodes + len(dist[id_high]))
+            for i in range(removed_nodes + 1):
+                for j in range(len(dist[id_high])):
+                    high_dist[i + j] = high_dist[i + j] + dist[id_high][j] * (
+                        math.comb(removed_nodes, i))
         # combine low and high distributions
         combine_distributions(id_node, dist, low_dist, high_dist)
 
@@ -116,7 +122,7 @@ def combine_distributions(id_node: int,
     dist[id_node] = node_dist
 
 
-def descriptive_statistics(prod_dist: list[int]) -> dict[str, Any]: # noqa: MC0001
+def descriptive_statistics(prod_dist: list[int]) -> dict[str, Any]:
     total_elements = sum(prod_dist)
     if total_elements == 0:
         return {

flamapy/metamodels/bdd_metamodel/transformations/dddmp_reader.py

@@ -34,12 +34,14 @@ def dddmp_v3_to_v2(filepath: str) -> str:
     with open(filepath, 'r', encoding='utf8') as file:
         lines = file.readlines()
         # Change version from 3.0 to 2.0
-        i, line = next((i, l) for i, l in enumerate(lines) if '.ver DDDMP-3.0' in l)
-        lines[i] = line.replace('3.0', '2.0')
+        index, line = next((index, line) for index, line in enumerate(lines) 
+                           if '.ver DDDMP-3.0' in line)
+        lines[index] = line.replace('3.0', '2.0')
 
         # Add '.varnames' field
-        i, line = next((i, l) for i, l in enumerate(lines) if '.varnames' in l)
-        lines.pop(i)
+        index, line = next((index, line) for index, line in enumerate(lines) 
+                           if '.varnames' in line)
+        lines.pop(index)
 
     with tempfile.NamedTemporaryFile(mode='w', encoding='utf8', delete=False) as temp_file:
         temp_file.writelines(lines)

flamapy/metamodels/bdd_metamodel/transformations/dddmp_writer.py

@@ -12,11 +12,11 @@ def get_destination_extension() -> str:
         return 'dddmp'
 
     def transform(self) -> str:
-        if self.path is None:  # type: ignore[has-type]
+        if self.path is None:
             with tempfile.NamedTemporaryFile(mode='w', encoding='utf8') as file:
                 self.path = file.name
                 result = write_to_file(self)
-                self.path = None  # type: ignore[assignment]
+                self.path = None
         else:
             result = write_to_file(self)
         return result
@@ -25,7 +25,10 @@ def transform(self) -> str:
 def write_to_file(writer: DDDMPWriter) -> str:
     try:
         super(type(writer), writer).transform()
-        result = dddmp_v2_to_v3(writer.path)
+        if writer.path is None:
+            raise ValueError("Writer path is None")
+        else:
+            result = dddmp_v2_to_v3(writer.path)
     except Exception as exc:
         raise FlamaException('DDDMPWriter is not supported.') from exc
     return result
@@ -39,12 +42,14 @@ def dddmp_v2_to_v3(filepath: str) -> str:
     with open(filepath, 'r', encoding='utf8') as file:
         lines = file.readlines()
         # Change version from 2.0 to 3.0
-        i, line = next((i, l) for i, l in enumerate(lines) if '.ver DDDMP-2.0' in l)
-        lines[i] = line.replace('2.0', '3.0')
+        index, line = next((index, line) for index, line in enumerate(lines) 
+                           if '.ver DDDMP-2.0' in line)
+        lines[index] = line.replace('2.0', '3.0')
 
         # Add '.varnames' field
-        i, line = next((i, l) for i, l in enumerate(lines) if '.orderedvarnames' in l)
-        lines.insert(i - 1, line.replace('.orderedvarnames', '.varnames'))
+        index, line = next((index, line) for index, line in enumerate(lines) 
+                           if '.orderedvarnames' in line)
+        lines.insert(index - 1, line.replace('.orderedvarnames', '.varnames'))
 
     with open(filepath, 'w', encoding='utf8') as file:
         file.writelines(lines)

flamapy/metamodels/bdd_metamodel/transformations/fm_to_bdd_cnf.py

@@ -1,4 +1,5 @@
 import itertools
+import copy
 from typing import Optional, Any
 
 from flamapy.core.transformations import ModelToModel
@@ -8,6 +9,12 @@
     Feature,
     Relation,
 )
+from flamapy.metamodels.fm_metamodel.transformations import (
+    FlatFM
+)
+from flamapy.metamodels.fm_metamodel.transformations.refactorings import (
+    FeatureCardinalityRefactoring
+)
 from flamapy.metamodels.bdd_metamodel.models import BDDModel
 
 
@@ -165,15 +172,24 @@ def get_term_variable(term: Any) -> int:
             self.clauses.append(clause_variables)
 
     def transform(self) -> BDDModel:
-        for feature in self.source_model.get_features():
+        # FlatFM if the feature model contains imports
+        feature_model = self.source_model
+        if feature_model.imports:
+            feature_model = FlatFM(feature_model).transform()
+        # Apply the feature cardinality refactoring to the source model
+        if FeatureCardinalityRefactoring(feature_model).is_applicable():
+            feature_model = copy.deepcopy(feature_model)
+            feature_model = FeatureCardinalityRefactoring(feature_model).transform()
+
+        for feature in feature_model.get_features():
             self.add_feature(feature)
 
-        self.add_root(self.source_model.root)
+        self.add_root(feature_model.root)
 
-        for relation in self.source_model.get_relations():
+        for relation in feature_model.get_relations():
             self.add_relation(relation)
 
-        for constraint in self.source_model.get_constraints():
+        for constraint in feature_model.get_constraints():
             self.add_constraint(constraint)
 
         # Transform clauses to textual CNF notation required by the BDD

flamapy/metamodels/bdd_metamodel/transformations/fm_to_bdd_pl.py

@@ -1,11 +1,16 @@
 import re
+import copy
 import itertools
 from typing import Optional
 
 from flamapy.core.models.ast import ASTOperation
 from flamapy.core.transformations import ModelToModel
 from flamapy.metamodels.fm_metamodel.models import FeatureModel, Feature, Relation, Constraint
 from flamapy.metamodels.bdd_metamodel.models import BDDModel
+from flamapy.metamodels.fm_metamodel.transformations import (
+    FeatureCardinalityRefactoring
+)
+from flamapy.metamodels.fm_metamodel.transformations import FMSecureFeaturesNames, FlatFM
 
 
 class FmToBDD(ModelToModel):
@@ -24,6 +29,22 @@ def __init__(self, source_model: FeatureModel) -> None:
         self._counter: int = 0
 
     def transform(self) -> BDDModel:
+        # FlatFM if the feature model contains imports
+        feature_model = self.source_model
+        if feature_model.imports:
+            feature_model = FlatFM(feature_model).transform()
+        # Apply the feature cardinality refactoring to the source model
+        if FeatureCardinalityRefactoring(feature_model).is_applicable():
+            feature_model = copy.deepcopy(feature_model)
+            feature_model = FeatureCardinalityRefactoring(feature_model).transform()
+
+        # Secure the features names and create a mapping with the original names
+        fmsfn = FMSecureFeaturesNames(feature_model)
+        secure_fm = fmsfn.transform()
+        mapping_names = fmsfn.mapping_names
+        self.bdd_model.mapping_names = mapping_names
+        self.source_model = secure_fm
+
         self.destination_model = BDDModel()
         for feature in self.source_model.get_features():
             self._add_feature(feature)

flamapy/metamodels/bdd_metamodel/transformations/json_writer.py

@@ -11,11 +11,11 @@ def get_destination_extension() -> str:
         return 'json'
 
     def transform(self) -> str:
-        if self.path is None:  # type: ignore[has-type]
+        if self.path is None:
             with tempfile.NamedTemporaryFile(mode='w', encoding='utf8') as file:
                 self.path = file.name
                 result = write_to_file(self)
-                self.path = None  # type: ignore[assignment]
+                self.path = None
         else:
             result = write_to_file(self)
         return result

tests/test_bdd_metamodel.py

@@ -1,7 +1,6 @@
 import pytest
 from collections import defaultdict
 
-from flamapy.metamodels.configuration_metamodel.models import Configuration
 from flamapy.metamodels.fm_metamodel.transformations import UVLReader
 from flamapy.metamodels.bdd_metamodel.models import BDDModel
 from flamapy.metamodels.bdd_metamodel.transformations import (
@@ -15,7 +14,6 @@
     BDDFeatureInclusionProbability,
     BDDSampling,
     BDDConfigurationsNumber,
-    BDDConfigurations,
     BDDCoreFeatures,
     BDDDeadFeatures,
     BDDVariantFeatures,