added new PyReason Rule object

pyreason/__init__.py

@@ -19,7 +19,7 @@
 
     settings.verbose = False
     load_graphml(graph_path)
-    add_rule('popular(x) <-1 popular(y), Friends(x,y), owns(y,z), owns(x,z)', 'popular_rule')
+    add_rule(Rule('popular(x) <-1 popular(y), Friends(x,y), owns(y,z), owns(x,z)', 'popular_rule'))
     add_fact(Fact('popular-fact', 'Mary', 'popular', [1, 1], 0, 2))
     reason(timesteps=2)
 

pyreason/pyreason.py

@@ -16,6 +16,7 @@
 import pyreason.scripts.numba_wrapper.numba_types.label_type as label
 import pyreason.scripts.numba_wrapper.numba_types.rule_type as rule
 from pyreason.scripts.facts.fact import Fact
+from pyreason.scripts.rules.rule import Rule
 import pyreason.scripts.numba_wrapper.numba_types.fact_node_type as fact_node
 import pyreason.scripts.numba_wrapper.numba_types.fact_edge_type as fact_edge
 import pyreason.scripts.numba_wrapper.numba_types.interval_type as interval
@@ -449,29 +450,15 @@ def load_inconsistent_predicate_list(path: str) -> None:
     __ipl = yaml_parser.parse_ipl(path)
 
 
-def add_rule(rule_text: str, name: str, infer_edges: bool = False, set_static: bool = False, immediate_rule: bool = False) -> None:
+def add_rule(pr_rule: Rule) -> None:
     """Add a rule to pyreason from text format. This format is not as modular as the YAML format.
-    1. It is not possible to specify thresholds. Threshold is greater than or equal to 1 by default
-    2. It is not possible to have weights for different clauses. Weights are 1 by default with bias 0
-    TODO: Add threshold class where we can pass this as a parameter
-    TODO: Add weights as a parameter
-
-    Example:
-    `'pred1(x,y) : [0.2, 1] <- pred2(a, b) : [1,1], pred3(b, c)'`
-
-    :param rule_text: The rule in text format
-    :param name: The name of the rule. This will appear in the rule trace
-    :param infer_edges: Whether to infer new edges after edge rule fires
-    :param set_static: Whether to set the atom in the head as static if the rule fires. The bounds will no longer change
-    :param immediate_rule: Whether the rule is immediate. Immediate rules check for more applicable rules immediately after being applied
     """
     global __rules
 
-    r = rule_parser.parse_rule(rule_text, name, infer_edges, set_static, immediate_rule)
     # Add to collection of rules
     if __rules is None:
         __rules = numba.typed.List.empty_list(rule.rule_type)
-    __rules.append(r)
+    __rules.append(pr_rule.rule)
 
 
 def add_rules_from_file(file_path: str, infer_edges: bool = False) -> None:
@@ -488,7 +475,7 @@ def add_rules_from_file(file_path: str, infer_edges: bool = False) -> None:
 
     rule_offset = 0 if __rules is None else len(__rules)
     for i, r in enumerate(rules):
-        add_rule(r, f'rule_{i+rule_offset}', infer_edges)
+        add_rule(Rule(r, f'rule_{i+rule_offset}', infer_edges))
 
 
 def add_fact(pyreason_fact: Fact) -> None:
@@ -578,7 +565,7 @@ def _reason(timesteps, convergence_threshold, convergence_bound_threshold):
     if __graph is None:
         raise Exception('Graph not loaded. Use `load_graph` to load the graphml file')
     if __rules is None:
-        raise Exception('Rules not loaded. Use `load_rules` to load the rules yaml file')
+        raise Exception('There are no rules, use `add_rule` or `add_rules_from_file`')
 
     # Check variables that are highly recommended. Warnings
     if __node_labels is None and __edge_labels is None:

pyreason/scripts/numba_wrapper/numba_types/rule_type.py

@@ -1,6 +1,6 @@
 import pyreason.scripts.numba_wrapper.numba_types.label_type as label
 import pyreason.scripts.numba_wrapper.numba_types.interval_type as interval
-from pyreason.scripts.rules.rule import Rule
+from pyreason.scripts.rules.rule_internal import Rule
 
 from numba import types
 from numba.extending import typeof_impl

pyreason/scripts/rules/rule.py

@@ -1,48 +1,22 @@
-class Rule:
-
-    def __init__(self, rule_name, rule_type, target, delta, clauses, bnd, thresholds, ann_fn, weights, edges, static, immediate_rule):
-        self._rule_name = rule_name
-        self._type = rule_type
-        self._target = target
-        self._delta = delta
-        self._clauses = clauses
-        self._bnd = bnd
-        self._thresholds = thresholds
-        self._ann_fn = ann_fn
-        self._weights = weights
-        self._edges = edges
-        self._static = static
-        self._immediate_rule = immediate_rule
-
-    def get_rule_name(self):
-        return self._rule_name
-
-    def get_rule_type(self):
-        return self._type
-
-    def get_target(self):
-        return self._target
+import pyreason.scripts.utils.rule_parser as rule_parser
 
-    def get_delta(self):
-        return self._delta
 
-    def get_neigh_criteria(self):
-        return self._clauses
-
-    def get_bnd(self):
-        return self._bnd
-
-    def get_thresholds(self):
-        return self._thresholds 
-
-    def get_annotation_function(self):
-        return self._ann_fn
-
-    def get_edges(self):
-        return self._edges
-
-    def is_static(self):
-        return self._static
-
-    def is_immediate_rule(self):
-        return self._immediate_rule
+class Rule:
+    """
+    Example text:
+            `'pred1(x,y) : [0.2, 1] <- pred2(a, b) : [1,1], pred3(b, c)'`
+
+    1. It is not possible to specify thresholds. Threshold is greater than or equal to 1 by default
+    2. It is not possible to have weights for different clauses. Weights are 1 by default with bias 0
+    TODO: Add threshold class where we can pass this as a parameter
+    TODO: Add weights as a parameter
+    """
+    def __init__(self, rule_text: str, name: str, infer_edges: bool = False, set_static: bool = False, immediate_rule: bool = False):
+        """
+        :param rule_text: The rule in text format
+        :param name: The name of the rule. This will appear in the rule trace
+        :param infer_edges: Whether to infer new edges after edge rule fires
+        :param set_static: Whether to set the atom in the head as static if the rule fires. The bounds will no longer change
+        :param immediate_rule: Whether the rule is immediate. Immediate rules check for more applicable rules immediately after being applied
+        """
+        self.rule = rule_parser.parse_rule(rule_text, name, infer_edges, set_static, immediate_rule)

pyreason/scripts/rules/rule_internal.py

@@ -0,0 +1,48 @@
+class Rule:
+
+    def __init__(self, rule_name, rule_type, target, delta, clauses, bnd, thresholds, ann_fn, weights, edges, static, immediate_rule):
+        self._rule_name = rule_name
+        self._type = rule_type
+        self._target = target
+        self._delta = delta
+        self._clauses = clauses
+        self._bnd = bnd
+        self._thresholds = thresholds
+        self._ann_fn = ann_fn
+        self._weights = weights
+        self._edges = edges
+        self._static = static
+        self._immediate_rule = immediate_rule
+
+    def get_rule_name(self):
+        return self._rule_name
+
+    def get_rule_type(self):
+        return self._type
+
+    def get_target(self):
+        return self._target
+
+    def get_delta(self):
+        return self._delta
+
+    def get_neigh_criteria(self):
+        return self._clauses
+
+    def get_bnd(self):
+        return self._bnd
+
+    def get_thresholds(self):
+        return self._thresholds 
+
+    def get_annotation_function(self):
+        return self._ann_fn
+
+    def get_edges(self):
+        return self._edges
+
+    def is_static(self):
+        return self._static
+
+    def is_immediate_rule(self):
+        return self._immediate_rule

setup.py

@@ -8,7 +8,7 @@
 
 setup(
     name='pyreason',
-    version='2.0.4',
+    version='2.1.0',
     author='Dyuman Aditya',
     author_email='[email protected]',
     description='An explainable inference software supporting annotated, real valued, graph based and temporal logic',

tests/test_hello_world.py

@@ -11,7 +11,7 @@ def test_hello_world():
 
     # Load all the files into pyreason
     pr.load_graphml(graph_path)
-    pr.add_rule('popular(x) <-1 popular(y), Friends(x,y), owns(y,z), owns(x,z)', 'popular_rule')
+    pr.add_rule(pr.Rule('popular(x) <-1 popular(y), Friends(x,y), owns(y,z), owns(x,z)', 'popular_rule'))
     pr.add_fact(pr.Fact('popular-fact', 'Mary', 'popular', [1, 1], 0, 2))
 
     # Run the program for two timesteps to see the diffusion take place