Minor modifications

.gitignore

@@ -69,4 +69,7 @@ multirun/
 
 
 # Scratch notebooks
-scratch_notebooks/
+scratch_notebooks/
+
+nohup.out
+last_50_nohup.out

frame/configs/funsearch-low-budget.yaml

@@ -15,7 +15,7 @@ defaults:
 
 programs_database:
   functions_per_prompt: 2
-  num_islands: 3
+  num_islands: 1
   reset_period: 360000 # 1 hour in seconds
   cluster_sampling_temperature_init: 0.01
   cluster_sampling_temperature_period: 1000
@@ -55,12 +55,12 @@ output_directory: "${hydra:runtime.output_dir}"
 evaluations_dir: "${hydra:runtime.output_dir}/evaluations"
 
 # Iterations of the Funsearch sampling loop
-iterations: 32
+iterations: 24
 
 # --- FunSearch Abstraction Settings ---
 abstraction:
   enabled: true                      # Master switch: set to false to disable the entire abstraction mechanism
-  frequency: 3                       # Run the abstraction step every N main FunSearch iterations (low for testing).
+  frequency: 2                       # Run the abstraction step every N main FunSearch iterations (low for testing).
   programs_to_sample: 2              # How many top programs to analyze (low for testing).
   max_abstractions_per_step: 2       # Target new abstractions per step (low for testing).
   # Optional LLM override block for abstraction task

frame/configs/models/gpt4o-mini.yaml

@@ -1,7 +1,7 @@
 name: gpt-4o
 provider: openai
 temperature: 0.9
-max_tokens: 8192
+max_tokens: 16384
 top_p: 1.0
 frequency_penalty: 0.0
 presence_penalty: 0.0 

frame/configs/succ_zero_generate_interestingness_funsearch.yaml

@@ -8,42 +8,42 @@ defaults:
   - override hydra/hydra_logging: default
 
 # # Note(George; 5/5): Due to some config loading issues, I've just pasted this directly here, change for diff FunSearch experiments.
-initial_state:
-  _target_: frame.knowledge_base.knowledge_graph.KnowledgeGraph
-  name: "succ_zero_eq_initial"
-  description: "Initial knowledge graph with Zero, Successor, and Equality"
-
-  # Import settings
-  import_from: "frame.knowledge_base.initial_concepts"
-  concepts:
-    - "create_zero_concept"
-    - "create_successor_concept"
-    - "create_equality_concept" 
-
 # initial_state:
 #   _target_: frame.knowledge_base.knowledge_graph.KnowledgeGraph
-#   name: "arithmetic_basic_initial"
-#   description: "Initial knowledge graph with arithmetic basic concepts"
+#   name: "succ_zero_eq_initial"
+#   description: "Initial knowledge graph with Zero, Successor, and Equality"
 
 #   # Import settings
 #   import_from: "frame.knowledge_base.initial_concepts"
 #   concepts:
 #     - "create_zero_concept"
-#     - "create_one_concept"
-#     - "create_two_concept"
-#     - "create_addition_concept"
-#     - "create_multiplication_concept"
-#     - "create_divides_concept"
-#     - "create_leq_than_concept"
+#     - "create_successor_concept"
 #     - "create_equality_concept" 
 
+initial_state:
+  _target_: frame.knowledge_base.knowledge_graph.KnowledgeGraph
+  name: "arithmetic_basic_initial"
+  description: "Initial knowledge graph with arithmetic basic concepts"
+
+  # Import settings
+  import_from: "frame.knowledge_base.initial_concepts"
+  concepts:
+    - "create_zero_concept"
+    - "create_one_concept"
+    - "create_two_concept"
+    - "create_addition_concept"
+    - "create_multiplication_concept"
+    - "create_divides_concept"
+    - "create_leq_than_concept"
+    - "create_equality_concept" 
+
 # Experiment settings override
 experiment:
   name: "succ_zero_multi_generated_interestingness_eval"
   description: "Generate N interestingness functions and evaluate each M times, starting from successor and zero."
   max_steps: 1000
   num_episodes: 16
-  num_workers: 2
+  num_workers: 4
   seed: 0 #${oc.env:RANDOM_SEED,12345}
   episode_timeout_seconds: 30 # Timeout for the main process waiting for a worker episode result
 

frame/interestingness/learning/tests/gpt4o_test_1.py

@@ -0,0 +1,55 @@
+import math
+import numpy as np
+from typing import Dict, Any, List, Set, Optional, Union
+from frame.knowledge_base.knowledge_graph import KnowledgeGraph, NodeType
+
+from frame.interestingness.learning.dsl_primitives import (
+    get_ancestors, get_descendants, get_construction_depth,
+    get_in_degree, get_out_degree, get_construction_history_rule_names,
+    get_entity_step_age, get_num_concepts, get_num_conjectures,
+    get_entity_node_type, get_concept_category, get_input_arity, get_num_component_types,
+    get_examples, get_nonexamples,
+    get_num_construction_inputs, is_proven, create_weighted_interestingness_function
+)
+
+
+def calculate_interestingness(entity_id: str, graph: KnowledgeGraph) -> float:
+  """Calculate the interestingness score for a given entity.
+
+    Args:
+        entity_id: The ID of the entity to score.
+        graph: The knowledge graph containing the entity.
+
+    Returns:
+        A float value representing the interestingness score (higher is more interesting).
+    """
+  try:
+      node_type = get_entity_node_type(entity_id, graph)
+      num_descendants = len(get_descendants(entity_id, graph))
+      depth = get_construction_depth(entity_id, graph)
+      in_degree = get_in_degree(entity_id, graph)
+      out_degree = get_out_degree(entity_id, graph)
+      step_age = get_entity_step_age(entity_id, graph)
+      is_proven_value = is_proven(entity_id, graph)
+
+      # Calculate base scores based on node type
+      base_score = 0.0
+      if node_type == 'Theorem':
+          base_score = 2.0
+      elif node_type == 'Conjecture':
+          base_score = 1.0
+      elif node_type == 'Concept':
+          base_score = 0.5
+
+      # Calculate derived features
+      connectivity_score = (in_degree + out_degree) / (depth + 1)
+      temporal_score = math.exp(-step_age) * 5
+
+      print(f"temporal_score:, step_age: {step_age}, temporal_score: {temporal_score}")
+
+      # Aggregate score
+      score = base_score + 0.5 * num_descendants + connectivity_score + temporal_score + 3 * is_proven_value
+      return score
+
+  except Exception:
+      return 0.0

frame/knowledge_base/knowledge_graph.py

@@ -241,7 +241,7 @@ def get_entity_step_age(self, entity_id: str) -> int:
         if entity_id not in self:
             raise ValueError(f"Entity {entity_id} not found in graph")
         creation_step = self.nodes[entity_id].get("creation_step", 0)
-        return self._step_counter - creation_step
+        return max(self._step_counter - creation_step, 0)
 
     def get_node(self, node_id: str) -> Tuple[Entity, NodeType, Optional[ConstructionStep]]:
         """Get a node's data from the graph."""

run_parallel_funsearch.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 # Configuration
-NUM_PARALLEL_RUNS=32  # N: Number of parallel FunSearch instances per iteration
+NUM_PARALLEL_RUNS=16  # N: Number of parallel FunSearch instances per iteration
 FUNSEARCH_SCRIPT="scripts/run_funsearch_smaller_budget.sh" # Relative path from project root
 NUM_ITERATIONS=1     # M: Number of times to run the block of N parallel instances
 

scripts/run_experiment_existing_interestingness.sh

@@ -13,12 +13,12 @@ export INTERESTINGNESS_DEBUG=1
 
 # TODO(George; 4/23): THIS IS FOR TESTING SPECIFIC PATHS!
 # Run the experiment with the existing interestingness config
-nohup python -m frame.theory_builder \
+python -m frame.theory_builder \
   --config-name succ_zero_existing_interestingness_experiment \
-  experiment.num_episodes=128 \
+  experiment.num_episodes=1 \
   experiment.max_steps=1000 \
   experiment.episode_timeout_seconds=30 \
-  experiment.num_workers=128 \
+  experiment.num_workers=1 \
   z3_usage.use_z3_prover=true \
   z3_usage.use_z3_example_search=true \
   ++policy.params.interestingness_function_path="frame/interestingness/learning/tests/gpt4o_test_1.py"

scripts/run_funsearch_smaller_budget.sh

@@ -22,7 +22,7 @@ echo "  Abstraction: ENABLED"
 # Only enabling abstraction explicitly.
 nohup python -m frame.funsearch.main \
   --config-name funsearch-low-budget \
-  abstraction.enabled=true
+  abstraction.enabled=false
 
 echo "FunSearch experiment with abstraction completed!"
 # Output directory location depends on Hydra configuration (e.g., ./outputs/YYYY-MM-DD/HH-MM-SS)