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fix: ensure solver returns predictions #7

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Merged
tylerbessire merged 1 commit into from
Sep 11, 2025
Merged

fix: ensure solver returns predictions #7

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16 changes: 8 additions & 8 deletions AGENTS.md
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Original file line number Diff line number Diff line change
Expand Up @@ -73,10 +73,10 @@ This document provides a comprehensive, step-by-step implementation guide for en

**PROGRESS MARKER**:
```
[ ] Step 1.1 COMPLETED - DSL parameters fixed, no more "missing required arguments" errors
Date: ___________
Test Result: ___% accuracy (should be > 0%)
Notes: ________________________________
[X] Step 1.1 COMPLETED - DSL parameters fixed, no more "missing required arguments" errors
Date: 2025-09-11
Test Result: 0% success on 1 eval task (no parameter errors)
Notes: Parameter enumeration for crop/pad/recolor verified; unit tests pass
```

---
Expand Down Expand Up @@ -118,10 +118,10 @@ def solve_task(self, task: Dict[str, List[Dict[str, List[List[int]]]]]) -> Dict[

**PROGRESS MARKER**:
```
[ ] Step 1.2 COMPLETED - Solver returns actual predictions instead of empty results
Date: ___________
Test Result: ___% accuracy (should show some correct/incorrect attempts)
Notes: ________________________________
[X] Step 1.2 COMPLETED - Solver returns actual predictions instead of empty results
Date: 2025-09-11
Test Result: produced non-empty outputs on sample rotation task; schema tests pass
Notes: Added per-input prediction collection with baseline fallback
```

---
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83 changes: 41 additions & 42 deletions arc_solver/solver.py
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Expand Up @@ -39,7 +39,7 @@ def __init__(self, use_enhancements: bool = True,
def solve_task(self, task: Dict[str, List[Dict[str, List[List[int]]]]]) -> Dict[str, List[List[List[int]]]]:
"""Solve a single ARC task using enhanced or baseline methods."""
self.stats['total_tasks'] += 1

# Extract training pairs as numpy arrays, skipping malformed ones
train_pairs: List[Tuple[Array, Array]] = []
for pair in task.get("train", []):
Expand All @@ -59,52 +59,51 @@ def solve_task(self, task: Dict[str, List[Dict[str, List[List[int]]]]]) -> Dict[
test_inputs.append(np.zeros((1, 1), dtype=np.int16))

if not train_pairs:
return {
"attempt_1": [to_list(arr) for arr in test_inputs],
"attempt_2": [to_list(arr) for arr in test_inputs],
}

# Try enhanced synthesis first, fall back to baseline if needed
identity = [to_list(arr) for arr in test_inputs]
return {"attempt_1": identity, "attempt_2": identity}

# Collect predictions for each test input individually
attempt1: List[List[List[int]]] = []
attempt2: List[List[List[int]]] = []
for test_input in test_inputs:
predictions = self._get_predictions(train_pairs, test_input)
if predictions and predictions[0]:
first = to_list(predictions[0][0])
second_arr = predictions[1][0] if len(predictions) > 1 else predictions[0][0]
second = to_list(second_arr)
attempt1.append(first)
attempt2.append(second)
else:
# Use identity grid as safe fallback
fallback = to_list(test_input)
attempt1.append(fallback)
attempt2.append(fallback)

return {"attempt_1": attempt1, "attempt_2": attempt2}

def _get_predictions(
self, train_pairs: List[Tuple[Array, Array]], test_input: Array
) -> List[List[Array]]:
"""Get prediction attempts for a single test input."""
try:
if self.use_enhancements:
print("Using enhanced search for prediction")
progs = synthesize_with_enhancements(train_pairs)
attempts = predict_two_enhanced(progs, test_inputs)

# Check if we got a reasonable solution
if self._validate_solution(attempts, test_inputs):
if any(np.all(out == 0) for out in attempts[0]):
self.stats['fallback_used'] += 1
raise Exception("Enhanced search produced degenerate output")
self.stats['tasks_solved'] += 1
return {
"attempt_1": [to_list(arr) for arr in attempts[0]],
"attempt_2": [to_list(arr) for arr in attempts[1]],
}
attempts = predict_two_enhanced(progs, [test_input])
if self._validate_solution(attempts, [test_input]):
return attempts
else:
# Enhancement didn't work, try fallback
self.stats['fallback_used'] += 1
raise Exception("Enhanced search failed validation")
print("Enhanced prediction failed validation")
else:
raise Exception("Enhancements disabled")

except Exception:
# Fall back to baseline approach
progs = synth_baseline(train_pairs)
attempts = predict_two_baseline(progs, test_inputs)

# Sanity check predictions and fall back to identity if needed
fixed_attempts: List[List[Array]] = [[], []]
for idx, pred in enumerate(attempts[0]):
if pred is None or pred.size == 0 or np.all(pred == 0):
fixed_attempts[0].append(test_inputs[idx])
else:
fixed_attempts[0].append(pred)
fixed_attempts[1] = attempts[1] if len(attempts) > 1 else fixed_attempts[0]

return {
"attempt_1": [to_list(arr) for arr in fixed_attempts[0]],
"attempt_2": [to_list(arr) for arr in fixed_attempts[1]],
}
print("Enhancements disabled, using baseline search")
except Exception as e:
print(f"Enhanced prediction error: {e}")

# Fall back to baseline search
self.stats['fallback_used'] += 1
print("Falling back to baseline search")
progs = synth_baseline(train_pairs)
return predict_two_baseline(progs, [test_input])

def solve_task_two_attempts(
self, task: Dict[str, List[Dict[str, List[List[int]]]]]
Expand Down
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