Add default cascade settings (#25)

Moves num quantiles (default 50) and IS weight (default 0.5) to the
default settings.

lotus/sem_ops/cascade_utils.py

@@ -10,7 +10,8 @@ def importance_sampling(
     """Uses importance sampling and returns the list of indices from which to learn cascade thresholds."""
 
     w = np.sqrt(proxy_scores)
-    w = 0.5 * w / np.sum(w) + 0.5 * np.ones((len(proxy_scores))) / len(proxy_scores)
+    is_weight = lotus.settings.cascade_is_weight
+    w = is_weight * w / np.sum(w) + (1 - is_weight) * np.ones((len(proxy_scores))) / len(proxy_scores)
     indices = np.arange(len(proxy_scores))
     sample_size = (int) (sample_percentage * len(proxy_scores))
     sample_indices = np.random.choice(indices, sample_size, p=w)
@@ -20,7 +21,7 @@ def importance_sampling(
 
 def calibrate_llm_logprobs(true_probs: list[float]) -> list[float]:
     """Transforms true probabilities to calibrate LLM proxies."""
-    num_quantiles = 50
+    num_quantiles = lotus.settings.cascade_num_calibration_quantiles
     quantile_values = np.percentile(true_probs, np.linspace(0, 100, num_quantiles + 1))
     true_probs = ((np.digitize(true_probs, quantile_values) - 1) / num_quantiles)
     true_probs = np.clip(true_probs, 0, 1)

lotus/settings.py

@@ -113,3 +113,4 @@ def __repr__(self) -> str:
 
 # set defaults
 settings = Settings()
+settings.configure(cascade_is_weight=0.5, cascade_num_calibration_quantiles=50)