alloy_changes w_out ipynb

bolift/aqfxns.py

@@ -80,28 +80,32 @@ def greedy_d(probs, values, best):
 
 def expected_improvement_g(mean, std, best):
     """Expected improvement for the given Gaussian distribution"""
-    z = (mean - best) / std
+    eps=1e-15
+    z = (mean - best) / (std+eps)
     ei = (mean - best) * norm.cdf(z) + std * norm.pdf(z)
     return ei
 
 def log_expected_improvement_g(mean, std, best):
     """Log Expected improvement for the given Gaussian distribution"""
-    z = (mean - best) / std 
+    eps=1e-15
+    z = (mean - best) / (std+eps)
     # ei = std * h(z)
     # ei = std * (norm.pdf(z) + z * norm.cdf(z))
     log_ei = np.log(std) + np.log((norm.pdf(z) + z * norm.cdf(z)))
     return log_ei
 
 def probability_of_improvement_g(mean, std, best):
     """Probability of improvement for the given Gaussian distribution"""
-    z = (mean - best) / std
+    eps=1e-15
+    z = (mean - best) / (std+eps)
     pi = norm.cdf(z)
     return pi
 
 
 def upper_confidence_bound_g(mean, std, best, _lambda):
     """Upper confidence bound for the given Gaussian distribution"""
-    return mean + _lambda * std
+    eps=1e-15
+    return mean + _lambda * (std+eps)
 
 
 def greedy_g(mean, std, best):

bolift/asktell.py

@@ -145,7 +145,7 @@ def __init__(
         self._answer_choices = _answer_choices[:k]
         self.use_quantiles = use_quantiles
         self.n_quantiles = n_quantiles
-        self._calibration_factor = None
+        self._calibration_factor = 1.0
         self._verbose = verbose
         self.tokens_used = 0
         self.cos_sim = cos_sim
@@ -309,6 +309,7 @@ def ask(
             aug_random_filter: Add this man y random examples to the pool to increase diversity after reducing pool with inverse model
             _lambda: Lambda value to use for UCB
             lambda_mult: control MMR diversity ,0-1 lower = more diverse
+
         Return:
             The selected x values, their acquisition function values, and the predicted y modes.
             Sorted by acquisition function value (descending)
@@ -323,6 +324,7 @@ def ask(
                 init_pnt,
                 [0] * k,
                 [0] * k,
+                [0] * k,
             )
 
         if aq_fxn == "probability_of_improvement":
@@ -340,6 +342,8 @@ def ask(
                 possible_x.sample(k),
                 [0] * k,
                 [0] * k,
+                [0] * k,
+
             )
         else:
             raise ValueError(f"Unknown acquisition function: {aq_fxn}")
@@ -352,7 +356,7 @@ def ask(
         if inv_filter+aug_random_filter < len(possible_x):
             possible_x_l = []
             if inv_filter:
-                approx_x = self.inv_predict(best * np.random.normal(1.2, 0.05), system_message=inv_system_message)
+                approx_x = self.inv_predict(best + np.abs(best)*np.random.normal(0.2, 0.05), system_message=inv_system_message)
                 possible_x_l.extend(possible_x.approx_sample(approx_x, inv_filter, lambda_mult=lambda_mult))
 
             if aug_random_filter:
@@ -367,6 +371,8 @@ def ask(
                 possible_x.sample(k),
                 [0] * k,
                 [0] * k,
+                [0] * k,
+                [0] * k
             )
         return results
 
@@ -456,7 +462,7 @@ def _setup_prompt(
     def _setup_inverse_prompt(self, example: Dict):
         prompt_template = PromptTemplate(
             input_variables=["x", "y", "y_name", "x_name"],
-            template="If {y_name} is {y}, then {x_name} is @@@\n{x}###",
+            template="If {y_name} is {y}, then the {x_name} is @@@\n{x}###",
         )
         if example is not None:
             prompt_template.format(**example)
@@ -468,7 +474,7 @@ def _setup_inverse_prompt(self, example: Dict):
             if len(examples) == 0:
                 raise ValueError("Cannot do zero-shot with selector")
 
-            sim_selector = SemanticSimilarityExampleSelector if self.cos_sim else MaxMarginalRelevanceExampleSelector #LabelSimilarityExampleSelector 
+            sim_selector = SemanticSimilarityExampleSelector if self.cos_sim else MaxMarginalRelevanceExampleSelector # LabelSimilarityExampleSelector 
             example_selector = sim_selector.from_examples(
                 [example],
                 OpenAIEmbeddings(),
@@ -541,11 +547,13 @@ def _ask(
         aq_vals = [aq_fxn(r, best) for r in results]
         selected = np.argsort(aq_vals)[::-1][:k]
         means = [r.mean() for r in results]
+        stds = [r.std() for r in results]
 
         return (
             [possible_x[i] for i in selected],
             [aq_vals[i] for i in selected],
             [means[i] for i in selected],
+            [stds[i] for i in selected],
         )
 
 

bolift/pool.py

@@ -27,7 +27,7 @@ def __init__(self, pool: List[Any], formatter: Callable = lambda x: str(x)) -> N
         self.format = formatter
         self._db = FAISS.from_texts(
             [formatter(x) for x in pool],
-            OpenAIEmbeddings(), # model="text-embedding-3-large" 
+            OpenAIEmbeddings(), #model="text-embedding-3-large" 
             metadatas=[dict(data=p) for p in pool],
         )
 
@@ -72,5 +72,4 @@ def __str__(self) -> str:
         return f"Pool of {len(self)} items with {len(self._selected)} selected"
 
     def __iter__(self):
-        return iter(self._available)
-
+        return iter(self._available)

bolift/tool.py

@@ -4,16 +4,16 @@
 from typing import *
 import os
 import pandas as pd
+from pydantic import BaseModel
 
-
-class BOLiftTool(BaseTool): 
-    name = "Experiment Designer"
-    description = ("Propose or predict experiments using stateful ask-and-tell Bayes Optimizer. "
-    "Syntax: Tell {{CSV_FILE}}. Adds training examples to model, {{CSV_FILE}}. No header and only two columns: x in column 0, y in column 1. "
-    "Ask. Returns optimal experiment to run next. Must call Tell first. "
-    "Best. Returns predicted experiment. Must call Tell first.")
-    asktell: AskTellFewShotTopk = None
-    pool: Pool = None
+class BOLiftTool(BaseTool):
+    name: str = "Experiment Designer"
+    description: str = (
+        "Propose or predict experiments using stateful ask-and-tell Bayes Optimizer. "
+        "Syntax: Tell {{CSV_FILE}}. Adds training examples to model, {{CSV_FILE}}. No header and only two columns: x in column 0, y in column 1. "
+        "Ask. Returns optimal experiment to run next. Must call Tell first. "
+        "Best. Returns predicted experiment. Must call Tell first."
+    )
 
     def __init__(self, pool: Pool, asktell: Optional[AskTellFewShotTopk] = None, ):
         # call the parent class constructor