Fixed most issues with Gemini

Author: nikivanstein

examples/run-automl.py

@@ -13,7 +13,7 @@
 
     llm1 = Gemini_LLM(api_key_gemini, "gemini-2.0-flash")
     #llm2 = OpenAI_LLM(api_key,"gpt-4.1-nano-2025-04-14", temperature=1.0)
-    budget = 10
+    budget = 3
 
     mutation_prompts = [
         "Refine the strategy of the selected solution to improve it.",  # small mutation
@@ -23,11 +23,11 @@
 
     for llm in [llm1]:
         LLaMEA_method = LLaMEA(llm, budget=budget, name="LLaMEA", mutation_prompts=mutation_prompts, n_parents=1, n_offspring=1, elitism=True)
-        ReEvo_method = ReEvo(llm, budget=budget, name="ReEvo", output_path="results/automl-breast-cancer", pop_size=2, init_pop_size=4)
-        EoH_method = EoH(llm, budget=budget, name="EoH", output_path="results/automl-breast-cancer")
+        #ReEvo_method = ReEvo(llm, budget=budget, name="ReEvo", output_path="results/automl-breast-cancer", pop_size=2, init_pop_size=4)
+        #EoH_method = EoH(llm, budget=budget, name="EoH", output_path="results/automl-breast-cancer")
         methods = [LLaMEA_method] #EoH_method, ReEvo_method, 
         logger = ExperimentLogger("results/automl-breast-cancer-new")
         problems = [AutoML()]
-        experiment = Experiment(methods=methods, problems=problems, runs=1, show_stdout=False, exp_logger=logger, budget=budget, n_jobs=3) #normal run
+        experiment = Experiment(methods=methods, problems=problems, runs=1, show_stdout=True, exp_logger=logger, budget=budget, n_jobs=3) #normal run
 
         experiment() #run the experiment

examples/run-mabbob.py

@@ -1,5 +1,5 @@
 from iohblade.experiment import MA_BBOB_Experiment
-from iohblade.llm import Gemini_LLM, Ollama_LLM
+from iohblade.llm import Gemini_LLM, Ollama_LLM, Dummy_LLM
 from iohblade.methods import LLaMEA, RandomSearch
 from iohblade.loggers import ExperimentLogger
 import numpy as np
@@ -10,7 +10,7 @@
     ai_model = "gemini-2.0-flash"
     llm1 = Gemini_LLM(api_key, ai_model)
     llm2 = Ollama_LLM("codestral")
-    budget = 100
+    budget = 10
 
 
     mutation_prompts1 = [
@@ -34,15 +34,15 @@
 
     for llm in [llm1]:#, llm2]:
         #RS = RandomSearch(llm, budget=budget) #LLaMEA(llm)
-        LLaMEA_method1 = LLaMEA(llm, budget=budget, name="LLaMEA-1", mutation_prompts=mutation_prompts1, n_parents=4, n_offspring=12, elitism=False)
-        LLaMEA_method2 = LLaMEA(llm, budget=budget, name="LLaMEA-2", mutation_prompts=mutation_prompts2, n_parents=4, n_offspring=12, elitism=False)
-        LLaMEA_method3 = LLaMEA(llm, budget=budget, name="LLaMEA-3", mutation_prompts=mutation_prompts3, n_parents=4, n_offspring=12, elitism=False)
-        LLaMEA_method4 = LLaMEA(llm, budget=budget, name="LLaMEA-4", mutation_prompts=mutation_prompts4, n_parents=4, n_offspring=12, elitism=False)
-        LLaMEA_method5 = LLaMEA(llm, budget=budget, name="LLaMEA-5", mutation_prompts=mutation_prompts5, n_parents=4, n_offspring=12, elitism=False) 
+        LLaMEA_method1 = LLaMEA(llm, budget=budget, name="LLaMEA-1", mutation_prompts=mutation_prompts1, n_parents=1, n_offspring=1, elitism=False)
+        # LLaMEA_method2 = LLaMEA(llm, budget=budget, name="LLaMEA-2", mutation_prompts=mutation_prompts2, n_parents=4, n_offspring=12, elitism=False)
+        # LLaMEA_method3 = LLaMEA(llm, budget=budget, name="LLaMEA-3", mutation_prompts=mutation_prompts3, n_parents=4, n_offspring=12, elitism=False)
+        # LLaMEA_method4 = LLaMEA(llm, budget=budget, name="LLaMEA-4", mutation_prompts=mutation_prompts4, n_parents=4, n_offspring=12, elitism=False)
+        # LLaMEA_method5 = LLaMEA(llm, budget=budget, name="LLaMEA-5", mutation_prompts=mutation_prompts5, n_parents=4, n_offspring=12, elitism=False) 
 
         methods = [LLaMEA_method1] #, LLaMEA_method4, LLaMEA_method5]#, LLaMEA_method4, LLaMEA_method5]
-        logger = ExperimentLogger("results/MA-BBOB")
-        experiment = MA_BBOB_Experiment(methods=methods, runs=2, seeds=[4,7], dims=[5], budget_factor=2000, budget=100, eval_timeout=60, show_stdout=True, exp_logger=logger) #normal run
+        logger = ExperimentLogger("results/MA-BBOB-test")
+        experiment = MA_BBOB_Experiment(methods=methods, runs=2, seeds=[4,7], dims=[5], budget_factor=200, budget=10, eval_timeout=60, show_stdout=True, exp_logger=logger) #normal run
         experiment() #run the experiment
 
 

iohblade/llm.py

@@ -420,6 +420,7 @@ def __init__(
             }
 
         self.client = genai.Client(api_key=api_key)
+        self.api_key = api_key
         self.generation_config = generation_config
 
     def _query(
@@ -470,6 +471,27 @@ def _query(
 
                 time.sleep(wait)
 
+    # ---------- pickling / deepcopy helpers ----------
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state.pop("client", None)
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        self.client = genai.Client(api_key=self.api_key)
+
+    def __deepcopy__(self, memo):
+        cls = self.__class__
+        new = cls.__new__(cls)
+        memo[id(self)] = new
+        for k, v in self.__dict__.items():
+            if k == "client":
+                continue
+            setattr(new, k, copy.deepcopy(v, memo))
+        new.client = genai.Client(api_key=new.api_key)
+        return new
+
 
 class Ollama_LLM(LLM):
     def __init__(self, model="llama3.2", **kwargs):

iohblade/problem.py

@@ -15,12 +15,9 @@
 
 # Standard packages installed in every evaluation environment
 BASE_DEPENDENCIES = [
-    "numpy>=1.26.3,<2",
+    "numpy>=2",
     "cloudpickle>=3.1.0,<4",
     "joblib>=1.4.2,<2",
-    #    "pandas==2.0.3",
-    #    "polars==1.31.0",
-    #    "scikit-learn==1.3.0",
 ]
 
 import copy
@@ -72,15 +69,14 @@ def evaluate_in_subprocess(problem, conn, solution):
                 [str(python_bin), str(script_path)],
                 check=True,
                 env=env,
-                capture_output=False,
+                capture_output=True,
                 text=True,
             )
             with open(result_pickle, "rb") as f:
                 result = cloudpickle.load(f)
             conn.send(result)
         except subprocess.CalledProcessError as e:
             # Process returned non-zero exit code
-            print(e.stderr)
             conn.send(e.stderr)
 
     except Exception as e:
@@ -159,7 +155,6 @@ def __call__(self, solution: Solution, logger=None):
         Returns:
             Solution: The evaluated solution with updated fitness and scores.
         """
-        print("in __call__")
         if logger != None:
             print("LOGGER is NOT NONE (UNEXPECTED)")
             self.logger = logger
@@ -218,7 +213,6 @@ def __call__(self, solution: Solution, logger=None):
                 process.join()
             except Exception:
                 pass
-        print("sol", solution.name)
         if self.logger is not None:
             self.logger.log_individual(solution)
         return solution

iohblade/problems/automl.py

@@ -34,7 +34,7 @@ def __init__(
         imports=None,
     ):
         if dependencies is None:
-            dependencies = ["pandas==2.0.3", "polars==1.31.0", "scikit-learn==1.3.0"]
+            dependencies = ["pandas==2.2.3", "polars==1.31.0", "scikit-learn==1.3.0"]
         if imports is None:
             imports = "import pandas as pd\nimport polars\nimport sklearn\n"
 

iohblade/problems/bbob_sboxcost.py

@@ -71,8 +71,8 @@ def __init__(
         """
         if dependencies is None:
             dependencies = [
-                "pandas==2.0.3",
-                "ioh==0.3.18",
+                "pandas==2.2.3",
+                "ioh==0.3.19",
                 "configspace==1.2.1",
                 "smac==2.3.1",
             ]

iohblade/problems/kerneltuner.py

@@ -78,8 +78,8 @@ def __init__(
             dependencies = [
                 "kernel-tuner @ git+https://github.com/XAI-liacs/kernel_tuner.git@hyperparametertuning_custom_strategies",
                 "autotuning-methodology @ git+https://github.com/AutoTuningAssociation/autotuning_methodology.git@6a9a50a5a49bc104469b3b753fd43a5324241702",
-                "pandas==2.0.3",
-                "ioh==0.3.18",
+                "pandas==2.2.3",
+                "ioh==0.3.19",
                 "configspace==1.2.1",
                 "smac==2.3.1",
             ]

iohblade/problems/mabbob.py

@@ -50,8 +50,8 @@ def __init__(
         """
         if dependencies is None:
             dependencies = [
-                "pandas==2.0.3",
-                "ioh==0.3.18",
+                "pandas==2.2.3",
+                "ioh==0.3.19",
                 "configspace==1.2.1",
                 "smac==2.3.1",
             ]

iohblade/problems/photonics.py

@@ -45,7 +45,7 @@ def __init__(
             seeds (int): Number of random runs.
         """
         if dependencies is None:
-            dependencies = ["ioh==0.3.18", "pandas==2.0.3"]
+            dependencies = ["ioh==0.3.19", "pandas==2.2.3"]
         if imports is None:
             imports = "import numpy as np\nimport ioh\n"
 

iohblade/solution.py

@@ -164,6 +164,38 @@ def to_dict(self):
             "task_prompt": self.task_prompt,
         }
 
+    def from_dict(self, data):
+        """
+        Updates the Solution instance from a dictionary.
+        Args:
+            data (dict): A dictionary representation of the individual.
+        Returns:
+            None
+        """
+        configspace = data.get("configspace", None)
+
+        if isinstance(configspace, dict):  # Deserialize if necessary
+            try:
+                configspace = ConfigSpace()  # Replace with actual class
+                configspace.from_serialized_dict(data["configspace"])
+            except Exception as e:
+                print(f"Warning: Failed to deserialize configspace - {e}")
+                configspace = None
+
+        # Update instance attributes
+        self.id = data.get("id")
+        self.fitness = data.get("fitness")
+        self.name = data.get("name")
+        self.description = data.get("description")
+        self.code = data.get("code")
+        self.configspace = configspace
+        self.generation = data.get("generation")
+        self.feedback = data.get("feedback")
+        self.error = data.get("error")
+        self.parent_ids = data.get("parent_ids", [])
+        self.operator = data.get("operator")
+        self.metadata = data.get("metadata", {})
+
     def to_json(self):
         """
         Converts the individual to a JSON string.