remove ensemble from hypo_gen

Author: Hoder-zyf

rdagent/scenarios/data_science/proposal/exp_gen/ensemble/ensemble.py

@@ -1,4 +1,5 @@
 import re
+
 from rdagent.core.proposal import ExpGen
 from rdagent.core.scenario import Scenario
 from rdagent.oai.llm_utils import APIBackend

rdagent/scenarios/data_science/proposal/exp_gen/prompts_v2.yaml

@@ -573,7 +573,7 @@ output_format:
       "problem name 1 (should be exactly same as the problem name provided)": {
         {% if enable_idea_pool %}"inspired": "True or False. Set to True if the hypothesis is inspired by the user provided ideas. Otherwise, set it to False.",{% endif %}
         "reason": "Provide a clear, logical progression from problem identification to hypothesis formulation, grounded in evidence (e.g., trace history, domain principles, or competition constraints). Refer to the Hypothesis Guidelines for better understanding. Reason should be short with no more than two sentences.",
-        "component": "The component tag of the hypothesis. Must be one of ('DataLoadSpec', 'FeatureEng', 'Model', 'Ensemble', 'Workflow').",
+        "component": "The component tag of the hypothesis. Must be one of ('DataLoadSpec', 'FeatureEng', 'Model', 'Workflow').",
         "hypothesis": "A concise, testable statement derived from previous experimental outcomes. Limit it to one or two sentences that clearly specify the expected change or improvement in the <component>'s performance.",
         "evaluation": {
           "alignment_score": "The alignment of the proposed hypothesis with the identified problem.",

rdagent/scenarios/data_science/proposal/exp_gen/proposal.py

@@ -1,13 +1,11 @@
+import asyncio
 import json
+import re
 from enum import Enum
 from typing import Any, Dict, List, Optional, Tuple
 
 import pandas as pd
 from pydantic import BaseModel, Field
-from rdagent.oai.backend.base import RD_Agent_TIMER_wrapper
-from rdagent.log.timer import RDAgentTimer
-from rdagent.core.conf import RD_AGENT_SETTINGS
-import asyncio
 
 from rdagent.app.data_science.conf import DS_RD_SETTING
 from rdagent.components.coder.data_science.ensemble.exp import EnsembleTask
@@ -16,9 +14,12 @@
 from rdagent.components.coder.data_science.pipeline.exp import PipelineTask
 from rdagent.components.coder.data_science.raw_data_loader.exp import DataLoaderTask
 from rdagent.components.coder.data_science.workflow.exp import WorkflowTask
+from rdagent.core.conf import RD_AGENT_SETTINGS
 from rdagent.core.proposal import ExpGen
 from rdagent.core.scenario import Scenario
 from rdagent.log import rdagent_logger as logger
+from rdagent.log.timer import RDAgentTimer
+from rdagent.oai.backend.base import RD_Agent_TIMER_wrapper
 from rdagent.oai.llm_utils import APIBackend, md5_hash
 from rdagent.scenarios.data_science.dev.feedback import ExperimentFeedback
 from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
@@ -30,7 +31,6 @@
 from rdagent.utils.agent.tpl import T
 from rdagent.utils.repo.diff import generate_diff_from_dict
 from rdagent.utils.workflow import wait_retry
-import re
 
 _COMPONENT_META: Dict[str, Dict[str, Any]] = {
     "DataLoadSpec": {
@@ -583,8 +583,6 @@ def hypothesis_gen(
         sys_prompt = T(".prompts_v2:hypothesis_gen.system").r(
             hypothesis_output_format=(
                 T(".prompts_v2:output_format.hypothesis").r(pipeline=pipeline, enable_idea_pool=enable_idea_pool)
-                if not self.supports_response_schema
-                else None
             ),
             pipeline=pipeline,
             enable_idea_pool=enable_idea_pool,
@@ -600,30 +598,10 @@ def hypothesis_gen(
         response = APIBackend().build_messages_and_create_chat_completion(
             user_prompt=user_prompt,
             system_prompt=sys_prompt,
-            response_format=HypothesisList if self.supports_response_schema else {"type": "json_object"},
-            json_target_type=(
-                Dict[str, Dict[str, str | Dict[str, str | int]]] if not self.supports_response_schema else None
-            ),
+            response_format={"type": "json_object"},
+            json_target_type=Dict[str, Dict[str, str | Dict[str, str | int]]],
         )
-        if self.supports_response_schema:
-            hypotheses = HypothesisList(**json.loads(response))
-            resp_dict = {
-                h.caption: {
-                    "reason": h.challenge,
-                    "component": h.component,
-                    "hypothesis": h.hypothesis,
-                    "evaluation": {
-                        "alignment_score": h.evaluation.alignment.score,
-                        "impact_score": h.evaluation.impact.score,
-                        "novelty_score": h.evaluation.novelty.score,
-                        "feasibility_score": h.evaluation.feasibility.score,
-                        "risk_reward_balance_score": h.evaluation.risk_reward_balance.score,
-                    },
-                }
-                for h in hypotheses.hypotheses
-            }
-        else:
-            resp_dict = json.loads(response)
+        resp_dict = json.loads(response)
         logger.info(f"Generated hypotheses:\n" + json.dumps(resp_dict, indent=2))
 
         # make sure the problem name is aligned