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refine class design and inheritance first version code (#41)

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* refine class design and inheritance first version code

* fix all typos

---------

Co-authored-by: xuyang1 <xuyang1@microsoft.com>
This commit is contained in:
Xu Yang
2024-07-02 17:58:37 +08:00
committed by GitHub
parent 0faf232b81
commit 1b31bcd2c2
39 changed files with 717 additions and 482 deletions
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rdagent/components/task_implementation/factor_implementation/evolving/scheduler.py
+24 -16
View File
@@ -1,12 +1,12 @@
import json
from pathlib import Path
from jinja2 import Template
from jinja2 import Environment, StrictUndefined
from rdagent.components.task_implementation.factor_implementation.evolving.factor import (
FactorEvovlingItem,
from rdagent.components.task_implementation.factor_implementation.evolving.evolvable_subjects import (
FactorEvolvingItem,
)
from rdagent.components.task_implementation.factor_implementation.share_modules.factor_implementation_utils import (
from rdagent.components.task_implementation.factor_implementation.utils import (
get_data_folder_intro,
)
from rdagent.core.conf import RD_AGENT_SETTINGS
@@ -29,18 +29,22 @@ def RandomSelect(to_be_finished_task_index, implementation_factors_per_round):
return to_be_finished_task_index
def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo: FactorEvovlingItem, former_trace):
def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo: FactorEvolvingItem, former_trace):
tasks = []
for i in to_be_finished_task_index:
# find corresponding former trace for each task
target_factor_task_information = evo.target_factor_tasks[i].get_factor_information()
target_factor_task_information = evo.sub_tasks[i].get_factor_information()
if target_factor_task_information in former_trace:
tasks.append((i, evo.target_factor_tasks[i], former_trace[target_factor_task_information]))
tasks.append((i, evo.sub_tasks[i], former_trace[target_factor_task_information]))
system_prompt = Template(
scheduler_prompts["select_implementable_factor_system"],
).render(
data_info=get_data_folder_intro(),
system_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
scheduler_prompts["select_implementable_factor_system"],
)
.render(
data_info=get_data_folder_intro(),
)
)
session = APIBackend(use_chat_cache=False).build_chat_session(
@@ -48,11 +52,15 @@ def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo:
)
while True:
user_prompt = Template(
scheduler_prompts["select_implementable_factor_user"],
).render(
factor_num=implementation_factors_per_round,
target_factor_tasks=tasks,
user_prompt = (
Environment(undefined=StrictUndefined)
.from_string(
scheduler_prompts["select_implementable_factor_user"],
)
.render(
factor_num=implementation_factors_per_round,
sub_tasks=tasks,
)
)
if (
session.build_chat_completion_message_and_calculate_token(