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Code Issues Packages Projects Releases Wiki Activity

feat: support more models for kaggle scenario (#223)

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* init commit for XGBoost

* fix some bugs

* CI issues

* CI issues

* CI issue

* edit prompts for kaggle scenario & fix some bugs

* Revised Prompts To Improve Performance on Model Type & Support of Random Forest

* edit prompts & modify evaluator.py to adapt to Kaggle scenario

* edit prompts

* fix some bugs

* CI issues

---------

Co-authored-by: Taozhi Wang <taozhi.mark.wang@gmail.com>
Co-authored-by: Xisen-Wang <xisen_application@163.com>
This commit is contained in:
TPLin22
2024-08-23 15:13:50 +08:00
committed by GitHub
parent 28166fc4f5
commit 89d4f92051
8 changed files with 226 additions and 98 deletions
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rdagent/components/coder/model_coder/model.py
+33 -16
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@@ -6,7 +6,9 @@ import uuid
from pathlib import Path
from typing import Any, Dict, Optional
import numpy as np
import torch
import xgboost as xgb
from rdagent.components.coder.model_coder.conf import MODEL_IMPL_SETTINGS
from rdagent.core.exception import CodeFormatError
@@ -32,9 +34,7 @@ class ModelTask(Task):
self.architecture: str = architecture
self.variables: str = variables
self.hyperparameters: str = hyperparameters
self.model_type: str = (
model_type # Tabular for tabular model, TimesSeries for time series model, Graph for graph model
)
self.model_type: str = model_type # Tabular for tabular model, TimesSeries for time series model, Graph for graph model, XGBoost for XGBoost model
def get_task_information(self):
return f"""name: {self.name}
@@ -95,9 +95,17 @@ class ModelFBWorkspace(FBWorkspace):
if cache_file_path.exists():
return pickle.load(open(cache_file_path, "rb"))
mod = get_module_by_module_path(str(self.workspace_path / "model.py"))
model_cls = mod.model_cls
if self.target_task.model_type == "Tabular":
if self.target_task.model_type != "XGBoost":
model_cls = mod.model_cls
if self.target_task.model_type == "XGBoost":
X_simulated = np.random.rand(100, num_features) # 100 samples, `num_features` features each
y_simulated = np.random.randint(0, 2, 100) # Binary target for example
params = mod.get_params()
num_round = mod.get_num_round()
dtrain = xgb.DMatrix(X_simulated, label=y_simulated)
elif self.target_task.model_type == "Tabular":
input_shape = (batch_size, num_features)
m = model_cls(num_features=input_shape[1])
data = torch.full(input_shape, input_value)
@@ -113,21 +121,30 @@ class ModelFBWorkspace(FBWorkspace):
else:
raise ValueError(f"Unsupported model type: {self.target_task.model_type}")
# Initialize all parameters of `m` to `param_init_value`
for _, param in m.named_parameters():
param.data.fill_(param_init_value)
# Execute the model
if self.target_task.model_type == "Graph":
out = m(*data)
if self.target_task.model_type == "XGBoost":
bst = xgb.train(params, dtrain, num_round)
y_pred = bst.predict(dtrain)
execution_model_output = y_pred
execution_feedback_str = "Execution successful, model trained and predictions made."
else:
out = m(data)
# Initialize all parameters of `m` to `param_init_value`
for _, param in m.named_parameters():
param.data.fill_(param_init_value)
execution_model_output = out.cpu().detach()
execution_feedback_str = f"Execution successful, output tensor shape: {execution_model_output.shape}"
# Execute the model
if self.target_task.model_type == "Graph":
out = m(*data)
else:
out = m(data)
execution_model_output = out.cpu().detach()
execution_feedback_str = f"Execution successful, output tensor shape: {execution_model_output.shape}"
if MODEL_IMPL_SETTINGS.enable_execution_cache:
pickle.dump((execution_feedback_str, execution_model_output), open(cache_file_path, "wb"))
pickle.dump(
(execution_feedback_str, execution_model_output),
open(cache_file_path, "wb"),
)
except Exception as e:
execution_feedback_str = f"Execution error: {e}\nTraceback: {traceback.format_exc()}"