apunkt/MetaGPT
1
0
Fork 0
mirror of https://github.com/FoundationAgents/MetaGPT.git synced 2026-06-11 15:15:18 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

add tuning data for tabular mode

Browse source
This commit is contained in:
Rayhao 2024-09-25 23:32:52 -07:00
parent 1a1855f21a
commit 3c397387f9
1 changed files with 6 additions and 6 deletions
Download patch file Download diff file Expand all files Collapse all files

12
expo/experimenter/autogluon.py
View file

@ -1,5 +1,6 @@
from datetime import datetime
from expo.experimenter.custom import CustomExperimenter
from autogluon.tabular import TabularDataset, TabularPredictor
import os
import pandas as pd
@ -10,23 +11,22 @@ class AGRunner:
self.datasets = self.state["datasets_dir"]
def run(self):
from autogluon.tabular import TabularDataset, TabularPredictor
train_path = self.datasets["train"]
dev_path = self.datasets["dev"]
dev_wo_target_path = self.datasets["dev_wo_target"]
test_wo_target_path = self.datasets["test_wo_target"]
target_col = self.state["dataset_config"]["target_col"]
train_data = TabularDataset(train_path)
dev_data = TabularDataset(dev_wo_target_path)
dev_data = TabularDataset(dev_path)
dev_wo_target_data = TabularDataset(dev_wo_target_path)
test_data = TabularDataset(test_wo_target_path)
eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_")
# predictor = TabularPredictor(label=target_col, eval_metric=eval_metric, path="AutogluonModels/ag-{}-{}".format(self.state['task'], datetime.now().strftime("%y%m%d_%H%M"))).fit(train_data, presets=self.preset, time_limit=self.time_limit, fit_weighted_ensemble=False, num_gpus=1)
predictor = TabularPredictor(
label=target_col,
eval_metric=eval_metric,
path="AutogluonModels/ag-{}-{}".format(self.state["task"], datetime.now().strftime("%y%m%d_%H%M")),
).fit(train_data, num_gpus=1)
dev_preds = predictor.predict(dev_data)
).fit(train_data=train_data, tuning_data=dev_data, num_gpus=1)
dev_preds = predictor.predict(dev_wo_target_data)
test_preds = predictor.predict(test_data)
return {"test_preds": test_preds, "dev_preds": dev_preds}