[BUG] fit_summary plot does not save to the model path directory #4708
Description
Bug Report Checklist
- I provided code that demonstrates a minimal reproducible example.
- I confirmed bug exists on the latest mainline of AutoGluon via source install.
- I confirmed bug exists on the latest stable version of AutoGluon.
Describe the bug
Plot generated by predictor.fit_summary does not save to predictor.path directory.
Expected behavior
Plot should be saved under the predictor.path directory.
To Reproduce
import pandas as pd
from autogluon.tabular import TabularPredictor
data_url = 'https://raw.githubusercontent.com/mli/ag-docs/main/knot_theory/'
train_data = pd.read_csv(f'{data_url}train.csv')
predictor = TabularPredictor(label="signature", path="/tmp/test")
predictor.fit(train_data, time_limit=5, included_model_types=["XGB"])
predictor.fit_summary()Plot is saved to /tmp/testSummaryOfModels.html, but it should be /tmp/test/SummaryOfModels.html, at least according to the docstring of this method. Change the path value to /tmp/test/ (with a trailing slash) does not help since it always translate to non-slash-ending value in predictor.path.
Screenshots / Logs
Installed Versions
INSTALLED VERSIONS
------------------
date : 2024-12-04
time : 10:07:00.779902
python : 3.10.12.final.0
OS : Darwin
OS-release : 23.6.0
Version : Darwin Kernel Version 23.6.0: Thu Sep 12 23:35:29 PDT 2024; root:xnu-10063.141.1.701.1~1/RELEASE_ARM64_T6000
machine : arm64
processor : arm
num_cores : 10
cpu_ram_mb : 32768.0
cuda version : None
num_gpus : 0
gpu_ram_mb : []
avail_disk_size_mb : 22278
accelerate : 0.34.2
autogluon : 1.2
autogluon.common : 1.2
autogluon.core : 1.2
autogluon.features : 1.2
autogluon.multimodal : 1.2
autogluon.tabular : 1.2
autogluon.timeseries : 1.2
boto3 : 1.35.73
catboost : 1.2.7
coreforecast : 0.0.12
defusedxml : 0.7.1
einops : 0.8.0
evaluate : 0.4.3
fastai : 2.7.18
fugue : 0.9.1
gluonts : 0.16.0
huggingface-hub : 0.26.3
hyperopt : 0.2.7
imodels : None
jinja2 : 3.1.4
joblib : 1.4.2
jsonschema : 4.21.1
lightgbm : 4.5.0
lightning : 2.4.0
matplotlib : 3.9.3
mlforecast : 0.13.4
networkx : 3.4.2
nlpaug : 1.1.11
nltk : 3.8.1
numpy : 1.26.4
nvidia-ml-py3 : 7.352.0
omegaconf : 2.2.3
onnx : None
onnxruntime : None
onnxruntime-gpu : None
openmim : 0.3.9
optimum : None
optimum-intel : None
orjson : 3.10.12
pandas : 2.2.3
pdf2image : 1.17.0
Pillow : 11.0.0
psutil : 6.1.0
pyarrow : 18.1.0
pytesseract : 0.3.10
pytorch-lightning : 2.4.0
pytorch-metric-learning: 2.3.0
ray : 2.39.0
requests : 2.32.3
scikit-image : 0.24.0
scikit-learn : 1.5.2
scikit-learn-intelex : None
scipy : 1.14.1
seqeval : 1.2.2
skl2onnx : None
spacy : 3.7.5
statsforecast : 1.7.8
tabpfn : None
tensorboard : 2.18.0
text-unidecode : 1.3
timm : 1.0.3
torch : 2.5.1
torchmetrics : 1.2.1
torchvision : 0.20.1
tqdm : 4.67.1
transformers : 4.46.3
utilsforecast : 0.2.4
vowpalwabbit : None
xgboost : 2.1.3