AutoGluon Tabular - Quick Start¶
In this tutorial, we will see how to use AutoGluon’s TabularPredictor to predict the values of a target column based on the other columns in a tabular dataset.
Begin by making sure AutoGluon is installed, and then import AutoGluon’s TabularDataset and TabularPredictor. We will use the former to load data and the latter to train models and make predictions.
!python -m pip install --upgrade pip
!python -m pip install autogluon
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Requirement already satisfied: colorama in /home/ci/opt/venv/lib/python3.10/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.1.1b20240613->autogluon) (0.4.4)
Requirement already satisfied: model-index in /home/ci/opt/venv/lib/python3.10/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.1.1b20240613->autogluon) (0.1.11)
Requirement already satisfied: opendatalab in /home/ci/opt/venv/lib/python3.10/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.1.1b20240613->autogluon) (0.0.10)
Requirement already satisfied: rich in /home/ci/opt/venv/lib/python3.10/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.1.1b20240613->autogluon) (13.7.1)
Requirement already satisfied: tabulate in /home/ci/opt/venv/lib/python3.10/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.1.1b20240613->autogluon) (0.9.0)
Requirement already satisfied: coloredlogs in /home/ci/opt/venv/lib/python3.10/site-packages (from optimum<1.19,>=1.17->optimum[onnxruntime]<1.19,>=1.17->autogluon.timeseries[all]==1.1.1b20240613->autogluon) (15.0.1)
Requirement already satisfied: sympy in /home/ci/opt/venv/lib/python3.10/site-packages (from optimum<1.19,>=1.17->optimum[onnxruntime]<1.19,>=1.17->autogluon.timeseries[all]==1.1.1b20240613->autogluon) (1.12.1)
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Requirement already satisfied: onnxruntime>=1.11.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from optimum[onnxruntime]<1.19,>=1.17->autogluon.timeseries[all]==1.1.1b20240613->autogluon) (1.18.0)
Requirement already satisfied: protobuf>=3.20.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from optimum[onnxruntime]<1.19,>=1.17->autogluon.timeseries[all]==1.1.1b20240613->autogluon) (3.20.2)
Requirement already satisfied: python-dateutil>=2.8.2 in /home/ci/opt/venv/lib/python3.10/site-packages (from pandas<2.3.0,>=2.0.0->autogluon.core==1.1.1b20240613->autogluon.core[all]==1.1.1b20240613->autogluon) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from pandas<2.3.0,>=2.0.0->autogluon.core==1.1.1b20240613->autogluon.core[all]==1.1.1b20240613->autogluon) (2024.1)
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Requirement already satisfied: filelock in /home/ci/opt/venv/lib/python3.10/site-packages (from ray<2.11,>=2.10.0->ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (3.15.1)
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Requirement already satisfied: aiosignal in /home/ci/opt/venv/lib/python3.10/site-packages (from ray<2.11,>=2.10.0->ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (1.3.1)
Requirement already satisfied: frozenlist in /home/ci/opt/venv/lib/python3.10/site-packages (from ray<2.11,>=2.10.0->ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (1.4.1)
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Requirement already satisfied: pyarrow>=6.0.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (16.1.0)
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Requirement already satisfied: colorful in /home/ci/opt/venv/lib/python3.10/site-packages (from ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (0.5.6)
Requirement already satisfied: py-spy>=0.2.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (0.3.14)
Requirement already satisfied: opencensus in /home/ci/opt/venv/lib/python3.10/site-packages (from ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (0.11.4)
Requirement already satisfied: prometheus-client>=0.7.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (0.20.0)
Requirement already satisfied: smart-open in /home/ci/opt/venv/lib/python3.10/site-packages (from ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (7.0.4)
Requirement already satisfied: virtualenv!=20.21.1,>=20.0.24 in /home/ci/opt/venv/lib/python3.10/site-packages (from ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (20.26.2)
Requirement already satisfied: grpcio>=1.42.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (1.64.1)
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Requirement already satisfied: idna<4,>=2.5 in /home/ci/opt/venv/lib/python3.10/site-packages (from requests->autogluon.core==1.1.1b20240613->autogluon.core[all]==1.1.1b20240613->autogluon) (3.7)
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Requirement already satisfied: certifi>=2017.4.17 in /home/ci/opt/venv/lib/python3.10/site-packages (from requests->autogluon.core==1.1.1b20240613->autogluon.core[all]==1.1.1b20240613->autogluon) (2024.6.2)
Requirement already satisfied: imageio>=2.4.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from scikit-image<0.21.0,>=0.19.1->autogluon.multimodal==1.1.1b20240613->autogluon) (2.34.1)
Requirement already satisfied: tifffile>=2019.7.26 in /home/ci/opt/venv/lib/python3.10/site-packages (from scikit-image<0.21.0,>=0.19.1->autogluon.multimodal==1.1.1b20240613->autogluon) (2024.5.22)
Requirement already satisfied: PyWavelets>=1.1.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from scikit-image<0.21.0,>=0.19.1->autogluon.multimodal==1.1.1b20240613->autogluon) (1.6.0)
Requirement already satisfied: lazy_loader>=0.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from scikit-image<0.21.0,>=0.19.1->autogluon.multimodal==1.1.1b20240613->autogluon) (0.4)
Requirement already satisfied: threadpoolctl>=2.0.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from scikit-learn<1.4.1,>=1.3.0->autogluon.core==1.1.1b20240613->autogluon.core[all]==1.1.1b20240613->autogluon) (3.5.0)
Requirement already satisfied: statsmodels>=0.13.2 in /home/ci/opt/venv/lib/python3.10/site-packages (from statsforecast<1.5,>=1.4.0->autogluon.timeseries==1.1.1b20240613->autogluon.timeseries[all]==1.1.1b20240613->autogluon) (0.14.2)
Requirement already satisfied: absl-py>=0.4 in /home/ci/opt/venv/lib/python3.10/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.1.1b20240613->autogluon) (2.1.0)
Requirement already satisfied: markdown>=2.6.8 in /home/ci/opt/venv/lib/python3.10/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.1.1b20240613->autogluon) (3.6)
Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.1.1b20240613->autogluon) (0.7.2)
Requirement already satisfied: werkzeug>=1.0.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.1.1b20240613->autogluon) (3.0.3)
Requirement already satisfied: safetensors in /home/ci/opt/venv/lib/python3.10/site-packages (from timm<0.10.0,>=0.9.5->autogluon.multimodal==1.1.1b20240613->autogluon) (0.4.3)
Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (12.1.105)
Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (12.1.105)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (12.1.105)
Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (8.9.2.26)
Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (12.1.3.1)
Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (11.0.2.54)
Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (10.3.2.106)
Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (11.4.5.107)
Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (12.1.0.106)
Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (2.20.5)
Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (12.1.105)
Requirement already satisfied: triton==2.3.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (2.3.1)
Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/ci/opt/venv/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch<2.4,>=2.2->autogluon.multimodal==1.1.1b20240613->autogluon) (12.5.40)
Requirement already satisfied: tokenizers<0.19,>=0.14 in /home/ci/opt/venv/lib/python3.10/site-packages (from transformers<4.41.0,>=4.38.0->transformers[sentencepiece]<4.41.0,>=4.38.0->autogluon.multimodal==1.1.1b20240613->autogluon) (0.15.2)
Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /home/ci/opt/venv/lib/python3.10/site-packages (from transformers[sentencepiece]<4.41.0,>=4.38.0->autogluon.multimodal==1.1.1b20240613->autogluon) (0.2.0)
Requirement already satisfied: contourpy>=1.0.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from matplotlib->autogluon.core==1.1.1b20240613->autogluon.core[all]==1.1.1b20240613->autogluon) (1.2.1)
Requirement already satisfied: cycler>=0.10 in /home/ci/opt/venv/lib/python3.10/site-packages (from matplotlib->autogluon.core==1.1.1b20240613->autogluon.core[all]==1.1.1b20240613->autogluon) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from matplotlib->autogluon.core==1.1.1b20240613->autogluon.core[all]==1.1.1b20240613->autogluon) (4.53.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from matplotlib->autogluon.core==1.1.1b20240613->autogluon.core[all]==1.1.1b20240613->autogluon) (1.4.5)
Requirement already satisfied: pyparsing>=2.2.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from matplotlib->autogluon.core==1.1.1b20240613->autogluon.core[all]==1.1.1b20240613->autogluon) (3.1.2)
Requirement already satisfied: multidict<7.0,>=4.5 in /home/ci/opt/venv/lib/python3.10/site-packages (from aiohttp>=3.7->ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (6.0.5)
Requirement already satisfied: yarl<2.0,>=1.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from aiohttp>=3.7->ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (1.9.4)
Requirement already satisfied: async-timeout<5.0,>=4.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from aiohttp>=3.7->ray[default,tune]<2.11,>=2.10.0; extra == "all"->autogluon.core[all]==1.1.1b20240613->autogluon) (4.0.3)
Requirement already satisfied: pyarrow-hotfix in /home/ci/opt/venv/lib/python3.10/site-packages (from datasets>=2.0.0->evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.1.1b20240613->autogluon) (0.6)
Requirement already satisfied: beautifulsoup4 in /home/ci/opt/venv/lib/python3.10/site-packages (from gdown>=4.0.0->nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==1.1.1b20240613->autogluon) (4.12.3)
Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /home/ci/opt/venv/lib/python3.10/site-packages (from numba->mlforecast<0.10.1,>=0.10.0->autogluon.timeseries==1.1.1b20240613->autogluon.timeseries[all]==1.1.1b20240613->autogluon) (0.43.0)
Requirement already satisfied: flatbuffers in /home/ci/opt/venv/lib/python3.10/site-packages (from onnxruntime>=1.11.0->optimum[onnxruntime]<1.19,>=1.17->autogluon.timeseries[all]==1.1.1b20240613->autogluon) (24.3.25)
Requirement already satisfied: annotated-types>=0.4.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from pydantic<3,>=1.7->gluonts==0.15.1->autogluon.timeseries==1.1.1b20240613->autogluon.timeseries[all]==1.1.1b20240613->autogluon) (0.7.0)
Requirement already satisfied: pydantic-core==2.18.4 in /home/ci/opt/venv/lib/python3.10/site-packages (from pydantic<3,>=1.7->gluonts==0.15.1->autogluon.timeseries==1.1.1b20240613->autogluon.timeseries[all]==1.1.1b20240613->autogluon) (2.18.4)
Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /home/ci/opt/venv/lib/python3.10/site-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.1.1b20240613->autogluon) (3.0.12)
Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.1.1b20240613->autogluon) (1.0.5)
Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.1.1b20240613->autogluon) (1.0.10)
Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /home/ci/opt/venv/lib/python3.10/site-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.1.1b20240613->autogluon) (2.0.8)
Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /home/ci/opt/venv/lib/python3.10/site-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.1.1b20240613->autogluon) (3.0.9)
Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /home/ci/opt/venv/lib/python3.10/site-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.1.1b20240613->autogluon) (8.2.4)
Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /home/ci/opt/venv/lib/python3.10/site-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.1.1b20240613->autogluon) (1.1.3)
Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /home/ci/opt/venv/lib/python3.10/site-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.1.1b20240613->autogluon) (2.4.8)
Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /home/ci/opt/venv/lib/python3.10/site-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.1.1b20240613->autogluon) (2.0.10)
Requirement already satisfied: weasel<0.5.0,>=0.1.0 in /home/ci/opt/venv/lib/python3.10/site-packages (from spacy<4->fastai<2.8,>=2.3.1->autogluon.tabular[all]==1.1.1b20240613->autogluon) (0.4.1)
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from autogluon.tabular import TabularDataset, TabularPredictor
Example Data¶
For this tutorial we will use a dataset from the cover story of Nature issue 7887: AI-guided intuition for math theorems. The goal is to predict a knot’s signature based on its properties. We sampled 10K training and 5K test examples from the original data. The sampled dataset make this tutorial run quickly, but AutoGluon can handle the full dataset if desired.
We load this dataset directly from a URL. AutoGluon’s TabularDataset is a subclass of pandas DataFrame, so any DataFrame methods can be used on TabularDataset as well.
data_url = 'https://raw.githubusercontent.com/mli/ag-docs/main/knot_theory/'
train_data = TabularDataset(f'{data_url}train.csv')
train_data.head()
| Unnamed: 0 | chern_simons | cusp_volume | hyperbolic_adjoint_torsion_degree | hyperbolic_torsion_degree | injectivity_radius | longitudinal_translation | meridinal_translation_imag | meridinal_translation_real | short_geodesic_imag_part | short_geodesic_real_part | Symmetry_0 | Symmetry_D3 | Symmetry_D4 | Symmetry_D6 | Symmetry_D8 | Symmetry_Z/2 + Z/2 | volume | signature | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 70746 | 0.090530 | 12.226322 | 0 | 10 | 0.507756 | 10.685555 | 1.144192 | -0.519157 | -2.760601 | 1.015512 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 11.393225 | -2 |
| 1 | 240827 | 0.232453 | 13.800773 | 0 | 14 | 0.413645 | 10.453156 | 1.320249 | -0.158522 | -3.013258 | 0.827289 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 12.742782 | 0 |
| 2 | 155659 | -0.144099 | 14.761030 | 0 | 14 | 0.436928 | 13.405199 | 1.101142 | 0.768894 | 2.233106 | 0.873856 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 15.236505 | 2 |
| 3 | 239963 | -0.171668 | 13.738019 | 0 | 22 | 0.249481 | 27.819496 | 0.493827 | -1.188718 | -2.042771 | 0.498961 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 17.279890 | -8 |
| 4 | 90504 | 0.235188 | 15.896359 | 0 | 10 | 0.389329 | 15.330971 | 1.036879 | 0.722828 | -3.056138 | 0.778658 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 16.749298 | 4 |
Our targets are stored in the “signature” column, which has 18 unique integers. Even though pandas didn’t correctly recognize this data type as categorical, AutoGluon will fix this issue.
label = 'signature'
train_data[label].describe()
count 10000.000000
mean -0.022000
std 3.025166
min -12.000000
25% -2.000000
50% 0.000000
75% 2.000000
max 12.000000
Name: signature, dtype: float64
Training¶
We now construct a TabularPredictor by specifying the label column name and then train on the dataset with TabularPredictor.fit(). We don’t need to specify any other parameters. AutoGluon will recognize this is a multi-class classification task, perform automatic feature engineering, train multiple models, and then ensemble the models to create the final predictor.
predictor = TabularPredictor(label=label).fit(train_data)
Show code cell output
No path specified. Models will be saved in: "AutogluonModels/ag-20240614_002801"
Verbosity: 2 (Standard Logging)
=================== System Info ===================
AutoGluon Version: 1.1.1b20240613
Python Version: 3.10.13
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Fri May 17 18:07:48 UTC 2024
CPU Count: 8
Memory Avail: 28.83 GB / 30.95 GB (93.2%)
Disk Space Avail: 209.41 GB / 255.99 GB (81.8%)
===================================================
No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets.
Recommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):
presets='best_quality' : Maximize accuracy. Default time_limit=3600.
presets='high_quality' : Strong accuracy with fast inference speed. Default time_limit=3600.
presets='good_quality' : Good accuracy with very fast inference speed. Default time_limit=3600.
presets='medium_quality' : Fast training time, ideal for initial prototyping.
Beginning AutoGluon training ...
AutoGluon will save models to "AutogluonModels/ag-20240614_002801"
Train Data Rows: 10000
Train Data Columns: 18
Label Column: signature
AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == int, but few unique label-values observed).
First 10 (of 13) unique label values: [-2, 0, 2, -8, 4, -4, -6, 8, 6, 10]
If 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])
Problem Type: multiclass
Preprocessing data ...
Warning: Some classes in the training set have fewer than 10 examples. AutoGluon will only keep 9 out of 13 classes for training and will not try to predict the rare classes. To keep more classes, increase the number of datapoints from these rare classes in the training data or reduce label_count_threshold.
Fraction of data from classes with at least 10 examples that will be kept for training models: 0.9984
Train Data Class Count: 9
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 29522.47 MB
Train Data (Original) Memory Usage: 1.37 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 5 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Stage 5 Generators:
Fitting DropDuplicatesFeatureGenerator...
Useless Original Features (Count: 1): ['Symmetry_D8']
These features carry no predictive signal and should be manually investigated.
This is typically a feature which has the same value for all rows.
These features do not need to be present at inference time.
Types of features in original data (raw dtype, special dtypes):
('float', []) : 14 | ['chern_simons', 'cusp_volume', 'injectivity_radius', 'longitudinal_translation', 'meridinal_translation_imag', ...]
('int', []) : 3 | ['Unnamed: 0', 'hyperbolic_adjoint_torsion_degree', 'hyperbolic_torsion_degree']
Types of features in processed data (raw dtype, special dtypes):
('float', []) : 9 | ['chern_simons', 'cusp_volume', 'injectivity_radius', 'longitudinal_translation', 'meridinal_translation_imag', ...]
('int', []) : 3 | ['Unnamed: 0', 'hyperbolic_adjoint_torsion_degree', 'hyperbolic_torsion_degree']
('int', ['bool']) : 5 | ['Symmetry_0', 'Symmetry_D3', 'Symmetry_D4', 'Symmetry_D6', 'Symmetry_Z/2 + Z/2']
0.1s = Fit runtime
17 features in original data used to generate 17 features in processed data.
Train Data (Processed) Memory Usage: 0.96 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.09s ...
AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.1, Train Rows: 8985, Val Rows: 999
User-specified model hyperparameters to be fit:
{
'NN_TORCH': {},
'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],
'CAT': {},
'XGB': {},
'FASTAI': {},
'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],
}
Fitting 13 L1 models ...
Fitting model: KNeighborsUnif ...
0.2232 = Validation score (accuracy)
0.05s = Training runtime
0.02s = Validation runtime
Fitting model: KNeighborsDist ...
0.2132 = Validation score (accuracy)
0.03s = Training runtime
0.01s = Validation runtime
Fitting model: NeuralNetFastAI ...
0.9459 = Validation score (accuracy)
9.94s = Training runtime
0.02s = Validation runtime
Fitting model: LightGBMXT ...
0.9459 = Validation score (accuracy)
4.27s = Training runtime
0.05s = Validation runtime
Fitting model: LightGBM ...
0.956 = Validation score (accuracy)
3.86s = Training runtime
0.03s = Validation runtime
Fitting model: RandomForestGini ...
0.9449 = Validation score (accuracy)
1.43s = Training runtime
0.08s = Validation runtime
Fitting model: RandomForestEntr ...
0.9499 = Validation score (accuracy)
1.8s = Training runtime
0.08s = Validation runtime
Fitting model: CatBoost ...
0.956 = Validation score (accuracy)
20.98s = Training runtime
0.0s = Validation runtime
Fitting model: ExtraTreesGini ...
0.9469 = Validation score (accuracy)
1.15s = Training runtime
0.08s = Validation runtime
Fitting model: ExtraTreesEntr ...
0.9429 = Validation score (accuracy)
1.15s = Training runtime
0.08s = Validation runtime
Fitting model: XGBoost ...
0.957 = Validation score (accuracy)
5.33s = Training runtime
0.07s = Validation runtime
Fitting model: NeuralNetTorch ...
0.9419 = Validation score (accuracy)
35.97s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBMLarge ...
0.9499 = Validation score (accuracy)
8.22s = Training runtime
0.07s = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
Ensemble Weights: {'NeuralNetFastAI': 0.333, 'RandomForestEntr': 0.333, 'RandomForestGini': 0.167, 'XGBoost': 0.167}
0.965 = Validation score (accuracy)
0.21s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 96.35s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 4122.1 rows/s (999 batch size)
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20240614_002801")
Model fitting should take a few minutes or less depending on your CPU. You can make training faster by specifying the time_limit argument. For example, fit(..., time_limit=60) will stop training after 60 seconds. Higher time limits will generally result in better prediction performance, and excessively low time limits will prevent AutoGluon from training and ensembling a reasonable set of models.
Prediction¶
Once we have a predictor that is fit on the training dataset, we can load a separate set of data to use for prediction and evaulation.
test_data = TabularDataset(f'{data_url}test.csv')
y_pred = predictor.predict(test_data.drop(columns=[label]))
y_pred.head()
Loaded data from: https://raw.githubusercontent.com/mli/ag-docs/main/knot_theory/test.csv | Columns = 19 / 19 | Rows = 5000 -> 5000
0 -4
1 0
2 0
3 4
4 2
Name: signature, dtype: int64
Evaluation¶
We can evaluate the predictor on the test dataset using the evaluate() function, which measures how well our predictor performs on data that was not used for fitting the models.
predictor.evaluate(test_data, silent=True)
{'accuracy': 0.9492,
'balanced_accuracy': 0.7476648119039666,
'mcc': 0.9377360375954512}
AutoGluon’s TabularPredictor also provides the leaderboard() function, which allows us to evaluate the performance of each individual trained model on the test data.
predictor.leaderboard(test_data)
| model | score_test | score_val | eval_metric | pred_time_test | pred_time_val | fit_time | pred_time_test_marginal | pred_time_val_marginal | fit_time_marginal | stack_level | can_infer | fit_order | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | WeightedEnsemble_L2 | 0.9492 | 0.964965 | accuracy | 0.819802 | 0.242352 | 18.699631 | 0.006168 | 0.000978 | 0.206938 | 2 | True | 14 |
| 1 | LightGBM | 0.9456 | 0.955956 | accuracy | 0.158733 | 0.029037 | 3.856032 | 0.158733 | 0.029037 | 3.856032 | 1 | True | 5 |
| 2 | XGBoost | 0.9448 | 0.956957 | accuracy | 0.469600 | 0.073006 | 5.327560 | 0.469600 | 0.073006 | 5.327560 | 1 | True | 11 |
| 3 | LightGBMLarge | 0.9444 | 0.949950 | accuracy | 0.433138 | 0.071398 | 8.222697 | 0.433138 | 0.071398 | 8.222697 | 1 | True | 13 |
| 4 | CatBoost | 0.9432 | 0.955956 | accuracy | 0.021801 | 0.003731 | 20.980770 | 0.021801 | 0.003731 | 20.980770 | 1 | True | 8 |
| 5 | RandomForestEntr | 0.9384 | 0.949950 | accuracy | 0.136356 | 0.075624 | 1.796609 | 0.136356 | 0.075624 | 1.796609 | 1 | True | 7 |
| 6 | NeuralNetFastAI | 0.9360 | 0.945946 | accuracy | 0.067896 | 0.016886 | 9.935997 | 0.067896 | 0.016886 | 9.935997 | 1 | True | 3 |
| 7 | ExtraTreesGini | 0.9360 | 0.946947 | accuracy | 0.202265 | 0.076256 | 1.147988 | 0.202265 | 0.076256 | 1.147988 | 1 | True | 9 |
| 8 | ExtraTreesEntr | 0.9358 | 0.942943 | accuracy | 0.227419 | 0.075732 | 1.149055 | 0.227419 | 0.075732 | 1.149055 | 1 | True | 10 |
| 9 | RandomForestGini | 0.9352 | 0.944945 | accuracy | 0.139782 | 0.075856 | 1.432528 | 0.139782 | 0.075856 | 1.432528 | 1 | True | 6 |
| 10 | LightGBMXT | 0.9320 | 0.945946 | accuracy | 0.252768 | 0.046458 | 4.272427 | 0.252768 | 0.046458 | 4.272427 | 1 | True | 4 |
| 11 | NeuralNetTorch | 0.9258 | 0.941942 | accuracy | 0.020241 | 0.008389 | 35.969113 | 0.020241 | 0.008389 | 35.969113 | 1 | True | 12 |
| 12 | KNeighborsDist | 0.2210 | 0.213213 | accuracy | 0.026492 | 0.014510 | 0.025824 | 0.026492 | 0.014510 | 0.025824 | 1 | True | 2 |
| 13 | KNeighborsUnif | 0.2180 | 0.223223 | accuracy | 0.026466 | 0.015305 | 0.053556 | 0.026466 | 0.015305 | 0.053556 | 1 | True | 1 |
Conclusion¶
In this quickstart tutorial we saw AutoGluon’s basic fit and predict functionality using TabularDataset and TabularPredictor. AutoGluon simplifies the model training process by not requiring feature engineering or model hyperparameter tuning. Check out the in-depth tutorials to learn more about AutoGluon’s other features like customizing the training and prediction steps or extending AutoGluon with custom feature generators, models, or metrics.