Use AutoGluon in Kaggle Kernels

A majority of recent Kaggle competitions require to submit via notebooks.

We could use !pip install autogluon, but got the a bunch of errors, even just importing the tabular task. For example

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-17-6f7d1b4fed2f> in <module>
----> 1 from autogluon import TabularPrediction as task

/opt/conda/lib/python3.7/site-packages/autogluon/__init__.py in <module>
     15 logger.setLevel(logging.ERROR)
     16 
---> 17 from .utils.try_import import *
     18 try_import_mxnet()
     19 

/opt/conda/lib/python3.7/site-packages/autogluon/utils/__init__.py in <module>
     18 from .util_decorator import classproperty
     19 from .custom_process import CustomProcess
---> 20 from .openml_download import *
     21 

/opt/conda/lib/python3.7/site-packages/autogluon/utils/openml_download.py in <module>
      1 import os
      2 import numpy as np
----> 3 from sklearn.impute import SimpleImputer
      4 from sklearn.model_selection import train_test_split
      5 

/opt/conda/lib/python3.7/site-packages/sklearn/impute/__init__.py in <module>
      2 
      3 from ._base import MissingIndicator, SimpleImputer
----> 4 from ._knn import KNNImputer
      5 
      6 __all__ = [

/opt/conda/lib/python3.7/site-packages/sklearn/impute/_knn.py in <module>
      7 from ._base import _BaseImputer
      8 from ..utils.validation import FLOAT_DTYPES
----> 9 from ..metrics import pairwise_distances_chunked
     10 from ..metrics.pairwise import _NAN_METRICS
     11 from ..neighbors._base import _get_weights

/opt/conda/lib/python3.7/site-packages/sklearn/metrics/__init__.py in <module>
      5 
      6 
----> 7 from ._ranking import auc
      8 from ._ranking import average_precision_score
      9 from ._ranking import coverage_error

/opt/conda/lib/python3.7/site-packages/sklearn/metrics/_ranking.py in <module>
     33 from ..utils.sparsefuncs import count_nonzero
     34 from ..exceptions import UndefinedMetricWarning
---> 35 from ..preprocessing import label_binarize
     36 from ..preprocessing._label import _encode
     37 

/opt/conda/lib/python3.7/site-packages/sklearn/preprocessing/__init__.py in <module>
      6 from ._function_transformer import FunctionTransformer
      7 
----> 8 from ._data import Binarizer
      9 from ._data import KernelCenterer
     10 from ._data import MinMaxScaler

/opt/conda/lib/python3.7/site-packages/sklearn/preprocessing/_data.py in <module>
     34 from ._csr_polynomial_expansion import _csr_polynomial_expansion
     35 
---> 36 from ._encoders import OneHotEncoder
     37 
     38 BOUNDS_THRESHOLD = 1e-7

/opt/conda/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py in <module>
      8 from ..base import BaseEstimator, TransformerMixin
      9 from ..utils import check_array
---> 10 from ..utils.fixes import _argmax
     11 from ..utils.validation import check_is_fitted
     12 

ImportError: cannot import name '_argmax' from 'sklearn.utils.fixes' (/opt/conda/lib/python3.7/site-packages/sklearn/utils/fixes.py)

Several competitions don’t allow use Internet, which means we cannot pip install. Instead, we should submit a PR to add autogluon at https://github.com/Kaggle/docker-python/blob/master/Dockerfile#L129 and

[Innixma]

For your reference.

I was also in trouble with a similar problem. The following is one example of the solution.

https://www.kaggle.com/daikikatsuragawa/a-beginner-s-guide-to-autogluon

Awesome! Thanks for giving an example.

One note (and unfortunately I expect this is going to happen a lot, so is the way of optional dependencies in python…):

!pip install autogluon.tabular only installs a minimal install of Tabular, with most of the models missing.

You’ll want to do !pip install autogluon.tabular[all] to get all the models.

[Innixma]

Closing as it appears there are no issues with installing AutoGluon on Kaggle as of v0.1 and v0.2.