from lightautoml.automl.presets.tabular_presets import TabularAutoML, TabularUtilizedAutoML returns error #147
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Hello @numomcmc! The problem is that A temporary solution is to install LightAutoML from the |
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Hello Peter
Thank you for your reply. I am having all kinds of problems using LAMA.
The same call made with TabularAutoML results the following err msg with
TabularUtilizedAutoML. So I'd like to have a clean reinstall to make sure
everything is current. The link to the notebook returns a "No results
matched your search", could you check that? I am unfortunately running
Conda on Windows 11, so I hope the new version and the installation play
nice together.
Thank you and I look forward to hearing from you.
Stefan
AttributeError: 'TabularUtilizedAutoML' object has no attribute 'reader'
…On Wed, Jan 10, 2024 at 2:04 AM PeterBel ***@***.***> wrote:
Hello @numomcmc <https://github.com/numomcmc>! The problem is that pip
installs an old version of LightAutoML (*<0.3.0*). By default, kaggle
uses python3.10 and the latest stable versions of LightAutoML are not
seen for pip3.10. We are working on this.
A temporary solution is to install LightAutoML from the
lightautoml-0.3.8-py3-none-any.whl
<https://www.kaggle.com/datasets/alexryzhkov/lightautoml-v0-3-8> file
provided by @alexmryzhkov <https://github.com/alexmryzhkov>. See the
notebook <https://github.com/sb-ai-lab/LightAutoML/issues/url> for an
example.
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Stefan, hello once again! I can not figure out why url is not working, so I will provide the code snippet here.
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UpdateNow, you can install LightAutoML on |
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Hi Peter
The new LAMA doesn't play nice with python 3.9.x, so I ran my notebook on
Kaggle w/ 3.10, and it was a success. Thank you for your swift response!
In the meantime, a question about GPU support. Both LightGBM and Catboost
can utilize GPU via CUDA. However, LAMA seems to have its own version of
Catboost and LightGBM packaged together, and the fact Github page indicated
that GPU support is conditionally available, it suggests to me that I can
not run LightGBM on GPU via parameters (same for Catboost) the usual way.
Is that the correct understanding?
Thank you for your support.
S
…On Thu, Jan 11, 2024 at 12:25 AM PeterBel ***@***.***> wrote:
Update
Now, you can install LightAutoML on python3.10 by directly specifying the
version0.3.8.b1,
i.e pip install -U lightautoml==0.3.8b1
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Hello, Stephen. LightGBM and Catboost automatically switch to GPU, if available. Moreover, there is an explicit GPU implementation of LightAutoML. |
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Hi Peter
A few follow up questions.
1. I would like to give LightAutoML_GPU a try, is the GPU version in synch
with the regular LAMA? Meaning, it's now based 0.3.8b1?
2. Also, I like to learn more about how to use LAMA to tune
hyperparameters. The YouTube video talked about how LAMA tunes
hyperparameters, but there is no mention of how to actually do it. There
is hardly anything about hyperparameter tuning in the documentation as
well. Could you provide more information?
3. In the Kaggle Home Price regression example, as well as the
documentation (
https://lightautoml.readthedocs.io/en/latest/_modules/lightautoml/automl/presets/tabular_presets.html#).
There are mentions of "lgb_tuned" and "cb_tuned". So I assume that allows
users to specify hyperparameter tuning as a part of the training. But how
does LAMA actually tune them? Which parameters are tuned, to what range of
values? Are the user supplied parameter values considered as the starting
value? What are the final resulting hyperparameter values? How does the
"optimized" validation compare to non-optimized results, where can I find
the comparison? Are they available in the report?
Many thanks for your help.
Stefan
…On Fri, Jan 12, 2024 at 7:44 AM PeterBel ***@***.***> wrote:
Hello, Stephen. Yes, this LAMA is developed only for *CPU* computations.
However, there is an explicit *GPU* implementation of LightAutoML that
could be run on a *GPU* with CUDA.
Check LightAutoML_GPU <https://github.com/sb-ai-lab/LightAutoML_GPU>.
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🐛 Bug
To Reproduce
Steps to reproduce the behavior:
!pip install -U LightAutoMLERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
yellowbrick 1.3.post1 requires numpy<1.20,>=1.16.0, but you have numpy 1.21.6 which is incompatible.
tensorflow 2.4.1 requires numpy~=1.19.2, but you have numpy 1.21.6 which is incompatible.
pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.4.2 which is incompatible.
matrixprofile 1.1.10 requires protobuf==3.11.2, but you have protobuf 3.17.3 which is incompatible.
kornia 0.5.5 requires numpy<=1.19, but you have numpy 1.21.6 which is incompatible.
imbalanced-learn 0.8.0 requires scikit-learn>=0.24, but you have scikit-learn 0.23.2 which is incompatible.
Successfully installed CairoSVG-2.7.1 LightAutoML-0.3.7.3 Pyphen-0.14.0 StrEnum-0.4.15 alabaster-0.7.13 autowoe-1.3.2 cairocffi-1.6.1 catboost-1.2.2 cssselect2-0.7.0 dask-2022.2.0 distributed-2022.2.0 efficientnet-pytorch-0.7.1 featuretools-1.11.1 holidays-0.27.1 imagesize-1.4.1 importlib-metadata-1.7.0 json2html-1.3.0 numpy-1.21.6 opencv-python-4.5.2.52 pandas-1.3.5 poetry-core-1.6.1 snowballstemmer-2.2.0 sphinx-4.3.2 sphinxcontrib-applehelp-1.0.2 sphinxcontrib-devhelp-1.0.2 sphinxcontrib-htmlhelp-2.0.0 sphinxcontrib-jsmath-1.0.1 sphinxcontrib-qthelp-1.0.3 sphinxcontrib-serializinghtml-1.1.5 tinycss2-1.2.1 tqdm-4.66.1 weasyprint-52.5 woodwork-0.16.4
WARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv
from lightautoml.automl.presets.tabular_presets import TabularAutoML, TabularUtilizedAutoMLTypeError Traceback (most recent call last)
in
11
12 # LightAutoML presets, task and report generation
---> 13 from lightautoml.automl.presets.tabular_presets import TabularAutoML, TabularUtilizedAutoML
14 from lightautoml.tasks import Task
15 from lightautoml.report.report_deco import ReportDeco
/opt/conda/lib/python3.7/site-packages/lightautoml/automl/presets/tabular_presets.py in
21 from tqdm import tqdm
22
---> 23 from ...addons.utilization import TimeUtilization
24 from ...dataset.np_pd_dataset import NumpyDataset
25 from ...ml_algo.boost_cb import BoostCB
/opt/conda/lib/python3.7/site-packages/lightautoml/addons/utilization/init.py in
1 """Tools to configure resources utilization."""
----> 2 from .utilization import TimeUtilization
3
4
5 all = ["TimeUtilization"]
/opt/conda/lib/python3.7/site-packages/lightautoml/addons/utilization/utilization.py in
11 from typing import Union
12
---> 13 from ...automl.base import AutoML
14 from ...automl.blend import BestModelSelector
15 from ...automl.blend import Blender
/opt/conda/lib/python3.7/site-packages/lightautoml/automl/base.py in
10 from typing import Sequence
11
---> 12 from ..dataset.base import LAMLDataset
13 from ..dataset.utils import concatenate
14 from ..pipelines.ml.base import MLPipeline
/opt/conda/lib/python3.7/site-packages/lightautoml/dataset/base.py in
10 from typing import Union
11
---> 12 from ..tasks.base import Task
13 from .roles import ColumnRole
14
/opt/conda/lib/python3.7/site-packages/lightautoml/tasks/init.py in
1 """Define the task to solve its loss, metric."""
2
----> 3 from .base import Task
4
5
/opt/conda/lib/python3.7/site-packages/lightautoml/tasks/base.py in
16 from .common_metric import _valid_metric_args
17 from .common_metric import _valid_str_metric_names
---> 18 from .losses import CBLoss
19 from .losses import LGBLoss
20 from .losses import SKLoss
/opt/conda/lib/python3.7/site-packages/lightautoml/tasks/losses/init.py in
3 from .base import _valid_str_metric_names
4 from .cb import CBLoss
----> 5 from .lgb import LGBLoss
6 from .sklearn import SKLoss
7 from .torch import TORCHLoss
/opt/conda/lib/python3.7/site-packages/lightautoml/tasks/losses/lgb.py in
10 from typing import Union
11
---> 12 import lightgbm as lgb
13 import numpy as np
14
/opt/conda/lib/python3.7/site-packages/lightgbm/init.py in
6 import os
7
----> 8 from .basic import Booster, Dataset, register_logger
9 from .callback import early_stopping, print_evaluation, record_evaluation, reset_parameter
10 from .engine import CVBooster, cv, train
/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py in
15 import scipy.sparse
16
---> 17 from .compat import PANDAS_INSTALLED, concat, dt_DataTable, is_dtype_sparse, pd_DataFrame, pd_Series
18 from .libpath import find_lib_path
19
/opt/conda/lib/python3.7/site-packages/lightgbm/compat.py in
113 from dask import delayed
114 from dask.array import Array as dask_Array
--> 115 from dask.dataframe import DataFrame as dask_DataFrame
116 from dask.dataframe import Series as dask_Series
117 from dask.distributed import Client, default_client, wait
/opt/conda/lib/python3.7/site-packages/dask/dataframe/init.py in
1 try:
2 from ..base import compute
----> 3 from . import backends, dispatch, rolling
4 from .core import (
5 DataFrame,
/opt/conda/lib/python3.7/site-packages/dask/dataframe/backends.py in
21
22 from ..utils import is_arraylike, typename
---> 23 from .core import DataFrame, Index, Scalar, Series, _Frame
24 from .dispatch import (
25 categorical_dtype_dispatch,
/opt/conda/lib/python3.7/site-packages/dask/dataframe/core.py in
79 no_default = "no_default"
80
---> 81 pd.set_option("compute.use_numexpr", False)
82
83
/opt/conda/lib/python3.7/site-packages/pandas/_config/config.py in call(self, *args, **kwds)
231 # class below which wraps functions inside a callable, and converts
232 # doc into a property function. The doctsrings below are templates
--> 233 # using the py2.6+ advanced formatting syntax to plug in a concise list
234 # of options, and option descriptions.
235
/opt/conda/lib/python3.7/site-packages/pandas/_config/config.py in _set_option(*args, **kwargs)
139 if o and o.validator:
140 o.validator(v)
--> 141
142 # walk the nested dict
143 root, k = _get_root(key)
/opt/conda/lib/python3.7/site-packages/pandas/core/config_init.py in use_numexpr_cb(key)
48
49
---> 50 def use_numexpr_cb(key):
51 from pandas.core.computation import expressions
52
/opt/conda/lib/python3.7/site-packages/pandas/core/computation/expressions.py in
17 from pandas._typing import FuncType
18
---> 19 from pandas.core.computation.check import NUMEXPR_INSTALLED
20 from pandas.core.ops import roperator
21
/opt/conda/lib/python3.7/site-packages/pandas/core/computation/check.py in
1 from pandas.compat._optional import import_optional_dependency
2
----> 3 ne = import_optional_dependency("numexpr", errors="warn")
4 NUMEXPR_INSTALLED = ne is not None
5 if NUMEXPR_INSTALLED:
TypeError: import_optional_dependency() got an unexpected keyword argument 'errors'
You can duplicate this behavior by running the notebook here
Expected behavior
Installation with compatibility via pip is not unusual. Maybe introduce a conda installation procedure?
from lightautoml.automl.presets.tabular_presets import TabularAutoML, TabularUtilizedAutoMLshouldn't return errors...
Additional context
Checklist
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