LightAutoML
v0.3.0
Lightautoml（Lama）是Sber AI实验室的汽车框架。
它为以下任务提供了自动模型创建：
该软件包的当前版本处理每行具有独立样本的数据集。即每一行都是具有其特定功能和目标的对象。多功能数据集和序列正在进行中:)
注意：我们使用AutoWoE库自动创建可解释的模型。
作者：Alexander Ryzhkov，Anton Vakhrushev，Dmitry Simakov，Vasilii Bunakov，Rinchin Damdinov，Pavel Shvets，Alexander Kirilin。
Lightautoml的文档可在此处获得，您也可以生成它。
目前可用于开发人员测试的Lightautoml的完整GPU管道（仍在进行中）。这里可用的代码和教程
要通过PYPI在计算机上安装LAMA框架，请执行以下命令：
# Install base functionality:
pip install -U lightautoml
# For partial installation use corresponding option.
# Extra dependecies: [nlp, cv, report]
# Or you can use 'all' to install everything
pip install -U lightautoml[nlp]
额外，运行以下命令以启用PDF报告生成：
# MacOS
brew install cairo pango gdk-pixbuf libffi
# Debian / Ubuntu
sudo apt-get install build-essential libcairo2 libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf2.0-0 libffi-dev shared-mime-info
# Fedora
sudo yum install redhat-rpm-config libffi-devel cairo pango gdk-pixbuf2
# Windows
# follow this tutorial https://weasyprint.readthedocs.io/en/stable/install.html#windows回到顶部
让我们在下面解决流行的Kaggle Titanic竞赛。使用Lightautoml有两种主要方法来解决机器学习问题：
import pandas as pd
from sklearn . metrics import f1_score
from lightautoml . automl . presets . tabular_presets import TabularAutoML
from lightautoml . tasks import Task
df_train = pd . read_csv ( '../input/titanic/train.csv' )
df_test = pd . read_csv ( '../input/titanic/test.csv' )
automl = TabularAutoML (
task = Task (
name = 'binary' ,
metric = lambda y_true , y_pred : f1_score ( y_true , ( y_pred > 0.5 ) * 1 ))
)
oof_pred = automl . fit_predict (
df_train ,
roles = { 'target' : 'Survived' , 'drop' : [ 'PassengerId' ]}
)
test_pred = automl . predict ( df_test )
pd . DataFrame ({
'PassengerId' : df_test . PassengerId ,
'Survived' : ( test_pred . data [:, 0 ] > 0.5 ) * 1
}). to_csv ( 'submit.csv' , index = False )Lighautoml Framework具有许多现成的零件和广泛的自定义选项，以了解更多信息，请查看资源部分。
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Tutorial_1_basics.ipynb从表格数据上开始使用Lightautoml。Tutorial_2_WhiteBox_AutoWoE.ipynb创建可解释的模型。Tutorial_3_sql_data_source.ipynb展示了如何使用lightautoml预设（既独立和使用的变体）来从SQL数据库而不是CSV求解ML任务。Tutorial_4_NLP_Interpretation.ipynb使用tabularnlpautoml预设，limetextexplainer的示例。Tutorial_5_uplift.ipynb显示了如何使用Lightautoml进行提升模型任务。Tutorial_6_custom_pipeline.ipynb显示如何从指定块中创建自己的管道：用于特征生成和功能选择的管道，ML算法，超参数优化等。Tutorial_7_ICE_and_PDP_interpretation.ipynb显示了如何使用ICE和PDP方法获得模型结果的本地和全局解释。注1 ：对于生产，您无需使用Profiler（这会增加工作时间和内存完善），因此请不要打开它 - 默认情况下处于OFF状态
注2 ：要在运行后查看此报告，请用报告删除命令对演示的最后一行。
Lightautoml碰撞课程：
视频指南：
论文：
有关Lightautoml的文章：
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如果您有兴趣为Lightautoml做出贡献，请阅读《入门贡献指南》。
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该项目是根据Apache许可证的2.0版获得许可的。有关更多详细信息，请参见许可证文件。
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首先，您需要安装GIT和诗歌。
# Load LAMA source code
git clone https://github.com/sberbank-ai-lab/LightAutoML.git
cd LightAutoML/
# !!!Choose only one item!!!
# 1. Global installation: Don't create virtual environment
poetry config virtualenvs.create false --local
# 2. Recommended: Create virtual environment inside your project directory
poetry config virtualenvs.in-project true
# For more information read poetry docs
# Install LAMA
poetry lock
poetry install import pandas as pd
from sklearn . metrics import f1_score
from lightautoml . automl . presets . tabular_presets import TabularAutoML
from lightautoml . tasks import Task
df_train = pd . read_csv ( '../input/titanic/train.csv' )
df_test = pd . read_csv ( '../input/titanic/test.csv' )
# define that machine learning problem is binary classification
task = Task ( "binary" )
reader = PandasToPandasReader ( task , cv = N_FOLDS , random_state = RANDOM_STATE )
# create a feature selector
model0 = BoostLGBM (
default_params = { 'learning_rate' : 0.05 , 'num_leaves' : 64 ,
'seed' : 42 , 'num_threads' : N_THREADS }
)
pipe0 = LGBSimpleFeatures ()
mbie = ModelBasedImportanceEstimator ()
selector = ImportanceCutoffSelector ( pipe0 , model0 , mbie , cutoff = 0 )
# build first level pipeline for AutoML
pipe = LGBSimpleFeatures ()
# stop after 20 iterations or after 30 seconds
params_tuner1 = OptunaTuner ( n_trials = 20 , timeout = 30 )
model1 = BoostLGBM (
default_params = { 'learning_rate' : 0.05 , 'num_leaves' : 128 ,
'seed' : 1 , 'num_threads' : N_THREADS }
)
model2 = BoostLGBM (
default_params = { 'learning_rate' : 0.025 , 'num_leaves' : 64 ,
'seed' : 2 , 'num_threads' : N_THREADS }
)
pipeline_lvl1 = MLPipeline ([
( model1 , params_tuner1 ),
model2
], pre_selection = selector , features_pipeline = pipe , post_selection = None )
# build second level pipeline for AutoML
pipe1 = LGBSimpleFeatures ()
model = BoostLGBM (
default_params = { 'learning_rate' : 0.05 , 'num_leaves' : 64 ,
'max_bin' : 1024 , 'seed' : 3 , 'num_threads' : N_THREADS },
freeze_defaults = True
)
pipeline_lvl2 = MLPipeline ([ model ], pre_selection = None , features_pipeline = pipe1 ,
post_selection = None )
# build AutoML pipeline
automl = AutoML ( reader , [
[ pipeline_lvl1 ],
[ pipeline_lvl2 ],
], skip_conn = False )
# train AutoML and get predictions
oof_pred = automl . fit_predict ( df_train , roles = { 'target' : 'Survived' , 'drop' : [ 'PassengerId' ]})
test_pred = automl . predict ( df_test )
pd . DataFrame ({
'PassengerId' : df_test . PassengerId ,
'Survived' : ( test_pred . data [:, 0 ] > 0.5 ) * 1
}). to_csv ( 'submit.csv' , index = False )回到顶部
在Slack Community或Telegram Group中寻求及时的建议。
打开有关GitHub问题的错误报告和功能请求。
