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README
Apache-2.0

English | 简体中文

AutoX是什么?

AutoX一个高效的自动化机器学习工具,它主要针对于表格类型的数据挖掘竞赛。 它的特点包括:

  • 效果出色: AutoX在多个kaggle数据集上,效果显著优于其他解决方案(见效果对比)。
  • 简单易用: AutoX的接口和sklearn类似,方便上手使用。
  • 通用: 适用于分类和回归问题。
  • 自动化: 无需人工干预,全自动的数据清洗、特征工程、模型调参等步骤。
  • 灵活性: 各组件解耦合,能单独使用,对于自动机器学习效果不满意的地方,可以结合专家知识,AutoX提供灵活的接口。
  • 比赛上分点总结:整理并公开历史比赛的上分点。

目录

安装

1. git clone https://github.com/4paradigm/autox.git
2. cd autox
3. python setup.py install

架构

├── autox
│   ├── ensemble
│   ├── feature_engineer
│   ├── feature_selection
│   ├── file_io
│   ├── join_tables
│   ├── metrics
│   ├── models
│   ├── process_data
│   └── util.py
│   ├── CONST.py
│   ├── autox.py
├── run_oneclick.py
└── demo
└── test
├── setup.py
├── README.md

快速上手

单表数据集

Kaggle_Santander-autox解决方案-colab

多表数据集

时序数据集

2021阿里云供应链大赛-autox解决方案-colab

效果对比:

data_type data_name(link) metric single-table or multi-table AutoX AutoGluon H2o
binary classification single-table Springleaf auc 0.78865 0.61141 0.78186
binary classification single-table stumbleupon auc 0.87177 0.81025 0.79039
binary classification single-table santander auc 0.89196 0.64643 0.88775
binary classification single-table Titanic accuracy 0.77751 0.78229 0.79186
binary classification multi-table IEEE accuracy 0.920809 0.724925 0.907818
binary classification single-table categorical_feature_encoding_challenge auc 0.66070 0.79609
regression single-table ventilator mae 0.755 8.434 4.221
regression single-table Allstate Claims Severity mae 1137.07885 1173.35917 1163.12014
regression single-table zhidemai mse 1.0034 1.9466 1.1927
regression single-table Tabular Playground Series - Aug 2021 rmse 7.87731 10.3944 7.8895
regression single-table House Prices rmse 0.13043 0.13104 0.13161
regression single-table Restaurant Revenue rmse 2133204.32146 31913829.59876 28958013.69639
regression multi-table Elo Merchant Category Recommendation rmse 3.72228 3.80801 22.88899
regression-ts single-table Demand Forecasting smape 13.79241 25.39182 18.89678
regression-ts multi-table Walmart Recruiting wmae 4660.99174 5024.16179 5128.31622
regression-ts multi-table Rossmann Store Sales RMSPE 0.14230 0.20453 0.35757

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AutoX社区

数据类型

  • cat: Categorical,类别型无序变量
  • ord: Ordinal,类别型有序变量
  • num: Numeric,连续型变量
  • datetime: Datetime型时间变量
  • timestamp: imestamp型时间变量

表关系

"relations": [ # 表关系(可以包含为1-1, 1-M, M-1, M-M四种)
        {
            "related_to_main_table": "true", # 是否为和主表的关系
            "left_entity": "overdue",  # 左表名字
            "left_on": ["new_user_id"],  # 左表拼表键
            "right_entity": "userinfo",  # 右表名字
            "right_on": ["new_user_id"], # 右表拼表键
            "type": "1-1" # 左表与右表的连接关系
        },
        {
            "related_to_main_table": "true",
            "left_entity": "overdue",
            "left_on": ["new_user_id"],
            "left_time_col": "flag1",
            "right_entity": "bank",
            "right_on": ["new_user_id"],
            "right_time_col": "flag1",
            "type": "1-M"
        },
        {
            "related_to_main_table": "true",
            "left_entity": "overdue",
            "left_on": ["new_user_id"],
            "left_time_col": "flag1",
            "right_entity": "browse",
            "right_on": ["new_user_id"],
            "right_time_col": "flag1",
            "type": "1-M"
        },
        {
            "related_to_main_table": "true",
            "left_entity": "overdue",
            "left_on": ["new_user_id"],
            "left_time_col": "flag1",
            "right_entity": "bill",
            "right_on": ["new_user_id"],
            "right_time_col": "flag1",
            "type": "1-M"
        }
    ]

pipeline的逻辑

  • 1.初始化AutoX类
1.1 读数据
1.2 合并train和test
1.3 识别数据表中列的类型
1.4 数据预处理
  • 2.特征工程
特征工程包含单表特征和多表特征。
每一个特征工程类都包含以下功能:
    一、自动筛选要执行当前操作的特征;
    二、查看筛选出来的特征
    三、修改要执行当前操作的特征
    四、执行特征数据的计算,返回和主表样本条数以及顺序一致的特征
  • 3.特征合并
将构造出来的特征进行合并,行数不变,列数增加,返回大的宽表
  • 4.训练集和测试集的划分
将宽表划分成训练集和测试集
  • 5.特征过滤
通过train和test的特征列数据分布情况,对构造出来的特征进行过滤,避免过拟合
  • 6.模型训练
利用过滤后的宽表特征对模型进行训练
模型类提供功能包括:
    一、查看模型默认参数;
    二、模型训练;
    三、模型调参;
    四、查看模型对应的特征重要性;
    五、模型预测
  • 7.模型预测

AutoX类

AutoX类自动为用户管理数据集和数据集信息。
初始化AutoX类之后会执行以下操作:
一、读数据;
二、合并train和test;
三、识别数据表中列的类型;
四、数据预处理。

属性

info_: info_属性用于保存数据集的信息。

  • info_['id']: List,用于标识样本的唯一Key
  • info_['target']: String,用于标识数据表的标签列
  • info_['shape_of_train']: Int,train数据集的数据样本条数
  • info_['shape_of_test']: Int,test数据集的数据样本条数
  • info_['feature_type']: Dict of Dict,标识数据表中特征列的数据类型
  • info_['train_name']: String,用于训练集主表表名
  • info_['test_name']: String,用于测试集主表表名

dfs_: dfs_属性用于保存所有的DataFrame,包含原始表数据和构造的表数据。

  • dfs_['train_test']: train数据和test数据合并后的数据
  • dfs_['FE_feature_name']:特征工程所构造出的数据,例如FE_count,FE_groupby
  • dfs_['FE_all']:原始特征和所有特征工程合并后的数据集

方法

  • concat_train_test: 将训练集和测试集拼接起来,一般在读取数据之后执行
  • split_train_test: 将训练集和测试集分开,一般在完成特征工程之后执行
  • get_submit: 获得预测结果(中间过程执行了完成的机器学习pipeline,包括数据预处理,特征工程,模型训练,模型调参,模型融合,模型预测等)

AutoX的pipeline中的操作对应的具体细节:

读数据

读取给定路径下的所有文件。默认情况下,会将训练集主表和测试集主表进行拼接,
再进行后续的数据预处理以及特征工程等操作,并在模型预测开始前,将训练集和测试进行拆分。

数据预处理

- 对时间列解析年, 月, 日, 时、星期几等信息
- 在每次训练前,会对输入到模型的数据删除无效(nunique为1)的特征
- 去除异常样本,去除label为nan的样本

特征工程

  • 1-1拼表特征

  • 1-M拼表特征
- time diff特征
- 聚合统计类特征
  • count特征
对要操作的特征列,将全体数据集中,和当前样本特征属性一致的样本计数作为特征
  • target encoding特征

  • 统计类特征

使用两层for训练提取统计类特征。
第一层for循环遍历所有筛选出来的分组特征(group_col),
第二层for循环遍历所有筛选出来的聚合特征(agg_col),
在第二层for循环中,
若遇到类别型特征,计算的统计特征为nunique,
若遇到数值型特征,计算的统计特征包括[median, std, sum, max, min, mean].
  • shift特征

模型训练

AutoX目前支持以下模型:
1. Lightgbm
2. Xgboost
3. TabNet

模型融合

AutoX支持的模型融合方式包括一下两种,默认情况下,使用Bagging的方式进行融合。
1. Stacking;
2. Bagging。

比赛上分点总结:

比赛 magics
kaggle criteo 对于nunique很大的特征列,进行分桶操作。例如,对于nunique大于10000的特征,做hash后截断保留4位,再进行label_encode。
zhidemai article_id隐含了时间信息,增加article_id的排序特征。例如,groupby(['date'])['article_id'].rank()。
kaggle StumbleUpon 以文本列特征作为输入,使用Bert模型进行训练。
kaggle ventilator 对breath_id聚合的shift、diff、cumsum特征
kaggle Santander 识别出fake test,剔除之后再和train合并,构造全局的count特征。识别的方法:真实的样本至少有一个特征对应的值是全局唯一的,而fake的样本没有全局唯一的特征值。参考: List of Fake Samples and Public/Private LB split
kaggle Allstate Claims Severity label取log1p后训练模型,获得结果后取expm1,mae能降低35+

错误排查

错误信息 解决办法
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