萧箫 发自 凹非寺
量子位 报道 | QbitAI
Python开源机器学习建模库PyCaret，刚刚发布了2.0版本。
这款堪称「调包侠神器」的模型训练工具包，几行代码就能搞定模型编写、改进和微调。
从数据预处理到模型效果对比，PyCaret都能自动实现。
所以，PyCaret长啥样，2.0的版本又做了什么改进？
一起来看看。
机器学习库的「炼丹炉」
PyCaret说白了，有点像一个机器学习库的炼丹炉。
以下是它「熔」进来的部分库：
数据处理：pandas、numpy…
数据可视化：matplotlib、seaborn…
各种模型：sklearn、xgboost、catboost、lightgbm…
嗯…sklearn直接就给封装进去了，调用很方便。
然后，PyCaret这个炼丹炉，自带功能“按键”（定义了一些函数），包括数据预处理、模型训练、模型集成、模型分析、模型测试等。
只需要写上几行Python代码，这些功能“按键”就会被按下，PyCaret自动帮你实现。
至于实现过程中需要调用什么基本库，那些可以放弃思考不需要考虑。
从下图来看，仅仅是预处理阶段，就包含样本划分、数据预处理、缺失值处理、归一化、独热编码等功能。
如果要实现必需的预处理功能，需要多少行代码来调用？
答案是0行。
因为，当使用setup()进行初始化时，PyCaret将自动执行机器学习必需的数据预处理步骤，包括缺失值插入、分类变量编码、标签编码、数据集拆分等。
例如，在数据处理前，你发现数据集有空缺的地方（下图中NaN部分）。
别怕，PyCaret会自动分析数据，进行缺失值插入。
预处理后，PyCaret还贴心地帮你准备了各种模型。
从模型训练、选用到测试，只有你想不到的，没有它做不了的。
如果已经将数据处理好，并交给PyCaret，一个compare_models函数就能训练库中的所有模型，进行结果比较后，标出最佳模型。
如下图，各种模型指标的最优值会被一键标黄，就看你怎么选择了。
选好后，想对模型进行一点优化？一个tune_model函数就能帮你搞定。
或者，不想仅仅选用一个模型？
PyCaret也准备了模型集成的函数，blend和stack任你选。
除此之外，模型参数的分析（包括可视化）也只需要几行代码就能实现，功能非常强大。
最后，PyCaret还能为新数据提供迭代预测结果，下面的效果，同样只需要几行代码就能完成。
那么，这次PyCaret增强，进行了什么改进呢？（项目见传送门）
PyCaret 2.0增强版
这是PyCaret 2.0的6大特色，有些在1.0就有了，有些功能如实验日志，看起来是更新后新加入的功能。
实验日志，对于模型的调整不可或缺。
例如，想要将训练过程中模型的精度变化可视化，通常我们会在模型中加入生成日志文件的函数，生成一个更直观的时间-精度变化图。
PyCaret 2.0加入了实验日志的功能，自动帮你跟踪模型实验过程中的各项指标，以及生成视觉效果等。
不仅如此，在2.0中，模型生成到预测的所有工作流程，现在可以被设计了。
也就是说，你可以设置一条自定义「流水线」，在这个过程中，从训练到测试，所有模块的功能都会被自动化完成。
甚至，PyCaret 2.0还提供了机器学习模型前端软件的搭建工具。
以及，PyCaret 2.0现在几乎支持所有算法的并行处理，xgboost和catboost模型也支持GPU训练。
除此之外，还有一些新的程序功能，等待你去发现。
传送门
项目地址：
https://github.com/pycaret/pycaret/releases/tag/2.0
参考链接：
https://towardsdatascience.com/announcing-pycaret-2-0-39c11014540e
end
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