DFFML Models For scikit / sklearn

关于

使用scikit创建的模型。

安装

$ python3 -m pip install --user dffml-model-scikit

用法

1. 线性回归模型

要在一个数据集上实现线性回归，让我们来看一个简单的例子

$ cat > train.csv << EOF
Years,Expertise,Trust,Salary
0,1,0.2,10
1,3,0.4,20
2,5,0.6,30
3,7,0.8,40
EOF
$ cat > test.csv << EOF
Years,Expertise,Trust,Salary
4,9,1.0,50
5,11,1.2,60
EOF
$ dffml train \
    -model scikitlr \
    -model-features Years:int:1 Expertise:int:1 Trust:float:1 \
    -model-predict Salary \
    -model-directory tempdir \
    -sources f=csv \
    -source-filename train.csv \
    -source-readonly \
    -log debug
$ dffml accuracy \
    -model scikitlr \
    -model-features Years:int:1 Expertise:int:1 Trust:float:1 \
    -model-predict Salary \
    -model-directory tempdir \
    -sources f=csv \
    -source-filename test.csv \
    -source-readonly \
    -log debug
$ echo -e 'Years,Expertise,Trust\n6,13,1.4\n' | \
  dffml predict all \
    -model scikitlr \
    -model-features Years:int:1 Expertise:int:1 Trust:float:1 \
    -model-predict Salary \
    -model-directory tempdir \
    -sources f=csv \
    -source-filename /dev/stdin \
    -source-readonly \
    -log debug

许可证

Scikit模型根据MIT许可证条款分发。