An easy-to-use ML framework
Project description
classicML: 简单易用的经典机器学习框架
classicML 是一个用Python和C++混编的机器学习项目,它既实现了Python的简单易用快速上手,又实现了C++的高效性能。classicML的设计目标是简单易用,快速入门,编程风格简洁。
多后端支持
classicML 本身是一个Python项目,但是机器学习中涉及到的复杂的矩阵运算对于Python有点儿捉襟见肘,因此我们提供了使用C++后端的函数的加速版本。为了保证兼容性,classicML默认使用Python后端,部分算法支持了使用C++作为后端进行加速,你需要安装标准版的classicML,然后在开头使用这条语句切换后端。
import os
os.environ['CLASSICML_ENGINE'] = 'CC'
第一个机器学习程序
使用线性判别分析进行二分类
- 下载示例数据集
wget https://github.com/sun1638650145/classicML/blob/master/datasets/西瓜数据集alpha.csv
- 运行下面的代码
import classicML as cml
DATASET_PATH = '/path/to/西瓜数据集alpha.csv'
# 读取数据
ds = cml.data.Dataset()
ds.from_csv(DATASET_PATH)
# 生成模型
model = cml.models.LDA()
# 训练模型
model.fit(ds.x, ds.y)
# 可视化模型
cml.plots.plot_lda(model, ds.x, ds.y, '密度', '含糖率')
v0.6.1 版本预览
- 增加数据读取的模块, 简化读取数据的流程
from_csv
自动读取CSV文件from_dataframe
自动加载pandas.DataFramefrom_tensor_slices
自动加载numpy.ndarray
- 增加数据预处理的模块
DummyEncoder
对标签进行Dummy编码Imputer
自动填充缺失值MaxMarginEncoder
对标签进行最大间隔编码MinMaxScaler
进行归一化OneHotEncoder
对标签进行独热编码StandardScaler
进行标准化
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