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mlplot的Python项目详细描述
mlplot
使用matplotlib和sklearn绘制机器学习评估图。
安装
pip install mlplot
ml plot与python 3.5及更高版本一起运行！（使用格式字符串和类型批注）
贡献
创建公关！
绘图
作品灵感来自sklearn model evaluation。
分类
AUC数
的ROCfrom mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.roc_curve()
校准
from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.calibration()
精确召回
from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.precision_recall(x_axis='recall')
eval.precision_recall(x_axis='thresold')
分布
from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.distribution()
混淆矩阵
from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.confusion_matrix(threshold=0.5)
分类报告
from mlplot.evaluation import ClassificationEvaluation
eval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)
eval.report_table()
回归
散点图
from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.scatter()
残差图
from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.residuals()
残差直方图
from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.residuals_histogram()
回归报告
from mlplot.evaluation import RegressionEvaluation
eval = RegressionEvaluation(y_true, y_pred, class_names, model_name)
eval.report_table()
预测
- 待定
排名
- 待定
开发
发布到pypi
python setup.py sdist bdist_wheel
twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
设计
基本界面思想
from mlplot.evaluation import ClassificationEvaluation
from mlplot.evaluation import RegressorEvaluation
from mlplot.evaluation import MultiClassificationEvaluation
from mlplot.evaluation import MultiRegressorEvaluation
from mlplot.evaluation import ModelComparison
from mlplot.feature_evaluation import *
eval = ClassificationEvaluation(y_true, y_pred)
ax = eval.roc_curve()
auc = eval.auc_score()
f1_score = eval.f1_score()
ax = eval.confusion_matrix(threshold=0.7)
- 模型评估基类
- 分类估价类
- 输入y_true、y_pred、类名、模型名
- 回归估值类
- 多分类评估类
- 模型比较
- 接受两个相同类型的评估
待办事项
- 固定分布图，制作线条
- 将带有r2的图例添加到回归图中
- 添加回归比较的测试
- 为比较类拆分文件
- 向自述文件添加比较
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