automl/auto-sklearn

convert to scikit learn code.

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#388 创建于 2017年11月14日

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 (20 评论) (1 反应) (0 负责人)Python (7,270 star) (1,265 fork)batch import
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描述

[(0.666667, SimpleRegressionPipeline({'imputation:strategy': 'mean', 'one_hot_encoding:use_minimum_fraction': 'True', 'preprocessor:choice': 'no_preprocessing', 'regressor:choice': 'adaboost', 'rescaling:choice': 'minmax', 'one_hot_encoding:minimum_fraction': 0.010000000000000004, 'regressor:adaboost:learning_rate': 0.9890631979261445, 'regressor:adaboost:loss': 'linear', 'regressor:adaboost:max_depth': 10, 'regressor:adaboost:n_estimators': 127}, dataset_properties={ 'task': 4, 'sparse': False, 'multilabel': False, 'multiclass': False, 'target_type': 'regression', 'signed': False})), (0.333333, SimpleRegressionPipeline({'imputation:strategy': 'mean', 'one_hot_encoding:use_minimum_fraction': 'True', 'preprocessor:choice': 'random_trees_embedding', 'regressor:choice': 'liblinear_svr', 'rescaling:choice': 'standardize', 'one_hot_encoding:minimum_fraction': 0.00011808426850838513, 'preprocessor:random_trees_embedding:max_depth': 3, 'preprocessor:random_trees_embedding:max_leaf_nodes': 'None', 'preprocessor:random_trees_embedding:min_samples_leaf': 3, 'preprocessor:random_trees_embedding:min_samples_split': 3, 'preprocessor:random_trees_embedding:min_weight_fraction_leaf': 1.0, 'preprocessor:random_trees_embedding:n_estimators': 68, 'regressor:liblinear_svr:C': 1.4174149191248073, 'regressor:liblinear_svr:dual': 'False', 'regressor:liblinear_svr:epsilon': 0.0328370684051209, 'regressor:liblinear_svr:fit_intercept': 'True', 'regressor:liblinear_svr:intercept_scaling': 1, 'regressor:liblinear_svr:loss': 'squared_epsilon_insensitive', 'regressor:liblinear_svr:tol': 0.0012221149693867595}, dataset_properties={ 'task': 4, 'sparse': False, 'multilabel': False, 'multiclass': False, 'target_type': 'regression', 'signed': False})), ] R2 score: 0.87227602958 How to convert the model I run to sklearn code？could you give me some example code?

贡献者指南

技术栈
pythonscikit learn
领域
documentation
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
1-3 hours
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
stale
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
basic understanding of auto sklearnknowledge of scikit learn pipeline API
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
30
研究方向
Review the auto sklearn source code for the SimpleRegressionPipeline class (likely in auto sklearn/pipeline/). Examine how configurations are used to instantiate sklearn estimators. Provide example code that manually creates a sklearn Pipeline using the hyperparameters given in the issue, such as AdaBoostRegressor with specified learning rate, loss, max depth, n estimators. Also check if an existing export or conversion function exists (e.g., in auto sklearn/export/). If not, propose a new utility function that takes an AutoML object and returns sklearn code.