yggdrasil decision forests下载yggdrasil decision forests来源代码下载

yggdrasil decision forests

C/C++

Python API 0.9.0

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YDF （Yggdrasil决策林）是一个培训，评估，解释和服务随机森林，梯度促进决策树，购物车和隔离森林模型的图书馆。

有关YDF的更多信息，请参见文档。

安装

要从PYPI安装YDF，请运行：

pip install ydf -U

用法示例

 import ydf
import pandas as pd

# Load dataset with Pandas
ds_path = "https://raw.githubusercontent.com/google/yggdrasil-decision-forests/main/yggdrasil_decision_forests/test_data/dataset/"
train_ds = pd . read_csv ( ds_path + "adult_train.csv" )
test_ds = pd . read_csv ( ds_path + "adult_test.csv" )

# Train a Gradient Boosted Trees model
model = ydf . GradientBoostedTreesLearner ( label = "income" ). train ( train_ds )

# Look at a model (input features, training logs, structure, etc.)
model . describe ()

# Evaluate a model (e.g. roc, accuracy, confusion matrix, confidence intervals)
model . evaluate ( test_ds )

# Generate predictions
model . predict ( test_ds )

# Analyse a model (e.g. partial dependence plot, variable importance)
model . analyze ( test_ds )

# Benchmark the inference speed of a model
model . benchmark ( test_ds )

# Save the model
model . save ( "/tmp/my_model" )

用C ++ API的示例。

 auto dataset_path = " csv:train.csv " ;

// List columns in training dataset
DataSpecification spec;
CreateDataSpec (dataset_path, false , {}, &spec);

// Create a training configuration
TrainingConfig train_config;
train_config.set_learner( " RANDOM_FOREST " );
train_config.set_task(Task::CLASSIFICATION);
train_config.set_label( " my_label " );

// Train model
std::unique_ptr<AbstractLearner> learner;
GetLearner (train_config, &learner);
auto model = learner-> Train (dataset_path, spec);

// Export model
SaveModel ( " my_model " , model.get());

（基于示例/初学者）

下一步

检查入门教程？

引用

如果您在科学出版物中我们的Yggdrasil决策森林，请引用以下论文：Yggdrasil决策森林：快速且可扩展的决策森林图书馆。

Bibtex

 @inproceedings{GBBSP23,
  author       = {Mathieu Guillame{-}Bert and
                  Sebastian Bruch and
                  Richard Stotz and
                  Jan Pfeifer},
  title        = {Yggdrasil Decision Forests: {A} Fast and Extensible Decision Forests
                  Library},
  booktitle    = {Proceedings of the 29th {ACM} {SIGKDD} Conference on Knowledge Discovery
                  and Data Mining, {KDD} 2023, Long Beach, CA, USA, August 6-10, 2023},
  pages        = {4068--4077},
  year         = {2023},
  url          = {https://doi.org/10.1145/3580305.3599933},
  doi          = {10.1145/3580305.3599933},
}

生的

Yggdrasil决策森林：一个快速而可扩展的决策森林图书馆，Guillame-Bert等，KDD 2023：4068-4077。 doi：10.1145/3580305.3599933