HyperImpute - 用于NaN和null的库。

HyperImpute简化了为您的ML管道选择数据插补算法的过程。它包含各种用于缺失数据的创新算法，并与sklearn兼容。

HyperImpute特性

• :rocket: 快速且可扩展的数据集插补算法，与sklearn兼容。
• :key: 新的迭代插补方法：HyperImpute。
• :cyclone: 经典方法：MICE、MissForest、GAIN、MIRACLE、MIWAE、Sinkhorn、SoftImpute等。
• :fire: 可插拔的架构。

:rocket: 安装

可以使用以下命令从PyPI安装此库：

$ pip install hyperimpute

或从源代码安装，使用

$ pip install .

:boom: 示例用法

列出可用的插补器

from hyperimpute.plugins.imputers import Imputers

imputers = Imputers()

imputers.list()

使用其中一种方法对数据集进行插补

import pandas as pd
import numpy as np
from hyperimpute.plugins.imputers import Imputers

X = pd.DataFrame([[1, 1, 1, 1], [4, 5, np.nan, np.nan], [3, 3, 9, 9], [2, 2, 2, 2]])

method = "gain"

plugin = Imputers().get(method)
out = plugin.fit_transform(X.copy())

print(method, out)

指定HyperImpute的基线模型

import pandas as pd
import numpy as np
from hyperimpute.plugins.imputers import Imputers

X = pd.DataFrame([[1, 1, 1, 1], [4, 5, np.nan, np.nan], [3, 3, 9, 9], [2, 2, 2, 2]])

plugin = Imputers().get(
    "hyperimpute",
    optimizer="hyperband",
    classifier_seed=["logistic_regression"],
    regression_seed=["linear_regression"],
)

out = plugin.fit_transform(X.copy())
print(out)

使用SKLearn管道的插补器

import pandas as pd
import numpy as np

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor

from hyperimpute.plugins.imputers import Imputers

X = pd.DataFrame([[1, 1, 1, 1], [4, 5, np.nan, np.nan], [3, 3, 9, 9], [2, 2, 2, 2]])
y = pd.Series([1, 2, 1, 2])

imputer = Imputers().get("hyperimpute")

estimator = Pipeline(
    [
        ("imputer", imputer),
        ("forest", RandomForestRegressor(random_state=0, n_estimators=100)),
    ]
)

estimator.fit(X, y)

编写新的插补插件

from sklearn.impute import KNNImputer
from hyperimpute.plugins.imputers import Imputers, ImputerPlugin

imputers = Imputers()

knn_imputer = "custom_knn"

class KNN(ImputerPlugin):
    def __init__(self) -> None:
        super().__init__()
        self._model = KNNImputer(n_neighbors=2, weights="uniform")

    @staticmethod
    def name():
        return knn_imputer

    @staticmethod
    def hyperparameter_space():
        return []

    def _fit(self, *args, **kwargs):
        self._model.fit(*args, **kwargs)
        return self

    def _transform(self, *args, **kwargs):
        return self._model.transform(*args, **kwargs)

imputers.add(knn_imputer, KNN)

assert imputers.get(knn_imputer) is not None

在数据集上对插补模型进行基准测试

from sklearn.datasets import load_iris
from hyperimpute.plugins.imputers import Imputers
from hyperimpute.utils.benchmarks import compare_models

X, y = load_iris(as_frame=True, return_X_y=True)

imputer = Imputers().get("hyperimpute")

compare_models(
    name="example",
    evaluated_model=imputer,
    X_raw=X,
    ref_methods=["ice", "missforest"],
    scenarios=["MAR"],
    miss_pct=[0.1, 0.3],
    n_iter=2,
)

📓 教程

• 教程 0：插补基础
• 教程 1：用于插补的AutoML
• 教程 2：基准

:zap: 插补方法

以下表格包含默认的插补插件

:hammer: 测试

使用以下命令安装测试依赖项

pip install .[testing]

可以使用以下命令执行测试

pytest -vsx

引用

如果您使用此代码，请引用相关论文

@article{Jarrett2022HyperImpute,
  doi = {10.48550/ARXIV.2206.07769},
  url = {https://arxiv.org/abs/2206.07769},
  author = {Jarrett, Daniel and Cebere, Bogdan and Liu, Tennison and Curth, Alicia and van der Schaar, Mihaela},
  keywords = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {HyperImpute: Generalized Iterative Imputation with Automatic Model Selection},
  year = {2022},
  booktitle={39th International Conference on Machine Learning},
}