Welcome to HyperImpute’s documentation!
HyperImpute - A library for NaNs and nulls.
HyperImpute simplifies the selection process of a data imputation algorithm for your ML pipelines. It includes various novel algorithms for missing data and is compatible with sklearn.
HyperImpute features
🚀 Fast and extensible dataset imputation algorithms, compatible with sklearn.
🔑 New iterative imputation method: HyperImpute.
🌀 Classic methods: MICE, MissForest, GAIN, MIRACLE, MIWAE, Sinkhorn, SoftImpute, etc.
🔥 Pluginable architecture.
🚀 Installation
The library can be installed from PyPI using
$ pip install hyperimpute
or from source, using
$ pip install .
💥 Sample Usage
List available imputers
from hyperimpute.plugins.imputers import Imputers
imputers = Imputers()
imputers.list()
Impute a dataset using one of the available methods
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)
Specify the baseline models for HyperImpute
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)
Use an imputer with a SKLearn pipeline
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)
Write a new imputation plugin
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
Benchmark imputation models on a dataset
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,
)
📓 Tutorials
⚡ Imputation methods
The following table contains the default imputation plugins:
Strategy |
Description |
Code |
---|---|---|
HyperImpute |
Iterative imputer using both regression and classification methods based on linear models, trees, XGBoost, CatBoost and neural nets |
``plugin_hyperimpute.py` <src/hyperimpute/plugins/imputers/plugin_hyperimpute.py>`_ |
Mean |
Replace the missing values using the mean along each column with ``SimpleImputer` <https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html>`_ |
``plugin_mean.py` <src/hyperimpute/plugins/imputers/plugin_mean.py>`_ |
Median |
Replace the missing values using the median along each column with ``SimpleImputer` <https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html>`_ |
``plugin_median.py` <src/hyperimpute/plugins/imputers/plugin_median.py>`_ |
Most-frequent |
Replace the missing values using the most frequent value along each column with ``SimpleImputer` <https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html>`_ |
``plugin_most_freq.py` <src/hyperimpute/plugins/imputers/plugin_most_freq.py>`_ |
MissForest |
Iterative imputation method based on Random Forests using ``IterativeImputer` <https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer>`_ and ``ExtraTreesRegressor` <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html>`_ |
``plugin_missforest.py` <src/hyperimpute/plugins/imputers/plugin_missforest.py>`_ |
ICE |
Iterative imputation method based on regularized linear regression using ``IterativeImputer` <https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer>`_ and ``BayesianRidge` <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html>`_ |
``plugin_ice.py` <src/hyperimpute/plugins/imputers/plugin_ice.py>`_ |
MICE |
Multiple imputations based on ICE using ``IterativeImputer` <https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer>`_ and ``BayesianRidge` <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html>`_ |
``plugin_mice.py` <src/hyperimpute/plugins/imputers/plugin_mice.py>`_ |
SoftImpute |
``Low-rank matrix approximation via nuclear-norm regularization` <https://jmlr.org/papers/volume16/hastie15a/hastie15a.pdf>`_ |
``plugin_softimpute.py` <src/hyperimpute/plugins/imputers/plugin_softimpute.py>`_ |
EM |
Iterative procedure which uses other variables to impute a value (Expectation), then checks whether that is the value most likely (Maximization) - ``EM imputation algorithm` <https://joon3216.github.io/research_materials/2019/em_imputation.html>`_ |
``plugin_em.py` <src/hyperimpute/plugins//imputers/plugin_em.py>`_ |
Sinkhorn |
``Missing Data Imputation using Optimal Transport` <https://arxiv.org/pdf/2002.03860.pdf>`_ |
``plugin_sinkhorn.py` <src/hyperimpute/plugins/imputers/plugin_sinkhorn.py>`_ |
GAIN |
``GAIN: Missing Data Imputation using Generative Adversarial Nets` <https://arxiv.org/abs/1806.02920>`_ |
``plugin_gain.py` <src/hyperimpute/plugins/imputers/plugin_gain.py>`_ |
MIRACLE |
``MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms` <https://arxiv.org/abs/2111.03187>`_ |
``plugin_miracle.py` <src/hyperimpute/plugins/imputers/plugin_miracle.py>`_ |
MIWAE |
``MIWAE: Deep Generative Modelling and Imputation of Incomplete Data` <https://arxiv.org/abs/1812.02633>`_ |
``plugin_miwae.py` <src/hyperimpute/plugins/imputers/plugin_miwae.py>`_ |
🔨 Tests
Install the testing dependencies using
pip install .[testing]
The tests can be executed using
pytest -vsx
Citing
If you use this code, please cite the associated paper:
@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},
}