HyperImpute - A library for NaNs and nulls.

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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

• :rocket: Fast and extensible dataset imputation algorithms, compatible with sklearn.
• :key: New iterative imputation method: HyperImpute.
• :cyclone: Classic methods: MICE, MissForest, GAIN, MIRACLE, MIWAE, Sinkhorn, SoftImpute, etc.
• :fire: Pluginable architecture.

:rocket: Installation

The library can be installed from PyPI using

$ pip install hyperimpute

or from source, using

$ pip install .

:boom: 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

• Tutorial 0: Imputation basics
• Tutorial 1: AutoML for imputation
• Tutorial 2: Benchmark

:zap: 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 | |Mean|Replace the missing values using the mean along each column with SimpleImputer| plugin_mean.py | |Median|Replace the missing values using the median along each column with SimpleImputer | plugin_median.py | |Most-frequent|Replace the missing values using the most frequent value along each column with SimpleImputer|plugin_most_freq.py | |MissForest|Iterative imputation method based on Random Forests using IterativeImputer and ExtraTreesRegressor| plugin_missforest.py | |ICE| Iterative imputation method based on regularized linear regression using IterativeImputer and BayesianRidge| plugin_ice.py| |MICE| Multiple imputations based on ICE using IterativeImputer and BayesianRidge| plugin_mice.py | |SoftImpute| Low-rank matrix approximation via nuclear-norm regularization| 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|plugin_em.py | |Sinkhorn|Missing Data Imputation using Optimal Transport|plugin_sinkhorn.py | |GAIN|GAIN: Missing Data Imputation using Generative Adversarial Nets|plugin_gain.py | |MIRACLE|MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms|plugin_miracle.py | |MIWAE|MIWAE: Deep Generative Modelling and Imputation of Incomplete Data|plugin_miwae.py |

:hammer: 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},
}