# 5.4. Imputation of missing values¶

For various reasons, many real world datasets contain missing values, often encoded as blanks, NaNs or other placeholders. Such datasets however are incompatible with scikit-learn estimators which assume that all values in an array are numerical, and that all have and hold meaning. A basic strategy to use incomplete datasets is to discard entire rows and/or columns containing missing values. However, this comes at the price of losing data which may be valuable (even though incomplete). A better strategy is to impute the missing values, i.e., to infer them from the known part of the data. See the Glossary of Common Terms and API Elements entry on imputation.

## 5.4.1. Univariate vs. Multivariate Imputation¶

One type of imputation algorithm is univariate, which imputes values in the
i-th feature dimension using only non-missing values in that feature dimension
(e.g. `impute.SimpleImputer`

). By contrast, multivariate imputation
algorithms use the entire set of available feature dimensions to estimate the
missing values (e.g. `impute.IterativeImputer`

).

## 5.4.2. Univariate feature imputation¶

The `SimpleImputer`

class provides basic strategies for imputing missing
values. Missing values can be imputed with a provided constant value, or using
the statistics (mean, median or most frequent) of each column in which the
missing values are located. This class also allows for different missing values
encodings.

The following snippet demonstrates how to replace missing values,
encoded as `np.nan`

, using the mean value of the columns (axis 0)
that contain the missing values:

```
>>> import numpy as np
>>> from sklearn.impute import SimpleImputer
>>> imp = SimpleImputer(missing_values=np.nan, strategy='mean')
>>> imp.fit([[1, 2], [np.nan, 3], [7, 6]])
SimpleImputer(add_indicator=False, copy=True, fill_value=None,
missing_values=nan, strategy='mean', verbose=0)
>>> X = [[np.nan, 2], [6, np.nan], [7, 6]]
>>> print(imp.transform(X))
[[4. 2. ]
[6. 3.666...]
[7. 6. ]]
```

The `SimpleImputer`

class also supports sparse matrices:

```
>>> import scipy.sparse as sp
>>> X = sp.csc_matrix([[1, 2], [0, -1], [8, 4]])
>>> imp = SimpleImputer(missing_values=-1, strategy='mean')
>>> imp.fit(X)
SimpleImputer(add_indicator=False, copy=True, fill_value=None,
missing_values=-1, strategy='mean', verbose=0)
>>> X_test = sp.csc_matrix([[-1, 2], [6, -1], [7, 6]])
>>> print(imp.transform(X_test).toarray())
[[3. 2.]
[6. 3.]
[7. 6.]]
```

Note that this format is not meant to be used to implicitly store missing values in the matrix because it would densify it at transform time. Missing values encoded by 0 must be used with dense input.

The `SimpleImputer`

class also supports categorical data represented as
string values or pandas categoricals when using the `'most_frequent'`

or
`'constant'`

strategy:

```
>>> import pandas as pd
>>> df = pd.DataFrame([["a", "x"],
... [np.nan, "y"],
... ["a", np.nan],
... ["b", "y"]], dtype="category")
...
>>> imp = SimpleImputer(strategy="most_frequent")
>>> print(imp.fit_transform(df))
[['a' 'x']
['a' 'y']
['a' 'y']
['b' 'y']]
```

## 5.4.3. Multivariate feature imputation¶

A more sophisticated approach is to use the `IterativeImputer`

class,
which models each feature with missing values as a function of other features,
and uses that estimate for imputation. It does so in an iterated round-robin
fashion: at each step, a feature column is designated as output `y`

and the
other feature columns are treated as inputs `X`

. A regressor is fit on ```
(X,
y)
```

for known `y`

. Then, the regressor is used to predict the missing values
of `y`

. This is done for each feature in an iterative fashion, and then is
repeated for `max_iter`

imputation rounds. The results of the final
imputation round are returned.

Note

This estimator is still **experimental** for now: the predictions
and the API might change without any deprecation cycle. To use it,
you need to explicitly import `enable_iterative_imputer`

.

```
>>> import numpy as np
>>> from sklearn.experimental import enable_iterative_imputer
>>> from sklearn.impute import IterativeImputer
>>> imp = IterativeImputer(max_iter=10, random_state=0)
>>> imp.fit([[1, 2], [3, 6], [4, 8], [np.nan, 3], [7, np.nan]])
IterativeImputer(add_indicator=False, estimator=None,
imputation_order='ascending', initial_strategy='mean',
max_iter=10, max_value=None, min_value=None,
missing_values=nan, n_nearest_features=None,
random_state=0, sample_posterior=False, tol=0.001,
verbose=0)
>>> X_test = [[np.nan, 2], [6, np.nan], [np.nan, 6]]
>>> # the model learns that the second feature is double the first
>>> print(np.round(imp.transform(X_test)))
[[ 1. 2.]
[ 6. 12.]
[ 3. 6.]]
```

Both `SimpleImputer`

and `IterativeImputer`

can be used in a
Pipeline as a way to build a composite estimator that supports imputation.
See Imputing missing values before building an estimator.

### 5.4.3.1. Flexibility of IterativeImputer¶

There are many well-established imputation packages in the R data science
ecosystem: Amelia, mi, mice, missForest, etc. missForest is popular, and turns
out to be a particular instance of different sequential imputation algorithms
that can all be implemented with `IterativeImputer`

by passing in
different regressors to be used for predicting missing feature values. In the
case of missForest, this regressor is a Random Forest.
See sphx_glr_auto_examples_plot_iterative_imputer_variants_comparison.py.

### 5.4.3.2. Multiple vs. Single Imputation¶

In the statistics community, it is common practice to perform multiple
imputations, generating, for example, `m`

separate imputations for a single
feature matrix. Each of these `m`

imputations is then put through the
subsequent analysis pipeline (e.g. feature engineering, clustering, regression,
classification). The `m`

final analysis results (e.g. held-out validation
errors) allow the data scientist to obtain understanding of how analytic
results may differ as a consequence of the inherent uncertainty caused by the
missing values. The above practice is called multiple imputation.

Our implementation of `IterativeImputer`

was inspired by the R MICE
package (Multivariate Imputation by Chained Equations) [1], but differs from
it by returning a single imputation instead of multiple imputations. However,
`IterativeImputer`

can also be used for multiple imputations by applying
it repeatedly to the same dataset with different random seeds when
`sample_posterior=True`

. See [2], chapter 4 for more discussion on multiple
vs. single imputations.

It is still an open problem as to how useful single vs. multiple imputation is in the context of prediction and classification when the user is not interested in measuring uncertainty due to missing values.

Note that a call to the `transform`

method of `IterativeImputer`

is
not allowed to change the number of samples. Therefore multiple imputations
cannot be achieved by a single call to `transform`

.

## 5.4.4. References¶

[1] | Stef van Buuren, Karin Groothuis-Oudshoorn (2011). “mice: Multivariate Imputation by Chained Equations in R”. Journal of Statistical Software 45: 1-67. |

[2] | Roderick J A Little and Donald B Rubin (1986). “Statistical Analysis with Missing Data”. John Wiley & Sons, Inc., New York, NY, USA. |

## 5.4.5. Marking imputed values¶

The `MissingIndicator`

transformer is useful to transform a dataset into
corresponding binary matrix indicating the presence of missing values in the
dataset. This transformation is useful in conjunction with imputation. When
using imputation, preserving the information about which values had been
missing can be informative.

`NaN`

is usually used as the placeholder for missing values. However, it
enforces the data type to be float. The parameter `missing_values`

allows to
specify other placeholder such as integer. In the following example, we will
use `-1`

as missing values:

```
>>> from sklearn.impute import MissingIndicator
>>> X = np.array([[-1, -1, 1, 3],
... [4, -1, 0, -1],
... [8, -1, 1, 0]])
>>> indicator = MissingIndicator(missing_values=-1)
>>> mask_missing_values_only = indicator.fit_transform(X)
>>> mask_missing_values_only
array([[ True, True, False],
[False, True, True],
[False, True, False]])
```

The `features`

parameter is used to choose the features for which the mask is
constructed. By default, it is `'missing-only'`

which returns the imputer
mask of the features containing missing values at `fit`

time:

```
>>> indicator.features_
array([0, 1, 3])
```

The `features`

parameter can be set to `'all'`

to returned all features
whether or not they contain missing values:

```
>>> indicator = MissingIndicator(missing_values=-1, features="all")
>>> mask_all = indicator.fit_transform(X)
>>> mask_all
array([[ True, True, False, False],
[False, True, False, True],
[False, True, False, False]])
>>> indicator.features_
array([0, 1, 2, 3])
```

When using the `MissingIndicator`

in a `Pipeline`

, be sure to use
the `FeatureUnion`

or `ColumnTransformer`

to add the indicator
features to the regular features. First we obtain the `iris`

dataset, and add
some missing values to it.

```
>>> from sklearn.datasets import load_iris
>>> from sklearn.impute import SimpleImputer, MissingIndicator
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.pipeline import FeatureUnion, make_pipeline
>>> from sklearn.tree import DecisionTreeClassifier
>>> X, y = load_iris(return_X_y=True)
>>> mask = np.random.randint(0, 2, size=X.shape).astype(np.bool)
>>> X[mask] = np.nan
>>> X_train, X_test, y_train, _ = train_test_split(X, y, test_size=100,
... random_state=0)
```

Now we create a `FeatureUnion`

. All features will be imputed using
`SimpleImputer`

, in order to enable classifiers to work with this data.
Additionally, it adds the the indicator variables from
`MissingIndicator`

.

```
>>> transformer = FeatureUnion(
... transformer_list=[
... ('features', SimpleImputer(strategy='mean')),
... ('indicators', MissingIndicator())])
>>> transformer = transformer.fit(X_train, y_train)
>>> results = transformer.transform(X_test)
>>> results.shape
(100, 8)
```

Of course, we cannot use the transformer to make any predictions. We should
wrap this in a `Pipeline`

with a classifier (e.g., a
`DecisionTreeClassifier`

) to be able to make predictions.

```
>>> clf = make_pipeline(transformer, DecisionTreeClassifier())
>>> clf = clf.fit(X_train, y_train)
>>> results = clf.predict(X_test)
>>> results.shape
(100,)
```