Imputer

---

class atom.data_cleaning.Imputer(strat_num="mean", strat_cat="most_frequent", max_nan_rows=None, max_nan_cols=None, n_jobs=1, device="cpu", engine=None, verbose=0, random_state=None)[source]

Handle missing values in the data.

Impute or remove missing values according to the selected strategy. Also removes rows and columns with too many missing values. Use the missing_ attribute to customize what are considered "missing values".

This class can be accessed from atom through the impute method. Read more in the user guide.

Parameters

strat_num: int, float, str or callable, default="mean" Imputing strategy for numerical columns. Choose from: "drop": Drop rows containing missing values. "mean": Impute with mean of column. "median": Impute with median of column. "most_frequent": Impute with the most frequent value. "knn": Impute using a K-Nearest Neighbors approach. "iterative": Impute using a multivariate imputer. "drift": Impute values using a PolynomialTrend model. "linear": Impute using linear interpolation. "nearest": Impute with nearest value. "bfill": Impute by using the next valid observation to fill the gap. "ffill": Impute by propagating the last valid observation to next valid. "random": Impute with random values between the min and max of column. int or float: Impute with provided numerical value. callable: Replace missing values using the scalar statistic returned by running the callable over a dense 1d array containing non-missing values of each column. strat_cat: str, default="most_frequent" Imputing strategy for categorical columns. Choose from: "drop": Drop rows containing missing values. "most_frequent": Impute with the most frequent value. str: Impute with provided string. max_nan_rows: int, float or None, default=None Maximum number or fraction of missing values in a row (if more, the row is removed). If None, ignore this step. max_nan_cols: int, float or None, default=None Maximum number or fraction of missing values in a column (if more, the column is removed). If None, ignore this step. n_jobs: int, default=1 Number of cores to use for parallel processing. If >0: Number of cores to use. If -1: Use all available cores. If <-1: Use number of cores - 1 - value. device: str, default="cpu" Device on which to run the estimators. Use any string that follows the SYCL_DEVICE_FILTER filter selector, e.g. device="gpu" to use the GPU. Read more in the user guide. engine: str or None, default=None Execution engine to use for estimators. If None, the default value is used. Choose from: "sklearn" (default) "cuml" verbose: int, default=0 Verbosity level of the class. Choose from: 0 to not print anything. 1 to print basic information. 2 to print detailed information. random_state: int or None, default=None Seed used by the random number generator. If None, the random number generator is the RandomState used by np.random. Only used when strat_num="iterative".

Attributes

missing_: list Values that are considered "missing". Default values are: None, NaN, NA, NaT, +inf, -inf, "", "?", "NA", "nan", "NaN", "NaT", "none", "None", "inf", "-inf". Note that None, NaN, NA, +inf and -inf are always considered missing since they are incompatible with sklearn estimators. feature_names_in_: np.ndarray Names of features seen during fit. n_features_in_: int Number of features seen during fit.

See Also

Balancer

Balance the number of samples per class in the target column.

Discretizer

Bin continuous data into intervals.

Encoder

Perform encoding of categorical features.

Example

atomstand-alone

>>> import numpy as np
>>> from atom import ATOMClassifier
>>> from numpy.random import randint
>>> from sklearn.datasets import load_breast_cancer

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)

>>> # Add some random missing values to the data
>>> for i, j in zip(randint(0, X.shape[0], 600), randint(0, 4, 600)):
...     X.iloc[i, j] = np.NaN

>>> atom = ATOMClassifier(X, y, random_state=1)
>>> print(atom.nans)

mean radius                130
mean texture               141
mean perimeter             124
mean area                  136
mean smoothness              0
mean compactness             0
mean concavity               0
mean concave points          0
mean symmetry                0
mean fractal dimension       0
radius error                 0
texture error                0
perimeter error              0
area error                   0
smoothness error             0
compactness error            0
concavity error              0
concave points error         0
symmetry error               0
fractal dimension error      0
worst radius                 0
worst texture                0
worst perimeter              0
worst area                   0
worst smoothness             0
worst compactness            0
worst concavity              0
worst concave points         0
worst symmetry               0
worst fractal dimension      0
target                       0
dtype: int64

>>> atom.impute(strat_num="median", max_nan_rows=0.1, verbose=2)

Fitting Imputer...
Imputing missing values...
 --> Imputing 130 missing values with median (13.27) in column mean radius.
 --> Imputing 141 missing values with median (18.87) in column mean texture.
 --> Imputing 124 missing values with median (85.66) in column mean perimeter.
 --> Imputing 136 missing values with median (555.1) in column mean area.

>>> print(atom.n_nans)

0

>>> import numpy as np
>>> from atom.data_cleaning import Imputer
>>> from numpy.random import randint
>>> from sklearn.datasets import load_breast_cancer

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)

>>> # Add some random missing values to the data
>>> for i, j in zip(randint(0, X.shape[0], 600), randint(0, 4, 600)):
...     X.iloc[i, j] = np.nan

>>> imputer = Imputer(strat_num="median", max_nan_rows=0.1, verbose=2)
>>> X, y = imputer.fit_transform(X, y)

Fitting Imputer...
Imputing missing values...
 --> Dropping 2 samples for containing more than 3 missing values.
 --> Imputing 124 missing values with median (13.38) in column mean radius.
 --> Imputing 127 missing values with median (18.87) in column mean texture.
 --> Imputing 137 missing values with median (86.54) in column mean perimeter.
 --> Imputing 134 missing values with median (561.3) in column mean area.

>>> print(X)

     mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  ...  worst texture  worst perimeter  worst area  worst smoothness  worst compactness  worst concavity  worst concave points  worst symmetry  worst fractal dimension
0          13.38        10.380         122.800     1001.0          0.11840           0.27760         0.30010              0.14710         0.2419  ...          17.33           184.60      2019.0           0.16220            0.66560           0.7119                0.2654          0.4601                  0.11890
1          20.57        17.770          86.545      561.3          0.08474           0.07864         0.08690              0.07017         0.1812  ...          23.41           158.80      1956.0           0.12380            0.18660           0.2416                0.1860          0.2750                  0.08902
2          19.69        21.250         130.000     1203.0          0.10960           0.15990         0.19740              0.12790         0.2069  ...          25.53           152.50      1709.0           0.14440            0.42450           0.4504                0.2430          0.3613                  0.08758
3          11.42        20.380          77.580      386.1          0.14250           0.28390         0.24140              0.10520         0.2597  ...          26.50            98.87       567.7           0.20980            0.86630           0.6869                0.2575          0.6638                  0.17300
4          13.38        14.340         135.100     1297.0          0.10030           0.13280         0.19800              0.10430         0.1809  ...          16.67           152.20      1575.0           0.13740            0.20500           0.4000                0.1625          0.2364                  0.07678
..           ...           ...             ...        ...              ...               ...             ...                  ...            ...  ...            ...              ...         ...               ...                ...              ...                   ...             ...                      ...
564        21.56        22.390          86.545      561.3          0.11100           0.11590         0.24390              0.13890         0.1726  ...          26.40           166.10      2027.0           0.14100            0.21130           0.4107                0.2216          0.2060                  0.07115
565        20.13        18.865         131.200     1261.0          0.09780           0.10340         0.14400              0.09791         0.1752  ...          38.25           155.00      1731.0           0.11660            0.19220           0.3215                0.1628          0.2572                  0.06637
566        13.38        28.080          86.545      561.3          0.08455           0.10230         0.09251              0.05302         0.1590  ...          34.12           126.70      1124.0           0.11390            0.30940           0.3403                0.1418          0.2218                  0.07820
567        20.60        29.330         140.100     1265.0          0.11780           0.27700         0.35140              0.15200         0.2397  ...          39.42           184.60      1821.0           0.16500            0.86810           0.9387                0.2650          0.4087                  0.12400
568        13.38        24.540          47.920      181.0          0.05263           0.04362         0.00000              0.00000         0.1587  ...          30.37            59.16       268.6           0.08996            0.06444           0.0000                0.0000          0.2871                  0.07039

[567 rows x 30 columns]

Methods

fit	Fit to data.
fit_transform	Fit to data, then transform it.
get_feature_names_out	Get output feature names for transformation.
get_params	Get parameters for this estimator.
inverse_transform	Do nothing.
set_output	Set output container.
set_params	Set the parameters of this estimator.
transform	Impute the missing values.

method fit(X, y=None)[source]

Fit to data.

Parameters	X: dataframe-like Feature set with shape=(n_samples, n_features). y: sequence, dataframe-like or None, default=None Do nothing. Implemented for continuity of the API.
Returns	Self Estimator instance.

method fit_transform(X=None, y=None, **fit_params)[source]

Fit to data, then transform it.

Parameters	X: dataframe-like or None, default=None Feature set with shape=(n_samples, n_features). If None, X is ignored. y: sequence, dataframe-like or None, default=None Target column(s) corresponding to X. If None, y is ignored. **fit_params Additional keyword arguments for the fit method.
Returns	dataframe Transformed feature set. Only returned if provided. series or dataframe Transformed target column. Only returned if provided.

method get_feature_names_out(input_features=None)[source]

Get output feature names for transformation.

Parameters	input_features: sequence or None, default=None Only used to validate feature names with the names seen in fit.
Returns	np.ndarray Transformed feature names.

method get_params(deep=True)[source]

Get parameters for this estimator.

Parameters	deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns	params : dict Parameter names mapped to their values.

method inverse_transform(X=None, y=None, **fit_params)[source]

Do nothing.

Returns the input unchanged. Implemented for continuity of the API.

Parameters	X: dataframe-like or None, default=None Feature set with shape=(n_samples, n_features). If None, X is ignored. y: sequence, dataframe-like or None, default=None Target column(s) corresponding to X. If None, y is ignored.
Returns	dataframe Feature set. Only returned if provided. series or dataframe Target column(s). Only returned if provided.

method set_output(transform=None)[source]

Set output container.

See sklearn's user guide on how to use the set_output API. See here a description of the choices.

Parameters	transform: str or None, default=None Configure the output of the transform, fit_transform, and inverse_transform method. If None, the configuration is not changed. Choose from: "numpy" "pandas" (default) "pandas-pyarrow" "polars" "polars-lazy" "pyarrow" "modin" "dask" "pyspark" "pyspark-pandas"
Returns	Self Estimator instance.

method set_params(**params)[source]

Set the parameters of this estimator.

Parameters	**params : dict Estimator parameters.
Returns	self : estimator instance Estimator instance.

method transform(X, y=None)[source]

Impute the missing values.

Note that leaving y=None can lead to inconsistencies in data length between X and y if rows are dropped during the transformation.

Parameters	X: dataframe-like Feature set with shape=(n_samples, n_features). y: sequence, dataframe-like or None, default=None Target column(s) corresponding to X.
Returns	dataframe Imputed dataframe. series or dataframe Transformed target column. Only returned if provided.