Imputation

class ai4water.preprocessing.imputation.Imputation(data: Union[DataFrame, ndarray, list], method: str = 'KNNImputer', features=None, imputer_args: Optional[dict] = None)

Bases: object

Implements imputation of missing values using a range of methods.

Imputation Methods

• pandas:

  Pandas library provides two methods for filling input data. interpolate: filling by interpolation

  Example of imputer_args can be

  {‘method’: ‘spline’: ‘order’: 2}

  For detailed args to be passed see interpolate

  fillna:

  example of imputer_args can be

  {‘method’: ‘ffill’}

  For detailed args to be passed see fillna

• sklearn:

  scikit-learn library provides 3 different imputation methods. SimplteImputer:

  For details see SimpleImputer

  IterativeImputer:

  imputer_args example: {‘n_nearest_features’: 2} For details see IterativeImputer

  KNNIMputer:

  All the args accepted by KNNImputer of sklearn can be passed as in imputer_args. imputer_args example: {‘n_neighbors’: 3}. For details KNNImputer

• fancyimpute:

  knn: NuclearnNormMinimization SoftImpute Biscaler

transdim:

- :py:meth:`ai4water.preprocessing.imputation.Imputation.plot` plots the imputed values.

- :py:meth:`ai4water.preprocessing.imputation.Imputation.missing_indices` indices of missing data.

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from ai4water.preprocessing import Imputation
>>> df = pd.DataFrame([1,3,np.nan,  np.nan, 9, np.nan, 11])
>>> imputer = Imputation(df, method='fillna', imputer_args={'method': 'ffill'})
>>> imputer()
# change the imputation method
>>> imputer.method = 'interpolate'
>>> imputer(method='cubic')
# Now try with KNN imputation
>>> imputer.method = 'KNNImputer'
>>> imputer(n_neighbors=3)

__init__(data: Union[DataFrame, ndarray, list], method: str = 'KNNImputer', features=None, imputer_args: Optional[dict] = None)

Parameters:

• data – the data which contains missing values

• method – the method to apply for missing

• features – the features on which imputation is to be applied

• imputer_args – arguments for underlying imputer function

call(*args, **kwargs)

maybe_make_df(data)

property method

missing_indices() → dict

plot(cols=None, st=0, en=None)

cols: columns to plot from data st: int en: int

Example

>>> imputer.plot(cols=['in1', 'in2'], st=0, en=25)