pyts.preprocessing
.InterpolationImputer¶
-
class
pyts.preprocessing.
InterpolationImputer
(missing_values=nan, strategy='linear')[source]¶ Impute missing values using interpolation.
Parameters: - missing_values : None, np.nan, integer or float (default = np.nan)
The placeholder for the missing values. All occurrences of missing_values will be imputed. If an integer or a float, the input data must not contain NaN or infinity values.
- strategy : str or int (default = ‘linear’)
Specifies the kind of interpolation as a string (‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, ‘next’, where ‘zero’, ‘slinear’, ‘quadratic’ and ‘cubic’ refer to a spline interpolation of zeroth, first, second or third order; ‘previous’ and ‘next’ simply return the previous or next value of the point) or as an integer specifying the order of the spline interpolator to use. Default is ‘linear’.
Examples
>>> import numpy as np >>> from pyts.preprocessing import InterpolationImputer >>> X = [[1, None, 3, 4], [8, None, 4, None]] >>> imputer = InterpolationImputer() >>> imputer.transform(X) array([[1., 2., 3., 4.], [8., 6., 4., 2.]])
Methods
__init__
([missing_values, strategy])Initialize self. fit
([X, y])Pass. fit_transform
(X[, y])Fit to data, then transform it. get_params
([deep])Get parameters for this estimator. set_params
(**params)Set the parameters of this estimator. transform
(X)Perform imputation using interpolation. -
__init__
(missing_values=nan, strategy='linear')[source]¶ Initialize self. See help(type(self)) for accurate signature.
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fit_transform
(X, y=None, **fit_params)¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: - X : array-like, shape = (n_samples, n_timestamps)
Univariate time series.
- y : None or array-like, shape = (n_samples,) (default = None)
Target values (None for unsupervised transformations).
- **fit_params : dict
Additional fit parameters.
Returns: - X_new : array
Transformed array.
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get_params
(deep=True)¶ 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.
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set_params
(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.Parameters: - **params : dict
Estimator parameters.
Returns: - self : estimator instance
Estimator instance.