pyts.preprocessing.KBinsDiscretizer¶
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class
pyts.preprocessing.KBinsDiscretizer(n_bins=5, strategy='quantile', raise_warning=True)[source]¶ Bin continuous data into intervals sample-wise.
Parameters: - n_bins : int (default = 5)
The number of bins to produce. The intervals for the bins are determined by the minimum and maximum of the input data. It must be greater than or equal to 2.
- strategy : ‘uniform’, ‘quantile’ or ‘normal’ (default = ‘quantile’)
Strategy used to define the widths of the bins:
- ‘uniform’: All bins in each sample have identical widths
- ‘quantile’: All bins in each sample have the same number of points
- ‘normal’: Bin edges are quantiles from a standard normal distribution
- raise_warning : bool (default = True)
If True, a warning is raised when the number of bins is smaller for at least one sample. In this case, you should consider decreasing the number of bins or removing these samples.
Examples
>>> from pyts.preprocessing import KBinsDiscretizer >>> X = [[0, 1, 0, 2, 3, 3, 2, 1], ... [7, 0, 6, 1, 5, 3, 4, 2]] >>> discretizer = KBinsDiscretizer(n_bins=2) >>> print(discretizer.transform(X)) [[0 0 0 1 1 1 1 0] [1 0 1 0 1 0 1 0]]
Methods
__init__([n_bins, strategy, raise_warning])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)Bin the data. -
__init__(n_bins=5, strategy='quantile', raise_warning=True)[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.
