PowerTransformer : Perform mapping to a normal distribution using a power transform. See also - quantile_transform : Equivalent function without the estimator API. References_ : ndarray, shape(n_quantiles, ) Quantiles of references.Įxamples - > import numpy as np > from sklearn.preprocessing import QuantileTransformer > rng = np.random.RandomState(0) > X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0) > qt = QuantileTransformer(n_quantiles=10, random_state=0) > qt.fit_transform(X) array(.
Quantiles_ : ndarray, shape (n_quantiles, n_features) The values corresponding the quantiles of reference. See :term:`Glossary `Ĭopy : boolean, optional, (default=True) Set to False to perform inplace transformation and avoid a copy (if the input is already a numpy array).Īttributes - n_quantiles_ : integer The actual number of quantiles used to discretize the cumulative distribution function. Pass an int for reproducible results across multiple function calls. Please see ``subsample`` for more details. Random_state : int, RandomState instance or None, optional (default=None) Determines random number generation for subsampling and smoothing noise. Note that the subsampling procedure may differ for value-identical sparse and dense matrices. Subsample : int, optional (default=1e5) Maximum number of samples used to estimate the quantiles for computational efficiency. If False, these entries are treated as zeros. If True, the sparse entries of the matrix are discarded to compute the quantile statistics. Ignore_implicit_zeros : bool, optional (default=False) Only applies to sparse matrices.
The choices are 'uniform' (default) or 'normal'. Output_distribution : str, optional (default='uniform') Marginal distribution for the transformed data. If n_quantiles is larger than the number of samples, n_quantiles is set to the number of samples as a larger number of quantiles does not give a better approximation of the cumulative distribution function estimator. It corresponds to the number of landmarks used to discretize the cumulative distribution function. Parameters - n_quantiles : int, optional (default=1000 or n_samples) Number of quantiles to be computed. It may distort linear correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable. Features values of new/unseen data that fall below or above the fitted range will be mapped to the bounds of the output distribution. The obtained values are then mapped to the desired output distribution using the associated quantile function. First an estimate of the cumulative distribution function of a feature is used to map the original values to a uniform distribution. The transformation is applied on each feature independently. It also reduces the impact of (marginal) outliers: this is therefore a robust preprocessing scheme.
Therefore, for a given feature, this transformation tends to spread out the most frequent values. This method transforms the features to follow a uniform or a normal distribution. The bug is tracked in GitHub issue #22731 and GitHub issue #22841 and solved with PR #22735.Transform features using quantiles information. It seems the problem is generated by the encode="ordinal" parameter passed to the KBinsDiscretizer constructor. If you run into similar issues, then make sure that this method has the following signature: get_feature_names_out(self, input_features) -> List. I implemented the get_feature_names_out method, but it was not accepting any parameter on my end and that was the problem. So it turns out, aside from the KBinsDiscretizer there was another bug in my custom transformer classes. I just wanted to add another important point for posterity: I was having other problems with my own custom pipeline and I was getting an error get_feature_names_out() takes 1 positional argument but 2 were given. Is there a better way to do it?ĮDIT: A big thank you to for answering the question, that was indeed the problem. Pipe.named_steps.get_feature_names_out()īut I’m running into get_feature_names_out() takes 1 positional argument but 2 were given, not sure what’s going on but this entire process doesn’t feel right.
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