| import numpy as np |
|
|
| from sklearn.preprocessing import RobustScaler |
| from scipy import sparse |
| from sklearn.utils import check_array |
| from sklearn.utils.validation import FLOAT_DTYPES |
| from sklearn.preprocessing.data import _handle_zeros_in_scale |
|
|
| class RobustNanScaler(RobustScaler): |
| |
| def _check_array(self, X, copy): |
| """Makes sure centering is not enabled for sparse matrices.""" |
| X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy, |
| estimator=self, dtype=FLOAT_DTYPES, force_all_finite=False) |
|
|
| if sparse.issparse(X): |
| if self.with_centering: |
| raise ValueError( |
| "Cannot center sparse matrices: use `with_centering=False`" |
| " instead. See docstring for motivation and alternatives.") |
| return X |
|
|
| |
| def fit(self, X, y=None): |
| if sparse.issparse(X): |
| raise TypeError("RobustScaler cannot be fitted on sparse inputs") |
| X = self._check_array(X, self.copy) |
| if self.with_centering: |
| self.center_ = np.nanmedian(X, axis=0) |
|
|
| if self.with_scaling: |
| q_min, q_max = self.quantile_range |
| if not 0 <= q_min <= q_max <= 100: |
| raise ValueError("Invalid quantile range: %s" % |
| str(self.quantile_range)) |
|
|
| q = np.nanpercentile(X, self.quantile_range, axis=0) |
| self.scale_ = (q[1] - q[0]) |
| self.scale_ = _handle_zeros_in_scale(self.scale_, copy=False) |
| return self |
|
|
