Source code for feets.extractors.ext_percent_diff_percentile
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2017-2024, Cabral, Juan
# Copyright (c) 2025, QuatroPe; Clariá, Felipe
# License: MIT
# Full Text:
# https://github.com/quatrope/feets/blob/master/LICENSE
# =============================================================================
# DOC
# =============================================================================
"""Percent difference magnitude percentile extractor."""
# =============================================================================
# IMPORTS
# =============================================================================
from light_curve import (
PercentDifferenceMagnitudePercentile as _PercentDifferenceMagnitudePercentile,
)
from .light_curve_extractor import LightCurveExtractor
from ..libs import doctools
# =============================================================================
# EXTRACTOR CLASS
# =============================================================================
[docs]
class PercentDiffPercentile(LightCurveExtractor):
r"""Ratio of p-th inter-percentile range to the median.
.. math::
p\mathrm{~percent~difference~magnitude~percentile}
= \frac{Q(1-p) - Q(p)}{\mathrm{Median}(m)}.
Parameters
----------
quantile : positive float, default=0.05
Relative range size, default is 0.05
transform : str or bool or None, optional
Transformer to apply to the feature values. If str, must be one of:
- 'default' - use default transformer for the feature, it same as
giving True. The default for this feature is 'clipped_lg'
- 'arcsinh' - Hyperbolic arcsine feature transformer
- 'clipped_lg' - Decimal logarithm of a value clipped to a minimum
value
- 'identity' - Identity feature transformer
- 'lg' - Decimal logarithm feature transformer
- 'ln1p' - :math:`ln(1+x)` feature transformer
- 'sqrt' - Square root feature transformer
f bool, must be True to use default transformer or False to disable.
If None, no transformation is applied.
References
----------
.. [disanto2016feature] D'Isanto, A., Cavuoti, S., Brescia, M., Donalek,
C., Longo, G., Riccio, G., & Djorgovski, S. G. (2016).
An analysis of feature relevance in the classification of astronomical
transients with machine learning methods.
Monthly Notices of the Royal Astronomical Society, 457(3), 3119-3132.
"""
features = ["PercentDiffPercentile"]
def __init__(self, quantile=0.05, transform=None):
self.quantile = quantile
self.transform = transform
self.lightcurve_ext = _PercentDifferenceMagnitudePercentile(
**self.params
)
[docs]
@doctools.doc_inherit(LightCurveExtractor.flatten_feature)
def flatten_feature(self, feature, value):
if feature == "PercentDiffPercentile":
[name] = self.lightcurve_ext.names
percentile = name.split("_")[4]
return {f"PercentDiffPercentile_{percentile}": value}
return super().flatten_feature(feature, value)
