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#!/usr/bin/env python
"""Stationary and transition entropy module.
"""
__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "BSD"
from typing import Tuple
from dataclasses import dataclass, field
from argaze import GazeFeatures, DataFeatures
from argaze.GazeAnalysis import TransitionMatrix
import pandas
import numpy
@dataclass
class AOIScanPathAnalyzer(GazeFeatures.AOIScanPathAnalyzer):
"""Implementation of entropy algorithm as described in:
**Krejtz K., Szmidt T., Duchowski A.T. (2014).**
*Entropy-based statistical analysis of eye movement transitions.*
Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA'14, 159-166).
[https://doi.org/10.1145/2578153.2578176](https://doi.org/10.1145/2578153.2578176)
"""
transition_matrix_analyzer: TransitionMatrix.AOIScanPathAnalyzer = field(default_factory=TransitionMatrix.AOIScanPathAnalyzer)
"""To get its transition_matrix_probabilities result.
!!! warning "Mandatory"
TransitionMatrix analyzer have to be loaded before.
"""
def __post_init__(self):
super().__init__()
self.__stationary_entropy = -1
self.__transition_entropy = -1
@DataFeatures.PipelineStepMethod
def analyze(self, aoi_scan_path: GazeFeatures.AOIScanPathType):
assert(len(aoi_scan_path) > 1)
# Count total number of fixations and how many fixations are there per aoi
scan_fixations_count, aoi_fixations_count = aoi_scan_path.fixations_count()
# Probability to have a fixation onto each aoi
stationary_probalities = {aoi: count/scan_fixations_count for aoi, count in aoi_fixations_count.items()}
# Stationary entropy
self.__stationary_entropy = 0
for aoi, p in stationary_probalities.items():
self.__stationary_entropy += p * numpy.log(p + 1e-9)
self.__stationary_entropy *= -1
# Transition entropy
self.__transition_entropy = 0
destination_p_log_sum = self.transition_matrix_analyzer.transition_matrix_probabilities.apply(lambda row: row.apply(lambda p: p * numpy.log(p + 1e-9)).sum(), axis=1)
for aoi, s in destination_p_log_sum.items():
self.__transition_entropy += s * stationary_probalities[aoi]
self.__transition_entropy *= -1
@property
def stationary_entropy(self) -> float:
"""Stationary entropy."""
return self.__stationary_entropy
@property
def transition_entropy(self) -> float:
"""Transition entropy."""
return self.__transition_entropy
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