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"""Stationary and transition entropy module."""
"""
This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with
this program. If not, see <https://www.gnu.org/licenses/>.
"""
__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "GPLv3"
import numpy
from argaze import GazeFeatures, DataFeatures
from argaze.GazeAnalysis import TransitionMatrix
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)
"""
@DataFeatures.PipelineStepInit
def __init__(self, **kwargs):
# Init AOIScanPathAnalyzer class
super().__init__()
self.__transition_matrix_analyzer = None
self.__stationary_entropy = -1
self.__transition_entropy = -1
@property
def transition_matrix_analyzer(self) -> TransitionMatrix.AOIScanPathAnalyzer:
"""Bind to TransitionMatrix analyzer to get its transition_matrix_probabilities.
!!! warning "Mandatory"
TransitionMatrix analyzer have to be loaded before.
"""
return self.__transition_matrix_analyzer
@transition_matrix_analyzer.setter
def transition_matrix_analyzer(self, transition_matrix_analyzer: TransitionMatrix.AOIScanPathAnalyzer):
self.__transition_matrix_analyzer = transition_matrix_analyzer
@DataFeatures.PipelineStepMethod
def analyze(self, aoi_scan_path: GazeFeatures.AOIScanPath):
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_probabilities = {aoi: count/scan_fixations_count for aoi, count in aoi_fixations_count.items()}
# Stationary entropy
self.__stationary_entropy = 0
for aoi, p in stationary_probabilities.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_probabilities[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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