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"""Transition matrix probabilities and density 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 <http://www.gnu.org/licenses/>.
"""
__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "GPLv3"
import numpy
import pandas
from argaze import GazeFeatures, DataFeatures
class AOIScanPathAnalyzer(GazeFeatures.AOIScanPathAnalyzer):
"""Implementation of transition matrix probabilities and density 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_probabilities = pandas.DataFrame()
self.__transition_matrix_density = 0.
@DataFeatures.PipelineStepMethod
def analyze(self, aoi_scan_path: GazeFeatures.AOIScanPath):
assert(len(aoi_scan_path) > 1)
# Sum transitions starting from each aoi
row_sum = aoi_scan_path.transition_matrix.apply(lambda row: row.sum(), axis=1)
# Editing transition matrix probabilities
# Note: when no transition starts from an aoi, destination probabilities is equal to 1/S where S is the number of aoi
self.__transition_matrix_probabilities = aoi_scan_path.transition_matrix.apply(lambda row: row.apply(lambda p: p / row_sum[row.name] if row_sum[row.name] > 0 else 1 / row_sum.size), axis=1)
# Calculate matrix density
self.__transition_matrix_density = (self.__transition_matrix_probabilities != 0.).astype(int).sum(axis=1).sum() / self.__transition_matrix_probabilities.size
@property
def transition_matrix_probabilities(self) -> pandas.DataFrame:
"""Transition matrix probabilities"""
return self.__transition_matrix_probabilities
@property
def transition_matrix_density(self) -> float:
"""Transition matrix density."""
return self.__transition_matrix_density
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