pychronicles 0.0.9

A package for chronicle recognition

PyChronicle package

A chronicle is a specification of the complex temporal behaviors as a graph of temporal constraints. More specifically, a chronicle is a multiset of events and a set of temporal constraints specifying that occurrences of pairs of events must occurs within a given temporal interval. It can be used to recognize complex behaviors in sequence the temporal events.

This library proposes a Python class to define a chronicle, to read/save them in a standard CRS format. The main useful functionnality for chronicle is their efficient matching in a temporal sequence.

A temporal sequence may have three different format:

• a simple list of items (str, int or None) with implicit timestamps : ['a', 'b', ..., None, 'c', None, 'd']
• a list of explicitly timestamped items (str or int) : [ (1,'a'), (23,'b'), (30,'c'), (45, 'd')]
• a pandas dataframe indexed by timestamps and using the items in a columns (named label)

The chronicles handles to model of timestamps (for the last two types of sequence models):

• discrete timestamps using integers
• continuous timestamps using datetime format. In this case the temporal constraints of a chronicle must be defined using timedelta values.

The package implements efficient algorithms to recognize it. It benefits from pandas functionalities to increase their efficiency. There are three different ways to recognize a chronicle in a sequence of a events:

• the absence/presence recognition (c.match(seq)): its result is a boolean stating whether the chronicle occur at least once in the sequence, this is the most efficient algorithm
• the occurrence enumeration (c.recognize(seq)): its result is a list of occurrence of the chronicle in a sequence. Contrary to the first implementation, it looks for all possible combinasion of events. Thus it is less efficient
• the approximate occurrence enumeration (c.cmp(seq, 0.7)): its result is a list of occurrences that are similar of the chronicle with a similarity threshold of 0.7.

In addition, when using a pandas dataframe which contains several sequences (indexed with an attribute), it is possible to request for matching a chronicle in all sequences (no specific optimisation).

Please note that the author is not fully satisfied by the function name and that it appeals to change them in a short delay ...

Perspectives

• extend the chronicle model for pandas dataframe by specifying event by a couple (attribute, value), that can be used to specify a wider range of complex behavior in multidimensional sequences
• graphical interfaces to edit chronicle (for instance with dash)
• better cythonize the recognition
• optimized matching of several chronicles at the same time

Requirements

Use pip install -r requirements.txt to install requirements.

Naturally, the latter may require superuser rights (consider prefixing the commands by sudo).

If you want to use Python 3 and your system defaults on Python 2.7, you may need to adjust the above commands, e.g., replace pip by pip3.

The required libraries are the following

• numpy
• scipy
• lazr.restfulclient
• larz.uri
• typing
• pandas

LAZR is used to instantiate chronicles from CRS files (with simple grammar).

Usage

Example of usage:

from pychronicles import *
#define a sequence of events
seq = [3,4,'b','a','a',1,3,'coucou','b','coucou',5,'coucou',5]

#define a chronicle
c=Chronicle()
c.add_event(0,'b')
c.add_event(1,1)
c.add_constraint(1,3, (3,45))
print(c)

#recognize the chronicle in the sequence
occs=c.recognize(seq)
print("occurrences: "+str(occs))

It is possible to specify chronicles using the CRS format. The following code illustrate the syntax for specifying a chronicle in this format.

chronicle C27_sub_0[]()
{
    event(Event_Type1[], t006)
    event(Event_Type1[], t004)
    event(Event_Type2[], t002)
    event(Event_Type3[], t001)

    t004-t006 in [17,25]
    t006-t002 in [-16,-10]
    t002-t001 in [14,29]
    t004-t001 in [27,35]
}

Authorship

• Author: Thomas Guyet
• Institution: Inria
• date: 8/2022