RNNExploration4SymbolicTS

Library to explore the hyper-parameter space of Recurrent Neural Networks (RNNs)

This repository contains a library for the generation of strings of tunable complexity using the LZW compressing method as base to approximate Kolmogorov complexity. It also contains the tools for the exploration of the hyperparameter space of commonly used RNNs as well as novel ones.

Prerequisites

Pandas 1.2.3, NumPy 1.19.2, TensorFlow 2.4.1, and TextDistance 4.2.0

Example

>>> import sys
>>> sys.path.append('./Code')
>>> from LZWStringGenerator import *
>>> from RNNExploration4SymbolicTS import *
>>> df_strings = LZWStringLibrary(symbols=3, complexity=[3, 9])
>>> df_strings

Processing: 2 of 2

	nr_symbols	LZW_complexity	length	string
0	3	3	3	BCA
1	3	9	12	ABCBBCBBABCC

>>> df_iters = pd.DataFrame()
>>> for i, string in enumerate(df_strings['string']):
>>>     kwargs = df_strings.iloc[i,:-1].to_dict()
>>>     seed_string = df_strings.iloc[i,-1]
>>>     df_iter = RNN_Iteration(seed_string, iterations=2, architecture='LSTM', **kwargs)
>>>     df_iter.loc[:, kwargs.keys()] = kwargs.values()
>>>     df_iters = df_iters.append(df_iter)
>>> df_iter.reset_index(drop=True, inplace=True)

...

>>> df_iters.reset_index(drop=True, inplace=True)
>>> df_iters

	jw	dl	total_epochs	seq_test	seq_forecast	total_time	nr_symbols	LZW_complexity	length
0	1.000000	1.0	12	ABCABCABCA	ABCABCABCA	2.685486	3	3	3
1	1.000000	1.0	14	ABCABCABCA	ABCABCABCA	2.436733	3	3	3
2	0.657143	0.5	36	CBBCBBABCC	AABCABCABC	3.352712	3	9	12
3	0.704762	0.4	36	CBBCBBABCC	ABCBABBBBB	3.811584	3	9	12