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.
Pandas 1.2.3, NumPy 1.19.2, TensorFlow 2.4.1, and TextDistance 4.2.0
>>> import sys
>>> sys.path.append('./Code')
>>> from LZWStringGenerator import *
>>> from RNNExploration4SymbolicTS import *
>>> df_strings = LZWStringLibrary(symbols=3, complexity=[3, 9])
>>> df_stringsProcessing: 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 |