Since version 0.40, Nkululeko can now show the best performing X acoustic features according to some model.

There is a new section call EXPL (short for exploration), and you could state

[EXPL]
feature_distributions = True
model = ['tree', 'gmm', 'svm']
permutation = True
max_feats = 5
sample_selection = test

in your config file, and then run the exploration module like this:

python -m nkululeko.explore --config my_config.ini

The following values can be set in the config:

• feature_distributions: use this analysis.
• sample_selection: Which sample set/split to use, either all, train or test
• model: Which models to use to estimate feature importance. Can be all models from the MODEL section, If several are stated, the mean result is used.
• max_feats: The best n feats are shown
• permutation: use feature permutation to determine the best features. Make sure to test the models before.

The resulting list will then appear in the result folder and a barplot image in the image folder.

Afterwards you could inspect single features as described here

As shown in this post, with Nkululeko you can select only specific features from your features sets by specifying them in the [FEAT] section:

[FEATS]
features = ['JitterPCA', 'meanF0Hz', 'hld_sylRate']

What you can also do, is plotting them per category (only for classification), by specifying in the PLOT section if you would like that for all samples or only test or train samples:

[EXPL]
# turn it on
feature_distributions = True 
# use only training samples
sample_selection = train 
# only plot the 5 most important features 
max_feats = 5  

You would have to call nkululeko with the explore interface:

python -m nkululeko.explore --config <myConfig.ini>

The image file is in the image folder and should look similar to this:

There are three ways to predict a number of samples:

1. If you want to save the predictions of an experiment for later use, you can do so by stating in the EXP section

   [EXP]
   save_test = ./my_saved_test_predictions.csv

   The output format is CSV, comma seperated values.

2. Alternatively, you can test an existing database against the best model you trained before, by stating the databases as tests in the DATA section:

   [DATA]
   tests = ['my_testdb']
   my_testdb = /mypath/my_testdb
   ...

   and then calling Nkululeko's test module

   python -m nkululeko.test --config mycoonfg.ini --outfile myresults.csv

3. Run the demo module simply for a set of files:

   python -m nkululeko.demo --config mycoonfg.ini --list my_filelist.txt