Nkululeko: show feature importance

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

model = tree
sample_num = 15

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

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

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

Nkululeko: how to plot distributions of feature values

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

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:

# 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:

Nkululeko: how to predict many samples

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

    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:

    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