Advanced Functionalities¶
The guide provided on Getting Started page uses the wrapper
function hardy_main which sequentially executes
data_wrapper, classifier_wrapper and report generation.
The structure of HARDy package is shown in the image below:
data_wrapper parses through the transformation configuration
file and loads it into the environment. The data wrapper also
loads the .csv files into the environment. The data wrapper
then applies the transformations, outlined in the configuration file,
to the data files. The data_wrapper stores the tranformed
information into the .pkl file or into .png files
depending on the user defined arguments.
To save time data_wrapper is capable of parallelizing the
transformations process. The parallelization is controlled through the
n_threads parameter. By default, n_threads is set a 1.
classifier_wrapper loads the data generated by data_wrapper.
By default, classifier_wrapper deletes the .pkl generated
by the data_wrapper. The loaded data is then ran through the
convolutional neural network (CNN) or hyperparameter tuning sessions
depending upon the specified inputs from the user.
Each tuning session is then reported into data_reporting module.
The tuned/trained model is then validated againt num_test_files_class.
Then the report folder having project_name in data_path
is created. The report folder contains folders for each transformation run.
These transformation folders contains model validation results, best tuned model
for a particular transformation and model hyperparameter details.