This is the reference implementation of my Master Thesis Optimizer Ensembles for Automated Machine Learning.
Simple usage:
from frankensteins_automl.frankensteins_automl import (
FrankensteinsAutoMLConfig,
FrankensteinsAutoML,
)
config = FrankensteinsAutoMLConfig()
# Either use an ARFF file as input
config.data_input_from_arff_file("<path/to/data/arff/file>", <target_column_index>)
# Or alternatively, provide the data as two arrays yourself
config.direct_data_input(<data_array>, <target_class_array>)
automl = FrankensteinsAutoML(config)
results = automl.run()
pipeline = results["pipeline_object"]
score = results["search_score"]
For other configuration possibilities please refer to the FrankensteinsAutoMLConfig
class.
The default timeouts there are really short, so you probably want to adjust them for any non-toy dataset.
Visualize search:
- Add the following line to the config:
config.event_send_url = "http://localhost:3000/event"
- Run
yarn install
inside of thesearch_visualization/
folder - Run
yarn electron:serve
inside of thesearch_visualization/
folder before you start frankensteins-automl