aikit
Automatic Tool Kit for Machine Learning and Datascience.
The objective is to provide tools to ease the repetitive part of the DataScientist job and so that he/she can focus on modelization. This package is still in alpha and more features will be added. Its mains features are:
- improved and new "scikit-learn like" transformers ;
- GraphPipeline : an extension of sklearn Pipeline that handles more generic chains of tranformations ;
- an AutoML to automatically search throught several transformers and models.
Full documentation is available here: https://aikit.readthedocs.io/en/latest/
You can run examples here, thanks to Binder.
GraphPipeline
The GraphPipeline object is an extension of sklearn.pipeline.Pipeline but the transformers/models can be chained with any directed graph.
The objects takes as input two arguments:
- models: dictionary of models (each key is the name of a given node, and each corresponding value is the transformer corresponding to that node)
- edges: list of tuples that links the nodes to each other
Example:
gpipeline = GraphPipeline(
models = {
"vect": CountVectorizerWrapper(analyzer="char",
ngram_range=(1, 4),
columns_to_use=["text1", "text2"]),
"cat": NumericalEncoder(columns_to_use=["cat1", "cat2"]),
"rf": RandomForestClassifier(n_estimators=100)
},
edges = [("vect", "rf"), ("cat", "rf")]
)
AutoML
Aikit contains an AutoML part which will test several models and transformers for a given dataset.
For example, you can create the following python script run_automl_titanic.py:
from aikit.datasets import load_dataset, DatasetEnum
from aikit.ml_machine import MlMachineLauncher
def loader():
dfX, y, *_ = load_dataset(DatasetEnum.titanic)
return dfX, y
def set_configs(launcher):
""" modify that function to change launcher configuration """
launcher.job_config.score_base_line = 0.75
launcher.job_config.allow_approx_cv = True
return launcher
if __name__ == "__main__":
launcher = MlMachineLauncher(base_folder = "~/automl/titanic",
name = "titanic",
loader = loader,
set_configs = set_configs)
launcher.execute_processed_command_argument()And then run the command:
python run_automl_titanic.py run -n 4
To run the automl using 4 workers, the results will be stored in the specified folder You can aggregate those result using:
python run_automl_titanic.py result