Note
Click here to download the full example code
API¶
Here are some examples of the machine learning API applied to clustering models.
Import¶
from __future__ import absolute_import, division, print_function, unicode_literals
from future import standard_library
from gemseo.api import configure_logger, load_dataset
from gemseo.mlearning.api import (
create_clustering_model,
get_clustering_models,
get_clustering_options,
)
configure_logger()
standard_library.install_aliases()
Get clustering model options¶
print(get_clustering_options("GaussianMixture"))
Out:
{'type': 'object', 'properties': {'transformer': {'description': 'transformation strategy for data groups.\nIf None, do not transform data. Default: None.\n:type transformer: dict(str)\n'}, 'var_names': {'description': 'names of the variables to consider.\n:type var_names: list(str)\n'}, 'n_components': {'type': 'integer', 'description': 'number of Gaussian mixture components.\nDefault: 5.\n:type n_components: int\n'}}, 'required': ['n_components']}
Create clustering model¶
iris = load_dataset("IrisDataset")
model = create_clustering_model("KMeans", data=iris, n_clusters=3)
model.learn()
print(model)
Out:
KMeans(var_names=None, n_clusters=3, random_state=0)
| built from 150 learning samples
Total running time of the script: ( 0 minutes 0.041 seconds)