API

Here are some examples of the machine learning API applied to clustering models.

Import

from __future__ import division, unicode_literals

from gemseo.api import configure_logger, load_dataset
from gemseo.mlearning.api import (
    create_clustering_model,
    get_clustering_models,
    get_clustering_options,
)

configure_logger()

Out:

<RootLogger root (INFO)>

Get available clustering models

print(get_clustering_models())

Out:

['GaussianMixture', 'KMeans', 'MLPredictiveClusteringAlgo']

Get clustering model options

print(get_clustering_options("GaussianMixture"))

Out:

{'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'transformer': {'type': 'null'}, 'var_names': {'type': 'null'}, 'n_components': {'description': 'The number of components of the Gaussian mixture.', 'type': 'integer'}}, '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(n_clusters=3, random_state=0, var_names=None)
   built from 150 learning samples