Classification API

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

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_classification_model,
    get_classification_models,
    get_classification_options,
)

configure_logger()

standard_library.install_aliases()

Get available classification models

print(get_classification_models())

Out:

['KNNClassifier', 'RandomForestClassifier']

Get classification model options

print(get_classification_options("KNNClassifier"))

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'}, 'input_names': {'description': 'names of the input variables.\n:type input_names: list(str)\n'}, 'output_names': {'description': 'names of the output variables.\n:type output_names: list(str)\n'}, 'n_neighbors': {'type': 'integer', 'description': 'number of neighbors.\n:type n_neighbords: int\n'}}, 'required': ['n_neighbors']}

Create classification model

iris = load_dataset("IrisDataset", as_io=True)

model = create_classification_model("KNNClassifier", data=iris)
model.learn()

print(model)

Out:

KNNClassifier(n_neighbors=5)
| based on the scikit-learn library
| built from 150 learning samples