.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/mlearning/clustering_model/plot_clustering_api.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_mlearning_clustering_model_plot_clustering_api.py: API === Here are some examples of the machine learning API applied to clustering models. .. GENERATED FROM PYTHON SOURCE LINES 32-34 Import ------ .. GENERATED FROM PYTHON SOURCE LINES 34-46 .. code-block:: default 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() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 47-49 Get available clustering models ------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 49-51 .. code-block:: default print(get_clustering_models()) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ['GaussianMixture', 'KMeans', 'MLPredictiveClusteringAlgo'] .. GENERATED FROM PYTHON SOURCE LINES 52-54 Get clustering model options ---------------------------- .. GENERATED FROM PYTHON SOURCE LINES 54-56 .. code-block:: default print(get_clustering_options("GaussianMixture")) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ | Name | Description | Type | +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ | n_components | The number of components of the gaussian mixture. | integer | | transformer | The strategies to transform the variables. the values are instances of | null | | | :class:`.transformer` while the keys are the names of either the variables or the groups | | | | of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. if a | | | | group is specified, the :class:`.transformer` will be applied to all the variables of this | | | | group. if none, do not transform the variables. | | | var_names | The names of the variables. if none, consider all variables mentioned in the learning | null | | | dataset. | | +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ INFO - 14:41:07: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ INFO - 14:41:07: | Name | Description | Type | INFO - 14:41:07: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ INFO - 14:41:07: | n_components | The number of components of the gaussian mixture. | integer | INFO - 14:41:07: | transformer | The strategies to transform the variables. the values are instances of | null | INFO - 14:41:07: | | :class:`.transformer` while the keys are the names of either the variables or the groups | | INFO - 14:41:07: | | of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. if a | | INFO - 14:41:07: | | group is specified, the :class:`.transformer` will be applied to all the variables of this | | INFO - 14:41:07: | | group. if none, do not transform the variables. | | INFO - 14:41:07: | var_names | The names of the variables. if none, consider all variables mentioned in the learning | null | INFO - 14:41:07: | | dataset. | | INFO - 14:41:07: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'transformer': {'description': 'The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If None, do not transform the variables.', 'type': 'null'}, 'var_names': {'description': 'The names of the variables. If None, consider all variables mentioned in the learning dataset.', 'type': 'null'}, 'n_components': {'description': 'The number of components of the Gaussian mixture.', 'type': 'integer'}}, 'required': ['n_components']} .. GENERATED FROM PYTHON SOURCE LINES 57-59 Create clustering model ----------------------- .. GENERATED FROM PYTHON SOURCE LINES 59-65 .. code-block:: default iris = load_dataset("IrisDataset") model = create_clustering_model("KMeans", data=iris, n_clusters=3) model.learn() print(model) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none KMeans(n_clusters=3, random_state=0, var_names=None) built from 150 learning samples .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.037 seconds) .. _sphx_glr_download_examples_mlearning_clustering_model_plot_clustering_api.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_clustering_api.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_clustering_api.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_