.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/mlearning/classification_model/plot_classification_api.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_mlearning_classification_model_plot_classification_api.py: Classification API ================== Here are some examples of the machine learning API applied to classification models. .. GENERATED FROM PYTHON SOURCE LINES 28-40 .. code-block:: Python from __future__ import annotations from gemseo import configure_logger from gemseo import create_benchmark_dataset from gemseo.mlearning import create_classification_model from gemseo.mlearning import get_classification_models from gemseo.mlearning import get_classification_options configure_logger() .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 41-43 Get available classification models ----------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 43-45 .. code-block:: Python get_classification_models() .. rst-class:: sphx-glr-script-out .. code-block:: none ['KNNClassifier', 'RandomForestClassifier', 'SVMClassifier'] .. GENERATED FROM PYTHON SOURCE LINES 46-48 Get classification model options -------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 48-50 .. code-block:: Python get_classification_options("KNNClassifier") .. rst-class:: sphx-glr-script-out .. code-block:: none +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ | Name | Description | Type | +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ | input_names | The names of the input variables | array | | n_neighbors | The number of neighbors. | integer | | output_names | The names of the output variables | array | | parameters | Other parameters. | object | | transformer | The strategies to transform the variables. the values are instances of | object | | | :class:`.basetransformer` 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:`.basetransformer` will be applied to all | | | | the variables of this group. if :attr:`.identity`, do not transform the variables. | | +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ INFO - 08:37:27: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ INFO - 08:37:27: | Name | Description | Type | INFO - 08:37:27: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ INFO - 08:37:27: | input_names | The names of the input variables | array | INFO - 08:37:27: | n_neighbors | The number of neighbors. | integer | INFO - 08:37:27: | output_names | The names of the output variables | array | INFO - 08:37:27: | parameters | Other parameters. | object | INFO - 08:37:27: | transformer | The strategies to transform the variables. the values are instances of | object | INFO - 08:37:27: | | :class:`.basetransformer` while the keys are the names of either the variables or the | | INFO - 08:37:27: | | groups of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression | | INFO - 08:37:27: | | algorithms. if a group is specified, the :class:`.basetransformer` will be applied to all | | INFO - 08:37:27: | | the variables of this group. if :attr:`.identity`, do not transform the variables. | | INFO - 08:37:27: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ {'additionalProperties': False, 'description': 'The settings of the k-nearest neighbors classification algorithm.', 'properties': {'transformer': {'description': 'The strategies to transform the variables.\n\nThe values are instances of :class:`.BaseTransformer`\nwhile the keys are the names of\neither the variables\nor the groups of variables,\ne.g. ``"inputs"`` or ``"outputs"``\nin the case of the regression algorithms.\nIf a group is specified,\nthe :class:`.BaseTransformer` will be applied\nto all the variables of this group.\nIf :attr:`.IDENTITY`, do not transform the variables.', 'title': 'Transformer', 'type': 'object'}, 'parameters': {'description': 'Other parameters.', 'title': 'Parameters', 'type': 'object'}, 'input_names': {'default': [], 'description': 'The names of the input variables', 'items': {'type': 'string'}, 'title': 'Input Names', 'type': 'array'}, 'output_names': {'default': [], 'description': 'The names of the output variables', 'items': {'type': 'string'}, 'title': 'Output Names', 'type': 'array'}, 'n_neighbors': {'default': 5, 'description': 'The number of neighbors.', 'exclusiveMinimum': 0, 'title': 'N Neighbors', 'type': 'integer'}}, 'title': 'KNNClassifier_Settings', 'type': 'object'} .. GENERATED FROM PYTHON SOURCE LINES 51-53 Create classification model --------------------------- .. GENERATED FROM PYTHON SOURCE LINES 53-58 .. code-block:: Python iris = create_benchmark_dataset("IrisDataset", as_io=True) model = create_classification_model("KNNClassifier", data=iris) model.learn() model .. raw:: html
KNNClassifier(input_names=(), n_neighbors=5, output_names=(), parameters={}, transformer={'inputs': <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x7f6dfa960ee0>})
  • based on the scikit-learn library
  • built from 150 learning samples


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