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Classification API¶
Here are some examples of the machine learning API applied to classification models.
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()
<RootLogger root (INFO)>
Get available classification models¶
print(get_classification_models())
['KNNClassifier', 'RandomForestClassifier', 'SVMClassifier']
Get classification model options¶
print(get_classification_options("KNNClassifier"))
+---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
| Name | Description | Type |
+---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
| input_names | The names of the input variables. if ``none``, consider all the input variables of the | null |
| | learning dataset. | |
| n_neighbors | The number of neighbors. | integer |
| output_names | The names of the output variables. if ``none``, consider all the output variables of the | null |
| | learning dataset. | |
| transformer | The strategies to transform the variables. the values are instances of | object |
| | :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 :attr:`.identity`, do not transform the variables. | |
+---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
INFO - 12:54:37: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
INFO - 12:54:37: | Name | Description | Type |
INFO - 12:54:37: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
INFO - 12:54:37: | input_names | The names of the input variables. if ``none``, consider all the input variables of the | null |
INFO - 12:54:37: | | learning dataset. | |
INFO - 12:54:37: | n_neighbors | The number of neighbors. | integer |
INFO - 12:54:37: | output_names | The names of the output variables. if ``none``, consider all the output variables of the | null |
INFO - 12:54:37: | | learning dataset. | |
INFO - 12:54:37: | transformer | The strategies to transform the variables. the values are instances of | object |
INFO - 12:54:37: | | :class:`.transformer` while the keys are the names of either the variables or the groups | |
INFO - 12:54:37: | | of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. | |
INFO - 12:54:37: | | if a group is specified, the :class:`.transformer` will be applied to all the variables of | |
INFO - 12:54:37: | | this group. if :attr:`.identity`, do not transform the variables. | |
INFO - 12:54:37: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
{'$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 :attr:`.IDENTITY`, do not transform the variables.', 'type': 'object'}, 'input_names': {'description': 'The names of the input variables. If ``None``, consider all the input variables of the learning dataset.', 'type': 'null'}, 'output_names': {'description': 'The names of the output variables. If ``None``, consider all the output variables of the learning dataset.', 'type': 'null'}, 'n_neighbors': {'description': 'The number of neighbors.', 'type': 'integer'}}, 'required': ['n_neighbors', 'transformer']}
Create classification model¶
iris = create_benchmark_dataset("IrisDataset", as_io=True)
model = create_classification_model("KNNClassifier", data=iris)
model.learn()
print(model)
KNNClassifier(n_neighbors=5)
based on the scikit-learn library
built from 150 learning samples
Total running time of the script: ( 0 minutes 0.040 seconds)