Note
Click here to download the full example code
Classification API¶
Here are some examples of the machine learning API applied to classification models.
from __future__ import division, unicode_literals
from gemseo.api import configure_logger, load_dataset
from gemseo.mlearning.api import (
create_classification_model,
get_classification_models,
get_classification_options,
)
configure_logger()
Out:
<RootLogger root (INFO)>
Get available classification models¶
print(get_classification_models())
Out:
['KNNClassifier', 'RandomForestClassifier', 'SVMClassifier']
Get classification model options¶
print(get_classification_options("KNNClassifier"))
Out:
+---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
| Name | Description | Type |
+---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
| input_names | The names of the input variables. if none, consider all input variables mentioned in the | null |
| | learning dataset. | |
| n_neighbors | The number of neighbors. | integer |
| output_names | The names of the output variables. if none, consider all input variables mentioned in the | null |
| | learning dataset. | |
| 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. | |
+---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
INFO - 12:56:37: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
INFO - 12:56:37: | Name | Description | Type |
INFO - 12:56:37: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
INFO - 12:56:37: | input_names | The names of the input variables. if none, consider all input variables mentioned in the | null |
INFO - 12:56:37: | | learning dataset. | |
INFO - 12:56:37: | n_neighbors | The number of neighbors. | integer |
INFO - 12:56:37: | output_names | The names of the output variables. if none, consider all input variables mentioned in the | null |
INFO - 12:56:37: | | learning dataset. | |
INFO - 12:56:37: | transformer | The strategies to transform the variables. the values are instances of | null |
INFO - 12:56:37: | | :class:`.transformer` while the keys are the names of either the variables or the groups | |
INFO - 12:56:37: | | of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. if a | |
INFO - 12:56:37: | | group is specified, the :class:`.transformer` will be applied to all the variables of this | |
INFO - 12:56:37: | | group. if none, do not transform the variables. | |
INFO - 12:56:37: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
{'$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 None, do not transform the variables.', 'type': 'null'}, 'input_names': {'description': 'The names of the input variables. If None, consider all input variables mentioned in the learning dataset.', 'type': 'null'}, 'output_names': {'description': 'The names of the output variables. If None, consider all input variables mentioned in the learning dataset.', 'type': 'null'}, 'n_neighbors': {'description': 'The number of neighbors.', 'type': 'integer'}}, '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
Total running time of the script: ( 0 minutes 0.053 seconds)