182 lines
8.2 KiB
Plaintext
182 lines
8.2 KiB
Plaintext
---
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title: SklearnSVM
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description: API reference for qiskit.aqua.algorithms.SklearnSVM
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.aqua.algorithms.SklearnSVM
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---
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<span id="qiskit-aqua-algorithms-sklearnsvm" />
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# qiskit.aqua.algorithms.SklearnSVM
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<Class id="qiskit.aqua.algorithms.SklearnSVM" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.8/qiskit/aqua/algorithms/classifiers/sklearn_svm/sklearn_svm.py" signature="SklearnSVM(training_dataset, test_dataset=None, datapoints=None, gamma=None, multiclass_extension=None)" modifiers="class">
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The Sklearn SVM algorithm (classical).
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This scikit-learn based SVM algorithm uses a classical approach to experiment with feature map classification problems. See also the quantum classifier [`QSVM`](qiskit.aqua.algorithms.QSVM "qiskit.aqua.algorithms.QSVM").
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Internally, this algorithm will run the binary classification or multiclass classification based on how many classes the data has. If the data has more than 2 classes then a *multiclass\_extension* is required to be supplied. Aqua provides several [`multiclass_extensions`](qiskit.aqua.components.multiclass_extensions#module-qiskit.aqua.components.multiclass_extensions "qiskit.aqua.components.multiclass_extensions").
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**Parameters**
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* **training\_dataset** (`Dict`\[`str`, `ndarray`]) – Training dataset.
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* **test\_dataset** (`Optional`\[`Dict`\[`str`, `ndarray`]]) – Testing dataset.
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* **datapoints** (`Optional`\[`ndarray`]) – Prediction dataset.
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* **gamma** (`Optional`\[`int`]) – Used as input for sklearn rbf\_kernel which is used internally. See [sklearn.metrics.pairwise.rbf\_kernel](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html) for more information about gamma.
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* **multiclass\_extension** (`Optional`\[`MulticlassExtension`]) – If number of classes is greater than 2 then a multiclass scheme must be supplied, in the form of a multiclass extension.
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**Raises**
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[**AquaError**](qiskit.aqua.AquaError "qiskit.aqua.AquaError") – Multiclass extension not supplied when number of classes > 2
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### \_\_init\_\_
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<Function id="qiskit.aqua.algorithms.SklearnSVM.__init__" signature="__init__(training_dataset, test_dataset=None, datapoints=None, gamma=None, multiclass_extension=None)">
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**Parameters**
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* **training\_dataset** (`Dict`\[`str`, `ndarray`]) – Training dataset.
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* **test\_dataset** (`Optional`\[`Dict`\[`str`, `ndarray`]]) – Testing dataset.
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* **datapoints** (`Optional`\[`ndarray`]) – Prediction dataset.
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* **gamma** (`Optional`\[`int`]) –
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Used as input for sklearn rbf\_kernel which is used internally. See [sklearn.metrics.pairwise.rbf\_kernel](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html) for more information about gamma.
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* **multiclass\_extension** (`Optional`\[`MulticlassExtension`]) – If number of classes is greater than 2 then a multiclass scheme must be supplied, in the form of a multiclass extension.
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**Raises**
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[**AquaError**](qiskit.aqua.AquaError "qiskit.aqua.AquaError") – Multiclass extension not supplied when number of classes > 2
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</Function>
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## Methods
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| ---------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------- |
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| [`__init__`](#qiskit.aqua.algorithms.SklearnSVM.__init__ "qiskit.aqua.algorithms.SklearnSVM.__init__")(training\_dataset\[, test\_dataset, …]) | **type training\_dataset**`Dict`\[`str`, `ndarray`] |
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| [`load_model`](#qiskit.aqua.algorithms.SklearnSVM.load_model "qiskit.aqua.algorithms.SklearnSVM.load_model")(file\_path) | Load a model from a file path. |
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| [`predict`](#qiskit.aqua.algorithms.SklearnSVM.predict "qiskit.aqua.algorithms.SklearnSVM.predict")(data) | Predict using the SVM |
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| [`run`](#qiskit.aqua.algorithms.SklearnSVM.run "qiskit.aqua.algorithms.SklearnSVM.run")() | Execute the classical algorithm. |
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| [`save_model`](#qiskit.aqua.algorithms.SklearnSVM.save_model "qiskit.aqua.algorithms.SklearnSVM.save_model")(file\_path) | Save the model to a file path. |
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| [`test`](#qiskit.aqua.algorithms.SklearnSVM.test "qiskit.aqua.algorithms.SklearnSVM.test")(data, labels) | Test the SVM |
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| [`train`](#qiskit.aqua.algorithms.SklearnSVM.train "qiskit.aqua.algorithms.SklearnSVM.train")(data, labels) | Train the SVM |
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## Attributes
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| ------------------------------------------------------------------------------------------------------------------------ | ---------------------- |
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| [`class_to_label`](#qiskit.aqua.algorithms.SklearnSVM.class_to_label "qiskit.aqua.algorithms.SklearnSVM.class_to_label") | returns class to label |
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| [`label_to_class`](#qiskit.aqua.algorithms.SklearnSVM.label_to_class "qiskit.aqua.algorithms.SklearnSVM.label_to_class") | returns label to class |
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| [`random`](#qiskit.aqua.algorithms.SklearnSVM.random "qiskit.aqua.algorithms.SklearnSVM.random") | Return a numpy random. |
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| [`ret`](#qiskit.aqua.algorithms.SklearnSVM.ret "qiskit.aqua.algorithms.SklearnSVM.ret") | returns result |
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### class\_to\_label
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<Attribute id="qiskit.aqua.algorithms.SklearnSVM.class_to_label">
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returns class to label
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</Attribute>
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### label\_to\_class
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<Attribute id="qiskit.aqua.algorithms.SklearnSVM.label_to_class">
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returns label to class
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</Attribute>
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### load\_model
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<Function id="qiskit.aqua.algorithms.SklearnSVM.load_model" signature="load_model(file_path)">
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Load a model from a file path.
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**Parameters**
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**file\_path** (*str*) – the path of the saved model.
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</Function>
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### predict
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<Function id="qiskit.aqua.algorithms.SklearnSVM.predict" signature="predict(data)">
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Predict using the SVM
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**Parameters**
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**data** (*numpy.ndarray*) – NxD array, where N is the number of data, D is the feature dimension.
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**Returns**
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predicted labels, Nx1 array
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**Return type**
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numpy.ndarray
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</Function>
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### random
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<Attribute id="qiskit.aqua.algorithms.SklearnSVM.random">
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Return a numpy random.
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</Attribute>
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### ret
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<Attribute id="qiskit.aqua.algorithms.SklearnSVM.ret">
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returns result
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</Attribute>
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### run
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<Function id="qiskit.aqua.algorithms.SklearnSVM.run" signature="run()">
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Execute the classical algorithm.
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**Returns**
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results of an algorithm.
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**Return type**
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dict
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</Function>
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### save\_model
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<Function id="qiskit.aqua.algorithms.SklearnSVM.save_model" signature="save_model(file_path)">
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Save the model to a file path.
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**Parameters**
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**file\_path** (*str*) – a path to save the model.
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</Function>
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### test
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<Function id="qiskit.aqua.algorithms.SklearnSVM.test" signature="test(data, labels)">
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Test the SVM
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**Parameters**
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* **data** (*numpy.ndarray*) – NxD array, where N is the number of data, D is the feature dimension.
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* **labels** (*numpy.ndarray*) – Nx1 array, where N is the number of data
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**Returns**
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accuracy
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**Return type**
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float
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</Function>
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### train
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<Function id="qiskit.aqua.algorithms.SklearnSVM.train" signature="train(data, labels)">
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Train the SVM
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**Parameters**
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* **data** (*numpy.ndarray*) – NxD array, where N is the number of data, D is the feature dimension.
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* **labels** (*numpy.ndarray*) – Nx1 array, where N is the number of data
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</Function>
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</Class>
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