95 lines
5.8 KiB
Plaintext
95 lines
5.8 KiB
Plaintext
---
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title: AllPairs
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description: API reference for qiskit.aqua.components.multiclass_extensions.AllPairs
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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.components.multiclass_extensions.AllPairs
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---
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# qiskit.aqua.components.multiclass\_extensions.AllPairs
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<Class id="qiskit.aqua.components.multiclass_extensions.AllPairs" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/aqua/components/multiclass_extensions/all_pairs.py" signature="AllPairs" modifiers="class">
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The All-Pairs multiclass extension.
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In the **all-pairs** reduction, one trains $k(k−1)/2$ binary classifiers for a $k$-way multiclass problem; each receives the samples of a pair of classes from the original training set, and must learn to distinguish these two classes. At prediction time, a **weighted voting scheme** is used: all $k(k−1)/2$ classifiers are applied to an unseen sample, and each class gets assigned the sum of all the scores obtained by the various classifiers. The combined classifier returns as a result the class getting the highest value.
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### \_\_init\_\_
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<Function id="qiskit.aqua.components.multiclass_extensions.AllPairs.__init__" signature="__init__()">
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Initialize self. See help(type(self)) for accurate signature.
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</Function>
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## Methods
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| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [`__init__`](#qiskit.aqua.components.multiclass_extensions.AllPairs.__init__ "qiskit.aqua.components.multiclass_extensions.AllPairs.__init__")() | Initialize self. |
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| [`predict`](#qiskit.aqua.components.multiclass_extensions.AllPairs.predict "qiskit.aqua.components.multiclass_extensions.AllPairs.predict")(x) | Applying multiple estimators for prediction. |
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| [`set_estimator`](#qiskit.aqua.components.multiclass_extensions.AllPairs.set_estimator "qiskit.aqua.components.multiclass_extensions.AllPairs.set_estimator")(estimator\_cls\[, params]) | Called internally to set `Estimator` and parameters :type estimator\_cls: `Callable`\[\[`List`], `Estimator`] :param estimator\_cls: An `Estimator` class :type params: `Optional`\[`List`] :param params: Parameters for the estimator |
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| [`test`](#qiskit.aqua.components.multiclass_extensions.AllPairs.test "qiskit.aqua.components.multiclass_extensions.AllPairs.test")(x, y) | Testing multiple estimators each for distinguishing a pair of classes. |
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| [`train`](#qiskit.aqua.components.multiclass_extensions.AllPairs.train "qiskit.aqua.components.multiclass_extensions.AllPairs.train")(x, y) | Training multiple estimators each for distinguishing a pair of classes. |
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### predict
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<Function id="qiskit.aqua.components.multiclass_extensions.AllPairs.predict" signature="predict(x)">
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Applying multiple estimators for prediction.
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**Parameters**
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**x** (*numpy.ndarray*) – NxD array
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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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### set\_estimator
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<Function id="qiskit.aqua.components.multiclass_extensions.AllPairs.set_estimator" signature="set_estimator(estimator_cls, params=None)">
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Called internally to set `Estimator` and parameters :type estimator\_cls: `Callable`\[\[`List`], `Estimator`] :param estimator\_cls: An `Estimator` class :type params: `Optional`\[`List`] :param params: Parameters for the estimator
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**Return type**
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`None`
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</Function>
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### test
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<Function id="qiskit.aqua.components.multiclass_extensions.AllPairs.test" signature="test(x, y)">
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Testing multiple estimators each for distinguishing a pair of classes.
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**Parameters**
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* **x** (*numpy.ndarray*) – input points
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* **y** (*numpy.ndarray*) – input labels
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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.components.multiclass_extensions.AllPairs.train" signature="train(x, y)">
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Training multiple estimators each for distinguishing a pair of classes.
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**Parameters**
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* **x** (*numpy.ndarray*) – input points
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* **y** (*numpy.ndarray*) – input labels
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**Raises**
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**ValueError** – can not be fit when only one class is present.
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</Function>
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</Class>
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