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48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
# Titan Robotics Team 2022: KNN submodule
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# Written by Arthur Lu
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# Notes:
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# this should be imported as a python module using 'from tra_analysis import KNN'
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# setup:
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__version__ = "1.0.2"
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__changelog__ = """changelog:
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1.0.2:
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- generalized optional args to **kwargs
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1.0.1:
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- optimized imports
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1.0.0:
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- ported analysis.KNN() here
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- removed classness
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"""
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__author__ = (
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"Arthur Lu <learthurgo@gmail.com>",
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"James Pan <zpan@imsa.edu>"
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)
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__all__ = [
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'knn_classifier',
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'knn_regressor'
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]
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import sklearn
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from . import ClassificationMetric, RegressionMetric
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def knn_classifier(data, labels, n_neighbors = 5, test_size = 0.3, **kwargs): #expects *2d data and 1d labels post-scaling
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.neighbors.KNeighborsClassifier(n_neighbors = n_neighbors, **kwargs)
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model.fit(data_train, labels_train)
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predictions = model.predict(data_test)
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return model, ClassificationMetric(predictions, labels_test)
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def knn_regressor(data, outputs, n_neighbors = 5, test_size = 0.3, **kwargs):
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data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
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model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, **kwargs)
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model.fit(data_train, outputs_train)
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predictions = model.predict(data_test)
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return model, RegressionMetric.RegressionMetric(predictions, outputs_test) |