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67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
# Titan Robotics Team 2022: NaiveBayes 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 NaiveBayes'
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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.NaiveBayes() 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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)
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__all__ = [
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'gaussian',
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'multinomial',
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'bernoulli',
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'complement',
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]
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import sklearn
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from . import ClassificationMetric
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def gaussian(data, labels, test_size = 0.3, **kwargs):
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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.naive_bayes.GaussianNB(**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 multinomial(data, labels, test_size = 0.3, **kwargs):
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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.naive_bayes.MultinomialNB(**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 bernoulli(data, labels, test_size = 0.3, **kwargs):
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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.naive_bayes.BernoulliNB(**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 complement(data, labels, test_size = 0.3, **kwargs):
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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.naive_bayes.ComplementNB(**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) |