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43 lines
2.2 KiB
Python
43 lines
2.2 KiB
Python
# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported.
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import sklearn
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from sklearn import model_selection, naive_bayes
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from . import ClassificationMetric, RegressionMetric
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class NaiveBayes:
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def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
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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(priors = priors, var_smoothing = var_smoothing)
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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(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
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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(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
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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(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
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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(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
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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(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
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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(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
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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) |