tra-analysis/analysis-master/tra_analysis/NaiveBayes_obj.py

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tra-analysis v 3.0.0 aggregate PR (#73) * reflected doc changes to README.md Signed-off-by: Arthur Lu <learthurgo@gmail.com> * tra_analysis v 2.1.0-alpha.1 Signed-off-by: Arthur Lu <learthurgo@gmail.com> * changed setup.py to use __version__ from source added Topic and keywords Signed-off-by: Arthur Lu <learthurgo@gmail.com> * updated Supported Platforms in README.md Signed-off-by: Arthur Lu <learthurgo@gmail.com> * moved required files back to parent Signed-off-by: Arthur Lu <learthurgo@gmail.com> * moved security back to parent Signed-off-by: Arthur Lu <learthurgo@gmail.com> * moved security back to parent moved contributing back to parent Signed-off-by: Arthur Lu <learthurgo@gmail.com> * add PR template Signed-off-by: Arthur Lu <learthurgo@gmail.com> * moved to parent folder Signed-off-by: Arthur Lu <learthurgo@gmail.com> * moved meta files to .github folder Signed-off-by: Arthur Lu <learthurgo@gmail.com> * Analysis.py v 3.0.1 Signed-off-by: Arthur Lu <learthurgo@gmail.com> * updated test_analysis for submodules, and added missing numpy import in Sort.py * fixed item one of Issue #58 Signed-off-by: Arthur Lu <learthurgo@gmail.com> * readded cache searching in postCreateCommand Signed-off-by: Arthur Lu <learthurgo@gmail.com> * added myself as an author * feat: created kivy gui boilerplate * added Kivy to requirements.txt Signed-off-by: Arthur Lu <learthurgo@gmail.com> * feat: gui with placeholders * fix: changed config.json path * migrated docker base image to debian Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * style: spaces to tabs * migrated to ubuntu Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * fixed issues Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * fix: docker build? * fix: use ubuntu bionic * fix: get kivy installed * @ltcptgeneral can't spell * optim dockerfile for not installing unused packages * install basic stuff while building the container * use prebuilt image for development * install pylint on base image * rename and use new kivy * tests: added tests for Array and CorrelationTest Both are not working due to errors * use new thing * use 20.04 base * symlink pip3 to pip * use pip instead of pip3 * equation.Expression.py v 0.0.1-alpha added corresponding .pyc to .gitignore * parser.py v 0.0.2-alpha * added pyparsing to requirements.txt * parser v 0.0.4-alpha * Equation v 0.0.1-alpha * added Equation to tra_analysis imports * tests: New unit tests for submoduling (#66) * feat: created kivy gui boilerplate * migrated docker base image to debian Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * migrated to ubuntu Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * fixed issues Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * fix: docker build? * fix: use ubuntu bionic * fix: get kivy installed * @ltcptgeneral can't spell * optim dockerfile for not installing unused packages * install basic stuff while building the container * use prebuilt image for development * install pylint on base image * rename and use new kivy * tests: added tests for Array and CorrelationTest Both are not working due to errors * fix: Array no longer has *args and CorrelationTest functions no longer have self in the arguments * use new thing * use 20.04 base * symlink pip3 to pip * use pip instead of pip3 * tra_analysis v 2.1.0-alpha.2 SVM v 1.0.1 added unvalidated SVM unit tests Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * fixed version number Signed-off-by: ltcptgeneral <learthurgo@gmail.com> * tests: added tests for ClassificationMetric * partially fixed and commented out svm unit tests * fixed some SVM unit tests * added installing pytest to devcontainer.json * fix: small fixes to KNN Namely, removing self from parameters and passing correct arguments to KNeighborsClassifier constructor * fix, test: Added tests for KNN and NaiveBayes. Also made some small fixes in KNN, NaiveBayes, and RegressionMetric * test: finished unit tests except for StatisticalTest Also made various small fixes and style changes * StatisticalTest v 1.0.1 * fixed RegressionMetric unit test temporarily disabled CorrelationTest unit tests * tra_analysis v 2.1.0-alpha.3 * readded __all__ * fix: floating point issues in unit tests for CorrelationTest Co-authored-by: AGawde05 <agawde05@gmail.com> Co-authored-by: ltcptgeneral <learthurgo@gmail.com> Co-authored-by: Dev Singh <dev@devksingh.com> Co-authored-by: jzpan1 <panzhenyu2014@gmail.com> * fixed depreciated escape sequences * ficed tests, indent, import in test_analysis * changed version to 3.0.0 added backwards compatibility * ficed pytest install in container * removed GUI changes Signed-off-by: Arthur Lu <learthurgo@gmail.com> * incremented version to rc.1 (release candidate 1) Signed-off-by: Arthur Lu <learthurgo@gmail.com> * fixed NaiveBayes __changelog__ Signed-off-by: Arthur Lu <learthurgo@gmail.com> * fix: __setitem__ == to single = * Array v 1.0.1 * Revert "Array v 1.0.1" This reverts commit 59783b79f7451586bc9741794589e00f0c625348. * Array v 1.0.1 * Array.py v 1.0.2 added more Array unit tests * cleaned .gitignore tra_analysis v 3.0.0-rc2 Signed-off-by: Arthur Lu <learthurgo@gmail.com> * added *.pyc to gitignore finished subdividing test_analysis * feat: gui layout + basic func * Froze and removed superscript (data-analysis) * remove data-analysis deps install for devcontainer * tukey pairwise comparison and multicomparison but no critical q-values * quick patch for devcontainer.json * better fix for devcontainer.json * fixed some styling in StatisticalTest removed print statement in StatisticalTest unit tests * update analysis tests to be more effecient * don't use loop for test_nativebayes * removed useless secondary docker files * tra-analysis v 3.0.0 Co-authored-by: James Pan <panzhenyu2014@gmail.com> Co-authored-by: AGawde05 <agawde05@gmail.com> Co-authored-by: zpan1 <72054510+zpan1@users.noreply.github.com> Co-authored-by: Dev Singh <dev@devksingh.com> Co-authored-by: = <=> Co-authored-by: Dev Singh <dsingh@imsa.edu> Co-authored-by: zpan1 <zpan@imsa.edu>
2021-04-29 00:33:50 +00:00
# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported.
import sklearn
from sklearn import model_selection, naive_bayes
from . import ClassificationMetric, RegressionMetric
class NaiveBayes:
def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
return model, ClassificationMetric(predictions, labels_test)
def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
return model, ClassificationMetric(predictions, labels_test)
def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
return model, ClassificationMetric(predictions, labels_test)
def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
model.fit(data_train, labels_train)
predictions = model.predict(data_test)
return model, ClassificationMetric(predictions, labels_test)