tra-analysis/analysis-master/tra_analysis/NaiveBayes_obj.py
Arthur Lu 9f71ab3aad
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 59783b79f7.

* 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>
2021-04-28 19:33:50 -05:00

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2.2 KiB
Python

# 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)