quit() FrovedisServer.initialize(argvs[1]) mat = pd.DataFrame([[1, 0, 1, 0, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 0, 1, 0, 1], [1, 0, 0, 1, 0, 1, 0]], dtype=np.float64) lbl = np.array([0, 1, 1, 0], dtype=np.float64) # fitting input matrix and label on linear nbm object nbm = MultinomialNB(alpha=1.4).fit(mat, lbl) nbm.debug_print() print("predicting on nbm multinomial classifier model: ") mnb = nbm.predict(mat) print(mnb) print("Accuracy of model") nbm.score(mat, lbl) nbm2 = BernoulliNB(alpha=1.4).fit(mat, lbl) nbm2.debug_print() print("predicting on nbm bernoulli classifier model: ") bnb = nbm2.predict(mat) print(bnb) print("Accuracy of model") nbm2.score(mat, lbl) if (lbl == mnb).all() and (lbl == bnb).all(): print("Status: Passed") else: print("Status: Failed") nbm.release()
argc = len(argvs) if (argc < 2): print ('Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 -x /opt/nec/nosupport/frovedis/ve/bin/frovedis_server")') quit() FrovedisServer.initialize(argvs[1]) # Demo of Multinomial Naive Bayes mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/multi.txt") lbl = np.array([1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0]) nbm1 = MultinomialNB(alpha=1.0).fit(mat,lbl) print("\nmultinomial model: ") nbm1.debug_print() mnb = nbm1.predict(mat) print("prediction on multinomial classifier model: ", mnb) print("accuracy of model: ", nbm1.score(mat,lbl)) # Demo of Bernoulli Naive Bayes mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/bern.txt") lbl = np.array([1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0]) nbm2 = BernoulliNB(alpha=1.0).fit(mat,lbl) print("\nbernoulli model: ") nbm2.debug_print() bnb = nbm2.predict(mat) print("prediction on bernoulli classifier model: ", bnb) print("accuracy of model", nbm2.score(mat,lbl)) # Clean-up nbm1.release() nbm2.release()
print 'Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 -x /opt/nec/nosupport/frovedis/ve/bin/frovedis_server")' quit() FrovedisServer.initialize(argvs[1]) mat = np.asmatrix([[1, 0, 1, 0, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 0, 1, 0, 1], [1, 0, 0, 1, 0, 1, 0]],dtype=np.float64) lbl = np.array([0, 1, 1, 0],dtype=np.float64) # fitting input matrix and label on linear nbm object nbm = MultinomialNB(alpha=1, fit_prior=True, class_prior=None, verbose = 0).fit(mat,lbl) nbm.debug_print() print("predicting on nbm multinomial classifier model: ") mnb = nbm.predict(mat) print("Accuracy of model", nbm.score(mat,lbl)) nbm2 = BernoulliNB(alpha=1.0,fit_prior=True,class_prior=None,binarize=0.0,verbose=0).fit(mat,lbl) nbm2.debug_print() print("predicting on nbm bernoulli classifier model: ") bnb = nbm2.predict(mat) print("Accuracy of model", nbm2.score(mat,lbl)) if (lbl == mnb).all() and (lbl == bnb).all(): print("Status: Passed") else: print("Status: Failed") nbm.release() nbm2.release() FrovedisServer.shut_down()
# initializing the Frovedis server argvs = sys.argv argc = len(argvs) if (argc < 2): print( 'Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 /opt/nec/frovedis/ve/bin/frovedis_server")' ) quit() from frovedis.exrpc.server import FrovedisServer FrovedisServer.initialize(argvs[1]) # classification data from sklearn.datasets import load_breast_cancer mat, lbl = load_breast_cancer(return_X_y=True) mnb = MultinomialNB(alpha=1.0).fit(mat, lbl) pred = mnb.predict(mat) print("prediction on multinomial classifier model: ") print(pred) print("prediction accuracy: %.4f" % (mnb.score(mat, lbl))) bnb = BernoulliNB(alpha=1.0).fit(mat, lbl) pred = bnb.predict(mat) print("prediction on bernoulli classifier model: ") print(pred) print("prediction accuracy: %.4f" % (bnb.score(mat, lbl))) # Clean-up FrovedisServer.shut_down()