)
quit()
FrovedisServer.initialize(argvs[1])
# sample 3x3 scipy csr matrix creation
data = np.array([1, 2, 3, 4, 5, 6])
indices = np.array([0, 2, 2, 0, 1, 2])
indptr = np.array([0, 2, 3, 6])
mat = csr_matrix((data, indices, indptr), dtype=np.float64, shape=(3, 3))
# Creating Frovedis server side crs matrix from scipy data
# "mat" can be any sparse matrix or array-like python object
fmat = FrovedisCRSMatrix(mat)
# Viewing the created matrix (for debugging)
fmat.debug_print()
# sparse to dense conversion at server side
print("crs -> rowmajor")
fmat.to_frovedis_rowmajor_matrix().debug_print()
print("crs -> colmajor")
fmat.to_frovedis_colmajor_matrix().debug_print()
# Saving the created matrix
fmat.save("./out/crs_3x3")
# asCRS demo
asmat = FrovedisCRSMatrix.asCRS(
fmat) # no constructor (returns self) + no destructor
asmat.debug_print()
quit()
FrovedisServer.initialize(argvs[1])
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])
nbm = MultinomialNB(alpha=1.0, 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(mnb)
print("Accuracy of model: ", nbm.score(mat, lbl))
ret1 = (mnb == lbl).all()
mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/bern.txt")
mat.debug_print()
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_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(bnb)
print("Accuracy of model", nbm2.score(mat, lbl))
ret2 = (bnb == lbl).all()
if ret1 and ret2: print("Status: Passed")
else: print("Status: Failed")