default=100,
type=int,
help='Data multiplier')
parser.add_argument('--maxiter',
type=int,
default=100,
help='Maximum number of iterations')
params = parse_args(parser, loop_types=('fit', 'predict'), prefix='daal4py')
# Load generated data
X = np.load(params.filex)
X_init = np.load(params.filei)
X_mult = np.vstack((X, ) * params.data_multiplier)
tol = np.load(params.filet)
params.size = size_str(X.shape)
params.n_clusters = X_init.shape[0]
params.dtype = X.dtype
# Define functions to time
def test_fit(X, X_init):
algorithm = kmeans(fptype=getFPType(X),
nClusters=params.n_clusters,
maxIterations=params.maxiter,
assignFlag=True,
accuracyThreshold=tol)
return algorithm.compute(X, X_init)
def test_predict(X, X_init):
def main():
parser = argparse.ArgumentParser(description='daal4py SVC benchmark with '
'linear kernel')
parser.add_argument('-x',
'--filex',
'--fileX',
type=argparse.FileType('r'),
required=True,
help='Input file with features, in NPY format')
parser.add_argument('-y',
'--filey',
'--fileY',
type=argparse.FileType('r'),
required=True,
help='Input file with labels, in NPY format')
parser.add_argument('-C',
dest='C',
type=float,
default=0.01,
help='SVM slack parameter')
parser.add_argument('--kernel',
choices=('linear', ),
default='linear',
help='SVM kernel function')
parser.add_argument('--maxiter',
type=int,
default=2000,
help='Maximum iterations for the iterative solver. '
'-1 means no limit.')
parser.add_argument('--max-cache-size',
type=int,
default=64,
help='Maximum cache size, in gigabytes, for SVM.')
parser.add_argument('--tau',
type=float,
default=1e-12,
help='Tau parameter for working set selection scheme')
parser.add_argument('--tol', type=float, default=1e-16, help='Tolerance')
parser.add_argument('--no-shrinking',
action='store_false',
default=True,
dest='shrinking',
help="Don't use shrinking heuristic")
params = parse_args(parser,
loop_types=('fit', 'predict'),
prefix='daal4py')
# Load data and cast to float64
X_train = np.load(params.filex.name).astype('f8')
y_train = np.load(params.filey.name).astype('f8')
cache_size_bytes = get_optimal_cache_size(X_train.shape[0],
max_cache=params.max_cache_size)
params.cache_size_mb = cache_size_bytes / 2**20
params.cache_size_bytes = cache_size_bytes
params.n_classes = np.unique(y_train).size
# This is necessary for daal
y_train[y_train == 0] = -1
y_train = y_train[:, np.newaxis]
columns = ('batch', 'arch', 'prefix', 'function', 'threads', 'dtype',
'size', 'kernel', 'cache_size_mb', 'C', 'sv_len', 'n_classes',
'accuracy', 'time')
params.size = size_str(X_train.shape)
params.dtype = X_train.dtype
print_header(columns, params)
# Time fit and predict
fit_time, res = time_mean_min(test_fit,
X_train,
y_train,
params,
outer_loops=params.fit_outer_loops,
inner_loops=params.fit_inner_loops)
res, support, indices, n_support = res
params.sv_len = support.shape[0]
print_row(columns, params, function='SVM.fit', time=fit_time)
predict_time, yp = time_mean_min(test_predict,
X_train,
res,
params,
outer_loops=params.predict_outer_loops,
inner_loops=params.predict_inner_loops)
print_row(columns,
params,
function='SVM.predict',
time=predict_time,
accuracy=f'{100*accuracy_score(yp, y_train):.3}')
