def get_args(filename, sd, bn, im):
x = ArffToArgs()
x.set_input(filename)
x.set_class_index("last")
x.set_impute(im)
x.set_binarize(bn)
x.set_standardize(sd)
args = x.get_args()
x.close()
return args
bs = args["batch_size"]
else:
bs = 128
X_test = args["X_test"]
preds = iter_test(X_test).tolist()
new = []
for pred in preds:
new.append(np.eye(args["num_classes"])[pred].tolist())
return new
if __name__ == '__main__':
x = ArffToArgs()
x.set_input("data/cpu_act.arff")
x.set_class_index("last")
x.set_impute(True)
x.set_binarize(True)
x.set_standardize(True)
x.set_arguments(
"adaptive=True;alpha=0.01;lambda=0;epochs=500;rmsprop=True")
args = x.get_args()
#args["debug"] = True
args["X_test"] = np.asarray(args["X_train"], dtype="float32")
model = train(args)
test(args, model)
from pyscript.pyscript import ArffToArgs
# test saving the pkl to an output file
f = ArffToArgs()
f.set_input("../datasets/iris.arff")
args = f.get_args()
f.save("iris.pkl.gz")
f.close()
# test normal
f = ArffToArgs()
f.set_input("../datasets/iris.arff")
args = f.get_args()
print f.output
f.close()
