Using the data from Mehta et al. (specifically the two datasets named Ising2DFM_reSample_L40_T=All.pkl and Ising2DFM_reSample_L40_T=All_labels.pkl) we have to unpack the data into numpy arrays.
filenames = glob.glob(os.path.join("..", "dat", "*"))
label_filename = list(filter(lambda x: "label" in x, filenames))[0]
dat_filename = list(filter(lambda x: "label" not in x, filenames))[0]
# Read in the labels
with open(label_filename, "rb") as f:
labels = pickle.load(f)
# Read in the corresponding configurations
with open(dat_filename, "rb") as f:
data = np.unpackbits(pickle.load(f)).reshape(-1, 1600).astype("int")
# Set spin-down to -1
data[data == 0] = -1
This dataset consists of \( 10000 \) samples, i.e., \( 10000 \) spin configurations with \( 40 \times 40 \) spins each, for \( 16 \) temperatures between \( 0.25 \) to \( 4.0 \). Next we create a train/test-split and keep the data in the critical phase as a separate dataset for extrapolation-testing.
# Set up slices of the dataset
ordered = slice(0, 70000)
critical = slice(70000, 100000)
disordered = slice(100000, 160000)
X_train, X_test, y_train, y_test = skms.train_test_split(
np.concatenate((data[ordered], data[disordered])),
np.concatenate((labels[ordered], labels[disordered])),
test_size=0.95
)
Using a small training set yields a better accuracy. This will be discussed in the end.