merylldindin

# Load the dataset
x,y = load_iris(return_X_y=True)
# Split it in training and validation
x_t, x_v, y_t, y_v = train_test_split(x, y, test_size=0.2, shuffle=True, random_state=42)
# Build a representation of the problem
arg = {'threads': cpu_count(), 'weights': False}
prb = Prototype(x_t, x_v, y_t, y_v, 'ETS', 'classification', 'acc', **arg)
# Instantiate and run a one-shot learning based on the given configuration
exp = Experiment()
exp.single(prb, random_state=42)

merylldindin

from sklearn.metrics import confusion_matrix

def kappa_score(y_true, y_pred):

    cfm = confusion_matrix(y_true, y_pred)
    n_c = len(np.unique(y_true))
    s_0 = np.sum(cfm, axis=0)
    s_1 = np.sum(cfm, axis=1)
    exp = np.outer(s_0, s_1).astype(np.double) / np.sum(s_0) 
    mat = np.ones([n_c, n_c], dtype=np.int)

merylldindin

def define_clusters(lst, fil, neighbors):
    
    # lst -> ordered list of indexes
    # fil -> mapped dictionnary of filtration values with indexes
    # neighbors -> number of closest elements to consider per query

    unf = UnionFind()

    for idx in lst:
        grp, srt = [], np.where(lst == idx)[0][0]