transformer.py

# Load the data streamer:
ds = IOutils.data_streamer(num_sets='all')

# obtains the unique rows in a dataset
def unique_rows(data):
    uniq = np.unique(data.view(data.dtype.descr * data.shape[1]))
    return uniq.view(data.dtype).reshape(-1, data.shape[1])
    
X, Y = ds.next()  
Y = np.array(Y)
urows = list(unique_rows(Y))
def transform_vector(y):
    for i, u in enumerate(urows):
        if all(y == u):
            return i


# now use as follows:
for X,Y in ds:
  Y = np.array(Y)
  Y_transformed = np.apply_along_axis(transform_vector, axis=1, arr=Y).astype(np.int32)
  # you might need to do some reshaping here.
  MyClassifier.fit(X,Y_transformed)