def transform_to_supervised_series()

in sagemaker/trainLSTM.py [0:0]

• 18 lines of code
• 9 McCabe index (conditional complexity)

    def transform_to_supervised_series(data, columns, n_in=1, n_out=1, dropnan=True):
        n_vars = 1 if type(data) is list else data.shape[1]
        df = DataFrame(data)
        cols, names = list(), list()
        # input sequence (t-n, ... t-1)
        for i in range(n_in, 0, -1):
            cols.append(df.shift(i))
            names += [('%s(t-%d)' % (columns[j], i)) for j in range(n_vars)]
        # forecast sequence (t, t+1, ... t+n)
        for i in range(0, n_out):
            cols.append(df.shift(-i))
            if i == 0:
                names += [('%s(t)' % (columns[j])) for j in range(n_vars)]
            else:
                names += [('%s(t+%d)' % (columns[j], i)) for j in range(n_vars)]
        # put it all together
        agg = concat(cols, axis=1)
        agg.columns = names
        # drop rows with NaN values
        if dropnan:
            agg.dropna(inplace=True)
        return agg