system: You are an expert security code reviewer. You will be shown a source code file along with a multiple-choice question listing several function or class names from that file. Your task is to identify which one contains the specified CWE vulnerability.

OUTPUT FORMAT: Respond with the single letter of the correct option, wrapped in \boxed{...}. Example: `\boxed{B}`. Output the boxed letter ONLY -- no explanation, no other text.

user: ```python
""" Wrapper class for MPTinR.

"""

import math
import re

import numpy as np

from mptpy.fitting import fitter
from . import likelihood as lh
from . import optimize as optim


FUNCS = {"rmse": optim.optim_rmse, "llik": optim.optim_llik}


# def __init__(self, data_path, sep=',', func="rmse", header=None):
#    super().__init__(data_path, sep=sep, header=header)
#    self.func = func


def fit_easy(
        easy_file_path,
        data_path,
        func,
        sep=',',
        n_optim=10,
        use_fia=False):
    """ Fit the given tree using SciPy

    Parameters
    ----------
    easy_file_path : str
        Path to the tree file in the easy format

    n_optim : int, optional
        number of optimization steps

    use_fia : boolean, optional
        wether FIA is wished to be used.
        Default: False.

    Returns
    -------
    dict
        BIC, GSQ, Likelihood (and optionally FIA)

    """
    # read the file
    easy = open(easy_file_path, 'r')
    cat_formulae = easy.readlines()
    cat_formulae = [formula.strip().split(" #")[0]
                    for formula in cat_formulae]
    easy.close()

    # find all parameters
    pattern = re.compile(r"[A-z_]+\d*")
    params = []
    for line in cat_formulae:
        params.extend(pattern.findall(line))
    params = list(set(params))

    # setup kwargs and fit
    kwargs = _setup_args(cat_formulae, params, func, data_path, sep)
    return _fit(kwargs, n_optim=n_optim)


def fit_mpt(mpt, func, data_path, sep=',', n_optim=10, use_fia=False):
    """ Fit the given tree using SciPy

    Parameters
    ----------
    mpt : MPT
        mpt model

    n_optim : int, optional
        number of optimization steps

    use_fia : boolean, optional
        wether FIA is wished to be used.
        Default: False.

    Returns
    -------
    dict
        BIC, GSQ, Likelihood (and optionally FIA)

    """
    # TODO what to do with restricted parameters?

    """
    if mpt.prefix_tree is not None:
        # the model consists of several subtrees, create restriction files
        self._clear_temp_dir("temp/")
        self._compute_parameter_ratios(mpt, "temp/")
    """

    cat_formulae = _get_cat_formulae(mpt)

    params = list(set(mpt.word.parameters))
    static_params = [x for x in params if x.startswith('y')]
    free_params = [x for x in params if not x.startswith('y')]

    kwargs = _setup_args(cat_formulae, free_params, func, data_path, sep)
    static_params = _determine_static_params_values(cat_formulae, static_params, kwargs['data'])
    kwargs['static_params'] = static_params
    return _fit(kwargs, n_optim=n_optim)

def _determine_static_params_values(cat_formulae, static_params, data):
    data = np.array(data)

    split_index = [[y.replace(' ', '') for y in re.split(r'[\+\* \(\)]+', x)] for x in cat_formulae]
    split_index = list(zip(split_index, range(len(split_index))))

    static_assignment = {}
    for param in static_params:
        indices_pos = [y for x, y in split_index if param in x]
        indices_neg = [y for x, y in split_index if '1-{}'.format(param) in x]

        static_assignment[param] = data[indices_pos].sum() / (data[indices_pos].sum() + data[indices_neg].sum())

    return static_assignment

def _fit(kwargs, n_optim=10):
    """ Fit the model

    Parameters
    ----------
    kwargs : dict
        func, data, cat_formulae, param_names

    """

    res, errs = optim.fit_classical(**kwargs, n_optim=n_optim)
    #print(res)

    # Compute the correct criteria (without ignoring factorials)
    measures = _compute_measures(res, kwargs)

    result = {
        'n_params': len(kwargs['free_params']),
        'n_datasets': len(kwargs['data']),
        'func_min': res.fun,
        'LogLik': measures['llik'],
        'LogLik-R': measures['llik-r'],
        'AIC': measures['aic'],
        'BIC': measures['bic'],
        'AIC-R': measures['aic-r'],
        'BIC-R': measures['bic-r'],
        'RMSE': measures['rmse'],
        'G2' : measures['G2'],
        'FIA': -999,
        'aRMSE' : measures['aRMSE'],
        'OptimErrorRatio': errs * 100,
        'ParamAssignment': measures['ass']
    }

    return result


def _compute_measures(res, kwargs):
    """ Compute the correct criteria (without ignoring factorials)
    """
    data = kwargs['data']
    formulae = kwargs['cat_formulae']

    free_params = kwargs['free_params']
    measures = {}
    measures['ass'] = dict(list(zip(kwargs['free_params'], res.x)))
    measures['ass'].update(kwargs['static_params'])

    probabilities = lh.category_probabilities(formulae, measures['ass'])
    predicted_data = predict_data(probabilities, data.sum())

    # Compute the RMSE
    measures['rmse'] = _rmse(data, predicted_data)

    measures['llik'] = lh.log_likelihood(
        probabilities, data, ignore_factorials=False)
    measures['llik-r'] = lh.log_likelihood(
        probabilities, data, ignore_factorials=True)
    measures['aic'] = -2 * measures['llik'] + 2 * len(free_params)
    measures['bic'] = -2 * measures['llik'] + \
        np.log(data.sum()) * len(free_params)

    measures['G2'] = _g2(data, predicted_data)
    measures['aic-r'] = measures['G2'] + 2* len(free_params)
    measures['bic-r'] = measures['G2'] + np.log(data.sum()) * len(free_params)

    sse = np.sum((data - predicted_data)**2)
    if (len(data) -1 -len(free_params)) > 0:
        measures['aRMSE'] = np.sqrt(sse / (len(data) - len(free_params)))
    else:
        # TODO
        measures['aRMSE'] = np.sqrt(sse / (len(data)))

    return measures


def predict_data(probabilities, N):
    preds = probabilities * N
    return preds


def _g2(data, predict_data):
    g2 = 2 * np.sum(data * np.log(data / predict_data))
    return g2

def _rmse(observed, predicted):
    """ Compute the RMSE

    Parameters
    ----------
    observed : np array

    predicted : np array

    """
    #return np.sqrt(np.mean((predicted - observed) ** 2))
    return np.sqrt((np.sum((predicted - observed) ** 2)) / len(predicted))


def _get_cat_formulae(mpt):
    """ Retrieve categories and respective formulae

    Parameters
    ----------
    mpt : MPT
        model

    """

    values = []
    for _, value in mpt.formulae().items():
        values.append(" + ".join(value))

    return values


def _setup_args(cat_formulae, free_params, func, data_path, sep=','):
    """ Compute the arguments needed for the fitting

    Parameters
    ----------
    mpt : MPT
        MPT to be fitted

    Returns
    -------
    dict
    """
    kwargs = {}
    kwargs['fun'] = FUNCS[func]

    data = np.array(fitter.read_data(data_path, sep))
    kwargs['data'] = data

    #ed = self._compute_parameter_ratios(mpt, data)

    kwargs['cat_formulae'] = cat_formulae

    kwargs['free_params'] = sorted(free_params)

    return kwargs

```

Which of the following functions or classes contains a `CWE-20: Improper Input Validation` vulnerability?

  A) fit_easy
  B) _compute_measures
  C) fit_mpt
  D) predict_data
Answer: A