Pure Python implementation of the full DDM log-likelihood function with exponential contaminants

ddm_lik_pure_python.py

"""Attempts to implement DDM likelihoods in Python.

"""
from math import pi, sqrt, log, ceil, floor, exp, sin, fabs, inf




def simpson_1d(x, v, sv, a, z, t, err, lb_z, ub_z, n_sz, lb_t, ub_t, n_st):
    n = max(n_st, n_sz)
    if n_st == 0:  # integration over z
        hz = (ub_z - lb_z) / n
        ht = 0
        lb_t = t
        ub_t = t
    else:  # integration over t
        hz = 0
        ht = (ub_t - lb_t) / n
        lb_z = z
        ub_z = z

    s = pdf_sv(x - lb_t, v, sv, a, lb_z, err)

    for i in range(1, n + 1):
        z_tag = lb_z + hz * i
        t_tag = lb_t + ht * i
        y = pdf_sv(x - t_tag, v, sv, a, z_tag, err)
        if i & 1:  # check if i is odd
            s += 4 * y
        else:
            s += 2 * y
    s = s - y  # the last term should be f(b) and not 2*f(b) so we subtract y
    s = s / (
        (ub_t - lb_t) + (ub_z - lb_z)
    )  # the right function if pdf_sv()/sz or pdf_sv()/st

    return (ht + hz) * s / 3



def simpson_2d(x, v, sv, a, z, t, err, lb_z, ub_z, n_sz, lb_t, ub_t, n_st):
    ht = (ub_t - lb_t) / n_st

    s = simpson_1d(x, v, sv, a, z, lb_t, err, lb_z, ub_z, n_sz, 0, 0, 0)

    for i_t in range(1, n_st + 1):
        t_tag = lb_t + ht * i_t
        y = simpson_1d(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0)
        if i_t & 1:  # check if i is odd
            s += 4 * y
        else:
            s += 2 * y
    s = s - y  # the last term should be f(b) and not 2*f(b) so we subtract y
    s = s / (ub_t - lb_t)

    return ht * s / 3



def adapt_simpson_aux(
    x,
    v,
    sv,
    a,
    z,
    t,
    pdf_err,
    lb_z,
    ub_z,
    lb_t,
    ub_t,
    ZT,
    simps_err,
    S,
    f_beg,
    f_end,
    f_mid,
    bottom,
):

    if (ub_t - lb_t) == 0:  # integration over sz
        h = ub_z - lb_z
        z_c = (ub_z + lb_z) / 2.0
        z_d = (lb_z + z_c) / 2.0
        z_e = (z_c + ub_z) / 2.0
        t_c = t
        t_d = t
        t_e = t

    else:  # integration over t
        h = ub_t - lb_t
        t_c = (ub_t + lb_t) / 2.0
        t_d = (lb_t + t_c) / 2.0
        t_e = (t_c + ub_t) / 2.0
        z_c = z
        z_d = z
        z_e = z

    fd = pdf_sv(x - t_d, v, sv, a, z_d, pdf_err) / ZT
    fe = pdf_sv(x - t_e, v, sv, a, z_e, pdf_err) / ZT

    Sleft = (h / 12) * (f_beg + 4 * fd + f_mid)
    Sright = (h / 12) * (f_mid + 4 * fe + f_end)
    S2 = Sleft + Sright
    if bottom <= 0 or fabs(S2 - S) <= 15 * simps_err:
        return S2 + (S2 - S) / 15
    return adapt_simpson_aux(
        x,
        v,
        sv,
        a,
        z,
        t,
        pdf_err,
        lb_z,
        z_c,
        lb_t,
        t_c,
        ZT,
        simps_err / 2,
        Sleft,
        f_beg,
        f_mid,
        fd,
        bottom - 1,
    ) + adapt_simpson_aux(
        x,
        v,
        sv,
        a,
        z,
        t,
        pdf_err,
        z_c,
        ub_z,
        t_c,
        ub_t,
        ZT,
        simps_err / 2,
        Sright,
        f_mid,
        f_end,
        fe,
        bottom - 1,
    )



def adapt_simpson_1d(
    x, v, sv, a, z, t, pdf_err, lb_z, ub_z, lb_t, ub_t, simps_err, maxRecursionDepth
):

    if (ub_t - lb_t) == 0:  # integration over z
        lb_t = t
        ub_t = t
        h = ub_z - lb_z
    else:  # integration over t
        h = ub_t - lb_t
        lb_z = z
        ub_z = z
    ZT = h
    c_t = (lb_t + ub_t) / 2.0
    c_z = (lb_z + ub_z) / 2.0

    f_beg = pdf_sv(x - lb_t, v, sv, a, lb_z, pdf_err) / ZT
    f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err) / ZT
    f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err) / ZT
    S = (h / 6) * (f_beg + 4 * f_mid + f_end)
    res = adapt_simpson_aux(
        x,
        v,
        sv,
        a,
        z,
        t,
        pdf_err,
        lb_z,
        ub_z,
        lb_t,
        ub_t,
        ZT,
        simps_err,
        S,
        f_beg,
        f_end,
        f_mid,
        maxRecursionDepth,
    )
    return res



def adapt_simpson_aux_2d(
    x,
    v,
    sv,
    a,
    z,
    t,
    pdf_err,
    err_1d,
    lb_z,
    ub_z,
    lb_t,
    ub_t,
    st,
    err_2d,
    S,
    f_beg,
    f_end,
    f_mid,
    maxRecursionDepth_sz,
    bottom,
):

    t_c = (ub_t + lb_t) / 2.0
    t_d = (lb_t + t_c) / 2.0
    t_e = (t_c + ub_t) / 2.0
    h = ub_t - lb_t

    fd = (
        adapt_simpson_1d(
            x, v, sv, a, z, t_d, pdf_err, lb_z, ub_z, 0, 0, err_1d, maxRecursionDepth_sz
        )
        / st
    )
    fe = (
        adapt_simpson_1d(
            x, v, sv, a, z, t_e, pdf_err, lb_z, ub_z, 0, 0, err_1d, maxRecursionDepth_sz
        )
        / st
    )

    Sleft = (h / 12) * (f_beg + 4 * fd + f_mid)
    Sright = (h / 12) * (f_mid + 4 * fe + f_end)
    S2 = Sleft + Sright

    if bottom <= 0 or fabs(S2 - S) <= 15 * err_2d:
        return S2 + (S2 - S) / 15

    return adapt_simpson_aux_2d(
        x,
        v,
        sv,
        a,
        z,
        t,
        pdf_err,
        err_1d,
        lb_z,
        ub_z,
        lb_t,
        t_c,
        st,
        err_2d / 2,
        Sleft,
        f_beg,
        f_mid,
        fd,
        maxRecursionDepth_sz,
        bottom - 1,
    ) + adapt_simpson_aux_2d(
        x,
        v,
        sv,
        a,
        z,
        t,
        pdf_err,
        err_1d,
        lb_z,
        ub_z,
        t_c,
        ub_t,
        st,
        err_2d / 2,
        Sright,
        f_mid,
        f_end,
        fe,
        maxRecursionDepth_sz,
        bottom - 1,
    )



def adapt_simpson_2d(
    x,
    v,
    sv,
    a,
    z,
    t,
    pdf_err,
    lb_z,
    ub_z,
    lb_t,
    ub_t,
    simps_err,
    maxRecursionDepth_sz,
    maxRecursionDepth_st,
):

    h = ub_t - lb_t

    st = ub_t - lb_t
    c_t = (lb_t + ub_t) / 2.0
    c_z = (lb_z + ub_z) / 2.0

    err_1d = simps_err
    err_2d = simps_err

    f_beg = (
        adapt_simpson_1d(
            x,
            v,
            sv,
            a,
            z,
            lb_t,
            pdf_err,
            lb_z,
            ub_z,
            0,
            0,
            err_1d,
            maxRecursionDepth_sz,
        )
        / st
    )

    f_end = (
        adapt_simpson_1d(
            x,
            v,
            sv,
            a,
            z,
            ub_t,
            pdf_err,
            lb_z,
            ub_z,
            0,
            0,
            err_1d,
            maxRecursionDepth_sz,
        )
        / st
    )
    f_mid = (
        adapt_simpson_1d(
            x,
            v,
            sv,
            a,
            z,
            (lb_t + ub_t) / 2,
            pdf_err,
            lb_z,
            ub_z,
            0,
            0,
            err_1d,
            maxRecursionDepth_sz,
        )
        / st
    )
    S = (h / 6) * (f_beg + 4 * f_mid + f_end)
    res = adapt_simpson_aux_2d(
        x,
        v,
        sv,
        a,
        z,
        t,
        pdf_err,
        err_1d,
        lb_z,
        ub_z,
        lb_t,
        ub_t,
        st,
        err_2d,
        S,
        f_beg,
        f_end,
        f_mid,
        maxRecursionDepth_sz,
        maxRecursionDepth_st,
    )
    return res



def ftt_01w(tt, w, err=1e-4):
    """Compute f(t|0,1,w) according to Navarro and Fuss (2009)."""

    # calculate number of terms needed for large t
    if pi * tt * err < 1:  # if error threshold is set low enough
        kl = sqrt(-2 * log(pi * tt * err) / (pi ** 2 * tt))  # bound
        kl = max(kl, 1.0 / (pi * sqrt(tt)))  # ensure boundary conditions met
    else:  # if error threshold set too high
        kl = 1.0 / (pi * sqrt(tt))  # set to boundary condition

    # calculate number of terms needed for small t
    if 2 * sqrt(2 * pi * tt) * err < 1:  # if error threshold is set low enough
        ks = 2 + sqrt(-2 * tt * log(2 * sqrt(2 * pi * tt) * err))  # bound
        ks = max(ks, sqrt(tt) + 1)  # ensure boundary conditions are met
    else:  # if error threshold was set too high
        ks = 2  # minimal kappa for that case

    # compute f(tt|0,1,w)
    p = 0  # initialize density
    if ks < kl:  # if small t is better (i.e., lambda<0) ...
        K = ceil(ks)  # round to smallest integer meeting error
        lower = -floor((K - 1) / 2.0)
        upper = ceil((K - 1) / 2.0)
        for k in range(lower, upper + 1):  # loop over k
            p += (w + 2 * k) * exp(-(pow((w + 2 * k), 2)) / 2 / tt)  # increment sum
        p /= sqrt(2 * pi * pow(tt, 3))  # add constant term

    else:  # if large t is better ...
        K = ceil(kl)  # round to smallest integer meeting error
        for k in range(1, K + 1):
            p += (
                k * exp(-(pow(k, 2)) * (pi ** 2) * tt / 2) * sin(k * pi * w)
            )  # increment sum
        p *= pi  # add constant term

    return p



def pdf(x, v, a, w, err=1e-4):
    """Compute f(t|v,a,z) according to Navarro and Fuss (2009)."""
    # time must be positive
    if x <= 0:
        return 0

    tt = x / a ** 2  # use normalized time
    p = ftt_01w(tt, w, err)  # get f(t|0,1,w)

    # convert to f(t|v,a,w)
    return p * exp(-v * a * w - (pow(v, 2)) * x / 2.0) / (pow(a, 2))



def pdf_sv(x, v, sv, a, z, err=1e-4):
    """Compute f(t|v,a,z,sv) using the method of Navarro and Fuss (2009), with analytic
    integration of v according to Tuerlinckx et al. (2001)."""
    # time must be positive
    if x <= 0:
        return 0

    # if sv=0 don't integrate
    if sv == 0:
        return pdf(x, v, a, z, err)

    tt = x / (pow(a, 2))  # use normalized time
    p = ftt_01w(tt, z, err)  # get f(t|0,1,w)

    # TODO: hack to prevent math domain error; fix this!
    if p == 0:
        logp = -500
    else:
        logp = log(p)

    # convert to f(t|v,a,w)
    return (
        exp(
            logp
            + ((a * z * sv) ** 2 - 2 * a * v * z - (v ** 2) * x)
            / (2 * (sv ** 2) * x + 2)
        )
        / sqrt((sv ** 2) * x + 1)
        / (a ** 2)
    )



def full_pdf(
    x, v, sv, a, z, sz, t, st, err=1e-4, n_st=2, n_sz=2, use_adaptive=1, simps_err=1e-3
):
    """Compute the probability density function of the full drift diffusion model.

    Computes f(t|v,a,z,sv,sz,st) using the method of Navarro and Fuss (2009) to compute
    the basic DDM likelihood, analytic integration of v when sv is non-zero (Tuerlinckx
    et al., 2001), and numeric of t_er and/or z when st and/or sz are non-zero,
    respectively (Ratcliff & Tuerlinckx, 2002).

    This function excepts negative or positive reaction times. Negative values
    correspond to the lower bound whereas positive responses correspond to the upper
    bound. Drift rates are flipped and biases are inverted for upper-bound responses.

    """

    # transform x, v, z if x is upper bound response
    if x > 0:
        v = -v
        z = 1.0 - z

    # absolute RT
    x = fabs(x)

    # set st and sz to 0 if really small
    if st < 1e-3:
        st = 0
    if sz < 1e-3:
        sz = 0
    if sv < 1e-3:
        sv = 0

    if sz == 0:
        if st == 0:  # sv=0,sz=0,st=0
            return pdf_sv(x - t, v, sv, a, z, err)
        else:  # sv=0,sz=0,st=$
            if use_adaptive > 0:
                return adapt_simpson_1d(
                    x,
                    v,
                    sv,
                    a,
                    z,
                    t,
                    err,
                    z,
                    z,
                    t - st / 2.0,
                    t + st / 2.0,
                    simps_err,
                    n_st,
                )
            else:
                return simpson_1d(
                    x, v, sv, a, z, t, err, z, z, 0, t - st / 2.0, t + st / 2.0, n_st
                )

    else:  # sz=$
        if st == 0:  # sv=0,sz=$,st=0
            if use_adaptive:
                return adapt_simpson_1d(
                    x,
                    v,
                    sv,
                    a,
                    z,
                    t,
                    err,
                    z - sz / 2.0,
                    z + sz / 2.0,
                    t,
                    t,
                    simps_err,
                    n_sz,
                )
            else:
                return simpson_1d(
                    x, v, sv, a, z, t, err, z - sz / 2.0, z + sz / 2.0, n_sz, t, t, 0
                )
        else:  # sv=0,sz=$,st=$
            if use_adaptive:
                return adapt_simpson_2d(
                    x,
                    v,
                    sv,
                    a,
                    z,
                    t,
                    err,
                    z - sz / 2.0,
                    z + sz / 2.0,
                    t - st / 2.0,
                    t + st / 2.0,
                    simps_err,
                    n_sz,
                    n_st,
                )
            else:
                return simpson_2d(
                    x,
                    v,
                    sv,
                    a,
                    z,
                    t,
                    err,
                    z - sz / 2.0,
                    z + sz / 2.0,
                    n_sz,
                    t - st / 2.0,
                    t + st / 2.0,
                    n_st,
                )


def pdf_contaminant_uniform(x, w=0.05):
    """Compute the probability density of a uniform contaminant distribution."""
    if -(0.5 / w) <= x <= (0.5 / w):
        return w
    else:
        return 0


def pdf_contaminant_exponential(x, l):
    """Compute the probability density of an exponential contaminant distribution."""
    return l * exp(-l * fabs(x))


def logpdf_with_contaminant_exponential(x, v, sv, a, z, sz, t, st, p_outlier, l):
    """Compute the log likelihood of the full DDM mixed with exponential contaminant
    distribution."""
    # check if all parameters are valid
    if (
            (z < 0)
            or (z > 1)
            or (a < 0)
            or (t < 0)
            or (st < 0)
            or (sv < 0)
            or (sz < 0)
            or (sz > 1)
            or (z + sz / 2.0 > 1)
            or (z - sz / 2.0 < 0)
            or (t - st / 2.0 < 0)
            or (p_outlier < 0)
            or (p_outlier > 1)
    ):
        return -inf

    if p_outlier == 0:
        return log(full_pdf(x, v, sv, a, z, sz, t, st))
    else:
        if l <= 0:
            return -inf
        p0 = full_pdf(x, v, sv, a, z, sz, t, st) * (1 - p_outlier)
        p1 = pdf_contaminant_exponential(x, l) * p_outlier
        return log(p0 + p1)


def test():
    from scipy.stats import lognorm
    import numpy as np
    np.random.seed(0)
    x = lognorm.rvs(s=0.5, size=10000)
    x = np.concatenate([x , -x[:1000]])
    v = -1.1
    sv = 0.01
    a = 1.
    z = 0.5
    sz = 0.01
    t = 0.5
    st = 0.01
    p_outlier = 0.001
    l = 0.1
    for i, _x in enumerate(x):
        print("i =", i)
        print("x =", _x)
        y = logpdf_with_contaminant_exponential(_x, v, sv, a, z, sz, t, st, p_outlier, l)
        print("y =", y)


if __name__ == '__main__':
    test()