コード例 #1
0
ファイル: seq_likelihood.py プロジェクト: mdrasmus/spimap
    def test_branch_likelihood_hky(self):
        """Test likelihood function"""

        # params
        bgfreq = [0.2, 0.3, 0.3, 0.2]
        kappa = 1.59
        seqlen = 100
        div = 0.1
        t0 = 0
        t1 = 0.5
        step = 0.001

        # prep probabilities
        probs1 = []
        probs2 = []
        for i in xrange(seqlen):
            if random.random() < div:
                for j in xrange(4):
                    probs1.append(0.01)
                    probs2.append(0.01)
                k = random.randint(1, 4)
                probs1[-k] = 1.0
                k = (k % 4) + 1
                probs2[-k] = 1.0
            else:
                for j in xrange(4):
                    probs1.append(0.2)
                    probs2.append(0.2)
                k = random.randint(1, 4)
                probs1[-k] = 1.0
                probs2[-k] = 1.0

        # estimate MLE
        mle = spidir.mle_distance_hky(probs1, probs2, seqlen, bgfreq, kappa, t0, t1)

        x = list(frange(0, 0.5, 0.01))
        y = [spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]

        top = x[argmax(y)]

        print div, top, mle

        prep_dir("test/output/branch_likelihood/")

        rplot_start("test/output/branch_likelihood/branch_function.pdf")
        rplot("plot", x, y, t="l", xlab="distance", ylab="likelihood")
        # rp.lines([div, div], [-1e300, 0], col="green")
        rp.lines([top, top], [-1e300, 0], col="blue")
        rp.lines([mle, mle], [-1e300, 0], col="red")
        rplot_end(True)

        # ================================
        # 1st derivative

        x = list(frange(0, 0.5, 0.01))
        dy = [spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
        dy2 = [
            (
                spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa, t + 0.01)
                - spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa, t)
            )
            / 0.01
            for t in x
        ]

        rplot_start("test/output/branch_likelihood/deriv_branch_function.pdf")
        rplot("plot", x, dy2, t="l", xlab="distance", ylab="d/dt likelihood")
        rp.lines([min(x), max(x)], [0, 0], col="black")
        rp.lines(x, dy, col="grey")
        # rp.lines([div, div], [-1e300, 0], col="green")
        rp.lines([top, top], [-1e300, 0], col="blue")
        rp.lines([mle, mle], [-1e300, 0], col="red")
        rplot_end(True)

        # =============================
        # 2nd derivative

        x = list(frange(0, 0.5, 0.01))
        d2y = [spidir.branch_likelihood_hky_deriv2(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
        d2y2 = [
            (
                spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq, kappa, t + 0.01)
                - spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq, kappa, t)
            )
            / 0.01
            for t in x
        ]

        rplot_start("test/output/branch_likelihood/deriv2_branch_function.pdf")
        rplot("plot", x, d2y2, t="l", xlab="distance", ylab="d^2/dt^2 likelihood")
        rp.lines([min(x), max(x)], [0, 0], col="black")
        rp.lines(x, d2y, col="grey")
        # rp.lines([div, div], [-1e300, 0], col="green")
        # rp.lines([top, top], [-1e300, 0], col="blue")
        # rp.lines([mle, mle], [-1e300, 0], col="red")
        rplot_end(True)
コード例 #2
0
    def test_branch_likelihood_hky(self):
        """Test likelihood function"""

        # params
        bgfreq = [.2, .3, .3, .2]
        kappa = 1.59
        seqlen = 100
        div = .1
        t0 = 0
        t1 = .5
        step = .001

        # prep probabilities
        probs1 = []
        probs2 = []
        for i in xrange(seqlen):
            if random.random() < div:
                for j in xrange(4):
                    probs1.append(.01)
                    probs2.append(.01)
                k = random.randint(1, 4)
                probs1[-k] = 1.0
                k = (k % 4) + 1
                probs2[-k] = 1.0
            else:
                for j in xrange(4):
                    probs1.append(.2)
                    probs2.append(.2)
                k = random.randint(1, 4)
                probs1[-k] = 1.0
                probs2[-k] = 1.0

        # estimate MLE
        mle = spidir.mle_distance_hky(probs1, probs2, seqlen, bgfreq, kappa,
                                      t0, t1)

        x = list(frange(0, .5, .01))
        y = [
            spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa,
                                         t) for t in x
        ]

        top = x[argmax(y)]

        print div, top, mle

        prep_dir("test/output/branch_likelihood/")

        rplot_start("test/output/branch_likelihood/branch_function.pdf")
        rplot("plot", x, y, t="l", xlab="distance", ylab="likelihood")
        #rp.lines([div, div], [-1e300, 0], col="green")
        rp.lines([top, top], [-1e300, 0], col="blue")
        rp.lines([mle, mle], [-1e300, 0], col="red")
        rplot_end(True)

        #================================
        # 1st derivative

        x = list(frange(0, .5, .01))
        dy = [
            spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq,
                                               kappa, t) for t in x
        ]
        dy2 = [(spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq,
                                             kappa, t + .01) -
                spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq,
                                             kappa, t)) / .01 for t in x]

        rplot_start("test/output/branch_likelihood/deriv_branch_function.pdf")
        rplot("plot", x, dy2, t="l", xlab="distance", ylab="d/dt likelihood")
        rp.lines([min(x), max(x)], [0, 0], col="black")
        rp.lines(x, dy, col="grey")
        #rp.lines([div, div], [-1e300, 0], col="green")
        rp.lines([top, top], [-1e300, 0], col="blue")
        rp.lines([mle, mle], [-1e300, 0], col="red")
        rplot_end(True)

        #=============================
        # 2nd derivative

        x = list(frange(0, .5, .01))
        d2y = [
            spidir.branch_likelihood_hky_deriv2(probs1, probs2, seqlen, bgfreq,
                                                kappa, t) for t in x
        ]
        d2y2 = [(spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
                                                    bgfreq, kappa, t + .01) -
                 spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
                                                    bgfreq, kappa, t)) / .01
                for t in x]

        rplot_start("test/output/branch_likelihood/deriv2_branch_function.pdf")
        rplot("plot",
              x,
              d2y2,
              t="l",
              xlab="distance",
              ylab="d^2/dt^2 likelihood")
        rp.lines([min(x), max(x)], [0, 0], col="black")
        rp.lines(x, d2y, col="grey")
        #rp.lines([div, div], [-1e300, 0], col="green")
        #rp.lines([top, top], [-1e300, 0], col="blue")
        #rp.lines([mle, mle], [-1e300, 0], col="red")
        rplot_end(True)
コード例 #3
0
ファイル: seq_likelihood.py プロジェクト: mdrasmus/spimap
    def _test_calc_lktable_row(self):
        """test the function CalcLktbaleRow"""

        def branchlk(probs1, probs2, seqlen, bgfreq, kappa, t):

            model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
            model2 = spidir.make_hky_matrix(bgfreq, kappa, 0)

            logl = 0.0
            for j in xrange(seqlen):
                s = sum(
                    bgfreq[k]
                    * sum(model1[k][x] * probs1[4 * j + x] for x in xrange(4))
                    * sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
                    for k in xrange(4)
                )
                logl += safelog(s, e)

            return logl

        def dbranchlk(probs1, probs2, seqlen, bgfreq, kappa, t):

            model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
            model2 = spidir.make_hky_matrix(bgfreq, kappa, 0.0)

            dmodel1 = spidir.make_hky_deriv_matrix(bgfreq, kappa, t)
            dmodel2 = spidir.make_hky_deriv_matrix(bgfreq, kappa, 0.0)

            logl = 0.0
            for j in xrange(seqlen):
                ds = sum(
                    bgfreq[k]
                    * sum(dmodel1[k][x] * probs1[4 * j + x] for x in xrange(4))
                    * sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
                    for k in xrange(4)
                )

                s = sum(
                    bgfreq[k]
                    * sum(model1[k][x] * probs1[4 * j + x] for x in xrange(4))
                    * sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
                    for k in xrange(4)
                )

                logl += safediv(ds, s, INF)
            return logl

        def d2branchlk(probs1, probs2, seqlen, bgfreq, kappa, t):

            model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
            model2 = spidir.make_hky_matrix(bgfreq, kappa, 0.0)

            dmodel1 = spidir.make_hky_deriv_matrix(bgfreq, kappa, t)
            dmodel2 = spidir.make_hky_deriv_matrix(bgfreq, kappa, 0.0)

            d2model1 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, t)
            d2model2 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, 0.0)

            logl = 0.0
            for j in xrange(seqlen):
                g = sum(
                    bgfreq[k]
                    * sum(model1[k][x] * probs1[4 * j + x] for x in xrange(4))
                    * sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
                    for k in xrange(4)
                )

                dg = sum(
                    bgfreq[k]
                    * sum(dmodel1[k][x] * probs1[4 * j + x] for x in xrange(4))
                    * sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
                    for k in xrange(4)
                )

                d2g = sum(
                    bgfreq[k]
                    * sum(d2model1[k][x] * probs1[4 * j + x] for x in xrange(4))
                    * sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
                    for k in xrange(4)
                )

                logl += -safediv(dg * dg, g * g, INF) + safediv(d2g, g, INF)
            return logl

        bgfreq = [0.25, 0.25, 0.25, 0.25]
        kappa = 1.59
        seqlen = 100

        # prep probabilities
        div = 0.1
        probs1 = []
        probs2 = []
        for i in xrange(seqlen):
            if random.random() < div:
                for j in xrange(4):
                    probs1.append(0.01)
                    probs2.append(0.01)
                k = random.randint(1, 4)
                probs1[-k] = 1.0
                k = (k % 4) + 1
                probs2[-k] = 1.0
            else:
                for j in xrange(4):
                    probs1.append(0.2)
                    probs2.append(0.2)
                k = random.randint(1, 4)
                probs1[-k] = 1.0
                probs2[-k] = 1.0

        # probs1 = [0.0, 0.0, 1.0, 0.0] + \
        #         [1.0, 0.0, 0.0, 0.0] * 5
        # probs2 = [0.0, 0.0, 0.0, 1.0] + \
        #         [1.0, 0.0, 0.0, 0.0] * 5
        # seqlen = len(probs1) / 4

        x = list(frange(0, 1.0, 0.01))

        y = [spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
        y2 = [branchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]

        prep_dir("test/output/branch_likelihood_simple/")

        rplot_start("test/output/branch_likelihood_simple/cmp_c_py.pdf")
        rplot("plot", x, y, t="l")
        rp.lines(x, y2, t="l", col="red")
        rplot_end(True)

        x = list(frange(0, 1.0, 0.01))

        y = [spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
        y2 = [dbranchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]

        rplot_start("test/output/branch_likelihood_simple/cmp_c_py_deriv.pdf")
        rplot("plot", x, y, t="l")
        rp.lines(x, y2, t="l", col="red")
        rplot_end(True)

        x = list(frange(0, 1.0, 0.01))

        y = [spidir.branch_likelihood_hky_deriv2(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
        # y = [(spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
        #                                         bgfreq, kappa, t+.01) -
        #      spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
        #                                         bgfreq, kappa, t)) / .01
        #     for t in x]
        y2 = [d2branchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]

        rplot_start("test/output/branch_likelihood_simple/cmp_c_py_deriv2.pdf")
        rplot("plot", x, y, t="l")
        rp.lines(x, y2, t="l", col="red")
        rplot_end(True)
コード例 #4
0
    def _test_calc_lktable_row(self):
        """test the function CalcLktbaleRow"""
        def branchlk(probs1, probs2, seqlen, bgfreq, kappa, t):

            model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
            model2 = spidir.make_hky_matrix(bgfreq, kappa, 0)

            logl = 0.0
            for j in xrange(seqlen):
                s = sum(bgfreq[k] * sum(model1[k][x] * probs1[4 * j + x]
                                        for x in xrange(4)) *
                        sum(model2[k][y] * probs2[4 * j + y]
                            for y in xrange(4)) for k in xrange(4))
                logl += safelog(s, e)

            return logl

        def dbranchlk(probs1, probs2, seqlen, bgfreq, kappa, t):

            model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
            model2 = spidir.make_hky_matrix(bgfreq, kappa, 0.0)

            dmodel1 = spidir.make_hky_deriv_matrix(bgfreq, kappa, t)
            dmodel2 = spidir.make_hky_deriv_matrix(bgfreq, kappa, 0.0)

            logl = 0.0
            for j in xrange(seqlen):
                ds = sum(bgfreq[k] * sum(dmodel1[k][x] * probs1[4 * j + x]
                                         for x in xrange(4)) *
                         sum(model2[k][y] * probs2[4 * j + y]
                             for y in xrange(4)) for k in xrange(4))

                s = sum(bgfreq[k] * sum(model1[k][x] * probs1[4 * j + x]
                                        for x in xrange(4)) *
                        sum(model2[k][y] * probs2[4 * j + y]
                            for y in xrange(4)) for k in xrange(4))

                logl += safediv(ds, s, INF)
            return logl

        def d2branchlk(probs1, probs2, seqlen, bgfreq, kappa, t):

            model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
            model2 = spidir.make_hky_matrix(bgfreq, kappa, 0.0)

            dmodel1 = spidir.make_hky_deriv_matrix(bgfreq, kappa, t)
            dmodel2 = spidir.make_hky_deriv_matrix(bgfreq, kappa, 0.0)

            d2model1 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, t)
            d2model2 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, 0.0)

            logl = 0.0
            for j in xrange(seqlen):
                g = sum(bgfreq[k] * sum(model1[k][x] * probs1[4 * j + x]
                                        for x in xrange(4)) *
                        sum(model2[k][y] * probs2[4 * j + y]
                            for y in xrange(4)) for k in xrange(4))

                dg = sum(bgfreq[k] * sum(dmodel1[k][x] * probs1[4 * j + x]
                                         for x in xrange(4)) *
                         sum(model2[k][y] * probs2[4 * j + y]
                             for y in xrange(4)) for k in xrange(4))

                d2g = sum(bgfreq[k] * sum(d2model1[k][x] * probs1[4 * j + x]
                                          for x in xrange(4)) *
                          sum(model2[k][y] * probs2[4 * j + y]
                              for y in xrange(4)) for k in xrange(4))


                logl += - safediv(dg*dg, g*g, INF) + \
                        safediv(d2g, g, INF)
            return logl

        bgfreq = [.25, .25, .25, .25]
        kappa = 1.59
        seqlen = 100

        # prep probabilities
        div = .1
        probs1 = []
        probs2 = []
        for i in xrange(seqlen):
            if random.random() < div:
                for j in xrange(4):
                    probs1.append(.01)
                    probs2.append(.01)
                k = random.randint(1, 4)
                probs1[-k] = 1.0
                k = (k % 4) + 1
                probs2[-k] = 1.0
            else:
                for j in xrange(4):
                    probs1.append(.2)
                    probs2.append(.2)
                k = random.randint(1, 4)
                probs1[-k] = 1.0
                probs2[-k] = 1.0

        #probs1 = [0.0, 0.0, 1.0, 0.0] + \
        #         [1.0, 0.0, 0.0, 0.0] * 5
        #probs2 = [0.0, 0.0, 0.0, 1.0] + \
        #         [1.0, 0.0, 0.0, 0.0] * 5
        #seqlen = len(probs1) / 4

        x = list(frange(0, 1.0, .01))

        y = [
            spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa,
                                         t) for t in x
        ]
        y2 = [branchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]

        prep_dir("test/output/branch_likelihood_simple/")

        rplot_start("test/output/branch_likelihood_simple/cmp_c_py.pdf")
        rplot("plot", x, y, t="l")
        rp.lines(x, y2, t="l", col="red")
        rplot_end(True)

        x = list(frange(0, 1.0, .01))

        y = [
            spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq,
                                               kappa, t) for t in x
        ]
        y2 = [dbranchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]

        rplot_start("test/output/branch_likelihood_simple/cmp_c_py_deriv.pdf")
        rplot("plot", x, y, t="l")
        rp.lines(x, y2, t="l", col="red")
        rplot_end(True)

        x = list(frange(0, 1.0, .01))

        y = [
            spidir.branch_likelihood_hky_deriv2(probs1, probs2, seqlen, bgfreq,
                                                kappa, t) for t in x
        ]
        #y = [(spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
        #                                         bgfreq, kappa, t+.01) -
        #      spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
        #                                         bgfreq, kappa, t)) / .01
        #     for t in x]
        y2 = [d2branchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]

        rplot_start("test/output/branch_likelihood_simple/cmp_c_py_deriv2.pdf")
        rplot("plot", x, y, t="l")
        rp.lines(x, y2, t="l", col="red")
        rplot_end(True)