Repository: pints-team/pints (language: python)

Issue:
Bug in DramACMC
Tried running DramACMC on `pints.GaussianLogPDF` and it looks like the distribution converges to a stationary distribution that is substantially wider than the target.
Bug in DramACMC
Tried running DramACMC on `pints.GaussianLogPDF` and it looks like the distribution converges to a stationary distribution that is substantially wider than the target.
Bug in DramACMC
Tried running DramACMC on `pints.GaussianLogPDF` and it looks like the distribution converges to a stationary distribution that is substantially wider than the target.


Discussion / hints:

Fix patch:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index e59b9d2c9..9273b907d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -24,6 +24,7 @@ All notable changes to this project will be documented in this file.
 
 ### Fixed
 - [#1449](https://github.com/pints-team/pints/pull/1449) Fixed a bug in `MarkovJumpModel.interpolate_mol_counts`.
+- [#1399](https://github.com/pints-team/pints/pull/1339) Fixed a bug in `DramACMC`, and fixed the number of proposal kernels to 2.
 
 
 ## [0.4.0] - 2021-12-07
diff --git a/examples/sampling/adaptive-covariance-dram.ipynb b/examples/sampling/adaptive-covariance-dram.ipynb
index 2ea726d6f..4fa5d88ee 100644
--- a/examples/sampling/adaptive-covariance-dram.ipynb
+++ b/examples/sampling/adaptive-covariance-dram.ipynb
@@ -6,25 +6,19 @@
    "source": [
     "# Inference: Delayed Rejection Adaptive Covariance MCMC\n",
     "\n",
-    "This example shows you how to perform Bayesian inference on a time series, using [DRAM ACMC](http://pints.readthedocs.io/en/latest/mcmc_samplers/dram_ac_mcmc.html) as described in [1]. This method allows users to have a number of proposal kernels. Typically, the first proposal kernel is wider (and hence can explore more aggressively); if this proposal is rejected, then subsequent kernels are more narrower to encourage, at least, some movement of the chains.\n",
-    "\n",
+    "This example shows you how to perform Bayesian inference on a time series, using [DRAM ACMC](http://pints.readthedocs.io/en/latest/mcmc_samplers/dram_ac_mcmc.html) as described in [1]. This method allows users to have two proposal kernels. Typically, the first proposal kernel is wider; if this proposal is rejected, then subsequent kernels are more narrower to encourage, at least, some movement of the chains.\n",
     "\n",
     "[1] \"DRAM: Efficient adaptive MCMC\".\n",
     "    H Haario, M Laine, A Mira, E Saksman, (2006) Statistical Computing\n",
     "    https://doi.org/10.1007/s11222-006-9438-0"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In our first use of the model, we try using 3 different proposal kernels that scale the proposal distributions to be of three different orders of magnitude."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -36,40 +30,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/michael/dev/pints/pints/_mcmc/_dram_ac.py:131: RuntimeWarning: divide by zero encountered in log\n",
-      "  i, Y_rev[0:(i + 2)], log_Y_rev[0:(i + 2)]))) -\n",
-      "/home/michael/dev/pints/pints/_mcmc/_dram_ac.py:133: RuntimeWarning: divide by zero encountered in log\n",
-      "  i, Y[0:(i + 2)], log_Y[0:(i + 2)])))\n",
-      "/home/michael/dev/pints/pints/_mcmc/_dram_ac.py:133: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  i, Y[0:(i + 2)], log_Y[0:(i + 2)])))\n",
-      "/home/michael/dev/pints/pints/_mcmc/_dram_ac.py:113: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  alpha_log = log_Y[n + 1] - log_Y[0]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Running...\n",
       "Done!\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 6 Axes>"
       ]
@@ -120,18 +94,14 @@
     "mcmc = pints.MCMCController(log_posterior, len(xs), xs, method=pints.DramACMC)\n",
     "\n",
     "# Add stopping criterion\n",
-    "mcmc.set_max_iterations(2000)\n",
+    "mcmc.set_max_iterations(10000)\n",
     "\n",
     "# Start adapting after 1000 iterations\n",
-    "mcmc.set_initial_phase_iterations(1000)\n",
+    "mcmc.set_initial_phase_iterations(2000)\n",
     "\n",
     "# Disable logging mode\n",
     "mcmc.set_log_to_screen(False)\n",
     "\n",
-    "# Try 3 proposal kernels\n",
-    "for i in range(len(xs)):\n",
-    "    mcmc.samplers()[i].set_n_kernels(3)\n",
-    "\n",
     "# Run!\n",
     "print('Running...')\n",
     "chains = mcmc.run()\n",
@@ -153,18 +123,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "param    mean    std.    2.5%    25%     50%     75%     97.5%    rhat    ess    ess per sec.\n",
-      "-------  ------  ------  ------  ------  ------  ------  -------  ------  -----  --------------\n",
-      "r        0.02    0.00    0.01    0.01    0.02    0.02    0.02     1.47    38.59  0.98\n",
-      "k        507.83  24.53   481.25  498.14  499.99  517.15  598.20   1.20    78.72  1.99\n",
-      "sigma    23.98   22.61   6.07    9.43    12.02   25.23   82.11    1.88    16.92  0.43\n"
+      "param    mean    std.    2.5%    25%     50%     75%     97.5%    rhat    ess     ess per sec.\n",
+      "-------  ------  ------  ------  ------  ------  ------  -------  ------  ------  --------------\n",
+      "r        0.02    0.00    0.01    0.01    0.01    0.02    0.02     1.03    146.67  4.10\n",
+      "k        499.27  6.65    483.48  498.95  499.30  499.66  522.28   1.11    72.32   2.02\n",
+      "sigma    11.30   7.54    9.27    9.56    9.72    9.92    26.64    1.05    111.42  3.11\n"
      ]
     }
    ],
@@ -172,101 +142,6 @@
     "results = pints.MCMCSummary(chains=chains, time=mcmc.time(), parameter_names=[\"r\", \"k\", \"sigma\"])\n",
     "print(results)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With three kernels, things look a little unstable in the sampling, so let's try just two instead."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running...\n",
-      "Done!\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create mcmc routine\n",
-    "mcmc = pints.MCMCController(log_posterior, len(xs), xs, method=pints.DramACMC)\n",
-    "\n",
-    "# Add stopping criterion\n",
-    "mcmc.set_max_iterations(2000)\n",
-    "\n",
-    "# Start adapting after 1000 iterations\n",
-    "mcmc.set_initial_phase_iterations(1000)\n",
-    "\n",
-    "# Disable logging mode\n",
-    "mcmc.set_log_to_screen(False)\n",
-    "\n",
-    "# Try 3 proposal kernels\n",
-    "for i in range(len(xs)):\n",
-    "    mcmc.samplers()[i].set_n_kernels(2)\n",
-    "\n",
-    "# Run!\n",
-    "print('Running...')\n",
-    "chains = mcmc.run()\n",
-    "print('Done!')\n",
-    "\n",
-    "# Show traces and histograms\n",
-    "pints.plot.trace(chains)\n",
-    "\n",
-    "# Show graphs\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above looks much better and we typically get a higher ESS than for the three kernel case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "param    mean    std.    2.5%    25%     50%     75%     97.5%    rhat    ess    ess per sec.\n",
-      "-------  ------  ------  ------  ------  ------  ------  -------  ------  -----  --------------\n",
-      "r        0.01    0.00    0.01    0.01    0.01    0.02    0.02     1.08    51.72  3.35\n",
-      "k        511.43  31.31   461.74  498.78  500.40  517.70  599.05   1.48    29.29  1.90\n",
-      "sigma    26.70   25.97   6.07    9.19    10.44   45.41   83.55    2.19    13.58  0.88\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = pints.MCMCSummary(\n",
-    "    chains=chains, \n",
-    "    time=mcmc.time(), \n",
-    "    parameter_names=['r', 'k', 'sigma']\n",
-    ")\n",
-    "print(results)"
-   ]
   }
  ],
  "metadata": {
diff --git a/pints/_mcmc/_dram_ac.py b/pints/_mcmc/_dram_ac.py
index d377f831a..2d7d2b83e 100644
--- a/pints/_mcmc/_dram_ac.py
+++ b/pints/_mcmc/_dram_ac.py
@@ -7,7 +7,6 @@
 #
 import pints
 import numpy as np
-import scipy.stats as stats
 
 
 class DramACMC(pints.AdaptiveCovarianceMC):
@@ -19,9 +18,10 @@ class DramACMC(pints.AdaptiveCovarianceMC):
     typically from a narrower (i.e. more conservative) proposal kernel than was
     used for the first proposal.
 
+    The number of proposal kernels is fixed to 2.
+
     In this approach, in each iteration, the following steps return the next
-    state of the Markov chain (assuming the current state is ``theta_0`` and
-    that there are 2 proposal kernels)::
+    state of the Markov chain (assuming the current state is ``theta_0``)::
 
         theta_1 ~ N(theta_0, lambda * scale_1 * sigma)
         alpha_1(theta_0, theta_1) = min(1, p(theta_1|X) / p(theta_0|X))
@@ -38,27 +38,6 @@ class DramACMC(pints.AdaptiveCovarianceMC):
         else:
             return theta_0
 
-    Our implementation also allows more than 2 proposal kernels to be used.
-    This means that ``k`` accept-reject steps are taken. In each step (``i``),
-    the probability that a proposal ``theta_i`` is accepted is::
-
-        alpha_i(theta_0, theta_1, ..., theta_i) = min(1, p(theta_i|X) /
-                                                  p(theta_0|X) * n_i / d_i)
-
-    where::
-
-        n_i = (1 - alpha_1(theta_i, theta_i-1)) *
-              (1 - alpha_2(theta_i, theta_i-1, theta_i-2)) *
-               ...
-              (1 - alpha_i-1(theta_i, theta_i-1, ..., theta_0))
-        d_i = (1 - alpha_1(theta_0, theta_1)) *
-              (1 - alpha_2(theta_0, theta_1, theta_2)) *
-              ...
-              (1 - alpha_i-1(theta_0, theta_1, ..., theta_i-1))
-
-    If ``k`` proposals have been rejected, the initial point ``theta_0`` is
-    returned.
-
     At the end of each iterations, a 'base' proposal kernel is adapted::
 
         mu = (1 - gamma) mu + gamma theta
@@ -67,8 +46,8 @@ class DramACMC(pints.AdaptiveCovarianceMC):
 
     where ``gamma = adaptations^-eta``, ``theta`` is the current state of
     the Markov chain and ``accepted`` is a binary indicator for whether any of
-    the series of proposals were accepted. The kernels for the all proposals
-    are then adapted as ``[scale_1, scale_2, ..., scale_k] * sigma``, where the
+    the series of proposals were accepted. The kernels for the two proposals
+    are then adapted as ``[scale_1, scale_2] * sigma``, where the
     scale factors are set using ``set_sigma_scale``.
 
     *Extends:* :class:`GlobalAdaptiveCovarianceMC`
@@ -82,20 +61,17 @@ class DramACMC(pints.AdaptiveCovarianceMC):
     def __init__(self, x0, sigma0=None):
         super(DramACMC, self).__init__(x0, sigma0)
 
-        self._adapt_kernel = True
-        self._before_kernels_set = True
         self._log_lambda = 0
-        self._n_kernels = 2
+        self._n_kernels = 2  # This is fixed!
         self._proposal_count = 0
         self._sigma_base = np.copy(self._sigma)
-        self._upper_scale = 1000
-        self._Y = [None] * self._n_kernels
         self._Y_log_pdf = np.zeros(self._n_kernels)
+        self.set_sigma_scale([1, 0.01])  # scale used in [1]_ for experiments
 
     def _adapt_sigma(self):
         """
-        Updates the covariance matrices of the various kernels being used
-        according to adaptive Metropolis routine.
+        Updates the covariance matrices of the two kernels being used according
+        to adaptive Metropolis routine.
         """
         dsigm = np.reshape(self._current - self._mu, (self._n_parameters, 1))
         self._sigma_base = ((1 - self._gamma) * self._sigma_base +
@@ -103,110 +79,71 @@ def _adapt_sigma(self):
         self._sigma = [self._sigma_scale[i] * self._sigma_base
                        for i in range(self._n_kernels)]
 
-    def _calculate_alpha_log(self, n, Y, log_Y):
+    def _alpha_1_log(self, fx, fy1):
         """
-        Calculates alpha expression necessary in eq. 3 of Haario et al. for
-        determining accept/reject
+        Calculates probability of acceptance in stage 1 of DRAM (eq. 1 in
+        [1]_).
         """
-        alpha_log = log_Y[n + 1] - log_Y[0]
-        if n == 0:
-            return min(0, alpha_log)
-        Y_rev = Y[::-1]
-        log_Y_rev = log_Y[::-1]
-        for i in range(n):
-            alpha_log += (
-                stats.multivariate_normal.logpdf(
-                    x=Y[n - i - 1],
-                    mean=Y[n + 1],
-                    cov=self._sigma[n],
-                    allow_singular=True) -
-                stats.multivariate_normal.logpdf(
-                    x=Y[i],
-                    mean=self._current,
-                    cov=self._sigma[0],
-                    allow_singular=True) +
-                np.log(1 - np.exp(self._calculate_alpha_log(
-                    i, Y_rev[0:(i + 2)], log_Y_rev[0:(i + 2)]))) -
-                np.log(1 - np.exp(self._calculate_alpha_log(
-                    i, Y[0:(i + 2)], log_Y[0:(i + 2)])))
-            )
+        alpha_log = fy1 - fx
         return min(0, alpha_log)
 
-    def _calculate_r_log(self, fx):
+    def _alpha_2_log(self, fx, fy1, fy2):
         """
-        Calculates value of logged acceptance ratio (eq. 3 in [1]_).
+        Calculates probability of acceptance in stage 1 of DRAM (eq. 2 in
+        [1]_).
         """
-        c = self._proposal_count
-        temp_Y = np.concatenate([[self._current], self._Y[0:(c + 1)]])
-        temp_log_Y = np.concatenate(
-            [[self._current_log_pdf], self._Y_log_pdf[0:(c + 1)]])
-        self._r_log = self._calculate_alpha_log(c, temp_Y, temp_log_Y)
+        alpha_log = (fy2 - fx +
+                     np.log1p(np.exp(self._alpha_1_log(fy2, fy1))) -
+                     np.log1p(np.exp(self._alpha_1_log(fx, fy1))))
+        return min(0, alpha_log)
 
     def _generate_proposal(self):
         """ See :meth:`AdaptiveCovarianceMC._generate_proposal()`. """
-        if self._before_kernels_set:
-            self.set_sigma_scale()
-            self._Y = [None] * self._n_kernels
-            self._Y_log_pdf = np.zeros(self._n_kernels)
-            self._before_kernels_set = False
-
         proposed = np.random.multivariate_normal(
             self._current, np.exp(self._log_lambda) *
             self._sigma[self._proposal_count])
-        self._Y[self._proposal_count] = proposed
         return proposed
 
     def name(self):
         """ See :meth:`pints.MCMCSampler.name()`. """
         return 'Delayed Rejection Adaptive Metropolis (Dram) MCMC'
 
-    def n_kernels(self):
-        """ Returns number of proposal kernels. """
-        return self._n_kernels
-
     def n_hyper_parameters(self):
         """ See :meth:`TunableMethod.n_hyper_parameters()`. """
         return 3
 
+    def _r_log(self):
+        """
+        Calculates value of logged acceptance ratio (eq. 3 in [1]_).
+        """
+        if self._proposal_count == 0:
+            r_log = self._alpha_1_log(
+                self._current_log_pdf, self._Y_log_pdf[0])
+        else:
+            r_log = self._alpha_2_log(
+                self._current_log_pdf, self._Y_log_pdf[0], self._Y_log_pdf[1])
+        return r_log
+
     def set_hyper_parameters(self, x):
         """
-        The hyper-parameter vector is ``[eta, n_kernels, upper_scale]``.
+        The hyper-parameter vector is ``[eta, sigma_scale_1, sigma_scale_2]``.
 
         See :meth:`TunableMethod.set_hyper_parameters()`.
         """
         self.set_eta(x[0])
-        self.set_n_kernels(x[1])
-        self.set_upper_scale(x[2])
-
-    def set_n_kernels(self, n_kernels):
-        """ Sets number of proposal kernels. """
-        if n_kernels < 1:
-            raise ValueError('Number of proposal kernels must be equal to ' +
-                             'or greater than 1.')
-        self._n_kernels = int(n_kernels)
+        self.set_sigma_scale(x[1:3])
 
-    def set_upper_scale(self, upper_scale):
+    def set_sigma_scale(self, scales):
         """
-        Set the upper scale of initial covariance matrix multipliers for each
-        of the kernels: ``[0,...,upper]`` where the gradations are uniform on
-        the log10 scale meaning the proposal covariance matrices are:
-        ``[10^upper,..., 1] * sigma``.
+        Set the scale of the mulipliers for the two proposal kernel
+        covariance matrices. Must be of the form ``[scale_1, scale_2]``.
         """
-        if upper_scale < 0:
-            raise ValueError('Upper scale must be positive.')
-        self._upper_scale = upper_scale
-
-    def set_sigma_scale(self):
-        """
-        Set the scale of initial covariance matrix multipliers for each of the
-        kernels: ``[0,...,upper]`` where the gradations are uniform on the
-        log10 scale meaning the proposal covariance matrices are:
-        ``[10^upper,..., 1] * sigma``.
-        """
-        a_min = np.log10(1)
-        a_max = np.log10(self._upper_scale)
-        self._sigma_scale = 10**np.linspace(a_min, a_max, self._n_kernels)
-        self._sigma_scale = self._sigma_scale[::-1]
+        if len(scales) != 2:
+            raise ValueError('Scales must be of length 2.')
+        for scale in scales:
+            if scale <= 0:
+                raise ValueError('Scales must be positive.')
+        self._sigma_scale = [scales[0], scales[1]]
         self._sigma = [self._sigma_scale[i] * self._sigma_base
                        for i in range(self._n_kernels)]
 
@@ -226,6 +163,9 @@ def tell(self, fx):
         if self._proposed is None:
             raise RuntimeError('Tell called before proposal was set.')
 
+        if self._proposal_count == 0:
+            self._iterations += 1
+
         # Ensure fx is a float
         fx = float(fx)
         self._Y_log_pdf[self._proposal_count] = fx
@@ -236,53 +176,40 @@ def tell(self, fx):
                 raise ValueError(
                     'Initial point for MCMC must have finite logpdf.')
 
-            # Accept
             self._current = self._proposed
             self._current_log_pdf = fx
-
-            # Increase iteration count
-            self._iterations += 1
-
-            # Clear proposal
             self._proposed = None
 
             # Return first point for chain
             return self._current, self._current_log_pdf, True
 
         # Check if the proposed point can be accepted
-        accepted = 0
-
+        accept = 0
         if np.isfinite(fx):
-            self._calculate_r_log(fx)
+            r_log = self._r_log()
             u = np.log(np.random.uniform(0, 1))
-            if u < self._r_log:
-                accepted = 1
+            if u < r_log:
+                accept = 1
+                self._acceptance_count += 1
                 self._current = self._proposed
                 self._current_log_pdf = fx
-
+                self._proposal_count = 0
+                self._Y_log_pdf = np.zeros(self._n_kernels)
         self._proposed = None
 
-        if accepted == 0:
-            # rejected proposal
-            if self._n_kernels > 1 and (
-               self._proposal_count < (self._n_kernels - 1)):
+        if accept == 0:
+            if self._proposal_count < (self._n_kernels - 1):
                 self._proposal_count += 1
                 return None
             else:
                 self._proposal_count = 0
-        self._gamma = (self._adaptations**-self._eta)
-        self._adaptations += 1
 
         # Update mu, covariance matrix and log lambda
+        self._acceptance_rate = self._acceptance_count / self._iterations
+        self._gamma = self._adaptations**-self._eta
+        self._adaptations += 1
         self._adapt_mu()
         self._adapt_sigma()
         self._log_lambda += (self._gamma *
-                             (accepted - self._target_acceptance))
-        return self._current, self._current_log_pdf, accepted != 0
-
-    def upper_scale(self):
-        """
-        Returns upper scale limit (see
-        :meth:`pints.DramACMC.set_upper_scale()`).
-        """
-        return self._upper_scale
+                             (accept - self._target_acceptance))
+        return self._current, self._current_log_pdf, accept != 0


Test patch:
diff --git a/pints/tests/test_mcmc_dram_ac.py b/pints/tests/test_mcmc_dram_ac.py
index 356eb7289..3eff7dc0e 100755
--- a/pints/tests/test_mcmc_dram_ac.py
+++ b/pints/tests/test_mcmc_dram_ac.py
@@ -95,9 +95,6 @@ def test_method(self):
         x0 = self.real_parameters * 1.1
         mcmc = pints.DramACMC(x0)
 
-        # Configure
-        mcmc.set_n_kernels(4)
-
         # Perform short run
         rate = []
         chain = []
@@ -150,21 +147,19 @@ def test_options(self):
         self.assertRaises(ValueError, mcmc.set_hyper_parameters, [-0.1, 1, 3])
         self.assertRaises(ValueError, mcmc.set_hyper_parameters, [0.5, 0, 3])
         self.assertRaises(ValueError, mcmc.set_hyper_parameters, [0.5, 1, -1])
-        mcmc.set_hyper_parameters([0.1, 4, 3.5])
+        mcmc.set_hyper_parameters([0.1, 4, 3])
         self.assertEqual(mcmc.eta(), 0.1)
-        self.assertEqual(mcmc.n_kernels(), 4)
-        self.assertEqual(mcmc.upper_scale(), 3.5)
         mcmc.ask()
-        mcmc.set_sigma_scale()
+        scale1 = [2, 3]
+        mcmc.set_sigma_scale(scale1)
         scale = mcmc.sigma_scale()
-        a_min = np.log10(1)
-        a_max = np.log10(3.5)
-        scale1 = 10**np.linspace(a_min, a_max, 4)
-        scale1 = scale1[::-1]
         self.assertTrue(np.array_equal(scale, scale1))
 
-        self.assertEqual(mcmc.name(), (
-            'Delayed Rejection Adaptive Metropolis (Dram) MCMC'))
+        self.assertRaisesRegex(ValueError, 'must be of length 2',
+                               mcmc.set_sigma_scale, [1])
+
+        self.assertEqual(mcmc.name(),
+                         'Delayed Rejection Adaptive Metropolis (Dram) MCMC')
 
     def test_logging(self):