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limix · Apr 25, 2017 · 49b7033 · 49b7033
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commit 49b7033
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Showing 6 changed files with 39 additions and 48 deletions.
diff --git a/.asv/results/benchmarks.json b/.asv/results/benchmarks.json
@@ -1,6 +1,6 @@
 {
     "benchmarks.TimeSuite.time_qep_binomial_1k_learn": {
-        "code": "def time_qep_binomial_1k_learn(self):\n    glmm = GLMM((self._nsuc1k, self._ntri1k), 'binomial', self._X1k,\n                self._QS1k)\n    glmm.feed().maximize(progress=False)\n\n    glmm = GLMM((nsuc1k, ntri1k), 'binomial', X1k,\n                QS1k)\n    glmm.feed().maximize(progress=False)\n    print(\".12f\" % glmm.feed().value())\n    assert_allclose(glmm.feed().value(), -2611.6160207784023)\n",
+        "code": "def time_qep_binomial_1k_learn(self):\n    glmm = GLMM((self._nsuc1k, self._ntri1k), 'binomial', self._X1k,\n                self._QS1k)\n    glmm.feed().maximize(progress=False)\n\n    glmm = GLMM((nsuc1k, ntri1k), 'binomial', X1k,\n                QS1k)\n    import pdb; pdb.set_trace()\n    glmm.feed().maximize(progress=False)\n    print(\".12f\" % glmm.feed().value())\n    assert_allclose(glmm.feed().value(), -2611.6160207784023)\n",
         "goal_time": 0.1,
         "name": "benchmarks.TimeSuite.time_qep_binomial_1k_learn",
         "number": 0,

diff --git a/benchmarks/benchmarks.py b/benchmarks/benchmarks.py
@@ -21,6 +21,7 @@ def setup(self):
 
         self._X1k = random.randn(n1k, 5)
         self._K1k = self._X1k.dot(self._X1k.T)
+        self._K1k /= self._K1k.diagonal().mean()
         sum2diag(self._K1k, 1e-3, out=self._K1k)
         self._QS1k = economic_qs(self._K1k)
 
@@ -31,9 +32,5 @@ def time_qep_binomial_1k_learn(self):
         glmm = GLMM((self._nsuc1k, self._ntri1k), 'binomial', self._X1k,
                     self._QS1k)
         glmm.feed().maximize(progress=False)
-
-        glmm = GLMM((nsuc1k, ntri1k), 'binomial', X1k,
-                    QS1k)
-        glmm.feed().maximize(progress=False)
         print(".12f" % glmm.feed().value())
         assert_allclose(glmm.feed().value(), -2611.6160207784023)
diff --git a/glimix_core/ep/ep.py b/glimix_core/ep/ep.py
@@ -12,8 +12,8 @@
 from .site import Site
 
 MAX_ITERS = 100
-RTOL = epsilon.small
-ATOL = epsilon.small
+RTOL = epsilon.small * 1000
+ATOL = epsilon.small * 1000
 
 
 class EP(object):  # pylint: disable=R0903
@@ -63,6 +63,7 @@ def _compute_moments(self):
 
     def _initialize(self, mean, QS):
         self._logger.debug("EP parameters initialization.")
+        self._need_params_update = True
 
         nsamples = len(mean)
 
@@ -177,8 +178,9 @@ def _params_update(self):
 
         if i == MAX_ITERS:
             msg = ('Maximum number of EP iterations has' + ' been attained.')
-            msg = " Last EP step was: %.10f." % norm(self._site.tau - self._psite.tau)
+            msg += " Last EP step was: %.10f." % norm(self._site.tau -
+                                                      self._psite.tau)
             raise ValueError(msg)
 
-        self._need_params_update = True
+        self._need_params_update = False
         self._logger.debug('EP loop has performed %d iterations.', i)
diff --git a/glimix_core/ggp/test/test_expfamgp.py b/glimix_core/ggp/test/test_expfamgp.py
@@ -59,6 +59,8 @@ def test_expfam_ep():
 
 
 def test_expfam_ep_function():
+    import logging
+    logging.basicConfig(level=logging.DEBUG)
     data = _get_data()
     ep = ExpFamGP((data['y'], ), 'bernoulli', data['mean'], data['cov'])
 
@@ -70,5 +72,5 @@ def test_expfam_ep_optimize():
     ep = ExpFamGP((data['y'], ), 'bernoulli', data['mean'], data['cov'])
     data['cov_left'].fix('logscale')
     ep.feed().maximize(progress=False)
-    assert_allclose(data['cov_right'].scale, 4.165865119892221e-06)
+    assert_allclose(data['cov_right'].scale, 4.165865119892221e-06, atol=1e-5)
     assert_allclose(data['mean'].offset, 1.0326586373049373)
diff --git a/glimix_core/glmm/glmm.py b/glimix_core/glmm/glmm.py
@@ -48,7 +48,7 @@ class GLMM(EP, Function):
 
     .. doctest::
 
-        >>> from numpy import dot
+        >>> from numpy import dot, sqrt, zeros
         >>> from numpy.random import RandomState
         >>>
         >>> from numpy_sugar.linalg import economic_qs
@@ -62,17 +62,22 @@ class GLMM(EP, Function):
         >>> G = random.randn(50, 100)
         >>> K = dot(G, G.T)
         >>> ntrials = random.randint(1, 100, nsamples)
-        >>> successes = [random.randint(0, i + 1) for i in ntrials]
+        >>> z = dot(G, random.randn(100)) / sqrt(100)
+        >>>
+        >>> successes = zeros(len(ntrials), int)
+        >>> for i in range(len(ntrials)):
+        ...     for j in range(ntrials[i]):
+        ...         successes[i] += int(z[i] + 0.5 * random.randn() > 0)
         >>>
         >>> y = (successes, ntrials)
         >>>
         >>> QS = economic_qs(K)
         >>> glmm = GLMM(y, 'binomial', X, QS)
         >>> print('Before: %.4f' % glmm.feed().value())
-        Before: -210.3568
+        Before: -151.5476
         >>> glmm.feed().maximize(progress=False)
         >>> print('After: %.2f' % glmm.feed().value())
-        After: -182.57
+        After: -147.53
     """
 
     def __init__(self, y, lik_name, X, QS):
@@ -86,10 +91,14 @@ def __init__(self, y, lik_name, X, QS):
         self._logger = logging.getLogger(__name__)
 
         logscale = self.variables()['logscale']
-        logscale.bounds = (-10, 4.0)
+        logscale.bounds = (-5, 4.0)
+        logscale.listen(self._clear_cache)
 
         logitdelta = self.variables()['logitdelta']
-        logitdelta.bounds = (-40.0, +40.0)
+        logitdelta.bounds = (-30.0, +30.0)
+        logitdelta.listen(self._clear_cache)
+
+        self.variables()['beta'].listen(self._clear_cache)
 
         if not isinstance(y, tuple):
             y = (y, )
@@ -112,6 +121,9 @@ def __init__(self, y, lik_name, X, QS):
         self._machine = LikNormMachine(lik_name, 500)
         self.set_nodata()
 
+    def _clear_cache(self, _):
+        self._need_params_update = True
+
     def __del__(self):
         if hasattr(self, '_machine'):
             self._machine.finish()
@@ -133,7 +145,7 @@ def fix(self, var_name):
             Function.fix(self, 'beta')
         else:
             raise ValueError("Unknown parameter name %s." % var_name)
-        self._need_params_update = True
+        self._clear_cache(None)
 
     def unfix(self, var_name):
         if var_name == 'scale':
@@ -144,7 +156,7 @@ def unfix(self, var_name):
             Function.unfix(self, 'beta')
         else:
             raise ValueError("Unknown parameter name %s." % var_name)
-        self._need_params_update = True
+        self._clear_cache(None)
 
     @property
     def scale(self):
@@ -153,7 +165,7 @@ def scale(self):
     @scale.setter
     def scale(self, v):
         self.variables().get('logscale').value = log(v)
-        self._need_params_update = True
+        self._clear_cache(None)
 
     @property
     def delta(self):
@@ -163,7 +175,7 @@ def delta(self):
     def delta(self, v):
         v = clip(v, epsilon.small, 1 - epsilon.small)
         self.variables().get('logitdelta').value = log(v / (1 - v))
-        self._need_params_update = True
+        self._clear_cache(None)
 
     @property
     def beta(self):
@@ -172,7 +184,7 @@ def beta(self):
     @beta.setter
     def beta(self, v):
         self.variables().get('beta').value = v
-        self._need_params_update = True
+        self._clear_cache(None)
 
     def _eigval_derivative_over_logscale(self):
         x = self.variables().get('logscale').value
@@ -185,7 +197,11 @@ def _eigval_derivative_over_logitdelta(self):
         s = self.scale
         c = (s * exp(x) / (1 + exp(-x))**2)
         E = self._QS[1]
-        return c * (1 - E)
+        cond = abs(E.max())/abs(E.min())
+        if cond > 1e5:
+            self._logger.info("Conditioning number too high: %.5f", cond)
+        res = c * (1 - E)
+        return res
 
     def _S(self):
         s = self.scale

diff --git a/test2.py b/test2.py