Add ability to fix/unfix parameters

mxfusion/inference/inference_parameters.py

@@ -140,6 +140,35 @@ def fix_all(self):
         for p in self.param_dict.values():
             p.grad_req = 'null'
 
+    def fix_variable(self, variable, value=None):
+        """
+        Fixes a variable so that it isn't changed by the inference algorithm. Optionally a value can be specified to
+        which to variable's value will be fixed. If a value is not specified, the variable will be fixed to it's current
+        value
+
+        :param variable: The variable to be fixed
+        :type variable: MXFusion Variable
+        :param value: (Optional) Value to which the variable will be fixed
+        :type value: MXNet NDArray
+        """
+
+        if value is not None:
+            self[variable] = value
+
+        parameter = self.param_dict[variable.uuid]
+        parameter.grad_req = 'null'
+
+    def unfix_variable(self, variable):
+        """
+        Allows a variable to be changed by the inference algorithm if it has been previously fixed
+
+        :param variable: The variable to be unfixed
+        :type variable: MXFusion Variable
+        """
+
+        parameter = self.param_dict[variable.uuid]
+        parameter.grad_req = 'write'
+
     @property
     def param_dict(self):
         return self._params

testing/inference/test_inference_parameters.py

@@ -0,0 +1,62 @@
+import unittest
+
+import mxnet as mx
+import numpy as np
+
+from mxfusion.common.config import get_default_dtype
+from mxfusion.components.variables import PositiveTransformation
+from mxfusion.inference import BatchInferenceLoop, GradBasedInference, MAP
+import mxfusion as mf
+
+
+class InferenceParameterTests(unittest.TestCase):
+    def setUp(self):
+        dtype = get_default_dtype()
+        m = mf.models.Model()
+        m.mean = mf.components.Variable()
+        m.var = mf.components.Variable(transformation=PositiveTransformation())
+        m.N = mf.components.Variable()
+        m.x = mf.components.distributions.Normal.define_variable(mean=m.mean, variance=m.var, shape=(m.N,))
+        m.y = mf.components.distributions.Normal.define_variable(mean=m.x, variance=mx.nd.array([1], dtype=dtype), shape=(m.N,))
+        self.m = m
+
+    def test_variable_fixing(self):
+        N = 10
+        dtype = get_default_dtype()
+        observed = [self.m.y]
+
+        # First check the parameter varies if it isn't fixed
+        alg = MAP(model=self.m, observed=observed)
+        infr = GradBasedInference(inference_algorithm=alg, grad_loop=BatchInferenceLoop())
+        infr.initialize(y=mx.nd.array(np.random.rand(N)))
+        infr.run(y=mx.nd.array(np.random.rand(N), dtype=dtype), max_iter=10)
+        assert infr.params[self.m.x.factor.mean] != mx.nd.ones(1)
+
+        # Now fix parameter and check it does not change
+        alg = MAP(model=self.m, observed=observed)
+        infr = GradBasedInference(inference_algorithm=alg, grad_loop=BatchInferenceLoop())
+        infr.initialize(y=mx.nd.array(np.random.rand(N)))
+        infr.params.fix_variable(self.m.x.factor.mean, mx.nd.ones(1))
+        infr.run(y=mx.nd.array(np.random.rand(N), dtype=dtype), max_iter=10)
+        assert infr.params[self.m.x.factor.mean] == mx.nd.ones(1)
+
+    def test_variable_unfixing(self):
+        N = 10
+        y = np.random.rand(N)
+        dtype = get_default_dtype()
+        observed = [self.m.y]
+
+        # First fix variable and run inference
+        alg = MAP(model=self.m, observed=observed)
+        infr = GradBasedInference(inference_algorithm=alg, grad_loop=BatchInferenceLoop())
+        infr.initialize(y=mx.nd.array(np.random.rand(N)))
+        infr.params.fix_variable(self.m.x.factor.mean, mx.nd.ones(1))
+        infr.run(y=mx.nd.array(y, dtype=dtype), max_iter=10)
+
+        assert infr.params[self.m.x.factor.mean] == mx.nd.ones(1)
+
+        # Now unfix and run inference again
+        infr.params.unfix_variable(self.m.x.factor.mean)
+        infr.run(y=mx.nd.array(y, dtype=dtype), max_iter=10)
+
+        assert infr.params[self.m.x.factor.mean] != mx.nd.ones(1)