Remove outdated TODOs in LikelihoodMaximization

qunfold/methods/likelihood.py

@@ -22,7 +22,6 @@ def __init__(
       solver_options = {"gtol": 1e-8, "maxiter": 1000}, # , "disp": True
       tau_0 = 0,
       tau_1 = 0,
-      epsilon = 0,
       fit_classifier = True,
       seed = None,
       ):
@@ -31,7 +30,6 @@ def __init__(
     self.solver_options = solver_options
     self.tau_0 = tau_0
     self.tau_1 = tau_1
-    self.epsilon = epsilon
     self.fit_classifier = fit_classifier
     self.seed = seed
   def fit(self, X, y, n_classes=None):
@@ -43,21 +41,10 @@ def fit(self, X, y, n_classes=None):
   def predict(self, X):
     pXY = jnp.array(self.classifier.predict_proba(X) / self.p_trn) # proportional to P(X|Y)
     pXY = pXY / pXY.sum(axis=1, keepdims=True) # normalize to P(X|Y)
-
-    # TODO 1) filter out all rows from pXY that contain zeros or ones, or values close to zero or one up to some self.epsilon. Goal: to reduce thrown errors / warnings and to replace the corresponding estimates with proper ones.
-    # epsilon_filtered_rows = pXY[jnp.any(pXY <= self.epsilon, axis=1), :]
-    # pXY = pXY[jnp.all(pXY > self.epsilon, axis=1),:]
-
-    # TODO 2) "side-chain" those rows that have contained values close to one, by setting up a classify-and-count estimate that is later added to opt.x. Appropriately weight both the CC estimate and opt.x by the fraction of rows that has lead to each of these estimates. Goal: to further improve the estimation (see also the todo 3).
-
-    # TODO 3) consider self.epsilon as a hyper-parameter, assuming that all fairly confident predictions are probably correct, not only the extemely confident exceptions.
-
-    # minimize the negative log-likelihood loss
-    def loss(p):
-      # xi_0 = jnp.sum((p[1:] - p[:-1])**2) / 2 # deviation from a uniform prediction
-      # xi_1 = jnp.sum((-p[:-2] + 2 * p[1:-1] - p[2:])**2) / 2 # deviation from non-ordinal
-      # return -jnp.log(pXY @ p).mean() + self.tau_0 * xi_0 + self.tau_1 * xi_1
-      return -jnp.log(pXY @ p).mean()
+    def loss(p): # the (regularized) negative log-likelihood loss
+      xi_0 = jnp.sum((p[1:] - p[:-1])**2) / 2 # deviation from a uniform prediction
+      xi_1 = jnp.sum((-p[:-2] + 2 * p[1:-1] - p[2:])**2) / 2 # deviation from non-ordinal
+      return -jnp.log(pXY @ p).mean() + self.tau_0 * xi_0 + self.tau_1 * xi_1
     return minimize(
       loss,
       len(self.p_trn),
@@ -67,7 +54,7 @@ def loss(p):
     )
 
 class ExpectationMaximizer(AbstractMethod):
-  """The expectation maximization-based method  by Saerens et al. (2002).
+  """The expectation maximization-based method by Saerens et al. (2002).
 
   This method is proven to be asymptotically equivalent to the `LikelihoodMaximizer` by Alexandari et al. (2020).