Develop metatree categorical

bayesml/categorical/_categorical.py

@@ -9,6 +9,7 @@
 import warnings
 import numpy as np
 from scipy.stats import dirichlet as ss_dirichlet
+from scipy.special import gammaln
 import matplotlib.pyplot as plt
 
 from .. import base
@@ -553,3 +554,16 @@ def pred_and_update(self, x, loss="squared",onehot=True):
         prediction = self.make_prediction(loss,onehot)
         self.update_posterior(x,onehot)
         return prediction
+
+    def calc_log_marginal_likelihood(self):
+        """Calculate log marginal likelihood
+
+        Returns
+        -------
+        log_marginal_likelihood : float
+            The log marginal likelihood.
+        """
+        return (gammaln(self.h0_alpha_vec.sum())
+                -gammaln(self.h0_alpha_vec).sum()
+                -gammaln(self.hn_alpha_vec.sum())
+                +gammaln(self.hn_alpha_vec).sum())

bayesml/metatree/metatree_test.py

@@ -1,5 +1,5 @@
 from bayesml import metatree
-from bayesml import linearregression
+from bayesml import categorical
 import numpy as np
 import time
 
@@ -12,6 +12,8 @@
     c_max_depth=5,
     h_g=1.0,
     seed=0,
+    SubModel=categorical,
+    sub_constants={'c_degree':3},
 )
 gen_model.gen_params()
 true_root = gen_model.get_params()
@@ -21,10 +23,20 @@
     c_dim_continuous=dim_continuous,
     c_dim_categorical=dim_categorical,
     c_max_depth=5,
+    SubModel=categorical,
+    sub_constants={'c_degree':3},
 )
 learn_model.set_h0_params(h0_metatree_list=[true_root['root']])
 learn_model.set_h0_params(h0_g=0.5)
 
+# learn_model.update_posterior(
+#     x_continuous,
+#     x_categorical,
+#     y,
+#     alg_type='given_MT',
+# )
+# learn_model.visualize_posterior(filename='posterior3.gv')
+
 time_vec = np.empty(100)
 for i in range(100):
     learn_model.reset_hn_params()