revise h_params of exponential

Author: yuta-nakahara

bayesml/exponential/_exponential.py

@@ -36,37 +36,19 @@ def __init__(self,*,lambda_=1.0,h_alpha=1.0,h_beta=1.0,seed=None):
         self.h_alpha = _check.pos_float(h_alpha,'h_alpha',ParameterFormatError)
         self.h_beta = _check.pos_float(h_beta,'h_beta',ParameterFormatError)
         self.rng = np.random.default_rng(seed)
-        self._H_PARAM_KEYS = {'h_alpha','h_beta'}
-        self._H0_PARAM_KEYS = {'h0_alpha','h0_beta'}
-        self._HN_PARAM_KEYS = {'hn_alpha','hn_beta'}
 
-    def set_h_params(self,**kwargs):
+    def set_h_params(self,h_alpha,h_beta):
         """Set the hyperparameters of the prior distribution.
         
         Parameters
         ----------
-        **kwargs
-            a python dictionary {'h_alpha':float, 'h_beta':float} or
-            {'h0_alpha':float, 'h0_beta':float} or {'hn_alpha':float, 'hn_beta':float}
-            They are obtained by ``get_h_params()`` of GenModel,
-            ``get_h0_params`` of LearnModel or ``get_hn_params`` of LearnModel.
+        h_alpha : float
+            a positive real number
+        h_beta : float
+            a positive real number
         """
-        if kwargs.keys() == self._H_PARAM_KEYS:
-            self.h_alpha = _check.pos_float(kwargs['h_alpha'],'h_alpha',ParameterFormatError)
-            self.h_beta = _check.pos_float(kwargs['h_beta'],'h_beta',ParameterFormatError)
-        elif kwargs.keys() == self._H0_PARAM_KEYS:
-            self.h_alpha = _check.pos_float(kwargs['h0_alpha'],'h_alpha',ParameterFormatError)
-            self.h_beta = _check.pos_float(kwargs['h0_beta'],'h_beta',ParameterFormatError)
-        elif kwargs.keys() == self._HN_PARAM_KEYS:
-            self.h_alpha = _check.pos_float(kwargs['hn_alpha'],'h_alpha',ParameterFormatError)
-            self.h_beta = _check.pos_float(kwargs['hn_beta'],'h_beta',ParameterFormatError)
-        else:
-            raise(ParameterFormatError(
-                "The input of this function must be a python dictionary with keys:"
-                +str(self._H_PARAM_KEYS)+" or "
-                +str(self._H0_PARAM_KEYS)+" or "
-                +str(self._HN_PARAM_KEYS)+".")
-                )
+        self.h_alpha = _check.pos_float(h_alpha,'h_alpha',ParameterFormatError)
+        self.h_beta = _check.pos_float(h_beta,'h_beta',ParameterFormatError)
 
     def get_h_params(self):
         """Get the hyperparameters of the prior distribution.
@@ -212,37 +194,19 @@ def __init__(self,h0_alpha=2.0, h0_beta=1.0):
         self.hn_beta = self.h0_beta
         self.p_kappa = self.hn_alpha
         self.p_lambda = self.hn_beta
-        self._H_PARAM_KEYS = {'h_alpha', 'h_beta'}
-        self._H0_PARAM_KEYS = {'h0_alpha', 'h0_beta'}
-        self._HN_PARAM_KEYS = {'hn_alpha', 'hn_beta'}
 
-    def set_h0_params(self,**kwargs):
+    def set_h0_params(self,h0_alpha, h0_beta):
         """Set initial values of the hyperparameter of the posterior distribution.
 
         Parameters
         ----------
-        **kwargs
-            a python dictionary {'h_alpha':float, 'h_beta':float} or
-            {'h0_alpha':float, 'h0_beta':float} or {'hn_alpha':float, 'hn_beta':float}
-            They are obtained by ``get_h_params()`` of GenModel,
-            ``get_h0_params`` of LearnModel or ``get_hn_params`` of LearnModel.
+        h0_alpha : float
+            a positive real number
+        h0_beta : float
+            a positibe real number
         """
-        if kwargs.keys() == self._H_PARAM_KEYS:
-            self.h0_alpha = _check.pos_float(kwargs['h_alpha'],'h0_alpha',ParameterFormatError)
-            self.h0_beta = _check.pos_float(kwargs['h_beta'],'h0_beta',ParameterFormatError)
-        elif kwargs.keys() == self._H0_PARAM_KEYS:
-            self.h0_alpha = _check.pos_float(kwargs['h0_alpha'],'h0_alpha',ParameterFormatError)
-            self.h0_beta = _check.pos_float(kwargs['h0_beta'],'h0_beta',ParameterFormatError)
-        elif kwargs.keys() == self._HN_PARAM_KEYS:
-            self.h0_alpha = _check.pos_float(kwargs['hn_alpha'],'h0_alpha',ParameterFormatError)
-            self.h0_beta = _check.pos_float(kwargs['hn_beta'],'h0_beta',ParameterFormatError)
-        else:
-            raise(ParameterFormatError(
-                "The input of this function must be a python dictionary with keys:"
-                +str(self._H_PARAM_KEYS)+" or "
-                +str(self._H0_PARAM_KEYS)+" or "
-                +str(self._HN_PARAM_KEYS)+".")
-                )
+        self.h0_alpha = _check.pos_float(h0_alpha, 'h0_alpha', ParameterFormatError)
+        self.h0_beta = _check.pos_float(h0_beta, 'h0_beta', ParameterFormatError)
         self.reset_hn_params()
 
     def get_h0_params(self):
@@ -256,33 +220,18 @@ def get_h0_params(self):
         """
         return {"h0_alpha":self.h0_alpha, "h0_beta": self.h0_beta}
 
-    def set_hn_params(self, **kwargs):
+    def set_hn_params(self,hn_alpha, hn_beta):
         """Set updated values of the hyperparameter of the posterior distribution.
 
         Parameters
         ----------
-        **kwargs
-            a python dictionary {'h_alpha':float, 'h_beta':float} or
-            {'h0_alpha':float, 'h0_beta':float} or {'hn_alpha':float, 'hn_beta':float}
-            They are obtained by ``get_h_params()`` of GenModel,
-            ``get_h0_params`` of LearnModel or ``get_hn_params`` of LearnModel.
+        hn_alpha : float
+            a positive real number
+        hn_beta : float
+            a positibe real number
         """
-        if kwargs.keys() == self._H_PARAM_KEYS:
-            self.hn_alpha = _check.pos_float(kwargs['h_alpha'], 'hn_alpha', ParameterFormatError)
-            self.hn_beta = _check.pos_float(kwargs['h_beta'], 'hn_beta', ParameterFormatError)
-        elif kwargs.keys() == self._H0_PARAM_KEYS:
-            self.hn_alpha = _check.pos_float(kwargs['h0_alpha'], 'hn_alpha', ParameterFormatError)
-            self.hn_beta = _check.pos_float(kwargs['h0_beta'], 'hn_beta', ParameterFormatError)
-        elif kwargs.keys() == self._HN_PARAM_KEYS:
-            self.hn_alpha = _check.pos_float(kwargs['hn_alpha'], 'hn_alpha', ParameterFormatError)
-            self.hn_beta = _check.pos_float(kwargs['hn_beta'], 'hn_beta', ParameterFormatError)
-        else:
-            raise (ParameterFormatError(
-                "The input of this function must be a python dictionary with keys:"
-                + str(self._H_PARAM_KEYS) + " or "
-                + str(self._H0_PARAM_KEYS) + " or "
-                + str(self._HN_PARAM_KEYS) + ".")
-            )
+        self.hn_alpha = _check.pos_float(hn_alpha, 'hn_alpha', ParameterFormatError)
+        self.hn_beta = _check.pos_float(hn_beta, 'hn_beta', ParameterFormatError)
         self.calc_pred_dist()
 
     def get_hn_params(self):

doc/devdoc/examples/h_params_test.py

@@ -1,7 +1,7 @@
-from bayesml import multivariate_normal as bayesml_model
+from bayesml import exponential as bayesml_model
 import numpy as np
 
-h0_params = {'h0_m_vec':np.ones(2),'h0_kappa':3,'h0_nu':2,'h0_w_mat':np.eye(2)*2}
+h0_params = {'h0_alpha':2,'h0_beta':3}
 
 print('Gen to Learn 1')
 model = bayesml_model.GenModel()