Merge pull request #31 from eth-cscs/release-0.4.0

Release 0.4.0

VERSION

@@ -1 +1 @@
-0.3
+0.4.0

abcpy/approx_lhd.py

@@ -6,27 +6,28 @@
 
 class Approx_likelihood(metaclass = ABCMeta):
     """This abstract base class defines the approximate likelihood 
-    function. To approximate the likelihood function at a parameter value given observed dataset, 
-    we need to pass a dataset simulated from model set at the parameter value and the observed dataset. 
+    function. To approximate the likelihood function at a parameter value given observed data set,
+    we need to pass a data set simulated from model set at the parameter value and the observed data set.
     """
 
     @abstractmethod
     def __init__(self, statistics_calc):       
-        """    The constructor of a sub-class must accept a non-optional statistics
-    calculator, which is stored to self.statistics_calc.
+        """
+        The constructor of a sub-class must accept a non-optional statistics
+        calculator, which is stored to self.statistics_calc.
 
-    Parameters
-    ----------
-    statistics_calc : abcpy.stasistics.Statistics 
-        Statistics extractor object that conforms to the Statistics class.
+        Parameters
+        ----------
+        statistics_calc : abcpy.stasistics.Statistics
+            Statistics extractor object that conforms to the Statistics class.
         """
 
         raise NotImplemented
 
     @abstractmethod
     def likelihood(y_obs, y_sim):
         """To be overwritten by any sub-class: should compute the approximate likelihood 
-        value given the observed dataset y_obs and dataset y_sim simulated from
+        value given the observed data set y_obs and the data set y_sim simulated from
         model set at the parameter value.
 
         Parameters
@@ -46,7 +47,7 @@ def likelihood(y_obs, y_sim):
 
 
 class SynLiklihood(Approx_likelihood):
-    """This class implements the aproximate likelihood function which computes the pproximate
+    """This class implements the approximate likelihood function which computes the approximate
     likelihood using the synthetic likelihood approach described in Wood [1].
     For synthetic likelihood approximation, we compute the robust precision matrix using Ledoit and Wolf's [2]
     method.
@@ -92,8 +93,8 @@ def likelihood(self, y_obs, y_sim):
 
 
 class PenLogReg(Approx_likelihood):
-    """This class implements the aproximate likelihood function which computes the pproximate
-    likelihood upto a constant using penalized logistic regression described in 
+    """This class implements the approximate likelihood function which computes the approximate
+    likelihood up to a constant using penalized logistic regression described in
     Dutta et. al. [1]. It takes one additional function handler defining the 
     true model and two additional parameters n_folds and n_simulate correspondingly defining number
     of folds used to estimate prediction error using cross-validation and the number 
@@ -106,10 +107,8 @@ class PenLogReg(Approx_likelihood):
     
     [2] Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization 
     paths for generalized linear models via coordinate descent. Journal of Statistical 
-    Software, 33(1), 1–22.      
-    """
-    def __init__(self, statistics_calc, model, n_simulate, n_folds=10, max_iter = 100000, seed = None):
-        """
+    Software, 33(1), 1–22.
+
     Parameters
     ----------
     statistics_calc : abcpy.stasistics.Statistics
@@ -124,17 +123,18 @@ def __init__(self, statistics_calc, model, n_simulate, n_folds=10, max_iter = 10
         Maximum passes over the data. The default is 100000.
     seed: int, optional
         Seed for the random number generator. The used glmnet solver is not
-        deterministic, this seed is used for determining the cv folds. The default value is 
+        deterministic, this seed is used for determining the cv folds. The default value is
         None.
-        """
-
+    """
+    def __init__(self, statistics_calc, model, n_simulate, n_folds=10, max_iter = 100000, seed = None):
+
         self.model = model
         self.statistics_calc = statistics_calc
         self.n_folds = n_folds
         self.n_simulate = n_simulate
         self.seed = seed
         self.max_iter = max_iter
-        # Simulate reference data and extract summary statistics from the reffernce data      
+        # Simulate reference data and extract summary statistics from the reference data
         self.ref_data_stat = self._simulate_ref_data()
 
 
@@ -164,7 +164,7 @@ def likelihood(self, y_obs, y_sim):
 
     def _simulate_ref_data(self):
         """
-        Simulate the reference dataset. This code is run at the initializtion of 
+        Simulate the reference data set. This code is run at the initialization of
         Penlogreg
         """
 

abcpy/backends/base.py

@@ -30,7 +30,7 @@ def parallelize(self, list):
             A reference object that represents the parallelized list
         """
 
-        raise NotImplemented
+        raise NotImplementedError
 
 
     @abstractmethod
@@ -49,7 +49,7 @@ def broadcast(self, object):
             A reference to the broadcasted object
         """
 
-        raise NotImplemented
+        raise NotImplementedError
 
 
     @abstractmethod
@@ -72,7 +72,7 @@ def map(self, func, pds):
             a new parallel data set that contains the result of the map
         """
 
-        raise NotImplemented
+        raise NotImplementedError
 
 
     @abstractmethod
@@ -91,7 +91,7 @@ def collect(self, pds):
             all elements of pds as a list
         """
 
-        raise NotImplemented
+        raise NotImplementedError
 
 
 class PDS:
@@ -101,7 +101,7 @@ class PDS:
 
     @abstractmethod
     def __init__(self):
-        raise NotImplemented
+        raise NotImplementedError
 
 
 class BDS:
@@ -111,15 +111,15 @@ class BDS:
 
     @abstractmethod
     def __init__(self):
-        raise NotImplemented
+        raise NotImplementedError
 
 
     @abstractmethod
     def value(self):
         """
         This method should return the actual object that the broadcast data set represents. 
         """
-        raise NotImplemented
+        raise NotImplementedError
 
 
 class BackendDummy(Backend):