quick bugfix to beta distribution with nonstandard bounds

src/ensemble/distributions.py

@@ -43,6 +43,8 @@ def __init__(
             # not np.isinf(self.support[1]),
         ):
             case (True, True):
+                # print(np.isinf(self.support[0]))
+                # print(np.isinf(self.support[1]))
                 if np.isinf(self.support[0]) or np.isinf(self.support[1]):
                     raise ValueError(
                         "You may not change an infinite bound to be finite or"
@@ -348,8 +350,8 @@ def __init__(
         lb: float = 0,
         ub: float = 1,
     ):
-        super().__init__(mean, variance, lb, ub)
         self.width = np.abs(ub - lb)
+        super().__init__(mean, variance, lb, ub)
 
     def _squeeze(self, x: float) -> float:
         """transform x to be within (0, 1)
@@ -392,8 +394,7 @@ def _create_scipy_dist(self, csd_mean) -> None:
                 + "combinations. The supplied variance must be in between "
                 + "(0, mean^2)"
             )
-        beta_bounds(self.mean)
-        if self.lb != 0 and self.ub != 1:
+        if self.lb != 0 or self.ub != 1:
             mean = (self.mean - self.lb) / self.width
             var = self.variance / self.width
         else:
@@ -511,6 +512,6 @@ def strict_positive_support(mean: float) -> None:
         raise ValueError("This distribution is only supported on (0, np.inf)")
 
 
-def beta_bounds(mean: float) -> None:
-    if (mean < 0) or (mean > 1):
-        raise ValueError("This distribution is only supposrted on [0, 1]")
+# def beta_bounds(mean: float) -> None:
+#     if (mean < 0) or (mean > 1):
+#         raise ValueError("This distribution is only supposrted on [0, 1]")

src/ensemble/model.py

@@ -307,6 +307,28 @@ def to_json(self, file_path: str, appending: bool = False) -> None:
     def from_objs(
         cls, fitted_distributions: List[Distribution]
     ) -> EnsembleDistribution:
+        """generates ensemble distribution from Distribution objects also from
+        the ensemble package. all parameters, such as mean, variance, upper
+        bound, and lower bound, must match
+
+        Parameters
+        ----------
+        fitted_distributions : List[Distribution]
+            list of distribution objects from the ensemble package
+
+        Returns
+        -------
+        EnsembleDistribution
+            ensemble distribution object with parameters equal to individual
+            input distributions
+
+        Raises
+        ------
+        ValueError
+            if parameters across individual distributions don't match
+        ValueError
+            if the weight of a distribution is not set
+        """
         try:
             mean, variance, lb, ub = (
                 fitted_distributions[0].mean,
@@ -441,23 +463,19 @@ class EnsembleFitter:
         names of distributions in ensemble
     objective: str
         name of objective function for use in fitting ensemble
-    lb: str
-
 
     """
 
     def __init__(
         self,
         distributions: List[str],
         objective: str,
-        lb: str = None,
-        ub: str = None,
     ):
         self.support = _check_supports_match(distributions)
         self.distributions = distributions
         self.objective = objective
-        self.lb = lb
-        self.ub = ub
+        # self.lb = lb
+        # self.ub = ub
 
     def _objective_func(self, vec: np.ndarray) -> float:
         """applies different penalties to vector of distances given by user
@@ -517,13 +535,18 @@ def fit(
         lb: float | None = None,
         ub: float | None = None,
     ) -> EnsembleResult:
+        # TODO: HOW SHOULD WE DESCRIBE UB AND LB?
         """fits weighted sum of CDFs corresponding to distributions in
         EnsembleModel object to empirical CDF of given data
 
         Parameters
         ----------
         data : npt.ArrayLike
             individual-level data (i.e. microdata)
+        lb: float, optional
+            lower allowable bound of data, by default None
+        ub: float, optional
+            upper allowable bound of data, by default None
 
         Returns
         -------
@@ -604,7 +627,25 @@ def fit(
 ### HELPER FUNCTIONS
 
 
-def _check_valid_ensemble(distributions: List[str], weights: List[float]):
+def _check_valid_ensemble(
+    distributions: List[str], weights: List[float]
+) -> None:
+    """checks if ensemble distribution is valid
+
+    Parameters
+    ----------
+    distributions : List[str]
+        list of named distributions, as strings
+    weights : List[float]
+        list of weights, in order of provided distribution list
+
+    Raises
+    ------
+    ValueError
+        if there is a mismatch between num distributions and num weights
+    ValueError
+        if weights do not sum to 1
+    """
     if len(distributions) != len(weights):
         raise ValueError(
             "there must be the same number of distributions as weights!"

tests/test_distributions.py

@@ -106,6 +106,15 @@ def test_beta():
     assert np.isclose(res[0], BETA_MEAN)
     assert np.isclose(res[1], BETA_VARIANCE)
 
+    mean = 3
+    vari = 1
+    lb = 0
+    ub = 5
+    beta_alt = Beta(mean, vari, lb, ub)
+    res_alt = beta_alt.stats(moments="mv")
+    assert np.isclose(res_alt[0], mean)
+    assert np.isclose(res_alt[1], vari)
+
 
 def test_invalid_means():
     # negative means for only positive RVs