more doc

convoys/multi.py

@@ -11,9 +11,15 @@ class MultiModel:
 
 class RegressionToMulti(MultiModel):
     def __init__(self, *args, **kwargs):
-        self.base_model = self.base_model_cls(*args, **kwargs)
+        self.base_model = self._base_model_cls(*args, **kwargs)
 
     def fit(self, G, B, T):
+        ''' Fits the model
+
+        :param G: numpy vector of shape :math:`n`
+        :param B: numpy vector of shape :math:`n`
+        :param T: numpy vector of shape :math:`n`
+        '''
         self._n_groups = max(G) + 1
         X = numpy.zeros((len(G), self._n_groups))
         for i, group in enumerate(G):
@@ -34,9 +40,15 @@ def rvs(self, group, *args, **kwargs):
 
 class SingleToMulti(MultiModel):
     def __init__(self, *args, **kwargs):
-        self.base_model_init = lambda: self.base_model_cls(*args, **kwargs)
+        self.base_model_init = lambda: self._base_model_cls(*args, **kwargs)
 
     def fit(self, G, B, T):
+        ''' Fits the model
+
+        :param G: numpy vector of shape :math:`n`
+        :param B: numpy vector of shape :math:`n`
+        :param T: numpy vector of shape :math:`n`
+        '''
         group2bt = {}
         for g, b, t in zip(G, B, T):
             group2bt.setdefault(g, []).append((b, t))
@@ -50,20 +62,25 @@ def cdf(self, group, t, *args, **kwargs):
 
 
 class Exponential(RegressionToMulti):
-    base_model_cls = regression.Exponential
+    ''' Multi-group version of :class:`convoys.regression.Exponential`. '''
+    _base_model_cls = regression.Exponential
 
 
 class Weibull(RegressionToMulti):
-    base_model_cls = regression.Weibull
+    ''' Multi-group version of :class:`convoys.regression.Weibull`. '''
+    _base_model_cls = regression.Weibull
 
 
 class Gamma(RegressionToMulti):
-    base_model_cls = regression.Gamma
+    ''' Multi-group version of :class:`convoys.regression.Gamma`. '''
+    _base_model_cls = regression.Gamma
 
 
 class GeneralizedGamma(RegressionToMulti):
-    base_model_cls = regression.GeneralizedGamma
+    ''' Multi-group version of :class:`convoys.regression.GeneralizedGamma`. '''
+    _base_model_cls = regression.GeneralizedGamma
 
 
 class KaplanMeier(SingleToMulti):
-    base_model_cls = single.KaplanMeier
+    ''' Multi-group version of :class:`convoys.single.KaplanMeier`. '''
+    _base_model_cls = single.KaplanMeier

convoys/regression.py

@@ -144,7 +144,14 @@ def __init__(self, ci=False):
         self._ci = ci
 
     def fit(self, X, B, T, W=None, fix_k=None, fix_p=None):
-        # Sanity check input:
+        '''Fits the model.
+
+        :param X: numpy matrix of shape :math:`k \cdot n`
+        :param B: numpy vector of shape :math:`n`
+        :param T: numpy vector of shape :math:`n`
+        :param W: (optional) numpy vector of shape :math:`n`
+        '''
+
         if W is None:
             W = [1] * len(X)
         XBTW = [(x, b, t, w) for x, b, t, w in zip(X, B, T, W)

convoys/single.py

@@ -10,7 +10,13 @@ class SingleModel:
 
 
 class KaplanMeier(SingleModel):
+    ''' Implementation of the Kaplan-Meier nonparametric method. '''
     def fit(self, B, T):
+        ''' Fits the model
+
+        :param B: numpy vector of shape :math:`n`
+        :param T: numpy vector of shape :math:`n`
+        '''
         # See https://www.math.wustl.edu/~sawyer/handouts/greenwood.pdf
         n = len(T)
         self._ts = []

docs/index.rst

@@ -6,12 +6,14 @@ Full API documentation
 
 .. automodule:: convoys.regression
    :members:
+   :inherited-members:
 
 .. automodule:: convoys.single
    :members:
 
 .. automodule:: convoys.multi
    :members:
+   :inherited-members:
 
 .. automodule:: convoys.utils
    :members: