Resolve recurrent dead-code/docs items (DEVELOPMENT.md 4B)

DEVELOPMENT.md

@@ -53,18 +53,6 @@ A current-state audit of the whole `recurrent/` package (parametric intensity,
 renewal, regression, nonparametric, competing risks). Findings are grouped by
 theme; items already resolved are marked **[done]**.
 
-**B. Dead code / stubs / docs**
-- `ParametricRecurrenceRegressionModel` (`parametric_regression.py`) — was a
-  `# TODO` stub, never imported/exported and a duplicate of
-  `ParametricRecurrenceModel`; recurrence-regression results are served by
-  `ProportionalIntensityModel`. **[done]** (file removed)
-- `_validate_memory` duplicated verbatim in `renewal/ara.py` and
-  `renewal/ari.py`.
-- 16 copy-pasted docstrings read "Parameters of the Duane model" inside
-  `CrowAMSAA`/`CoxLewis`.
-- `has_left_censoing` typo (`nhpp_fitter.py`, NHPP regression file).
-- Doubled `to_xrd()` call in `nonparametric/mcf.py:106-107`.
-
 **C. Simplification**
 - The multi-start MLE fit scaffolding is copy-pasted across the four repair
   fitters (`GeneralizedRenewal`, `GeneralizedOneRenewal`, `ARA`, `ARI`): the

surpyval/recurrent/parametric/crow_amsaa.py

@@ -61,7 +61,7 @@ def cif(self, x, *params):
         x : float
             The value at which CIF is evaluated.
         params : tuple
-            Parameters of the Duane model.
+            Parameters of the Crow-AMSAA model.
 
 
         Returns
@@ -85,7 +85,7 @@ def iif(self, x, *params):
         x : float
             The value at which IIF is evaluated.
         params : tuple
-            Parameters of the Duane model.
+            Parameters of the Crow-AMSAA model.
 
         Returns
         -------
@@ -108,7 +108,7 @@ def log_iif(self, x, *params):
         x : float
             The value at which log(IIF) is evaluated.
         params : tuple
-            Parameters of the Duane model.
+            Parameters of the Crow-AMSAA model.
 
         Returns
         -------
@@ -131,7 +131,7 @@ def inv_cif(self, N, *params):
         N : float
             The number of events expected to have occured.
         params : tuple
-            Parameters of the Duane model.
+            Parameters of the Crow-AMSAA model.
 
         Returns
         -------

surpyval/recurrent/parametric/nhpp_fitter.py

@@ -16,7 +16,7 @@ def create_negll_func(self, data):
 
         has_interval_censoring = True if 2 in c else False
         has_observed = True if 0 in c else False
-        has_left_censoing = True if -1 in c else False
+        has_left_censoring = True if -1 in c else False
         has_right_censoring = True if 1 in c else False
 
         x_l = x if x.ndim == 1 else x[:, 0]
@@ -41,8 +41,8 @@ def create_negll_func(self, data):
                 x_prev_l[c == 1] if has_right_censoring else np.array([])
             )
 
-        x_left = x_l[c == -1] if has_left_censoing else np.array([])
-        n_left = n[c == -1] if has_left_censoing else np.array([])
+        x_left = x_l[c == -1] if has_left_censoring else np.array([])
+        n_left = n[c == -1] if has_left_censoring else np.array([])
 
         x_i_l = x_l[c == 2] if has_interval_censoring else np.array([])
         x_i_r = x_r[c == 2] if has_interval_censoring else np.array([])

surpyval/recurrent/regression/nhpp_proportional_intensity.py

@@ -80,7 +80,7 @@ def create_negll_func(self, data, dist):
 
         has_interval_censoring = True if 2 in c else False
         has_observed = True if 0 in c else False
-        has_left_censoing = True if -1 in c else False
+        has_left_censoring = True if -1 in c else False
         has_right_censoring = True if 1 in c else False
 
         x_l = x if x.ndim == 1 else x[:, 0]
@@ -112,9 +112,11 @@ def create_negll_func(self, data, dist):
         )
 
         # Untangle the left censored data
-        x_left = x_l[c == -1] if has_left_censoing else np.array([])
-        n_left = n[c == -1] if has_left_censoing else np.array([])
-        Z_left = Z[c == -1] if has_left_censoing else np.zeros((1, Z.shape[1]))
+        x_left = x_l[c == -1] if has_left_censoring else np.array([])
+        n_left = n[c == -1] if has_left_censoring else np.array([])
+        Z_left = (
+            Z[c == -1] if has_left_censoring else np.zeros((1, Z.shape[1]))
+        )
 
         # Untangle the interval censored data
         x_i_l = x_l[c == 2] if has_interval_censoring else np.array([])

surpyval/recurrent/renewal/ara.py

@@ -8,6 +8,7 @@
 from surpyval.utils.recurrent_utils import (
     handle_xicn,
     reject_left_truncation,
+    validate_memory,
     validate_renewal_censoring,
 )
 
@@ -81,15 +82,6 @@ class ARA:
     >>> model = ARA.fit(x, i, c=c, m=2)
     """
 
-    @staticmethod
-    def _validate_memory(m):
-        if m == np.inf:
-            return
-        if not (isinstance(m, (int, np.integer)) and m >= 1):
-            raise ValueError(
-                "m must be a positive integer or numpy.inf; got {!r}".format(m)
-            )
-
     @staticmethod
     def _build_sampler(model):
         rho = model.rho
@@ -179,7 +171,7 @@ def fit_from_recurrent_data(self, data, dist=Weibull, m=1, init=None):
         RenewalModel
             A fitted renewal model.
         """
-        self._validate_memory(m)
+        validate_memory(m)
         validate_renewal_censoring(data.c, type(self).__name__)
         reject_left_truncation(data, type(self).__name__)
 
@@ -293,6 +285,6 @@ def fit_from_parameters(self, params, rho, m=1, dist=Weibull):
         ARA
             An ARA object built from the supplied parameters.
         """
-        self._validate_memory(m)
+        validate_memory(m)
         model = dist.from_params(params)
         return self._make_model(model, rho, m)

surpyval/recurrent/renewal/ari.py

@@ -7,6 +7,7 @@
 from surpyval.utils.recurrent_utils import (
     handle_xicn,
     reject_left_truncation,
+    validate_memory,
     validate_renewal_censoring,
 )
 
@@ -69,15 +70,6 @@ class ARI:
     >>> model = ARI.fit(x, i, m=1, dist=CrowAMSAA)
     """
 
-    @staticmethod
-    def _validate_memory(m):
-        if m == np.inf:
-            return
-        if not (isinstance(m, (int, np.integer)) and m >= 1):
-            raise ValueError(
-                "m must be a positive integer or numpy.inf; got {!r}".format(m)
-            )
-
     @staticmethod
     def _build_sampler(model):
         dist = model.model.dist
@@ -185,7 +177,7 @@ def fit_from_recurrent_data(self, data, dist=CrowAMSAA, m=1, init=None):
         ARI
             A fitted ARI object.
         """
-        self._validate_memory(m)
+        validate_memory(m)
         validate_renewal_censoring(data.c, type(self).__name__)
         reject_left_truncation(data, type(self).__name__)
 
@@ -294,5 +286,5 @@ def fit_from_parameters(self, dist_params, rho, m=1, dist=CrowAMSAA):
         ARI
             An ARI object built from the supplied parameters.
         """
-        self._validate_memory(m)
+        validate_memory(m)
         return self._make_model(dist, dist_params, rho, m)

surpyval/utils/recurrent_utils.py

@@ -54,6 +54,21 @@ def reject_unsupported_nonparametric(
         )
 
 
+def validate_memory(m: object) -> None:
+    """
+    The Arithmetic Reduction of Age/Intensity models (``ARA``/``ARI``) are
+    parameterised by an integer memory ``m`` (how many prior failures the
+    repair acts on), with ``m = numpy.inf`` recovering the infinite-memory
+    limit. Reject anything that is neither a positive integer nor ``inf``.
+    """
+    if m == np.inf:
+        return
+    if not (isinstance(m, (int, np.integer)) and m >= 1):
+        raise ValueError(
+            "m must be a positive integer or numpy.inf; got {!r}".format(m)
+        )
+
+
 def validate_renewal_censoring(c: npt.ArrayLike, model_name: str) -> None:
     """
     The renewal models only define likelihood contributions for exact events