Merge pull request #279 from jGaboardi/update_testing

testing overhaul, etc
pysal · Oct 17, 2022 · 336f235 · 336f235
2 parents 1be809f + 0d7da9e
commit 336f235
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Showing 28 changed files with 632 additions and 529 deletions.
diff --git a/.coveragerc b/.coveragerc
@@ -25,6 +25,7 @@ ignore_errors = True
 omit =
     */tests/*
     *__init__.py
+    *_version.py
 
 [html]
 directory = coverage_html_report
diff --git a/spopt/BaseClass.py b/spopt/BaseClass.py
@@ -2,9 +2,7 @@
 
 
 class BaseSpOptSolver(ABC):
-    """Base class for all spatial optimization model solvers.
-
-    """
+    """Base class for all spatial optimization model solvers."""
 
     @abstractmethod
     def solve(self):
@@ -56,4 +54,4 @@ class BaseSpOptHeuristicSolver(BaseSpOptSolver):
     @abstractmethod
     def solve(self):
         """Solve the optimization model."""
-        pass
+        pass
diff --git a/spopt/locate/base.py b/spopt/locate/base.py
@@ -76,7 +76,8 @@ def client_facility_array(self) -> None:
                     self.cli2fac[fac_site].append(i)
         else:
             raise AttributeError(
-                "The attribute `fac2cli` is not set. See `facility_client_array` method to set the attribute"
+                "The attribute `fac2cli` is not set. "
+                "See `facility_client_array` method to set the attribute"
             )
 
 
@@ -311,7 +312,7 @@ def add_set_covering_constraint(
                 )
         else:
             raise AttributeError(
-                "before setting constraints must set facility variable"
+                "Before setting coverage constraints facility variables must be set."
             )
 
     @staticmethod
@@ -364,7 +365,8 @@ def add_backup_covering_constraint(
                     )
         else:
             raise AttributeError(
-                "before setting constraints must set facility variable"
+                "Before setting backup coverage constraints "
+                "facility variables must be set."
             )
 
     @staticmethod
@@ -393,7 +395,7 @@ def add_facility_constraint(
             model += pulp.lpSum(fac_vars) == p_facilities
         else:
             raise AttributeError(
-                "before setting constraints must set facility variable"
+                "Before setting facility constraint facility variables must be set."
             )
 
     @staticmethod
@@ -424,7 +426,8 @@ def add_predefined_facility_constraint(
                     fac_vars[ind].fixValue()
         else:
             raise AttributeError(
-                "before predefined facility must set facility variable"
+                "Before setting predefined facility constraints "
+                "facility variables must be set."
             )
 
     @staticmethod
@@ -463,7 +466,8 @@ def add_maximal_coverage_constraint(
                 )
         else:
             raise AttributeError(
-                "before setting constraints must set facility and demand variable"
+                "Before setting maximal coverage constraints facility "
+                "and demand variables must be set."
             )
 
     @staticmethod
@@ -496,7 +500,8 @@ def add_assignment_constraint(
                 model += pulp.lpSum([cli_assgn_vars[i][j] for j in range_facility]) == 1
         else:
             raise AttributeError(
-                "before setting constraints must set client assignment variable"
+                "Before setting assignment constraints "
+                "client assignment variables must be set."
             )
 
     @staticmethod
@@ -531,7 +536,8 @@ def add_opening_constraint(
                     model += fac_vars[j] - cli_assgn_vars[i][j] >= 0
         else:
             raise AttributeError(
-                "before setting constraints must set client assignment variable"
+                "Before setting opening constraints "
+                "client assignment variables must be set."
             )
 
     @staticmethod
@@ -579,7 +585,8 @@ def add_minimized_maximum_constraint(
                 )
         else:
             raise AttributeError(
-                "before setting constraints must set weight and client assignment variables"
+                "Before setting minimized maximum constraints weight "
+                "and client assignment variables must be set."
             )
 
     @staticmethod
@@ -621,5 +628,6 @@ def add_p_dispersion_interfacility_constraint(
                         )
         else:
             raise AttributeError(
-                "before setting constraints must set dispersion objective value and facility assignment variables"
+                "Before setting interfacility distance constraints dispersion "
+                "objective value and facility assignment variables must be set."
             )
diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py
@@ -237,26 +237,25 @@ def from_geodataframe(
         dem_type_geom = dem.geom_type.unique()
         fac_type_geom = fac.geom_type.unique()
 
+        _msg = (
+            " geodataframe contains mixed type geometries or is not a point. Be "
+            "sure deriving centroid from geometries doesn't affect the results."
+        )
         if len(dem_type_geom) > 1 or not "Point" in dem_type_geom:
-            warnings.warn(
-                "Demand geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
-            )
+            warnings.warn(f"Demand{_msg}", UserWarning)
             dem = dem.centroid
 
         if len(fac_type_geom) > 1 or not "Point" in fac_type_geom:
-            warnings.warn(
-                "Facility geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
-            )
+            warnings.warn(f"Facility{_msg}", UserWarning)
             fac = fac.centroid
 
         dem_data = np.array([dem.x.to_numpy(), dem.y.to_numpy()]).T
         fac_data = np.array([fac.x.to_numpy(), fac.y.to_numpy()]).T
 
         if gdf_demand.crs != gdf_fac.crs:
             raise ValueError(
-                f"geodataframes crs are different: gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
+                "Geodataframes crs are different: "
+                f"gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
             )
 
         distances = cdist(dem_data, fac_data, distance_metric)
@@ -554,26 +553,25 @@ def from_geodataframe(
         dem_type_geom = dem.geom_type.unique()
         fac_type_geom = fac.geom_type.unique()
 
+        _msg = (
+            " geodataframe contains mixed type geometries or is not a point. Be "
+            "sure deriving centroid from geometries doesn't affect the results."
+        )
         if len(dem_type_geom) > 1 or not "Point" in dem_type_geom:
-            warnings.warn(
-                "Demand geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
-            )
+            warnings.warn(f"Demand{_msg}", UserWarning)
             dem = dem.centroid
 
         if len(fac_type_geom) > 1 or not "Point" in fac_type_geom:
-            warnings.warn(
-                "Facility geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
-            )
+            warnings.warn(f"Facility{_msg}", UserWarning)
             fac = fac.centroid
 
         dem_data = np.array([dem.x.to_numpy(), dem.y.to_numpy()]).T
         fac_data = np.array([fac.x.to_numpy(), fac.y.to_numpy()]).T
 
         if gdf_demand.crs != gdf_fac.crs:
             raise ValueError(
-                f"geodataframes crs are different: gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
+                "Geodataframes crs are different: "
+                f"gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
             )
 
         distances = cdist(dem_data, fac_data, distance_metric)
@@ -898,26 +896,25 @@ def from_geodataframe(
         dem_type_geom = dem.geom_type.unique()
         fac_type_geom = fac.geom_type.unique()
 
+        _msg = (
+            " geodataframe contains mixed type geometries or is not a point. Be "
+            "sure deriving centroid from geometries doesn't affect the results."
+        )
         if len(dem_type_geom) > 1 or not "Point" in dem_type_geom:
-            warnings.warn(
-                "Demand geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
-            )
+            warnings.warn(f"Demand{_msg}", UserWarning)
             dem = dem.centroid
 
         if len(fac_type_geom) > 1 or not "Point" in fac_type_geom:
-            warnings.warn(
-                "Facility geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
-            )
+            warnings.warn(f"Facility{_msg}", UserWarning)
             fac = fac.centroid
 
         dem_data = np.array([dem.x.to_numpy(), dem.y.to_numpy()]).T
         fac_data = np.array([fac.x.to_numpy(), fac.y.to_numpy()]).T
 
         if gdf_demand.crs != gdf_fac.crs:
             raise ValueError(
-                f"geodataframes crs are different: gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
+                "Geodataframes crs are different: "
+                f"gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
             )
 
         distances = cdist(dem_data, fac_data, distance_metric)

diff --git a/spopt/locate/p_center.py b/spopt/locate/p_center.py
@@ -33,7 +33,7 @@ class PCenter(LocateSolver, BaseOutputMixin):
     cli2fac: np.array
         2-d MxN, where m is number of clients and n is number of facilities. Each row represent a client and has an array containing facility index meaning that the client is covered by the facility ith.
     aij: np.array
-        Cost matrix 2-d array 
+        Cost matrix 2-d array
 
     """
 
@@ -244,26 +244,25 @@ def from_geodataframe(
         dem_type_geom = dem.geom_type.unique()
         fac_type_geom = fac.geom_type.unique()
 
+        _msg = (
+            " geodataframe contains mixed type geometries or is not a point. Be "
+            "sure deriving centroid from geometries doesn't affect the results."
+        )
         if len(dem_type_geom) > 1 or not "Point" in dem_type_geom:
-            warnings.warn(
-                "Demand geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
-            )
+            warnings.warn(f"Demand{_msg}", UserWarning)
             dem = dem.centroid
 
         if len(fac_type_geom) > 1 or not "Point" in fac_type_geom:
-            warnings.warn(
-                "Facility geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
-            )
+            warnings.warn(f"Facility{_msg}", UserWarning)
             fac = fac.centroid
 
         dem_data = np.array([dem.x.to_numpy(), dem.y.to_numpy()]).T
         fac_data = np.array([fac.x.to_numpy(), fac.y.to_numpy()]).T
 
         if gdf_demand.crs != gdf_fac.crs:
             raise ValueError(
-                f"geodataframes crs are different: gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
+                "Geodataframes crs are different: "
+                f"gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
             )
 
         distances = cdist(dem_data, fac_data, distance_metric)
@@ -301,7 +300,7 @@ def solve(self, solver: pulp.LpSolver, results: bool = True):
         ----------
         solver: pulp.LpSolver
             solver supported by pulp package
-        
+
         results: bool
             if True it will create metainfo - which facilities cover which demand and vice-versa, and the uncovered demand - about the model results
 

diff --git a/spopt/locate/p_dispersion.py b/spopt/locate/p_dispersion.py
@@ -215,8 +215,12 @@ def from_geodataframe(
 
         if len(fac_type_geom) > 1 or not "Point" in fac_type_geom:
             warnings.warn(
-                "Facility geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
+                (
+                    "Facility geodataframe contains mixed type geometries "
+                    "or is not a point. Be sure deriving centroid from "
+                    "geometries doesn't affect the results."
+                ),
+                UserWarning,
             )
             fac = fac.centroid
 

diff --git a/spopt/locate/p_median.py b/spopt/locate/p_median.py
@@ -38,7 +38,7 @@ class PMedian(LocateSolver, BaseOutputMixin, MeanDistanceMixin):
     cli2fac: np.array
         2-d MxN, where m is number of clients and n is number of facilities. Each row represent a client and has an array containing facility index meaning that the client is covered by the facility ith.
     aij: np.array
-        Cost matrix 2-d array 
+        Cost matrix 2-d array
 
     """
 
@@ -280,26 +280,25 @@ def from_geodataframe(
         dem_type_geom = dem.geom_type.unique()
         fac_type_geom = fac.geom_type.unique()
 
+        _msg = (
+            " geodataframe contains mixed type geometries or is not a point. Be "
+            "sure deriving centroid from geometries doesn't affect the results."
+        )
         if len(dem_type_geom) > 1 or not "Point" in dem_type_geom:
-            warnings.warn(
-                "Demand geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
-            )
+            warnings.warn(f"Demand{_msg}", UserWarning)
             dem = dem.centroid
 
         if len(fac_type_geom) > 1 or not "Point" in fac_type_geom:
-            warnings.warn(
-                "Facility geodataframe contains mixed type geometries or is not a point. Be sure deriving centroid from geometries doesn't affect the results.",
-                Warning,
-            )
+            warnings.warn(f"Facility{_msg}", UserWarning)
             fac = fac.centroid
 
         dem_data = np.array([dem.x.to_numpy(), dem.y.to_numpy()]).T
         fac_data = np.array([fac.x.to_numpy(), fac.y.to_numpy()]).T
 
         if gdf_demand.crs != gdf_fac.crs:
             raise ValueError(
-                f"geodataframes crs are different: gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
+                "Geodataframes crs are different: "
+                f"gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
             )
 
         distances = cdist(dem_data, fac_data, distance_metric)
@@ -329,7 +328,7 @@ def facility_client_array(self) -> None:
 
             self.fac2cli.append(array_cli)
 
-    def solve(self, solver: pulp.LpSolver, results: bool=True):
+    def solve(self, solver: pulp.LpSolver, results: bool = True):
         """
         Solve the PMedian model
 

diff --git a/spopt/region/azp.py b/spopt/region/azp.py
@@ -162,7 +162,7 @@ class AZP_orig:
     labels_ : numpy.ndarray
         Each element is a region label specifying to which region the
         corresponding area was assigned to by the last run of a fit-method.
-    
+
     """
 
     def __init__(self, allow_move_strategy=None, random_state=None):
@@ -175,7 +175,7 @@ def __init__(self, allow_move_strategy=None, random_state=None):
             instance can be passed as argument. Default is ``None``.
         random_state : None, int, str, bytes, or bytearray
             Random seed. Default is ``None``.
-        
+
         """
 
         self.n_regions = None
@@ -225,7 +225,7 @@ def fit_from_scipy_sparse_matrix(
         objective_func : region.objective_function.ObjectiveFunction
             The objective function to use. Default is
             ``ObjectiveFunctionPairwise()``.
-            
+
         """
 
         if attr.ndim == 1:
@@ -375,7 +375,7 @@ def fit_from_geodataframe(
             ``fit_from_scipy_sparse_matrix``.
         contiguity : str
             Defines the contiguity relationship between areas.
-            Default is ``'rook'``. Possible contiguity definitions are: 
+            Default is ``'rook'``. Possible contiguity definitions are:
 
             * "rook" - Rook contiguity.
             * "queen" - Queen contiguity.
@@ -750,7 +750,7 @@ def fit_from_networkx(
             Refer to the corresponding argument in
             ``AZP.fit_from_networkx``.
             Default is ``ObjectiveFunctionPairwise()``.
-        
+
         """
 
         adj = nx.to_scipy_sparse_matrix(graph)