Merge branch 'master' of github.com:industrial-optimization-group/des…

…deo-mcdm

README.md

@@ -1,6 +1,6 @@
 # desdeo-mcdm
 
-Contains interactive optimization methods for solving multiobjective optimizaton problems. This package is part of the DESDEO framework.
+Contains interactive optimization methods for solving multiobjective optimization problems. This package is part of the DESDEO framework.
 
 ## Installation
 

desdeo_mcdm/interactive/ENautilus.py

@@ -167,7 +167,7 @@ def __init__(
         if objective_names:
             if not len(objective_names) == ideal.shape[0]:
                 raise ENautilusException(
-                    "The supplied objective names must have a leangth equal to " "the numbr of objectives."
+                    "The supplied objective names must have a length equal to " "the number of objectives."
                 )
             self._objective_names = objective_names
         else:
@@ -186,7 +186,7 @@ def __init__(
         # in objective space!
         self._pareto_front = pareto_front
 
-        # bounds of the rechable region
+        # bounds of the reachable region
         self._reachable_ub = self._nadir
         self._reachable_lb = self._ideal
 
@@ -211,7 +211,7 @@ def start(self) -> ENautilusInitialRequest:
     def iterate(
         self, request: Union[ENautilusInitialRequest, ENautilusRequest]
     ) -> Union[ENautilusRequest, ENautilusStopRequest]:
-        """Perform the next logical iteratino step based on the given request type.
+        """Perform the next logical iteration step based on the given request type.
 
         """
         if type(request) is ENautilusInitialRequest:
@@ -223,7 +223,7 @@ def iterate(
             return request
 
     def handle_initial_request(self, request: ENautilusInitialRequest) -> ENautilusRequest:
-        """Handles the initial request by parsing the response appropiately.
+        """Handles the initial request by parsing the response appropriately.
 
         """
         self._n_iterations = request.response["n_iterations"]
@@ -277,7 +277,7 @@ def handle_request(self, request: ENautilusRequest) -> Union[ENautilusRequest, E
     def calculate_representative_points(
         self, pareto_front: np.ndarray, subset_indices: List[int], n_points: int
     ) -> np.ndarray:
-        """Calcualtes the most representative points on the Pareto front. The points are clustered using k-means.
+        """Calculates the most representative points on the Pareto front. The points are clustered using k-means.
 
         Args:
             pareto_front (np.ndarray): The Pareto front.
@@ -307,7 +307,7 @@ def calculate_representative_points(
     def calculate_intermediate_points(
         self, preferred_point: np.ndarray, zbars: np.ndarray, n_iterations_left: int,
     ) -> np.ndarray:
-        """Calcualtes the intermediate points between representative points an a preferred point.
+        """Calculates the intermediate points between representative points an a preferred point.
 
         Args:
             preferred_point (np.ndarray): The preferred point, 1D array.
@@ -328,7 +328,7 @@ def calculate_bounds(
 
         Args:
             pareto_front (np.ndarray): The Pareto optimal front.
-            intermediate_points (np.ndarray): The current intermedaite points as a 2D array.
+            intermediate_points (np.ndarray): The current intermediate points as a 2D array.
 
         Returns:
             Tuple[np.ndarray, np.ndarray]: The lower and upper bounds for each of the intermediate points.

desdeo_mcdm/interactive/NIMBUS.py

@@ -41,7 +41,7 @@ def __init__(self, method: NIMBUS, ref: np.ndarray):
             "\n\t4. values which may be impaired until some upper bound is reached '>='"
             "\n\t5. values which are free to change '0'"
             "\nProvide the aspiration levels and upper bounds as a vector. For categories 1, 3, and 5,"
-            "the value in the vector at the objective's position is ignored. Suppy also the number of maximum"
+            "the value in the vector at the objective's position is ignored. Supply also the number of maximum"
             "solutions to be generated."
         )
 
@@ -87,7 +87,7 @@ def validator(self, response: Dict) -> None:
         is_valid_cls = map(lambda x: x in self._valid_classifications, response["classifications"],)
 
         if not all(list(is_valid_cls)):
-            raise NimbusException(f"Invalid classificaiton found in {response['classifications']}")
+            raise NimbusException(f"Invalid classification found in {response['classifications']}")
 
         # check the levels
         if len(np.array(response["levels"]).squeeze()) != self._method._problem.n_of_objectives:
@@ -142,7 +142,7 @@ def __init__(
     ):
         msg = (
             "Please specify which solutions shown you would like to save for later viewing. Supply the "
-            "indices of such solutions as a list, or supply an empty list if none of the shown soulutions "
+            "indices of such solutions as a list, or supply an empty list if none of the shown solutions "
             "should be saved."
         )
         content = {
@@ -203,7 +203,7 @@ def __init__(
         self, solution_vectors: List[np.ndarray], objective_vectors: List[np.ndarray],
     ):
         msg = (
-            "Would you like to see intermediate solutions between two previusly computed solutions? "
+            "Would you like to see intermediate solutions between two previously computed solutions? "
             "If so, please supply two indices corresponding to the solutions."
         )
 
@@ -267,7 +267,7 @@ class NimbusMostPreferredRequest(BaseRequest):
 
     Note:
         The objective vector at position 'i' in `objective_vectors` should correspond to the decision variables at
-        position 'i' in `solution_vectors`. Only the two first entries in each of the lists is relevant. The preferred
+        position 'i' in `solution_vectors`. Only the two first entries in each of the lists are relevant. The preferred
         solution will be selected from `objective_vectors`.
 
     """
@@ -467,7 +467,7 @@ def handle_classification_request(
         """Handles a classification request.
         
         Args:
-            request (NimbusClassificationReuest): A classification request with the
+            request (NimbusClassificationRequest): A classification request with the
                 response attribute set.
         
         Returns:
@@ -545,15 +545,15 @@ def handle_intermediate_solutions_request(
     def handle_most_preferred_request(
         self, request: NimbusMostPreferredRequest
     ) -> Tuple[Union[NimbusClassificationRequest, NimbusStopRequest], SimplePlotRequest]:
-        """Handles a preferres solution request.
+        """Handles a preferred solution request.
         
         Args:
             request (NimbusMostPreferredRequest): A NIMBUS preferred solution request with the
                 response attribute set.
         
         Returns:
             Tuple[Union[NimbusClassificationRequest, NimbusStopRequest], SimplePlotRequest]:
-            Return a classificaiton request if the decision maker wishes to continue. If the
+            Return a classification request if the decision maker wishes to continue. If the
             decision maker wishes to stop, return a stop request. Also return a plot 
             request with all the solutions saved so far.
         """
@@ -611,18 +611,18 @@ def request_most_preferred_solution(
     def compute_intermediate_solutions(
         self, solutions: np.ndarray, n_desired: int,
     ) -> Tuple[NimbusSaveRequest, SimplePlotRequest]:
-        """Computs intermediate solution between two solutions computed earlier.
+        """Computes intermediate solution between two solutions computed earlier.
 
         Args:
-            solutions (np.ndarray): The solutions between which the intermediat solutions should
+            solutions (np.ndarray): The solutions between which the intermediate solutions should
                 be computed.
             n_desired (int): The number of intermediate solutions desired.
         
         Raises:
             NimbusException
         
         Returns:
-            Tuple[NimbusSaveRequest, SimplePlotRequest]: A save request with the compured intermediate
+            Tuple[NimbusSaveRequest, SimplePlotRequest]: A save request with the computed intermediate
             points, and a plot request to visualize said points.
         """
         # vector between the two solutions
@@ -676,7 +676,7 @@ def compute_intermediate_solutions(
                 intermediate_solutions[i] = self._problem.decision_variables[res["x"]]
                 intermediate_objectives[i] = self._problem.objectives[res["x"]]
 
-        # create appropiate requests
+        # create appropriate requests
         save_request = NimbusSaveRequest(list(intermediate_solutions), list(intermediate_objectives))
 
         msg = "Computed intermediate solutions"
@@ -730,7 +730,7 @@ def calculate_new_solutions(
         impaire_until_inds: np.ndarray,
         free_inds: np.ndarray,
     ) -> Tuple[NimbusSaveRequest, SimplePlotRequest]:
-        """Calcualtes new solutions based on classifications supplied by the decision maker by
+        """Calculates new solutions based on classifications supplied by the decision maker by
             solving ASF problems.
         
         Args:
@@ -744,7 +744,7 @@ def calculate_new_solutions(
             free_inds (np.ndarray): Indices of objectives which may change freely.
         
         Returns:
-            Tuple[NimbusSaveRequest, SimplePlotRequest]: A save request with the newly computed soutions, and 
+            Tuple[NimbusSaveRequest, SimplePlotRequest]: A save request with the newly computed solutions, and 
             a plot request to visualize said solutions.
         """
         results = []
@@ -960,14 +960,14 @@ def iterate(
         Union[NimbusClassificationRequest, NimbusSaveRequest, NimbusIntermediateSolutionsRequest,],
         Union[SimplePlotRequest, None],
     ]:
-        """Implements a finite state machine to iterate over the different steps defined in Synchronous NIMBUS based on a supllied request.
+        """Implements a finite state machine to iterate over the different steps defined in Synchronous NIMBUS based on a supplied request.
         
         Args:
             request (Union[NimbusClassificationRequest,NimbusSaveRequest,NimbusIntermediateSolutionsRequest,NimbusMostPreferredRequest,NimbusStopRequest,]):
                 A request based on the next step in the NIMBUS algorithm is taken.
         
         Raises:
-            NimbusException: If a wrong type of request is supllied based on the current state NIMBUS is in.
+            NimbusException: If a wrong type of request is supplied based on the current state NIMBUS is in.
         
         Returns:
             Tuple[Union[NimbusClassificationRequest,NimbusSaveRequest,NimbusIntermediateSolutionsRequest,],Union[SimplePlotRequest, None],]:
@@ -1141,7 +1141,7 @@ def f_2(xs: np.ndarray):
 
     print(classification_request.content["objective_values"])
 
-    # Ploting F1!
+    # Plotting F1!
     plt.scatter(x1, y, label="Range")
     plt.scatter(-15, 0, label="P1", c="r")
     plt.scatter(-18, 0, label="P2", c="r")
@@ -1152,7 +1152,7 @@ def f_2(xs: np.ndarray):
 
     plt.show()
 
-    # Ploting F2!
+    # Plotting F2!
     plt.scatter(x2, y, label="Range")
     plt.scatter(-6, 0, label="P1", c="r")
     plt.scatter(-9, 0, label="P2", c="r")
@@ -1171,7 +1171,7 @@ def f_2(xs: np.ndarray):
     print(save_request.content["objectives"])
     print(save_request.content["solutions"])
 
-    # Ploting F1!
+    # Plotting F1!
     plt.scatter(x1, y, label="Range")
     plt.scatter(-15, 0, label="P1", c="r")
     plt.scatter(-18, 0, label="P2", c="r")
@@ -1182,7 +1182,7 @@ def f_2(xs: np.ndarray):
 
     plt.show()
 
-    # Ploting F2!
+    # Plotting F2!
     plt.scatter(x2, y, label="Range")
     plt.scatter(-6, 0, label="P1", c="r")
     plt.scatter(-9, 0, label="P2", c="r")

desdeo_mcdm/interactive/Nautilus.py

@@ -85,7 +85,7 @@ def validate_preferences(n_objectives: int, response: Dict) -> None:
             raise NautilusException(msg)
         elif not (1 <= max(response["preference_info"]) <= n_objectives):
             msg = "The minimum index of importance must be greater or equal "
-            "to 1 and the maximum index of improtance must be less "
+            "to 1 and the maximum index of importance must be less "
             "than or equal to the number of objectives in the "
             "problem, which is {}. Check the indices {}" \
                 .format(n_objectives, response["preference_info"])
@@ -359,7 +359,7 @@ def __init__(
         if objective_names:
             if not len(objective_names) == ideal.shape[0]:
                 raise NautilusException(
-                    "The supplied objective names must have a length equal to " "the numbr of objectives."
+                    "The supplied objective names must have a length equal to " "the number of objectives."
                 )
             self._objective_names = objective_names
         else:

desdeo_mcdm/interactive/NautilusNavigator.py

@@ -24,7 +24,7 @@ def __init__(
         step_number: int,
         steps_remaining: int,
         distance: float,
-        allowed_speeds: [int],
+        allowed_speeds: List[int],
         current_speed: int,
         navigation_point: np.ndarray,
     ):
@@ -36,7 +36,7 @@ def __init__(
             "desired, please set `go_to_previous` to True, otherwise it should "
             "be False. "
             "Lastly, if stopping is desired, `stop` should be True, "
-            "otherweise it should be set to False."
+            "otherwise it should be set to False."
         )
         content = {
             "message": msg,
@@ -182,7 +182,7 @@ def __init__(
         if objective_names:
             if not len(objective_names) == ideal.shape[0]:
                 raise NautilusNavigatorException(
-                    "The supplied objective names must have a leangth equal to " "the numbr of objectives."
+                    "The supplied objective names must have a length equal to " "the number of objectives."
                 )
             self._objective_names = objective_names
         else:
@@ -203,7 +203,7 @@ def __init__(
         # in objective space!
         self._pareto_front = pareto_front
 
-        # bounds of the rechable region
+        # bounds of the reachable region
         self._reachable_ub = self._nadir
         self._reachable_lb = self._ideal
 
@@ -295,7 +295,7 @@ def update(
         distance: Optional[float] = None,
         steps_remaining: Optional[int] = None,
     ) -> Optional[NautilusNavigatorRequest]:
-        """Update the inernal state of self.
+        """Update the internal state of self.
 
         Args:
             ref_point (np.ndarray): A reference point given by a decision maker.
@@ -309,10 +309,10 @@ def update(
             navigation point. Relevant if go_to_previous is True. Defaults to
             None.
             lower_bounds (Optional[np.ndarray], optional): Lower bounds of
-            the reachable objective vector valus. Relevant if go_to_previous
+            the reachable objective vector values. Relevant if go_to_previous
             is True. Defaults to None.
             upper_bounds (Optional[np.ndarray], optional): Upper bounds of
-            the reachable objective vector valus. Relevant if go_to_previous
+            the reachable objective vector values. Relevant if go_to_previous
             is True. Defaults to None.
             reachable_idx (Optional[List[int]], optional): Indices of the
             reachable Pareto optimal solutions. Relevant if go_to_previous is
@@ -324,7 +324,7 @@ def update(
             navigation. Relevant if go_to_previous is True. Defaults to None.
 
         Returns:
-            NautilusNavigatorRequest: Some of the given parameters are erraneous.
+            NautilusNavigatorRequest: Some of the given parameters are erroneous.
         """
         # go to a previous state
         if go_to_previous:
@@ -405,10 +405,10 @@ def calculate_reachable_point_indices(
         return reachable_idx
 
     def solve_nautilus_asf_problem(
-        self, pareto_f: np.ndarray, subset_indices: [int], ref_point: np.ndarray, ideal: np.ndarray, nadir: np.ndarray,
+        self, pareto_f: np.ndarray, subset_indices: List[int], ref_point: np.ndarray, ideal: np.ndarray, nadir: np.ndarray,
     ) -> int:
         """Forms and solves the achievement scalarizing function to find the
-        closesto point on the Pareto optimal front to the given reference
+        closest point on the Pareto optimal front to the given reference
         point.
 
         Args: