src/processing.py

@@ -1,8 +1,6 @@
 from preprocessing import parse_file
-from pandas import DataFrame, Series
+from pandas import DataFrame
 from sys import argv
-from random import seed, randint
-from time import time
 
 
 def get_first_solution(n, data):
@@ -13,7 +11,7 @@ def get_first_solution(n, data):
         distance_sum = distance_sum.append(
             {"point": element, "distance": distance}, ignore_index=True
         )
-    furthest_index = distance_sum["distance"].astype(float).idxmax()
+    furthest_index = distance_sum["distance"].idxmax()
     furthest_row = distance_sum.iloc[furthest_index]
     furthest_row["distance"] = 0
     return furthest_row
@@ -27,14 +25,11 @@ def get_different_element(original, row):
 
 def get_furthest_element(element, data):
     element_df = data.query(f"source == {element} or destination == {element}")
-    furthest_index = element_df["distance"].astype(float).idxmax()
+    furthest_index = element_df["distance"].idxmax()
     furthest_row = data.iloc[furthest_index]
     furthest_point = get_different_element(original=element, row=furthest_row)
-    return Series(data={"point": furthest_point, "distance": furthest_row["distance"]})
-
-
-def remove_solution_dataset(data, solution):
-    return data.query(f"source != {solution} and destination != {solution}")
+    furthest_element = {"point": furthest_point, "distance": furthest_row["distance"]}
+    return furthest_element, furthest_index
 
 
 def greedy_algorithm(n, m, data):
@@ -42,64 +37,29 @@ def greedy_algorithm(n, m, data):
     first_solution = get_first_solution(n, data)
     solutions = solutions.append(first_solution, ignore_index=True)
     for _ in range(m):
-        last_solution = int(solutions["point"].tail(n=1))
-        centroid = get_furthest_element(element=last_solution, data=data)
-        solutions = solutions.append(centroid, ignore_index=True)
-        data = remove_solution_dataset(data=data, solution=last_solution)
+        last_solution = solutions["point"].tail(n=1)
+        centroid, index = get_furthest_element(element=int(last_solution), data=data)
+        solutions = solutions.append(dict(centroid), ignore_index=True)
+        data = data.drop(index)
     return solutions
 
 
-def get_pseudorandom_solution(n, data):
-    seed(42)
-    solution = data.iloc[randint(a=0, b=n)]
-    return Series(data={"point": solution["destination"], "distance": 0})
-
-
-def local_search(n, m, data):
-    solutions = DataFrame(columns=["point", "distance"])
-    first_solution = get_pseudorandom_solution(n=n, data=data)
-    solutions = solutions.append(first_solution, ignore_index=True)
-    for _ in range(m):
-        pass
-    return solutions
-
-
-def execute_algorithm(choice, n, m, data):
-    if choice == "greedy":
-        return greedy_algorithm(n, m, data)
-    elif choice == "local":
-        return local_search(n, m, data)
-    else:
-        print("The valid algorithm choices are 'greedy' and 'local'")
-        exit(1)
-
-
-def show_results(solutions, time_delta):
-    distance_sum = solutions["distance"].sum()
-    duplicates = solutions.duplicated()
-    print(solutions)
-    print("Total distance: " + str(distance_sum))
-    if solutions[duplicates].empty:
-        print("No duplicates found")
-    print("Execution time: " + str(time_delta))
+# NOTE In each step, switch to the element that gives the least amount
+def local_search():
+    pass
 
 
 def usage(argv):
-    print(f"Usage: python {argv[0]} <file> <algorithm choice>")
-    print("algorithm choices:")
-    print("greedy: greedy algorithm")
-    print("local: local search algorithm")
+    print(f"Usage: python {argv[0]} <file>")
     exit(1)
 
 
 def main():
-    if len(argv) != 3:
+    if len(argv) != 2:
         usage(argv)
     n, m, data = parse_file(argv[1])
-    start_time = time()
-    solutions = execute_algorithm(choice=argv[2], n=n, m=m, data=data)
-    end_time = time()
-    show_results(solutions, time_delta=end_time - start_time)
+    solutions = greedy_algorithm(n, m, data)
+    print(solutions)
 
 
 if __name__ == "__main__":