Add execution module

src/execution.py

@@ -0,0 +1,84 @@
+from glob import glob
+from subprocess import run
+from sys import executable
+
+from numpy import mean, std
+from pandas import DataFrame, ExcelWriter
+
+
+def file_list(path):
+    file_list = []
+    for fname in glob(path):
+        file_list.append(fname)
+    return file_list
+
+
+def create_dataframes():
+    return [DataFrame() for _ in range(2)]
+
+
+def process_output(results):
+    distances = []
+    time = []
+    for line in results:
+        if line.startswith(bytes("Total distance:", encoding="utf-8")):
+            line_elements = line.split(sep=bytes(":", encoding="utf-8"))
+            distances.append(float(line_elements[1]))
+        if line.startswith(bytes("Execution time:", encoding="utf-8")):
+            line_elements = line.split(sep=bytes(":", encoding="utf-8"))
+            time.append(float(line_elements[1]))
+    return distances, time
+
+
+def populate_dataframe(df, output_cmd, dataset):
+    distances, time = process_output(output_cmd)
+    data_dict = {
+        "dataset": dataset.removeprefix("data/"),
+        "media distancia": mean(distances),
+        "desviacion distancia": std(distances),
+        "media tiempo": mean(time),
+        "desviacion tiempo": std(time),
+    }
+    df = df.append(data_dict, ignore_index=True)
+    return df
+
+
+def script_execution(filenames, df_list):
+    script = "src/main.py"
+    parameters = [["annealing"], ["multistart"]]
+    for dataset in filenames:
+        print(f"Running on dataset {dataset}")
+        for index, params in zip(range(4), parameters):
+            print(f"Running {params} algorithm")
+            output_cmd = run(
+                [executable, script, dataset, *params], capture_output=True
+            ).stdout.splitlines()
+            df_list[index] = populate_dataframe(df_list[index], output_cmd, dataset)
+    return df_list
+
+
+def export_results(df_list):
+    dataframes = {
+        "Simulated annealing": df_list[0],
+        "Multi-start local search": df_list[1],
+    }
+    writer = ExcelWriter(path="docs/algorithm-results.xlsx", engine="xlsxwriter")
+    for name, df in dataframes.items():
+        df.to_excel(writer, sheet_name=name, index=False)
+        worksheet = writer.sheets[name]
+        for index, column in enumerate(df):
+            series = df[column]
+            max_length = max(series.astype(str).str.len().max(), len(str(series.name)))
+            worksheet.set_column(index, index, width=max_length + 5)
+    writer.save()
+
+
+def main():
+    datasets = file_list(path="data/*.txt")
+    df_list = create_dataframes()
+    populated_df_list = script_execution(datasets, df_list)
+    export_results(populated_df_list)
+
+
+if __name__ == "__main__":
+    main()

src/main.py

@@ -23,7 +23,7 @@ def show_results(solution, time_delta):
 def parse_arguments():
     parser = ArgumentParser()
     parser.add_argument("file", help="dataset of choice")
-    parser.add_argument("algorithm", choices=["annealing", "multisearch"])
+    parser.add_argument("algorithm", choices=["annealing", "multistart"])
     return parser.parse_args()