final delivery, flake OK

Liste+dactions+-+P7+Python+-+Feuille+1.csv

@@ -0,0 +1,21 @@
+Actions #,Coût par action (en euros),Bénéfice (après 2 ans)
+Action-1,20,5%
+Action-2,30,10%
+Action-3,50,15%
+Action-4,70,20%
+Action-5,60,17%
+Action-6,80,25%
+Action-7,22,7%
+Action-8,26,11%
+Action-9,48,13%
+Action-10,34,27%
+Action-11,42,17%
+Action-12,110,9%
+Action-13,38,23%
+Action-14,14,1%
+Action-15,18,3%
+Action-16,8,8%
+Action-17,4,12%
+Action-18,10,14%
+Action-19,24,21%
+Action-20,114,18%

README.md

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+# AlgoInvest&Trade
+
+Déterminer un choix optimal d'actions caractérisées par un coût et un rendement, en fonction d'un coût maximum pour un profit maximal 
+
+## Introduction
+
+Ces instructions vous permettent de :
+- récupérer le programme, 
+- d'installer l'environnement nécessaire à son exécution, 
+- de l'exécuter,
+- d'en connaitre le résultat
+
+
+### Pré-requis
+
+```
+paquets : python 3.11, python3.11-venv, git 
+modules : csv
+```
+
+### Installation
+
+1. créer l'environnement virtuel :
+```
+python3.11 -m venv env
+source env/bin/activate
+```
+2. cloner le dépôt :
+```
+git clone https://mcstn.fr/gitea/Yann/Projet7.git
+```
+
+
+## Exécution
+ 
+Pour l'algorithme bruteforce sur le dataset0, 
+exécuter la commande :
+```
+python3 bruteforce.py
+```  
+
+Pour l'algorithme de DP, executer la commande : 
+```
+python3 optimized.py
+``` 
+
+
+## Résultat
+
+Optimized traite par défaut les datasets 1 et 2;
+Décommenter la ligne du dataset0 dans le main() si besoin
+```
+$ time python optimized.py 
+
+DATASET 1
+Cost: 499.43 €
+Profit: 196.84 €
+Shares : ['Share-HITN', 'Share-GRUT']
+
+DATASET 2
+Cost: 497.67 €
+Profit: 194.90 €
+Shares : ['Share-GEBJ', 'Share-LFXB', 'Share-FWBE', 'Share-PLLK', 'Share-ZKSN', 'Share-ZOFA', 'Share-PATS', 'Share-DWSK', 'Share-ALIY', 'Share-ECAQ', 'Share-FAPS', 'Share-JGTW', 'Share-QLWT', 'Share-OPBR', 'Share-ANFX', 'Share-IJFT', 'Share-JWGF']
+
+real	0m0,852s
+user	0m0,832s
+sys	0m0,018s
+```
+## Auteur
+
+Yann  <yann@needsome.coffee>
+
+
+
+## License
+
+N/A

bruteforce.py

@@ -1,12 +1,7 @@
 import csv
 
-
-def powerset(itemList):
-	result = [[]]
-	for item in itemList:
-		newsubsets = [subset + [item] for subset in result]
-		result.extend(newsubsets)
-	return result
+MAX_COST = 500
+FILE = "Liste+dactions+-+P7+Python+-+Feuille+1.csv"
 
 def listFromFile(csv_file):
     """
@@ -22,61 +17,71 @@ def listFromFile(csv_file):
     liste.pop(0)
     for item in liste:
         item[1] = int(item[1])
-        item[2] = float(item[2].strip("%"))
+        item[2] = item[1] * float(item[2].strip("%")) / 100
     return liste
 
-def splitActions(actionList):
-    """
-    split list in two parts, just in case we need to divide the operation for
-    more efficiency
-    returns a tuple with two lists
-    """
-    liste1 = []
-    liste2 = []
-    for i in range(len(actionList)):
-        if (i < 10):
-            liste1.append(actionList[i])
-        if (i >= 10):
-            liste2.append(actionList[i])
-    return (liste1, liste2)
 
-def selectActions(actionList, max):
+def powerset(itemList):
     """
-    :param actionList: takes a list of combinations and a max
-    :return: a list of selected combinations where cost is under max
+    Generate every subset (combination) for a given list
+    :param itemList: a list of items
+    :return: a list of combinations(lists)
+    """
+    result = [[]]
+    for item in itemList:
+        newsubsets = [subset + [item] for subset in result]
+        result.extend(newsubsets)
+    return result
+
+
+def transformData(dataset):
+    """
+    Transform in a list of dict with computed values as gain, ratio
+    Sorted by gain
+    :param dataset: list of items
+    :return: a sorted list of dict
+    """
+    tmpset = [{'nom': x[0], 'cout': x[1],
+               'rendement': x[2],
+               'gain': x[1] * x[2] / 100,
+               'ratio1': x[2] / x[1],
+               'ratio2': (x[1] * x[2] / 100) / x[1]}
+              for x in dataset if x[1] > 0.0 and x[2] > 0.0]
+
+    return sorted(tmpset, key=lambda x: x['gain'], reverse=True)
+
+
+def selectActions(actionList, maximal_cost):
+    """
+    select combination corresponding to max cost
+    :param actionList: list of combinations
+    :param maximal_cost: maximal cost
+    :return: a list of selected items
     """
     best = []
-    best2 = []
     for i in actionList:
-        cout = 0
-        rendement = 0
+        cost = 0
+        gain = 0
         for action in i:
-            cout += action[1]
-            rendement += action[2]
-        if cout < int(max):
-            best.append((rendement, cout, i))
-            best2.append(i)
-    return best, best2
+            cost += action[1]
+            gain += action[2]
+        if cost < int(maximal_cost):
+            best.append((gain, cost, i))
+
+    sortedBest = sorted(best, key=lambda k: k[0], reverse=True)
+
+    return sortedBest.pop(0)
 
 
-
-actions = listFromFile("/home/b/Documents/OCR/projet7/actions.csv")
-powerActions = powerset(actions)
-selectedActions, selected = selectActions(powerActions, 500)
-
-print("Longueur de la liste d'actions:", len(actions))
-print("Nb de combinaisons:", len(powerActions))
-print("Nb de combinaisons au cout inferieur à 500:", len(selectedActions))
-
+def main():
+    actions = listFromFile(FILE)
+    power_actions = powerset(actions)
+    selected_actions = selectActions(power_actions, MAX_COST)
     # tri des actions sur le rendement
-best_sorted = sorted(selectedActions, key=lambda k: k[0], reverse=True)
-best2 = sort(selected, key=lambda k:[])
-#print("\nfive last sorted :")
-#for i in range(len(best_sorted)-1, len(best_sorted)-10, -1):
-#    print("set", i, ":", best_sorted[i])
-#print(f"Rendement: {sum(x[2][1] * x[2][2]/100 for x in best_sorted[0])}")
-print(selected[1])
-print("Meilleur rendement:", best_sorted[0][0], "%")
-print("Actions sélectionnées:")
-for action in best_sorted[0][2]:
-    print(f"Nom: {action[0]}, Cout: {action[1]}, Rendement: {action[2]}%")
+    print("Cost:", selected_actions[1], "€")
+    print("Profit: %.2f €" % selected_actions[0])
+    print(f"Shares: {[x[0] for x in selected_actions[2]]}")
+
+
+if __name__ == '__main__':
+    main()

optimized.py

@@ -0,0 +1,109 @@
+import csv
+
+MAX_COST = 500
+DATASET1 = "dataset1_Python+P7.csv"
+DATASET2 = "dataset2_Python+P7.csv"
+DATASET0 = "Liste+dactions+-+P7+Python+-+Feuille+1.csv"
+
+
+def listFromFile(csv_file):
+    """
+    Extract and format data from file(csv)
+    :param csv_file: full path
+    :return: a list of items
+    """
+    liste = []
+    with open(csv_file) as file:
+        data = csv.reader(file)
+        for i in data:
+            liste.append(i)
+    liste.pop(0)
+    for item in liste:
+        item[1] = float(item[1])
+        if item[2][-1] == "%":
+            item[2] = item[2].strip("%")
+        item[2] = float(item[2])
+    return liste
+
+
+def transformData(dataset):
+    """
+    Transform in a list of dict with computed values as gain, ratio
+    Sorted by gain
+    :param dataset: list of items
+    :return: a sorted list of dict
+    """
+    tmpset = [{'nom': x[0], 'cout': x[1],
+               'rendement': x[2],
+               'gain': x[1] * x[2] / 100,
+               'ratio1': x[2] / x[1],
+               'ratio2': (x[1] * x[2] / 100) / x[1]}
+              for x in dataset if x[1] > 0.0 and x[2] > 0.0]
+
+    return sorted(tmpset, key=lambda x: x['gain'], reverse=True)
+
+
+def get_results(filepath, maximum, nbr):
+    """
+    load, transform data then run the algorithm and print results
+    :param filepath: full path to csv
+    :param maximum: maximum cost
+    :param nbr: set number
+    :return: print results
+    """
+
+    action_list = transformData(listFromFile(filepath))
+    maximum_gain, selection = sacADosFloat(action_list, maximum)
+
+    print("\nDATASET", nbr)
+    print(f"Cost: {sum(x['cout'] for x in selection):.2f} €")
+    print("Profit: %.2f €" % maximum_gain)
+    print(f"Shares : {[x['nom'] for x in selection]}")
+
+
+def sacADosFloat(actions, maximum_cost):
+    """
+    Use dynamic approach
+    :param actions: a list of dict with minimum key as cost and gain
+    :param maximum_cost: the constraint, our max cost
+    :return: maximum gain: int, selected items: list
+    """
+    n = len(actions)
+    table = [[0.0 for x in range(int(maximum_cost) + 1)] for x in range(n + 1)]
+
+    # Dynamic programing table
+    for i in range(n + 1):
+        for w in range(int(maximum_cost) + 1):
+            if i == 0 or w == 0:
+                table[i][w] = 0.0
+            elif actions[i-1]['cout'] <= w:
+                table[i][w] = (
+                    max(
+                        actions[i-1]['gain'] +
+                        table[i-1][int(w-actions[i-1]['cout'])],
+                        table[i-1][w]
+                    )
+                )
+
+            else:
+                table[i][w] = table[i-1][w]
+
+    # Selection
+    w = maximum_cost
+    selected_actions = []
+    for i in range(n, 0, -1):
+        if table[i][int(w)] != table[i-1][int(w)]:
+            selected_actions.append(actions[i-1])
+            w -= actions[i-1]['cout']
+
+    return table[n][int(maximum_cost)], selected_actions
+
+
+def main():
+    # get_results(DATASET0, MAX_COST, 0)
+    get_results(DATASET1, MAX_COST, 1)
+    get_results(DATASET2, MAX_COST, 2)
+
+
+if __name__ == '__main__':
+    main()