Initial commit

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

@@ -1,21 +0,0 @@
-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

@@ -1,77 +1,3 @@
-# AlgoInvest&Trade
+# Projet7
 
-Déterminer un choix optimal d'actions caractérisées par un coût et un rendement, en fonction d'un coût maximum pour un profit maximal 
+Solve problems using algorithms
-
-## 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

@@ -1,87 +0,0 @@
-import csv
-
-MAX_COST = 500
-FILE = "Liste+dactions+-+P7+Python+-+Feuille+1.csv"
-
-def listFromFile(csv_file):
-    """
-    get data from a csv file and :
-    converts numbers, remove first title line
-    returns a list including name, cost: int, roi: float
-    """
-    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] = int(item[1])
-        item[2] = item[1] * float(item[2].strip("%")) / 100
-    return liste
-
-
-def powerset(itemList):
-    """
-    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 = []
-    for i in actionList:
-        cost = 0
-        gain = 0
-        for action in i:
-            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)
-
-
-def main():
-    actions = listFromFile(FILE)
-    power_actions = powerset(actions)
-    selected_actions = selectActions(power_actions, MAX_COST)
-    # tri des actions sur le rendement
-    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

@@ -1,109 +0,0 @@
-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()