class TopsisEvaluator(BaseEvaluator):
def __init__(self, candidate_names=None):
self.candidate_names = candidate_names
self.data_topsis = None
def get_optimal_candidates(self, candidates, criteria):
names = self.candidate_names.iloc[candidates.index].values
min_max = []
weights = []
columns = []
for c in criteria:
if c.maximize:
min_max.append(MAX)
else:
min_max.append(MIN)
weights.append(c.weight)
columns.append(c.index)
self.data_topsis = Data(candidates[columns].as_matrix().tolist(),
min_max,
weights=weights,
anames=names,
cnames=columns)
model = closeness.TOPSIS()
choice = model.decide(self.data_topsis)
return [candidates.index[int(choice.best_alternative_)]
] # Return must be a list
def print(self):
if self.data_topsis != None:
self.data_topsis.plot()
0.0555555555555556, 0.0555555555555556, 0.0555555555555556
],
anames=[
"SriLanka", "India", "England", "South Africa", "Australia",
"NewZealand", "Pakistan", "West Indies", "Bangladesh", "Zimbabwe",
"Ireland"
],
cnames=[
"Run", "HS", "Average", "BF", "Strikerate", "Centuries", "Fifties",
"zeros", "Fours", "Sixes", "Maiden", "Runs_bowl", "Wickets",
"Average_bowling", "Economical", "Strikerate_bowl", "4wickets",
"5wickets"
])
#data.plot();
data.plot("box")
#data.plot.violin();
#data.plot.radar(cmap="inferno", show_criteria=False);
# first create the decision maker
# (with the default hiper parameters)
dm = simple.WeightedSum()
print(dm)
# Now lets decide the ranking
dec = dm.decide(data)
print(dec)
print(dec.e_)
print(dec.e_.points)