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Merge branch 'main' of github.com:Berack96/upo-ml

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Giacomo Bertolazzi
2024-08-19 21:01:41 +02:00
parent 8b1c149535 ef3669978d
commit f823479afa
3 changed files with 24 additions and 15 deletions
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1
src/app.py
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@@ -103,6 +103,7 @@ if __name__ == "__main__":
rand = np.random.randint(0, 4294967295)
#rand = 2205910060 # LiR for power_plant
#rand = 347617386 # LoR for electrical_grid
#rand = 834535453 # LoR for heart
#rand = 1793295160 # MLP for iris
#rand = 2914000170 # MLP for frogs
#rand = 885416001 # KMe for frogs_no_target

35
src/learning/data.py
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@@ -132,6 +132,7 @@ class ConfusionMatrix:
self.matrix = conf_matrix
self.classes = classes
self.total = dataset_y.shape[0]
self.weights = np.sum(conf_matrix, axis=1)
self.tp = np.diagonal(conf_matrix)
self.fp = np.sum(conf_matrix, axis=0) - self.tp
self.fn = np.sum(conf_matrix, axis=1) - self.tp
@@ -152,33 +153,39 @@ class ConfusionMatrix:
def recall_per_class(self) -> np.ndarray:
return self.divide_ignore_zero(self.tp, self.tp + self.fn)
def specificity_per_class(self) -> np.ndarray:
return self.divide_ignore_zero(self.tn, self.tn + self.fp)
def cohen_kappa_per_class(self) -> np.ndarray:
p_pl = (self.tp + self.fn) * (self.tp + self.fp) / (self.total ** 2)
p_ne = (self.tn + self.fp) * (self.tn + self.fn) / (self.total ** 2)
p = p_pl + p_ne
return (self.accuracy_per_class() - p) / (1 - p)
def f1_score_per_class(self) -> np.ndarray:
prec = self.precision_per_class()
rec = self.recall_per_class()
return self.divide_ignore_zero(2 * prec * rec, prec + rec)
def specificity_per_class(self) -> np.ndarray:
return self.divide_ignore_zero(self.tn, self.tn + self.fp)
def accuracy(self) -> float:
return self.tp.sum() / self.total
def precision(self) -> float:
precision_per_class = self.precision_per_class()
support = np.sum(self.matrix, axis=1)
return np.average(precision_per_class, weights=support)
return np.average(precision_per_class, weights=self.weights)
def recall(self) -> float:
recall_per_class = self.recall_per_class()
support = np.sum(self.matrix, axis=1)
return np.average(recall_per_class, weights=support)
def f1_score(self) -> float:
f1_per_class = self.f1_score_per_class()
support = np.sum(self.matrix, axis=1)
return np.average(f1_per_class, weights=support)
return np.average(recall_per_class, weights=self.weights)
def specificity(self) -> float:
specificity_per_class = self.specificity_per_class()
support = np.sum(self.matrix, axis=1)
return np.average(specificity_per_class, weights=support)
return np.average(specificity_per_class, weights=self.weights)
def f1_score(self) -> float:
f1_per_class = self.f1_score_per_class()
return np.average(f1_per_class, weights=self.weights)
def cohen_kappa(self) -> float:
kappa_per_class = self.cohen_kappa_per_class()
return np.average(kappa_per_class, weights=self.weights)

3
src/learning/ml.py
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@@ -90,8 +90,9 @@ class MLAlgorithm(ABC):
print(f"Accuracy : {conf.accuracy():0.5f} - classes {conf.accuracy_per_class()}")
print(f"Precision : {conf.precision():0.5f} - classes {conf.precision_per_class()}")
print(f"Recall : {conf.recall():0.5f} - classes {conf.recall_per_class()}")
print(f"F1 score : {conf.f1_score():0.5f} - classes {conf.f1_score_per_class()}")
print(f"Specificity: {conf.specificity():0.5f} - classes {conf.specificity_per_class()}")
print(f"Cohen Kappa: {conf.cohen_kappa():0.5f} - classes {conf.cohen_kappa_per_class()}")
print(f"F1 score : {conf.f1_score():0.5f} - classes {conf.f1_score_per_class()}")
def test_confusion_matrix(self) -> ConfusionMatrix:
if self._target_type != TargetType.Classification\