class TestFCM(unittest.TestCase):
def setUp(self):
self.df = pd.read_csv("iris-setosa.csv")
self.X = self.df.iloc[:, 1:5].values
self.y = self.df.iloc[:, 0].values
self.model = FCM()
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
self.X, self.y)
def test_isScoreValid(
self): # Controlla che l'accuratezza sia un valore valido
n_clusters = 2
train_membership, centers = self.model.fuzzy_train(
self.X_train, n_clusters, 2)
test_membership = self.model.fuzzy_predict(self.X_test, n_clusters,
centers, 2)
#warnings.filterwarnings("ignore")
acc = self.model.accuracy(test_membership, self.y_test)
self.assertGreaterEqual(acc, 0)
self.assertLessEqual(acc, 1)
def test_errorThreshold(
self
): # Controlla che l'errore RMSE delle membership sia minore di una certa soglia
threshold = 0.4
n_clusters = 2
train_membership, centers = self.model.fuzzy_train(
self.X_train, n_clusters, 2)
test_membership = self.model.fuzzy_predict(self.X_test, n_clusters,
centers, 2)
#warnings.filterwarnings("ignore")
err = self.model.RMSE_membership(test_membership, self.y_test)
self.assertLessEqual(err, threshold)
def fcm_script(data):
#Da utilizzare se so a priori i nomi delle colonne
#feat1=sys.argv[2]
#feat2=sys.argv[3]
#labels=sys.argv[4]
dataset = pd.read_csv(data)
# extract features and labels
#X = dataset[[feat1, feat2]].values
#y = dataset[labels].values
X = dataset.iloc[:, 1:]
y = dataset.iloc[:, 0]
X = np.asarray(X)
y = np.asarray(y)
model = FCM()
N_SPLITS = 5
N_CLUSTER = 2
error = []
score = []
#cross validation
skf = StratifiedKFold(n_splits=N_SPLITS, shuffle=True, random_state=1)
for train_index, test_index in skf.split(X, y):
#print("TRAIN:", train_index, "\nTEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
#training
train_membership, centers = model.fuzzy_train(X_train, N_CLUSTER, 2)
#test
test_membership = model.fuzzy_predict(X_test, N_CLUSTER, centers, 2)
if (N_CLUSTER == 2):
error.append(model.RMSE_membership(test_membership, y_test))
else:
error.append(model.RMSE(test_membership, y_test))
score.append(model.accuracy(test_membership, y_test))
return model, score, error
