alpha=0.95,
activation_func='multiquadric',
activation_args=None,
user_components=None,
regressor=linear_model.Ridge(),
random_state=None)
#======================
# TRAIN ELM
#======================
elm.fit(TRAIN, Y_train)
#======================
# ELM PREDICTION
#======================
Y_pred = elm.predict(TEST)
#======================
# SCORE CALCULATION
#======================
score = elm.score(TEST, Y_test)
f1_weighted = metrics.f1_score(
Y_test,
Y_pred,
labels=None,
pos_label=1,
average='weighted',
sample_weight=None) #toma en cuenta el desbalance de etiqueta
precision_score = metrics.precision_score(Y_test,
Y_pred,
y_test = X[X[:, 0] == v][:, 1]
y_train_real = y_train.copy()
#%%
#### ELM DEFINIDO PELO SKLEARN
clsf = ELMClassifier(random_state=100)
#clsf.fit(X[:v, 2:], y[:v])
clsf.fit(X_train, y_train)
Htreino = clsf._get_weights()
print()
print(clsf.predict(X_test))
#print()
#print(y[v:])
print('Score:', clsf.score(X_test, y_test))
Hteste = clsf._get_weights()
#%%
################# ELM IMPLENTÇÃO DO KAGGLE ################
# https://www.kaggle.com/robertbm/extreme-learning-machine-example #
# funções
rot.append([*arq.split('.')])
#%%
#arr = np.array(arr)
#class_dict = dict(zip(np.unique(arr[:,2]), range(len(np.unique(arr[:,2])))))
rot = np.array(rot)
class_dict = dict(zip(np.unique(rot[:, 1]), range(len(np.unique(rot[:, 1])))))
print('Quantidade de rotulos:', len(np.unique(rot[:, 1])))
print('Quantidade de pessoas:', len(np.unique(rot[:, 0])))
X = np.array(arr) # TRANSFORMANDO VETOR DAS IMAGENS EM ARRAY
y = rot[:, 1] # PEGANDO APENAS OS LABELS
y = np.vectorize(class_dict.get)(y) # TRANSFORMANDO LABELS EM NUMEROS
#a = ELMRegressor()
#a.fit(X, y)
v = -11
clsf = ELMClassifier()
clsf.fit(X[:v], y[:v])
print()
print(clsf.predict(X[v:]))
print(y[v:])
print('Socore:', clsf.score(X[v:], y[v:]))
TRAIN = sX_train # --> Datos ESTANDARIZADOS
TEST = sX_test # --> Datos ESTANDARIZADOS
clf=ELMClassifier(n_hidden=4000,alpha=0.95,activation_func='multiquadric',activation_args=None,
user_components=None,regressor=linear_model.Ridge(),random_state=None)
clf.fit(TRAIN, Y_train)
#TEST = X_test # --> Datos CRUDOS
TEST = sX_test # --> Datos ESTANDARIZADOS
#TEST = nX_test # --> Datos NORMALIZADOS
Y_pred = clf.predict(TEST)
#======================
# SCORE CALCULATION
#======================
score = clf.score(TEST, Y_test)
print("\nAccuracy: %0.2f" % (score))
#Cohen’s kappa-[-1,1]->Si>0.8 se considera ok
score=metrics.cohen_kappa_score(Y_test, Y_pred)
print("Cohen’s kappa: %0.2f " % score)
# Hamming loss¶ . when 0 excelent
score=metrics.hamming_loss(Y_test, Y_pred)
print("Hamming loss: %0.2f " % score)