def strategy05(X_train, labels_train, X_test, labels_test, groups):
'''
Estrategia número 5 para el segundo clasificador
'''
print('\nEjecutando la estrategia número 5 del segundo clasificador...')
# *** DEFINCION DE DATOS PARA EL TRAINING ***
# Paso 1: Clean
# > Training: 5040 x 82
s_clean = clean(X_train)
X_train = X_train[:, s_clean]
# Paso 2: PCA
# > Training: 5040 x 82
X_train, _, A1, Xm1, _ = pca(X_train, n_components=X_train.shape[1])
# Paso 3: Normalizacion
# > Training: 5040 x 82
X_train, a, b = normalize(X_train)
# Paso 4: SFS
# > Training: 5040 x 80
s_sfs = sfs(X_train, labels_train, n_features=80, method="fisher")
X_train = X_train[:, s_sfs]
X_train_sfs80 = X_train.copy()
# Paso 5: PCA
# > Training: 5040 x 10
X_train, _, A2, Xm2, _ = pca(X_train, n_components=10)
#Paso 6: SFS
# > Trainning: 5040 x 20
X_train = np.concatenate((X_train, X_train_sfs80), axis=1)
s_sfs2 = sfs(X_train, labels_train, n_features=20, method="fisher")
X_train = X_train[:, s_sfs2]
# *** DEFINCION DE DATOS PARA EL TESTING ***
X_test = X_test[:, s_clean] # Paso 1: clean
X_test = np.matmul(X_test - Xm1, A1) # Paso 2: PCA
X_test = X_test * a + b # Paso 3: normalizacion
X_test = X_test[:, s_sfs] # Paso 4: SFS
X_test_sfs80 = X_test.copy()
X_test = np.matmul(X_test - Xm2, A2) # Paso 5: PCA
X_test = np.concatenate((X_test, X_test_sfs80), axis=1)
X_test = X_test[:, s_sfs2] # Paso 6: SFS
# *** ENTRENAMIENTO CON DATOS DE TRAINING Y PRUEBA CON DATOS DE TESTING ***
return classifier_tests(X_train, labels_train, X_test, labels_test, groups)
def strategy02(X_train, labels_train, X_test, labels_test):
'''
Estrategia número 2 para el primer clasificador
'''
print('\nEjecutando la estrategia número 2 del primer clasificador...')
# *** DEFINCION DE DATOS PARA EL TRAINING ***
# Paso 1: Clean
# > Training: 5040 x 82
s_clean = clean(X_train)
X_train = X_train[:, s_clean]
# Paso 2: PCA de 70 componentes
# > Training: 5040 x 70
X_train, _, A, Xm, _ = pca(X_train, n_components=70)
# Paso 3: Normalizacion
# > Training: 5040 x 70
X_train, a, b = normalize(X_train)
# Paso 4: SFS
# > Training: 5040 x 20
s_sfs = sfs(X_train, labels_train, n_features=20, method="fisher")
X_train = X_train[:, s_sfs]
# *** DEFINCION DE DATOS PARA EL TESTING ***
X_test = X_test[:, s_clean] # Paso 1: Clean
X_test = np.matmul(X_test - Xm, A) # Paso 2: PCA
X_test = X_test * a + b # Paso 3: Normalizacion
X_test = X_test[:, s_sfs] # Paso 4: SFS
return classifier_tests(X_train, labels_train, X_test, labels_test)
def strategy01(X_train, labels_train, X_test, labels_test):
'''
Estrategia número 1 para el primer clasificador
'''
print('\nEjecutando la estrategia número 1 del primer clasificador...')
# *** DEFINCION DE DATOS PARA EL TRAINING ***
# Paso 1: Cleaning de los datos
# > Training: 5040 x 82
s_clean = clean(X_train)
X_train = X_train[:, s_clean]
# Paso 2: Normalización Mean-Std de los datos
X_train, a, b = normalize(X_train)
# Paso 3: Selección de características
# Acá se utilizó el criterio de fisher
# > Training: 5040 x 50
s_sfs = sfs(X_train, labels_train, n_features=50, method="fisher")
X_train = X_train[:, s_sfs]
# Paso 4: PCA
# > Training: 5040 x 50
X_train, _, A, Xm, _ = pca(X_train, n_components=50)
# *** DEFINCION DE DATOS PARA EL TESTING ***
X_test = X_test[:, s_clean] # Paso 1: Clean
X_test = X_test * a + b # Paso 2: Normalizacion
X_test = X_test[:, s_sfs] # Paso 3: SFS
X_test = np.matmul(X_test - Xm, A) # Paso 4: PCA
return classifier_tests(X_train, labels_train, X_test, labels_test)
def PCA(x_train, x_test, x_val, n_components):
"""
Realiza la transformación PCA de los datos a tan solo 'n_components' características.
n_components: número de características.
"""
x_train, _, A, Xm, _ = pca(x_train, n_components=n_components)
x_test = np.matmul(x_test - Xm, A)
x_val = np.matmul(x_val - Xm, A)
return x_train, x_test, x_val
def sfspca_old(X, d, m1, m2, cc, ex, m3):
s1 = sfsfisher(X, d, m1)
X = X[:, s1]
Y, _, _, _, _ = pca(X, n_components=m2)
if cc == 1:
Y = np.concatenate((X, Y), axis=1)
if ex == 1:
s2 = exsearch(Y, d, m3)
else:
s2 = sfs(Y, d, m3)
X = Y[:, s2]
return X
def WinnerStrategy(X_train, labels_train, X_test, labels_test):
'''
Estrategia Número 1 con redes neuronales,
Reescrita para poder obtener estadísticas
'''
# *** DEFINCION DE DATOS PARA EL TRAINING ***
# Paso 1: Cleaning de los datos
# > Training: 5040 x 82
s_clean = clean(X_train)
X_train = X_train[:, s_clean]
# Paso 2: Normalización Mean-Std de los datos
X_train, a, b = normalize(X_train)
# Paso 3: Selección de características
# Acá se utilizó el criterio de fisher
# > Training: 5040 x 50
s_sfs = sfs(X_train, labels_train, n_features=50, method="fisher")
X_train = X_train[:, s_sfs]
# Paso 4: PCA
# > Training: 5040 x 50
X_train, _, A, Xm, _ = pca(X_train, n_components=50)
# *** DEFINCION DE DATOS PARA EL TESTING ***
X_test = X_test[:, s_clean] # Paso 1: Clean
X_test = X_test * a + b # Paso 2: Normalizacion
X_test = X_test[:, s_sfs] # Paso 3: SFS
X_test = np.matmul(X_test - Xm, A) # Paso 4: PCA
classifier = MLPClassifier(alpha=1, max_iter=1000, random_state=2)
results = {}
# Clasificamos las muestras de Testing
classifier.fit(X_train, labels_train)
Y_pred = classifier.predict(X_test)
accuracy = performance(Y_pred, labels_test)
results['Accuracy'] = accuracy * 100
results['Y_pred'] = Y_pred
results['labels_test'] = labels_test
return results
def sfspca(bcl, X, d, m1, m2, cc, ex, m3):
s1 = fsel(bcl, X, d, m1)
X = X[:, s1]
if m2 > 0:
Y, _, _, _, _ = pca(X, n_components=m2)
else:
Y = X
if cc == 1:
Y = np.concatenate((X, Y), axis=1)
if m3 > 0:
if ex == 1:
s2 = exsearch(Y, d, m3)
else:
s2 = fsel(bcl, Y, d, m3)
X = Y[:, s2]
else:
X = Y
return X
def strategy03(X_train, labels_train, X_test, labels_test, groups):
'''
Estrategia número 3 para el segundo clasificador
'''
print('\nEjecutando la estrategia número 3 del segundo clasificador...')
# *** DEFINCION DE DATOS PARA EL TRAINING ***
# Paso 1: Clean
# > Training: 5040 x 82
s_clean = clean(X_train)
X_train = X_train[:, s_clean]
# Paso 2: Normalizacion
# > Training: 5040 x 82
X_train, a, b = normalize(X_train)
# Paso 3: SFS
# > Training: 5040 x 80
s_sfs = sfs(X_train, labels_train, n_features=80, method="fisher")
X_train = X_train[:, s_sfs]
# Paso 4: PCA
# > Training: 5040 x 20
X_train, _, A, Xm, _ = pca(X_train, n_components=20)
# Paso 5: SFS
# > Training: 5040 x 15
s_sfs2 = sfs(X_train, labels_train, n_features=15, method="fisher")
X_train = X_train[:, s_sfs2]
# *** DEFINCION DE DATOS PARA EL TESTING ***
X_test = X_test[:, s_clean] # Paso 1: Clean
X_test = X_test * a + b # Paso 2: Normalizacion
X_test = X_test[:, s_sfs] # Paso 3: SFS
X_test = np.matmul(X_test - Xm, A) # Paso 4: PCA
X_test = X_test[:, s_sfs2] # Paso 5: SFS
# *** ENTRENAMIENTO CON DATOS DE TRAINING Y PRUEBA CON DATOS DE TESTING ***
return classifier_tests(X_train, labels_train, X_test, labels_test, groups)
# > Training: 211 x 387 s_clean = clean(X_train) X_train = X_train[:, s_clean] # Paso 3-Training: Normalizacion # > Training: 211 x 387 X_train, a, b = normalize(X_train) # Paso 4-Training: SFS # > Training: 211 x 40 s_sfs = sfs(X_train, d_train, n_features=40, method="fisher", show=True) X_train = X_train[:, s_sfs] # Paso 5-Training: PCA # > Training: 211 x 10 X_train, _, A, Xm, _ = pca(X_train, n_components=10) # *** DEFINCION DE DATOS PARA EL TESTING *** X_test = X_test[:, s_clean] # Paso 2: clean X_test = X_test * a + b # Paso 3: normalizacion X_test = X_test[:, s_sfs] # Paso 4: SFS X_test = np.matmul(X_test - Xm, A) # Paso 5: PCA # *** ENTRENAMIENTO CON DATOS DE TRAINING Y PRUEBA CON DATOS DE TESTING *** knn = KNN(n_neighbors=1) knn.fit(X_train, d_train) Y_pred = knn.predict(X_test) accuracy = performance(Y_pred, d_test)
from pyxvis.features.selection import fse_model, fse_sbs, clean_norm, clean_norm_transform
from pyxvis.io.plots import plot_features3, print_confusion
from sklearn.neighbors import KNeighborsClassifier as knn
# Training-Data
path = '../images/threatobjects/'
fx = ['basicgeo', 'ellipse', 'hugeo', 'flusser', 'fourierdes', 'gupta']
X, d = extract_features_labels(fx,
path + 'train',
'jpg',
segmentation='bimodal')
X, sclean, a, b = clean_norm(X)
(name, params) = fse_model('LDA')
ssbs = fse_sbs([name, params], X, d, 20)
X = X[:, ssbs]
Ypca, _, _, _, _ = pca(X, n_components=3)
plot_features3(Ypca, d, 'PCA - Threat Objects', view=(-160, 120))
# Testing-Data
Xt, dt = extract_features_labels(fx,
path + 'test',
'jpg',
segmentation='bimodal')
Xt = clean_norm_transform(Xt, sclean, a, b)
Xt = Xt[:, ssbs]
# Classification and Evaluation
clf = knn(n_neighbors=5)
clf.fit(X, d)
ds = clf.predict(Xt)
print_confusion(dt, ds)
from pybalu.feature_transformation import pca
from pybalu.feature_analysis import jfisher
from pyxvis.features.extraction import extract_features_labels
from pyxvis.features.selection import fsel, fse_model, clean_norm, clean_norm_transform
from pyxvis.io.plots import plot_features3, print_confusion
from sklearn.neighbors import KNeighborsClassifier as KNN
# Training-Data
path = '../images/fishbones/'
fx = ['basicint', 'gabor-ri', 'lbp-ri', 'haralick-2', 'fourier', 'dct', 'hog']
X, d = extract_features_labels(fx, path + 'train', 'jpg')
X, sclean, a, b = clean_norm(X)
(name, params) = fse_model('QDA')
ssfs = fsel([name, params], X, d, 15, cv=5, show=1)
X = X[:, ssfs]
Ypca, _, A, Mx, _ = pca(X, n_components=6)
X = np.concatenate((X, Ypca), axis=1)
sf = exsearch(X, d, n_features=3, method="fisher", show=True)
X = X[:, sf]
print('Jfisher = ' + str(jfisher(X, d)))
plot_features3(X, d, 'Fishbones')
# Testing-Data
Xt, dt = extract_features_labels(fx, path + 'test', 'jpg')
Xt = clean_norm_transform(Xt, sclean, a, b)
Xt = Xt[:, ssfs]
Ytpca = np.matmul(Xt - Mx, A)
Xt = np.concatenate((Xt, Ytpca), axis=1)
Xt = Xt[:, sf]
# Classification and Evaluation