def plot_attAUC(GT, attributepattern, clf):
AUC = []
P = np.loadtxt(attributepattern)
attributes = get_attributes()
# Loading ground truth
test_index = bzUnpickle('./CreatedData/test_features_index.txt')
test_attributes = get_class_attributes('./Classes/', name='test')
_, y_true = create_data('./CreatedData/test_featuresVGG19.pic.bz2',
test_index, test_attributes)
for i in range(y_true.shape[1]):
fp, tp, _ = roc_curve(y_true[:, i], P[:, i])
roc_auc = auc(fp, tp)
AUC.append(roc_auc)
print("Mean attrAUC %g" % (np.nanmean(AUC)))
xs = np.arange(y_true.shape[1])
width = 0.5
fig = plt.figure(figsize=(15, 5))
ax = fig.add_subplot(1, 1, 1)
rects = ax.bar(xs, AUC, width, align='center')
ax.set_xticks(xs)
ax.set_xticklabels(attributes, rotation=90)
ax.set_ylabel("area under ROC curve")
autolabel(rects, ax)
plt.savefig('results/AwA-AttAUC-DAP-%s.pdf' % clf)
def indirectAttributePrediction(classifier='SVM'):
# Get features index to recover samples
train_index = bzUnpickle('./CreatedData/train_features_index.txt')
test_index = bzUnpickle('./CreatedData/test_features_index.txt')
# Get classes-attributes relationship
train_attributes = get_class_attributes('./', name='train')
test_attributes = get_class_attributes('./', name='test')
# Create training Dataset
print('Creating training dataset...')
X_train, a_train = create_data('./CreatedData/train_featuresVGG19.pic.bz2',
train_index, train_attributes)
y_train = []
for (animal, num) in train_index:
y_train += num * [animal_dict[animal]]
y_train = np.array(y_train)
print('X_train to dense...')
X_train = X_train.toarray()
print('Creating test dataset...')
X_test, a_test = create_data('./CreatedData/test_featuresVGG19.pic.bz2',
test_index, test_attributes)
print('X_test to dense...')
X_test = X_test.toarray()
clf = SVMClassifierIAP(n_components=100, C=1.0)
print('Training model... (takes around 10 min)')
t0 = time()
clf.fit(X_train, y_train)
print('Training finished in', time() - t0)
y_pred = clf.predict(X_test)
y_proba = clf.predict_proba(X_test)
print('Saving files...')
np.savetxt('./IAP/prediction_SVM', y_pred)
np.savetxt('./IAP/probabilities_SVM', y_proba)
def DirectAttributePrediction(classifier='SVM',
predicate_type='binary',
C=10.0):
# Get features index to recover samples
train_index = bzUnpickle('./CreatedData/train_features_index.txt')
test_index = bzUnpickle('./CreatedData/test_features_index.txt')
# Get classes-attributes relationship
train_attributes = get_class_attributes('./',
name='train',
predicate_type=predicate_type)
test_attributes = get_class_attributes('./',
name='test',
predicate_type=predicate_type)
N_ATTRIBUTES = train_attributes.shape[1]
# Create training Dataset
print('Creating training dataset...')
X_train, y_train = create_data('./CreatedData/train_featuresVGG19.pic.bz2',
train_index, train_attributes)
print('X_train to dense...')
X_train = X_train.toarray()
Xplat_train, Xplat_val, yplat_train, yplat_val = train_test_split(
X_train, y_train, test_size=0.10, random_state=42)
print('Creating test dataset...')
X_test, y_test = create_data('./CreatedData/test_featuresVGG19.pic.bz2',
test_index, test_attributes)
y_pred = np.zeros(y_test.shape)
y_proba = np.copy(y_pred)
print('X_test to dense...')
X_test = X_test.toarray()
# CHOOSING SVM
if classifier == 'SVM':
platt_params = []
for i in range(N_ATTRIBUTES):
print('--------- Attribute %d/%d ---------' %
(i + 1, N_ATTRIBUTES))
t0 = time()
# SVM classifier
if predicate_type == 'binary':
clf = SVMClassifier()
else:
clf = SVMRegressor()
# Training
clf.fit(X_train, y_train[:, i])
print('Fitted classifier in: %fs' % (time() - t0))
if predicate_type == 'binary':
clf.set_platt_params(Xplat_val, yplat_val[:, i])
# Predicting
print('Predicting for attribute %d...' % (i + 1))
y_pred[:, i] = clf.predict(X_test)
if predicate_type == 'binary':
y_proba[:, i] = clf.predict_proba(X_test)
print('Saving files...')
np.savetxt('./DAP_' + predicate_type + '/prediction_SVM', y_pred)
if predicate_type == 'binary':
np.savetxt('./DAP_' + predicate_type + '/platt_params_SVM',
platt_params)
np.savetxt('./DAP_' + predicate_type + '/probabilities_SVM',
y_proba)
# CHOOSING NEURAL NETWORK
if classifier == 'NN':
if predicate_type != 'binary':
clf = NeuralNetworkRegressor(dim_features=X_train.shape[1],
nb_attributes=N_ATTRIBUTES)
else:
clf = NeuralNetworkClassifier(dim_features=X_train.shape[1],
nb_attributes=N_ATTRIBUTES)
print('Fitting Neural Network...')
clf.fit(X_train, y_train)
print('Predicting attributes...')
y_pred = np.array(clf.predict(X_test))
y_pred = y_pred.reshape((y_pred.shape[0], y_pred.shape[1])).T
y_proba = y_pred
print('Saving files...')
np.savetxt('./DAP_' + predicate_type + '/prediction_NN', y_pred)
if predicate_type == 'binary':
np.savetxt('./DAP_' + predicate_type + '/probabilities_NN',
y_proba)
def DirectAttributePrediction(predicate_type='binary'):
# Get features index to recover samples
train_index = bzUnpickle('./CreatedData/train_features_index.txt')
test_index = bzUnpickle('./CreatedData/test_features_index.txt')
val_index = bzUnpickle('./CreatedData/validation_features_index.txt')
# Get classes-attributes relationship
train_attributes = get_class_attributes('./Classes/',
name='train',
predicate_type=predicate_type)
test_attributes = get_class_attributes('./Classes/',
name='test',
predicate_type=predicate_type)
N_ATTRIBUTES = train_attributes.shape[1]
# Create training Dataset
print('Creating training dataset...')
X_train, y_train = create_data('./CreatedData/train_featuresVGG19.pic.bz2',
train_index, train_attributes)
print('Creating seen test dataset...')
X_test_seen, y_test_seen = create_data(
'./CreatedData/validation_featuresVGG19.pic.bz2', val_index,
train_attributes)
y_pred_ = np.zeros(y_test_seen.shape)
y_proba_ = np.copy(y_pred_)
print('X_train to dense...')
X_train = X_train.toarray()
print('X_test_seen to dense...')
X_test_seen = X_test_seen.toarray()
print('Creating test dataset...')
X_test, y_test = create_data('./CreatedData/test_featuresVGG19.pic.bz2',
test_index, test_attributes)
y_pred = np.zeros(y_test.shape)
y_proba = np.copy(y_pred)
print('X_test to dense...')
X_test = X_test.toarray()
if predicate_type != 'binary':
clf = NeuralNetworkRegressor(dim_features=X_train.shape[1],
nb_attributes=N_ATTRIBUTES)
else:
clf = NeuralNetworkClassifier(dim_features=X_train.shape[1],
nb_attributes=N_ATTRIBUTES)
print('Fitting Neural Network...')
# fix random seed for reproducibility
# seed = 7
# numpy.random.seed(seed)
# X_train_, X_test_, y_train_, y_test_ = train_test_split(X_train, y_train, test_size=1, random_state=seed)
his = clf.fit(X_train, y_train)
print('Predicting attributes...')
y_pred = np.array(clf.predict(X_test))
y_pred = y_pred.reshape((y_pred.shape[0], y_pred.shape[1])).T
y_proba = y_pred
y_pred_ = np.array(clf.predict(X_test_seen))
y_pred_ = y_pred_.reshape((y_pred_.shape[0], y_pred_.shape[1])).T
y_proba_ = y_pred_
print('Saving files...')
np.savetxt('./DAP_' + predicate_type + '/prediction_NN', y_pred)
np.savetxt('./DAP_' + predicate_type + '/xprediction_NN', y_pred_)
if predicate_type == 'binary':
np.savetxt('./DAP_' + predicate_type + '/probabilities_NN', y_proba)
np.savetxt('./DAP_' + predicate_type + '/xprobabilities_NN', y_proba_)