def three():
km_trainer = kmeans.kMeansTrainer(nr_centroids=nr_centroids,
nr_it=nr_iterations)
print "Creating centroids"
centroids = km_trainer.fit()
print "Saving centroids to file"
km_trainer.save_centroids(centroids, file_path=centroids_folder)
print "Done"
def steven():
calculator = ac.ActivationCalculation()
km = kmeans.kMeansTrainer()
print "Loading centroids"
centroids = km.get_saved_centroids(nr_centroids,
file_path=centroids_folder)
print "Calculating activations of test data"
calculator.pipeline(centroids,
n_pool_regions=nr_pool_regions,
file_path=activations_folder_test,
data_file=processed_test_filename)
print "Done"
f, ax = plt.subplots(length, length)
for i in range(0, length):
for j in range(0, length):
ax[i, j].imshow(reshaped[i * length + j],
cmap='Greys',
interpolation='nearest')
ax[i, j].axis('off')
plt.show()
def visualize_activation_alt(self, activations):
patch_size = np.sqrt(activations.shape[0])
one = activations[:, 0]
im = np.reshape(one, (patch_size, patch_size))
plt.imshow(im, cmap='Greys', interpolation='nearest')
plt.show()
if __name__ == '__main__':
km = kmeans.kMeansTrainer()
centroids = km.get_saved_centroids(100)
#util.plot_centroids(centroids, "../data/centroidskmeans")
sup_km = ActivationCalculation()
sup_km.pipeline(centroids=centroids,
data_file="../data/preprocessed_test.h5")
def singlePipeline(nr_centroids, nr_it,
label_path = "../data/preprocessed.h5",
clsfr = "SGD",
calc_centroids = True,
dogfeed=True,
train_model=True,
cache_size=4000,
degree=3,
tol=1e-3,
max_iter=-1,
kernel='rbf',
model_file='UNSPECIFIED'):
if calc_centroids:
print "calculating centroids..."
#Finds the features using kmeans
kmTrainer = kmeans.kMeansTrainer(nr_centroids = nr_centroids, nr_it = nr_it)
centroids = kmTrainer.fit()
kmTrainer.save_centroids(centroids)
print "calculating activations..."
#Calculates the activaiton of the test set
act_calc = act.ActivationCalculation()
features = act_calc.pipeline(centroids)
else:
print "loading activations from file..."
#loads feature data
feature_data = h5py.File("../data/activations_train/"+str(nr_centroids)+"activationkmeans.h5")
features = feature_data["activations"]
print "Loading labels from file..."
#get the labels
labels = util.load_labels(label_path)
label_names = util.load_label_names(label_path)
print "Got labels"
if clsfr == "SGD":
if train_model:
#Train the SGD classifier
print "Begin training of SGD..."
train.trainSGD(features, labels, nr_centroids)
print "Training done"
if not dogfeed:
return
print "Dogfeeding"
#Predict based on SGD training
print "Begin SGD predictions..."
classified = classifier.predict(features, nr_centroids, degree=degree, cache_size=cache_size)
print "Predicting done"
elif clsfr == "SVC" or clsfr == "NUSVR":
if train_model:
print "Begin training of Model..."
if clsfr=="SVC":
#Train SVC classifier
model = svc.train_svc(features,
labels,
nr_centroids,
degree=degree,
cache_size=cache_size,
tol=tol,
max_iter=max_iter,
kernel = kernel)
else :
#Train SVC classifier
model = svc.train_svc(features,
labels,
nr_centroids,
degree=degree,
cache_size=cache_size,
tol=tol,
max_iter=max_iter,
kernel=kernel)
print "Training done"
else:
print "Loading model"
model = joblib.load(model_file)
if not dogfeed:
return
print "Dogfeeding"
#Predict based on SVC training
print "Begin SVC predictions..."
classified = model.predict_proba(features)
print "Predicting done"
else:
print "Selected classifier not available, please use an available classifier"
return
print "Calculating log loss..."
summing = 0
correct = 0
np.savetxt("meuk.csv", classified, delimiter=";")
loss = metrics.log_loss(labels, classified)
print loss
print -np.mean(np.log(classified)[np.arange(len(labels)), labels])
#calculate the log loss
for i, label in enumerate(labels):
actual = labels[i]
if(classified[i][label] == 0):
summing+= np.log(10e-15)
else:
summing+= np.log(classified[i][label])
if actual == np.argmax(classified[i]):
correct += 1
image = np.zeros((len(label_names),len(labels)))
for j, label_index in enumerate(labels):
image[label_index,j] = 1
scipy.misc.imsave('correct.png', image)
scipy.misc.imsave('predicted.png', classified.T)
error = image - classified.T
scipy.misc.imsave('error.png', error)
print "Calculation finished"
summing = -summing/len(labels)
print "log loss: ", summing
print "correct/amount_of_labels: ", correct/len(labels)
print "lowest classification score: ", np.min(classified)
# print summing
np.savetxt( "realLabel.csv", labels, delimiter=";")
# np.savetxt( "SGD_label.csv", max_SGD, delimiter=";")
if calc_centroids is False:
feature_data.close()