def main():
# Get training file name from the command line
traindatafile = sys.argv[1]
# The training file is in libSVM format
with open(traindatafile, mode="r") as myFile:
lines = myFile.readlines()
random.shuffle(lines)
open("tempdata.dat", 'w').writelines(lines)
tr_data = load_svmlight_file("tempdata.dat")
#To randomly select 5000 points
Xtr = tr_data[0].toarray()
# Converts sparse matrices to dense
Ytr = tr_data[1]
# The trainig labels
Xtr = Xtr[:5000]
Ytr = Ytr[:5000]
# Cast data to Shogun format to work with LMNN
features = RealFeatures(Xtr.T)
labels = MulticlassLabels(Ytr.astype(np.float64))
#print(Xtr.shape)
### Do magic stuff here to learn the best metric you can ###
kmax = 25 #inductive bias
values = list(range(1, kmax + 1))
k = predict(Xtr, Ytr, values)
# Number of target neighbours per example - tune this using validation
#print(k)
# Initialize the LMNN package
print("K : "),
print(k)
k = 5
lmnn = LMNN(features, labels, k)
init_transform = np.eye(Xtr.shape[1])
# Choose an appropriate timeout
lmnn.set_maxiter(25000)
lmnn.train(init_transform)
# Let LMNN do its magic and return a linear transformation
# corresponding to the Mahalanobis metric it has learnt
L = lmnn.get_linear_transform()
M = np.matrix(np.dot(L.T, L))
print("LMNN done")
#print(M)
# Save the model for use in testing phase
# Warning: do not change this file name
np.save("model.npy", M)
def test_LMNN():
X = np.eye(80)
Y = np.array([i for j in range(4) for i in range(20)])
feats = RealFeatures(X.T)
labs = MulticlassLabels(Y.astype(np.float64))
arr = LMNN(feats, labs, 2)
arr.train()
L = arr.get_linear_transform()
X_proj = np.dot(L, X.T)
test_x = np.eye(80)[0:20:]
test = RealFeatures(test_x.T)
test_proj = np.dot(L, test_x.T)
pdb.set_trace()
def run_knn(Xtrain,Ytrain,Xtest,Ytest):
prod_features = RealFeatures(Xtrain)
prod_labels = MulticlassLabels(Ytrain)
test_features = RealFeatures(Xtest)
test_labels = MulticlassLabels(Ytest)
if os.path.exists(".lmnn_model30000_5_reg05_cor20"):
print "Using LMNN distance"
lmnn = LMNN()
sf = SerializableAsciiFile(".lmnn_model30000_5_reg05_cor20", 'r')
lmnn.load_serializable(sf)
diagonal = np.diag(lmnn.get_linear_transform())
#print('%d out of %d elements are non-zero.' % (np.sum(diagonal != 0), diagonal.size))
#diagonal = lmnn.get_linear_transform()
np.set_printoptions(precision=1,threshold=1e10,linewidth=500)
#lmnn.set_diagonal(True)
dist = lmnn.get_distance()
else:
dist = EuclideanDistance()
# classifier
knn = KNN(K, dist, prod_labels)
#knn.set_use_covertree(True)
parallel = knn.get_global_parallel()
parallel.set_num_threads(4)
knn.set_global_parallel(parallel)
knn.train(prod_features)
print "Classifying test set..."
pred = knn.apply_multiclass(test_features)
print "Accuracy = %2.2f%%" % (100*np.mean(pred == Ytest))
cm = build_confusion_matrix(Ytest, pred, NCLASSES)
#save_confusion_matrix(cm)
#cm = load_confusion_matrix()
print "Confusion matrix: "
print cm
#plot_confusion_matrix(cm)
#results = predict_class_prob(pred, cm)
#nn = build_neighbours_matrix(knn, prod_labels)
#results = predict_class_from_neighbours(nn)
#print "Log loss: " + str(calculate_log_loss(results, Ytest))
#print_prediction_output(results)
return cm
def main():
# Get training file name from the command line
traindatafile = sys.argv[1]
# The training file is in libSVM format
tr_data = load_svmlight_file(traindatafile);
Xtr = tr_data[0].toarray(); # Converts sparse matrices to dense
Ytr = tr_data[1]; # The trainig labels
Indices_array = np.arange(Ytr.shape[0]);
np.random.shuffle(Indices_array);
Xtr = Xtr[Indices_array];
Xtr = Xtr[:6000];
Ytr = Ytr[Indices_array];
Ytr = Ytr[:6000];
# Cast data to Shogun format to work with LMNN
features = RealFeatures(Xtr.T)
labels = MulticlassLabels(Ytr.astype(np.float64))
### Do magic stuff here to learn the best metric you can ###
# Number of target neighbours per example - tune this using validation
k = 10
# Initialize the LMNN package
lmnn = LMNN(features, labels, k)
init_transform = np.eye(Xtr.shape[1])
# Choose an appropriate timeout
lmnn.set_maxiter(200000)
lmnn.train(init_transform)
# Let LMNN do its magic and return a linear transformation
# corresponding to the Mahalanobis metric it has learnt
L = lmnn.get_linear_transform()
M = np.matrix(np.dot(L.T, L))
# Save the model for use in testing phase
# Warning: do not change this file name
np.save("model.npy", M)
def RunLMNNShogun():
totalTimer = Timer()
# Load input dataset.
Log.Info("Loading dataset", self.verbose)
# Use the last row of the training set as the responses.
X, y = SplitTrainData(self.dataset)
try:
feat = RealFeatures(X.T)
labels = MulticlassLabels(y.astype(np.float64))
with totalTimer:
# Get the options for running LMNN.
if "k" in options:
self.k = int(options.pop("k"))
if "maxiter" in options:
n = int(options.pop("maxiter"))
else:
n = 2000
if len(options) > 0:
Log.Fatal("Unknown parameters: " + str(options))
raise Exception("unknown parameters")
# Perform LMNN.
prep = ShogunLMNN(feat, labels, self.k)
prep.set_maxiter(n)
prep.train()
except Exception as e:
return [-1, -1]
time = totalTimer.ElapsedTime()
# Get distance.
distance = prep.get_linear_transform()
dataList = [X, y]
accuracy1NN = Metrics.KNNAccuracy(distance, dataList, 1, False)
accuracy3NN = Metrics.KNNAccuracy(distance, dataList, 3, False)
accuracy3NNDW = Metrics.KNNAccuracy(distance, dataList, 3, True)
accuracy5NN = Metrics.KNNAccuracy(distance, dataList, 5, False)
accuracy5NNDW = Metrics.KNNAccuracy(distance, dataList, 5, True)
return [time, accuracy1NN, accuracy3NN, accuracy3NNDW,
accuracy5NN, accuracy5NNDW]
def main():
# Get training file name from the command line
traindatafile = sys.argv[1]
# The training file is in libSVM format
tr_data = load_svmlight_file(traindatafile)
print("loaded data")
init_transform = np.eye(tr_data[0].toarray().shape[1])
print(init_transform)
Xtr = tr_data[0][:6000].toarray()
# Converts sparse matrices to dense
Ytr = tr_data[1][:6000]
# The trainig labels
# Cast data to Shogun format to work with LMNN
features = RealFeatures(Xtr.T)
labels = MulticlassLabels(Ytr.astype(np.float64))
### Do magic stuff here to learn the best metric you can ###
# Number of target neighbours per example - tune this using validation
k = 21
# Initialize the LMNN package
print("starting lmnn train....")
lmnn = LMNN(features, labels, k)
# Choose an appropriate timeout
lmnn.set_maxiter(3000)
lmnn.train(init_transform)
# Let LMNN do its magic and return a linear transformation
# corresponding to the Mahalanobis metric it has learnt
L = lmnn.get_linear_transform()
M = np.matrix(np.dot(L.T, L))
print(M)
# Save the model for use in testing phase
# Warning: do not change this file name
statistics = lmnn.get_statistics()
pyplot.plot(statistics.obj.get())
pyplot.grid(True)
pyplot.xlabel('Number of iterations')
pyplot.ylabel('LMNN objective')
pyplot.show()
np.save("model.npy", M)
def main():
Xtr, Ytr = gettrainData()
Xtr = Xtr[:len(Xtr) // 6]
Ytr = Ytr[:len(Ytr) // 6]
# Cast data to Shogun format to work with LMNN
features = RealFeatures(Xtr.T)
labels = MulticlassLabels(Ytr.astype(np.float64))
print(2.1)
### Do magic stuff here to learn the best metric you can ###
# Number of target neighbours per example - tune this using validation
k = 10
# Initialize the LMNN package
lmnn = LMNN(features, labels, k)
print(2.2)
init_transform = np.eye(Xtr.shape[1])
print(2.3)
# Choose an appropriate timeout
lmnn.set_maxiter(8000)
print(2.4)
lmnn.train(init_transform)
print(2.5)
# Let LMNN do its magic and return a linear transformation
# corresponding to the Mahalanobis metric it has learnt
L = lmnn.get_linear_transform()
print(2.6)
M = np.matrix(np.dot(L.T, L))
print(2.7)
# Save the model for use in testing phase
# Warning: do not change this file name
np.save("model2.npy", M)
assert(features.get_num_vectors() == labels.get_num_labels())
distance = EuclideanDistance(features, features)
k = 2
knn = KNN(k, distance, labels)
plot_data(x, y, axarr[0])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[0])
axarr[0].set_aspect('equal')
axarr[0].set_xlim(-6, 4)
axarr[0].set_ylim(-3, 2)
lmnn = LMNN(features, labels, k)
lmnn.set_maxiter(10000)
lmnn.train()
L = lmnn.get_linear_transform()
knn.set_distance(lmnn.get_distance())
plot_data(x, y, axarr[1])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[1])
axarr[1].set_aspect('equal')
axarr[1].set_xlim(-6, 4)
axarr[1].set_ylim(-3, 2)
xL = numpy.dot(x, L.T) ## to see the data after the linear transformation
features = RealFeatures(xL.T)
distance = EuclideanDistance(features, features)
knn.set_distance(distance)
plot_data(xL, y, axarr[2])
plot_neighborhood_graph(xL, knn.nearest_neighbors(), axarr[2])
assert (features.get_num_vectors() == labels.get_num_labels())
distance = EuclideanDistance(features, features)
k = 2
knn = KNN(k, distance, labels)
plot_data(x, y, axarr[0])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[0])
axarr[0].set_aspect('equal')
axarr[0].set_xlim(-6, 4)
axarr[0].set_ylim(-3, 2)
lmnn = LMNN(features, labels, k)
lmnn.set_maxiter(10000)
lmnn.train()
L = lmnn.get_linear_transform()
knn.set_distance(lmnn.get_distance())
plot_data(x, y, axarr[1])
plot_neighborhood_graph(x, knn.nearest_neighbors(), axarr[1])
axarr[1].set_aspect('equal')
axarr[1].set_xlim(-6, 4)
axarr[1].set_ylim(-3, 2)
xL = numpy.dot(x, L.T) ## to see the data after the linear transformation
features = RealFeatures(xL.T)
distance = EuclideanDistance(features, features)
knn.set_distance(distance)
plot_data(xL, y, axarr[2])
plot_neighborhood_graph(xL, knn.nearest_neighbors(), axarr[2])
N = Xtest.shape[0]
prod_features = RealFeatures(Xtrain.T)
prod_labels = MulticlassLabels(Ytrain.T)
test_features = RealFeatures(Xtest.T)
k = 5
# load LMNN
if os.path.exists(".lmnn_model30000_5_reg05_cor20"):
sf = SerializableAsciiFile(".lmnn_model30000_5_reg05_cor20", 'r')
lmnn = LMNN()
lmnn.load_serializable(sf)
diagonal = np.diag(lmnn.get_linear_transform())
print('%d out of %d elements are non-zero.' % (np.sum(diagonal != 0), diagonal.size))
#print diagonal
dist = lmnn.get_distance()
else:
dist = EuclideanDistance()
cm = load_confusion_matrix()
print cm
# classifier
knn = KNN(k, dist, prod_labels)
parallel = knn.get_global_parallel()
parallel.set_num_threads(4)
knn.set_global_parallel(parallel)
knn.train(prod_features)
