def __init__(self, X_valid, Y_valid, qids_valid = None, measure=sqmprank, maxiter=10):
self.X_valid = array_tools.as_matrix(X_valid)
self.Y_valid = array_tools.as_2d_array(Y_valid)
self.qids_valid = qids_to_splits(qids_valid)
self.measure = measure
self.bestperf = None
self.bestA = None
self.iter = 0
self.last_update = 0
self.maxiter = maxiter
def __init__(self, X, Y, regparam = 1.0, qids = None, callbackfun=None, **kwargs):
self.regparam = regparam
self.callbackfun = None
self.Y = array_tools.as_2d_array(Y)
#Number of training examples
self.size = Y.shape[0]
if self.Y.shape[1] > 1:
raise Exception('CGRankRLS does not currently work in multi-label mode')
self.learn_from_labels = True
self.callbackfun = callbackfun
self.X = csc_matrix(X.T)
if qids is not None:
self.qids = map_qids(qids)
self.splits = qids_to_splits(self.qids)
else:
self.qids = None
regparam = self.regparam
qids = self.qids
if qids is not None:
P = sp.lil_matrix((self.size, len(set(qids))))
for qidind in range(len(self.splits)):
inds = self.splits[qidind]
qsize = len(inds)
for i in inds:
P[i, qidind] = 1. / sqrt(qsize)
P = P.tocsr()
PT = P.tocsc().T
else:
P = 1./sqrt(self.size)*(np.mat(np.ones((self.size,1), dtype=np.float64)))
PT = P.T
X = self.X.tocsc()
X_csr = X.tocsr()
def mv(v):
v = np.mat(v).T
return X_csr*(X.T*v)-X_csr*(P*(PT*(X.T*v)))+regparam*v
G = LinearOperator((X.shape[0],X.shape[0]), matvec=mv, dtype=np.float64)
Y = self.Y
if not self.callbackfun is None:
def cb(v):
self.A = np.mat(v).T
self.b = np.mat(np.zeros((1,1)))
self.callbackfun.callback(self)
else:
cb = None
XLY = X_csr*Y-X_csr*(P*(PT*Y))
try:
self.A = np.mat(cg(G, XLY, callback=cb)[0]).T
except Finished:
pass
self.b = np.mat(np.zeros((1,1)))
self.predictor = predictor.LinearPredictor(self.A, self.b)
def __init__(self,
X_valid,
Y_valid,
qids_valid=None,
measure=sqmprank,
maxiter=10):
self.X_valid = array_tools.as_matrix(X_valid)
self.Y_valid = array_tools.as_2d_array(Y_valid)
self.qids_valid = qids_to_splits(qids_valid)
self.measure = measure
self.bestperf = None
self.bestA = None
self.iter = 0
self.last_update = 0
self.maxiter = maxiter
def __init__(self, X, Y, qids, regparam = 1.0, kernel='LinearKernel', basis_vectors = None, **kwargs):
kwargs["bias"] = 0.
kwargs['kernel'] = kernel
kwargs['X'] = X
if basis_vectors is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.Y = np.mat(array_tools.as_2d_array(Y))
self.regparam = regparam
self.svals = np.mat(self.svdad.svals)
self.svecs = self.svdad.rsvecs
self.size = self.Y.shape[0]
self.size = self.Y.shape[0]
self.qids = map_qids(qids)
self.qidlist = qids_to_splits(self.qids)
self.solve(self.regparam)
def __init__(self,
X,
Y,
qids,
regparam=1.0,
kernel='LinearKernel',
basis_vectors=None,
**kwargs):
kwargs["bias"] = 0.
kwargs['kernel'] = kernel
kwargs['X'] = X
if basis_vectors is not None:
kwargs['basis_vectors'] = basis_vectors
self.svdad = adapter.createSVDAdapter(**kwargs)
self.Y = np.mat(array_tools.as_2d_array(Y))
self.regparam = regparam
self.svals = np.mat(self.svdad.svals)
self.svecs = self.svdad.rsvecs
self.size = self.Y.shape[0]
self.size = self.Y.shape[0]
self.qids = map_qids(qids)
self.qidlist = qids_to_splits(self.qids)
self.solve(self.regparam)
train_labels = np.loadtxt("./examples/data/rank_train.labels")
test_labels = np.loadtxt("./examples/data/rank_test.labels")
train_qids = read_qids("./examples/data/rank_train.qids")
test_features = read_sparse("./examples/data/rank_test.features")
train_features = read_sparse("./examples/data/rank_train.features")
test_qids = read_qids("./examples/data/rank_test.qids")
kwargs = {}
kwargs['measure'] = cindex
kwargs['regparams'] = [2**i for i in range(-10, 11)]
kwargs["Y"] = train_labels
kwargs["X"] = train_features
kwargs["qids"] = train_qids
learner = LeaveQueryOutRankRLS(**kwargs)
grid = kwargs['regparams']
perfs = learner.cv_performances
for i in range(len(grid)):
print "parameter %f cv_performance %f" % (grid[i], perfs[i])
P = learner.predict(test_features)
from rlscore.measure.measure_utilities import UndefinedPerformance
from rlscore.measure.measure_utilities import qids_to_splits
test_qids = qids_to_splits(test_qids)
perfs = []
for query in test_qids:
try:
perf = cindex(test_labels[query], P[query])
perfs.append(perf)
except UndefinedPerformance:
pass
test_perf = np.mean(perfs)
print "test set performance: %f" % test_perf
import numpy as np
from rlscore.learner.query_rankrls import QueryRankRLS
from rlscore.utilities.reader import read_qids
from rlscore.utilities.reader import read_sparse
from rlscore.measure import cindex
train_labels = np.loadtxt("./legacy_tests/data/rank_train.labels")
test_labels = np.loadtxt("./legacy_tests/data/rank_test.labels")
train_qids = read_qids("./legacy_tests/data/rank_train.qids")
test_features = read_sparse("./legacy_tests/data/rank_test.features")
train_features = read_sparse("./legacy_tests/data/rank_train.features")
test_qids = read_qids("./legacy_tests/data/rank_test.qids")
kwargs = {}
kwargs["Y"] = train_labels
kwargs["X"] = train_features
kwargs["qids"] = train_qids
kwargs["regparam"] = 1
learner = QueryRankRLS(**kwargs)
P = learner.predict(test_features)
from rlscore.measure.measure_utilities import UndefinedPerformance
from rlscore.measure.measure_utilities import qids_to_splits
test_qids = qids_to_splits(test_qids)
perfs = []
for query in test_qids:
try:
perf = cindex(test_labels[query], P[query])
perfs.append(perf)
except UndefinedPerformance:
pass
test_perf = np.mean(perfs)
print("test set performance: %f" %test_perf)
def __init__(self,
X,
Y,
regparam=1.0,
qids=None,
callbackfun=None,
**kwargs):
self.regparam = regparam
self.callbackfun = None
self.Y = array_tools.as_2d_array(Y)
#Number of training examples
self.size = Y.shape[0]
if self.Y.shape[1] > 1:
raise Exception(
'CGRankRLS does not currently work in multi-label mode')
self.learn_from_labels = True
self.callbackfun = callbackfun
self.X = csc_matrix(X.T)
if qids is not None:
self.qids = map_qids(qids)
self.splits = qids_to_splits(self.qids)
else:
self.qids = None
regparam = self.regparam
qids = self.qids
if qids is not None:
P = sp.lil_matrix((self.size, len(set(qids))))
for qidind in range(len(self.splits)):
inds = self.splits[qidind]
qsize = len(inds)
for i in inds:
P[i, qidind] = 1. / sqrt(qsize)
P = P.tocsr()
PT = P.tocsc().T
else:
P = 1. / sqrt(self.size) * (np.mat(
np.ones((self.size, 1), dtype=np.float64)))
PT = P.T
X = self.X.tocsc()
X_csr = X.tocsr()
def mv(v):
v = np.mat(v).T
return X_csr * (X.T * v) - X_csr * (P * (PT *
(X.T * v))) + regparam * v
G = LinearOperator((X.shape[0], X.shape[0]),
matvec=mv,
dtype=np.float64)
Y = self.Y
if not self.callbackfun is None:
def cb(v):
self.A = np.mat(v).T
self.b = np.mat(np.zeros((1, 1)))
self.callbackfun.callback(self)
else:
cb = None
XLY = X_csr * Y - X_csr * (P * (PT * Y))
try:
self.A = np.mat(cg(G, XLY, callback=cb)[0]).T
except Finished:
pass
self.b = np.mat(np.zeros((1, 1)))
self.predictor = predictor.LinearPredictor(self.A, self.b)
