def _test_gpr_model_selection(self): # pragma: no cover
"""Smoke test for running model selection while getting GPRWeights
TODO: DISABLED because setting of hyperparameters was not adopted for 0.6 (yet)
"""
if not externals.exists('openopt'):
return
amap = AttributeMap() # we would need to pass numbers into the GPR
dataset = datasets['uni2small'].copy(
) #data_generators.linear1d_gaussian_noise()
dataset.targets = amap.to_numeric(dataset.targets).astype(float)
k = GeneralizedLinearKernel()
clf = GPR(k, enable_ca=['log_marginal_likelihood'])
sa = clf.get_sensitivity_analyzer() # should be regular weights
sa_ms = clf.get_sensitivity_analyzer(
flavor='model_select') # with model selection
def prints():
print clf.ca.log_marginal_likelihood, clf.kernel.Sigma_p, clf.kernel.sigma_0
sa(dataset)
lml = clf.ca.log_marginal_likelihood
sa_ms(dataset)
lml_ms = clf.ca.log_marginal_likelihood
self.assertTrue(lml_ms > lml)
def test_attrmap_repr():
assert_equal(repr(AttributeMap()), "AttributeMap()")
assert_equal(repr(AttributeMap(dict(a=2, b=1))),
"AttributeMap({'a': 2, 'b': 1})")
assert_equal(repr(AttributeMap(dict(a=2, b=1), mapnumeric=True)),
"AttributeMap({'a': 2, 'b': 1}, mapnumeric=True)")
assert_equal(repr(AttributeMap(dict(a=2, b=1), mapnumeric=True, collisions_resolution='tuple')),
"AttributeMap({'a': 2, 'b': 1}, mapnumeric=True, collisions_resolution='tuple')")
def test_attrmap_repr():
assert_equal(repr(AttributeMap()), "AttributeMap()")
d = dict(a=2, b=1)
assert_equal(repr(AttributeMap(d)), "AttributeMap(%r)" % (d, ))
assert_equal(repr(AttributeMap(dict(a=2, b=1), mapnumeric=True)),
"AttributeMap(%r, mapnumeric=True)" % (d, ))
assert_equal(
repr(
AttributeMap(dict(a=2, b=1),
mapnumeric=True,
collisions_resolution='tuple')),
"AttributeMap(%r, mapnumeric=True, collisions_resolution='tuple')" %
(d, ))
def test_attrmap_conflicts():
am_n = AttributeMap({'a':1, 'b':2, 'c':1})
am_t = AttributeMap({'a':1, 'b':2, 'c':1}, collisions_resolution='tuple')
am_l = AttributeMap({'a':1, 'b':2, 'c':1}, collisions_resolution='lucky')
q_f = ['a', 'b', 'a', 'c']
# should have no effect on forward mapping
ok_(np.all(am_n.to_numeric(q_f) == am_t.to_numeric(q_f)))
ok_(np.all(am_t.to_numeric(q_f) == am_l.to_numeric(q_f)))
assert_raises(ValueError, am_n.to_literal, [2])
r_t = am_t.to_literal([2, 1])
r_l = am_l.to_literal([2, 1])
def _call(self, dataset):
sens = super(self.__class__, self)._call(dataset)
clf = self.clf
targets_attr = clf.get_space()
if targets_attr in sens.sa:
# if labels are present -- transform them into meaningful tuples
# (or not if just a single beast)
am = AttributeMap(dict([(l, -1) for l in clf.neglabels] + [(l, +1) for l in clf.poslabels]))
# XXX here we still can get a sensitivity per each label
# (e.g. with SMLR as the slave clf), so I guess we should
# tune up Multiclass...Analyzer to add an additional sa
# And here we might need to check if asobjarray call is necessary
# and should be actually done
# asobjarray(
sens.sa[targets_attr] = am.to_literal(sens.sa[targets_attr].value, recurse=True)
return sens
def _call(self, dataset):
sens = super(self.__class__, self)._call(dataset)
clf = self.clf
targets_attr = clf.get_space()
if targets_attr in sens.sa:
# if labels are present -- transform them into meaningful tuples
# (or not if just a single beast)
am = AttributeMap(dict([(l, -1) for l in clf.neglabels] +
[(l, +1) for l in clf.poslabels]))
# XXX here we still can get a sensitivity per each label
# (e.g. with SMLR as the slave clf), so I guess we should
# tune up Multiclass...Analyzer to add an additional sa
# And here we might need to check if asobjarray call is necessary
# and should be actually done
#asobjarray(
sens.sa[targets_attr] = \
am.to_literal(sens.sa[targets_attr].value, recurse=True)
return sens
def _call(self, ds):
y = ds.sa[self.space].value
if self.numeric or ((self.numeric is None) and y.dtype.char == 'S'):
y = AttributeMap().to_numeric(y)
# TODO:
if not self.uni:
out = self.fx(ds.samples, y)
else:
out = np.array([self.fx(feat, y) for feat in ds.samples.T])
return Dataset(out[None], fa=ds.fa)
def __init__(self, space=None, **kwargs):
# by default we want classifiers to use the 'targets' sample attribute
# for training/testing
if space is None:
space = 'targets'
Learner.__init__(self, space=space, **kwargs)
# XXX
# the place to map literal to numerical labels (and back)
# this needs to be in the base class, since some classifiers also
# have this nasty 'regression' mode, and the code in this class
# needs to deal with converting the regression output into discrete
# labels
# however, preferably the mapping should be kept in the respective
# low-level implementations that need it
self._attrmap = AttributeMap()
self.__trainednfeatures = 0
"""Stores number of features for which classifier was trained.
If 0 -- it wasn't trained at all"""
self._set_retrainable(self.params.retrainable, force=True)
def _test_gpr_model_selection(self): # pragma: no cover
"""Smoke test for running model selection while getting GPRWeights
TODO: DISABLED because setting of hyperparameters was not adopted for 0.6 (yet)
"""
if not externals.exists('openopt'):
return
amap = AttributeMap() # we would need to pass numbers into the GPR
dataset = datasets['uni2small'].copy() #data_generators.linear1d_gaussian_noise()
dataset.targets = amap.to_numeric(dataset.targets).astype(float)
k = GeneralizedLinearKernel()
clf = GPR(k, enable_ca=['log_marginal_likelihood'])
sa = clf.get_sensitivity_analyzer() # should be regular weights
sa_ms = clf.get_sensitivity_analyzer(flavor='model_select') # with model selection
def prints():
print clf.ca.log_marginal_likelihood, clf.kernel.Sigma_p, clf.kernel.sigma_0
sa(dataset)
lml = clf.ca.log_marginal_likelihood
sa_ms(dataset)
lml_ms = clf.ca.log_marginal_likelihood
self.assertTrue(lml_ms > lml)
def test_regressions_classifiers(self, clf):
"""Simple tests on regressions being used as classifiers
"""
# check if we get values set correctly
clf.ca.change_temporarily(enable_ca=['estimates'])
self.assertRaises(UnknownStateError, clf.ca['estimates']._get)
cv = CrossValidation(clf, NFoldPartitioner(),
enable_ca=['stats', 'training_stats'])
ds = datasets['uni2small'].copy()
# we want numeric labels to maintain the previous behavior, especially
# since we deal with regressions here
ds.sa.targets = AttributeMap().to_numeric(ds.targets)
cverror = cv(ds)
self.assertTrue(len(clf.ca.estimates) == ds[ds.chunks == 1].nsamples)
clf.ca.reset_changed_temporarily()
def test_degenerate_usage(self, clf):
"""Test how clf handles degenerate cases
"""
# Whenever we have only 1 feature with only 0s in it
ds1 = datasets['uni2small'][:, [0]]
# XXX this very line breaks LARS in many other unittests --
# very interesting effect. but screw it -- for now it will be
# this way
ds1.samples[:] = 0.0 # all 0s
# For regression we need numbers
if clf.__is_regression__:
ds1.targets = AttributeMap().to_numeric(ds1.targets)
#ds2 = datasets['uni2small'][[0], :]
#ds2.samples[:] = 0.0 # all 0s
clf.ca.change_temporarily(enable_ca=['estimates', 'training_stats'])
# Good pukes are good ;-)
# TODO XXX add
# - ", ds2):" to test degenerate ds with 1 sample
# - ds1 but without 0s -- just 1 feature... feature selections
# might lead to 'surprises' due to magic in combiners etc
for ds in (ds1, ):
try:
try:
clf.train(ds) # should not crash or stall
except (ValueError, AssertionError) as e:
self.fail(
"Failed to train on degenerate data. Error was %r" % e)
except DegenerateInputError:
# so it realized that data is degenerate and puked
continue
# could we still get those?
_ = clf.summary()
cm = clf.ca.training_stats
# If succeeded to train/predict (due to
# training_stats) without error -- results better be
# at "chance"
continue
if 'ACC' in cm.stats:
self.assertEqual(cm.stats['ACC'], 0.5)
else:
self.assertTrue(np.isnan(cm.stats['CCe']))
except tuple(_degenerate_allowed_exceptions):
pass
clf.ca.reset_changed_temporarily()
def __init__(self, space=None, **kwargs):
# by default we want classifiers to use the 'targets' sample attribute
# for training/testing
if space is None:
space = 'targets'
Learner.__init__(self, space=space, **kwargs)
# XXX
# the place to map literal to numerical labels (and back)
# this needs to be in the base class, since some classifiers also
# have this nasty 'regression' mode, and the code in this class
# needs to deal with converting the regression output into discrete
# labels
# however, preferably the mapping should be kept in the respective
# low-level implementations that need it
self._attrmap = AttributeMap()
self.__trainednfeatures = 0
"""Stores number of features for which classifier was trained.
If 0 -- it wasn't trained at all"""
self._set_retrainable(self.params.retrainable, force=True)
def test_null_dist_prob_any(self):
"""Test 'any' tail statistics estimation"""
skip_if_no_external('scipy')
# test 'any' mode
from mvpa2.measures.corrcoef import CorrCoef
# we will reassign targets later on, so let's operate on a
# copy
ds = datasets['uni2medium'].copy()
permutator = AttributePermutator('targets', count=20)
null = MCNullDist(permutator, tail='any')
assert_raises(ValueError, null.fit, CorrCoef(), ds)
# cheat and map to numeric for this test
ds.sa.targets = AttributeMap().to_numeric(ds.targets)
null.fit(CorrCoef(), ds)
# 100 and -100 should both have zero probability on their respective
# tails
pm100 = null.p([-100] + [0] * (ds.nfeatures - 1))
p100 = null.p([100] + [0] * (ds.nfeatures - 1))
assert_array_almost_equal(pm100, p100)
# With 20 samples it isn't that easy to get a reliable sampling for
# non-parametric, so we can allow somewhat low significance
self.assertTrue(pm100[0] <= 0.1)
self.assertTrue(p100[0] <= 0.1)
self.assertTrue(np.all(pm100[1:] > 0.05))
self.assertTrue(np.all(p100[1:] > 0.05))
# same test with just scalar measure/feature
null.fit(CorrCoef(), ds[:, 0])
p_100 = null.p(100)
self.failUnlessAlmostEqual(null.p(-100), p_100)
self.failUnlessAlmostEqual(p100[0], p_100)
class Classifier(Learner):
"""Abstract classifier class to be inherited by all classifiers
"""
# Kept separate from doc to don't pollute help(clf), especially if
# we including help for the parent class
_DEV__doc__ = """
Required behavior:
For every classifier is has to be possible to be instantiated without
having to specify the training pattern.
Repeated calls to the train() method with different training data have to
result in a valid classifier, trained for the particular dataset.
It must be possible to specify all classifier parameters as keyword
arguments to the constructor.
Recommended behavior:
Derived classifiers should provide access to *estimates* -- i.e. that
information that is finally used to determine the predicted class label.
Michael: Maybe it works well if each classifier provides a 'estimates'
state member. This variable is a list as long as and in same order
as Dataset.uniquetargets (training data). Each item in the list
corresponds to the likelyhood of a sample to belong to the
respective class. However the semantics might differ between
classifiers, e.g. kNN would probably store distances to class-
neighbors, where PLR would store the raw function value of the
logistic function. So in the case of kNN low is predictive and for
PLR high is predictive. Don't know if there is the need to unify
that.
As the storage and/or computation of this information might be
demanding its collection should be switchable and off be default.
Nomenclature
* predictions : result of the last call to .predict()
* estimates : might be different from predictions if a classifier's predict()
makes a decision based on some internal value such as
probability or a distance.
"""
# Dict that contains the parameters of a classifier.
# This shall provide an interface to plug generic parameter optimizer
# on all classifiers (e.g. grid- or line-search optimizer)
# A dictionary is used because Michael thinks that access by name is nicer.
# Additionally Michael thinks ATM that additional information might be
# necessary in some situations (e.g. reasonably predefined parameter range,
# minimal iteration stepsize, ...), therefore the value to each key should
# also be a dict or we should use mvpa2.base.param.Parameter'...
training_stats = ConditionalAttribute(enabled=False,
doc="Confusion matrix of learning performance")
predictions = ConditionalAttribute(enabled=True,
doc="Most recent set of predictions")
estimates = ConditionalAttribute(enabled=True,
doc="Internal classifier estimates the most recent " +
"predictions are based on")
predicting_time = ConditionalAttribute(enabled=True,
doc="Time (in seconds) which took classifier to predict")
__tags__ = []
"""Describes some specifics about the classifier -- is that it is
doing regression for instance...."""
# TODO: make it available only for actually retrainable classifiers
retrainable = Parameter(False, allowedtype='bool',
doc="""Either to enable retraining for 'retrainable' classifier.""",
index=1002)
def __init__(self, space=None, **kwargs):
# by default we want classifiers to use the 'targets' sample attribute
# for training/testing
if space is None:
space = 'targets'
Learner.__init__(self, space=space, **kwargs)
# XXX
# the place to map literal to numerical labels (and back)
# this needs to be in the base class, since some classifiers also
# have this nasty 'regression' mode, and the code in this class
# needs to deal with converting the regression output into discrete
# labels
# however, preferably the mapping should be kept in the respective
# low-level implementations that need it
self._attrmap = AttributeMap()
self.__trainednfeatures = 0
"""Stores number of features for which classifier was trained.
If 0 -- it wasn't trained at all"""
self._set_retrainable(self.params.retrainable, force=True)
# deprecate
#self.__trainedidhash = None
#"""Stores id of the dataset on which it was trained to signal
#in trained() if it was trained already on the same dataset"""
@property
def __summary_class__(self):
if 'regression' in self.__tags__:
return RegressionStatistics
else:
return ConfusionMatrix
@property
def __is_regression__(self):
return 'regression' in self.__tags__
def __str__(self, *args, **kwargs):
if __debug__ and 'CLF_' in debug.active:
return "%s / %s" % (repr(self), super(Classifier, self).__str__())
else:
return _str(self, *args, **kwargs)
def _pretrain(self, dataset):
"""Functionality prior to training
"""
# So we reset all conditional attributes and may be free up some memory
# explicitly
params = self.params
if not params.retrainable:
self.untrain()
else:
# just reset the ca, do not untrain
self.ca.reset()
if not self.__changedData_isset:
self.__reset_changed_data()
_changedData = self._changedData
__idhashes = self.__idhashes
__invalidatedChangedData = self.__invalidatedChangedData
# if we don't know what was changed we need to figure
# them out
if __debug__:
debug('CLF_', "IDHashes are %s", (__idhashes,))
# Look at the data if any was changed
for key, data_ in (('traindata', dataset.samples),
('targets', dataset.sa[self.get_space()].value)):
_changedData[key] = self.__was_data_changed(key, data_)
# if those idhashes were invalidated by retraining
# we need to adjust _changedData accordingly
if __invalidatedChangedData.get(key, False):
if __debug__ and not _changedData[key]:
debug('CLF_', 'Found that idhash for %s was '
'invalidated by retraining', (key,))
_changedData[key] = True
# Look at the parameters
for col in self._paramscols:
changedParams = self._collections[col].which_set()
if len(changedParams):
_changedData[col] = changedParams
self.__invalidatedChangedData = {} # reset it on training
if __debug__:
debug('CLF_', "Obtained _changedData is %s",
(self._changedData,))
def _posttrain(self, dataset):
"""Functionality post training
For instance -- computing confusion matrix.
Parameters
----------
dataset : Dataset
Data which was used for training
"""
super(Classifier, self)._posttrain(dataset)
ca = self.ca
# needs to be assigned first since below we use predict
self.__trainednfeatures = dataset.nfeatures
if __debug__ and 'CHECK_TRAINED' in debug.active:
self.__trainedidhash = dataset.idhash
if ca.is_enabled('training_stats') and \
not ca.is_set('training_stats'):
# we should not store predictions for training data,
# it is confusing imho (yoh)
ca.change_temporarily(
disable_ca=["predictions"])
if self.params.retrainable:
# we would need to recheck if data is the same,
# XXX think if there is a way to make this all
# efficient. For now, probably, retrainable
# classifiers have no chance but not to use
# training_stats... sad
self.__changedData_isset = False
predictions = self.predict(dataset)
ca.reset_changed_temporarily()
ca.training_stats = self.__summary_class__(
targets=dataset.sa[self.get_space()].value,
predictions=predictions)
def summary(self):
"""Providing summary over the classifier"""
s = "Classifier %s" % self
ca = self.ca
ca_enabled = ca.enabled
if self.trained:
s += "\n trained"
if ca.is_set('training_time'):
s += ' in %.3g sec' % ca.training_time
s += ' on data with'
if ca.is_set('trained_targets'):
s += ' targets:%s' % list(ca.trained_targets)
nsamples, nchunks = None, None
if ca.is_set('trained_nsamples'):
nsamples = ca.trained_nsamples
if ca.is_set('trained_dataset'):
td = ca.trained_dataset
nsamples, nchunks = td.nsamples, len(td.sa['chunks'].unique)
if nsamples is not None:
s += ' #samples:%d' % nsamples
if nchunks is not None:
s += ' #chunks:%d' % nchunks
s += " #features:%d" % self.__trainednfeatures
if ca.is_set('training_stats'):
s += ", training error:%.3g" % ca.training_stats.error
else:
s += "\n not yet trained"
if len(ca_enabled):
s += "\n enabled ca:%s" % ', '.join([str(ca[x])
for x in ca_enabled])
return s
def clone(self):
"""Create full copy of the classifier.
It might require classifier to be untrained first due to
present SWIG bindings.
TODO: think about proper re-implementation, without enrollment of deepcopy
"""
if __debug__:
debug("CLF", "Cloning %s%s", (self, _strid(self)))
try:
return deepcopy(self)
except:
self.untrain()
return deepcopy(self)
def _train(self, dataset):
"""Function to be actually overridden in derived classes
"""
raise NotImplementedError
def _prepredict(self, dataset):
"""Functionality prior prediction
"""
if not ('notrain2predict' in self.__tags__):
# check if classifier was trained if that is needed
if not self.trained:
raise FailedToPredictError(
"Classifier %s wasn't yet trained, therefore can't "
"predict" % self)
nfeatures = dataset.nfeatures #data.shape[1]
# check if number of features is the same as in the data
# it was trained on
if nfeatures != self.__trainednfeatures:
raise ValueError, \
"Classifier %s was trained on data with %d features, " % \
(self, self.__trainednfeatures) + \
"thus can't predict for %d features" % nfeatures
if self.params.retrainable:
if not self.__changedData_isset:
self.__reset_changed_data()
_changedData = self._changedData
data = np.asanyarray(dataset.samples)
_changedData['testdata'] = \
self.__was_data_changed('testdata', data)
if __debug__:
debug('CLF_', "prepredict: Obtained _changedData is %s",
(_changedData,))
def _postpredict(self, dataset, result):
"""Functionality after prediction is computed
"""
self.ca.predictions = result
if self.params.retrainable:
self.__changedData_isset = False
def _predict(self, dataset):
"""Actual prediction
"""
raise NotImplementedError
@accepts_samples_as_dataset
def predict(self, dataset):
"""Predict classifier on data
Shouldn't be overridden in subclasses unless explicitly needed
to do so. Also subclasses trying to call super class's predict
should call _predict if within _predict instead of predict()
since otherwise it would loop
"""
## ??? yoh: changed to asany from as without exhaustive check
data = np.asanyarray(dataset.samples)
if __debug__:
# Verify that we have no NaN/Inf's which we do not "support" ATM
if not np.all(np.isfinite(data)):
raise ValueError(
"Some input data for predict is not finite (NaN or Inf)")
debug("CLF", "Predicting classifier %s on ds %s",
(self, dataset))
# remember the time when started computing predictions
t0 = time.time()
ca = self.ca
# to assure that those are reset (could be set due to testing
# post-training)
ca.reset(['estimates', 'predictions'])
self._prepredict(dataset)
if self.__trainednfeatures > 0 \
or 'notrain2predict' in self.__tags__:
result = self._predict(dataset)
else:
warning("Trying to predict using classifier trained on no features")
if __debug__:
debug("CLF",
"No features were present for training, prediction is " \
"bogus")
result = [None]*data.shape[0]
ca.predicting_time = time.time() - t0
# with labels mapping in-place, we also need to go back to the
# literal labels
if self._attrmap:
try:
result = self._attrmap.to_literal(result)
except KeyError, e:
raise FailedToPredictError, \
"Failed to convert predictions from numeric into " \
"literals: %s" % e
self._postpredict(dataset, result)
return result
def _train(self, dataset):
"""Train SVM
"""
# XXX watchout
# self.untrain()
newkernel, newsvm = False, False
# local bindings for faster lookup
params = self.params
retrainable = self.params.retrainable
targets_sa_name = self.get_space() # name of targets sa
targets_sa = dataset.sa[targets_sa_name] # actual targets sa
if retrainable:
_changedData = self._changedData
# LABELS
ul = None
self.__traindataset = dataset
# OK -- we have to map labels since
# binary ones expect -1/+1
# Multiclass expect labels starting with 0, otherwise they puke
# when ran from ipython... yikes
if __debug__:
debug("SG_", "Creating labels instance")
if self.__is_regression__:
labels_ = np.asarray(targets_sa.value, dtype='double')
else:
ul = targets_sa.unique
# ul.sort()
if len(ul) == 2:
# assure that we have -1/+1
_labels_dict = {ul[0]:-1.0, ul[1]:+1.0}
elif len(ul) < 2:
raise FailedToTrainError, \
"We do not have 1-class SVM brought into SG yet"
else:
# can't use plain enumerate since we need them swapped
_labels_dict = dict([ (ul[i], i) for i in range(len(ul))])
# Create SG-customized attrmap to assure -1 / +1 if necessary
self._attrmap = AttributeMap(_labels_dict, mapnumeric=True)
if __debug__:
debug("SG__", "Mapping labels using dict %s" % _labels_dict)
labels_ = self._attrmap.to_numeric(targets_sa.value).astype(float)
labels = shogun.Features.Labels(labels_)
_setdebug(labels, 'Labels')
# KERNEL
# XXX cruel fix for now... whole retraining business needs to
# be rethought
if retrainable:
_changedData['kernel_params'] = _changedData.get('kernel_params', False)
# TODO: big RF to move non-kernel classifiers away
if 'kernel-based' in self.__tags__ and (not retrainable
or _changedData['traindata'] or _changedData['kernel_params']):
# If needed compute or just collect arguments for SVM and for
# the kernel
if retrainable and __debug__:
if _changedData['traindata']:
debug("SG",
"Re-Creating kernel since training data has changed")
if _changedData['kernel_params']:
debug("SG",
"Re-Creating kernel since params %s has changed" %
_changedData['kernel_params'])
k = self.params.kernel
k.compute(dataset)
self.__kernel = kernel = k.as_raw_sg()
newkernel = True
self.kernel_params.reset() # mark them as not-changed
#_setdebug(kernel, 'Kernels')
#self.__condition_kernel(kernel)
if retrainable:
if __debug__:
debug("SG_", "Resetting test kernel for retrainable SVM")
self.__kernel_test = None
# TODO -- handle _changedData['params'] correctly, ie without recreating
# whole SVM
Cs = None
if not retrainable or self.__svm is None or _changedData['params']:
# SVM
if self.params.has_key('C'):
Cs = self._get_cvec(dataset)
# XXX do not jump over the head and leave it up to the user
# ie do not rescale automagically by the number of samples
#if len(Cs) == 2 and not ('regression' in self.__tags__) and len(ul) == 2:
# # we were given two Cs
# if np.max(C) < 0 and np.min(C) < 0:
# # and both are requested to be 'scaled' TODO :
# # provide proper 'features' to the parameters,
# # so we could specify explicitely if to scale
# # them by the number of samples here
# nl = [np.sum(labels_ == _labels_dict[l]) for l in ul]
# ratio = np.sqrt(float(nl[1]) / nl[0])
# #ratio = (float(nl[1]) / nl[0])
# Cs[0] *= ratio
# Cs[1] /= ratio
# if __debug__:
# debug("SG_", "Rescaled Cs to %s to accomodate the "
# "difference in number of training samples" %
# Cs)
# Choose appropriate implementation
svm_impl_class = self.__get_implementation(ul)
if __debug__:
debug("SG", "Creating SVM instance of %s" % `svm_impl_class`)
if self._svm_impl in ['libsvr', 'svrlight']:
# for regressions constructor a bit different
self.__svm = svm_impl_class(Cs[0], self.params.tube_epsilon, self.__kernel, labels)
# we need to set epsilon explicitly
self.__svm.set_epsilon(self.params.epsilon)
elif self._svm_impl in ['krr']:
self.__svm = svm_impl_class(self.params.tau, self.__kernel, labels)
elif 'kernel-based' in self.__tags__:
self.__svm = svm_impl_class(Cs[0], self.__kernel, labels)
self.__svm.set_epsilon(self.params.epsilon)
else:
traindata_sg = _tosg(dataset.samples)
self.__svm = svm_impl_class(Cs[0], traindata_sg, labels)
self.__svm.set_epsilon(self.params.epsilon)
# To stay compatible with versions across API changes in sg 1.0.0
self.__svm_apply = externals.versions['shogun'] >= '1' \
and self.__svm.apply \
or self.__svm.classify # the last one for old API
# Set shrinking
if 'shrinking' in params:
shrinking = params.shrinking
if __debug__:
debug("SG_", "Setting shrinking to %s" % shrinking)
self.__svm.set_shrinking_enabled(shrinking)
if Cs is not None and len(Cs) == 2:
if __debug__:
debug("SG_", "Since multiple Cs are provided: %s, assign them" % Cs)
self.__svm.set_C(Cs[0], Cs[1])
self.params.reset() # mark them as not-changed
newsvm = True
_setdebug(self.__svm, 'SVM')
# Set optimization parameters
if self.params.has_key('tube_epsilon') and \
hasattr(self.__svm, 'set_tube_epsilon'):
self.__svm.set_tube_epsilon(self.params.tube_epsilon)
self.__svm.parallel.set_num_threads(self.params.num_threads)
else:
if __debug__:
debug("SG_", "SVM instance is not re-created")
if _changedData['targets']: # labels were changed
if __debug__: debug("SG__", "Assigning new labels")
self.__svm.set_labels(labels)
if newkernel: # kernel was replaced
if __debug__: debug("SG__", "Assigning new kernel")
self.__svm.set_kernel(self.__kernel)
assert(_changedData['params'] is False) # we should never get here
if retrainable:
# we must assign it only if it is retrainable
self.ca.retrained = not newsvm or not newkernel
# Train
if __debug__ and 'SG' in debug.active:
if not self.__is_regression__:
lstr = " with labels %s" % targets_sa.unique
else:
lstr = ""
debug("SG", "%sTraining %s on data%s" %
(("","Re-")[retrainable and self.ca.retrained],
self, lstr))
self.__svm.train()
if __debug__:
debug("SG_", "Done training SG_SVM %s" % self)
# Report on training
if (__debug__ and 'SG__' in debug.active) or \
self.ca.is_enabled('training_stats'):
if __debug__:
debug("SG_", "Assessing predictions on training data")
trained_targets = self.__svm_apply().get_labels()
else:
trained_targets = None
if __debug__ and "SG__" in debug.active:
debug("SG__", "Original labels: %s, Trained labels: %s" %
(targets_sa.value, trained_targets))
# Assign training confusion right away here since we are ready
# to do so.
# XXX TODO use some other conditional attribute like 'trained_targets' and
# use it within base Classifier._posttrain to assign predictions
# instead of duplicating code here
# XXX For now it can be done only for regressions since labels need to
# be remapped and that becomes even worse if we use regression
# as a classifier so mapping happens upstairs
if self.__is_regression__ and self.ca.is_enabled('training_stats'):
self.ca.training_stats = self.__summary_class__(
targets=targets_sa.value,
predictions=trained_targets)
class SVM(_SVM):
"""Support Vector Machine Classifier(s) based on Shogun
This is a simple base interface
"""
__default_kernel_class__ = _default_kernel_class_
num_threads = Parameter(1,
min=1,
doc='Number of threads to utilize')
_KNOWN_PARAMS = [ 'epsilon' ]
__tags__ = _SVM.__tags__ + [ 'sg', 'retrainable' ]
# Some words of wisdom from shogun author:
# XXX remove after proper comments added to implementations
"""
If you'd like to train linear SVMs use SGD or OCAS. These are (I am
serious) the fastest linear SVM-solvers to date. (OCAS cannot do SVMs
with standard additive bias, but will L2 reqularize it - though it
should not matter much in practice (although it will give slightly
different solutions)). Note that SGD has no stopping criterion (you
simply have to specify the number of iterations) and that OCAS has a
different stopping condition than svmlight for example which may be more
tight and more loose depending on the problem - I sugeest 1e-2 or 1e-3
for epsilon.
If you would like to train kernel SVMs use libsvm/gpdt/svmlight -
depending on the problem one is faster than the other (hard to say when,
I *think* when your dataset is very unbalanced chunking methods like
svmlight/gpdt are better), for smaller problems definitely libsvm.
If you use string kernels then gpdt/svmlight have a special 'linadd'
speedup for this (requires sg 0.6.2 - there was some inefficiency in the
code for python-modular before that). This is effective for big datasets
and (I trained on 10 million strings based on this).
And yes currently we only implemented parallel training for svmlight,
however all SVMs can be evaluated in parallel.
"""
_KNOWN_SENSITIVITIES={'linear':LinearSVMWeights,
}
_KNOWN_IMPLEMENTATIONS = {}
if externals.exists('shogun', raise_=True):
_KNOWN_IMPLEMENTATIONS = {
"libsvm" : (shogun.Classifier.LibSVM, ('C',),
('multiclass', 'binary'),
"LIBSVM's C-SVM (L2 soft-margin SVM)"),
"gmnp" : (shogun.Classifier.GMNPSVM, ('C',),
('multiclass', 'binary'),
"Generalized Nearest Point Problem SVM"),
# XXX should have been GPDT, shogun has it fixed since some version
"gpbt" : (shogun.Classifier.GPBTSVM, ('C',), ('binary',),
"Gradient Projection Decomposition Technique for " \
"large-scale SVM problems"),
"gnpp" : (shogun.Classifier.GNPPSVM, ('C',), ('binary',),
"Generalized Nearest Point Problem SVM"),
## TODO: Needs sparse features...
# "svmlin" : (shogun.Classifier.SVMLin, ''),
# "liblinear" : (shogun.Classifier.LibLinear, ''),
# "subgradient" : (shogun.Classifier.SubGradientSVM, ''),
## good 2-class linear SVMs
# "ocas" : (shogun.Classifier.SVMOcas, ''),
# "sgd" : ( shogun.Classifier.SVMSGD, ''),
# regressions
"libsvr": (shogun.Regression.LibSVR, ('C', 'tube_epsilon',),
('regression',),
"LIBSVM's epsilon-SVR"),
}
def __init__(self, **kwargs):
"""Interface class to Shogun's classifiers and regressions.
Default implementation is 'libsvm'.
"""
svm_impl = kwargs.get('svm_impl', 'libsvm').lower()
kwargs['svm_impl'] = svm_impl
# init base class
_SVM.__init__(self, **kwargs)
self.__svm = None
"""Holds the trained svm."""
self.__svm_apply = None
"""Compatibility convenience to bind to the classify/apply method
of __svm"""
# Need to store original data...
# TODO: keep 1 of them -- just __traindata or __traindataset
# For now it is needed for computing sensitivities
self.__traindataset = None
# internal SG swig proxies
self.__traindata = None
self.__kernel = None
self.__kernel_test = None
self.__testdata = None
# remove kernel-based for some
# TODO RF: provide separate handling for non-kernel machines
if svm_impl in ['svmocas']:
if not (self.__kernel is None
or self.__kernel.__kernel_name__ == 'linear'):
raise ValueError(
"%s is inherently linear, thus provided kernel %s "
"is of no effect" % (svm_impl, self.__kernel))
self.__tags__.pop(self.__tags__.index('kernel-based'))
self.__tags__.pop(self.__tags__.index('retrainable'))
# TODO: integrate with kernel framework
#def __condition_kernel(self, kernel):
## XXX I thought that it is needed only for retrainable classifier,
## but then krr gets confused, and svrlight needs it to provide
## meaningful results even without 'retraining'
#if self._svm_impl in ['svrlight', 'lightsvm']:
#try:
#kernel.set_precompute_matrix(True, True)
#except Exception, e:
## N/A in shogun 0.9.1... TODO: RF
#if __debug__:
#debug('SG_', "Failed call to set_precompute_matrix for %s: %s"
#% (self, e))
def _train(self, dataset):
"""Train SVM
"""
# XXX watchout
# self.untrain()
newkernel, newsvm = False, False
# local bindings for faster lookup
params = self.params
retrainable = self.params.retrainable
targets_sa_name = self.get_space() # name of targets sa
targets_sa = dataset.sa[targets_sa_name] # actual targets sa
if retrainable:
_changedData = self._changedData
# LABELS
ul = None
self.__traindataset = dataset
# OK -- we have to map labels since
# binary ones expect -1/+1
# Multiclass expect labels starting with 0, otherwise they puke
# when ran from ipython... yikes
if __debug__:
debug("SG_", "Creating labels instance")
if self.__is_regression__:
labels_ = np.asarray(targets_sa.value, dtype='double')
else:
ul = targets_sa.unique
# ul.sort()
if len(ul) == 2:
# assure that we have -1/+1
_labels_dict = {ul[0]:-1.0, ul[1]:+1.0}
elif len(ul) < 2:
raise FailedToTrainError, \
"We do not have 1-class SVM brought into SG yet"
else:
# can't use plain enumerate since we need them swapped
_labels_dict = dict([ (ul[i], i) for i in range(len(ul))])
# Create SG-customized attrmap to assure -1 / +1 if necessary
self._attrmap = AttributeMap(_labels_dict, mapnumeric=True)
if __debug__:
debug("SG__", "Mapping labels using dict %s" % _labels_dict)
labels_ = self._attrmap.to_numeric(targets_sa.value).astype(float)
labels = shogun.Features.Labels(labels_)
_setdebug(labels, 'Labels')
# KERNEL
# XXX cruel fix for now... whole retraining business needs to
# be rethought
if retrainable:
_changedData['kernel_params'] = _changedData.get('kernel_params', False)
# TODO: big RF to move non-kernel classifiers away
if 'kernel-based' in self.__tags__ and (not retrainable
or _changedData['traindata'] or _changedData['kernel_params']):
# If needed compute or just collect arguments for SVM and for
# the kernel
if retrainable and __debug__:
if _changedData['traindata']:
debug("SG",
"Re-Creating kernel since training data has changed")
if _changedData['kernel_params']:
debug("SG",
"Re-Creating kernel since params %s has changed" %
_changedData['kernel_params'])
k = self.params.kernel
k.compute(dataset)
self.__kernel = kernel = k.as_raw_sg()
newkernel = True
self.kernel_params.reset() # mark them as not-changed
#_setdebug(kernel, 'Kernels')
#self.__condition_kernel(kernel)
if retrainable:
if __debug__:
debug("SG_", "Resetting test kernel for retrainable SVM")
self.__kernel_test = None
# TODO -- handle _changedData['params'] correctly, ie without recreating
# whole SVM
Cs = None
if not retrainable or self.__svm is None or _changedData['params']:
# SVM
if self.params.has_key('C'):
Cs = self._get_cvec(dataset)
# XXX do not jump over the head and leave it up to the user
# ie do not rescale automagically by the number of samples
#if len(Cs) == 2 and not ('regression' in self.__tags__) and len(ul) == 2:
# # we were given two Cs
# if np.max(C) < 0 and np.min(C) < 0:
# # and both are requested to be 'scaled' TODO :
# # provide proper 'features' to the parameters,
# # so we could specify explicitely if to scale
# # them by the number of samples here
# nl = [np.sum(labels_ == _labels_dict[l]) for l in ul]
# ratio = np.sqrt(float(nl[1]) / nl[0])
# #ratio = (float(nl[1]) / nl[0])
# Cs[0] *= ratio
# Cs[1] /= ratio
# if __debug__:
# debug("SG_", "Rescaled Cs to %s to accomodate the "
# "difference in number of training samples" %
# Cs)
# Choose appropriate implementation
svm_impl_class = self.__get_implementation(ul)
if __debug__:
debug("SG", "Creating SVM instance of %s" % `svm_impl_class`)
if self._svm_impl in ['libsvr', 'svrlight']:
# for regressions constructor a bit different
self.__svm = svm_impl_class(Cs[0], self.params.tube_epsilon, self.__kernel, labels)
# we need to set epsilon explicitly
self.__svm.set_epsilon(self.params.epsilon)
elif self._svm_impl in ['krr']:
self.__svm = svm_impl_class(self.params.tau, self.__kernel, labels)
elif 'kernel-based' in self.__tags__:
self.__svm = svm_impl_class(Cs[0], self.__kernel, labels)
self.__svm.set_epsilon(self.params.epsilon)
else:
traindata_sg = _tosg(dataset.samples)
self.__svm = svm_impl_class(Cs[0], traindata_sg, labels)
self.__svm.set_epsilon(self.params.epsilon)
# To stay compatible with versions across API changes in sg 1.0.0
self.__svm_apply = externals.versions['shogun'] >= '1' \
and self.__svm.apply \
or self.__svm.classify # the last one for old API
# Set shrinking
if 'shrinking' in params:
shrinking = params.shrinking
if __debug__:
debug("SG_", "Setting shrinking to %s" % shrinking)
self.__svm.set_shrinking_enabled(shrinking)
if Cs is not None and len(Cs) == 2:
if __debug__:
debug("SG_", "Since multiple Cs are provided: %s, assign them" % Cs)
self.__svm.set_C(Cs[0], Cs[1])
self.params.reset() # mark them as not-changed
newsvm = True
_setdebug(self.__svm, 'SVM')
# Set optimization parameters
if self.params.has_key('tube_epsilon') and \
hasattr(self.__svm, 'set_tube_epsilon'):
self.__svm.set_tube_epsilon(self.params.tube_epsilon)
self.__svm.parallel.set_num_threads(self.params.num_threads)
else:
if __debug__:
debug("SG_", "SVM instance is not re-created")
if _changedData['targets']: # labels were changed
if __debug__: debug("SG__", "Assigning new labels")
self.__svm.set_labels(labels)
if newkernel: # kernel was replaced
if __debug__: debug("SG__", "Assigning new kernel")
self.__svm.set_kernel(self.__kernel)
assert(_changedData['params'] is False) # we should never get here
if retrainable:
# we must assign it only if it is retrainable
self.ca.retrained = not newsvm or not newkernel
# Train
if __debug__ and 'SG' in debug.active:
if not self.__is_regression__:
lstr = " with labels %s" % targets_sa.unique
else:
lstr = ""
debug("SG", "%sTraining %s on data%s" %
(("","Re-")[retrainable and self.ca.retrained],
self, lstr))
self.__svm.train()
if __debug__:
debug("SG_", "Done training SG_SVM %s" % self)
# Report on training
if (__debug__ and 'SG__' in debug.active) or \
self.ca.is_enabled('training_stats'):
if __debug__:
debug("SG_", "Assessing predictions on training data")
trained_targets = self.__svm_apply().get_labels()
else:
trained_targets = None
if __debug__ and "SG__" in debug.active:
debug("SG__", "Original labels: %s, Trained labels: %s" %
(targets_sa.value, trained_targets))
# Assign training confusion right away here since we are ready
# to do so.
# XXX TODO use some other conditional attribute like 'trained_targets' and
# use it within base Classifier._posttrain to assign predictions
# instead of duplicating code here
# XXX For now it can be done only for regressions since labels need to
# be remapped and that becomes even worse if we use regression
# as a classifier so mapping happens upstairs
if self.__is_regression__ and self.ca.is_enabled('training_stats'):
self.ca.training_stats = self.__summary_class__(
targets=targets_sa.value,
predictions=trained_targets)
# XXX actually this is the beast which started this evil conversion
# so -- make use of dataset here! ;)
@accepts_samples_as_dataset
def _predict(self, dataset):
"""Predict values for the data
"""
retrainable = self.params.retrainable
if retrainable:
changed_testdata = self._changedData['testdata'] or \
self.__kernel_test is None
if not retrainable:
if __debug__:
debug("SG__",
"Initializing SVMs kernel of %s with training/testing samples"
% self)
self.params.kernel.compute(self.__traindataset, dataset)
self.__kernel_test = self.params.kernel.as_sg()._k
# We can just reuse kernel used for training
#self.__condition_kernel(self.__kernel)
else:
if changed_testdata:
#if __debug__:
#debug("SG__",
#"Re-creating testing kernel of %s giving "
#"arguments %s" %
#(`self._kernel_type`, self.__kernel_args))
self.params.kernel.compute(self.__traindataset, dataset)
#_setdebug(kernel_test, 'Kernels')
#_setdebug(kernel_test_custom, 'Kernels')
self.__kernel_test = self.params.kernel.as_raw_sg()
elif __debug__:
debug("SG__", "Re-using testing kernel")
assert(self.__kernel_test is not None)
if 'kernel-based' in self.__tags__:
self.__svm.set_kernel(self.__kernel_test)
# doesn't do any good imho although on unittests helps tiny bit... hm
#self.__svm.init_kernel_optimization()
values_ = self.__svm_apply()
else:
testdata_sg = _tosg(dataset.samples)
self.__svm.set_features(testdata_sg)
values_ = self.__svm_apply()
if __debug__:
debug("SG_", "Classifying testing data")
if values_ is None:
raise RuntimeError, "We got empty list of values from %s" % self
values = values_.get_labels()
if retrainable:
# we must assign it only if it is retrainable
self.ca.repredicted = repredicted = not changed_testdata
if __debug__:
debug("SG__", "Re-assigning learing kernel. Repredicted is %s"
% repredicted)
# return back original kernel
if 'kernel-based' in self.__tags__:
self.__svm.set_kernel(self.__kernel)
if __debug__:
debug("SG__", "Got values %s" % values)
if (self.__is_regression__):
predictions = values
else:
if len(self._attrmap.keys()) == 2:
predictions = np.sign(values)
# since np.sign(0) == 0
predictions[predictions==0] = 1
else:
predictions = values
# remap labels back adjusting their type
# XXX YOH: This is done by topclass now (needs RF)
#predictions = self._attrmap.to_literal(predictions)
if __debug__:
debug("SG__", "Tuned predictions %s" % predictions)
# store conditional attribute
# TODO: extract values properly for multiclass SVMs --
# ie 1 value per label or pairs for all 1-vs-1 classifications
self.ca.estimates = values
## to avoid leaks with not yet properly fixed shogun
if not retrainable:
try:
testdata.free_features()
except:
pass
return predictions
def _untrain(self):
super(SVM, self)._untrain()
# untrain/clean the kernel -- we might not allow to drag SWIG
# instance around BUT XXX -- make it work fine with
# CachedKernel -- we might not want to fully "untrain" in such
# case
self.params.kernel.cleanup() # XXX unify naming
if not self.params.retrainable:
if __debug__:
debug("SG__", "Untraining %(clf)s and destroying sg's SVM",
msgargs={'clf':self})
# to avoid leaks with not yet properly fixed shogun
# XXX make it nice... now it is just stable ;-)
if True: # not self.__traindata is None:
if True:
# try:
if self.__kernel is not None:
del self.__kernel
self.__kernel = None
if self.__kernel_test is not None:
del self.__kernel_test
self.__kernel_test = None
if self.__svm is not None:
del self.__svm
self.__svm = None
self.__svm_apply = None
if self.__traindata is not None:
# Let in for easy demonstration of the memory leak in shogun
#for i in xrange(10):
# debug("SG__", "cachesize pre free features %s" %
# (self.__svm.get_kernel().get_cache_size()))
self.__traindata.free_features()
del self.__traindata
self.__traindata = None
self.__traindataset = None
#except:
# pass
if __debug__:
debug("SG__",
"Done untraining %(self)s and destroying sg's SVM",
msgargs=locals())
elif __debug__:
debug("SG__", "Not untraining %(self)s since it is retrainable",
msgargs=locals())
def __get_implementation(self, ul):
if self.__is_regression__ or len(ul) == 2:
svm_impl_class = SVM._KNOWN_IMPLEMENTATIONS[self._svm_impl][0]
else:
if self._svm_impl == 'libsvm':
svm_impl_class = shogun.Classifier.LibSVMMultiClass
elif self._svm_impl == 'gmnp':
svm_impl_class = shogun.Classifier.GMNPSVM
else:
raise RuntimeError, \
"Shogun: Implementation %s doesn't handle multiclass " \
"data. Got labels %s. Use some other classifier" % \
(self._svm_impl,
self.__traindataset.sa[self.get_space()].unique)
if __debug__:
debug("SG_", "Using %s for multiclass data of %s" %
(svm_impl_class, self._svm_impl))
return svm_impl_class
svm = property(fget=lambda self: self.__svm)
"""Access to the SVM model."""
traindataset = property(fget=lambda self: self.__traindataset)
"""Dataset which was used for training
def plot_decision_boundary_2d(dataset, clf=None,
targets=None, regions=None, maps=None,
maps_res=50, vals=None,
data_callback=None):
"""Plot a scatter of a classifier's decision boundary and data points
Assumes data is 2d (no way to visualize otherwise!!)
Parameters
----------
dataset : `Dataset`
Data points to visualize (might be the data `clf` was train on, or
any novel data).
clf : `Classifier`, optional
Trained classifier
targets : string, optional
What samples attributes to use for targets. If None and clf is
provided, then `clf.params.targets_attr` is used.
regions : string, optional
Plot regions (polygons) around groups of samples with the same
attribute (and target attribute) values. E.g. chunks.
maps : string in {'targets', 'estimates'}, optional
Either plot underlying colored maps, such as clf predictions
within the spanned regions, or estimates from the classifier
(might not work for some).
maps_res : int, optional
Number of points in each direction to evaluate.
Points are between axis limits, which are set automatically by
matplotlib. Higher number will yield smoother decision lines but come
at the cost of O^2 classifying time/memory.
vals : array of floats, optional
Where to draw the contour lines if maps='estimates'
data_callback : callable, optional
Callable object to preprocess the new data points.
Classified points of the form samples = data_callback(xysamples).
I.e. this can be a function to normalize them, or cache them
before they are classified.
"""
if vals is None:
vals = [-1, 0, 1]
if False:
## from mvpa2.misc.data_generators import *
## from mvpa2.clfs.svm import *
## from mvpa2.clfs.knn import *
## ds = dumb_feature_binary_dataset()
dataset = normal_feature_dataset(nfeatures=2, nchunks=5,
snr=10, nlabels=4, means=[ [0,1], [1,0], [1,1], [0,0] ])
dataset.samples += dataset.sa.chunks[:, None]*0.1 # slight shifts for chunks ;)
#dataset = normal_feature_dataset(nfeatures=2, nlabels=3, means=[ [0,1], [1,0], [1,1] ])
#dataset = normal_feature_dataset(nfeatures=2, nlabels=2, means=[ [0,1], [1,0] ])
#clf = LinearCSVMC(C=-1)
clf = kNN(4)#LinearCSVMC(C=-1)
clf.train(dataset)
#clf = None
#plot_decision_boundary_2d(ds, clf)
targets = 'targets'
regions = 'chunks'
#maps = 'estimates'
maps = 'targets'
#maps = None #'targets'
res = 50
vals = [-1, 0, 1]
data_callback=None
pl.clf()
if dataset.nfeatures != 2:
raise ValueError('Can only plot a decision boundary in 2D')
Pioff()
a = pl.gca() # f.add_subplot(1,1,1)
attrmap = None
if clf:
estimates_were_enabled = clf.ca.is_enabled('estimates')
clf.ca.enable('estimates')
if targets is None:
targets = clf.get_space()
# Lets reuse classifiers attrmap if it is good enough
attrmap = clf._attrmap
predictions = clf.predict(dataset)
targets_sa_name = targets # bad Yarik -- will rebind targets to actual values
targets_lit = dataset.sa[targets_sa_name].value
utargets_lit = dataset.sa[targets_sa_name].unique
if not (attrmap is not None
and len(attrmap)
and set(clf._attrmap.keys()).issuperset(utargets_lit)):
# create our own
attrmap = AttributeMap(mapnumeric=True)
targets = attrmap.to_numeric(targets_lit)
utargets = attrmap.to_numeric(utargets_lit)
vmin = min(utargets)
vmax = max(utargets)
cmap = pl.cm.RdYlGn # argument
# Scatter points
if clf:
all_hits = predictions == targets_lit
else:
all_hits = np.ones((len(targets),), dtype=bool)
targets_colors = {}
for l in utargets:
targets_mask = targets==l
s = dataset[targets_mask]
targets_colors[l] = c \
= cmap((l-vmin)/float(vmax-vmin))
# We want to plot hits and misses with different symbols
hits = all_hits[targets_mask]
misses = np.logical_not(hits)
scatter_kwargs = dict(
c=[c], zorder=10+(l-vmin))
if sum(hits):
a.scatter(s.samples[hits, 0], s.samples[hits, 1], marker='o',
label='%s [%d]' % (attrmap.to_literal(l), sum(hits)),
**scatter_kwargs)
if sum(misses):
a.scatter(s.samples[misses, 0], s.samples[misses, 1], marker='x',
label='%s [%d] (miss)' % (attrmap.to_literal(l), sum(misses)),
edgecolor=[c], **scatter_kwargs)
(xmin, xmax) = a.get_xlim()
(ymin, ymax) = a.get_ylim()
extent = (xmin, xmax, ymin, ymax)
# Create grid to evaluate, predict it
(x,y) = np.mgrid[xmin:xmax:np.complex(0, maps_res),
ymin:ymax:np.complex(0, maps_res)]
news = np.vstack((x.ravel(), y.ravel())).T
try:
news = data_callback(news)
except TypeError: # Not a callable object
pass
imshow_kwargs = dict(origin='lower',
zorder=1,
aspect='auto',
interpolation='bilinear', alpha=0.9, cmap=cmap,
vmin=vmin, vmax=vmax,
extent=extent)
if maps is not None:
if clf is None:
raise ValueError, \
"Please provide classifier for plotting maps of %s" % maps
predictions_new = clf.predict(news)
if maps == 'estimates':
# Contour and show predictions
trained_targets = attrmap.to_numeric(clf.ca.trained_targets)
if len(trained_targets)==2:
linestyles = []
for v in vals:
if v == 0:
linestyles.append('solid')
else:
linestyles.append('dashed')
vmin, vmax = -3, 3 # Gives a nice tonal range ;)
map_ = 'estimates' # should actually depend on estimates
else:
vals = (trained_targets[:-1] + trained_targets[1:])/2.
linestyles = ['solid'] * len(vals)
map_ = 'targets'
try:
clf.ca.estimates.reshape(x.shape)
a.imshow(map_values.T, **imshow_kwargs)
CS = a.contour(x, y, map_values, vals, zorder=6,
linestyles=linestyles, extent=extent, colors='k')
except ValueError, e:
print "Sorry - plotting of estimates isn't full supported for %s. " \
"Got exception %s" % (clf, e)
except ValueError, e:
print "Sorry - plotting of estimates isn't full supported for %s. " \
"Got exception %s" % (clf, e)
elif maps == 'targets':
map_values = attrmap.to_numeric(predictions_new).reshape(x.shape)
a.imshow(map_values.T, **imshow_kwargs)
#CS = a.contour(x, y, map_values, vals, zorder=6,
# linestyles=linestyles, extent=extent, colors='k')
# Plot regions belonging to the same pair of attribute given
# (e.g. chunks) and targets attribute
if regions:
chunks_sa = dataset.sa[regions]
chunks_lit = chunks_sa.value
uchunks_lit = chunks_sa.value
chunks_attrmap = AttributeMap(mapnumeric=True)
chunks = chunks_attrmap.to_numeric(chunks_lit)
uchunks = chunks_attrmap.to_numeric(uchunks_lit)
from matplotlib.delaunay.triangulate import Triangulation
from matplotlib.patches import Polygon
# Lets figure out convex halls for each chunk/label pair
for target in utargets:
t_mask = targets == target
for chunk in uchunks:
tc_mask = np.logical_and(t_mask,
chunk == chunks)
tc_samples = dataset.samples[tc_mask]
tr = Triangulation(tc_samples[:, 0],
tc_samples[:, 1])
poly = pl.fill(tc_samples[tr.hull, 0],
def to_lightsvm_format(dataset, out, targets_attr='targets',
domain=None, am=None):
"""Export dataset into LightSVM format
Parameters
----------
dataset : Dataset
out
Anything understanding .write(string), such as `File`
targets_attr : string, optional
Name of the samples attribute to be output
domain : {None, 'regression', 'binary', 'multiclass'}, optional
What domain dataset belongs to. If `None`, it would be deduced
depending on the datatype ('regression' if float, classification
in case of int or string, with 'binary'/'multiclass' depending on
the number of unique targets)
am : `AttributeMap` or None, optional
Which mapping to use for storing the non-conformant targets. If
None was provided, new one would be automagically generated
depending on the given/deduced domain.
Returns
-------
am
LightSVM format is an ASCII representation with a single sample per
each line::
output featureIndex:featureValue ... featureIndex:featureValue
where ``output`` is specific for a given domain:
regression
float number
binary
integer labels from {-1, 1}
multiclass
integer labels from {1..ds.targets_attr.nunique}
"""
targets_a = dataset.sa[targets_attr]
targets = targets_a.value
# XXX this all below
# * might become cleaner
# * might be RF to become more generic to be used may be elsewhere as well
if domain is None:
if targets.dtype.kind in ['S', 'i']:
if len(targets_a.unique) == 2:
domain = 'binary'
else:
domain = 'multiclass'
else:
domain = 'regression'
if domain in ['multiclass', 'binary']:
# check if labels are appropriate and provide mapping if necessary
utargets = targets_a.unique
if domain == 'binary' and set(utargets) != set([-1, 1]):
# need mapping
if len(utargets) != 2:
raise ValueError, \
"We need 2 unique targets in %s of %s. Got targets " \
"from set %s" % (targets_attr, dataset, utargets)
if am is None:
am = AttributeMap(dict(zip(utargets, [-1, 1])))
elif set(am.keys()) != set([-1, 1]):
raise ValueError, \
"Provided %s doesn't map into binary " \
"labels -1,+1" % (am,)
elif domain == 'multiclass' \
and set(utargets) != set(range(1, len(utargets)+1)):
if am is None:
am = AttributeMap(dict(zip(utargets,
range(1, len(utargets) + 1))))
elif set(am.keys()) != set([-1, 1]):
raise ValueError, \
"Provided %s doesn't map into multiclass " \
"range 1..N" % (am, )
if am is not None:
# map the targets
targets = am.to_numeric(targets)
for t, s in zip(targets, dataset.samples):
out.write('%g %s\n'
% (t,
' '.join(
'%i:%.8g' % (i, v)
for i,v in zip(range(1, dataset.nfeatures+1), s))))
out.flush() # push it out
return am
def test_attrmap():
map_default = {'eins': 0, 'zwei': 2, 'sieben': 1}
map_custom = {'eins': 11, 'zwei': 22, 'sieben': 33}
literal = ['eins', 'zwei', 'sieben', 'eins', 'sieben', 'eins']
literal_nonmatching = ['uno', 'dos', 'tres']
num_default = [0, 2, 1, 0, 1, 0]
num_custom = [11, 22, 33, 11, 33, 11]
# no custom mapping given
am = AttributeMap()
assert_false(am)
ok_(len(am) == 0)
assert_array_equal(am.to_numeric(literal), num_default)
assert_array_equal(am.to_literal(num_default), literal)
ok_(am)
ok_(len(am) == 3)
#
# Tests for recursive mapping + preserving datatype
class myarray(np.ndarray):
pass
assert_raises(KeyError, am.to_literal, [(1, 2), 2, 0])
literal_fancy = [(1, 2), 2, [0], np.array([0, 1]).view(myarray)]
literal_fancy_tuple = tuple(literal_fancy)
literal_fancy_array = np.array(literal_fancy, dtype=object)
for l in (literal_fancy, literal_fancy_tuple, literal_fancy_array):
res = am.to_literal(l, recurse=True)
assert_equal(res[0], ('sieben', 'zwei'))
assert_equal(res[1], 'zwei')
assert_equal(res[2], ['eins'])
assert_array_equal(res[3], ['eins', 'sieben'])
# types of result and subsequences should be preserved
ok_(isinstance(res, l.__class__))
ok_(isinstance(res[0], tuple))
ok_(isinstance(res[1], str))
ok_(isinstance(res[2], list))
ok_(isinstance(res[3], myarray))
# yet another example
a = np.empty(1, dtype=object)
a[0] = (0, 1)
res = am.to_literal(a, recurse=True)
ok_(isinstance(res[0], tuple))
#
# with custom mapping
am = AttributeMap(map=map_custom)
assert_array_equal(am.to_numeric(literal), num_custom)
assert_array_equal(am.to_literal(num_custom), literal)
# if not numeric nothing is mapped
assert_array_equal(am.to_numeric(num_custom), num_custom)
# even if the map doesn't fit
assert_array_equal(am.to_numeric(num_default), num_default)
# need to_numeric first
am = AttributeMap()
assert_raises(RuntimeError, am.to_literal, [1, 2, 3])
# stupid args
assert_raises(ValueError, AttributeMap, map=num_custom)
# map mismatch
am = AttributeMap(map=map_custom)
if __debug__:
# checked only in __debug__
assert_raises(KeyError, am.to_numeric, literal_nonmatching)
# needs reset and should work afterwards
am.clear()
assert_array_equal(am.to_numeric(literal_nonmatching), [2, 0, 1])
# and now reverse
am = AttributeMap(map=map_custom)
assert_raises(KeyError, am.to_literal, num_default)
# dict-like interface
am = AttributeMap()
ok_([(k, v) for k, v in am.items()] == [])
class Classifier(Learner):
"""Abstract classifier class to be inherited by all classifiers
"""
# Kept separate from doc to don't pollute help(clf), especially if
# we including help for the parent class
_DEV__doc__ = """
Required behavior:
For every classifier is has to be possible to be instantiated without
having to specify the training pattern.
Repeated calls to the train() method with different training data have to
result in a valid classifier, trained for the particular dataset.
It must be possible to specify all classifier parameters as keyword
arguments to the constructor.
Recommended behavior:
Derived classifiers should provide access to *estimates* -- i.e. that
information that is finally used to determine the predicted class label.
Michael: Maybe it works well if each classifier provides a 'estimates'
state member. This variable is a list as long as and in same order
as Dataset.uniquetargets (training data). Each item in the list
corresponds to the likelyhood of a sample to belong to the
respective class. However the semantics might differ between
classifiers, e.g. kNN would probably store distances to class-
neighbors, where PLR would store the raw function value of the
logistic function. So in the case of kNN low is predictive and for
PLR high is predictive. Don't know if there is the need to unify
that.
As the storage and/or computation of this information might be
demanding its collection should be switchable and off be default.
Nomenclature
* predictions : result of the last call to .predict()
* estimates : might be different from predictions if a classifier's predict()
makes a decision based on some internal value such as
probability or a distance.
"""
# Dict that contains the parameters of a classifier.
# This shall provide an interface to plug generic parameter optimizer
# on all classifiers (e.g. grid- or line-search optimizer)
# A dictionary is used because Michael thinks that access by name is nicer.
# Additionally Michael thinks ATM that additional information might be
# necessary in some situations (e.g. reasonably predefined parameter range,
# minimal iteration stepsize, ...), therefore the value to each key should
# also be a dict or we should use mvpa2.base.param.Parameter'...
training_stats = ConditionalAttribute(
enabled=False, doc="Confusion matrix of learning performance")
predictions = ConditionalAttribute(enabled=True,
doc="Most recent set of predictions")
estimates = ConditionalAttribute(
enabled=True,
doc="Internal classifier estimates the most recent " +
"predictions are based on")
predicting_time = ConditionalAttribute(
enabled=True, doc="Time (in seconds) which took classifier to predict")
__tags__ = []
"""Describes some specifics about the classifier -- is that it is
doing regression for instance...."""
# TODO: make it available only for actually retrainable classifiers
retrainable = Parameter(
False,
constraints='bool',
doc="""Either to enable retraining for 'retrainable' classifier.""",
index=1002)
def __init__(self, space=None, **kwargs):
# by default we want classifiers to use the 'targets' sample attribute
# for training/testing
if space is None:
space = 'targets'
Learner.__init__(self, space=space, **kwargs)
# XXX
# the place to map literal to numerical labels (and back)
# this needs to be in the base class, since some classifiers also
# have this nasty 'regression' mode, and the code in this class
# needs to deal with converting the regression output into discrete
# labels
# however, preferably the mapping should be kept in the respective
# low-level implementations that need it
self._attrmap = AttributeMap()
self.__trainednfeatures = 0
"""Stores number of features for which classifier was trained.
If 0 -- it wasn't trained at all"""
self._set_retrainable(self.params.retrainable, force=True)
# deprecate
#self.__trainedidhash = None
#"""Stores id of the dataset on which it was trained to signal
#in trained() if it was trained already on the same dataset"""
@property
def __summary_class__(self):
if 'regression' in self.__tags__:
return RegressionStatistics
else:
return ConfusionMatrix
@property
def __is_regression__(self):
return 'regression' in self.__tags__
def __str__(self, *args, **kwargs):
if __debug__ and 'CLF_' in debug.active:
return "%s / %s" % (repr(self), super(Classifier, self).__str__())
else:
return _str(self, *args, **kwargs)
def _pretrain(self, dataset):
"""Functionality prior to training
"""
# So we reset all conditional attributes and may be free up some memory
# explicitly
params = self.params
if not params.retrainable:
self.untrain()
else:
# just reset the ca, do not untrain
self.ca.reset()
if not self.__changedData_isset:
self.__reset_changed_data()
_changedData = self._changedData
__idhashes = self.__idhashes
__invalidatedChangedData = self.__invalidatedChangedData
# if we don't know what was changed we need to figure
# them out
if __debug__:
debug('CLF_', "IDHashes are %s", (__idhashes, ))
# Look at the data if any was changed
for key, data_ in (('traindata', dataset.samples),
('targets',
dataset.sa[self.get_space()].value)):
_changedData[key] = self.__was_data_changed(key, data_)
# if those idhashes were invalidated by retraining
# we need to adjust _changedData accordingly
if __invalidatedChangedData.get(key, False):
if __debug__ and not _changedData[key]:
debug(
'CLF_', 'Found that idhash for %s was '
'invalidated by retraining', (key, ))
_changedData[key] = True
# Look at the parameters
for col in self._paramscols:
changedParams = self._collections[col].which_set()
if len(changedParams):
_changedData[col] = changedParams
self.__invalidatedChangedData = {} # reset it on training
if __debug__:
debug('CLF_', "Obtained _changedData is %s",
(self._changedData, ))
def _posttrain(self, dataset):
"""Functionality post training
For instance -- computing confusion matrix.
Parameters
----------
dataset : Dataset
Data which was used for training
"""
super(Classifier, self)._posttrain(dataset)
ca = self.ca
# needs to be assigned first since below we use predict
self.__trainednfeatures = dataset.nfeatures
if __debug__ and 'CHECK_TRAINED' in debug.active:
self.__trainedidhash = dataset.idhash
if ca.is_enabled('training_stats') and \
not ca.is_set('training_stats'):
# we should not store predictions for training data,
# it is confusing imho (yoh)
ca.change_temporarily(disable_ca=["predictions"])
if self.params.retrainable:
# we would need to recheck if data is the same,
# XXX think if there is a way to make this all
# efficient. For now, probably, retrainable
# classifiers have no chance but not to use
# training_stats... sad
self.__changedData_isset = False
predictions = self.predict(dataset)
ca.reset_changed_temporarily()
targets = dataset.sa[self.get_space()].value
if is_datasetlike(predictions) and (self.get_space()
in predictions.fa):
# e.g. in case of pair-wise uncombined results - provide
# stats per each of the targets pairs
prediction_targets = predictions.fa[self.get_space()].value
ca.training_stats = dict(
(t,
self.__summary_class__(targets=targets,
predictions=predictions.samples[:,
i])
) for i, t in enumerate(prediction_targets))
else:
ca.training_stats = self.__summary_class__(
targets=targets, predictions=predictions)
def summary(self):
"""Providing summary over the classifier"""
s = "Classifier %s" % self
ca = self.ca
ca_enabled = ca.enabled
if self.trained:
s += "\n trained"
if ca.is_set('training_time'):
s += ' in %.3g sec' % ca.training_time
s += ' on data with'
if ca.is_set('trained_targets'):
s += ' targets:%s' % list(ca.trained_targets)
nsamples, nchunks = None, None
if ca.is_set('trained_nsamples'):
nsamples = ca.trained_nsamples
if ca.is_set('trained_dataset'):
td = ca.trained_dataset
nsamples, nchunks = td.nsamples, len(td.sa['chunks'].unique)
if nsamples is not None:
s += ' #samples:%d' % nsamples
if nchunks is not None:
s += ' #chunks:%d' % nchunks
s += " #features:%d" % self.__trainednfeatures
if ca.is_set('training_stats'):
s += ", training error:%.3g" % ca.training_stats.error
else:
s += "\n not yet trained"
if len(ca_enabled):
s += "\n enabled ca:%s" % ', '.join(
[str(ca[x]) for x in ca_enabled])
return s
def clone(self):
"""Create full copy of the classifier.
It might require classifier to be untrained first due to
present SWIG bindings.
TODO: think about proper re-implementation, without enrollment of deepcopy
"""
if __debug__:
debug("CLF", "Cloning %s%s", (self, _strid(self)))
try:
return deepcopy(self)
except:
self.untrain()
return deepcopy(self)
def _train(self, dataset):
"""Function to be actually overridden in derived classes
"""
raise NotImplementedError
def _prepredict(self, dataset):
"""Functionality prior prediction
"""
if not ('notrain2predict' in self.__tags__):
# check if classifier was trained if that is needed
if not self.trained:
raise FailedToPredictError(
"Classifier %s wasn't yet trained, therefore can't "
"predict" % self)
nfeatures = dataset.nfeatures #data.shape[1]
# check if number of features is the same as in the data
# it was trained on
if nfeatures != self.__trainednfeatures:
raise ValueError("Classifier %s was trained on data with %d features, " % \
(self, self.__trainednfeatures) + \
"thus can't predict for %d features" % nfeatures)
if self.params.retrainable:
if not self.__changedData_isset:
self.__reset_changed_data()
_changedData = self._changedData
data = np.asanyarray(dataset.samples)
_changedData['testdata'] = \
self.__was_data_changed('testdata', data)
if __debug__:
debug('CLF_', "prepredict: Obtained _changedData is %s",
(_changedData, ))
def _postpredict(self, dataset, result):
"""Functionality after prediction is computed
"""
self.ca.predictions = result
if self.params.retrainable:
self.__changedData_isset = False
def _predict(self, dataset):
"""Actual prediction
"""
raise NotImplementedError
@accepts_samples_as_dataset
def predict(self, dataset):
"""Predict classifier on data
Shouldn't be overridden in subclasses unless explicitly needed
to do so. Also subclasses trying to call super class's predict
should call _predict if within _predict instead of predict()
since otherwise it would loop
"""
## ??? yoh: changed to asany from as without exhaustive check
data = np.asanyarray(dataset.samples)
if __debug__:
# Verify that we have no NaN/Inf's which we do not "support" ATM
if not np.all(np.isfinite(data)):
raise ValueError(
"Some input data for predict is not finite (NaN or Inf)")
debug("CLF", "Predicting classifier %s on ds %s", (self, dataset))
# remember the time when started computing predictions
t0 = time.time()
ca = self.ca
# to assure that those are reset (could be set due to testing
# post-training)
ca.reset(['estimates', 'predictions'])
self._prepredict(dataset)
if self.__trainednfeatures > 0 \
or 'notrain2predict' in self.__tags__:
result = self._predict(dataset)
else:
warning(
"Trying to predict using classifier trained on no features")
if __debug__:
debug("CLF",
"No features were present for training, prediction is " \
"bogus")
result = [None] * data.shape[0]
ca.predicting_time = time.time() - t0
# with labels mapping in-place, we also need to go back to the
# literal labels
if self._attrmap:
try:
result = self._attrmap.to_literal(result)
except KeyError as e:
raise FailedToPredictError("Failed to convert predictions from numeric into " \
"literals: %s" % e)
self._postpredict(dataset, result)
return result
def _call(self, ds):
# get the predictions
# call with full dataset, since we might need it further down in
# the stream, e.g. for caching...
pred = self.predict(ds)
tattr = self.get_space()
# return the predictions and the targets in a dataset
if isinstance(pred, Dataset):
# it is already a dataset, e.g. as if we did not
# use any combiner for MulticlassClassifier
# to look at each pair
pred.sa[tattr] = ds.sa[tattr]
return pred
else:
return Dataset(pred, sa={tattr: ds.sa[tattr]})
# XXX deprecate ???
##REF: Name was automagically refactored
def is_trained(self, dataset=None):
"""Either classifier was already trained.
MUST BE USED WITH CARE IF EVER"""
if dataset is None:
# simply return if it was trained on anything
return not self.__trainednfeatures == 0
else:
res = (self.__trainednfeatures == dataset.nfeatures)
if __debug__ and 'CHECK_TRAINED' in debug.active:
res2 = (self.__trainedidhash == dataset.idhash)
if res2 != res:
raise RuntimeError("is_trained is weak and shouldn't be relied upon. " \
"Got result %b although comparing of idhash says %b" \
% (res, res2))
return res
@property
def trained(self):
"""Either classifier was already trained"""
return self.is_trained()
def _untrain(self):
"""Reset trained state"""
# any previous apping is obsolete now
self._attrmap.clear()
self.__trainednfeatures = 0
# probably not needed... retrainable shouldn't be fully untrained
# or should be???
#if self.params.retrainable:
# # ??? don't duplicate the code ;-)
# self.__idhashes = {'traindata': None, 'targets': None,
# 'testdata': None, 'testtraindata': None}
# no need to do this, as the Leaner class is doing it anyway
#super(Classifier, self).reset()
##REF: Name was automagically refactored
def get_sensitivity_analyzer(self, **kwargs):
"""Factory method to return an appropriate sensitivity analyzer for
the respective classifier."""
raise NotImplementedError
#
# Methods which are needed for retrainable classifiers
#
##REF: Name was automagically refactored
def _set_retrainable(self, value, force=False):
"""Assign value of retrainable parameter
If retrainable flag is to be changed, classifier has to be
untrained. Also internal attributes such as _changedData,
__changedData_isset, and __idhashes should be initialized if
it becomes retrainable
"""
pretrainable = self.params['retrainable']
if (force or value != pretrainable.value) \
and 'retrainable' in self.__tags__:
if __debug__:
debug("CLF_", "Setting retrainable to %s" % value)
if 'meta' in self.__tags__:
warning("Retrainability is not yet crafted/tested for "
"meta classifiers. Unpredictable behavior might occur")
# assure that we don't drag anything behind
if self.trained:
self.untrain()
ca = self.ca
if not value and 'retrained' in ca:
ca.pop('retrained')
ca.pop('repredicted')
if value:
if not 'retrainable' in self.__tags__:
warning(
"Setting of flag retrainable for %s has no effect"
" since classifier has no such capability. It would"
" just lead to resources consumption and slowdown" %
self)
ca['retrained'] = ConditionalAttribute(
enabled=True,
doc="Either retrainable classifier was retrained")
ca['repredicted'] = ConditionalAttribute(
enabled=True,
doc="Either retrainable classifier was repredicted")
pretrainable.value = value
# if retrainable we need to keep track of things
if value:
self.__idhashes = {
'traindata': None,
'targets': None,
'testdata': None
} #, 'testtraindata': None}
if __debug__ and 'CHECK_RETRAIN' in debug.active:
# ??? it is not clear though if idhash is faster than
# simple comparison of (dataset != __traineddataset).any(),
# but if we like to get rid of __traineddataset then we
# should use idhash anyways
self.__trained = self.__idhashes.copy() # just same Nones
self.__reset_changed_data()
self.__invalidatedChangedData = {}
elif 'retrainable' in self.__tags__:
#self.__reset_changed_data()
self.__changedData_isset = False
self._changedData = None
self.__idhashes = None
if __debug__ and 'CHECK_RETRAIN' in debug.active:
self.__trained = None
##REF: Name was automagically refactored
def __reset_changed_data(self):
"""For retrainable classifier we keep track of what was changed
This function resets that dictionary
"""
if __debug__:
debug('CLF_',
'Retrainable: resetting flags on either data was changed')
keys = list(self.__idhashes.keys()) + self._paramscols
# we might like to just reinit estimates to False???
#_changedData = self._changedData
#if isinstance(_changedData, dict):
# for key in _changedData.keys():
# _changedData[key] = False
self._changedData = dict(list(zip(keys, [False] * len(keys))))
self.__changedData_isset = False
##REF: Name was automagically refactored
def __was_data_changed(self, key, entry, update=True):
"""Check if given entry was changed from what known prior.
If so -- store only the ones needed for retrainable beastie
"""
idhash_ = idhash(entry)
__idhashes = self.__idhashes
changed = __idhashes[key] != idhash_
if __debug__ and 'CHECK_RETRAIN' in debug.active:
__trained = self.__trained
changed2 = entry != __trained[key]
if isinstance(changed2, np.ndarray):
changed2 = changed2.any()
if changed != changed2 and not changed:
raise RuntimeError('idhash found to be weak for %s. Though hashid %s!=%s %s, '\
'estimates %s!=%s %s' % \
(key, idhash_, __idhashes[key], changed,
entry, __trained[key], changed2))
if update:
__trained[key] = entry
if __debug__ and changed:
debug('CLF_', "Changed %s from %s to %s.%s",
(key, __idhashes[key], idhash_,
('', 'updated')[int(update)]))
if update:
__idhashes[key] = idhash_
return changed
# def __updateHashIds(self, key, data):
# """Is twofold operation: updates hashid if was said that it changed.
#
# or if it wasn't said that data changed, but CHECK_RETRAIN and it found
# to be changed -- raise Exception
# """
#
# check_retrain = __debug__ and 'CHECK_RETRAIN' in debug.active
# chd = self._changedData
#
# # we need to updated idhashes
# if chd[key] or check_retrain:
# keychanged = self.__was_data_changed(key, data)
# if check_retrain and keychanged and not chd[key]:
# raise RuntimeError, \
# "Data %s found changed although wasn't " \
# "labeled as such" % key
#
# Additional API which is specific only for retrainable classifiers.
# For now it would just puke if asked from not retrainable one.
#
# Might come useful and efficient for statistics testing, so if just
# labels of dataset changed, then
# self.retrain(dataset, labels=True)
# would cause efficient retraining (no kernels recomputed etc)
# and subsequent self.repredict(data) should be also quite fase ;-)
def retrain(self, dataset, **kwargs):
"""Helper to avoid check if data was changed actually changed
Useful if just some aspects of classifier were changed since
its previous training. For instance if dataset wasn't changed
but only classifier parameters, then kernel matrix does not
have to be computed.
Words of caution: classifier must be previously trained,
results always should first be compared to the results on not
'retrainable' classifier (without calling retrain). Some
additional checks are enabled if debug id 'CHECK_RETRAIN' is
enabled, to guard against obvious mistakes.
Parameters
----------
kwargs
that is what _changedData gets updated with. So, smth like
`(params=['C'], targets=True)` if parameter C and targets
got changed
"""
# Note that it also demolishes anything for repredicting,
# which should be ok in most of the cases
if __debug__:
if not self.params.retrainable:
raise RuntimeError("Do not use re(train,predict) on non-retrainable %s" % \
self)
if 'params' in kwargs or 'kernel_params' in kwargs:
raise ValueError(
"Retraining for changed params not working yet")
self.__reset_changed_data()
# local bindings
chd = self._changedData
ichd = self.__invalidatedChangedData
chd.update(kwargs)
# mark for future 'train()' items which are explicitely
# mentioned as changed
for key, value in kwargs.items():
if value:
ichd[key] = True
self.__changedData_isset = True
# To check if we are not fooled
if __debug__ and 'CHECK_RETRAIN' in debug.active:
for key, data_ in (('traindata', dataset.samples),
('targets',
dataset.sa[self.get_space()].value)):
# so it wasn't told to be invalid
if not chd[key] and not ichd.get(key, False):
if self.__was_data_changed(key, data_, update=False):
raise RuntimeError("Data %s found changed although wasn't " \
"labeled as such" % key)
# TODO: parameters of classifiers... for now there is explicit
# 'forbidance' above
# Below check should be superseeded by check above, thus never occur.
# remove later on ???
if __debug__ and 'CHECK_RETRAIN' in debug.active and self.trained \
and not self._changedData['traindata'] \
and self.__trained['traindata'].shape != dataset.samples.shape:
raise ValueError("In retrain got dataset with %s size, " \
"whenever previousely was trained on %s size" \
% (dataset.samples.shape, self.__trained['traindata'].shape))
self.train(dataset)
@accepts_samples_as_dataset
def repredict(self, dataset, **kwargs):
"""Helper to avoid check if data was changed actually changed
Useful if classifier was (re)trained but with the same data
(so just parameters were changed), so that it could be
repredicted easily (on the same data as before) without
recomputing for instance train/test kernel matrix. Should be
used with caution and always compared to the results on not
'retrainable' classifier. Some additional checks are enabled
if debug id 'CHECK_RETRAIN' is enabled, to guard against
obvious mistakes.
Parameters
----------
dataset
dataset which is conventionally given to predict
kwargs
that is what _changedData gets updated with. So, smth like
`(params=['C'], targets=True)` if parameter C and targets
got changed
"""
if len(kwargs) > 0:
raise RuntimeError("repredict for now should be used without params since " \
"it makes little sense to repredict if anything got changed")
if __debug__ and not self.params.retrainable:
raise RuntimeError(
"Do not use retrain/repredict on non-retrainable classifiers")
self.__reset_changed_data()
chd = self._changedData
chd.update(**kwargs)
self.__changedData_isset = True
# check if we are attempted to perform on the same data
if __debug__ and 'CHECK_RETRAIN' in debug.active:
for key, data_ in (('testdata', dataset.samples), ):
# so it wasn't told to be invalid
#if not chd[key]:# and not ichd.get(key, False):
if self.__was_data_changed(key, data_, update=False):
raise RuntimeError("Data %s found changed although wasn't " \
"labeled as such" % key)
# Should be superseded by above
# remove in future???
if __debug__ and 'CHECK_RETRAIN' in debug.active \
and not self._changedData['testdata'] \
and self.__trained['testdata'].shape != dataset.samples.shape:
raise ValueError("In repredict got dataset with %s size, " \
"whenever previously was trained on %s size" \
% (dataset.samples.shape, self.__trained['testdata'].shape))
return self.predict(dataset)
class Classifier(Learner):
"""Abstract classifier class to be inherited by all classifiers
"""
# Kept separate from doc to don't pollute help(clf), especially if
# we including help for the parent class
_DEV__doc__ = """
Required behavior:
For every classifier is has to be possible to be instantiated without
having to specify the training pattern.
Repeated calls to the train() method with different training data have to
result in a valid classifier, trained for the particular dataset.
It must be possible to specify all classifier parameters as keyword
arguments to the constructor.
Recommended behavior:
Derived classifiers should provide access to *estimates* -- i.e. that
information that is finally used to determine the predicted class label.
Michael: Maybe it works well if each classifier provides a 'estimates'
state member. This variable is a list as long as and in same order
as Dataset.uniquetargets (training data). Each item in the list
corresponds to the likelyhood of a sample to belong to the
respective class. However the semantics might differ between
classifiers, e.g. kNN would probably store distances to class-
neighbors, where PLR would store the raw function value of the
logistic function. So in the case of kNN low is predictive and for
PLR high is predictive. Don't know if there is the need to unify
that.
As the storage and/or computation of this information might be
demanding its collection should be switchable and off be default.
Nomenclature
* predictions : result of the last call to .predict()
* estimates : might be different from predictions if a classifier's predict()
makes a decision based on some internal value such as
probability or a distance.
"""
# Dict that contains the parameters of a classifier.
# This shall provide an interface to plug generic parameter optimizer
# on all classifiers (e.g. grid- or line-search optimizer)
# A dictionary is used because Michael thinks that access by name is nicer.
# Additionally Michael thinks ATM that additional information might be
# necessary in some situations (e.g. reasonably predefined parameter range,
# minimal iteration stepsize, ...), therefore the value to each key should
# also be a dict or we should use mvpa2.base.param.Parameter'...
training_stats = ConditionalAttribute(
enabled=False, doc="Confusion matrix of learning performance")
predictions = ConditionalAttribute(enabled=True,
doc="Most recent set of predictions")
estimates = ConditionalAttribute(
enabled=True,
doc="Internal classifier estimates the most recent " +
"predictions are based on")
predicting_time = ConditionalAttribute(
enabled=True, doc="Time (in seconds) which took classifier to predict")
__tags__ = []
"""Describes some specifics about the classifier -- is that it is
doing regression for instance...."""
# TODO: make it available only for actually retrainable classifiers
retrainable = Parameter(
False,
constraints='bool',
doc="""Either to enable retraining for 'retrainable' classifier.""",
index=1002)
def __init__(self, space=None, **kwargs):
# by default we want classifiers to use the 'targets' sample attribute
# for training/testing
if space is None:
space = 'targets'
Learner.__init__(self, space=space, **kwargs)
# XXX
# the place to map literal to numerical labels (and back)
# this needs to be in the base class, since some classifiers also
# have this nasty 'regression' mode, and the code in this class
# needs to deal with converting the regression output into discrete
# labels
# however, preferably the mapping should be kept in the respective
# low-level implementations that need it
self._attrmap = AttributeMap()
self.__trainednfeatures = 0
"""Stores number of features for which classifier was trained.
If 0 -- it wasn't trained at all"""
self._set_retrainable(self.params.retrainable, force=True)
# deprecate
#self.__trainedidhash = None
#"""Stores id of the dataset on which it was trained to signal
#in trained() if it was trained already on the same dataset"""
@property
def __summary_class__(self):
if 'regression' in self.__tags__:
return RegressionStatistics
else:
return ConfusionMatrix
@property
def __is_regression__(self):
return 'regression' in self.__tags__
def __str__(self, *args, **kwargs):
if __debug__ and 'CLF_' in debug.active:
return "%s / %s" % (repr(self), super(Classifier, self).__str__())
else:
return _str(self, *args, **kwargs)
def _pretrain(self, dataset):
"""Functionality prior to training
"""
# So we reset all conditional attributes and may be free up some memory
# explicitly
params = self.params
if not params.retrainable:
self.untrain()
else:
# just reset the ca, do not untrain
self.ca.reset()
if not self.__changedData_isset:
self.__reset_changed_data()
_changedData = self._changedData
__idhashes = self.__idhashes
__invalidatedChangedData = self.__invalidatedChangedData
# if we don't know what was changed we need to figure
# them out
if __debug__:
debug('CLF_', "IDHashes are %s", (__idhashes, ))
# Look at the data if any was changed
for key, data_ in (('traindata', dataset.samples),
('targets',
dataset.sa[self.get_space()].value)):
_changedData[key] = self.__was_data_changed(key, data_)
# if those idhashes were invalidated by retraining
# we need to adjust _changedData accordingly
if __invalidatedChangedData.get(key, False):
if __debug__ and not _changedData[key]:
debug(
'CLF_', 'Found that idhash for %s was '
'invalidated by retraining', (key, ))
_changedData[key] = True
# Look at the parameters
for col in self._paramscols:
changedParams = self._collections[col].which_set()
if len(changedParams):
_changedData[col] = changedParams
self.__invalidatedChangedData = {} # reset it on training
if __debug__:
debug('CLF_', "Obtained _changedData is %s",
(self._changedData, ))
def _posttrain(self, dataset):
"""Functionality post training
For instance -- computing confusion matrix.
Parameters
----------
dataset : Dataset
Data which was used for training
"""
super(Classifier, self)._posttrain(dataset)
ca = self.ca
# needs to be assigned first since below we use predict
self.__trainednfeatures = dataset.nfeatures
if __debug__ and 'CHECK_TRAINED' in debug.active:
self.__trainedidhash = dataset.idhash
if ca.is_enabled('training_stats') and \
not ca.is_set('training_stats'):
# we should not store predictions for training data,
# it is confusing imho (yoh)
ca.change_temporarily(disable_ca=["predictions"])
if self.params.retrainable:
# we would need to recheck if data is the same,
# XXX think if there is a way to make this all
# efficient. For now, probably, retrainable
# classifiers have no chance but not to use
# training_stats... sad
self.__changedData_isset = False
predictions = self.predict(dataset)
ca.reset_changed_temporarily()
targets = dataset.sa[self.get_space()].value
if is_datasetlike(predictions) and (self.get_space()
in predictions.fa):
# e.g. in case of pair-wise uncombined results - provide
# stats per each of the targets pairs
prediction_targets = predictions.fa[self.get_space()].value
ca.training_stats = dict(
(t,
self.__summary_class__(targets=targets,
predictions=predictions.samples[:,
i])
) for i, t in enumerate(prediction_targets))
else:
ca.training_stats = self.__summary_class__(
targets=targets, predictions=predictions)
def summary(self):
"""Providing summary over the classifier"""
s = "Classifier %s" % self
ca = self.ca
ca_enabled = ca.enabled
if self.trained:
s += "\n trained"
if ca.is_set('training_time'):
s += ' in %.3g sec' % ca.training_time
s += ' on data with'
if ca.is_set('trained_targets'):
s += ' targets:%s' % list(ca.trained_targets)
nsamples, nchunks = None, None
if ca.is_set('trained_nsamples'):
nsamples = ca.trained_nsamples
if ca.is_set('trained_dataset'):
td = ca.trained_dataset
nsamples, nchunks = td.nsamples, len(td.sa['chunks'].unique)
if nsamples is not None:
s += ' #samples:%d' % nsamples
if nchunks is not None:
s += ' #chunks:%d' % nchunks
s += " #features:%d" % self.__trainednfeatures
if ca.is_set('training_stats'):
s += ", training error:%.3g" % ca.training_stats.error
else:
s += "\n not yet trained"
if len(ca_enabled):
s += "\n enabled ca:%s" % ', '.join(
[str(ca[x]) for x in ca_enabled])
return s
def clone(self):
"""Create full copy of the classifier.
It might require classifier to be untrained first due to
present SWIG bindings.
TODO: think about proper re-implementation, without enrollment of deepcopy
"""
if __debug__:
debug("CLF", "Cloning %s%s", (self, _strid(self)))
try:
return deepcopy(self)
except:
self.untrain()
return deepcopy(self)
def _train(self, dataset):
"""Function to be actually overridden in derived classes
"""
raise NotImplementedError
def _prepredict(self, dataset):
"""Functionality prior prediction
"""
if not ('notrain2predict' in self.__tags__):
# check if classifier was trained if that is needed
if not self.trained:
raise FailedToPredictError(
"Classifier %s wasn't yet trained, therefore can't "
"predict" % self)
nfeatures = dataset.nfeatures #data.shape[1]
# check if number of features is the same as in the data
# it was trained on
if nfeatures != self.__trainednfeatures:
raise ValueError, \
"Classifier %s was trained on data with %d features, " % \
(self, self.__trainednfeatures) + \
"thus can't predict for %d features" % nfeatures
if self.params.retrainable:
if not self.__changedData_isset:
self.__reset_changed_data()
_changedData = self._changedData
data = np.asanyarray(dataset.samples)
_changedData['testdata'] = \
self.__was_data_changed('testdata', data)
if __debug__:
debug('CLF_', "prepredict: Obtained _changedData is %s",
(_changedData, ))
def _postpredict(self, dataset, result):
"""Functionality after prediction is computed
"""
self.ca.predictions = result
if self.params.retrainable:
self.__changedData_isset = False
def _predict(self, dataset):
"""Actual prediction
"""
raise NotImplementedError
@accepts_samples_as_dataset
def predict(self, dataset):
"""Predict classifier on data
Shouldn't be overridden in subclasses unless explicitly needed
to do so. Also subclasses trying to call super class's predict
should call _predict if within _predict instead of predict()
since otherwise it would loop
"""
## ??? yoh: changed to asany from as without exhaustive check
data = np.asanyarray(dataset.samples)
if __debug__:
# Verify that we have no NaN/Inf's which we do not "support" ATM
if not np.all(np.isfinite(data)):
raise ValueError(
"Some input data for predict is not finite (NaN or Inf)")
debug("CLF", "Predicting classifier %s on ds %s", (self, dataset))
# remember the time when started computing predictions
t0 = time.time()
ca = self.ca
# to assure that those are reset (could be set due to testing
# post-training)
ca.reset(['estimates', 'predictions'])
self._prepredict(dataset)
if self.__trainednfeatures > 0 \
or 'notrain2predict' in self.__tags__:
result = self._predict(dataset)
else:
warning(
"Trying to predict using classifier trained on no features")
if __debug__:
debug("CLF",
"No features were present for training, prediction is " \
"bogus")
result = [None] * data.shape[0]
ca.predicting_time = time.time() - t0
# with labels mapping in-place, we also need to go back to the
# literal labels
if self._attrmap:
try:
result = self._attrmap.to_literal(result)
except KeyError, e:
raise FailedToPredictError, \
"Failed to convert predictions from numeric into " \
"literals: %s" % e
self._postpredict(dataset, result)
return result
def test_attrmap_conflicts():
am_n = AttributeMap({'a': 1, 'b': 2, 'c': 1})
am_t = AttributeMap({
'a': 1,
'b': 2,
'c': 1
},
collisions_resolution='tuple')
am_l = AttributeMap({
'a': 1,
'b': 2,
'c': 1
},
collisions_resolution='lucky')
q_f = ['a', 'b', 'a', 'c']
# should have no effect on forward mapping
ok_(np.all(am_n.to_numeric(q_f) == am_t.to_numeric(q_f)))
ok_(np.all(am_t.to_numeric(q_f) == am_l.to_numeric(q_f)))
assert_raises(ValueError, am_n.to_literal, [2])
r_t = am_t.to_literal([2, 1])
r_l = am_l.to_literal([2, 1])
def test_attrmap():
map_default = {'eins': 0, 'zwei': 2, 'sieben': 1}
map_custom = {'eins': 11, 'zwei': 22, 'sieben': 33}
literal = ['eins', 'zwei', 'sieben', 'eins', 'sieben', 'eins']
literal_nonmatching = ['uno', 'dos', 'tres']
num_default = [0, 2, 1, 0, 1, 0]
num_custom = [11, 22, 33, 11, 33, 11]
# no custom mapping given
am = AttributeMap()
assert_false(am)
ok_(len(am) == 0)
assert_array_equal(am.to_numeric(literal), num_default)
assert_array_equal(am.to_literal(num_default), literal)
ok_(am)
ok_(len(am) == 3)
#
# Tests for recursive mapping + preserving datatype
class myarray(np.ndarray):
pass
assert_raises(KeyError, am.to_literal, [(1, 2), 2, 0])
literal_fancy = [(1, 2), 2, [0], np.array([0, 1]).view(myarray)]
literal_fancy_tuple = tuple(literal_fancy)
literal_fancy_array = np.array(literal_fancy, dtype=object)
for l in (literal_fancy, literal_fancy_tuple,
literal_fancy_array):
res = am.to_literal(l, recurse=True)
assert_equal(res[0], ('sieben', 'zwei'))
assert_equal(res[1], 'zwei')
assert_equal(res[2], ['eins'])
assert_array_equal(res[3], ['eins', 'sieben'])
# types of result and subsequences should be preserved
ok_(isinstance(res, l.__class__))
ok_(isinstance(res[0], tuple))
ok_(isinstance(res[1], str))
ok_(isinstance(res[2], list))
ok_(isinstance(res[3], myarray))
# yet another example
a = np.empty(1, dtype=object)
a[0] = (0, 1)
res = am.to_literal(a, recurse=True)
ok_(isinstance(res[0], tuple))
#
# with custom mapping
am = AttributeMap(map=map_custom)
assert_array_equal(am.to_numeric(literal), num_custom)
assert_array_equal(am.to_literal(num_custom), literal)
# if not numeric nothing is mapped
assert_array_equal(am.to_numeric(num_custom), num_custom)
# even if the map doesn't fit
assert_array_equal(am.to_numeric(num_default), num_default)
# need to_numeric first
am = AttributeMap()
assert_raises(RuntimeError, am.to_literal, [1,2,3])
# stupid args
assert_raises(ValueError, AttributeMap, map=num_custom)
# map mismatch
am = AttributeMap(map=map_custom)
if __debug__:
# checked only in __debug__
assert_raises(KeyError, am.to_numeric, literal_nonmatching)
# needs reset and should work afterwards
am.clear()
assert_array_equal(am.to_numeric(literal_nonmatching), [2, 0, 1])
# and now reverse
am = AttributeMap(map=map_custom)
assert_raises(KeyError, am.to_literal, num_default)
# dict-like interface
am = AttributeMap()
ok_([(k, v) for k, v in am.iteritems()] == [])
def to_lightsvm_format(dataset,
out,
targets_attr='targets',
domain=None,
am=None):
"""Export dataset into LightSVM format
Parameters
----------
dataset : Dataset
out
Anything understanding .write(string), such as `File`
targets_attr : string, optional
Name of the samples attribute to be output
domain : {None, 'regression', 'binary', 'multiclass'}, optional
What domain dataset belongs to. If `None`, it would be deduced
depending on the datatype ('regression' if float, classification
in case of int or string, with 'binary'/'multiclass' depending on
the number of unique targets)
am : `AttributeMap` or None, optional
Which mapping to use for storing the non-conformant targets. If
None was provided, new one would be automagically generated
depending on the given/deduced domain.
Returns
-------
am
LightSVM format is an ASCII representation with a single sample per
each line::
output featureIndex:featureValue ... featureIndex:featureValue
where ``output`` is specific for a given domain:
regression
float number
binary
integer labels from {-1, 1}
multiclass
integer labels from {1..ds.targets_attr.nunique}
"""
targets_a = dataset.sa[targets_attr]
targets = targets_a.value
# XXX this all below
# * might become cleaner
# * might be RF to become more generic to be used may be elsewhere as well
if domain is None:
if targets.dtype.kind in ['S', 'U', 'i']:
if len(targets_a.unique) == 2:
domain = 'binary'
else:
domain = 'multiclass'
else:
domain = 'regression'
if domain in ['multiclass', 'binary']:
# check if labels are appropriate and provide mapping if necessary
utargets = targets_a.unique
if domain == 'binary' and set(utargets) != set([-1, 1]):
# need mapping
if len(utargets) != 2:
raise ValueError, \
"We need 2 unique targets in %s of %s. Got targets " \
"from set %s" % (targets_attr, dataset, utargets)
if am is None:
am = AttributeMap(dict(zip(utargets, [-1, 1])))
elif set(am.keys()) != set([-1, 1]):
raise ValueError, \
"Provided %s doesn't map into binary " \
"labels -1,+1" % (am,)
elif domain == 'multiclass' \
and set(utargets) != set(range(1, len(utargets)+1)):
if am is None:
am = AttributeMap(
dict(zip(utargets, range(1,
len(utargets) + 1))))
elif set(am.keys()) != set([-1, 1]):
raise ValueError, \
"Provided %s doesn't map into multiclass " \
"range 1..N" % (am, )
if am is not None:
# map the targets
targets = am.to_numeric(targets)
for t, s in zip(targets, dataset.samples):
out.write(
('%g %s\n' %
(t, ' '.join('%i:%.8g' % (i, v)
for i, v in zip(range(1, dataset.nfeatures +
1), s)))).encode('ascii'))
out.flush() # push it out
return am
def test_regressions(self, regr):
"""Simple tests on regressions
"""
if not externals.exists('scipy'):
raise SkipTest
else:
from mvpa2.misc.errorfx import corr_error
ds = datasets['chirp_linear']
# we want numeric labels to maintain the previous behavior, especially
# since we deal with regressions here
ds.sa.targets = AttributeMap().to_numeric(ds.targets)
cve = CrossValidation(regr, NFoldPartitioner(), postproc=mean_sample(),
errorfx=corr_error, enable_ca=['training_stats', 'stats'])
# check the default
#self.assertTrue(cve.transerror.errorfx is corr_error)
corr = np.asscalar(cve(ds).samples)
# Our CorrErrorFx should never return NaN
self.assertTrue(not np.isnan(corr))
self.assertTrue(corr == cve.ca.stats.stats['CCe'])
splitregr = SplitClassifier(
regr, partitioner=OddEvenPartitioner(),
enable_ca=['training_stats', 'stats'])
splitregr.train(ds)
split_corr = splitregr.ca.stats.stats['CCe']
split_corr_tr = splitregr.ca.training_stats.stats['CCe']
for confusion, error in (
(cve.ca.stats, corr),
(splitregr.ca.stats, split_corr),
(splitregr.ca.training_stats, split_corr_tr),
):
#TODO: test confusion statistics
# Part of it for now -- CCe
for conf in confusion.summaries:
stats = conf.stats
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(stats['CCe'] < 0.5)
self.assertEqual(stats['CCe'], stats['Summary CCe'])
s0 = confusion.as_string(short=True)
s1 = confusion.as_string(short=False)
for s in [s0, s1]:
self.assertTrue(len(s) > 10,
msg="We should get some string representation "
"of regression summary. Got %s" % s)
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(error < 0.2,
msg="Regressions should perform well on a simple "
"dataset. Got correlation error of %s " % error)
# Test access to summary statistics
# YOH: lets start making testing more reliable.
# p-value for such accident to have is verrrry tiny,
# so if regression works -- it better has at least 0.5 ;)
# otherwise fix it! ;)
# YOH: not now -- issues with libsvr in SG and linear kernel
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(confusion.stats['CCe'] < 0.5)
# just to check if it works fine
split_predictions = splitregr.predict(ds.samples)
