def test_mrseql_on_gunpoint():
# load training data
X_train, y_train = load_gunpoint(split='train', return_X_y=True)
X_test, y_test = load_gunpoint(split='test', return_X_y=True)
sax_clf = MrSEQLClassifier(seql_mode='fs', symrep=['sax'])
sfa_clf = MrSEQLClassifier(seql_mode='fs', symrep=['sfa'])
ss_clf = MrSEQLClassifier(seql_mode='fs', symrep=['sax', 'sfa'])
# fit training data
sax_clf.fit(X_train, y_train)
sfa_clf.fit(X_train, y_train)
ss_clf.fit(X_train, y_train)
# prediction
sax_predicted = sax_clf.predict(X_test)
sfa_predicted = sfa_clf.predict(X_test)
ss_predicted = ss_clf.predict(X_test)
# test feature space dimension
# the multi-domain classifier (ss_clf) should produce as many features
# as the others (sax_clf and sfa_clf) combine
np.testing.assert_equal(
ss_clf.ots_clf.coef_.shape[1],
sfa_clf.ots_clf.coef_.shape[1] + sax_clf.ots_clf.coef_.shape[1])
# test number of correct predictions
np.testing.assert_equal((sax_predicted == y_test).sum(), 148)
np.testing.assert_equal((sfa_predicted == y_test).sum(), 150)
np.testing.assert_equal((ss_predicted == y_test).sum(), 150)
def set_classifier(cls, resampleId=None):
"""Construct a classifier.
Basic way of creating the classifier to build using the default settings. This
set up is to help with batch jobs for multiple problems to facilitate easy
reproducability. You can set up bespoke classifier in many other ways.
Parameters
----------
cls: String indicating which classifier you want
resampleId: classifier random seed
Return
------
A classifier.
"""
name = cls.lower()
# Distance based
if name == "pf" or name == "proximityforest":
return ProximityForest(random_state=resampleId)
elif name == "pt" or name == "proximitytree":
return ProximityTree(random_state=resampleId)
elif name == "ps" or name == "proximityStump":
return ProximityStump(random_state=resampleId)
elif name == "dtwcv" or name == "kneighborstimeseriesclassifier":
return KNeighborsTimeSeriesClassifier(distance="dtwcv")
elif name == "dtw" or name == "1nn-dtw":
return KNeighborsTimeSeriesClassifier(distance="dtw")
elif name == "msm" or name == "1nn-msm":
return KNeighborsTimeSeriesClassifier(distance="msm")
elif name == "ee" or name == "elasticensemble":
return ElasticEnsemble()
elif name == "shapedtw":
return ShapeDTW()
# Dictionary based
elif name == "boss" or name == "bossensemble":
return BOSSEnsemble(random_state=resampleId)
elif name == "cboss" or name == "contractableboss":
return ContractableBOSS(random_state=resampleId)
elif name == "tde" or name == "temporaldictionaryensemble":
return TemporalDictionaryEnsemble(random_state=resampleId)
elif name == "weasel":
return WEASEL(random_state=resampleId)
elif name == "muse":
return MUSE(random_state=resampleId)
# Interval based
elif name == "rise" or name == "randomintervalspectralforest":
return RandomIntervalSpectralForest(random_state=resampleId)
elif name == "tsf" or name == "timeseriesforestclassifier":
return TimeSeriesForestClassifier(random_state=resampleId)
elif name == "cif" or name == "canonicalintervalforest":
return CanonicalIntervalForest(random_state=resampleId)
elif name == "drcif":
return DrCIF(random_state=resampleId)
# Shapelet based
elif name == "stc" or name == "shapelettransformclassifier":
return ShapeletTransformClassifier(random_state=resampleId,
transform_contract_in_mins=60)
elif name == "mrseql" or name == "mrseqlclassifier":
return MrSEQLClassifier(seql_mode="fs", symrep=["sax", "sfa"])
elif name == "rocket":
return ROCKETClassifier(random_state=resampleId)
elif name == "arsenal":
return Arsenal(random_state=resampleId)
# Hybrid
elif name == "catch22":
return Catch22ForestClassifier(random_state=resampleId)
elif name == "hivecotev1":
return HIVECOTEV1(random_state=resampleId)
else:
raise Exception("UNKNOWN CLASSIFIER")
def set_classifier(cls, resample_id=None):
"""Construct a classifier.
Basic way of creating the classifier to build using the default settings. This
set up is to help with batch jobs for multiple problems to facilitate easy
reproducibility for use with load_and_run_classification_experiment. You can pass a
classifier object instead to run_classification_experiment.
Parameters
----------
cls : String
String indicating which classifier you want.
resample_id : Random seed as an int, default=None
Classifier random seed.
Return
------
classifier : A BaseClassifier.
The classifier matching the input classifier name.
"""
name = cls.lower()
# Dictionary based
if name == "boss" or name == "bossensemble":
return BOSSEnsemble(random_state=resample_id)
elif name == "cboss" or name == "contractableboss":
return ContractableBOSS(random_state=resample_id)
elif name == "tde" or name == "temporaldictionaryensemble":
return TemporalDictionaryEnsemble(random_state=resample_id)
elif name == "weasel":
return WEASEL(random_state=resample_id)
elif name == "muse":
return MUSE(random_state=resample_id)
# Distance based
elif name == "pf" or name == "proximityforest":
return ProximityForest(random_state=resample_id)
elif name == "pt" or name == "proximitytree":
return ProximityTree(random_state=resample_id)
elif name == "ps" or name == "proximityStump":
return ProximityStump(random_state=resample_id)
elif name == "dtwcv" or name == "kneighborstimeseriesclassifier":
return KNeighborsTimeSeriesClassifier(distance="dtwcv")
elif name == "dtw" or name == "1nn-dtw":
return KNeighborsTimeSeriesClassifier(distance="dtw")
elif name == "msm" or name == "1nn-msm":
return KNeighborsTimeSeriesClassifier(distance="msm")
elif name == "ee" or name == "elasticensemble":
return ElasticEnsemble()
elif name == "shapedtw":
return ShapeDTW()
# Feature based: Removed because of soft dependency
elif name == "catch22":
return Catch22Classifier(
random_state=resample_id,
estimator=RandomForestClassifier(n_estimators=500))
elif name == "matrixprofile":
return MatrixProfileClassifier(random_state=resample_id)
elif name == "signature":
return SignatureClassifier(
random_state=resample_id,
classifier=RandomForestClassifier(n_estimators=500),
)
elif name == "tsfresh":
return TSFreshClassifier(
random_state=resample_id,
estimator=RandomForestClassifier(n_estimators=500))
elif name == "tsfresh-r":
return TSFreshClassifier(
random_state=resample_id,
estimator=RandomForestClassifier(n_estimators=500),
relevant_feature_extractor=True,
)
# Hybrid
elif name == "hivecotev1":
return HIVECOTEV1(random_state=resample_id)
# Interval based
elif name == "rise" or name == "randomintervalspectralforest":
return RandomIntervalSpectralForest(random_state=resample_id,
n_estimators=500)
elif name == "tsf" or name == "timeseriesforestclassifier":
return TimeSeriesForestClassifier(random_state=resample_id,
n_estimators=500)
elif name == "cif" or name == "canonicalintervalforest":
return CanonicalIntervalForest(random_state=resample_id,
n_estimators=500)
elif name == "stsf":
return SupervisedTimeSeriesForest(random_state=resample_id,
n_estimators=500)
elif name == "drcif":
return DrCIF(random_state=resample_id, n_estimators=500)
# Kernel based
elif name == "rocket":
return ROCKETClassifier(random_state=resample_id)
elif name == "arsenal":
return Arsenal(random_state=resample_id)
# Shapelet based
elif name == "stc" or name == "shapelettransformclassifier":
return ShapeletTransformClassifier(random_state=resample_id,
n_estimators=500)
elif name == "mrseql" or name == "mrseqlclassifier":
return MrSEQLClassifier(seql_mode="fs", symrep=["sax", "sfa"])
else:
raise Exception("UNKNOWN CLASSIFIER")