def fit(self, X, y):
X, y = check_X_y(X, y, enforce_univariate=True, coerce_to_numpy=True)
self.n_classes = np.unique(y).shape[0]
self.classes_ = class_distribution(np.asarray(y).reshape(-1, 1))[0][0]
cv_size = 10
_, counts = np.unique(y, return_counts=True)
min_class = np.min(counts)
if min_class < cv_size:
cv_size = min_class
self.stc = ShapeletTransformClassifier(
random_state=self.random_state,
time_contract_in_mins=60,
)
self.stc.fit(X, y)
train_preds = cross_val_predict(
ShapeletTransformClassifier(
random_state=self.random_state,
time_contract_in_mins=60,
),
X=X,
y=y,
cv=cv_size,
)
self.stc_weight = accuracy_score(y, train_preds)**4
self.tsf = TimeSeriesForest(random_state=self.random_state)
self.tsf.fit(X, y)
train_preds = cross_val_predict(
TimeSeriesForest(random_state=self.random_state),
X=X,
y=y,
cv=cv_size,
)
self.tsf_weight = accuracy_score(y, train_preds)**4
self.rise = RandomIntervalSpectralForest(
random_state=self.random_state)
self.fit(X, y)
train_preds = cross_val_predict(
RandomIntervalSpectralForest(random_state=self.random_state),
X=X,
y=y,
cv=cv_size,
)
self.rise_weight = accuracy_score(y, train_preds)**4
self.cboss = ContractableBOSS(random_state=self.random_state)
self.cboss.fit(X, y)
train_probs = self.cboss._get_train_probs(X)
train_preds = self.cboss.classes_[np.argmax(train_probs, axis=1)]
self.cboss_weight = accuracy_score(y, train_preds)**4
return self
def test_contracted_stc_on_unit_test_data():
"""Test of contracted ShapeletTransformClassifier on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
# train contracted STC
stc = ShapeletTransformClassifier(
estimator=RotationForest(contract_max_n_estimators=3),
max_shapelets=20,
time_limit_in_minutes=0.25,
contract_max_n_shapelet_samples=500,
batch_size=100,
random_state=0,
)
stc.fit(X_train, y_train)
def test_stc_on_basic_motions():
"""Test of ShapeletTransformClassifier on basic motions data."""
# load basic motions data
X_train, y_train = load_basic_motions(split="train", return_X_y=True)
X_test, y_test = load_basic_motions(split="test", return_X_y=True)
indices = np.random.RandomState(4).choice(len(y_train), 15, replace=False)
# train STC
stc = ShapeletTransformClassifier(
estimator=RotationForest(n_estimators=3),
max_shapelets=20,
n_shapelet_samples=500,
batch_size=100,
random_state=0,
)
stc.fit(X_train.iloc[indices], y_train[indices])
# assert probabilities are the same
probas = stc.predict_proba(X_test.iloc[indices[:10]])
testing.assert_array_equal(probas, stc_basic_motions_probas)
def test_stc_train_estimate():
"""Test of ShapeletTransformClassifier train estimate on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
# train STC
stc = ShapeletTransformClassifier(
estimator=RotationForest(n_estimators=2),
max_shapelets=3,
n_shapelet_samples=10,
batch_size=5,
random_state=0,
save_transformed_data=True,
)
stc.fit(X_train, y_train)
# test train estimate
train_probas = stc._get_train_probs(X_train, y_train)
assert train_probas.shape == (20, 2)
train_preds = stc.classes_[np.argmax(train_probas, axis=1)]
assert accuracy_score(y_train, train_preds) >= 0.6
def test_stc_on_unit_test_data():
"""Test of ShapeletTransformClassifier on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train", return_X_y=True)
X_test, y_test = load_unit_test(split="test", return_X_y=True)
indices = np.random.RandomState(0).choice(len(y_train), 10, replace=False)
# train STC
stc = ShapeletTransformClassifier(
estimator=RotationForest(n_estimators=3),
max_shapelets=20,
n_shapelet_samples=500,
batch_size=100,
random_state=0,
save_transformed_data=True,
)
stc.fit(X_train, y_train)
# assert probabilities are the same
probas = stc.predict_proba(X_test.iloc[indices])
testing.assert_array_equal(probas, stc_unit_test_probas)
# test train estimate
train_probas = stc._get_train_probs(X_train, y_train)
train_preds = stc.classes_[np.argmax(train_probas, axis=1)]
assert accuracy_score(y_train, train_preds) >= 0.75
def fit(self, X, y):
"""Fit a HIVE-COTEv1.0 classifier.
Parameters
----------
X : nested pandas DataFrame of shape [n_instances, 1]
Nested dataframe with univariate time-series in cells.
y : array-like, shape = [n_instances] The class labels.
Returns
-------
self : object
"""
X, y = check_X_y(X, y, enforce_univariate=True)
self.n_classes = np.unique(y).shape[0]
self.classes_ = class_distribution(np.asarray(y).reshape(-1, 1))[0][0]
cv_size = 10
_, counts = np.unique(y, return_counts=True)
min_class = np.min(counts)
if min_class < cv_size:
cv_size = min_class
self.stc = ShapeletTransformClassifier(
**self.stc_params,
random_state=self.random_state,
)
self.stc.fit(X, y)
if self.verbose > 0:
print("STC ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
train_preds = cross_val_predict(
ShapeletTransformClassifier(
**self.stc_params,
random_state=self.random_state,
),
X=X,
y=y,
cv=cv_size,
n_jobs=self.n_jobs,
)
self.stc_weight = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"STC train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("STC weight = " + str(self.stc_weight)) # noqa
self.tsf = TimeSeriesForestClassifier(
**self.tsf_params,
random_state=self.random_state,
n_jobs=self.n_jobs,
)
self.tsf.fit(X, y)
if self.verbose > 0:
print("TSF ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
train_preds = cross_val_predict(
TimeSeriesForestClassifier(**self.tsf_params,
random_state=self.random_state),
X=X,
y=y,
cv=cv_size,
n_jobs=self.n_jobs,
)
self.tsf_weight = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"TSF train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("TSF weight = " + str(self.tsf_weight)) # noqa
self.rise = RandomIntervalSpectralForest(
**self.rise_params,
random_state=self.random_state,
n_jobs=self.n_jobs,
)
self.rise.fit(X, y)
if self.verbose > 0:
print("RISE ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
train_preds = cross_val_predict(
RandomIntervalSpectralForest(
**self.rise_params,
random_state=self.random_state,
),
X=X,
y=y,
cv=cv_size,
n_jobs=self.n_jobs,
)
self.rise_weight = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"RISE train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("RISE weight = " + str(self.rise_weight)) # noqa
self.cboss = ContractableBOSS(**self.cboss_params,
random_state=self.random_state,
n_jobs=self.n_jobs)
self.cboss.fit(X, y)
train_probs = self.cboss._get_train_probs(X)
train_preds = self.cboss.classes_[np.argmax(train_probs, axis=1)]
self.cboss_weight = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"cBOSS (estimate included) ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("cBOSS weight = " + str(self.cboss_weight)) # noqa
self._is_fitted = True
return self
def set_classifier(cls, resample_id=None, train_file=False):
"""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 : str
String indicating which classifier you want.
resample_id : int or None, default=None
Classifier random seed.
train_file : bool, default=False
Whether a train file is being produced.
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, save_train_predictions=train_file
)
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(random_state=resample_id)
elif name == "shapedtw":
return ShapeDTW()
# Feature based
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,
estimator=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 == "hc1" or name == "hivecotev1":
return HIVECOTEV1(random_state=resample_id)
elif name == "hc2" or name == "hivecotev2":
return HIVECOTEV2(random_state=resample_id)
# Interval based
elif name == "rise" or name == "randomintervalspectralforest":
return RandomIntervalSpectralEnsemble(
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" or name == "supervisedtimeseriesforest":
return SupervisedTimeSeriesForest(random_state=resample_id, n_estimators=500)
elif name == "drcif":
return DrCIF(
random_state=resample_id, n_estimators=500, save_transformed_data=train_file
)
# Kernel based
elif name == "rocket":
return ROCKETClassifier(random_state=resample_id)
elif name == "arsenal":
return Arsenal(random_state=resample_id, save_transformed_data=train_file)
# Shapelet based
elif name == "stc" or name == "shapelettransformclassifier":
return ShapeletTransformClassifier(
random_state=resample_id, save_transformed_data=train_file
)
elif name == "mrseql" or name == "mrseqlclassifier":
return MrSEQLClassifier(seql_mode="fs", symrep=["sax", "sfa"])
else:
raise Exception("UNKNOWN CLASSIFIER")
class HIVECOTEV2(BaseClassifier):
"""Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V2.
An ensemble of the STC, DrCIF, Arsenal and TDE classifiers from different feature
representations using the CAWPE structure as described in [1].
Parameters
----------
stc_params : dict or None, default=None
Parameters for the ShapeletTransformClassifier module. If None, uses the
default parameters with a 2 hour transform contract.
drcif_params : dict or None, default=None
Parameters for the DrCIF module. If None, uses the default parameters with
n_estimators set to 500.
arsenal_params : dict or None, default=None
Parameters for the Arsenal module. If None, uses the default parameters.
tde_params : dict or None, default=None
Parameters for the TemporalDictionaryEnsemble module. If None, uses the default
parameters.
time_limit_in_minutes : int, default=0
Time contract to limit build time in minutes, overriding
n_estimators/n_parameter_samples for each component.
Default of 0 means n_estimators/n_parameter_samples for each component is used.
save_component_probas : bool, default=False
When predict/predict_proba is called, save each HIVE-COTEV2 component
probability predictions in component_probas.
verbose : int, default=0
Level of output printed to the console (for information only).
n_jobs : int, default=1
The number of jobs to run in parallel for both `fit` and `predict`.
``-1`` means using all processors.
random_state : int or None, default=None
Seed for random number generation.
Attributes
----------
n_classes_ : int
The number of classes.
classes_ : list
The unique class labels.
stc_weight_ : float
The weight for STC probabilities.
drcif_weight_ : float
The weight for DrCIF probabilities.
arsenal_weight_ : float
The weight for Arsenal probabilities.
tde_weight_ : float
The weight for TDE probabilities.
component_probas : dict
Only used if save_component_probas is true. Saved probability predictions for
each HIVE-COTEV2 component.
See Also
--------
HIVECOTEV1, ShapeletTransformClassifier, DrCIF, Arsenal, TemporalDictionaryEnsemble
Notes
-----
For the Java version, see
`https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/
tsml/classifiers/hybrids/HIVE_COTE.java`_.
References
----------
.. [1] Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom,
and Anthony Bagnall. "HIVE-COTE 2.0: a new meta ensemble for time series
classification." Machine Learning (2021).
Examples
--------
>>> from sktime.classification.hybrid import HIVECOTEV2
>>> from sktime.contrib.vector_classifiers._rotation_forest import RotationForest
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
>>> clf = HIVECOTEV2(
... stc_params={
... "estimator": RotationForest(n_estimators=3),
... "n_shapelet_samples": 500,
... "max_shapelets": 20,
... "batch_size": 100,
... },
... drcif_params={"n_estimators": 10},
... arsenal_params={"num_kernels": 100, "n_estimators": 5},
... tde_params={
... "n_parameter_samples": 25,
... "max_ensemble_size": 5,
... "randomly_selected_params": 10,
... },
... )
>>> clf.fit(X_train, y_train)
HIVECOTEV2(...)
>>> y_pred = clf.predict(X_test)
"""
_tags = {
"capability:multivariate": True,
"capability:contractable": True,
"capability:multithreading": True,
}
def __init__(
self,
stc_params=None,
drcif_params=None,
arsenal_params=None,
tde_params=None,
time_limit_in_minutes=0,
save_component_probas=False,
verbose=0,
n_jobs=1,
random_state=None,
):
self.stc_params = stc_params
self.drcif_params = drcif_params
self.arsenal_params = arsenal_params
self.tde_params = tde_params
self.time_limit_in_minutes = time_limit_in_minutes
self.save_component_probas = save_component_probas
self.verbose = verbose
self.n_jobs = n_jobs
self.random_state = random_state
self.stc_weight_ = 0
self.drcif_weight_ = 0
self.arsenal_weight_ = 0
self.tde_weight_ = 0
self.component_probas = {}
self._stc_params = stc_params
self._drcif_params = drcif_params
self._arsenal_params = arsenal_params
self._tde_params = tde_params
self._stc = None
self._drcif = None
self._arsenal = None
self._tde = None
super(HIVECOTEV2, self).__init__()
def _fit(self, X, y):
"""Fit HIVE-COTE 2.0 to training data.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The training data.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self :
Reference to self.
Notes
-----
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
# Default values from HC2 paper
if self.stc_params is None:
self._stc_params = {"transform_limit_in_minutes": 120}
if self.drcif_params is None:
self._drcif_params = {"n_estimators": 500}
if self.arsenal_params is None:
self._arsenal_params = {}
if self.tde_params is None:
self._tde_params = {}
# If we are contracting split the contract time between each algorithm
if self.time_limit_in_minutes > 0:
# Leave 1/3 for train estimates
ct = self.time_limit_in_minutes / 6
self._stc_params["time_limit_in_minutes"] = ct
self._drcif_params["time_limit_in_minutes"] = ct
self._arsenal_params["time_limit_in_minutes"] = ct
self._tde_params["time_limit_in_minutes"] = ct
# Build STC
self._stc = ShapeletTransformClassifier(
**self._stc_params,
save_transformed_data=True,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._stc.fit(X, y)
if self.verbose > 0:
print("STC ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find STC weight using train set estimate
train_probs = self._stc._get_train_probs(X, y)
train_preds = self._stc.classes_[np.argmax(train_probs, axis=1)]
self.stc_weight_ = accuracy_score(y, train_preds) ** 4
if self.verbose > 0:
print( # noqa
"STC train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("STC weight = " + str(self.stc_weight_)) # noqa
# Build DrCIF
self._drcif = DrCIF(
**self._drcif_params,
save_transformed_data=True,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._drcif.fit(X, y)
if self.verbose > 0:
print("DrCIF ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find DrCIF weight using train set estimate
train_probs = self._drcif._get_train_probs(X, y)
train_preds = self._drcif.classes_[np.argmax(train_probs, axis=1)]
self.drcif_weight_ = accuracy_score(y, train_preds) ** 4
if self.verbose > 0:
print( # noqa
"DrCIF train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("DrCIF weight = " + str(self.drcif_weight_)) # noqa
# Build Arsenal
self._arsenal = Arsenal(
**self._arsenal_params,
save_transformed_data=True,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._arsenal.fit(X, y)
if self.verbose > 0:
print("Arsenal ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find Arsenal weight using train set estimate
train_probs = self._arsenal._get_train_probs(X, y)
train_preds = self._arsenal.classes_[np.argmax(train_probs, axis=1)]
self.arsenal_weight_ = accuracy_score(y, train_preds) ** 4
if self.verbose > 0:
print( # noqa
"Arsenal train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("Arsenal weight = " + str(self.arsenal_weight_)) # noqa
# Build TDE
self._tde = TemporalDictionaryEnsemble(
**self._tde_params,
save_train_predictions=True,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._tde.fit(X, y)
if self.verbose > 0:
print("TDE ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find TDE weight using train set estimate
train_probs = self._tde._get_train_probs(X, y, train_estimate_method="loocv")
train_preds = self._tde.classes_[np.argmax(train_probs, axis=1)]
self.tde_weight_ = accuracy_score(y, train_preds) ** 4
if self.verbose > 0:
print( # noqa
"TDE train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("TDE weight = " + str(self.tde_weight_)) # noqa
return self
def _predict(self, X):
"""Predicts labels for sequences in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predictions for.
Returns
-------
y : array-like, shape = [n_instances]
Predicted class labels.
"""
rng = check_random_state(self.random_state)
return np.array(
[
self.classes_[int(rng.choice(np.flatnonzero(prob == prob.max())))]
for prob in self.predict_proba(X)
]
)
def _predict_proba(self, X, return_component_probas=False):
"""Predicts labels probabilities for sequences in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predict probabilities for.
Returns
-------
y : array-like, shape = [n_instances, n_classes_]
Predicted probabilities using the ordering in classes_.
"""
dists = np.zeros((X.shape[0], self.n_classes_))
# Call predict proba on each classifier, multiply the probabilities by the
# classifiers weight then add them to the current HC2 probabilities
stc_probas = self._stc.predict_proba(X)
dists = np.add(
dists,
stc_probas * (np.ones(self.n_classes_) * self.stc_weight_),
)
drcif_probas = self._drcif.predict_proba(X)
dists = np.add(
dists,
drcif_probas * (np.ones(self.n_classes_) * self.drcif_weight_),
)
arsenal_probas = self._arsenal.predict_proba(X)
dists = np.add(
dists,
arsenal_probas * (np.ones(self.n_classes_) * self.arsenal_weight_),
)
tde_probas = self._tde.predict_proba(X)
dists = np.add(
dists,
tde_probas * (np.ones(self.n_classes_) * self.tde_weight_),
)
if self.save_component_probas:
self.component_probas = {
"STC": stc_probas,
"DrCIF": drcif_probas,
"Arsenal": arsenal_probas,
"TDE": tde_probas,
}
# Make each instances probability array sum to 1 and return
return dists / dists.sum(axis=1, keepdims=True)
def _fit(self, X, y):
"""Fit HIVE-COTE 2.0 to training data.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The training data.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self :
Reference to self.
Notes
-----
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
# Default values from HC2 paper
if self.stc_params is None:
self._stc_params = {"transform_limit_in_minutes": 120}
if self.drcif_params is None:
self._drcif_params = {"n_estimators": 500}
if self.arsenal_params is None:
self._arsenal_params = {}
if self.tde_params is None:
self._tde_params = {}
# If we are contracting split the contract time between each algorithm
if self.time_limit_in_minutes > 0:
# Leave 1/3 for train estimates
ct = self.time_limit_in_minutes / 6
self._stc_params["time_limit_in_minutes"] = ct
self._drcif_params["time_limit_in_minutes"] = ct
self._arsenal_params["time_limit_in_minutes"] = ct
self._tde_params["time_limit_in_minutes"] = ct
# Build STC
self._stc = ShapeletTransformClassifier(
**self._stc_params,
save_transformed_data=True,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._stc.fit(X, y)
if self.verbose > 0:
print("STC ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find STC weight using train set estimate
train_probs = self._stc._get_train_probs(X, y)
train_preds = self._stc.classes_[np.argmax(train_probs, axis=1)]
self.stc_weight_ = accuracy_score(y, train_preds) ** 4
if self.verbose > 0:
print( # noqa
"STC train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("STC weight = " + str(self.stc_weight_)) # noqa
# Build DrCIF
self._drcif = DrCIF(
**self._drcif_params,
save_transformed_data=True,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._drcif.fit(X, y)
if self.verbose > 0:
print("DrCIF ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find DrCIF weight using train set estimate
train_probs = self._drcif._get_train_probs(X, y)
train_preds = self._drcif.classes_[np.argmax(train_probs, axis=1)]
self.drcif_weight_ = accuracy_score(y, train_preds) ** 4
if self.verbose > 0:
print( # noqa
"DrCIF train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("DrCIF weight = " + str(self.drcif_weight_)) # noqa
# Build Arsenal
self._arsenal = Arsenal(
**self._arsenal_params,
save_transformed_data=True,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._arsenal.fit(X, y)
if self.verbose > 0:
print("Arsenal ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find Arsenal weight using train set estimate
train_probs = self._arsenal._get_train_probs(X, y)
train_preds = self._arsenal.classes_[np.argmax(train_probs, axis=1)]
self.arsenal_weight_ = accuracy_score(y, train_preds) ** 4
if self.verbose > 0:
print( # noqa
"Arsenal train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("Arsenal weight = " + str(self.arsenal_weight_)) # noqa
# Build TDE
self._tde = TemporalDictionaryEnsemble(
**self._tde_params,
save_train_predictions=True,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._tde.fit(X, y)
if self.verbose > 0:
print("TDE ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find TDE weight using train set estimate
train_probs = self._tde._get_train_probs(X, y, train_estimate_method="loocv")
train_preds = self._tde.classes_[np.argmax(train_probs, axis=1)]
self.tde_weight_ = accuracy_score(y, train_preds) ** 4
if self.verbose > 0:
print( # noqa
"TDE train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("TDE weight = " + str(self.tde_weight_)) # noqa
return self
RocketClassifier(num_kernels=500, random_state=0)
),
)
_print_array(
"RocketClassifier - BasicMotions",
_reproduce_classification_basic_motions(
RocketClassifier(num_kernels=500, random_state=0)
),
)
_print_array(
"ShapeletTransformClassifier - UnitTest",
_reproduce_classification_unit_test(
ShapeletTransformClassifier(
estimator=RotationForest(n_estimators=3),
max_shapelets=20,
n_shapelet_samples=500,
batch_size=100,
random_state=0,
)
),
)
_print_array(
"ShapeletTransformClassifier - BasicMotions",
_reproduce_classification_basic_motions(
ShapeletTransformClassifier(
estimator=RotationForest(n_estimators=3),
max_shapelets=20,
n_shapelet_samples=500,
batch_size=100,
random_state=0,
)
"RocketClassifier - UnitTest",
_reproduce_classification_unit_test(
RocketClassifier(num_kernels=100, random_state=0)),
)
_print_array(
"RocketClassifier - BasicMotions",
_reproduce_classification_basic_motions(
RocketClassifier(num_kernels=100, random_state=0)),
)
_print_array(
"ShapeletTransformClassifier - UnitTest",
_reproduce_classification_unit_test(
ShapeletTransformClassifier(
estimator=RandomForestClassifier(n_estimators=5),
n_shapelet_samples=50,
max_shapelets=10,
batch_size=10,
random_state=0,
)),
)
_print_array(
"ShapeletTransformClassifier - BasicMotions",
_reproduce_classification_basic_motions(
ShapeletTransformClassifier(
estimator=RandomForestClassifier(n_estimators=5),
n_shapelet_samples=50,
max_shapelets=10,
batch_size=10,
random_state=0,
)),
)
class HIVECOTEV1(BaseClassifier):
"""
Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V1
as described in [1].
An ensemble of the STC, TSF, RISE and cBOSS classifiers from different feature
representations using the CAWPE structure.
Parameters
----------
random_state : int or None, seed for random, integer,
optional (default to no seed)
Attributes
----------
n_classes : extracted from the data
Notes
-----
@article{bagnall2020usage,
title={On the Usage and Performance of The Hierarchical Vote Collective of
Transformation-based Ensembles version 1.0 (HIVE-COTE 1.0)},
author={Bagnall, Anthony and Flynn, Michael and Large, James and Lines, Jason and
Middlehurst, Matthew},
journal={arXiv preprint arXiv:2004.06069},
year={2020}
}
Java version
https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/
tsml/classifiers/hybrids/HIVE_COTE.java
"""
# Capability tags
capabilities = {
"multivariate": False,
"unequal_length": False,
"missing_values": False,
}
def __init__(
self,
random_state=None,
):
self.random_state = random_state
self.stc = None
self.tsf = None
self.rise = None
self.cboss = None
self.stc_weight = 0
self.tsf_weight = 0
self.rise_weight = 0
self.cboss_weight = 0
self.n_classes = 0
self.classes_ = []
super(HIVECOTEV1, self).__init__()
def fit(self, X, y):
X, y = check_X_y(X, y, enforce_univariate=True, coerce_to_numpy=True)
self.n_classes = np.unique(y).shape[0]
self.classes_ = class_distribution(np.asarray(y).reshape(-1, 1))[0][0]
cv_size = 10
_, counts = np.unique(y, return_counts=True)
min_class = np.min(counts)
if min_class < cv_size:
cv_size = min_class
self.stc = ShapeletTransformClassifier(
random_state=self.random_state,
time_contract_in_mins=60,
)
self.stc.fit(X, y)
train_preds = cross_val_predict(
ShapeletTransformClassifier(
random_state=self.random_state,
time_contract_in_mins=60,
),
X=X,
y=y,
cv=cv_size,
)
self.stc_weight = accuracy_score(y, train_preds)**4
self.tsf = TimeSeriesForest(random_state=self.random_state)
self.tsf.fit(X, y)
train_preds = cross_val_predict(
TimeSeriesForest(random_state=self.random_state),
X=X,
y=y,
cv=cv_size,
)
self.tsf_weight = accuracy_score(y, train_preds)**4
self.rise = RandomIntervalSpectralForest(
random_state=self.random_state)
self.fit(X, y)
train_preds = cross_val_predict(
RandomIntervalSpectralForest(random_state=self.random_state),
X=X,
y=y,
cv=cv_size,
)
self.rise_weight = accuracy_score(y, train_preds)**4
self.cboss = ContractableBOSS(random_state=self.random_state)
self.cboss.fit(X, y)
train_probs = self.cboss._get_train_probs(X)
train_preds = self.cboss.classes_[np.argmax(train_probs, axis=1)]
self.cboss_weight = accuracy_score(y, train_preds)**4
return self
def predict(self, X):
rng = check_random_state(self.random_state)
return np.array([
self.classes_[int(rng.choice(np.flatnonzero(prob == prob.max())))]
for prob in self.predict_proba(X)
])
def predict_proba(self, X):
self.check_is_fitted()
X = check_X(X, enforce_univariate=True, coerce_to_numpy=True)
dists = np.zeros((X.shape[0], self.n_classes))
dists = np.add(
dists,
self.stc.predict_proba(X) *
(np.ones(self.n_classes) * self.stc_weight),
)
dists = np.add(
dists,
self.tsf.predict_proba(X) *
(np.ones(self.n_classes) * self.tsf_weight),
)
dists = np.add(
dists,
self.rise.predict_proba(X) *
(np.ones(self.n_classes) * self.rise_weight),
)
dists = np.add(
dists,
self.cboss.predict_proba(X) *
(np.ones(self.n_classes) * self.cboss_weight),
)
return dists / dists.sum(axis=1, keepdims=True)
class HIVECOTEV1(BaseClassifier):
"""Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V1.
An ensemble of the STC, TSF, RISE and cBOSS classifiers from different feature
representations using the CAWPE structure as described in [1]_.
Parameters
----------
stc_params : dict or None, default=None
Parameters for the ShapeletTransformClassifier module. If None, uses the
default parameters with a 2 hour transform contract.
tsf_params : dict or None, default=None
Parameters for the TimeSeriesForestClassifier module. If None, uses the default
parameters with n_estimators set to 500.
rise_params : dict or None, default=None
Parameters for the RandomIntervalSpectralForest module. If None, uses the
default parameters with n_estimators set to 500.
cboss_params : dict or None, default=None
Parameters for the ContractableBOSS module. If None, uses the default
parameters.
verbose : int, default=0
Level of output printed to the console (for information only).
n_jobs : int, default=1
The number of jobs to run in parallel for both `fit` and `predict`.
``-1`` means using all processors.
random_state : int or None, default=None
Seed for random number generation.
Attributes
----------
n_classes_ : int
The number of classes.
classes_ : list
The unique class labels.
stc_weight_ : float
The weight for STC probabilities.
tsf_weight_ : float
The weight for TSF probabilities.
rise_weight_ : float
The weight for RISE probabilities.
cboss_weight_ : float
The weight for cBOSS probabilities.
See Also
--------
HIVECOTEV2, ShapeletTransformClassifier, TimeSeriesForestClassifier,
RandomIntervalSpectralForest, ContractableBOSS
Notes
-----
For the Java version, see
`https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/
tsml/classifiers/hybrids/HIVE_COTE.java`_.
References
----------
.. [1] Anthony Bagnall, Michael Flynn, James Large, Jason Lines and
Matthew Middlehurst. "On the usage and performance of the Hierarchical Vote
Collective of Transformation-based Ensembles version 1.0 (hive-cote v1.0)"
International Workshop on Advanced Analytics and Learning on Temporal Data 2020
Examples
--------
>>> from sktime.classification.hybrid import HIVECOTEV1
>>> from sktime.contrib.vector_classifiers._rotation_forest import RotationForest
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
>>> clf = HIVECOTEV1(
... stc_params={
... "estimator": RotationForest(n_estimators=3),
... "n_shapelet_samples": 500,
... "max_shapelets": 20,
... "batch_size": 100,
... },
... tsf_params={"n_estimators": 10},
... rise_params={"n_estimators": 10},
... cboss_params={"n_parameter_samples": 25, "max_ensemble_size": 5},
... )
>>> clf.fit(X_train, y_train)
HIVECOTEV1(...)
>>> y_pred = clf.predict(X_test)
"""
_tags = {
"capability:multithreading": True,
}
def __init__(
self,
stc_params=None,
tsf_params=None,
rise_params=None,
cboss_params=None,
verbose=0,
n_jobs=1,
random_state=None,
):
self.stc_params = stc_params
self.tsf_params = tsf_params
self.rise_params = rise_params
self.cboss_params = cboss_params
self.verbose = verbose
self.n_jobs = n_jobs
self.random_state = random_state
self.stc_weight_ = 0
self.tsf_weight_ = 0
self.rise_weight_ = 0
self.cboss_weight_ = 0
self._stc_params = stc_params
self._tsf_params = tsf_params
self._rise_params = rise_params
self._cboss_params = cboss_params
self._stc = None
self._tsf = None
self._rise = None
self._cboss = None
super(HIVECOTEV1, self).__init__()
def _fit(self, X, y):
"""Fit HIVE-COTE 1.0 to training data.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The training data.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self :
Reference to self.
Notes
-----
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
# Default values from HC1 paper
if self.stc_params is None:
self._stc_params = {"transform_limit_in_minutes": 120}
if self.tsf_params is None:
self._tsf_params = {"n_estimators": 500}
if self.rise_params is None:
self._rise_params = {"n_estimators": 500}
if self.cboss_params is None:
self._cboss_params = {}
# Cross-validation size for TSF and RISE
cv_size = 10
_, counts = np.unique(y, return_counts=True)
min_class = np.min(counts)
if min_class < cv_size:
cv_size = min_class
# Build STC
self._stc = ShapeletTransformClassifier(
**self._stc_params,
save_transformed_data=True,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._stc.fit(X, y)
if self.verbose > 0:
print("STC ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find STC weight using train set estimate
train_probs = self._stc._get_train_probs(X, y)
train_preds = self._stc.classes_[np.argmax(train_probs, axis=1)]
self.stc_weight_ = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"STC train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("STC weight = " + str(self.stc_weight_)) # noqa
# Build TSF
self._tsf = TimeSeriesForestClassifier(
**self._tsf_params,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._tsf.fit(X, y)
if self.verbose > 0:
print("TSF ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find TSF weight using train set estimate found through CV
train_preds = cross_val_predict(
TimeSeriesForestClassifier(**self._tsf_params,
random_state=self.random_state),
X=X,
y=y,
cv=cv_size,
n_jobs=self._threads_to_use,
)
self.tsf_weight_ = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"TSF train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("TSF weight = " + str(self.tsf_weight_)) # noqa
# Build RISE
self._rise = RandomIntervalSpectralEnsemble(
**self._rise_params,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._rise.fit(X, y)
if self.verbose > 0:
print("RISE ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find RISE weight using train set estimate found through CV
train_preds = cross_val_predict(
RandomIntervalSpectralEnsemble(
**self._rise_params,
random_state=self.random_state,
),
X=X,
y=y,
cv=cv_size,
n_jobs=self._threads_to_use,
)
self.rise_weight_ = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"RISE train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("RISE weight = " + str(self.rise_weight_)) # noqa
# Build cBOSS
self._cboss = ContractableBOSS(
**self._cboss_params,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._cboss.fit(X, y)
# Find cBOSS weight using train set estimate
train_probs = self._cboss._get_train_probs(X, y)
train_preds = self._cboss.classes_[np.argmax(train_probs, axis=1)]
self.cboss_weight_ = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"cBOSS (estimate included)",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("cBOSS weight = " + str(self.cboss_weight_)) # noqa
return self
def _predict(self, X):
"""Predicts labels for sequences in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predictions for.
Returns
-------
y : array-like, shape = [n_instances]
Predicted class labels.
"""
rng = check_random_state(self.random_state)
return np.array([
self.classes_[int(rng.choice(np.flatnonzero(prob == prob.max())))]
for prob in self.predict_proba(X)
])
def _predict_proba(self, X):
"""Predicts labels probabilities for sequences in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predict probabilities for.
Returns
-------
y : array-like, shape = [n_instances, n_classes_]
Predicted probabilities using the ordering in classes_.
"""
dists = np.zeros((X.shape[0], self.n_classes_))
# Call predict proba on each classifier, multiply the probabilities by the
# classifiers weight then add them to the current HC1 probabilities
dists = np.add(
dists,
self._stc.predict_proba(X) *
(np.ones(self.n_classes_) * self.stc_weight_),
)
dists = np.add(
dists,
self._tsf.predict_proba(X) *
(np.ones(self.n_classes_) * self.tsf_weight_),
)
dists = np.add(
dists,
self._rise.predict_proba(X) *
(np.ones(self.n_classes_) * self.rise_weight_),
)
dists = np.add(
dists,
self._cboss.predict_proba(X) *
(np.ones(self.n_classes_) * self.cboss_weight_),
)
# Make each instances probability array sum to 1 and return
return dists / dists.sum(axis=1, keepdims=True)
def _fit(self, X, y):
"""Fit HIVE-COTE 1.0 to training data.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The training data.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self :
Reference to self.
Notes
-----
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
# Default values from HC1 paper
if self.stc_params is None:
self._stc_params = {"transform_limit_in_minutes": 120}
if self.tsf_params is None:
self._tsf_params = {"n_estimators": 500}
if self.rise_params is None:
self._rise_params = {"n_estimators": 500}
if self.cboss_params is None:
self._cboss_params = {}
# Cross-validation size for TSF and RISE
cv_size = 10
_, counts = np.unique(y, return_counts=True)
min_class = np.min(counts)
if min_class < cv_size:
cv_size = min_class
# Build STC
self._stc = ShapeletTransformClassifier(
**self._stc_params,
save_transformed_data=True,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._stc.fit(X, y)
if self.verbose > 0:
print("STC ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find STC weight using train set estimate
train_probs = self._stc._get_train_probs(X, y)
train_preds = self._stc.classes_[np.argmax(train_probs, axis=1)]
self.stc_weight_ = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"STC train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("STC weight = " + str(self.stc_weight_)) # noqa
# Build TSF
self._tsf = TimeSeriesForestClassifier(
**self._tsf_params,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._tsf.fit(X, y)
if self.verbose > 0:
print("TSF ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find TSF weight using train set estimate found through CV
train_preds = cross_val_predict(
TimeSeriesForestClassifier(**self._tsf_params,
random_state=self.random_state),
X=X,
y=y,
cv=cv_size,
n_jobs=self._threads_to_use,
)
self.tsf_weight_ = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"TSF train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("TSF weight = " + str(self.tsf_weight_)) # noqa
# Build RISE
self._rise = RandomIntervalSpectralEnsemble(
**self._rise_params,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._rise.fit(X, y)
if self.verbose > 0:
print("RISE ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
# Find RISE weight using train set estimate found through CV
train_preds = cross_val_predict(
RandomIntervalSpectralEnsemble(
**self._rise_params,
random_state=self.random_state,
),
X=X,
y=y,
cv=cv_size,
n_jobs=self._threads_to_use,
)
self.rise_weight_ = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"RISE train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("RISE weight = " + str(self.rise_weight_)) # noqa
# Build cBOSS
self._cboss = ContractableBOSS(
**self._cboss_params,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._cboss.fit(X, y)
# Find cBOSS weight using train set estimate
train_probs = self._cboss._get_train_probs(X, y)
train_preds = self._cboss.classes_[np.argmax(train_probs, axis=1)]
self.cboss_weight_ = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"cBOSS (estimate included)",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("cBOSS weight = " + str(self.cboss_weight_)) # noqa
return self
class HIVECOTEV1(BaseClassifier):
"""Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V1.
An ensemble of the STC, TSF, RISE and cBOSS classifiers from different feature
representations using the CAWPE structure as described in [1].
Parameters
----------
verbose : int, level of output printed to
the console (for information only) (default = 0)
n_jobs : int, optional (default=1)
The number of jobs to run in parallel for both `fit` and `predict`.
``-1`` means using all processors.
random_state : int or None, seed for random, integer,
optional (default to no seed)
Attributes
----------
n_classes : extracted from the data
Notes
-----
..[1] Anthony Bagnall, Michael Flynn, James Large, Jason Lines and
Matthew Middlehurst.
"On the usage and performance of the Hierarchical Vote Collective of
Transformation-based Ensembles version 1.0 (hive-cote v1. 0)"
International Workshop on Advanced Analytics and Learning on Temporal
Data 2020
Java version
https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/
tsml/classifiers/hybrids/HIVE_COTE.java
"""
# Capability tags
capabilities = {
"multivariate": False,
"unequal_length": False,
"missing_values": False,
"train_estimate": False,
"contractable": False,
}
def __init__(
self,
stc_params=None,
tsf_params=None,
rise_params=None,
cboss_params=None,
verbose=0,
n_jobs=1,
random_state=None,
):
if stc_params is None:
stc_params = {"n_estimators": 500}
if tsf_params is None:
tsf_params = {"n_estimators": 500}
if rise_params is None:
rise_params = {"n_estimators": 500}
if cboss_params is None:
cboss_params = {}
self.stc_params = stc_params
self.tsf_params = tsf_params
self.rise_params = rise_params
self.cboss_params = cboss_params
self.verbose = verbose
self.n_jobs = n_jobs
self.random_state = random_state
self.stc = None
self.tsf = None
self.rise = None
self.cboss = None
self.stc_weight = 0
self.tsf_weight = 0
self.rise_weight = 0
self.cboss_weight = 0
self.n_classes = 0
self.classes_ = []
super(HIVECOTEV1, self).__init__()
def fit(self, X, y):
"""Fit a HIVE-COTEv1.0 classifier.
Parameters
----------
X : nested pandas DataFrame of shape [n_instances, 1]
Nested dataframe with univariate time-series in cells.
y : array-like, shape = [n_instances] The class labels.
Returns
-------
self : object
"""
X, y = check_X_y(X, y, enforce_univariate=True)
self.n_classes = np.unique(y).shape[0]
self.classes_ = class_distribution(np.asarray(y).reshape(-1, 1))[0][0]
cv_size = 10
_, counts = np.unique(y, return_counts=True)
min_class = np.min(counts)
if min_class < cv_size:
cv_size = min_class
self.stc = ShapeletTransformClassifier(
**self.stc_params,
random_state=self.random_state,
)
self.stc.fit(X, y)
if self.verbose > 0:
print("STC ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
train_preds = cross_val_predict(
ShapeletTransformClassifier(
**self.stc_params,
random_state=self.random_state,
),
X=X,
y=y,
cv=cv_size,
n_jobs=self.n_jobs,
)
self.stc_weight = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"STC train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("STC weight = " + str(self.stc_weight)) # noqa
self.tsf = TimeSeriesForestClassifier(
**self.tsf_params,
random_state=self.random_state,
n_jobs=self.n_jobs,
)
self.tsf.fit(X, y)
if self.verbose > 0:
print("TSF ", datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
train_preds = cross_val_predict(
TimeSeriesForestClassifier(**self.tsf_params,
random_state=self.random_state),
X=X,
y=y,
cv=cv_size,
n_jobs=self.n_jobs,
)
self.tsf_weight = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"TSF train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("TSF weight = " + str(self.tsf_weight)) # noqa
self.rise = RandomIntervalSpectralForest(
**self.rise_params,
random_state=self.random_state,
n_jobs=self.n_jobs,
)
self.rise.fit(X, y)
if self.verbose > 0:
print("RISE ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y")) # noqa
train_preds = cross_val_predict(
RandomIntervalSpectralForest(
**self.rise_params,
random_state=self.random_state,
),
X=X,
y=y,
cv=cv_size,
n_jobs=self.n_jobs,
)
self.rise_weight = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"RISE train estimate ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("RISE weight = " + str(self.rise_weight)) # noqa
self.cboss = ContractableBOSS(**self.cboss_params,
random_state=self.random_state,
n_jobs=self.n_jobs)
self.cboss.fit(X, y)
train_probs = self.cboss._get_train_probs(X)
train_preds = self.cboss.classes_[np.argmax(train_probs, axis=1)]
self.cboss_weight = accuracy_score(y, train_preds)**4
if self.verbose > 0:
print( # noqa
"cBOSS (estimate included) ",
datetime.now().strftime("%H:%M:%S %d/%m/%Y"),
)
print("cBOSS weight = " + str(self.cboss_weight)) # noqa
self._is_fitted = True
return self
def predict(self, X):
"""Make predictions for all cases in X.
Parameters
----------
X : The testing input samples of shape [n_instances,1].
Returns
-------
output : numpy array of shape = [n_instances]
"""
rng = check_random_state(self.random_state)
return np.array([
self.classes_[int(rng.choice(np.flatnonzero(prob == prob.max())))]
for prob in self.predict_proba(X)
])
def predict_proba(self, X):
"""Make class probability estimates on each case in X.
Parameters
----------
X - pandas dataframe of testing data of shape [n_instances,1].
Returns
-------
output : numpy array of shape =
[n_instances, num_classes] of probabilities
"""
self.check_is_fitted()
X = check_X(X, enforce_univariate=True)
dists = np.zeros((X.shape[0], self.n_classes))
dists = np.add(
dists,
self.stc.predict_proba(X) *
(np.ones(self.n_classes) * self.stc_weight),
)
dists = np.add(
dists,
self.tsf.predict_proba(X) *
(np.ones(self.n_classes) * self.tsf_weight),
)
dists = np.add(
dists,
self.rise.predict_proba(X) *
(np.ones(self.n_classes) * self.rise_weight),
)
dists = np.add(
dists,
self.cboss.predict_proba(X) *
(np.ones(self.n_classes) * self.cboss_weight),
)
return dists / dists.sum(axis=1, keepdims=True)
def set_classifier(cls, resampleId=None):
"""
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.
:param cls: String indicating which classifier you want
:param 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, time_contract_in_mins=1
)
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 build_model(dataset,
pipeline,
experiment,
current_target='class',
test_size=0.3):
models_dir = './results/{}_{}_{}/models/'.format(dataset, pipeline,
experiment)
reports_dir = './results/{}_{}_{}/reports/'.format(dataset, pipeline,
experiment)
experiment_index_file = './results/{}_{}_{}/index.json'.format(
dataset, pipeline, experiment)
log_file = './results/{}_{}_{}/model_build.log'.format(
dataset, pipeline, experiment)
scoring = make_scorer(precision_score, zero_division=1, average='micro')
os.makedirs(models_dir, exist_ok=True)
os.makedirs(reports_dir, exist_ok=True)
# Setup logging
logger.setup(filename=log_file,
filemode='w',
root_level=logging.DEBUG,
log_level=logging.DEBUG,
logger='build_model')
index_name = 'index'
if '.' in dataset:
splits = dataset.split(".")
dataset = splits[0]
index_name = splits[1]
# Load the dataset index
dataset_index = load_dataset(dataset,
return_index=True,
index_name=index_name)
# Dynamically import the pipeline we want to use for building the model
logger.info('Start experiment: {} using {} on {} with target {}'.format(
experiment, pipeline, dataset, current_target))
reports = ReportCollection(dataset, pipeline, experiment)
for _sym, data in {'BTC': dataset_index['BTC']}.items():
try:
logger.info('Start processing: {}'.format(_sym))
features = pd.read_csv(data['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
targets = pd.read_csv(data['target_csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
# Drop columns whose values are all NaN, as well as rows with ANY nan value, then
# replace infinity values with nan so that they can later be imputed to a finite value
features = features.dropna(
axis='columns', how='all').dropna().replace([np.inf, -np.inf],
np.nan)
target = targets.loc[features.index][current_target]
#X_train, X_test, y_train, y_test = train_test_split(features, target, shuffle=False, test_size=test_size)
all_size = features.shape[0]
train_size = int(all_size * (1 - test_size))
features = detabularise(
features[[c for c in features.columns if 'close' in c]])
X_train = features.iloc[0:train_size]
y_train = target.iloc[0:train_size]
X_test = features.iloc[train_size:all_size]
y_test = target.iloc[train_size:all_size]
# Summarize distribution
logger.info("Start Grid search")
clf = ShapeletTransformClassifier(time_contract_in_mins=5)
clf.fit(X_train, y_train)
print('{} Score: {}'.format(_sym, clf.score(X_test, y_test)))
pred = clf.predict(X_test)
print(classification_report(y_test, pred))
logger.info("End Grid search")
logger.info("--- {} end ---".format(_sym))
except Exception as e:
logger.error(
"Exception while building model pipeline: {} dataset: {} symbol: {}\nException:\n{}"
.format(pipeline, dataset, _sym, e))
traceback.print_exc()
return reports
