def test_y_proba_on_gunpoint():
X, y = load_gunpoint(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.1,
random_state=42)
estimator = RandomIntervalSpectralForest(random_state=42, n_estimators=20)
estimator.fit(X_train, y_train)
actual = estimator.predict_proba(X_test)
np.testing.assert_array_equal(actual, expected)
def test_risf_on_unit_test_data():
"""Test of RandomIntervalSpectralEnsemble on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
X_test, y_test = load_unit_test(split="test")
indices = np.random.RandomState(0).choice(len(y_train), 10, replace=False)
# train RISE
rise = RandomIntervalSpectralForest(n_estimators=10, random_state=0)
rise.fit(X_train, y_train)
# assert probabilities are the same
probas = rise.predict_proba(X_test.iloc[indices])
testing.assert_array_equal(probas, rise_unit_test_probas)
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)
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.
n_jobs_ : int
The number of threads used.
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:multivariate": False,
"capability:unequal_length": False,
"capability:missing_values": False,
"capability:train_estimate": False,
"capability: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,
):
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.n_classes_ = 0
self.classes_ = []
self.n_jobs_ = n_jobs
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.
"""
self.n_jobs_ = check_n_jobs(self.n_jobs)
self.n_classes_ = np.unique(y).shape[0]
self.classes_ = class_distribution(np.asarray(y).reshape(-1, 1))[0][0]
# 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.n_jobs_,
)
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.n_jobs_,
)
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.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
# Build RISE
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
# Find RISE weight using train set estimate found through CV
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
# Build cBOSS
self._cboss = ContractableBOSS(**self._cboss_params,
random_state=self.random_state,
n_jobs=self.n_jobs_)
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
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)