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 = TimeSeriesForestClassifier(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_tsf_on_unit_test_data():
"""Test of TimeSeriesForestClassifier 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 TSF
tsf = TimeSeriesForestClassifier(n_estimators=10, random_state=0)
tsf.fit(X_train, y_train)
# assert probabilities are the same
probas = tsf.predict_proba(X_test.iloc[indices])
testing.assert_array_equal(probas, tsf_unit_test_probas)
def StartTrain(self):
train_files = glob.glob(self.lineEdit.text() + "\\*.csv")
test_files = glob.glob(self.lineEdit_2.text() + "\\*.csv")
train_li = []
for filename in train_files:
df = pd.read_csv(filename, index_col=None, header=None,usecols=[2])
train_li.append(df)
X_df = pd.concat(train_li, axis=1, ignore_index=True)
X_df = X_df.T
test_li = []
for filename in test_files:
df = pd.read_csv(filename, index_col=None, header=None,usecols=[2])
test_li.append(df)
X_df_ng = pd.concat(test_li, axis=1, ignore_index=True)
X_df_ng = X_df_ng.T
X_df = X_df.append(X_df_ng)
X_df_tab = from_2d_array_to_nested(X_df)
Y_df_ok = np.zeros(len(test_li), dtype="int32")
Y_df_ng = np.ones(len(train_li), dtype="int32")
Y_df = np.concatenate([Y_df_ok, Y_df_ng], 0)
X_train, X_test, y_train, y_test = train_test_split(X_df_tab, Y_df, test_size= (100 - self.horizontalSlider.value()) / 100)
self.tableWidget.setRowCount(0)
selectedModel = self.comboBox.currentText()
if(selectedModel == "RandomForestClassifier"):
classifier = make_pipeline(Tabularizer(), RandomForestClassifier())
classifier.fit(X_train, y_train)
self.lineEdit_5.setText(str(classifier.score(X_train, y_train)))
self.lineEdit_6.setText(str(classifier.score(X_test, y_test)))
for i in range(len(X_test)):
row = self.tableWidget.rowCount()
self.tableWidget.setRowCount(row)
classifier_preds = classifier.predict(X_test.iloc[i].to_frame())
self.addTableRow(self.tableWidget, [str(i),str(y_test[i]), str(classifier_preds)])
elif(selectedModel == "RocketClassifier"):
rocket = RocketClassifier()
rocket.fit(X_train, y_train)
self.lineEdit_5.setText(str(rocket.score(X_train, y_train)))
self.lineEdit_6.setText(str(rocket.score(X_test, y_test)))
for i in range(len(X_test)):
row = self.tableWidget.rowCount()
self.tableWidget.setRowCount(row)
rocket_preds = rocket.predict(X_test.iloc[i].to_frame())
self.addTableRow(self.tableWidget, [str(i),str(y_test[i]), str(rocket_preds)])
elif(selectedModel == "TimeSeriesForestClassifier"):
tsf = TimeSeriesForestClassifier(n_estimators=50, random_state=47)
tsf.fit(X_train, y_train)
self.lineEdit_5.setText(str(tsf.score(X_train, y_train)))
self.lineEdit_6.setText(str(tsf.score(X_test, y_test)))
for i in range(len(X_test)):
row = self.tableWidget.rowCount()
self.tableWidget.setRowCount(row)
tsf_preds = tsf.predict(X_test.iloc[i].to_frame())
self.addTableRow(self.tableWidget, [str(i),str(y_test[i]), str(tsf_preds)])
elif(selectedModel == "RandomIntervalSpectralEnsemble"):
rise = RandomIntervalSpectralEnsemble(n_estimators=50, random_state=47)
rise.fit(X_train, y_train)
self.lineEdit_5.setText(str(rise.score(X_train, y_train)))
self.lineEdit_6.setText(str(rise.score(X_test, y_test)))
for i in range(len(X_test)):
row = self.tableWidget.rowCount()
self.tableWidget.setRowCount(row)
rise_preds = rise.predict(X_test.iloc[i].to_frame())
self.addTableRow(self.tableWidget, [str(i),str(y_test[i]), str(rise_preds)])
elif(selectedModel == "SupervisedTimeSeriesForest"):
stsf = SupervisedTimeSeriesForest(n_estimators=50, random_state=47)
stsf.fit(X_train, y_train)
self.lineEdit_5.setText(str(stsf.score(X_train, y_train)))
self.lineEdit_6.setText(str(stsf.score(X_test, y_test)))
for i in range(len(X_test)):
row = self.tableWidget.rowCount()
self.tableWidget.setRowCount(row)
stsf_preds = rise.predict(X_test.iloc[i].to_frame())
self.addTableRow(self.tableWidget, [str(i),str(y_test[i]), str(stsf_preds)])
else:
print("None")
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)
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # time series forest classifier from sktime.classification.interval_based import TimeSeriesForestClassifier TimeSeriesForestClassifier(n_estimators=200, min_interval=3, n_jobs=1, random_state=None|0) # cannot handle multivariate series .fit(X, y, **kwargs) .predict(X, **kwargs) .predict_proba(X, **kwargs) # example: time_series = [1,2,3,4,5,6,7,8,9] x_train = [ [1,2,3], [2,3,4], [3,4,5], [4,5,6] ] y_train = [ 1, 1, 0, 0, ] model = TimeSeriesForestClassifier(n_estimators=5, min_interval=2) model.fit(np.array(x_train), np.array(y_train)) y_pred = model.predict(np.array(x_train)) print(y_pred) # [1,1,0,0] # examine input output structure: 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) x = [ [list(j) for j in i] for i in X_train.to_numpy()] y = list(y_train) # multivariate workaround (chain 3rd dim into flat 2nd) time_series = [ [1,9], [2,8], [3,7], [4,6], [5,5], [6,4], [7,3], [8,2], [9,1] ] x_train_2d = [ [1,9,2,8,3,7], [2,8,3,7,4,6], [3,7,4,6,5,5], [4,6,5,5,6,4] ] # instead of: x_train_3d = [ [[1,9],[2,8],[3,7]], [[2,8],[3,7],[4,6]], [[3,7],[4,6],[5,5]], ... ]
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)