def test_heterogenous_pipeline_column_ensmbler():
X_train, y_train = load_basic_motions("TRAIN", return_X_y=True)
X_test, y_test = load_basic_motions("TEST", return_X_y=True)
n_intervals = 3
steps = [('segment', RandomIntervalSegmenter(n_intervals=n_intervals)),
('transform',
FeatureUnion([('mean',
RowwiseTransformer(
FunctionTransformer(func=np.mean,
validate=False))),
('std',
RowwiseTransformer(
FunctionTransformer(func=np.std,
validate=False)))])),
('clf', DecisionTreeClassifier())]
clf1 = Pipeline(steps, random_state=1)
# dims 0-3 with alternating classifiers.
ct = ColumnEnsembleClassifier([
("RandomIntervalTree", clf1, [0]),
("KNN4", KNNTSC(n_neighbors=1), [4]),
("BOSSEnsemble1 ", BOSSEnsemble(ensemble_size=3), [1]),
("KNN2", KNNTSC(n_neighbors=1), [2]),
("BOSSEnsemble3", BOSSEnsemble(ensemble_size=3), [3]),
])
ct.fit(X_train, y_train)
ct.score(X_test, y_test)
def test_homogeneous_column_ensembler():
X_train, y_train = load_basic_motions("TRAIN", return_X_y=True)
X_test, y_test = load_basic_motions("TEST", return_X_y=True)
cts = HomogeneousColumnEnsembleClassifier(KNNTSC(n_neighbors=1))
cts.fit(X_train, y_train)
cts.score(X_test, y_test) == 1.0
def test_homogeneous_pipeline_column_ensmbler():
X_train, y_train = load_basic_motions("TRAIN", return_X_y=True)
X_test, y_test = load_basic_motions("TEST", return_X_y=True)
ct = ColumnEnsembleClassifier([("KNN%d " % i, KNNTSC(n_neighbors=1), [i])
for i in range(0, X_train.shape[1])])
ct.fit(X_train, y_train)
ct.score(X_test, y_test)
def test_univariate_column_ensembler_init():
ct = ColumnEnsembleClassifier([("KNN1", KNNTSC(n_neighbors=1), [1]),
("KNN2", KNNTSC(n_neighbors=1), [2])])