def test_pipeline(network=CNNClassifier()):
'''
slightly more generalised test with sktime pipelines
load data,
construct pipeline with classifier,
fit,
score
'''
print("Start test_pipeline()")
from sktime.pipeline import Pipeline
# just a simple (useless) pipeline for the purposes of testing
# that the keras network is compatible with that system
steps = [
('clf', network)
]
clf = Pipeline(steps)
X_train, y_train = load_italy_power_demand(split='TRAIN', return_X_y=True)
X_test, y_test = load_italy_power_demand(split='TEST', return_X_y=True)
hist = clf.fit(X_train[:10], y_train[:10])
print(clf.score(X_test[:10], y_test[:10]))
print("End test_pipeline()")
def test_pipeline(network=catch22ForestClassifier()):
'''
slightly more generalised test with sktime pipelines
load data,
construct pipeline with classifier,
fit,
score
'''
print("Start test_pipeline()")
from sktime.pipeline import Pipeline
# just a simple (useless) pipeline
steps = [('clf', network)]
clf = Pipeline(steps)
X_train, y_train = load_gunpoint(split='TRAIN', return_X_y=True)
X_test, y_test = load_gunpoint(split='TEST', return_X_y=True)
hist = clf.fit(X_train[:10], y_train[:10])
print(clf.score(X_test[:10], y_test[:10]))
print("End test_pipeline()")
def concatenateMethod(Classifier, x_train, y_train, x_test, y_test):
steps = [('concatenate', ColumnConcatenator()),
('classify', Classifier(n_estimators=10))]
clf = Pipeline(steps)
clf.fit(x_train, y_train)
return clf.score(x_test, y_test)