def test_highLevelsktime(network=catch22ForestClassifier()):
'''
truly generalised test with sktime tasks/strategies
load data, build task
construct classifier, build strategy
fit,
score
'''
print("start test_highLevelsktime()")
from sktime.highlevel.tasks import TSCTask
from sktime.highlevel.strategies import TSCStrategy
from sklearn.metrics import accuracy_score
train = load_gunpoint(split='TRAIN')
test = load_gunpoint(split='TEST')
task = TSCTask(target='class_val', metadata=train)
strategy = TSCStrategy(network)
strategy.fit(task, train.iloc[:10])
y_pred = strategy.predict(test.iloc[:10])
y_test = test.iloc[:10][task.target].values.astype(np.float)
print(accuracy_score(y_test, y_pred))
print("End test_highLevelsktime()")
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 test_network(network=catch22ForestClassifier()):
# sklearn compatibility
test_basic_univariate(network)
# test_basic_multivariate(network)
test_pipeline(network)
test_highLevelsktime(network)
def all_networks_all_tests():
networks = [
catch22ForestClassifier(),
]
for network in networks:
print('\n\t\t' + network.__class__.__name__ + ' testing started')
test_network(network)
print('\t\t' + network.__class__.__name__ + ' testing finished')
def test_basic_univariate(network=catch22ForestClassifier()):
'''
just a super basic test with gunpoint,
load data,
construct classifier,
fit,
score
'''
print("Start test_basic()")
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 = network.fit(X_train[:10], y_train[:10])
print(network.score(X_test[:10], y_test[:10]))
print("End test_basic()")
if __name__ == "__main__":
X_train, y_train = load_gunpoint(split='TRAIN', return_X_y=True)
X_test, y_test = load_gunpoint(split='TEST', return_X_y=True)
# # alternatively load data from disk
# X_train, y_train = load_from_tsfile_to_dataframe(
# '/home/carl/Downloads/Matthew_errorData/UWaveGestureLibraryZ_TRAIN.ts')
# X_test, y_test = load_from_tsfile_to_dataframe(
# '/home/carl/Downloads/Matthew_errorData/UWaveGestureLibraryZ_TEST.ts')
# In case the labels are not numbers
if not isinstance(y_train[0], (int, float)):
le = LabelEncoder()
le.fit(np.unique(y_train))
y_train = le.transform(y_train)
y_test = le.transform(y_test)
else:
y_train = y_train.astype(np.int64)
y_test = y_test.astype(np.int64)
f = catch22ForestClassifier()
c = time.time()
f.fit(X_train, y_train)
print("Classes: ", f.classes_)
print("Num dimensions: ", X_train.shape[1])
print("Num patterns: ", X_train.shape[0])
print("Num timepoints: ", len(X_train.iloc[0].iloc[0]))
print("Accuracy: ", f.score(X_test, y_test))
print("Time spent: ", round(time.time() - c))