def test_cboss_on_power_demand():
# load power demand data
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)
indices = np.random.RandomState(0).permutation(100)
# train cBOSS
cboss = ContractableBOSS(n_parameter_samples=50,
max_ensemble_size=10,
random_state=0)
cboss.fit(X_train, y_train)
score = cboss.score(X_test.iloc[indices], y_test[indices])
assert score >= 0.9
def build_classifiers():
"""Examples of building a classifier.
1. Directly from 2D numpy arrays.
2. Directly from 3D numpy arrays.
3. From a nested pandas.
4. From a baked in dataset.
5. From any UCR/UEA dataset downloaded from timeseriesclassification.com.
"""
# Create an array
# Random forest, rocket and HC2.
randf = RandomForestClassifier()
trainX, train_y, testX, test_y = make_toy_2d_problem()
X = trainX.reshape(trainX.shape[0], 1, trainX.shape[1])
train_y = pd.Series(train_y)
test_y = pd.Series(test_y)
# randf.fit(trainX, train_y)
cls1 = ContractableBOSS(time_limit_in_minutes=1)
# cls2 = BOSSEnsemble()
cls1.fit(trainX, train_y)
print(" CBOSS acc = ", cls1.score(testX, test_y))