ROCKET

ROCKET (RandOm Convolutional KErnel Transform) functions for univariate and multivariate time series.

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RocketClassifier

 RocketClassifier (num_kernels=10000, normalize_input=True,
                   random_state=None, alphas=array([1.e-03, 1.e-02,
                   1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]),
                   normalize_features=True, memory=None, verbose=False,
                   scoring=None, class_weight=None, **kwargs)

Time series classification using ROCKET features and a linear classifier

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load_rocket

 load_rocket (fname='Rocket', path='./models')

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RocketRegressor

 RocketRegressor (num_kernels=10000, normalize_input=True,
                  random_state=None, alphas=array([1.e-03, 1.e-02, 1.e-01,
                  1.e+00, 1.e+01, 1.e+02, 1.e+03]),
                  normalize_features=True, memory=None, verbose=False,
                  scoring=None, **kwargs)

Time series regression using ROCKET features and a linear regressor

# Univariate classification with sklearn-type API
dsid = 'OliveOil'
fname = 'RocketClassifier'
X_train, y_train, X_test, y_test = get_UCR_data(dsid, Xdtype='float64')
cls = RocketClassifier()
cls.fit(X_train, y_train)
cls.save(fname)
del cls
cls = load_rocket(fname)
print(cls.score(X_test, y_test))

OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.

0.9

# Multivariate classification with sklearn-type API
dsid = 'NATOPS'
fname = 'RocketClassifier'
X_train, y_train, X_test, y_test = get_UCR_data(dsid, Xdtype='float64')
cls = RocketClassifier()
cls.fit(X_train, y_train)
cls.save(fname)
del cls
cls = load_rocket(fname)
print(cls.score(X_test, y_test))

0.8666666666666667

from sklearn.metrics import mean_squared_error

# Univariate regression with sklearn-type API
dsid = 'Covid3Month'
fname = 'RocketRegressor'
X_train, y_train, X_test, y_test = get_Monash_regression_data(dsid, Xdtype='float64')
if X_train is not None: 
    rmse_scorer = make_scorer(mean_squared_error, greater_is_better=False)
    reg = RocketRegressor(scoring=rmse_scorer)
    reg.fit(X_train, y_train)
    reg.save(fname)
    del reg
    reg = load_rocket(fname)
    y_pred = reg.predict(X_test)
    print(mean_squared_error(y_test, y_pred, squared=False))

0.03908714523468997

# Multivariate regression with sklearn-type API
dsid = 'AppliancesEnergy'
fname = 'RocketRegressor'
X_train, y_train, X_test, y_test = get_Monash_regression_data(dsid, Xdtype='float64')
if X_train is not None: 
    rmse_scorer = make_scorer(mean_squared_error, greater_is_better=False)
    reg = RocketRegressor(scoring=rmse_scorer)
    reg.fit(X_train, y_train)
    reg.save(fname)
    del reg
    reg = load_rocket(fname)
    y_pred = reg.predict(X_test)
    print(mean_squared_error(y_test, y_pred, squared=False))

2.287302226812576