MINIROCKET

A Very Fast (Almost) Deterministic Transform for Time Series Classification.

MiniRocketClassifier

 MiniRocketClassifier (num_features=10000, max_dilations_per_kernel=32,
                       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 MINIROCKET features and a linear classifier

source

load_minirocket

 load_minirocket (fname, path='./models')

source

MiniRocketRegressor

 MiniRocketRegressor (num_features=10000, max_dilations_per_kernel=32,
                      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 MINIROCKET features and a linear regressor

source

load_minirocket

 load_minirocket (fname, path='./models')

source

MiniRocketVotingClassifier

 MiniRocketVotingClassifier (n_estimators=5, weights=None, n_jobs=-1,
                             num_features=10000,
                             max_dilations_per_kernel=32,
                             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 ensemble using MINIROCKET features, a linear classifier and majority voting

source

get_minirocket_preds

 get_minirocket_preds (X, fname, path='./models', model=None)

source

MiniRocketVotingRegressor

 MiniRocketVotingRegressor (n_estimators=5, weights=None, n_jobs=-1,
                            num_features=10000,
                            max_dilations_per_kernel=32,
                            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 ensemble using MINIROCKET features, a linear regressor and a voting regressor

# Univariate classification with sklearn-type API
dsid = 'OliveOil'
fname = 'MiniRocketClassifier'
X_train, y_train, X_test, y_test = get_UCR_data(dsid)
cls = MiniRocketClassifier()
cls.fit(X_train, y_train)
cls.save(fname)
pred = cls.score(X_test, y_test)
del cls
cls = load_minirocket(fname)
test_eq(cls.score(X_test, y_test), pred)

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

# Multivariate classification with sklearn-type API
dsid = 'NATOPS'
X_train, y_train, X_test, y_test = get_UCR_data(dsid)
cls = MiniRocketClassifier()
cls.fit(X_train, y_train)
cls.score(X_test, y_test)

0.9277777777777778

# Multivariate classification with sklearn-type API
dsid = 'NATOPS'
X_train, y_train, X_test, y_test = get_UCR_data(dsid)
cls = MiniRocketVotingClassifier(5)
cls.fit(X_train, y_train)
cls.score(X_test, y_test)

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

0.9166666666666666

from sklearn.metrics import mean_squared_error

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

0.04099244037606886

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

2.2938026879322577

# Multivariate regression ensemble with sklearn-type API
if X_train is not None:
    reg = MiniRocketVotingRegressor(5, scoring=rmse_scorer)
    reg.fit(X_train, y_train)
    y_pred = reg.predict(X_test)
    print(mean_squared_error(y_test, y_pred, squared=False))

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

2.286295546348893