MINIROCKET

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

MiniRocketClassifier


def MiniRocketClassifier(
    num_features:int=10000, max_dilations_per_kernel:int=32, random_state:NoneType=None,
    alphas:tuple=(0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0), normalize_features:bool=True, memory:NoneType=None,
    verbose:bool=False, scoring:NoneType=None, class_weight:NoneType=None, kwargs:VAR_KEYWORD
):

Time series classification using MINIROCKET features and a linear classifier

source

load_minirocket


def load_minirocket(
    fname, path:str='./models'
):

Call self as a function.

source

MiniRocketRegressor


def MiniRocketRegressor(
    num_features:int=10000, max_dilations_per_kernel:int=32, random_state:NoneType=None,
    alphas:tuple=(0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0), normalize_features:bool=True, memory:NoneType=None,
    verbose:bool=False, scoring:NoneType=None, kwargs:VAR_KEYWORD
):

Time series regression using MINIROCKET features and a linear regressor

source

load_minirocket


def load_minirocket(
    fname, path:str='./models'
):

Call self as a function.

source

MiniRocketVotingClassifier


def MiniRocketVotingClassifier(
    n_estimators:int=5, weights:NoneType=None, n_jobs:int=-1, num_features:int=10000,
    max_dilations_per_kernel:int=32, random_state:NoneType=None,
    alphas:tuple=(0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0), normalize_features:bool=True, memory:NoneType=None,
    verbose:bool=False, scoring:NoneType=None, class_weight:NoneType=None, kwargs:VAR_KEYWORD
):

Time series classification ensemble using MINIROCKET features, a linear classifier and majority voting

source

get_minirocket_preds


def get_minirocket_preds(
    X, fname, path:str='./models', model:NoneType=None
):

Call self as a function.

source

MiniRocketVotingRegressor


def MiniRocketVotingRegressor(
    n_estimators:int=5, weights:NoneType=None, n_jobs:int=-1, num_features:int=10000,
    max_dilations_per_kernel:int=32, random_state:NoneType=None,
    alphas:tuple=(0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0), normalize_features:bool=True, memory:NoneType=None,
    verbose:bool=False, scoring:NoneType=None, kwargs:VAR_KEYWORD
):

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