--- title: ROCKET keywords: fastai sidebar: home_sidebar summary: "ROCKET (RandOm Convolutional KErnel Transform) functions for univariate and multivariate time series." description: "ROCKET (RandOm Convolutional KErnel Transform) functions for univariate and multivariate time series." nb_path: "nbs/111_models.ROCKET.ipynb" ---
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class RocketClassifier[source]

RocketClassifier() :: Pipeline

Time series classification using ROCKET features and a linear classifier

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load_rocket[source]

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

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class RocketRegressor[source]

RocketRegressor() :: Pipeline

Time series regression using ROCKET features and a linear regressor

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# 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))
0.9
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# 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))
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# Univariate regression with sklearn-type API
from sklearn.metrics import mean_squared_error
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))
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# Multivariate regression with sklearn-type API
from sklearn.metrics import mean_squared_error
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))
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