--- 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" ---
# 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))
# 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))
# 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))
# 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))