--- title: MINIROCKET keywords: fastai sidebar: home_sidebar summary: "A Very Fast (Almost) Deterministic Transform for Time Series Classification." description: "A Very Fast (Almost) Deterministic Transform for Time Series Classification." nb_path: "nbs/111b_models.MINIROCKET.ipynb" ---
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class MiniRocketClassifier[source]

MiniRocketClassifier() :: Pipeline

Time series classification using MINIROCKET features and a linear classifier

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load_minirocket[source]

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

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class MiniRocketRegressor[source]

MiniRocketRegressor() :: Pipeline

Time series regression using MINIROCKET features and a linear regressor

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load_minirocket[source]

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

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class MiniRocketVotingClassifier[source]

MiniRocketVotingClassifier() :: VotingClassifier

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

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get_minirocket_preds[source]

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

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class MiniRocketVotingRegressor[source]

MiniRocketVotingRegressor() :: VotingRegressor

Time series regression ensemble using MINIROCKET features, a linear regressor and a voting regressor

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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)
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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)
0.9222222222222223
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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)
0.9222222222222223
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from sklearn.metrics import mean_squared_error
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))
0.041632847308248865
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from sklearn.metrics import mean_squared_error
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))
2.390817419056604
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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))
2.208260730602593
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