--- 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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/Users/nacho/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/errors.py:154: UserWarning: Insufficiently recent colorama version found. Numba requires colorama >= 0.3.9
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create_scripts
<function tsai.imports.create_scripts(max_elapsed=60)>
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class MiniRocketClassifier[source]

MiniRocketClassifier() :: Pipeline

Time series classification using MINIROCKET features and a linear classifier

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MiniRocketClassifier.__doc__
'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)
0.9055555555555556
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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)
0.9166666666666666
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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)
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)
rmse = mean_squared_error(y_test, y_pred, squared=False)
rmse
0.041569300419880065
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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)
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)
rmse = mean_squared_error(y_test, y_pred, squared=False)
rmse
2.323818048053846
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reg = MiniRocketVotingRegressor(5, scoring=rmse_scorer)
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)
rmse = mean_squared_error(y_test, y_pred, squared=False)
rmse
2.2630456530301166
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