2b2e99fee3173fc14b98fca060eb83a3cbdfa0f9,seglearn/tests/test_pipe.py,,test_pipe_forecast,#,135

Before Change

    est = Pipeline([("ftr", FeatureRep()),
                    ("scaler", StandardScaler())])

    pipe = SegPipe(est, segmenter=SegmentXYForecast())


    Xt = [np.random.rand(1000, 10), np.random.rand(100, 10), np.random.rand(500, 10)]
    Xc = np.random.rand(3,3)

After Change

    X = [np.random.rand(1000,10)]
    y = [np.random.rand(1000)]
    
    pipe = Pype([("seg", SegmentXYForecast()),
                 ("ftr", FeatureRep()),
                 ("ridge", Ridge())])    

    pipe.fit(X, y)
    pipe.transform_predict(X, y)
    pipe.predict(X)
    pipe.score(X, y)

    // context data, single time seres
    Xt = [np.random.rand(1000, 10)]
    Xc = [np.random.rand(3)]
    X = TS_Data(Xt, Xc)
    y = [np.random.rand(1000)]

    pipe.fit(X, y)
    pipe.transform_predict(X, y)
    pipe.predict(X)
    pipe.score(X, y)

    // multiple time seres
    Xt = [np.random.rand(1000, 10), np.random.rand(100, 10), np.random.rand(500, 10)]
    Xc = np.random.rand(3,3)
    X = TS_Data(Xt, Xc)
    y = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)]

    pipe.fit(X, y)
    pipe.transform_predict(X, y)
    pipe.predict(X)
    pipe.score(X, y)

    // cross val

Instances