def test_segmentxyforecast():
Nt = 100
width = 5
vars = 5
# lets do a forecast test
seg = transform.SegmentXYForecast(width=width, forecast=5)
Xt = [
np.random.rand(Nt, vars),
np.random.rand(2 * Nt, vars),
np.random.rand(Nt, vars)
]
Xc = np.random.rand(3, 4)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = make_ts_data(Xt, Xc)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width, vars)
assert Xsc.shape == (N, 4)
# univariate X
vars = 1
seg = transform.SegmentXYForecast(width=width, forecast=5)
X = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width)
def test_util():
df = load_watch()
data = util.make_ts_data(df['X'], df['side'])
util.get_ts_data_parts(data)
util.check_ts_data(data, df['y'])
util.check_ts_data(df['X'], df['y'])
util.ts_stats(df['X'], df['y'], fs=1., class_labels=df['y_labels'])
def test_feature_rep():
# multivariate ts
frep = transform.FeatureRep(features=all_features())
X = np.random.rand(100, 10, 5)
y = np.ones(100)
frep.fit(X, y)
Xt = frep.transform(X)
assert Xt.shape[0] == len(X)
assert len(frep.f_labels) == Xt.shape[1]
# univariate ts
X = np.random.rand(100, 10)
y = np.ones(100)
frep.fit(X, y)
Xt = frep.transform(X)
assert Xt.shape[0] == len(X)
assert len(frep.f_labels) == Xt.shape[1]
# single feature
frep = transform.FeatureRep(features={'mean': mean})
frep.fit(X, y)
Xt = frep.transform(X)
assert Xt.shape[0] == len(X)
assert len(frep.f_labels) == Xt.shape[1]
assert Xt.shape[1] == 1
# ts with multivariate contextual data
frep = transform.FeatureRep(features=all_features())
X = make_ts_data(np.random.rand(100, 10, 5), np.random.rand(100, 3))
y = np.ones(100)
frep.fit(X, y)
Xt = frep.transform(X)
assert Xt.shape[0] == len(X)
assert len(frep.f_labels) == Xt.shape[1]
# ts with univariate contextual data
X = make_ts_data(np.random.rand(100, 10, 5), np.random.rand(100))
y = np.ones(100)
frep.fit(X, y)
Xt = frep.transform(X)
assert Xt.shape[0] == len(X)
assert len(frep.f_labels) == Xt.shape[1]
def test_pipe_regression():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [np.random.rand(1000)]
est = Pipeline([('ftr', FeatureRep()), ('ridge', Ridge())])
pipe = SegPipe(est, segmenter=SegmentXY())
pipe.fit(X, y)
pipe.predict(X, y)
pipe.score(X, y)
# context data, single time seres
Xt = [np.random.rand(1000, 10)]
Xc = [np.random.rand(3)]
X = make_ts_data(Xt, Xc)
y = [np.random.rand(1000)]
pipe.fit(X, y)
pipe.predict(X, y)
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 = make_ts_data(Xt, Xc)
y = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)]
pipe.fit(X, y)
pipe.predict(X, y)
pipe.score(X, y)
# cross val
Xt = np.array([np.random.rand(1000, 10) for i in range(5)])
Xc = np.random.rand(5, 3)
X = make_ts_data(Xt, Xc)
y = np.array([np.random.rand(1000) for i in range(5)])
cross_validate(pipe, X, y)
# transform pipe
est = Pipeline([('ftr', FeatureRep()), ('scaler', StandardScaler())])
pipe = SegPipe(est, segmenter=SegmentXY())
Xt = [
np.random.rand(1000, 10),
np.random.rand(100, 10),
np.random.rand(500, 10)
]
Xc = np.random.rand(3, 3)
X = make_ts_data(Xt, Xc)
y = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)]
pipe.fit(X, y)
pipe.transform(X, y)
pipe.fit_transform(X, y)
def test_pipe_classification():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [5]
est = Pipeline([('ftr', FeatureRep()),
('ridge', RandomForestClassifier())])
pipe = SegPipe(est, segmenter=SegmentX())
pipe.fit(X, y)
pipe.predict(X, y)
pipe.score(X, y)
# context data, single time seres
Xt = [np.random.rand(1000, 10)]
Xc = [np.random.rand(3)]
X = make_ts_data(Xt, Xc)
y = [5]
pipe.fit(X, y)
pipe.predict(X, y)
pipe.score(X, y)
# multiple time series
Xt = [
np.random.rand(1000, 10),
np.random.rand(100, 10),
np.random.rand(500, 10)
]
Xc = np.random.rand(3, 3)
X = make_ts_data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.predict(X, y)
pipe.score(X, y)
# univariate data
Xt = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)]
Xc = np.random.rand(3)
X = make_ts_data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.predict(X, y)
pipe.score(X, y)
# transform pipe
est = Pipeline([('ftr', FeatureRep()), ('scaler', StandardScaler())])
pipe = SegPipe(est, segmenter=SegmentX())
Xt = [
np.random.rand(1000, 10),
np.random.rand(100, 10),
np.random.rand(500, 10)
]
Xc = np.random.rand(3, 3)
X = make_ts_data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.transform(X, y)
pipe.fit_transform(X, y)
def test_segmentx():
width = 5
vars = 5
seg = transform.SegmentX(width=width)
# multivariate ts data without context data
Xt = [
np.random.rand(100, vars),
np.random.rand(100, vars),
np.random.rand(100, vars)
]
y = np.random.rand(3)
X = make_ts_data(Xt)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width, vars)
# univariate ts data without context
Xt = [np.random.rand(100), np.random.rand(100), np.random.rand(100)]
y = np.random.rand(3)
X = make_ts_data(Xt)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width)
# multivariate ts data with context data
Xt = [
np.random.rand(100, vars),
np.random.rand(200, vars),
np.random.rand(50, vars)
]
Xc = np.random.rand(3, 4)
y = np.random.rand(3)
X = make_ts_data(Xt, Xc)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width, vars)
assert Xsc.shape == (N, 4)
# ts data with univariate context data
Xt = [np.random.rand(100), np.random.rand(200), np.random.rand(50)]
Xc = np.random.rand(3)
y = np.random.rand(3)
X = make_ts_data(Xt, Xc)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width)
assert Xsc.shape == (N, 1)
# same number as context vars and time vars
# this would cause broadcasting failure before implementation of TS_Data class
Xt = [
np.random.rand(100, vars),
np.random.rand(200, vars),
np.random.rand(50, vars)
]
Xc = np.random.rand(3, vars)
y = np.random.rand(3)
X = make_ts_data(Xt, Xc)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width, vars)
assert Xsc.shape == (N, 5)