def test_pipe_PadTrunc():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [5]
pipe = Pype([('trunc', PadTrunc()), ('ftr', FeatureRep()),
('rf', RandomForestClassifier(n_estimators=10))])
classifier_test(pipe, X, y)
# context data, single time seres
Xt = [np.random.rand(1000, 10)]
Xc = [np.random.rand(3)]
X = TS_Data(Xt, Xc)
y = [5]
classifier_test(pipe, 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 = TS_Data(Xt, Xc)
y = [1, 2, 3]
classifier_test(pipe, X, y)
# univariate data
Xt = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)]
Xc = np.random.rand(3)
X = TS_Data(Xt, Xc)
y = [1, 2, 3]
classifier_test(pipe, X, y)
def test_pipe_transformation():
# SegmentX transform pipe
pipe = Pype([('seg', SegmentX()),
('ftr', FeatureRep()),
('scaler', StandardScaler())])
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 = [1, 2, 3]
transformation_test(pipe, X, y)
X = pd.DataFrame(Xc)
X['ts_data'] = Xt
X = TS_Data.from_df(X)
transformation_test(pipe, X, y)
# SegmentXY transform pipe
pipe = Pype([('seg', SegmentXY()),
('ftr', FeatureRep()),
('scaler', StandardScaler())])
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)]
transformation_test(pipe, X, y)
X = pd.DataFrame(Xc)
X['ts_data'] = Xt
X = TS_Data.from_df(X)
transformation_test(pipe, X, y)
# Forecast transform pipe
pipe = Pype([('seg', SegmentXYForecast()),
('ftr', FeatureRep()),
('scaler', StandardScaler())])
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)]
transformation_test(pipe, X, y)
X = pd.DataFrame(Xc)
X['ts_data'] = Xt
X = TS_Data.from_df(X)
transformation_test(pipe, X, y)
# Padtrunc transform pipe
pipe = Pype([('trunc', PadTrunc()),
('ftr', FeatureRep()),
('scaler', StandardScaler())])
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 = [1, 2, 3]
transformation_test(pipe, X, y)
X = pd.DataFrame(Xc)
X['ts_data'] = Xt
X = TS_Data.from_df(X)
transformation_test(pipe, X, y)
def test_ts_data():
# time series data
ts = np.array([
np.random.rand(100, 10),
np.random.rand(200, 10),
np.random.rand(20, 10)
])
c = np.random.rand(3, 10)
data = TS_Data(ts, c)
assert np.array_equal(data.context_data, c)
assert np.array_equal(data.ts_data, ts)
assert isinstance(data[1], TS_Data)
assert np.array_equal(data[1].ts_data, ts[1])
assert np.array_equal(data[1].context_data, c[1])
# segmented time series data
sts = np.random.rand(100, 10, 6)
c = np.random.rand(100, 6)
data = TS_Data(sts, c)
assert isinstance(data[4:10], TS_Data)
assert np.array_equal(data[4:10].ts_data, sts[4:10])
assert np.array_equal(data[4:10].context_data, c[4:10])
sts = np.random.rand(100, 10)
c = np.random.rand(100)
data = TS_Data(sts, c)
assert isinstance(data[4:10], TS_Data)
assert np.array_equal(data[4:10].ts_data, sts[4:10])
assert np.array_equal(data[4:10].context_data, c[4:10])
def test_pipe_regression():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [np.random.rand(1000)]
pipe = Pype([('seg', SegmentXY()), ('ftr', FeatureRep()),
('ridge', Ridge())])
regression_test(pipe, 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)]
regression_test(pipe, 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)]
regression_test(pipe, X, y)
# cross val
Xt = np.array([np.random.rand(1000, 10)] * 5)
Xc = np.random.rand(5, 3)
X = TS_Data(Xt, Xc)
y = np.array([np.random.rand(1000)] * 5)
cross_validate(pipe, X, y, cv=3)
def test_pipe_forecast():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [np.random.rand(1000)]
pipe = Pype([('seg', SegmentXYForecast()), ('ftr', FeatureRep()),
('ridge', Ridge())])
forecast_test(pipe, 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)]
forecast_test(pipe, X, y)
X = pd.DataFrame(Xc)
X['ts_data'] = Xt
X = TS_Data.from_df(X)
forecast_test(pipe, 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)]
forecast_test(pipe, X, y)
X = pd.DataFrame(Xc)
X['ts_data'] = Xt
X = TS_Data.from_df(X)
forecast_test(pipe, X, y)
# cross val
Xt = np.array([np.random.rand(1000, 10)] * 5)
Xc = np.random.rand(5, 3)
X = TS_Data(Xt, Xc)
y = np.array([np.random.rand(1000)] * 5)
cross_validate(pipe, X, y, cv=3)
X = pd.DataFrame(Xc)
Xt = [np.random.rand(1000, 10)] * 5
X['ts_data'] = Xt
X = TS_Data.from_df(X)
cross_validate(pipe, X, y, cv=3)
def test_pipe_classification():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [5]
pipe = Pype([('seg', SegmentX()), ('ftr', FeatureRep()),
('rf', RandomForestClassifier(n_estimators=10))])
classifier_test(pipe, X, y)
# context data, single time seres
Xt = [np.random.rand(1000, 10)]
Xc = [np.random.rand(3)]
X = TS_Data(Xt, Xc)
y = [5]
classifier_test(pipe, X, y)
X = pd.DataFrame(Xc)
X['ts_data'] = Xt
X = TS_Data.from_df(X)
classifier_test(pipe, 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 = TS_Data(Xt, Xc)
y = [1, 2, 3]
classifier_test(pipe, X, y)
X = pd.DataFrame(Xc)
X['ts_data'] = Xt
X = TS_Data.from_df(X)
classifier_test(pipe, X, y)
# univariate data
Xt = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)]
Xc = np.random.rand(3)
X = TS_Data(Xt, Xc)
y = [1, 2, 3]
classifier_test(pipe, X, y)
X = pd.DataFrame(Xc)
X['ts_data'] = Xt
X = TS_Data.from_df(X)
classifier_test(pipe, X, y)
def test_segmentxyforecast():
Nt = 100
width = 5
nvars = 5
# lets do a forecast test
seg = transform.SegmentXYForecast(width=width, forecast=5)
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, 4)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
assert Xsc.shape == (N, 4)
# univariate X
nvars = 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_feature_rep_mix():
union = transform.FeatureRepMix([
('a', transform.FeatureRep(features={'mean': mean}), 0),
('b', transform.FeatureRep(features={'mean': mean}), 1),
('c', transform.FeatureRep(features={'mean': mean}), [2, 3]),
('d', transform.FeatureRep(features={'mean': mean}), slice(0, 2)),
('e', transform.FeatureRep(features={'mean': mean}),
[False, False, True, True]),
])
# multivariate ts
X = np.random.rand(100, 10, 4)
y = np.ones(100)
union.fit(X, y)
Xt = union.transform(X)
assert Xt.shape[0] == len(X)
assert len(union.f_labels) == Xt.shape[1]
# ts with multivariate contextual data
X = TS_Data(np.random.rand(100, 10, 4), np.random.rand(100, 3))
y = np.ones(100)
union.fit(X, y)
Xt = union.transform(X)
assert Xt.shape[0] == len(X)
assert len(union.f_labels) == Xt.shape[1]
# ts with univariate contextual data
X = TS_Data(np.random.rand(100, 10, 4), np.random.rand(100))
y = np.ones(100)
union.fit(X, y)
Xt = union.transform(X)
assert Xt.shape[0] == len(X)
assert len(union.f_labels) == Xt.shape[1]
# univariate ts
uni_union = transform.FeatureRepMix([
('a', transform.FeatureRep(features={'mean': mean}), 0),
('b', transform.FeatureRep(features={'mean': mean}), [0]),
('c', transform.FeatureRep(features={'mean': mean}), slice(0, 1)),
('d', transform.FeatureRep(features={'mean': mean}), [True]),
])
X = np.random.rand(100, 10)
y = np.ones(100)
uni_union.fit(X, y)
Xt = uni_union.transform(X)
assert Xt.shape[0] == len(X)
assert len(uni_union.f_labels) == Xt.shape[1]
def test_temporal_k_fold():
# test length 1 series
splitter = TemporalKFold()
X = [rand(100, 10)]
y = [5]
Xs, ys, cv = splitter.split(X, y)
check_folds(Xs, ys, cv)
X = [rand(100, 10)]
y = [rand(100)]
Xs, ys, cv = splitter.split(X, y)
check_folds(Xs, ys, cv)
Xt = [rand(100, 10)]
Xc = [5]
X = TS_Data(Xt, Xc)
y = [rand(100)]
Xs, ys, cv = splitter.split(X, y)
check_folds(Xs, ys, cv)
# test with lots of series
splitter = TemporalKFold()
Ns = 5
X = np.array([rand(100, 10)] * Ns)
y = rand(Ns)
Xs, ys, cv = splitter.split(X, y)
check_folds(Xs, ys, cv)
X = np.array([rand(100, 10)] * Ns)
y = np.array([rand(100)] * Ns)
Xs, ys, cv = splitter.split(X, y)
check_folds(Xs, ys, cv)
Xt = np.array([rand(100, 10)] * Ns)
Xc = rand(Ns)
X = TS_Data(Xt, Xc)
y = np.array([rand(100)] * Ns)
Xs, ys, cv = splitter.split(X, y)
check_folds(Xs, ys, cv)
Xt = np.array([rand(100, 10)] * Ns)
Xc = rand(Ns)
X = TS_Data(Xt, Xc)
y = rand(Ns)
Xs, ys, cv = splitter.split(X, y)
check_folds(Xs, ys, cv)
def test_temporal_split():
# test with length 1 series
X = [rand(100, 10)]
y = [5]
Xtr, Xte, ytr, yte = temporal_split(X, y)
assert len(Xtr) == len(ytr)
assert len(Xte) == len(yte)
X = [rand(100, 10)]
y = [rand(100)]
Xtr, Xte, ytr, yte = temporal_split(X, y)
assert len(Xtr) == len(ytr)
assert len(Xte) == len(yte)
Xt = [rand(100, 10)]
Xc = [5]
X = TS_Data(Xt, Xc)
y = [rand(100)]
Xtr, Xte, ytr, yte = temporal_split(X, y)
assert len(Xtr) == len(ytr)
assert len(Xte) == len(yte)
# test with lots of series
Ns = 5
X = np.array([rand(100, 10) for i in range(Ns)])
y = rand(Ns)
Xtr, Xte, ytr, yte = temporal_split(X, y)
assert len(Xtr) == len(ytr)
assert len(Xte) == len(yte)
X = np.array([rand(100, 10) for i in range(Ns)])
y = np.array([rand(100) for i in range(Ns)])
Xtr, Xte, ytr, yte = temporal_split(X, y)
assert len(Xtr) == len(ytr)
assert len(Xte) == len(yte)
Xt = np.array([rand(100, 10) for i in range(Ns)])
Xc = rand(Ns)
X = TS_Data(Xt, Xc)
y = np.arange(Ns)
Xtr, Xte, ytr, yte = temporal_split(X, y)
assert len(Xtr) == len(ytr)
assert len(Xte) == len(yte)
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 = 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 = 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_util():
df = load_watch()
data = TS_Data(df['X'], df['side'])
Xt, Xc = util.get_ts_data_parts(data)
assert np.array_equal(Xc, df['side'])
assert np.all([np.array_equal(Xt[i], df['X'][i]) for i in range(len(df['X']))])
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_ts_data():
# time series data
ts = np.array([np.random.rand(100,10),np.random.rand(200,10),np.random.rand(20,10)])
c = np.random.rand(3,10)
data = TS_Data(ts, c)
assert type(data[1]) == TS_Data
# segmented time series data
sts = np.random.rand(100,10,6)
c = np.random.rand(100, 6)
data = TS_Data(sts, c)
assert type(data[4:10]) == TS_Data
sts = np.random.rand(100,10)
c = np.random.rand(100)
data = TS_Data(sts, c)
assert type(data[4:10]) == TS_Data
def test_temporal_split():
# test with length 1 series
X = [rand(100, 10)]
y = [5]
Xtr, Xte, ytr, yte = temporal_split(X, y)
check_split(X, Xtr, Xte, y, ytr, yte)
X = [rand(100, 10)]
y = [rand(100)]
Xtr, Xte, ytr, yte = temporal_split(X, y)
check_split(X, Xtr, Xte, y, ytr, yte)
Xt = [rand(100, 10)]
Xc = [5]
X = TS_Data(Xt, Xc)
y = [rand(100)]
Xtr, Xte, ytr, yte = temporal_split(X, y)
check_split(X, Xtr, Xte, y, ytr, yte)
# test with lots of series
Ns = 5
X = np.array([rand(100, 10)] * Ns)
y = rand(Ns)
Xtr, Xte, ytr, yte = temporal_split(X, y)
check_split(X, Xtr, Xte, y, ytr, yte)
X = np.array([rand(100, 10)] * Ns)
y = np.array([rand(100)] * Ns)
Xtr, Xte, ytr, yte = temporal_split(X, y)
check_split(X, Xtr, Xte, y, ytr, yte)
Xt = np.array([rand(100, 10)] * Ns)
Xc = rand(Ns)
X = TS_Data(Xt, Xc)
y = np.arange(Ns)
Xtr, Xte, ytr, yte = temporal_split(X, y)
check_split(X, Xtr, Xte, y, ytr, yte)
def test_trle():
# Multivariate data
Nt = 100
nvars = 5
X = [np.random.rand(Nt, nvars)]
y = [np.concatenate([np.full(3, 1), np.full(26, 2), np.full(1, 3), np.full(70, 4)])]
rle = TargetRunLengthEncoder(min_length=5)
rle.fit(X)
Xt, yt, _ = rle.transform(X, y)
assert len(Xt) == len(yt) and len(yt) == 2
assert yt[0] == 2 and yt[1] == 4
assert len(Xt[0]) == 26 and len(Xt[1]) == 70
# Nothing excluded
Nt = 100
nvars = 5
X = [np.random.rand(Nt, nvars)]
y = [np.concatenate([np.full(50,1), np.full(50,2)])]
rle = TargetRunLengthEncoder(min_length=5)
rle.fit(X, y)
Xt, yt, _ = rle.transform(X, y)
assert len(Xt) == len(yt) and len(yt) == 2
assert np.all(np.concatenate(Xt) == X)
assert yt[0] == 1 and yt[1] == 2
assert len(Xt[0]) == 50 and len(Xt[1]) == 50
# Univariate data with sample weight and context
Nt = 100
Xts = [np.random.rand(Nt)]
Xc = [5]
X = TS_Data(Xts, Xc)
y = [np.concatenate([np.full(3, 1), np.full(26, 2), np.full(1, 3), np.full(70, 4)])]
sw = [1]
rle = TargetRunLengthEncoder(min_length=5)
rle.fit(X)
Xt, yt, swt = rle.transform(X, y, sw)
Xtc = Xt.context_data
assert len(Xt) == len(yt) and len(swt) == len(yt) and len(yt) == 2
assert yt[0] == 2 and yt[1] == 4
assert len(Xt[0]) == 26 and len(Xt[1]) == 70
assert swt[0] == 1 and swt[1] == 1
assert Xtc[0] == 5 and Xtc[1] == 5
def make_ts_data(time_series, context_vars=None):
'''
Combines time series data and relational contextual variables into an ``TS_Data`` object compatible with ``SegPipe`` and related classes. If context_vars are none, a numpy array is returned.
Parameters
----------
time_series : array-like, shape [n_series, ]
Time series data - each element (series) may have a different length
context_vars : array-like, shape [n_series, n_context_variables]
contextual relational data
Returns
-------
X : array-like [n_series, ]
``TS_Data object containing time series and contextual data
'''
if context_vars is not None:
return TS_Data(time_series, context_vars)
else:
return np.array(time_series)
def test_pad_trunc():
Nt = 100
width = 5
nvars = 5
seg = transform.PadTrunc(width=width)
# multivariate ts data without context data
X = [
np.random.rand(Nt, nvars),
np.random.rand(Nt, nvars),
np.random.rand(Nt, nvars)
]
y = [np.random.rand(Nt), np.random.rand(Nt), np.random.rand(Nt)]
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width, nvars)
assert np.all([np.equal(X[i][0:width], Xs[i]) for i in range(len(X))])
assert np.all([np.equal(y[i][0:width], ys[i]) for i in range(len(y))])
# univariate ts data without context data
X = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(3 * Nt)]
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(3 * Nt)]
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width)
assert np.all([np.equal(X[i][0:width], Xs[i]) for i in range(len(X))])
assert np.all([np.equal(y[i][0:width], ys[i]) for i in range(len(y))])
# multivariate ts data with context data
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, 4)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
assert Xsc.shape == (N, 4)
assert np.all([np.equal(Xt[i][0:width], Xst[i]) for i in range(len(Xt))])
assert np.all([np.equal(Xc[i], Xsc[i]) for i in range(len(Xt))])
assert np.all([np.equal(y[i][0:width], ys[i]) for i in range(len(y))])
# ts data with univariate context data
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
assert Xsc.shape == (N, )
assert np.all([np.equal(Xt[i][0:width], Xst[i]) for i in range(len(Xt))])
assert np.all([np.equal(Xc[i], Xsc[i]) for i in range(len(Xt))])
assert np.all([np.equal(y[i][0:width], ys[i]) for i in range(len(y))])
# same number as context vars and time vars
# this would cause broadcasting failure before implementation of TS_Data class
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, nvars)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
assert Xsc.shape == (N, 5)
assert np.all([np.equal(Xt[i][0:width], Xst[i]) for i in range(len(Xt))])
assert np.all([np.equal(Xc[i], Xsc[i]) for i in range(len(Xt))])
assert np.all([np.equal(y[i][0:width], ys[i]) for i in range(len(y))])
width = 5
nvars = 5
seg = transform.PadTrunc(width=width)
# multivariate ts data without context data
X = [
np.random.rand(100, nvars),
np.random.rand(100, nvars),
np.random.rand(100, nvars)
]
y = np.random.rand(3)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width, nvars)
assert np.all([np.equal(X[i][0:width], Xs[i]) for i in range(len(Xt))])
assert np.all([np.equal(y[i], ys[i]) for i in range(len(y))])
# univariate ts data without context
X = [np.random.rand(100), np.random.rand(100), np.random.rand(100)]
y = np.random.rand(3)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width)
assert np.all([np.equal(X[i][0:width], Xs[i]) for i in range(len(Xt))])
assert np.all([np.equal(y[i], ys[i]) for i in range(len(y))])
# multivariate ts data with context data
Xt = [
np.random.rand(100, nvars),
np.random.rand(200, nvars),
np.random.rand(50, nvars)
]
Xc = np.random.rand(3, 4)
y = np.random.rand(3)
X = 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, nvars)
assert Xsc.shape == (N, 4)
assert np.all([np.equal(Xt[i][0:width], Xst[i]) for i in range(len(Xt))])
assert np.all([np.equal(Xc[i], Xsc[i]) for i in range(len(Xt))])
assert np.all([np.equal(y[i], ys[i]) for i in range(len(y))])
def test_watch():
df = load_watch()
data = TS_Data(df['X'], df['side'])
assert isinstance(data, TS_Data)
def test_segmentxy():
# test illegal parameter settings
with pytest.raises(ValueError):
transform.SegmentXY(width=0) # illegal width value
with pytest.raises(ValueError):
transform.SegmentXY(
overlap=None, step=None) # either overlap or step must be defined
with pytest.raises(ValueError):
transform.SegmentXY(overlap=-1, step=None) # illegal overlap value
with pytest.raises(ValueError):
transform.SegmentXY(step=0) # illegal step value
with pytest.raises(ValueError):
transform.SegmentXY(order=None) # illegal order
# test _step property working as expected
seg = transform.SegmentXY(width=10, overlap=0.5)
assert seg._step == 5
# test precedence of step over overlap
seg = transform.SegmentXY(width=10, overlap=1, step=1)
assert seg._step == 1
# illegal overlap value, but valid step value
seg = transform.SegmentXY(overlap=-1, step=1)
assert seg._step == 1
# test shape of segmented data
Nt = 100
width = 5
nvars = 5
seg = transform.SegmentXY(width=width)
# multivariate ts data without context data
X = [
np.random.rand(Nt, nvars),
np.random.rand(Nt, nvars),
np.random.rand(Nt, nvars)
]
y = [np.random.rand(Nt), np.random.rand(Nt), np.random.rand(Nt)]
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width, nvars)
# univariate ts data without context data
X = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(3 * Nt)]
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(3 * Nt)]
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(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, 4)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
assert Xsc.shape == (N, 4)
# ts data with univariate context data
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
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(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, nvars)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
assert Xsc.shape == (N, 5)
def test_segmentxyforecast():
# test illegal parameter settings
with pytest.raises(ValueError):
transform.SegmentXYForecast(width=0) # illegal width value
with pytest.raises(ValueError):
transform.SegmentXYForecast(
overlap=None, step=None) # either overlap or step must be defined
with pytest.raises(ValueError):
transform.SegmentXYForecast(overlap=-1,
step=None) # illegal overlap value
with pytest.raises(ValueError):
transform.SegmentXYForecast(step=0) # illegal step value
with pytest.raises(ValueError):
transform.SegmentXYForecast(order=None) # illegal order
with pytest.raises(ValueError):
transform.SegmentXYForecast(forecast=0) # illegal forecast value
# test _step property working as expected
seg = transform.SegmentXYForecast(width=10, overlap=0.5)
assert seg._step == 5
# test precedence of step over overlap
seg = transform.SegmentXYForecast(width=10, overlap=1, step=1)
assert seg._step == 1
# illegal overlap value, but valid step value
seg = transform.SegmentXYForecast(overlap=-1, step=1)
assert seg._step == 1
# test shape of segmented data
Nt = 100
width = 5
nvars = 5
# lets do a forecast test
seg = transform.SegmentXYForecast(width=width, forecast=5)
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, 4)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
assert Xsc.shape == (N, 4)
# univariate X
nvars = 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)
import pandas as pd
import matplotlib.pyplot as plt
data = load_watch()
y = data['y']
Xt = data['X']
fs = 50 # sampling frequency
# create time series data object with no contextual variables
check_ts_data(Xt)
# create time series data object with 2 contextual variables
Xs = np.column_stack([data['side'], data['subject']])
X = TS_Data(Xt, Xs)
check_ts_data(X)
# recover time series and contextual variables
Xt = X.ts_data
Xs = X.context_data
# generate some statistics from the time series data
results = ts_stats(X, y, fs=fs, class_labels=data['y_labels'])
print("DATA STATS - AGGREGATED")
print(results['total'])
print("")
print("DATA STATS - BY CLASS")
print(pd.DataFrame(results['by_class']))
# plot an instance from the data set
def test_watch():
df = load_watch()
data = TS_Data(df['X'], df['side'])
def test_patch_sampler():
# test patch_sampler on a class without a fit_resample function
class EmptyClass(object):
pass
with pytest.raises(TypeError):
transform.patch_sampler(EmptyClass)
# test patch_sampler on a mocked imbalanced-learn Sampler class
unpatched_sampler = MockImblearnSampler()
patched_sampler = transform.patch_sampler(MockImblearnSampler)(
shuffle=True, random_state=0)
assert str(patched_sampler.__class__) != str(unpatched_sampler.__class__)
pickled_sampler = pickle.dumps(patched_sampler)
unpickled_sampler = pickle.loads(pickled_sampler)
assert str(patched_sampler.__class__) == str(unpickled_sampler.__class__)
# test representation
assert "mocked_param" in repr(patched_sampler)
assert "random_state" in repr(patched_sampler)
assert "shuffle" in repr(patched_sampler)
# multivariate ts
X = np.random.rand(100, 10, 4)
y = np.ones(100)
Xt, yt, _ = patched_sampler.transform(X, y)
assert Xt is X
assert yt is y
Xt, yt, _ = patched_sampler.fit_transform(X, y)
X, y = shuffle(mock_resample(X), mock_resample(y), random_state=0)
assert np.array_equal(Xt, X)
assert np.array_equal(yt, y)
# ts with multivariate contextual data
X = TS_Data(np.random.rand(100, 10, 4), np.random.rand(100, 3))
Xt_orig, _ = get_ts_data_parts(X)
y = np.ones(100)
Xt, yt, _ = patched_sampler.transform(X, y)
assert Xt is X
assert yt is y
Xt, yt, _ = patched_sampler.fit_transform(X, y)
Xtt, Xtc = get_ts_data_parts(Xt)
Xt_orig, y = shuffle(mock_resample(Xt_orig),
mock_resample(y),
random_state=0)
assert np.array_equal(Xtt, Xt_orig)
assert np.array_equal(yt, y)
# ts with univariate contextual data
X = TS_Data(np.random.rand(100, 10, 4), np.random.rand(100))
Xt_orig, _ = get_ts_data_parts(X)
y = np.ones(100)
Xt, yt, _ = patched_sampler.transform(X, y)
assert Xt is X
assert yt is y
Xt, yt, _ = patched_sampler.fit_transform(X, y)
Xtt, Xtc = get_ts_data_parts(Xt)
Xt_orig, y = shuffle(mock_resample(Xt_orig),
mock_resample(y),
random_state=0)
assert np.array_equal(Xtt, Xt_orig)
assert np.array_equal(yt, y)
# univariate ts
X = np.random.rand(100, 10)
y = np.ones(100)
Xt, yt, _ = patched_sampler.transform(X, y)
assert Xt is X
assert yt is y
Xt, yt, _ = patched_sampler.fit_transform(X, y)
X, y = shuffle(mock_resample(X), mock_resample(y), random_state=0)
assert np.array_equal(Xt, X)
assert np.array_equal(yt, y)
def test_segmentxy():
Nt = 100
width = 5
nvars = 5
seg = transform.SegmentXY(width=width)
# multivariate ts data without context data
X = [
np.random.rand(Nt, nvars),
np.random.rand(Nt, nvars),
np.random.rand(Nt, nvars)
]
y = [np.random.rand(Nt), np.random.rand(Nt), np.random.rand(Nt)]
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width, nvars)
# univariate ts data without context data
X = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(3 * Nt)]
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(3 * Nt)]
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(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, 4)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
assert Xsc.shape == (N, 4)
# ts data with univariate context data
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
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(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, nvars)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = 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, nvars)
assert Xsc.shape == (N, 5)
def test_pipe_regression():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [np.random.rand(1000)]
pipe = Pype([('seg', SegmentXY()), ('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
Xt = np.array([np.random.rand(1000, 10)] * 5)
Xc = np.random.rand(5, 3)
X = TS_Data(Xt, Xc)
y = np.array([np.random.rand(1000)] * 5)
cross_validate(pipe, X, y, cv=3)
# transform pipe
pipe = Pype([('seg', SegmentXY()), ('ftr', FeatureRep()),
('scaler', StandardScaler())])
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(X, y)
pipe.fit_transform(X, y)
def test_function_transform():
constant = 10
identity = transform.FunctionTransformer()
def replace(Xt, value):
return np.ones(Xt.shape) * value
custom = transform.FunctionTransformer(replace,
func_kwargs={"value": constant})
# univariate ts
X = np.random.rand(100, 10)
y = np.ones(100)
identity.fit(X, y)
Xtrans = identity.transform(X)
assert Xtrans is X
custom.fit(X, y)
Xtrans = custom.transform(X)
assert np.array_equal(Xtrans, np.ones(X.shape) * constant)
# multivariate ts
X = np.random.rand(100, 10, 4)
y = np.ones(100)
identity.fit(X, y)
Xtrans = identity.transform(X)
assert Xtrans is X
custom.fit(X, y)
Xtrans = custom.transform(X)
assert np.array_equal(Xtrans, np.ones(X.shape) * constant)
# ts with univariate contextual data
Xt = np.random.rand(100, 10, 4)
Xc = np.random.rand(100)
X = TS_Data(Xt, Xc)
y = np.ones(100)
identity.fit(X, y)
Xtrans = identity.transform(X)
assert Xtrans is X
custom.fit(X, y)
Xtrans = custom.transform(X)
Xtt, Xtc = get_ts_data_parts(Xtrans)
assert np.array_equal(Xtt, np.ones(Xt.shape) * constant)
assert Xtc is Xc
# ts with multivariate contextual data
Xt = np.random.rand(100, 10, 4)
Xc = np.random.rand(100, 3)
X = TS_Data(Xt, Xc)
y = np.ones(100)
identity.fit(X, y)
Xtrans = identity.transform(X)
assert Xtrans is X
custom.fit(X, y)
Xtrans = custom.transform(X)
Xtt, Xtc = get_ts_data_parts(Xtrans)
assert np.array_equal(Xtt, np.ones(Xt.shape) * constant)
assert Xtc is Xc
# test resampling
def resample(Xt):
return Xt.reshape(1, -1)
illegal_resampler = transform.FunctionTransformer(resample)
X = np.random.rand(100, 10)
y = np.ones(100)
illegal_resampler.fit(X, y)
with pytest.raises(ValueError):
Xtrans = illegal_resampler.transform(X)
def transform(self, X, y, sample_weight=None):
check_is_fitted(self, ['reference_windows_', 'num_new_ts_'])
Xt, Xc = get_ts_data_parts(X)
yt = y
N = len(Xt) # Number of time series
# preallocate new time series data
Xt_trans = [None] * self.num_new_ts_
y_trans = [None] * self.num_new_ts_
Xc_trans = np.recarray(shape=(self.num_new_ts_, ),
dtype=contextual_recarray_dtype)
k = 0
pbar = tqdm(total=self.num_new_ts_,
desc="Segment",
disable=(not self._VERBOSE),
file=sys.stdout)
# get time series which should be segmented together
for window, selection_idx in zip(
self.reference_windows_,
moving_window(range(N), window_size=self.n)):
# get reference windows for segmentation
for starting_timestamp, ending_timestamp in window:
# segment each time series
for idx in selection_idx:
ts = Xt[idx]
start_idx = find_nearest(ts[:, 0], starting_timestamp)
if self.enforce_size and Xc is not None:
stop_idx = start_idx + round2int(
self.window_length * Xc[idx].sr)
else:
stop_idx = find_nearest(ts[:, 0], ending_timestamp)
if stop_idx < start_idx:
raise ValueError
Xt_trans[k] = ts[start_idx:stop_idx, :]
if Xc is not None:
Xc_trans[k] = Xc[idx]
if yt is not None and len(yt[0].shape) >= 2:
start_idx = find_nearest(yt[idx][:, 0],
starting_timestamp)
if self.enforce_size:
stop_idx = start_idx + round2int(
self.window_length * get_sampling_rate(
yt[idx][:, 0], t_unit=self._T_UNIT))
else:
stop_idx = find_nearest(yt[idx][:, 0],
ending_timestamp)
if stop_idx < start_idx:
raise ValueError
y_trans[k] = yt[idx][start_idx:stop_idx, :]
elif yt is not None:
y_trans[k] = yt[idx][start_idx:stop_idx]
pbar.update(1)
k += 1
pbar.close()
assert len([
1 for ts, y in zip(Xt_trans, y_trans) if ts is None or y is None
]) == 0, "[CRITICAL] Missing segments."
# --- find empty windows and delete them ---
empty_windows = []
for i, (ts, y) in enumerate(zip(Xt_trans, y_trans)):
if len(ts) <= 1 or len(y) <= 1:
empty_windows.append(i)
if len(empty_windows) > 0:
Xt_trans = np.delete(Xt_trans, empty_windows)
if Xc is not None:
Xc_trans = np.delete(Xc_trans, empty_windows)
if y is not None:
y_trans = np.delete(y_trans, empty_windows)
num_empty_windows = len(empty_windows)
if num_empty_windows > 0:
logger.warning(
f"[{self.__class__.__name__}] {num_empty_windows} windows could not be processed "
f"and thus removed")
# --- finalize ---
if Xc is not None:
Xt = TS_Data(Xt_trans, Xc_trans)
else:
Xt = Xt_trans
return Xt, y_trans, sample_weight
('rf', RandomForestClassifier(n_estimators=20))])
# split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
print("N series in train: ", len(X_train))
print("N series in test: ", len(X_test))
print("N segments in train: ", clf.N_train)
print("N segments in test: ", clf.N_test)
print("Accuracy score: ", score)
# now lets add some contextual data
Xc = np.column_stack((data['side'], data['subject']))
Xt = np.array(data['X'])
X = TS_Data(Xt, Xc)
y = np.array(data['y'])
# and do a cross validation
scoring = make_scorer(f1_score, average='macro')
cv_scores = cross_validate(clf, X, y, cv=4, return_train_score=True)
print("CV Scores: ", pd.DataFrame(cv_scores))
# lets see what feature we used
print("Features: ", clf.steps[1][1].f_labels)
img = mpimg.imread('feet.jpg')
plt.imshow(img)
def test_pipe_PadTrunc():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [5]
pipe = Pype([('trunc', PadTrunc()), ('ftr', FeatureRep()),
('rf', RandomForestClassifier(n_estimators=10))])
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 = [5]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
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 = TS_Data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
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 = TS_Data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# transform pipe
pipe = Pype([('trunc', PadTrunc()), ('ftr', FeatureRep()),
('scaler', StandardScaler())])
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 = [1, 2, 3]
pipe.fit(X, y)
pipe.transform(X, y)
pipe.fit_transform(X, y)