def test_tsb_not_one_dimensional():
tsb = np.array([[1, 1], [2, 2]])
ts = np.arange(10)
w = 32
with pytest.raises(ValueError) as excinfo:
mpdist(ts, tsb, w)
assert ('ts_b must be one dimensional!' == str(excinfo.value))
def test_against_matlab_parallel():
ts = np.loadtxt(os.path.join(MODULE_PATH, '..', 'tests', 'sampledata.txt'))
tsb = ts[199:300]
w = 32
desired = 1.460009659995543e-07
actual = mpdist(ts, tsb, w, n_jobs=-1)
np.testing.assert_almost_equal(actual, desired)
def test_small_series_multi_threaded():
ts = np.array([1, 2, 3, 1, 2, 3, 4, 5, 6, 0, 0, 1, 1, 2, 2, 4, 5, 1, 1,
9]).astype('d')
query = np.array([
0.23595094, 0.9865171, 0.1934413, 0.60880883, 0.55174926, 0.77139988,
0.33529215, 0.63215848
]).astype('d')
w = 4
desired = 0.437690617625298
actual = mpdist(ts, query, w, n_jobs=-1)
np.testing.assert_almost_equal(actual, desired)
def compute_dist(args):
"""
Helper function to parallelize pairwise distance calculation.
Parameters
----------
args : tuple
The arguments to pass to the mpdist calculation.
Returns
-------
values : tuple
The kth index and distance.
"""
k = args[0]
distance = mpdist(args[1], args[2], args[3], threshold=args[4])
return (k, distance)
def test_invalid_threshold():
ts = np.arange(100)
tsb = np.arange(100)
w = 32
threshold = -1
error = 'threshold must be a float greater than 0 and less than 1'
with pytest.raises(ValueError) as excinfo:
mpdist(ts, tsb, w, threshold=threshold)
assert (error == str(excinfo.value))
threshold = 'str'
with pytest.raises(ValueError) as excinfo:
mpdist(ts, tsb, w, threshold=threshold)
assert (error == str(excinfo.value))
threshold = 1
with pytest.raises(ValueError) as excinfo:
mpdist(ts, tsb, w, threshold=threshold)
assert (error == str(excinfo.value))
def pairwise_dist(X, window_size, threshold=0.05, n_jobs=1):
"""
Utility function to compute all pairwise distances between the timeseries
using MPDist.
Note
----
scipy.spatial.distance.pdist cannot be used because they
do not allow for jagged arrays, however their code was used as a reference
in creating this function.
https://github.com/scipy/scipy/blob/master/scipy/spatial/distance.py#L2039
Parameters
----------
X : array_like
An array_like object containing time series to compute distances for.
window_size : int
The window size to use in computing the MPDist.
threshold : float
The threshold used to compute MPDist.
n_jobs : int
Number of CPU cores to use during computation.
Returns
-------
Y : np.ndarray
Returns a condensed distance matrix Y. For
each :math:`i` and :math:`j` (where :math:`i<j<m`),where m is the
number of original observations. The metric ``dist(u=X[i], v=X[j])``
is computed and stored in entry ``ij``.
"""
if not core.is_array_like(X):
raise ValueError('X must be array_like!')
# identify shape based on iterable or np.ndarray.shape
m = 0
if isinstance(X, np.ndarray) and len(X.shape) == 2:
m = X.shape[0]
else:
m = len(X)
dm = np.empty((m * (m - 1)) // 2, dtype=np.double)
k = 0
if n_jobs == 1:
for i in range(0, m - 1):
for j in range(i + 1, m):
dm[k] = mpdist(X[i],
X[j],
window_size,
threshold=threshold,
n_jobs=n_jobs)
k = k + 1
else:
args = []
for i in range(0, m - 1):
for j in range(i + 1, m):
args.append((k, X[i], X[j], window_size, threshold))
k = k + 1
with core.mp_pool()(n_jobs) as pool:
results = pool.map(compute_dist, args)
# put results in the matrix
for result in results:
dm[result[0]] = result[1]
return dm