def test_time_series_default_values():
n_channels = 3
t, m, e = sample_time_series(channels=n_channels)
ts = TimeSeries(None, m[0], None)
npt.assert_allclose(
ts.time, np.linspace(0., time_series.DEFAULT_MAX_TIME, m.shape[1]))
npt.assert_allclose(ts.error,
np.repeat(time_series.DEFAULT_ERROR_VALUE, m.shape[1]))
assert ts.n_channels == 1
ts = TimeSeries(None, m, None)
npt.assert_allclose(
ts.time[0], np.linspace(0., time_series.DEFAULT_MAX_TIME, m.shape[1]))
npt.assert_allclose(ts.error[0],
np.repeat(time_series.DEFAULT_ERROR_VALUE, m.shape[1]))
assert ts.n_channels == n_channels
t = [t[i][0:i + 2] for i in range(len(t))]
m = [m[i][0:i + 2] for i in range(len(m))]
e = [e[i][0:i + 2] for i in range(len(e))]
ts = TimeSeries(None, m, None)
for i in range(n_channels):
npt.assert_allclose(
ts.time[i], np.linspace(0., time_series.DEFAULT_MAX_TIME,
len(m[i])))
npt.assert_allclose(
ts.error[i], np.repeat(time_series.DEFAULT_ERROR_VALUE, len(m[i])))
assert ts.n_channels == n_channels
def test_transform_ts_files():
n_class1 = 4
n_class2 = 8
transform_type = "Train/Test Split"
time_series = [TimeSeries(*sample_values(), target='class1')
for i in range(n_class1)]
time_series += [TimeSeries(*sample_values(), target='class2')
for i in range(n_class2)]
output = transformation.transform_ts_files(time_series, transform_type)
npt.assert_equal(len(output), 2)
def test_train_test_split_ratios():
n_class1 = 4
n_class2 = 8
transform_type = "Train/Test Split"
time_series = [TimeSeries(*sample_values(), target='class1')
for i in range(n_class1)]
time_series += [TimeSeries(*sample_values(), target='class2')
for i in range(n_class2)]
outputs = transformation.train_test_split(
time_series, test_size=0.5, train_size=0.5)
npt.assert_equal(len(outputs[1]), len(time_series) / 2)
npt.assert_equal(len(outputs[0]), len(time_series) / 2)
def sample_ts_files(size, targets=[None]):
temp_dir = tempfile.mkdtemp()
paths = []
for target in islice(cycle(targets), size):
t, m, e = sample_values()
name = str(uuid.uuid4())
path = pjoin(temp_dir, '{}.nc'.format(name))
ts = TimeSeries(t, m, e, target=target, path=path, name=name)
ts.to_netcdf(path)
paths.append(path)
yield paths
shutil.rmtree(temp_dir)
def sample_ts_files(size, labels=[None]):
temp_dir = tempfile.mkdtemp()
paths = []
for label in islice(cycle(labels), size):
t, m, e = sample_values()
name = str(uuid.uuid4())
path = pjoin(temp_dir, '{}.npz'.format(name))
ts = TimeSeries(t, m, e, label=label, path=path, name=name)
ts.save(path)
paths.append(path)
yield paths
shutil.rmtree(temp_dir)
def test_time_series_init_2d():
n_channels = 3
t, m, e = sample_time_series(channels=n_channels)
ts = TimeSeries(t, m, e)
assert ts.time.shape == t.shape and np.allclose(ts.time, t)
assert ts.measurement.shape == m.shape and np.allclose(ts.measurement, m)
assert ts.error.shape == e.shape and np.allclose(ts.error, e)
assert ts.n_channels == n_channels
ts = TimeSeries(t[0], m, e[0])
assert ts.time.shape == m.shape and np.allclose(ts.time[0], t[0])
assert ts.measurement.shape == m.shape and np.allclose(ts.measurement, m)
assert ts.error.shape == m.shape and np.allclose(ts.error[0], e[0])
assert ts.n_channels == n_channels
def sample_ts_files(size, targets=[None]):
temp_dir = tempfile.mkdtemp()
paths = []
for target in islice(cycle(targets), size):
t, m, e = sample_values()
name = str(uuid.uuid4())
path = pjoin(temp_dir, '{}.nc'.format(name))
ts = TimeSeries(t, m, e, target=target, path=path, name=name)
ts.to_netcdf(path)
paths.append(path)
yield paths
shutil.rmtree(temp_dir)
def sample_ts_files(size, labels=[None]):
temp_dir = tempfile.mkdtemp()
paths = []
for label in islice(cycle(labels), size):
t, m, e = sample_values()
name = str(uuid.uuid4())
path = pjoin(temp_dir, '{}.npz'.format(name))
ts = TimeSeries(t, m, e, label=label, path=path, name=name)
ts.save(path)
paths.append(path)
yield paths
shutil.rmtree(temp_dir)
def test_time_series_init_1d():
t, m, e = sample_time_series(channels=1)
ts = TimeSeries(t, m, e)
assert ts.time.shape == t.shape and np.allclose(ts.time, t)
assert ts.measurement.shape == m.shape and np.allclose(ts.measurement, m)
assert ts.error.shape == e.shape and np.allclose(ts.error, e)
assert ts.n_channels == 1
def test_train_test_split():
# Mock out unevenly-labeled test data: 4 class1, 8 class2
n_class1 = 4
n_class2 = 8
transform_type = "Train/Test Split"
time_series = [TimeSeries(*sample_values(), target='class1')
for i in range(n_class1)]
time_series += [TimeSeries(*sample_values(), target='class2')
for i in range(n_class2)]
np.random.seed(0)
train, test = transformation.transform_ts_files(time_series,
transform_type)
npt.assert_equal(
sum(ts.target == 'class1' for ts in train), 1 * n_class1 / 2)
npt.assert_equal(sum(ts.target == 'class1' for ts in test), n_class1 / 2)
npt.assert_equal(
sum(ts.target == 'class2' for ts in train), 1 * n_class2 / 2)
npt.assert_equal(sum(ts.target == 'class2' for ts in test), n_class2 / 2)
def test_time_series_init_ragged():
n_channels = 3
t, m, e = sample_time_series(channels=n_channels)
t = [t[i][0:i + 2] for i in range(len(t))]
m = [m[i][0:i + 2] for i in range(len(m))]
e = [e[i][0:i + 2] for i in range(len(e))]
ts = TimeSeries(t, m, e)
assert all(np.allclose(ts.time[i], t[i]) for i in range(len(t)))
assert all(np.allclose(ts.measurement[i], m[i]) for i in range(len(t)))
assert all(np.allclose(ts.error[i], e[i]) for i in range(len(t)))
assert ts.n_channels == n_channels
def to_cesium_dataset(X):
"""Transform a tslearn-compatible dataset into a cesium dataset.
Parameters
----------
X: array, shape = (n_ts, sz, d), where n_ts=1
tslearn-formatted dataset to be cast to cesium format
Returns
-------
list of cesium TimeSeries
cesium-formatted dataset (cf.
`link <http://cesium-ml.org/docs/api/cesium.time_series.html#cesium.time_series.TimeSeries>`_)
Examples
--------
>>> tslearn_arr = numpy.random.randn(3, 16, 1)
>>> cesium_ds = to_cesium_dataset(tslearn_arr)
>>> len(cesium_ds)
3
>>> cesium_ds[0].measurement.shape
(16,)
>>> tslearn_arr = numpy.random.randn(3, 16, 2)
>>> cesium_ds = to_cesium_dataset(tslearn_arr)
>>> len(cesium_ds)
3
>>> cesium_ds[0].measurement.shape
(2, 16)
>>> tslearn_arr = [[1, 2, 3], [1, 2, 3, 4]]
>>> cesium_ds = to_cesium_dataset(tslearn_arr)
>>> len(cesium_ds)
2
>>> cesium_ds[0].measurement.shape
(3,)
Notes
-----
Conversion from/to cesium format requires cesium to be installed.
""" # noqa: E501
try:
from cesium.time_series import TimeSeries
except ImportError:
raise ImportError("Conversion from/to cesium cannot be performed "
"if cesium is not installed.")
def transpose_or_flatten(ts):
ts_ = ts[:ts_size(ts)]
if ts.shape[1] == 1:
return ts_.reshape((-1, ))
else:
return ts_.transpose()
X_ = check_dataset(X)
return [TimeSeries(m=transpose_or_flatten(Xi)) for Xi in X_]
def test_time_series_sort():
t, m, e = sample_time_series(channels=1)
t[:2] = t[1::-1]
ts = TimeSeries(t, m, e)
npt.assert_allclose(ts.time, np.sort(t))
npt.assert_allclose(ts.measurement, m[np.argsort(t)])
npt.assert_allclose(ts.error, e[np.argsort(t)])
n_channels = 3
t, m, e = sample_time_series(channels=n_channels)
t[:, :2] = t[:, 1::-1]
ts = TimeSeries(t, m, e)
for i in range(len(m)):
npt.assert_allclose(ts.time[i], np.sort(t[i]))
npt.assert_allclose(ts.measurement[i], m[i][np.argsort(t[i])])
npt.assert_allclose(ts.error[i], e[i][np.argsort(t[i])])
ts = TimeSeries(t[0], m, e[0])
for i in range(len(m)):
npt.assert_allclose(ts.time[i], np.sort(t[0]))
npt.assert_allclose(ts.measurement[i], m[i][np.argsort(t[0])])
npt.assert_allclose(ts.error[i], e[0][np.argsort(t[0])])
def test_time_series_netCDF():
n_channels = 3
t, m, e = sample_time_series(channels=n_channels)
ts = TimeSeries(t[0], m[0], e[0])
ts.to_netcdf(TEST_TS_PATH)
ts_nc = time_series.from_netcdf(TEST_TS_PATH)
assert_ts_equal(ts, ts_nc)
ts = TimeSeries(t[0], m, e[0])
ts.to_netcdf(TEST_TS_PATH)
ts_nc = time_series.from_netcdf(TEST_TS_PATH)
assert_ts_equal(ts, ts_nc)
t = [t[i][0:i+2] for i in range(len(t))]
m = [m[i][0:i+2] for i in range(len(m))]
e = [e[i][0:i+2] for i in range(len(e))]
ts = TimeSeries(t, m, e)
ts.to_netcdf(TEST_TS_PATH)
ts_nc = time_series.from_netcdf(TEST_TS_PATH)
assert_ts_equal(ts, ts_nc)
def test_channels_iterator():
n_channels = 3
t, m, e = sample_time_series(channels=n_channels)
ts = TimeSeries(t[0], m[0], e[0])
for t_i, m_i, e_i in ts.channels():
npt.assert_allclose(t_i, t[0])
npt.assert_allclose(m_i, m[0])
npt.assert_allclose(e_i, e[0])
ts = TimeSeries(t, m, e)
for (t_i, m_i, e_i), i in zip(ts.channels(), range(n_channels)):
npt.assert_allclose(t_i, t[i])
npt.assert_allclose(m_i, m[i])
npt.assert_allclose(e_i, e[i])
t = [t[i][0:i + 2] for i in range(len(t))]
m = [m[i][0:i + 2] for i in range(len(m))]
e = [e[i][0:i + 2] for i in range(len(e))]
ts = TimeSeries(t, m, e)
for (t_i, m_i, e_i), i in zip(ts.channels(), range(n_channels)):
npt.assert_allclose(t_i, t[i])
npt.assert_allclose(m_i, m[i])
npt.assert_allclose(e_i, e[i])
def test_time_series_npz(tmpdir):
n_channels = 3
t, m, e = sample_time_series(channels=n_channels)
ts = TimeSeries(t[0], m[0], e[0])
ts_path = os.path.join(str(tmpdir), str(uuid4()) + '.npz')
ts.save(ts_path)
ts_loaded = time_series.load(ts_path)
assert_ts_equal(ts, ts_loaded)
ts = TimeSeries(t[0], m, e[0])
ts_path = os.path.join(str(tmpdir), str(uuid4()) + '.npz')
ts.save(ts_path)
ts_loaded = time_series.load(ts_path)
assert_ts_equal(ts, ts_loaded)
t = [t[i][0:i+2] for i in range(len(t))]
m = [m[i][0:i+2] for i in range(len(m))]
e = [e[i][0:i+2] for i in range(len(e))]
ts = TimeSeries(t, m, e)
ts_path = os.path.join(str(tmpdir), str(uuid4()) + '.npz')
ts.save(ts_path)
ts_loaded = time_series.load(ts_path)
assert_ts_equal(ts, ts_loaded)
def get_ts_obj(object_id, meta_data, ts_data, pbmap):
pbnames = list(pbmap.values())
row = meta_data.query('object_id == @object_id')
if 'target' in meta_data.columns:
target = row['target']
else:
target = None
extract_ts = ts_data.query('object_id == @object_id')
pbind = [(extract_ts['passband'] == pb) for pb in pbmap]
t = [extract_ts['mjd'][mask].values for mask in pbind]
m = [extract_ts['flux'][mask].values for mask in pbind]
e = [extract_ts['flux_err'][mask].values for mask in pbind]
ts_obj = TimeSeries(t=t,
m=m,
e=e,
label=target,
name=object_id,
channel_names=pbnames)
return (object_id, ts_obj)
def read_lightcurves():
# Load the light curve data from csv file to an Astropy table
lcfilename = f'{datadir}/training_set.csv'
lcdata = Table.read(lcfilename, format='csv')
# Now we make a cesium Timeseries object for each light curve
tsdict = OrderedDict()
for i in trange(nobjects, desc="Building Timeseries"):
row = metadata[i]
thisid = row['object_id']
target = row['target']
meta = {
'z': row['hostgal_photoz'],
'zerr': row['hostgal_photoz_err'],
'mwebv': row['mwebv']
}
ind = (lcdata['object_id'] == thisid)
thislc = lcdata[ind]
pbind = [(thislc['passband'] == pb) for pb in pbmap]
t = [thislc['mjd'][mask].data for mask in pbind]
m = [thislc['flux'][mask].data for mask in pbind]
e = [thislc['flux_err'][mask].data for mask in pbind]
tsdict[thisid] = TimeSeries(t=t,
m=m,
e=e,
label=target,
name=thisid,
meta_features=meta,
channel_names=pbnames)
del lcdata
return tsdict
def test_channels_iterator():
n_channels = 3
t, m, e = sample_time_series(channels=n_channels)
ts = TimeSeries(t[0], m[0], e[0])
for t_i, m_i, e_i in ts.channels():
npt.assert_allclose(t_i, t[0])
npt.assert_allclose(m_i, m[0])
npt.assert_allclose(e_i, e[0])
ts = TimeSeries(t, m, e)
for (t_i, m_i, e_i), i in zip(ts.channels(), range(n_channels)):
npt.assert_allclose(t_i, t[i])
npt.assert_allclose(m_i, m[i])
npt.assert_allclose(e_i, e[i])
t = [t[i][0:i+2] for i in range(len(t))]
m = [m[i][0:i+2] for i in range(len(m))]
e = [e[i][0:i+2] for i in range(len(e))]
ts = TimeSeries(t, m, e)
for (t_i, m_i, e_i), i in zip(ts.channels(), range(n_channels)):
npt.assert_allclose(t_i, t[i])
npt.assert_allclose(m_i, m[i])
npt.assert_allclose(e_i, e[i])
def featurize_data(pbmap, pbnames, lcdata, metadata, nobjects, featurefile):
"""
***Feature extractor for PLaSTiCC***
Extracts features from data by some Cesium library functions.
Builds a timeseries dictionary and for each time series extracts
features. Features described in file: feature_sets.
Created on Mon Apr 29 19:30:52 2019
@author: luisarribas
"""
print("")
print("EXTRACTING FEATURES")
print("===================")
print("")
print("Building Timeseries....wait")
print("===========================")
#**********************BUILD TIME SERIES**********************************
tsdict = OrderedDict()
for i in range(nobjects):
row = metadata[i]
thisid = row['object_id']
target = row['target']
meta = {'zBand':row['zBand'],\
'z':row['hostgal_photoz'],\
'zerr':row['hostgal_photoz_err'],\
'mag':row['magnitude'],\
'u-b':row['u-b'],\
'b-v':row['b-v']
}
ind = (lcdata['object_id'] == thisid)
thislc = lcdata[ind]
pbind = [(thislc['passband'] == pb) for pb in pbmap]
t = [thislc['mjd'][mask].data for mask in pbind]
m = [thislc['flux'][mask].data for mask in pbind]
e = [thislc['flux_err'][mask].data for mask in pbind]
tsdict[thisid] = TimeSeries(t=t, m=m, e=e,\
label=target, name=thisid, meta_features=meta,\
channel_names=pbnames )
print("")
print("OK!")
print(" ")
#***********************FEATURE EXTRACTION WITH CESIUM********************
warnings.simplefilter('ignore')
if os.path.exists(featurefile):
print("")
print("Loading features from file....wait")
print("==================================")
featuretable, _ = featurize.load_featureset(featurefile)
print("")
print("OK!")
print(" ")
else:
features_list = []
print("")
print("Computing features....wait")
print("==========================")
with schwimmbad.MultiPool() as pool:
results = pool.imap(worker, list(tsdict.values()))
for res in results:
features_list.append(res)
featuretable = featurize.assemble_featureset(features_list=features_list,\
time_series=tsdict.values())
featurize.impute_featureset(fset=featuretable,
strategy='constant',
value=0,
max_value=18446744073709551000,
inplace=True)
featurize.save_featureset(fset=featuretable, path=featurefile)
print("")
print("OK!")
print(" ")
#*******Build Pandas dataframe output*************************************
old_names = featuretable.columns.values
new_names = ['{}_{}'.format(x, pbmap.get(y, 'meta')) for x, y in old_names]
cols = [featuretable[col] for col in old_names]
allfeats = Table(cols, names=new_names, masked=False)
allfeats['target'] = metadata['target']
allfeats = allfeats.to_pandas()
allfeats = np.nan_to_num(allfeats)
new_names.append('target')
allfeats = Table(allfeats, names=new_names, masked=False)
allfeats = allfeats.to_pandas()
print("")
print("Extracted features = ", len(allfeats.columns))
print("==========================")
print("")
print("Nan Values detected = ", sum(len(allfeats) - allfeats.count()))
print("==========================")
return allfeats
#where the length equals how many total observations that object has in the time series data
pbind = [np.asarray(thislc['passband'] == pb) for pb in pbmap]
#Store the times of all the measurements for this object_id. this will be a list with length 6 (one for
# each passband) where each of the 6 items is a list of length = how many observations that object
#has for JUST THAT PASSBAND in the time series data
t = [np.asarray(thislc.loc[:,'mjd'][mask]) for mask in pbind]
#Store the flux measurements taken for this object_id. This is the same shape as t
m = [np.asarray(thislc['flux'][mask]) for mask in pbind]
#Store the flux measurement errors for this object_id. This is the same shape as t
e = [np.asarray(thislc['flux_err'][mask]) for mask in pbind]
#Create the TimeSeries() object using the variables defined above, and append it to the ordered dict.
tsdict[thisid] = TimeSeries(t=t, m=m, e=e, name=thisid, meta_features=meta,channel_names=pbnames)
#Occasionally print progress so we can assess speed
if i % 1000000 == 0:
print(str(i) + ' done out of ' + str(nobjects))
#Empty list for the actual features we want the TimeSeries() object to facilitate
features_list = []
print("Generating features from objects and storing to a table...")
with multiprocessing.Pool() as pool:
#Apply the worker function to each object in the dict of TimeSeries() objects. see ./testWorker.py
#this returns a single row for each object with hundreds of features determined by ./testWorker.py
results = pool.imap(worker, list(tsdict.values()))
i = 0
def test_time_series_npz():
n_channels = 3
t, m, e = sample_time_series(channels=n_channels)
ts = TimeSeries(t[0], m[0], e[0])
ts.save(TEST_TS_PATH)
ts_loaded = time_series.load(TEST_TS_PATH)
assert_ts_equal(ts, ts_loaded)
ts = TimeSeries(t[0], m, e[0])
ts.save(TEST_TS_PATH)
ts_loaded = time_series.load(TEST_TS_PATH)
assert_ts_equal(ts, ts_loaded)
t = [t[i][0:i+2] for i in range(len(t))]
m = [m[i][0:i+2] for i in range(len(m))]
e = [e[i][0:i+2] for i in range(len(e))]
ts = TimeSeries(t, m, e)
ts.save(TEST_TS_PATH)
ts_loaded = time_series.load(TEST_TS_PATH)
assert_ts_equal(ts, ts_loaded)
def test_time_series_npz(tmpdir):
n_channels = 3
t, m, e = sample_time_series(channels=n_channels)
ts = TimeSeries(t[0], m[0], e[0])
ts_path = os.path.join(str(tmpdir), str(uuid4()) + '.npz')
ts.save(ts_path)
ts_loaded = time_series.load(ts_path)
assert_ts_equal(ts, ts_loaded)
ts = TimeSeries(t[0], m, e[0])
ts_path = os.path.join(str(tmpdir), str(uuid4()) + '.npz')
ts.save(ts_path)
ts_loaded = time_series.load(ts_path)
assert_ts_equal(ts, ts_loaded)
t = [t[i][0:i + 2] for i in range(len(t))]
m = [m[i][0:i + 2] for i in range(len(m))]
e = [e[i][0:i + 2] for i in range(len(e))]
ts = TimeSeries(t, m, e)
ts_path = os.path.join(str(tmpdir), str(uuid4()) + '.npz')
ts.save(ts_path)
ts_loaded = time_series.load(ts_path)
assert_ts_equal(ts, ts_loaded)
