from joblib import Parallel, delayed
from sklearn.utils import gen_even_slices
import numpy as np
from ..trajectory_data import TrajectoryData
def filter_trajectory_length(data, min_length, max_length, inplace=True,
n_jobs=1):
"""Removes trajectories by length criteria.
Parameters
----------
data : :class:`trajminer.TrajectoryData`
The dataset to be filtered.
min_length : int
The minimum length required (inclusive) to keep trajectories in the
dataset. If `None`, then no minimum length is enforced.
max_length : int
The maximum length required (inclusive) to keep trajectories in the
dataset. If `None`, then no maximum length is enforced.
inplace : bool (default=True)
If `True` modifies the current object, otherwise returns a new
object.
n_jobs : int (default=1)
The number of parallel jobs.
Returns
-------
dataset : :class:`trajminer.TrajectoryData`
The filtered dataset. If `inplace=True`, then returns the modified
current object.
"""
has_labels = data.get_labels() is not None
tids = data.get_tids()
def filter_slice(s):
n_tids, n_data, n_labels = [], [], []
for i in range(s.start, s.stop):
tid = tids[i]
traj = data.get_trajectory(tid)
if (min_length is not None and len(traj) < min_length) or \
(max_length is not None and len(traj) > max_length):
continue
n_tids.append(tid)
n_data.append(traj)
if has_labels:
n_labels.append(data.get_label(tid))
return n_tids, n_data, n_labels
func = delayed(filter_slice)
ret = Parallel(n_jobs=n_jobs, verbose=0)(
func(s) for s in gen_even_slices(len(tids), n_jobs))
n_tids, n_data, n_labels = [], [], []
for job in ret:
n_tids.extend(job[0])
n_data.extend(job[1])
n_labels.extend(job[2])
if inplace:
data._update(data.get_attributes(), n_data, n_tids, n_labels)
return data
return TrajectoryData(data.get_attributes(), n_data, n_tids, n_labels)
def filter_label_size(data, min_size, max_size, inplace=True, n_jobs=1):
"""Removes trajectories corresponding to sets of labels that do not meet
size criteria.
Parameters
----------
data : :class:`trajminer.TrajectoryData`
The dataset to be filtered.
min_size : int
The minimum number of trajectories (inclusive) required of a certain
label to keep it in the dataset. If `None`, then no minimum size is
enforced.
max_size : int
The maximum number of trajectories (inclusive) required of a certain
label to keep it in the dataset. If `None`, then no maximum size is
enforced.
inplace : bool (default=True)
If `True` modifies the current object, otherwise returns a new
object.
n_jobs : int (default=1)
The number of parallel jobs.
Returns
-------
dataset : :class:`trajminer.TrajectoryData`
The filtered dataset. If `inplace=True`, then returns the modified
current object.
"""
labels = data.get_labels(unique=True)
def filter_slice(s):
labels_to_keep = []
for i in range(s.start, s.stop):
label = labels[i]
size = len(data.get_trajectories(label))
if (min_size is not None and size < min_size) or \
(max_size is not None and size > max_size):
continue
labels_to_keep.append(label)
n_tids, n_data, n_labels = [], [], []
for i, tid in enumerate(data.get_tids()):
traj = data.get_trajectory(tid)
label = data.get_label(tid)
if label not in labels_to_keep:
continue
n_tids.append(tid)
n_data.append(traj)
n_labels.append(label)
return n_tids, n_data, n_labels
func = delayed(filter_slice)
ret = Parallel(n_jobs=n_jobs, verbose=0)(
func(s) for s in gen_even_slices(len(labels), n_jobs))
n_tids, n_data, n_labels = [], [], []
for job in ret:
n_tids.extend(job[0])
n_data.extend(job[1])
n_labels.extend(job[2])
if inplace:
data._update(data.get_attributes(), n_data, n_tids, n_labels)
return data
return TrajectoryData(data.get_attributes(), n_data, n_tids, n_labels)
def filter_duplicate_points(data, criterium, remove_first=True, inplace=True,
n_jobs=1):
"""Removes duplicates of trajectory points according to the given criteria.
Parameters
----------
data : :class:`trajminer.TrajectoryData`
The dataset to be filtered.
criterium : callable
A callable that takes two trajectory points and decides wheter or not
they are duplicates. If `True`, then one of the points is removed from
the dataset (the first or the last point, depending on the
`remove_first` parameter).
remove_first : bool (default=True)
If `True`, then whenever duplicates are found, the first point is
removed. Otherwise, the last one is removed from the dataset.
inplace : bool (default=True)
If `True` modifies the current object, otherwise returns a new
object.
n_jobs : int (default=1)
The number of parallel jobs.
Returns
-------
dataset : :class:`trajminer.TrajectoryData`
The filtered dataset. If `inplace=True`, then returns the modified
current object.
"""
tids = data.get_tids()
def filter_slice(s):
n_data = []
for t in range(s.start, s.stop):
traj = np.copy(data.get_trajectory(tids[t]))
i = 1
while i < len(traj):
if not criterium(traj[i-1], traj[i]):
i += 1
elif remove_first:
traj = np.delete(traj, i-1, axis=0)
else:
traj = np.delete(traj, i, axis=0)
n_data.append(traj)
return n_data
func = delayed(filter_slice)
ret = Parallel(n_jobs=n_jobs, verbose=0)(
func(s) for s in gen_even_slices(len(tids), n_jobs))
n_data = np.concatenate(ret)
if inplace:
data._update(data.get_attributes(), n_data, data.get_tids(),
data.get_labels())
return data
return TrajectoryData(data.get_attributes(), n_data, data.get_tids(),
data.get_labels())
