tanat.metric.static package#

Submodules#

tanat.metric.static.metric module#

StaticMetric Class: base class for defining a static metric.

class tanat.metric.static.metric.StaticMetric(cmp_fnct: Callable | None = None)[source]#

Bases: object

Class for comparing static attributes between sequences or trajectories.

The comparison function between static information is to be provided by the user while creating the StaticMetric object. If not defined this class implements a binary similarity function that returns 0 if all attributes values are similars and 1 otherwise. It is based on the shared static attributes between compared objects. If none, it raises an error.

Computes a scalar distance between two Trajectory or Sequence objects.

Warning:

The comparison function must have two parameters that are dictionaries.
Use brackets with attribute names to access the value of a static
feature.

Static metric are objects that made to compute metrics between pairs of trajectories or sequences. It can be defined as it.

Example:

# Comparison between two dictionaries representing
# the static attributes of two trajectories/sequences.
# Values of the attributes of static features are accessed
# using named brackets only.

# This function must have two parameters and return a
# float.

# In this exemple, it compares the ages of the individuals
def static_metric(static1, static2) -> float:
    return abs(float(static1["age"]) - float(static2["age"]))


# Definition of a metric based on static features
csmet = StaticMetric(smet)

# Evaluate the metrics between two trajectories
val = csmet(traj1, traj2)

Static metric are more useful while hidden in the AggregationTrajectoryMetric. A static object comparison function or a StaticMetric can be defined to take into account static features in the aggregated metric.

Example:

# Same function to compare the `age` static attribute
def static_metric(static1, static2) -> float:
    return abs(float(static1["age"]) - float(static2["age"]))

# When an aggregation trajectory metric is defined, the user can
# add an additional component based on the static features by
# providing the function used to compare static features and also
# a weight in the weighted sum of metrics.
metric = AggregationTrajectoryMetric(static_metric=static_metric, static_metric_weight=0.5)
value = metric(traj1, traj2)

Information

StaticMetric can evaluate cross-distance matrix from a pool of trajectories
or sequences. Nonetheless, their computation is not optimized for large
datasets.
Warning ::

Contrary to the other functions that compute cross-distance matrix it does not provide a DistanceMatrix

__init__(cmp_fnct: Callable | None = None) → None[source]#
Parameters:

  • cmp_fnct – function to define the comparison between static

  • data (must have two dictionaries as parameters)

compute_cross_matrix(pool_rows: TrajectoryPool | SequencePool, pool_cols: TrajectoryPool | SequencePool) → ndarray[source]#

Compute cross distance matrix based on the static data between two pools.

Parameters:

  • pool_rows – Trajectory or Sequence pool for rows (N trajectories).

  • pool_cols – Trajectory or Sequence pool for columns (M trajectories).

Returns:

(N x M) matrix containing the pairwise distances computed based on static data.

Return type:

static_matrix

compute_matrix(pool: TrajectoryPool | SequencePool) → ndarray[source]#

Module contents#

Static metric sub-package.

class tanat.metric.static.StaticMetric(cmp_fnct: Callable | None = None)[source]#

Bases: object

Class for comparing static attributes between sequences or trajectories.

The comparison function between static information is to be provided by the user while creating the StaticMetric object. If not defined this class implements a binary similarity function that returns 0 if all attributes values are similars and 1 otherwise. It is based on the shared static attributes between compared objects. If none, it raises an error.

Computes a scalar distance between two Trajectory or Sequence objects.

Warning:

The comparison function must have two parameters that are dictionaries.
Use brackets with attribute names to access the value of a static
feature.

Static metric are objects that made to compute metrics between pairs of trajectories or sequences. It can be defined as it.

Example:

# Comparison between two dictionaries representing
# the static attributes of two trajectories/sequences.
# Values of the attributes of static features are accessed
# using named brackets only.

# This function must have two parameters and return a
# float.

# In this exemple, it compares the ages of the individuals
def static_metric(static1, static2) -> float:
    return abs(float(static1["age"]) - float(static2["age"]))


# Definition of a metric based on static features
csmet = StaticMetric(smet)

# Evaluate the metrics between two trajectories
val = csmet(traj1, traj2)

Static metric are more useful while hidden in the AggregationTrajectoryMetric. A static object comparison function or a StaticMetric can be defined to take into account static features in the aggregated metric.

Example:

# Same function to compare the `age` static attribute
def static_metric(static1, static2) -> float:
    return abs(float(static1["age"]) - float(static2["age"]))

# When an aggregation trajectory metric is defined, the user can
# add an additional component based on the static features by
# providing the function used to compare static features and also
# a weight in the weighted sum of metrics.
metric = AggregationTrajectoryMetric(static_metric=static_metric, static_metric_weight=0.5)
value = metric(traj1, traj2)

Information

StaticMetric can evaluate cross-distance matrix from a pool of trajectories
or sequences. Nonetheless, their computation is not optimized for large
datasets.
Warning ::

Contrary to the other functions that compute cross-distance matrix it does not provide a DistanceMatrix

__init__(cmp_fnct: Callable | None = None) → None[source]#
Parameters:

  • cmp_fnct – function to define the comparison between static

  • data (must have two dictionaries as parameters)

compute_cross_matrix(pool_rows: TrajectoryPool | SequencePool, pool_cols: TrajectoryPool | SequencePool) → ndarray[source]#

Compute cross distance matrix based on the static data between two pools.

Parameters:

  • pool_rows – Trajectory or Sequence pool for rows (N trajectories).

  • pool_cols – Trajectory or Sequence pool for columns (M trajectories).

Returns:

(N x M) matrix containing the pairwise distances computed based on static data.

Return type:

static_matrix

compute_matrix(pool: TrajectoryPool | SequencePool) → ndarray[source]#