matsimilarity.methods.mat.MUITAS package

Submodules

matsimilarity.methods.mat.MUITAS.MUITAS_T2T module

MAT-Tools: Python Framework for Multiple Aspect Trajectory Data Mining

The present package offers a tool, to support the user in the task of data analysis of multiple aspect trajectories. It integrates into a unique framework for multiple aspects trajectories and in general for multidimensional sequence data mining methods. Copyright (C) 2022, MIT license (this portion of code is subject to licensing from source project distribution)

Created in Dec, 2021 Copyright (C) 2024, License GPL Version 3 or superior (see LICENSE file)

Authors: - Vanessa Lago Machado - Tarlis Portela - Lucas May Petry (adapted)

class matsimilarity.methods.mat.MUITAS.MUITAS_T2T.Feature(attributes: List[int], weight: float)[source]

Bases: object

Feature: A class representing a feature in the MUITAS application.

_attributes

A list of attribute indices.

Type:

List[int]

_weight

The weight of the feature.

Type:

float

property attributes: List[int]

Getter for attributes.

property weight: float

Getter for weight.

class matsimilarity.methods.mat.MUITAS.MUITAS_T2T.MUITAS(dataset_descriptor: DataDescriptor | None = None)[source]

Bases: SimilarityMeasure

MUITAS: Similarity Measure for Multiple Aspect Trajectory

This class provides methods to analyze and measure the similarity between multiple aspect trajectory data.

thresholds

A dictionary to store threshold values for different attribute types.

Type:

Dict[int, float]

_parityT1T2

Parity score from trajectory T1 to T2.

Type:

float

_parityT2T1

Parity score from trajectory T2 to T1.

Type:

float

_data_descriptor

The data descriptor for the dataset.

Type:

DataDescriptor

features

A list to store features with their attributes and weights.

Type:

List[Feature]

_default_thresholds

Default threshold values based on attribute types.

Type:

Dict[str, float]

References

Petry, L. M., Ferrero, C. A., Alvares, L. O., Renso, C., & Bogorny, V. (2019). Towards Semantic-Aware Multiple-Aspect Trajectory Similarity Measuring. Transactions in GIS (accepted), XX(X), XXX-XXX.

add_feature(attributes: List[int] | List[FeatureDescriptor], weight: float)[source]

Add a feature to the MUITAS object.

Parameters:

  • attributes (List[int] or List[FeatureDescriptor]) – Attributes of a feature can be either a list of indices or a list of FeatureDescriptor objects (following attributes in dataset descriptor)

  • weight (float) – The weight of the feature.

property attributes

Getter for attributes from the data descriptor.

Returns:

List of attributes from the data descriptor.

Return type:

List[FeatureDescriptor]

display_attributes_and_thresholds()[source]

Display the attributes and their corresponding thresholds.

get_index_attribute(attribute: FeatureDescriptor) → int[source]

Get the index of an attribute in the data descriptor attributes.

Parameters:

attribute (FeatureDescriptor) – The attribute to find the index for.

Returns:

The index of the attribute in the data descriptor attributes.

Returns -1 if the attribute is not found.

Return type:

int

get_indices_by_dtype(dtype: str) → List[int][source]

Get indices of attributes in the data descriptor where the attribute’s dtype matches the given parameter.

Parameters:

dtype (str) – The dtype to match.

Returns:

A list of indices where the attribute’s dtype matches the given parameter.

Return type:

List[int]

identify_unique_types_descriptor() → set[source]

Identify unique types in the dataset descriptor attributes.

Returns:

A set of unique attribute types.

Return type:

set

score(p1: Point | None = None, p2: Point | None = None) → float[source]

Compute the score between two points based on their aspects and features.

Parameters:

  • p1 (Point, optional) – The first point.

  • p2 (Point, optional) – The second point.

Returns:

The computed score.

Return type:

float

set_threshold(att_type: str | None = None, threshold_value: float | List[float] | None = None)[source]

Set the threshold value for a given aspect or list of aspects.

Parameters:

  • att_type (str, optional) – The aspect type to set the threshold.

  • threshold_value (float or list, optional) – The threshold value to set or a list of threshold values.

Raises:

  • TypeError – If att_type is not a string or None.

  • ValueError – If no threshold is provided for given aspect types in the list.

similarity(t1: MultipleAspectSequence, t2: MultipleAspectSequence) → float[source]

Compute the similarity between two multiple aspect sequences.

Parameters:

  • t1 (MultipleAspectSequence) – The first multiple aspect trajectory or subtrajectory.

  • t2 (MultipleAspectSequence) – The second multiple aspect trajectory or subtrajectory.

Returns:

The computed similarity score.

Return type:

float

Module contents