matmodel.comparator package

Submodules

matmodel.comparator.distance module

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

The present application offers a tool, to support the user in the modeling of multiple aspect trajectory data. 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 on Apr, 2024 Copyright (C) 2024, License GPL Version 3 or superior (see LICENSE file)

Authors:

  • Tarlis Portela

  • Vanessa Lago Machado

class matmodel.comparator.distance.AbsoluteDistance(max_value=None)[source]

Bases: NumericDistance

distance(asp1, asp2)[source]

Calculates the absolute distance.

Arguments: asp1 (Aspect<numeric>) - value 1 to compare asp2 (Aspect<numeric>) - value 2 to compare

Return: distance - distance difference value, abs(asp1 - asp2).

class matmodel.comparator.distance.CaselessDistance(max_value=None)[source]

Bases: Comparator

class matmodel.comparator.distance.Comparator(max_value=None)[source]

Bases: object

<Abstract> Calculates the distance.

Properties: max_value=None - Maximum possible value for distance, default: Comparator.MAX_VALUE = float(‘inf’).

MAX_VALUE = inf
distance(asp1, asp2)[source]

Calculates the distance.

Arguments: asp1 (Aspect<?>) - value 1 to compare asp2 (Aspect<?>) - value 2 to compare

Return: distance - distance equality value (0 for equal values, 1 for different).

enhance(distance)[source]
static instantiate(json_obj)[source]
match(asp1, asp2, match_threshold=0)[source]

Determine whether two aspects match in equality Override this method to change behavior, can use match_threshold to match based on a specified threshold.

Parameters:

asp1Aspect

The first aspect to be compared.

asp2Aspect

The second aspect to be compared.

match_thresholdfloat, optional

A threshold for matching criteria (default 0). Currently, it is used to match based on 0 distance.

Returns:

bool

True if the two aspects match, False otherwise.

normalize(distance)[source]
class matmodel.comparator.distance.DatetimeDistance(max_value=None, units=None)[source]

Bases: Comparator

Calculates the date distance in one of the following units: D - days M - months Y - years w - weeks h - hours m - minutes s - seconds (default), which includes microseconds fraction

‘weekday’ - difference in weekdays ‘=weekday’ - equal weekday (0 for equal values, 1 for different) ‘isweekday’ - equal if both are weekdays or both are weekends (0 for equal values, 1 for different)

Properties: units=’ms’ - Unit measure to get distance. max_value=None - Maximum possible value for distance, default: Comparator.MAX_VALUE = float(‘inf’)

distance(asp1, asp2)[source]

Calculates the distance.

Arguments: asp1 (Aspect<?>) - value 1 to compare asp2 (Aspect<?>) - value 2 to compare

Return: distance - distance equality value (0 for equal values, 1 for different).

class matmodel.comparator.distance.EditlcsDistance(max_value=None)[source]

Bases: LcsDistance

distance(asp1, asp2)[source]

Calculates the Longest Common Subsequence difference.

Arguments: asp1 (Aspect<nominal>) - value 1 to compare asp2 (Aspect<nominal>) - value 2 to compare

Return: distance - LCS distance value.

class matmodel.comparator.distance.EqualsDistance(max_value=None)[source]

Bases: Comparator

distance(asp1, asp2)[source]

Calculates the distance for eqality ignoring case.

Arguments: asp1 (Aspect<nominal>) - value 1 to compare asp2 (Aspect<nominal>) - value 2 to compare

Return: distance - distance equality value (0 for equal values, 1 for different).

class matmodel.comparator.distance.EuclideanDistance(max_value=None)[source]

Bases: Comparator

distance(asp1, asp2)[source]

Calculates the Euclidean distance (works for points of 2D, 3D, and more).

Arguments: asp1 (Space2D, Space3D) - value 1 to compare asp2 (Space2D, Space3D) - value 2 to compare

Return: distance - distance value.

class matmodel.comparator.distance.InintervalDistance(max_value=None, units='m')[source]

Bases: DatetimeDistance

converge(asp1, asp2)[source]
distance(asp1, asp2)[source]

Calculates the distance.

Arguments: asp1 (Aspect<?>) - value 1 to compare asp2 (Aspect<?>) - value 2 to compare

Return: distance - distance equality value (0 for equal values, 1 for different).

match(asp1, asp2, match_threshold=0)[source]

Determine whether two aspects match in equality Override this method to change behavior, can use match_threshold to match based on a specified threshold.

Parameters:

asp1Aspect

The first aspect to be compared.

asp2Aspect

The second aspect to be compared.

match_thresholdfloat, optional

A threshold for matching criteria (default 0). Currently, it is used to match based on 0 distance.

Returns:

bool

True if the two aspects match, False otherwise.

match_date_interval(asp1, asp2)[source]
match_dates(asp1, asp2, match_threshold=0)[source]
class matmodel.comparator.distance.LcsDistance(max_value=None)[source]

Bases: Comparator

distance(asp1, asp2)[source]

Calculates the Longest Common Subsequence difference.

Arguments: asp1 (Aspect<nominal>) - value 1 to compare asp2 (Aspect<nominal>) - value 2 to compare

Return: distance - LCS distance value.

lcs(X, Y)[source]
lcs_distance(X, Y)[source]
class matmodel.comparator.distance.ManhattanDistance(max_value=None)[source]

Bases: Comparator

distance(asp1, asp2)[source]

Calculates the Manhattan distance (works for points of 2D, 3D, and more).

Arguments: asp1 (Space2D, Space3D) - value 1 to compare asp2 (Space2D, Space3D) - value 2 to compare

Return: distance - distance value.

class matmodel.comparator.distance.NumericDistance(max_value=None)[source]

Bases: Comparator

distance(asp1, asp2)[source]

Calculates the numeric distance.

Arguments: asp1 (Aspect<numeric>) - value 1 to compare asp2 (Aspect<numeric>) - value 2 to compare

Return: distance - distance difference value (asp1 - asp2).

class matmodel.comparator.distance.TimeDistance(max_value=None, units='m')[source]

Bases: Comparator

Calculates the closest time distance. Only works for time in hours, minutes, seconds, and microseconds. Ex.: difference between 22h and 2h is 3h.

Properties: units=’ms’ - Unit measure to get distance: h (hours), m (minutes), s (seconds), ms (microseconds) max_value=None - Maximum possible value for distance (Ex.: hours = 24)

abs_distance(asp1, asp2)[source]

Calculates the simple time distance.

Arguments: asp1 (Aspect<numeric>, DateTime Aspect) - value 1 to compare asp2 (Aspect<numeric>, DateTime Aspect) - value 2 to compare

Return: distance - distance difference in the informed units.

distance(asp1, asp2)[source]

Calculates the closest time distance.

Arguments: asp1 (Aspect<numeric>, DateTime Aspect) - value 1 to compare asp2 (Aspect<numeric>, DateTime Aspect) - value 2 to compare

Return: distance - distance difference in the informed units.

matmodel.comparator.helper module

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

The present application offers a tool, to support the user in the modeling of multiple aspect trajectory data. 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 on Apr, 2024 Copyright (C) 2024, License GPL Version 3 or superior (see LICENSE file)

Authors:

  • Tarlis Portela

  • Vanessa Lago Machado

Module contents