Hierarchy fragmentation curve

`FragmentationCurve`	This class represents a fragmentation curve, ie the evolution of the scores of the partitions of a hierarchy with respect to the number of regions in those partitions.
`OptimalCutMeasure`	Quality measures usable with optimal cut assessment
`assess_fragmentation_horizontal_cut`(tree, ...)	Fragmentation curve of the horizontal cuts in a hierarchy w.r.t.
`assess_fragmentation_optimal_cut`(tree, ...)	Fragmentation curve of the optimal cuts in a hierarchy w.r.t.
`make_assesser_fragmentation_optimal_cut`(...)	Creates an assesser for hierarchy optimal cuts w.r.t.
`AssesserFragmentationOptimalCut`

class FragmentationCurve

This class represents a fragmentation curve, ie the evolution of the scores of the partitions of a hierarchy with respect to the number of regions in those partitions.

Example

plt.plot(x=fg.num_regions(), y=fg.scores())

__init__(*args, **kwargs)

num_regions(self: higra.higram.FragmentationCurve) → numpy.ndarray[numpy.float64]: Array of number of regions in the different cuts

num_regions_ground_truth(self: higra.higram.FragmentationCurve) → int: Array of number of regions in the different cuts

num_regions_normalized(self: higra.higram.FragmentationCurve) → numpy.ndarray[numpy.float64]: Array of number of regions in the different cuts divided by the number of regions in the ground-truth

scores(self: higra.higram.FragmentationCurve) → numpy.ndarray[numpy.float64]: Array of scores of the different cuts

class OptimalCutMeasure

Quality measures usable with optimal cut assessment

Members:

BCE

DHamming

DCovering

BCE = <OptimalCutMeasure.BCE: 0>

DCovering = <OptimalCutMeasure.DCovering: 2>

DHamming = <OptimalCutMeasure.DHamming: 1>

__eq__(self: object, other: object) → bool

__getstate__(self: object) → int

__hash__(self: object) → int

__index__(self: higra.higram.OptimalCutMeasure) → int

__init__(self: higra.higram.OptimalCutMeasure, value: int) → None

__int__(self: higra.higram.OptimalCutMeasure) → int

__members__ = {'BCE': <OptimalCutMeasure.BCE: 0>, 'DCovering': <OptimalCutMeasure.DCovering: 2>, 'DHamming': <OptimalCutMeasure.DHamming: 1>}

__module__ = 'higra.higram'

__ne__(self: object, other: object) → bool

__repr__(self: object) → str

__setstate__(self: higra.higram.OptimalCutMeasure, state: int) → None

__str__(self: object) → str

property name

property value

assess_fragmentation_horizontal_cut(tree, altitudes, ground_truth, measure, max_regions=200, vertex_map=None)[source]

Fragmentation curve of the horizontal cuts in a hierarchy w.r.t. a given measure.

The base graph of the hierarchy is:

the leaf graph of the hierarchy if it is not a region adjacency graph

the original graph of the leaf graph of the hierarchy if it is a region adjacency graph

Parameters:

tree – input hierarchy (Concept CptHierarchy)
altitudes – altitudes of the nodes of the input hierarchy
ground_truth – labelisation of base graph vertices
measure – evaluation measure to use (see enumeration PartitionMeasure)
max_regions – maximum number of regions in the cuts
vertex_map – optional, vertex mapping if the hierarchy is build on a region adjacency graph (deduced from CptRegionAdjacencyGraph on the leaf graph of tree)

Returns:

an object of type FragmentationCurve

assess_fragmentation_optimal_cut(tree, ground_truth, measure, max_regions=200, vertex_map=None)[source]

Fragmentation curve of the optimal cuts in a hierarchy w.r.t. a given measure.

The base graph of the hierarchy is:

the leaf graph of the hierarchy if it is not a region adjacency graph

the original graph of the leaf graph of the hierarchy if it is a region adjacency graph

Parameters:

tree – input hierarchy (Concept CptHierarchy)
ground_truth – labelisation of base graph vertices
measure – evaluation measure to use (see enumeration OptimalCutMeasure)
max_regions – maximum number of regions in the cuts
vertex_map – optional, vertex mapping if the hierarchy is build on a region adjacency graph (deduced from CptRegionAdjacencyGraph on the leaf graph of tree)

Returns:

an object of type FragmentationCurve

make_assesser_fragmentation_optimal_cut(tree, ground_truth, measure, max_regions=200, vertex_map=None)[source]

Creates an assesser for hierarchy optimal cuts w.r.t. a given ground-truth partition of the base graph vertices and the given optimal cut measure (see OptimalCutMeasure). The algorithms will explore optimal cuts containing at most max_regions regions.

The base graph of the hierarchy is:

the leaf graph of the hierarchy if it is not a region adjacency graph

the original graph of the leaf graph of the hierarchy if it is a region adjacency graph

Parameters:

tree – input hierarchy (Concept CptHierarchy)
ground_truth – labelisation of base graph vertices
measure – evaluation measure to use (see enumeration OptimalCutMeasure)
max_regions – maximum number of regions in the cuts
vertex_map – optional, vertex mapping if the hierarchy is build on a region adjacency graph (deduced from CptRegionAdjacencyGraph on the leaf graph of tree)

Returns:

an object of type AssesserFragmentationOptimalCut

class AssesserFragmentationOptimalCut

fragmentation_curve(self: higra.higram.AssesserFragmentationOptimalCut) → higra.higram.FragmentationCurve: Fragmentation curve, i.e. for each number of region k between 1 and max_regions, the score of the optimal cut with k regions.

optimal_number_of_regions(self: higra.higram.AssesserFragmentationOptimalCut) → int: Number of regions in the optimal cut.

optimal_partition(self: higra.higram.AssesserFragmentationOptimalCut, num_regions: int = 0) → numpy.ndarray[numpy.uint64]: Labelisation of the tree vertices that corresponds to the optimal cut withthe given number of regions. If the number of regions is equal to 0 (default), the global optimal cut it returned (it will contain get_optimal_number_of_regions regions).

optimal_score(self: higra.higram.AssesserFragmentationOptimalCut) → float: Score of the optimal cut.