Hierarchy fragmentation curve¶
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. |
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Quality measures usable with optimal cut assessment |
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Fragmentation curve of the horizontal cuts in a hierarchy w.r.t. |
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Fragmentation curve of the optimal cuts in a hierarchy w.r.t. |
Creates an assesser for hierarchy optimal cuts w.r.t. |
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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())
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__init__
()¶ Initialize self. See help(type(self)) for accurate signature.
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num_regions
(self: higra.higram.FragmentationCurve) → numpy.ndarray[numpy.float64]¶ Array of number of regions in the different cuts
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num_regions_ground_truth
(self: higra.higram.FragmentationCurve) → int¶ Array of number of regions in the different cuts
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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
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scores
(self: higra.higram.FragmentationCurve) → numpy.ndarray[numpy.float64]¶ Array of scores of the different cuts
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class
OptimalCutMeasure
¶ Quality measures usable with optimal cut assessment
Members:
BCE
DHamming
DCovering
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BCE
= <OptimalCutMeasure.BCE: 0>¶
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DCovering
= <OptimalCutMeasure.DCovering: 2>¶
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DHamming
= <OptimalCutMeasure.DHamming: 1>¶
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__eq__
(self: object, other: object) → bool¶
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__getstate__
(self: object) → int¶
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__hash__
(self: object) → int¶
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__index__
(self: higra.higram.OptimalCutMeasure) → int¶
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__init__
(self: higra.higram.OptimalCutMeasure, value: int) → None¶
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__int__
(self: higra.higram.OptimalCutMeasure) → int¶
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__members__
= {'BCE': <OptimalCutMeasure.BCE: 0>, 'DCovering': <OptimalCutMeasure.DCovering: 2>, 'DHamming': <OptimalCutMeasure.DHamming: 1>}¶
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__module__
= 'higra.higram'¶
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__ne__
(self: object, other: object) → bool¶
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__repr__
(self: object) → str¶
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__setstate__
(self: higra.higram.OptimalCutMeasure, state: int) → None¶
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__str__
()¶ name(self: handle) -> str
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property
name
¶
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property
value
¶
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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
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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
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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
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class
AssesserFragmentationOptimalCut
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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.
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optimal_number_of_regions
(self: higra.higram.AssesserFragmentationOptimalCut) → int¶ Number of regions in the optimal cut.
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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).
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optimal_score
(self: higra.higram.AssesserFragmentationOptimalCut) → float¶ Score of the optimal cut.
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