src.utils.ptransf.point_cloud_sampler

Classes

PointCloudSampler(**kwargs)

class src.utils.ptransf.point_cloud_sampler.PointCloudSampler(**kwargs)

Author:: Alberto M. Esmoris Pena

Class for transforming points following one or many sampling strategies.

Variables:

neighborhood_sampling (dict or None) –

The specification of the neighborhood sampling strategy, if any.

The format for this attribute should be like:

{
    "support_conditions": [
        {
            "value_name": "ClassAmbiguity",
            "condition_type": "greater_than_or_equal_to",
            "value_target": 0.5,
            "action": "preserve"
        }
    ],
    "support_strategy": "fps",
    "support_strategy_num_points": 100000,
    "support_strategy_fast": true,
    "support_chunk_size": 50000,
    "center_on_pcloud": false,
    "neighborhood": {
        "type": "sphere",
        "radius": 2.5,
        "separation_factor": 0
    },
    "neighborhoods_per_iter": 10000,
    "nthreads": -1
}

static extract_ptransf_args(spec)

Extract the arguments to initialize/instantiate a PointCloudSampler.

Parameters:: spec – The key-word specification containing the arguments.
Returns:: The arguments to initialize/instantiate a PointCloudSampler.
Return type:: dict

__init__(**kwargs)

Initialize/instantiate a PointCloudSampler.

Parameters:: kwargs – The attributes for the PointCloudSampler.

transform(X=None, F=None, y=None, yhat=None, out_prefix=None, **kwargs)

The fundamental point transform logic defining the point cloud sampler.

See PointTransformer and PointTransformer.transform().

transform_pcloud(pcloud, out_prefix=None, **kwargs)

Update the input point cloud in place.

See PointCloudSampler.transform() and PointTransformer.transform_pcloud().

apply_neighborhood_sampling(X, F, y, yhat, out_prefix=None, **kwargs)

Compute a neighborhood sampling. This strategy considers each support point (for example, the entire X, or a subset of X that satisfied the given conditions) and computes it neighborhood. The indices of the points in the neighborhoods are then returned.

Parameters:

X (np.ndarray) – The structure space matrix representing the point cloud to be sampled.
F (np.ndarray or None) – the feature space matrix representing the point cloud to be sampled.
y (np.ndarray or None) – The references of the point cloud to be sampled (typically a vector of classes).
yhat (np.ndarray or None) – The predictions on the point cloud to be sampled ( typically a vector of classes).
out_prefix – See PointTransformer and PointTransformer.transform().

Returns:

List with the indices of the points to be included in the sampled version of the point cloud.

Return type:

List of int

static update_selected_indices(base_selected_indices, new_selected_indices, X, F, y, yhat)

Merge the current selected indices with the new selected indices. Note that the resulting selected indices are built by selecting those indices at base_selected_indices specified by new_selected_indices.

Returns:: The new list of selected indices.
Return type:: list

filter_support_points(conditions, X, F, y, yhat, min_distance=None, out_prefix=None)

Filter the given point cloud considering the given conditions to obtain the corresponding support points.

Parameters:

conditions (list of dict) –
A list with the dictionary-like specifications of each condition that must be applied. Each condition must specify:

– "value_name"The name of the value involved in the
condition.

– "condition_type"The relational governing the condition,
i.e., "not_equals" (\(\neq\)), "equals" (\(=\)), "less_than" (\(<\)), "less_than_or_equal_to" (\(\leq\)), "greater_than" (\(>\)), "greater_than_or_equal_to" (\(\geq\)), "in" (\(\in\)), or "not_in" (\(\notin\)).

– "value_target"The value for the rhs of the relational.
Note that the lhs will be the corresponding value from the point cloud.

– "action"Whether the conditions define points that must be
preserved ("preserve") or discarded ("discard").
X (np.ndarray or None) – The structure space matrix representing the point cloud.
F (np.ndarray or None) – The feature space matrix representing the point cloud.
y (np.ndarray or None) – The references of the point cloud.
yhat (np.ndarray or None) – The predictions of the point cloud.
min_distance (float or None) – When given, support points that are closer to other support points in less than min_distance will be discarded.
out_prefix – See PointTransformer and PointTransformer.transform().

Returns:

The structure space matrix representing the support points.

Return type:

np.ndarray

apply_condition(cond, X, F, y, yhat)

Apply the given condition to the given point cloud. See PointCloudSampler.filter_support_points() for further details when using this function to filter the support points.

Returns:: The structure space, feature space, references, and predictions of the filtered point cloud.
Return type:: tuple of np.ndarray

static compute_neighborhoods(X, X_sup, neighborhood_spec, support_chunk_size=0, kdt=None)

Compute the neighborhood of each support point on X.

See SupportNeighborhoods for more details.

Parameters:

X_sup (np.ndarray) – The support structure space.
X (np.ndarray) – The structure space where the neighbors must be.
neighborhood_spec (dict) – The key-word specification of the neighborhood.
support_chunk_size (int) – See SupportNeighborhoods.
kdt – See SupportNeighborhoods.compute().