diff options
| -rw-r--r-- | src/python/doc/point_cloud.rst | 17 | ||||
| -rw-r--r-- | src/python/gudhi/point_cloud/dtm.py | 6 | ||||
| -rw-r--r-- | src/python/gudhi/point_cloud/knn.py | 31 | ||||
| -rwxr-xr-x | src/python/test/test_dtm.py | 2 |
4 files changed, 39 insertions, 17 deletions
diff --git a/src/python/doc/point_cloud.rst b/src/python/doc/point_cloud.rst index c0d4b303..351b0786 100644 --- a/src/python/doc/point_cloud.rst +++ b/src/python/doc/point_cloud.rst @@ -21,10 +21,23 @@ Subsampling :special-members: :show-inheritance: -TimeDelayEmbedding ------------------- +Time Delay Embedding +-------------------- .. autoclass:: gudhi.point_cloud.timedelay.TimeDelayEmbedding :members: :special-members: __call__ +Nearest neighbors +----------------- + +.. automodule:: gudhi.point_cloud.knn + :members: + :special-members: __init__ + +Distance to measure +------------------- + +.. automodule:: gudhi.point_cloud.dtm + :members: + :special-members: __init__ diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py index 541b74a6..e4096c5e 100644 --- a/src/python/gudhi/point_cloud/dtm.py +++ b/src/python/gudhi/point_cloud/dtm.py @@ -11,11 +11,15 @@ from .knn import KNN class DTM: + """ + Class to compute the distance to the empirical measure defined by a point set. + """ + def __init__(self, k, q=2, **kwargs): """ Args: q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if input_type is 'distance_matrix'. - kwargs: Same parameters as KNN, except that metric="neighbors" means that transform() expects an array with the distances to the k nearest neighbors. + kwargs: Same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors. """ self.k = k self.q = q diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py index a4ea3acd..02448530 100644 --- a/src/python/gudhi/point_cloud/knn.py +++ b/src/python/gudhi/point_cloud/knn.py @@ -11,6 +11,10 @@ import numpy class KNN: + """ + Class wrapping several implementations for computing the k nearest neighbors in a point set. + """ + def __init__(self, k, return_index=True, return_distance=False, metric="euclidean", **kwargs): """ Args: @@ -19,22 +23,17 @@ class KNN: return_distance (bool): if True, return the distance to each neighbor. implementation (str): Choice of the library that does the real work. - * 'keops' for a brute-force, CUDA implementation through pykeops. Useful when the dimension becomes - large (10+) but the number of points remains low (less than a million). - Only "minkowski" and its aliases are supported. + * 'keops' for a brute-force, CUDA implementation through pykeops. Useful when the dimension becomes large (10+) but the number of points remains low (less than a million). Only "minkowski" and its aliases are supported. * 'ckdtree' for scipy's cKDTree. Only "minkowski" and its aliases are supported. - * 'sklearn' for scikit-learn's NearestNeighbors. - Note that this provides in particular an option algorithm="brute". - * 'hnsw' for hnswlib.Index. It is very fast but does not provide guarantees. - Only supports "euclidean" for now. + * 'sklearn' for scikit-learn's NearestNeighbors. Note that this provides in particular an option algorithm="brute". + * 'hnsw' for hnswlib.Index. It can be very fast but does not provide guarantees. Only supports "euclidean" for now. * None will try to select a sensible one (scipy if possible, scikit-learn otherwise). metric (str): see `sklearn.neighbors.NearestNeighbors`. eps (float): relative error when computing nearest neighbors with the cKDTree. p (float): norm L^p on input points (including numpy.inf) if metric is "minkowski". Defaults to 2. n_jobs (int): Number of jobs to schedule for parallel processing of nearest neighbors on the CPU. If -1 is given all processors are used. Default: 1. - - Additional parameters are forwarded to the backends. + kwargs: additional parameters are forwarded to the backends. """ self.k = k self.return_index = return_index @@ -75,20 +74,26 @@ class KNN: if self.params["implementation"] == "ckdtree": # sklearn could handle this, but it is much slower from scipy.spatial import cKDTree + self.kdtree = cKDTree(X) if self.params["implementation"] == "sklearn" and self.metric != "precomputed": # FIXME: sklearn badly handles "precomputed" from sklearn.neighbors import NearestNeighbors - nargs = {k: v for k, v in self.params.items() if k in {"p", "n_jobs", "metric_params", "algorithm", "leaf_size"}} + nargs = { + k: v for k, v in self.params.items() if k in {"p", "n_jobs", "metric_params", "algorithm", "leaf_size"} + } self.nn = NearestNeighbors(self.k, metric=self.metric, **nargs) self.nn.fit(X) if self.params["implementation"] == "hnsw": import hnswlib - self.graph = hnswlib.Index("l2", len(X[0])) # Actually returns squared distances - self.graph.init_index(len(X), **{k:v for k,v in self.params.items() if k in {"ef_construction", "M", "random_seed"}}) + + self.graph = hnswlib.Index("l2", len(X[0])) # Actually returns squared distances + self.graph.init_index( + len(X), **{k: v for k, v in self.params.items() if k in {"ef_construction", "M", "random_seed"}} + ) n = self.params.get("num_threads") if n is None: n = self.params.get("n_jobs", 1) @@ -154,7 +159,7 @@ class KNN: p = self.params["p"] if p == numpy.inf: - # Requires a version of pykeops strictly more recent than 1.3 + # Requires pykeops 1.4 or later mat = (LazyTensor(XX[:, None, :]) - LazyTensor(YY[None, :, :])).abs().max(-1) elif p == 2: # Any even integer? mat = ((LazyTensor(XX[:, None, :]) - LazyTensor(YY[None, :, :])) ** p).sum(-1) diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py index 841f8c3c..93b13e1a 100755 --- a/src/python/test/test_dtm.py +++ b/src/python/test/test_dtm.py @@ -47,4 +47,4 @@ def test_dtm_precomputed(): dist = numpy.array([[2.0, 2], [0, 1], [3, 4]]) dtm = DTM(2, q=2, metric="neighbors") r = dtm.fit_transform(dist) - assert r == pytest.approx([2.0, .707, 3.5355], rel=.01) + assert r == pytest.approx([2.0, 0.707, 3.5355], rel=0.01) |