# Source code for mat_discover.utils.nearest_neigh

```
"""Nearest neighbor helper functions for DISCOVER."""
import numpy as np
from sklearn.neighbors import NearestNeighbors
[docs]def nearest_neigh_props(
X,
target,
r_strength=None,
radius=None,
n_neighbors=10,
metric="precomputed",
**NN_kwargs,
):
"""Compute nearest neighbor properties for peak proxy using radius and kNN.
Parameters
----------
X : 2d array
Pairwise distance matrix (within single set).
target : 1d array
Target property values.
r_strength : float or None, optional
Radius strength used as a scaling value for `radius`, by default None.
If None, then a default value based on mean and standard deviation is used. See `_nearest_neigh_props`.
radius : float, optional
The radius within which to consider nearest neighbors, by default None
n_neighbors : int, optional
The number of nearest neighbors (kNNs) to consider for computing `k_neigh_avg_targ`, by default 10.
metric : str or callable
"The distance metric to use for the tree. The default metric is minkowski, and
with p=2 is equivalent to the standard Euclidean metric. See the documentation
of DistanceMetric for a list of available metrics. If metric is "precomputed", X
is assumed to be a distance matrix and must be square during fit. X may be a
sparse graph, in which case only "nonzero" elements may be considered
neighbors." (source: `sklearn.neighbors.NearestNeighbors` docs). By default "precomputed".
Returns
-------
rad_neigh_avg_targ, k_neigh_avg_targ : 1d array (X.shape[0],)
Radius- and kNN-based average of neighbor targets, respectively.
See Also
--------
sklearn.neighbors.NearestNeighbors : Unsupervised learner for implementing neighbor
searches. Text source: `sklearn.neighbors.NearestNeighbors` docs
"""
rad_neigh_avg_targ, num_neigh = _nearest_neigh_props(
X,
target,
type="radius",
r_strength=r_strength,
radius=radius,
n_neighbors=n_neighbors,
metric=metric,
)[1:3]
k_neigh_avg_targ = _nearest_neigh_props(
X,
target,
type="kneighbors",
r_strength=r_strength,
radius=radius,
n_neighbors=n_neighbors,
metric=metric,
)[1]
return rad_neigh_avg_targ, k_neigh_avg_targ
def _nearest_neigh_props(
X,
target,
type="radius",
r_strength=None,
radius=None,
n_neighbors=10,
metric="precomputed",
**NN_kwargs,
):
"""Compute nearest neighbor properties.
Parameters
----------
X : 2d array
Pairwise distance matrix (within single set).
target : 1d array
Target property values.
type : str, optional
[description], by default "radius"
r_strength : float or None, optional
Radius strength used as a scaling value for `radius`, by default None.
If None, then a default value based on mean and standard deviation is used. See `_nearest_neigh_props`.
radius : float, optional
The radius within which to consider nearest neighbors, by default None
n_neighbors : int, optional
The number of nearest neighbors (kNNs) to consider for computing `k_neigh_avg_targ`, by default 10.
"metric : str or callable
The distance metric to use for the tree. The default metric is minkowski, and
with p=2 is equivalent to the standard Euclidean metric. See the documentation
of DistanceMetric for a list of available metrics. If metric is "precomputed", X
is assumed to be a distance matrix and must be square during fit. X may be a
sparse graph, in which case only "nonzero" elements may be considered
neighbors." (source: `sklearn.neighbors.NearestNeighbors` docs). By default
"precomputed".
**NN_kwargs : `sklearn.neighbors.NearestNeighbors` keyword arguments.
Returns
-------
neigh_target : ndarray with dtype=object
Target properties of the neighbors.
neigh_avg_targ : 1d array
Average of neighbor targets for each compound.
num_neigh : 1d array
Number of neighbors for each compound.
See Also
--------
sklearn.neighbors.NearestNeighbors : Unsupervised learner for implementing neighbor
searches. Text source: `sklearn.neighbors.NearestNeighbors` docs
"""
if radius is None and metric == "precomputed":
if r_strength is None:
r_strength = 1.5
mean, std = (np.mean(X), np.std(X))
radius = mean - r_strength * std
if n_neighbors > X.shape[0]:
n_neighbors = X.shape[0]
NN = NearestNeighbors(
radius=radius, n_neighbors=n_neighbors, metric="precomputed", **NN_kwargs
)
NN.fit(X)
if type == "radius":
neigh_ind = NN.radius_neighbors(return_distance=False)
num_neigh = np.array([len(ind) for ind in neigh_ind])
elif type == "kneighbors":
neigh_ind = NN.kneighbors(return_distance=False)
num_neigh = n_neighbors * np.ones(neigh_ind.shape[0])
neigh_target = np.array([target[ind] for ind in neigh_ind], dtype="object")
neigh_avg_targ = np.array(
[np.mean(t) if len(t) > 0 else float(0) for t in neigh_target]
)
return neigh_target, neigh_avg_targ, num_neigh
```