My ultimate goal is replace some methods that use kneighbors_graph with transformers from the sklearn-ann package. These all use transformer objects. However, the function I'm trying to replace uses kneighbors_graph(mode="connectivity", include_self=True) and I'm having a hard time converting the distance output with include_self=False to this type of connectivity matrix.
I'm able to reproduce the kneighbors_graph(mode="connectivity", include_self=True) from kneighbors_graph(mode="distance", include_self=True) (referring to as nn_with_self). However, I'm unable to reproduce it from kneighbors_graph(mode="distance", include_self=False) (referring to as nn_without_self) which is the same output as KNeighborsTransformer(mode="distance").fit_transform. I see that the nn_without_self is a super set of nn_with_self but I don't know how the backend algorithm selects which fields are kept.
How can I recreate nn_with_self from the nn_with_self matrix below?
I tried looking at the backend code but it's like an inception of class inheritance and I find myself pouring through several files at the same time losing track on the GitHub.
from sklearn.datasets import make_classificationfrom sklearn.neighbors import kneighbors_graph, KNeighborsTransformerX, _ = make_classification(n_samples=10, n_features=4, n_classes=2, n_clusters_per_class=1, random_state=0)n_neighbors=3# Nearest neighborsnn_with_self = kneighbors_graph(X, n_neighbors=n_neighbors, mode="distance", metric="euclidean", include_self=True,n_jobs=-1).todense()nn_without_self = kneighbors_graph(X, n_neighbors=n_neighbors, mode="distance", metric="euclidean", include_self=False,n_jobs=-1).todense()nn_from_transformer = KNeighborsTransformer(mode="distance", n_neighbors=n_neighbors, metric="euclidean", n_jobs=-1).fit_transform(X)np.all(nn_from_transformer == nn_without_self)# Truenp.all(nn_with_self == nn_without_self)# False# Is `nn_with_self` symmetric?np.allclose(nn_with_self,nn_with_self.T)# False# Is `nn_without_self` symmetric?np.allclose(nn_without_self,nn_without_self.T)# FalseHere are the actual arrays:
nn_with_self# matrix([[0. , 0.70550439, 0. , 0.20463097, 0. ,# 0. , 0. , 0. , 0. , 0. ],# [0. , 0. , 0. , 0.51947869, 0. ,# 0. , 0. , 0. , 0. , 0.44145655],# [0. , 0. , 0. , 0. , 0.50025504,# 0. , 0. , 0. , 0.49481662, 0. ],# [0.20463097, 0.51947869, 0. , 0. , 0. ,# 0. , 0. , 0. , 0. , 0. ],# [0. , 0. , 0.50025504, 0. , 0. ,# 0. , 0. , 0. , 0.34132965, 0. ],# [0. , 0.88867318, 0. , 0. , 0. ,# 0. , 0. , 0. , 0. , 0.44956691],# [0. , 0. , 1.10390699, 0. , 1.52953542,# 0. , 0. , 0. , 0. , 0. ],# [0. , 0. , 0. , 0. , 0. ,# 3.62670755, 0. , 0. , 0. , 3.83571739],# [0. , 0. , 0.49481662, 0. , 0.34132965,# 0. , 0. , 0. , 0. , 0. ],# [0. , 0.44145655, 0. , 0. , 0. ,# 0.44956691, 0. , 0. , 0. , 0. ]])nn_without_self# matrix([[0. , 0.70550439, 0. , 0.20463097, 1.02852831,# 0. , 0. , 0. , 0. , 0. ],# [0.70550439, 0. , 0. , 0.51947869, 0. ,# 0. , 0. , 0. , 0. , 0.44145655],# [0. , 0. , 0. , 0. , 0.50025504,# 0. , 1.10390699, 0. , 0.49481662, 0. ],# [0.20463097, 0.51947869, 0. , 0. , 0. ,# 0. , 0. , 0. , 0. , 0.95611187],# [1.02852831, 0. , 0.50025504, 0. , 0. ,# 0. , 0. , 0. , 0.34132965, 0. ],# [0. , 0.88867318, 0. , 1.40547465, 0. ,# 0. , 0. , 0. , 0. , 0.44956691],# [0. , 0. , 1.10390699, 0. , 1.52953542,# 0. , 0. , 0. , 1.59848513, 0. ],# [0. , 4.1280709 , 0. , 0. , 0. ,# 3.62670755, 0. , 0. , 0. , 3.83571739],# [1.36553076, 0. , 0.49481662, 0. , 0.34132965,# 0. , 0. , 0. , 0. , 0. ],# [0. , 0.44145655, 0. , 0.95611187, 0. ,# 0.44956691, 0. , 0. , 0. , 0. ]])