def _local_umap_trustworthiness(local_X, local_y, n_neighbors, supervised):
"""
Train model on all data, report trustworthiness
"""
from cuml.manifold import UMAP
local_model = UMAP(n_neighbors=n_neighbors, random_state=42)
y_train = None
if supervised:
y_train = local_y
local_model.fit(local_X, y=y_train)
embedding = local_model.transform(local_X)
return trustworthiness(local_X,
embedding,
n_neighbors=n_neighbors,
batch_size=5000)
def local_umap_trustworthiness():
local_model = UMAP(n_neighbors=n_neighbors)
local_model.fit(local_X, local_y)
embedding = local_model.transform(local_X)
return trustworthiness(local_X, embedding, n_neighbors)
from sklearn.datasets import load_digits
from numba import cuda
from pyrr import Matrix44
import numpy as np
import cudatashader as ds
from cudatashader import transfer_functions as tf
from cudatashader.colors import Hot
from IPython.core.display import display, HTML, clear_output
digits = load_digits()
data, target_classes = digits.data, digits.target
n_samples = target_classes.shape[0]
reducer = UMAP(n_components=3)
reducer.fit(data)
embedding = reducer.transform(data)
maxThreadsPerBlock = cuda.get_current_device().MAX_THREADS_PER_BLOCK
@cuda.jit('void(int64[:], float64[:,:])')
def fill_agg_value(target_classes, result):
i = cuda.grid(1)
result[i, 2] = target_classes[i]
@cuda.jit('void(float64[:,:], float64[:,:], float64[:,:])')
def apply_projection(MVP, embedding, result):
i = cuda.grid(1)
x, y, z = embedding[i, 0], embedding[i, 1], embedding[i, 2]
result[i, 0] = MVP[0, 0] * x + MVP[1, 0] * y + MVP[2, 0] * z + MVP[3, 0]