File size: 6,519 Bytes
f291f4a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 |
"""
backend APIs for Single Visualization model trainer
"""
# import modules
import torch
import time
import numpy as np
import tensorflow as tf
from scipy.special import softmax
from pynndescent import NNDescent
import scipy
def get_graph_elements(graph_, n_epochs):
"""
gets elements of graphs, weights, and number of epochs per edge
Parameters
----------
graph_ : scipy.sparse.csr.csr_matrix
umap graph of probabilities
n_epochs : int
maximum number of epochs per edge
Returns
-------
graph scipy.sparse.csr.csr_matrix
umap graph
epochs_per_sample np.array
number of epochs to train each sample for
head np.array
edge head
tail np.array
edge tail
weight np.array
edge weight
n_vertices int
number of verticies in graph
"""
### should we remove redundancies () here??
# graph_ = remove_redundant_edges(graph_)
graph = graph_.tocoo()
# eliminate duplicate entries by summing them together
graph.sum_duplicates()
# number of vertices in dataset
n_vertices = graph.shape[1]
# # get the number of epochs based on the size of the dataset
if n_epochs is None:
# For smaller datasets we can use more epochs
if graph.shape[0] <= 10000:
n_epochs = 500
else:
n_epochs = 200
# remove elements with very low probability
if len(graph.data) >0:
graph.data[graph.data < (graph.data.max() / float(n_epochs)) + 1e-3] = 0.0
graph.eliminate_zeros()
head = graph.row
tail = graph.col
#! normalization
# weight = graph.data*n_epochs
weight = graph.data
return graph, head, tail, weight, n_vertices
def convert_distance_to_probability(distances, a=1.0, b=1.0):
"""convert distance to student-t distribution probability in low-dimensional space"""
return 1.0 / (1.0 + a * torch.pow(distances, 2 * b))
def compute_cross_entropy(
probabilities_graph, probabilities_distance, EPS=1e-4, repulsion_strength=1.0
):
"""
Compute cross entropy between low and high probability
Parameters
----------
probabilities_graph : torch.Tensor
high dimensional probabilities
probabilities_distance : torch.Tensor
low dimensional probabilities
EPS : float, optional
offset to to ensure log is taken of a positive number, by default 1e-4
repulsion_strength : float, optional
strength of repulsion between negative samples, by default 1.0
Returns
-------
attraction_term: torch.float
attraction term for cross entropy loss
repellent_term: torch.float
repellent term for cross entropy loss
cross_entropy: torch.float
cross entropy umap loss
"""
attraction_term = - probabilities_graph * torch.log(torch.clamp(probabilities_distance, min=EPS, max=1.0))
repellent_term = (
-(1.0 - probabilities_graph)
* torch.log(torch.clamp(1.0 - probabilities_distance, min=EPS, max=1.0))
* repulsion_strength
)
# balance the expected losses between attraction and repel
CE = attraction_term + repellent_term
return attraction_term, repellent_term, CE
def compute_cross_entropy_tf(
probabilities_graph, probabilities_distance, EPS=1e-4, repulsion_strength=1.0
):
attraction_term = - probabilities_graph * tf.math.log(tf.clip_by_value(probabilities_distance, clip_value_min=EPS, clip_value_max=1.0))
repellent_term = (
-(1.0 - probabilities_graph)
* tf.math.log(tf.clip_by_value(1.0 - probabilities_distance, clip_value_min=EPS, clip_value_max=1.0))
* repulsion_strength
)
# balance the expected losses between attraction and repel
CE = attraction_term + repellent_term
return attraction_term, repellent_term, CE
def find_neighbor_preserving_rate(prev_data, train_data, n_neighbors):
"""
neighbor preserving rate, (0, 1)
:param prev_data: ndarray, shape(N,2) low dimensional embedding from last epoch
:param train_data: ndarray, shape(N,2) low dimensional embedding from current epoch
:param n_neighbors:
:return alpha: ndarray, shape (N,)
"""
if prev_data is None:
return np.zeros(len(train_data))
# number of trees in random projection forest
n_trees = min(64, 5 + int(round((train_data.shape[0]) ** 0.5 / 20.0)))
# max number of nearest neighbor iters to perform
n_iters = max(5, int(round(np.log2(train_data.shape[0]))))
# distance metric
metric = "euclidean"
# get nearest neighbors
nnd = NNDescent(
train_data,
n_neighbors=n_neighbors,
metric=metric,
n_trees=n_trees,
n_iters=n_iters,
max_candidates=60,
verbose=False
)
train_indices, _ = nnd.neighbor_graph
prev_nnd = NNDescent(
prev_data,
n_neighbors=n_neighbors,
metric="euclidean",
n_trees=n_trees,
n_iters=n_iters,
max_candidates=60,
verbose=False
)
prev_indices, _ = prev_nnd.neighbor_graph
temporal_pres = np.zeros(len(train_data))
for i in range(len(train_indices)):
pres = np.intersect1d(train_indices[i], prev_indices[i])
temporal_pres[i] = len(pres) / float(n_neighbors)
return temporal_pres
def get_attention(model, data, device, temperature=.01, verbose=1):
t0 = time.time()
grad_list = []
for i in range(len(data)):
b = torch.from_numpy(data[i:i + 1]).to(device=device, dtype=torch.float)
b.requires_grad = True
out = model(b)
top1 = torch.argsort(out)[0][-1]
out[0][top1].backward()
grad_list.append(b.grad.data.detach().cpu().numpy())
grad_list2 = []
for i in range(len(data)):
b = torch.from_numpy(data[i:i + 1]).to(device=device, dtype=torch.float)
b.requires_grad = True
out = model(b)
top2 = torch.argsort(out)[0][-2]
out[0][top2].backward()
grad_list2.append(b.grad.data.detach().cpu().numpy())
t1 = time.time()
grad1 = np.array(grad_list)
grad2 = np.array(grad_list2)
grad1 = grad1.squeeze(axis=1)
grad2 = grad2.squeeze(axis=1)
grad = np.abs(grad1) + np.abs(grad2)
grad = softmax(grad/temperature, axis=1)
t2 = time.time()
if verbose:
print("Gradients calculation: {:.2f} seconds\tsoftmax with temperature: {:.2f} seconds".format(round(t1-t0), round(t2-t1)))
return grad
|