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"""The Sampeling class serve as a helper module for retriving subject model data"""
from abc import ABC, abstractmethod
import os
import gc
import time
import numpy as np
from sklearn.neighbors import NearestNeighbors
from scipy.special import gamma
import math
from pynndescent import NNDescent
from sklearn.cluster import KMeans
"""
DataContainder module
1. calculate information entropy for singel sample and subset
2. sample informative subset
"""
class SampelingAbstractClass(ABC):
def __init__(self, data_provider, epoch):
self.mode = "abstract"
self.data_provider = data_provider
# self.model = model
self.epoch = epoch
@abstractmethod
def info_calculator(self):
pass
class Sampleing(SampelingAbstractClass):
def __init__(self,data_provider, epoch, device):
self.data_provider = data_provider
# self.model = model
self.epoch = epoch
self.DEVICE = device
def probability_density_cal(self,X,dim,k):
"""
calculate the probability of each sample
:param data: numpy.ndarray
:param k: nearest neibour number
:return: probability, numpy.ndarray
"""
## p(xi) = k / (n * V * σ)
# step one: calculate volumes: V = π^(d/2) / Γ(d/2 + 1) * r^d
print("start calculate the nbrs")
# nbrs = NearestNeighbors(n_neighbors=k, algorithm='kd_tree').fit(X)
# distances, indicates = nbrs.kneighbors(X)
"""
construct a vietoris-rips complex
"""
# number of trees in random projection forest
n_trees = min(64, 5 + int(round((X.shape[0]) ** 0.5 / 20.0)))
# max number of nearest neighbor iters to perform
n_iters = max(5, int(round(np.log2(X.shape[0]))))
# distance metric
metric = "euclidean"
# get nearest neighbors
nnd = NNDescent(
X,
n_neighbors=k,
metric=metric,
n_trees=n_trees,
n_iters=n_iters,
max_candidates=60,
verbose=True
)
indicates, distances = nnd.neighbor_graph
print("finish calculate the nbrs")
pred = self.data_provider.get_pred(self.epoch,X)
d = dim # dimensional
volumes = []
variances = []
r_col = []
print("start calculate the volumes and variances")
for i in range(len(X)):
r = distances[i, -1] # farest neibour 's distance as r
V = (math.pi**(d/2) / gamma(d/2 + 1)) * (r**d) +1e-8 # calculate the volumes
volumes.append(V)
r_col.append(r)
# calculate prediction varians: σ
neighbor_indices = indicates[i]
neighbor_preds = pred[neighbor_indices] # get predictions of k neighbors
# Flatten the neighbor_preds array to a 1D array
flatten_preds = neighbor_preds.flatten()
# Calculate the variance for all predictions
variance = np.var(flatten_preds)
# variance = np.var(neighbor_preds, axis=0) # calculate variance for each class
variances.append(variance)
print("finsih calculate the volumes and variances")
# step three: calculate the probability: p(xi) = k / (n * V * σ)
n = len(X)
probabilities = []
for i in range(len(X)):
# p = k / (n * volumes[i] * variances[i])
p = k / (n * (r_col[i]) * (variances[i]))
probabilities.append(p)
return probabilities,volumes,variances,r_col
def info_calculator(self):
data = self.data_provider.train_representation(self.epoch)
# data_nbrs = NearestNeighbors(n_neibour = )
def clustering(self,data, n_clusters):
kmeans = KMeans(n_clusters=n_clusters, random_state=0)
# k-means
kmeans.fit(data)
# label
labels = kmeans.labels_
# print(labels)
# clustering center
centers = kmeans.cluster_centers_
# print(centers)
return labels,centers
def space_split(self, data):
self.pred = self.data_provider.get_pred(self.epoch, data)
cluster_idx = 10
print("clustering....")
labels, centers = self.clustering(data, n_clusters=cluster_idx)
print("clustering finfished")
for i in range(10):
subset_indices = np.where(labels == i)[0]
subset = data[subset_indices]
info = self.subset_info_cal(subset, centers[i])
# 如果信息量大于0，继续分割
print("info",info)
if info > 0:
labels, new_cluster_idx = self.split(data,subset_indices, labels, cluster_idx)
cluster_idx = new_cluster_idx
return labels
def split(self, org_data,indices, labels, cluster_idx, m=1.5, n=1.5):
data = org_data[indices] # get the data subset from global data using indices
print("data.shape",data.shape)
# divide clustering
sub_labels, centers = self.clustering(data, n_clusters=2)
# calculate the information of the subset's clustering
info = []
for i in range(2):
subset_indices = indices[sub_labels == i]
subset = data[sub_labels == i]
info_i = self.subset_info_cal(subset, centers[i], m, n)
info.append(info_i)
# if information > 0
for i in range(2):
if info[i] > 0:
subset_indices = indices[sub_labels == i]
labels, cluster_idx = self.split(org_data, subset_indices, labels, cluster_idx+1, m, n)
else:
subset_indices = indices[sub_labels == i]
labels[subset_indices] = cluster_idx
cluster_idx += 1
return labels, cluster_idx
def subset_info_cal(self,data,center_data,m=1.5,n=1.5):
"""
use infomration theroy quintify the information of each subset
information = - log(p(d < m)) - log(p(a < n))
"""
# calculat the center samples to each sample's distance
dists = np.sqrt(np.sum((data - center_data)**2, axis=1))
preds = self.data_provider.get_pred(self.epoch, data)
pred_i = self.data_provider.get_pred(self.epoch, np.array([center_data]))
# 计算预测值的误差，如果误差小于n，认为是True
diffs = np.abs(preds - pred_i[0])
# 计算满足条件的概率
p_d = np.mean(dists < m) + 1e-8
p_a = np.mean(diffs < n) + 1e-8
print("p_d",p_d, "p_a",p_a)
info = -np.log(p_d) - np.log(p_a)
return info
def sample_data(self, data, sample_ratio=0.2):
all_indices = [] # store the selected indices
labels = self.space_split(data)
unique_labels = np.unique(labels)
for label in unique_labels:
indices = np.where(labels == label)[0] # indices of data in the current cluster
sample_size = int(len(indices) * sample_ratio) # number of samples to select
if sample_size == 0 and len(indices) > 0: # in case sample_size is zero for small clusters, select at least one data point
sample_size = len(indices)
sampled_indices = np.random.choice(indices, size=sample_size, replace=False) # select samples without replacement
all_indices.append(sampled_indices)
return np.concatenate(all_indices) #
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