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workflow_learn_kmeans.py
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workflow_learn_kmeans.py
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'''
Main pipeline for running unsupervised learning methods: k-means, PCA, ITQ, random projection, or any additional method that implements a given interface.
Precise configuration can be adjusted in utils.py.
'''
import os, sys, math
import os.path as osp
import numpy as np
import sklearn
from sklearn.cluster import MiniBatchKMeans, KMeans
import pca
import cplsh
import utils
import pickle
import pdb
import time
import json
from collections import defaultdict
import kahip_solver
import kmeans
from datetime import date
import itq
import torch
data_dir = 'data'
#mbkm: minibatch k-means, km: k-means.
km_method = 'km'
max_loyd = 50
class KNode():
def __init__(self, d_idx, dataset, n_clusters, height, ds2bins, ht2cutsz, opt):
#d_idx are list of indices of current data in overall dataset
self.d_idx = d_idx
self.bin_idx = None
self.ds2bins = ds2bins
self.n_clusters = n_clusters
self.height = height
if height > 0:
self.create_child_nodes(dataset, ds2bins, ht2cutsz, opt)
if height == 0 or len(self.children) == 0:
#leaf node
self.bin_idx = len(set(ds2bins.values()))
opt.bin2len_all[self.bin_idx] = len(d_idx)
for idx in self.d_idx:
ds2bins[idx] = self.bin_idx
def create_child_nodes(self, dataset, ds2bins, ht2cutsz, opt):
self.children = []
if len(self.d_idx) < self.n_clusters:
if len(self.d_idx) <= 1:
return
if opt.cplsh:
self.n_clusters = 2**int(np.log(len(self.d_idx))/np.log(2))
else:
self.n_clusters = len(self.d_idx)
ds = dataset[self.d_idx]
#qu = queries[self.q_idx]
child_d_idx_l, self.solver = k_means(ds, self.d_idx, ht2cutsz, self.height, self.n_clusters, opt)
#self.d_idx2dist = {self.d_idx[i] : self.d_dist_idx[i] for i in range(len(self.d_idx)) }
#self.q_idx2dist = {self.q_idx[i] : self.q_dist_idx[i] for i in range(len(self.q_idx)) }
for i in range(self.n_clusters):
d_idx = self.d_idx[child_d_idx_l[i]]
#q_idx = self.q_idx[child_q_idx_l[i]]
node = KNode(d_idx, dataset, self.n_clusters, self.height-1, ds2bins, ht2cutsz, opt)
self.children.append(node)
'''
Input:
-dataset: dataset for current KNode.
-dataset_idx: indices in entire dataset for current dataset/partition/KNode.
'''
def k_means(dataset, dataset_idx, ht2cutsz, height, n_clusters, opt): #ranks
num_points = dataset.shape[0]
dimension = dataset.shape[1]
use_kahip_solver = False
if opt.kmeans_use_kahip_height == height:
use_kahip_solver = True
if use_kahip_solver:
solver = kahip_solver.KahipSolver()
elif opt.fast_kmeans:
solver = kmeans.FastKMeans(dataset, n_clusters, opt)
elif opt.itq:
solver = itq.ITQSolver(dataset, n_clusters)
elif opt.cplsh:
solver = cplsh.CPLshSolver(dataset, n_clusters, opt)
elif opt.pca:
assert n_clusters == 2
solver = pca.PCASolver(dataset, opt)
elif opt.st:
assert n_clusters == 2
solver = pca.STSolver(dataset, opt.glob_st_ranks, dataset_idx, opt)
elif opt.rp:
if n_clusters != 2:
raise Exception('n_cluster {} must be 2!'.format(n_clusters))
solver = pca.RPSolver(dataset, opt)
elif km_method == 'km':
solver = KMeans(n_clusters=n_clusters, max_iter=max_loyd)
solver.fit(dataset)
elif km_method == 'mbkm':
solver = MiniBatchKMeans(n_clusters=n_clusters, max_iter=max_loyd)
solver.fit(dataset)
else:
raise Exception('method {} not supported'.format(km_method))
#print("Ranking clusters for data and query points...")
#dataset_dist = solver.transform(dataset) #could be useful, commented out for speed
#queries_dist = solver.transform(queries)
#the distances to cluster centers, ranked smallest first.
#dataset_dist_idx = np.argsort(dataset_dist, axis=1)
#queries_dist_idx = np.argsort(queries_dist, axis=1)
if use_kahip_solver:
#output is numpy array
d_cls_idx = solver.predict(dataset_idx)
elif isinstance(solver, kmeans.FastKMeans):
d_cls_idx = solver.predict(dataset, k=1)
d_cls_idx = d_cls_idx.reshape(-1)
elif isinstance(solver, cplsh.CPLshSolver):
d_cls_idx = solver.predict(dataset, k=1)
elif isinstance(solver, itq.ITQSolver):
d_cls_idx = solver.predict(dataset, k=1)
d_cls_idx = d_cls_idx.reshape(-1)
else:
d_cls_idx = solver.predict(dataset)
#lists of indices (not dataset points) for each class. Note each list element is a tuple.
#list of np arrays
d_idx_l = [np.where(d_cls_idx==i)[0] for i in range(n_clusters)]
#q_idx_l = [np.where(q_cls_idx==i) for i in range(n_clusters)] #could be useful, commented out for speed
compute_cut_sz_b = False
if compute_cut_sz_b:
ranks = utils.dist_rank(dataset, k=opt.k, opt=opt)
#ranks are assumed to be 1-based
ranks += 1
cut_sz = compute_cut_size(d_cls_idx.tolist(), ranks)
ht2cutsz[height].append(cut_sz)
return d_idx_l, solver
'''
Check on nearest neighbors, which bins they land in.
against true NN
Input: n_bins
-neigh are indices in dataset, not feature vecs.
-ds buckets, bucket counts of elements in dataset.
Returns
acc and probe
'''
def check_res_single(dsroot, qu, neigh, n_bins, ds2bins, opt):
#check the bin of the nearest neighbor.
acc_ar = np.zeros(len(qu))
probe_ar = np.zeros(len(qu))
probe_counts = np.zeros(len(qu))
ds2bins = dsroot.ds2bins
for i, q in enumerate(qu):
bin2len = {}
targets = neigh[i]
#will contain the bins retrieved by query.
q_bins = set() #check_res_single2(node, q, probe_set, q_bins, n_bins=2):
check_res_single2(dsroot, q, i, bin2len, q_bins, n_bins, opt)
#get number of points probed
#compare with target buckets
cor = 0
for neighbor in targets:
target_bin = ds2bins[neighbor]
if target_bin in q_bins:
cor += 1
#target_bins.append(ds2bins[neighbor])
#len(targets) is k
acc_ar[i] = cor / len(targets)
#probe_ar[i] = sum([opt.bin2len_all[b] for b in q_bins])
probe_ar[i] = np.array(list(bin2len.values())).sum()
#compare buckets, how many of k neighbors buckets are in buckets queried
mean_probe = np.mean(probe_ar)
acc = np.mean(acc_ar)
n95 = int(len(qu)*.95)
#q_nn = solver.transform(q.reshape(1,-1)).reshape(-1).argpartition(n_bins-1)[:n_bins]
idx95 = probe_ar.argpartition(n95)[n95-1]
probe_count95 = probe_ar[idx95]
print('acc: {} mean_probe count {}'.format(acc, mean_probe))
return acc, mean_probe, probe_count95
'''
Recurse down the hierarchy.
'''
def check_res_single2(node, q, q_idx, bin2len, q_bins, n_bins, opt):
if node.height == 0 or len(node.children) == 0:
bin2len[node.bin_idx] = len(node.d_idx)
q_bins.add(node.bin_idx)
return
solver = node.solver
#stop as soon as none of the n_bins coincide
if isinstance(solver, kahip_solver.KahipSolver):
q_nn = solver.predict([q_idx])
elif isinstance(solver, kmeans.FastKMeans):
q_nn = solver.predict(q.reshape(1,-1), k=n_bins)
q_nn = q_nn.reshape(-1)
elif isinstance(solver, cplsh.CPLshSolver):
#if node.height == 1:
#print('heit 1')
#pdb.set_trace()
q_nn = solver.predict(q.reshape(1,-1), k=n_bins)
q_nn = q_nn.reshape(-1)
elif isinstance(solver, itq.ITQSolver):
q_nn = solver.predict(q.reshape(1,-1))
q_nn = q_nn.reshape(-1)
elif isinstance(solver, sklearn.cluster.KMeans) or isinstance(solver, sklearn.cluster.MiniBatchKMeans):
q_nn = solver.transform(q.reshape(1,-1)).reshape(-1).argpartition(n_bins-1)[:n_bins]
else:
q_nn = solver.predict(q.reshape(1,-1))
for bucket in q_nn:
#pdb.set_trace()
if bucket >= 0:
check_res_single2(node.children[int(bucket)], q, q_idx, bin2len, q_bins, n_bins, opt)
def load_data(data_dir, opt):
if opt.glove:
dataset = np.load(osp.join(utils.data_dir, 'glove_dataset.npy'))
queries = np.load(osp.join(utils.data_dir, 'glove_queries.npy'))
neigh = np.load(osp.join(utils.data_dir, 'glove_answers.npy'))
#if DEBUG:
#dataset = dataset[:5000]
#neigh = utils.dist_rank(torch.from_numpy(queries).to(utils.device), k=10, data_y=torch.from_numpy(dataset).to(utils.device), opt=opt).cpu().numpy()
elif opt.glove_c:
dataset = np.load(osp.join(utils.data_dir, 'glove_c0.08_dataset.npy'))
queries = np.load(osp.join(utils.data_dir, 'glove_c0.08_queries.npy'))
neigh = np.load(osp.join(utils.data_dir, 'glove_answers.npy'))
print('data loaded from {}'.format(osp.join(utils.data_dir, 'glove_c_dataset.npy')))
elif opt.sift:
dataset = np.load(osp.join(utils.data_dir, "sift_dataset_unnorm.npy"))
queries = np.load(osp.join(utils.data_dir, "sift_queries_unnorm.npy"))
neigh = np.load(osp.join(utils.data_dir, "sift_answers_unnorm.npy"))
elif opt.sift_c:
dataset = np.load(osp.join(utils.data_dir, 'sift_c_dataset.npy'))
queries = np.load(osp.join(utils.data_dir, 'sift_c_queries.npy'))
neigh = np.load(osp.join(utils.data_dir, 'sift_answers_unnorm.npy'))
print('data loaded from {}'.format(osp.join(utils.data_dir, 'sift_c_dataset_unnorm.npy')))
elif opt.prefix10m:
dataset = np.load(osp.join(utils.data_dir, 'prefix10m_dataset.npy'))
queries = np.load(osp.join(utils.data_dir, 'prefix10m_queries.npy'))
neigh = np.load(osp.join(utils.data_dir, 'prefix10m_answers.npy'))
print('data loaded from {}'.format(osp.join(utils.data_dir, 'prefix')))
else:
# Load MNIST data
npy_dataset_file_name = os.path.join(data_dir, "dataset_unnorm.npy")
npy_queries_file_name = os.path.join(data_dir, "queries_unnorm.npy")
npy_neigh_file_name = os.path.join(data_dir, "answers_unnorm.npy")
dataset = np.load(npy_dataset_file_name)
queries = np.load(npy_queries_file_name)
neigh = np.load(npy_neigh_file_name)
if opt.normalize_data:
dataset = utils.normalize_np(dataset)
queries = utils.normalize_np(queries)
return dataset, queries, neigh
'''
Save the tree.
'''
def save_data(dsroot):
print("Saving results...")
with open(os.path.join(data_dir, "kmeans_dsroot"), "wb") as output:
pickle.dump(dsroot, output)
print("Done.")
def run_kmeans(ds, qu, neigh, n_bins, n_clusters, height, ht2cutsz, opt):
#used if evaluating performance on training set
swap_query_to_data = False
if swap_query_to_data:
qu = ds
#nearest neighbor not itself
dist = utils.l2_dist(ds)
dist += 2*torch.max(dist).item()*torch.eye(len(ds))
val, neigh = torch.topk(dist, k=opt.k, dim=1, largest=False)
neigh = neigh.numpy()
if opt.sift:
kmeans_path = os.path.join(data_dir, 'sift', 'sift_dsroot{}ht{}'.format(n_clusters, height))
elif opt.glove:
if opt.fast_kmeans:
kmeans_path = os.path.join(data_dir, 'kmeans', 'fastkmeans_dsroot{}{}{}_{}'.format(n_clusters, km_method, max_loyd, height))
else:
kmeans_path = os.path.join(data_dir, 'kmeans', 'kmeans_dsroot{}{}{}_{}'.format(n_clusters, km_method, max_loyd, height))
elif opt.glove_c:
#if opt.fast_kmeans:
kmeans_path = os.path.join(data_dir, 'kmeans_glove_c', 'fastkmeans_dsroot{}{}{}_{}'.format(n_clusters, km_method, max_loyd, height))
elif opt.sift_c:
#if opt.fast_kmeans:
kmeans_path = os.path.join(data_dir, 'kmeans_sift_c', 'fastkmeans_dsroot{}{}{}_{}'.format(n_clusters, km_method, max_loyd, height))
else:
if opt.fast_kmeans:
kmeans_path = os.path.join(data_dir, 'kmeans_mnist', 'fastkmeans_dsroot{}{}{}_{}'.format(n_clusters, km_method, max_loyd, height))
else:
kmeans_path = os.path.join(data_dir, 'kmeans_mnist', 'kmeans_dsroot{}{}{}_{}'.format(n_clusters, km_method, max_loyd, height))
save_data = True #True
if os.path.exists(kmeans_path) and not (opt.pca or opt.rp or opt.st): #False and
with open(kmeans_path, 'rb') as file:
root = pickle.load(file)
elif opt.cplsh and hasattr(opt, 'cplsh_root'):
#can't serialize cpp object
root = opt.cplsh_root
else:
print("Building ...")
d_idx = np.array(list(range(len(ds))))
#q_idx = np.array(list(range(len(qu))))
#dataset element indices to bin indices
ds2bins = {}
root = KNode(d_idx, ds, n_clusters, height, ds2bins, ht2cutsz, opt)
if save_data:
if opt.cplsh:
opt.cplsh_root = root
elif not (opt.rp or opt.pca or opt.st):
with open(kmeans_path, "wb") as output:
pickle.dump(root, output)
opt.saved_path = kmeans_path
acc, probe, probe95 = check_res_single(root, qu, neigh, n_bins, root.ds2bins, opt)
print('n_clusters: {} n_bins: {} height: {} acc: {} probe: {} probe95: {}'.format(n_clusters, n_bins, height, acc, probe, probe95))
return acc, probe, probe95
def run_main(height_preset, ds, qu, neigh, opt):
if height_preset == 1:
n_clusters_l = [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]#, 16384, 32768, 60000] #65536]
n_clusters_l = [1<<16]
n_clusters_l = [16, 256] #[16]
n_clusters_l = [16]
#n_clusters_l = [1<<8]
elif height_preset == 2:
n_clusters_l = [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024] #2
n_clusters_l = [16, 256] #[16]
n_clusters_l = [256]
elif height_preset == 3:
n_clusters_l = [2, 4, 8, 16, 32, 64]
n_clusters_l = [2]
elif height_preset in range(11):
n_clusters_l = [2]
else:
raise Exception('No n_clusters for height {}'.format(height_preset))
print('HEIGHT: {} n_clusters: {}'.format(height_preset, n_clusters_l))
#if height_preset != 1 and opt.itq:
# raise Exception('Height must be 1 if using ITQ')
force_height = True
k = opt.k
n_repeat = opt.n_repeat_km
n_repeat = 1
neigh = neigh[:, 0:k]
ht2cutsz = defaultdict(list)
#acc_mx = np.zeros((len(n_clusters_l), len(n_bins_l)))
#probe_mx = np.zeros((len(n_clusters_l), len(n_bins_l)))
n_clusters_l_len = len(n_clusters_l)
acc_mx = [[] for i in range(n_clusters_l_len)]
probe_mx = [[] for i in range(n_clusters_l_len)]
probe95_mx = [[] for i in range(n_clusters_l_len)]
max_bin_count = 0
start_time = time.time()
serial_data = {}
serial_data['k'] = k
if opt.pca or opt.rp or opt.itq or opt.st:
#only 1-bin probe makes sense in these settings
opt.max_bin_count = 1
for i, n_clusters in enumerate(n_clusters_l):
if force_height:
height = height_preset
serial_data['height'] = height
else:
height = math.floor(math.log(len(ds), n_clusters))
bin_count = 40 #1
acc = 0
probe = 0
#if opt.itq or opt.pca or opt.rp:
# #only 1-bin probe makes sense in these settings
# opt.max_bin_count = 1
#keep expanding number of bins until acc reaches e.g. 0.97
while acc < opt.acc_thresh and bin_count <= min(n_clusters, opt.max_bin_count):
acc = 0
probe = 0
probe95 = 0
for l in range(n_repeat):
cur_acc, cur_probe, cur_probe95 = run_kmeans(ds, qu, neigh, bin_count, n_clusters, height, ht2cutsz, opt)
acc += cur_acc
probe += cur_probe
probe95 += cur_probe95
acc /= n_repeat
probe /= n_repeat
probe95 /= n_repeat
#bin_count += 1
bin_count += 1
acc_mx[i].append(acc)
probe_mx[i].append(probe)
probe95_mx[i].append(probe95)
max_bin_count = max(max_bin_count, bin_count-1)
end_time = time.time()
serial_data['acc_mx'] = acc_mx
serial_data['probe_mx'] = probe_mx
serial_data['max_loyd'] = max_loyd
serial_data['km_method'] = km_method
serial_data['ht2cutsz'] = ht2cutsz
print_output = True
if print_output:
print('total computation time: {} hrs'.format((end_time-start_time)/3600))
print('acc {}'.format(acc_mx))
print('probe count {}'.format(probe_mx))
print('ht2cutsz {}'.format(ht2cutsz))
row_label = ['{} clusters'.format(i) for i in n_clusters_l]
col_label = ['{} bins'.format(i+1) for i in range(max_bin_count)]
acc_mx0 = acc_mx
probe_mx0 = probe_mx
probe95_mx0 = probe95_mx
acc_mx = np.zeros((n_clusters_l_len, max_bin_count))
probe_mx = np.zeros((n_clusters_l_len, max_bin_count))
probe95_mx = np.zeros((n_clusters_l_len, max_bin_count))
for i in range(len(n_clusters_l)):
for j in range(len(acc_mx0[i])):
acc_mx[i][j] = acc_mx0[i][j]
probe_mx[i][j] = probe_mx0[i][j]
probe95_mx[i][j] = probe95_mx0[i][j]
#[acc_mx[i][j] = acc_mx0[i][j] for j in range(len(acc_mx0[i])) for i in range(len(n_clusters_l))]
#[probe_mx[i][j] = probe_mx0[i][j] for j in range(len(probe_mx0[i])) for i in range(len(n_clusters_l))]
acc_md = utils.mxs2md([np.around(acc_mx,3), np.rint(probe_mx), np.rint(probe95_mx)], row_label, col_label)
cur_method = 'k-means'
if opt.pca:
cur_method = 'PCA Tree'
elif opt.st:
cur_method = 'ST'
elif opt.itq:
cur_method = 'ITQ'
elif opt.rp:
cur_method = 'Random Projection'
elif opt.cplsh:
cur_method = 'Cross Polytope LSH'
if opt.write_res: #False
if opt.glove:
res_path = os.path.join('results', 'linear2_glove.md')
elif opt.glove_c:
res_path = os.path.join('results', 'linear2_glove_c.md')
elif opt.sift:
res_path = os.path.join('results', 'linear2_sift.md')
elif opt.sift_c:
res_path = os.path.join('results', 'linear2_sift_c.md')
else:
res_path = os.path.join('results', 'linear2_mnist.md')
with open(res_path, 'a') as file:
msg = '\n\n{} **For k = {}, height {}, method {}, max_iter: {}**\n\n'.format(str(date.today()), k, height, cur_method, max_loyd)
if opt.itq:
msg = '\n\n*ITQ*' + msg
file.write(msg)
file.write(acc_md)
if print_output:
print('acc_md\n {} \n'.format(acc_md))
if opt.glove:
pickle_path = os.path.join(data_dir, 'glove', 'kmeans_ht{}.pkl'.format(height))
json_path = os.path.join(data_dir, 'glove', 'kmeans_ht{}.json'.format(height))
elif opt.glove_c:
pickle_path = os.path.join(data_dir, 'glove_c', 'kmeans_ht{}.pkl'.format(height))
json_path = os.path.join(data_dir, 'glove_c', 'kmeans_ht{}.json'.format(height))
elif opt.sift:
pickle_path = os.path.join(data_dir, 'sift', 'kmeans_ht{}.pkl'.format(height))
json_path = os.path.join(data_dir, 'sift', 'kmeans_ht{}.json'.format(height))
elif opt.sift_c:
pickle_path = os.path.join(data_dir, 'sift_c', 'kmeans_ht{}.pkl'.format(height))
json_path = os.path.join(data_dir, 'sift_c', 'kmeans_ht{}.json'.format(height))
else:
pickle_path = os.path.join(data_dir, 'kmeans_ht{}.pkl'.format(height))
json_path = os.path.join(data_dir, 'kmeans_ht{}.json'.format(height))
if False: #march
utils.pickle_dump(serial_data, pickle_path)
with open(json_path, 'w') as file:
json.dump(serial_data, file)
return acc_mx, probe_mx, probe95_mx
if __name__ == '__main__':
opt = utils.parse_args()
if opt.kmeans_use_kahip_height > 0:
print('NOTE: will use kahip solver for height {}'.format(opt.kmeans_use_kahip_height))
height_l = range(2, 10)
height_l = [2]
#height_l = [2]
#height_l = range(1, 9)
#height_l = [9,10]
opt.bin2len_all = {}
res_l = []
if opt.glove_c or opt.sift_c:
res_l = ['Catalyzed data ']
ds, qu, neigh = load_data(utils.data_dir, opt)
if opt.cplsh and opt.sift:
ds = ds / np.sqrt((ds**2).sum(-1, keepdims=True))
qu = qu / np.sqrt((qu**2).sum(-1, keepdims=True))
qu = qu[:500]
neigh = neigh[:500]
n_repeat = 1
#search tree
#global glob_st_ranks
#if glob_st_ranks is None:
if opt.st:
opt.glob_st_ranks = utils.dist_rank(ds, opt.k, include_self=True, opt=opt)
torch.save(opt.glob_st_ranks, 'st_ranks_glove')
for i in range(n_repeat):
for height in height_l:
acc, probe, probe95 = run_main(height, ds, qu, neigh, opt)
res_l.append(str(height) + ' ' + ' '.join([str(acc[0,0]), str(probe[0,0]), str(probe95[0,0])]))
res_str = '\n'.join(res_l)
if opt.rp:
with open(osp.join(utils.data_dir, 'rp_data_mnist.md'), 'a') as file:
file.write(res_str +'\n')
print(res_str)
if hasattr(opt, 'saved_path'):
print('need to delete ', opt.saved_path)