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pca.py
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pca.py
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from sklearn.decomposition import PCA
import torch
import numpy as np
import utils
import torch.sparse
import pdb
'''
Classes for two linear models.
Linear model: PCA, used to compared with trained supervised models learned from kahip partitions.
Linear model: random projections, used to compared with trained supervised models learned using kahip partitions.
'''
class PCASolver():
#Guaranteed to only need top component
def __init__(self, dataset, opt):
if isinstance(dataset, torch.Tensor):
dataset = torch.tensor(dataset).cpu().numpy()
#scale features
dataset_t = np.transpose(dataset)
self.ds_mean = dataset_t.mean(axis=-1, keepdims=True)
dataset_t = dataset_t - self.ds_mean
self.ds_std = dataset_t.std(axis=-1, keepdims=True).clip(min=0.1)
dataset_t = dataset_t / self.ds_std
dataset = np.transpose(dataset_t)
self.pca = PCA(n_components=1)
#shape: n_sample x 1
self.pca.fit(dataset)
out = self.pca.transform(dataset)
self.median = np.median(out)
'''
Input: k here to satisfy uniform interface with kmeans solver.
query: 2D vec
Output:
-1 D np array
'''
def predict(self, query):
if isinstance(query, torch.Tensor):
query = torch.tensor(query).cpu().numpy()
cls = np.zeros(len(query))
query = np.transpose((np.transpose(query) - self.ds_mean) / self.ds_std)
out = self.pca.transform(query).reshape(-1)
cls[out >= self.median] = 1
return cls
from scipy.stats import ortho_group
'''
Another linear method, random projection.
'''
class RPSolver():
def __init__(self, dataset, opt):
if isinstance(dataset, torch.Tensor):
dataset = torch.tensor(dataset).cpu().numpy()
self.data_mean = dataset.mean(axis=0)
#orthogonal projection
self.orth_mx = ortho_group.rvs(dataset.shape[-1])
#self.rand_vec = np.random.randn(dataset.shape[-1]) #np.random.multivariate_normal(mean, cov)
'''
Input: k here to satisfy uniform interface with kmeans solver.
query: 2D vec
Output:
-1 D np array
'''
def predict(self, query):
if isinstance(query, torch.Tensor):
query = torch.tensor(query).cpu().numpy()
query = query - self.data_mean
out = np.matmul(query, self.orth_mx).sum(axis=-1)
cls = np.zeros(len(query))
cls[out > 0] = 1
return cls
'''
Search tree solver.
'''
class STSolver():
'''
Input:
-dataset: dataset for current node, ie subset of full dataset.
-knn_graph: knn graph
-ranks: nearest neighbor ranks matrix (as original distances), indices are as original dataset. ranks
for index i includes the i itself.
-idx: indices of dataset used in cur iteration, indices are wrt original dataset.
'''
def __init__(self, dataset, ranks, idx, opt):
if isinstance(dataset, np.ndarray):
dataset = torch.from_numpy(dataset).to(utils.device)
idx = torch.from_numpy(idx).to(utils.device)
#augment last component with 1's
dataset = torch.cat((dataset, torch.zeros(len(dataset), 1, device=utils.device)), dim=-1)
if len(dataset) != len(ranks):
long_vec = -torch.ones(len(ranks), device=utils.device)
src_vec = torch.cuda.FloatTensor(range(len(idx)))
long_vec.scatter_(dim=0, index=idx, src=src_vec)
long_vec = long_vec.cpu().numpy()
#sparse_idx = torch.LongTensor(len(idx ) )
sparse_idx_l = []
for i, t in enumerate(idx):
cur_vec = []
for j in ranks[t]:
if long_vec[j] != -1:
cur_vec.append(long_vec[j])
idx_i = torch.cat((torch.ones(1, len(cur_vec), dtype=torch.int64, device=utils.device)*i, torch.cuda.LongTensor(cur_vec).unsqueeze(0)), dim=0)
sparse_idx_l.append(idx_i)
#2 x number of non-zero entries
sparse_idx = torch.cat(sparse_idx_l, dim=-1)
else:
#pdb.set_trace()
range_vec = torch.cuda.LongTensor(range(len(dataset))).unsqueeze(-1).repeat(1, ranks.size(-1))
ranks = ranks.to(utils.device)
sparse_idx = torch.cat((range_vec.view(1, -1), ranks.view(1, -1)), dim=0)
sparse_idx1 = torch.clone(sparse_idx)
sparse_idx1[0] = sparse_idx[1]
sparse_idx1[1] = sparse_idx[0]
sparse_idx = torch.cat((sparse_idx, sparse_idx1), dim=-1)
sparse_val = torch.ones(sparse_idx.size(-1), device=utils.device)
sparse_vec = torch.sparse.FloatTensor(sparse_idx, sparse_val, torch.Size([len(dataset), len(dataset)]))
sparse_vec = sparse_vec.coalesce()
sparse_vec = torch.sparse.FloatTensor(sparse_vec._indices(), torch.ones_like(sparse_vec._values()), torch.Size([len(dataset), len(dataset)]) )
lamb = sparse_vec._values().sum().item()/len(dataset)**2 #.001
print('lamb {}'.format(lamb))
ones = torch.ones(1, dataset.size(0), device=utils.device)
W = torch.mm(torch.sparse.mm(sparse_vec.t(), dataset).t(), dataset) - lamb*torch.mm(torch.mm(dataset.t(), ones.t()), torch.mm(ones, dataset))
eval_, evec_ = torch.eig(W, eigenvectors=True)
eval_ = eval_[:, 0]
evec_ = evec_.t()
#pdb.set_trace()
max_idx = torch.argmax(eval_)
self.top_evec = evec_[max_idx]
self.top_evec = self.top_evec.cpu().numpy()
'''
Input: k here to satisfy uniform interface with kmeans solver.
query: 2D vec
Output:
-1 D np array
'''
def predict(self, query):
query = np.concatenate((query, np.ones((len(query), 1))), axis=-1)
projected = (self.top_evec * query).sum(-1)
cls = np.zeros(len(query))
cls[projected > 0] = 1
#print('sum! {}'.format( cls.sum()))
#pdb.set_trace()
return cls