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hypergraph.py
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hypergraph.py
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'''
using hypergraph representations for document classification.
'''
import _init_paths
import torch
import torch.nn as nn
import numpy as np
from torch.nn import Parameter
import torch.optim as optim
import torch.nn.functional as F
import torch.optim as optim
import utils
import math
from sklearn.decomposition import TruncatedSVD
from collections import defaultdict
import sys
import time
import pdb
device = utils.device
class HyperMod(nn.Module):
def __init__(self, input_dim, vidx, eidx, nv, ne, v_weight, e_weight, args, is_last=False, use_edge_lin=False):
super(HyperMod, self).__init__()
self.args = args
self.eidx = eidx
self.vidx = vidx
self.v_weight = v_weight
self.e_weight = e_weight
self.nv, self.ne = args.nv, args.ne
self.W_v2e = Parameter(torch.randn(args.n_hidden, args.n_hidden))
self.W_e2v = Parameter(torch.randn(args.n_hidden, args.n_hidden))
self.b_v = Parameter(torch.zeros(args.n_hidden))
self.b_e = Parameter(torch.zeros(args.n_hidden))
self.is_last_mod = is_last
self.use_edge_lin = use_edge_lin
if is_last and self.use_edge_lin:
self.edge_lin = torch.nn.Linear(args.n_hidden, args.final_edge_dim)
def forward(self, v, e):
if args.edge_linear:
ve = torch.matmul(v, self.W_v2e) + self.b_v
else:
ve = F.relu(torch.matmul(v, self.W_v2e) + self.b_v)
#weigh ve according to how many edges a vertex is connected to
v_fac = 4 if args.predict_edge else 1
v = v*self.v_weight*v_fac
eidx = self.eidx.unsqueeze(-1).expand(-1, self.args.n_hidden)
e = e.clone()
ve = (ve*self.v_weight)[self.args.paper_author[:, 0]]
ve *= args.v_reg_weight
e.scatter_add_(src=ve, index=eidx, dim=0)
e /= args.e_reg_sum
#e = e*self.e_weight
ev = F.relu(torch.matmul(e, self.W_e2v) + self.b_e)
#e = e*self.e_weight
#ev *= self.e_weight
#v = torch.zeros(self.nv , self.n_hidden)
vidx = self.vidx.unsqueeze(-1).expand(-1, self.args.n_hidden)
ev_vtx = (ev*self.e_weight)[self.args.paper_author[:, 1]]
#ev_vtx = (ev)[self.args.paper_author[:, 1]]
ev_vtx *= args.e_reg_weight
#v = v.clone()
v.scatter_add_(src=ev_vtx, index=vidx, dim=0)
#v = v*self.v_weight
v /= args.v_reg_sum
if not self.is_last_mod:
v = F.dropout(v, args.dropout_p)
if self.is_last_mod and self.use_edge_lin:
ev_edge = (ev*torch.exp(self.e_weight)/np.exp(2))[self.args.paper_author[:, 1]]
v2 = torch.zeros_like(v)
v2.scatter_add_(src=ev_edge, index=vidx, dim=0)
v2 = self.edge_lin(v2)
v = torch.cat([v, v2], -1)
return v, e
def forward00(self, v, e):
#March normalization
v = self.lin1(v)
ve = F.relu(torch.matmul(v, self.W_v2e) + self.b_v)
#weigh ve according to how many edges a vertex is connected to
#ve *= self.v_weight
v = v*self.v_weight #*2
eidx = self.eidx.unsqueeze(-1).expand(-1, self.args.n_hidden)
e = e.clone()
ve = (ve*self.v_weight)[self.args.paper_author[:, 0]]
e.scatter_add_(src=ve, index=eidx, dim=0)
ev = F.relu(torch.matmul(e, self.W_e2v) + self.b_e)
e = e*self.e_weight
#ev *= self.e_weight
#v = torch.zeros(self.nv , self.n_hidden)
vidx = self.vidx.unsqueeze(-1).expand(-1, self.args.n_hidden)
ev_vtx = (ev*self.e_weight)[self.args.paper_author[:, 1]]
#v = v.clone()
v.scatter_add_(src=ev_vtx, index=vidx, dim=0)
#v = v*self.v_weight
if self.is_last_mod:
ev_edge = (ev*torch.exp(self.e_weight)/np.exp(2))[self.args.paper_author[:, 1]]
pdb.set_trace()
v2 = torch.zeros_like(v)
v2.scatter_add_(src=ev_edge, index=vidx, dim=0)
v2 = self.edge_lin(v2)
v = torch.cat([v, v2], -1)
return v, e
class Hypergraph(nn.Module):
'''
Hypergraph class, uses weights for vertex-edge and edge-vertex incidence matrix.
One large graph.
'''
def __init__(self, vidx, eidx, nv, ne, v_weight, e_weight, args):
'''
vidx: idx tensor of elements to select, shape (ne, max_n),
shifted by 1 to account for 0th elem (which is 0)
eidx has shape (nv, max n)..
'''
super(Hypergraph, self).__init__()
self.args = args
self.hypermods = []
is_first = True
for i in range(args.n_layers):
is_last = True if i == args.n_layers-1 else False
self.hypermods.append(HyperMod(args.input_dim if is_first else args.n_hidden, vidx, eidx, nv, ne, v_weight, e_weight, args, is_last=is_last))
is_first = False
if args.predict_edge:
self.edge_lin = torch.nn.Linear(args.input_dim, args.n_hidden)
self.vtx_lin = torch.nn.Linear(args.input_dim, args.n_hidden)
#insetad of A have vector of indices
#self.cls = nn.Linear(args.n_hidden+args.final_edge_dim, args.n_cls)
self.cls = nn.Linear(args.n_hidden, args.n_cls)
def to_device(self, device):
self.to(device)
for mod in self.hypermods:
mod.to('cuda')
return self
def all_params(self):
params = []
for mod in self.hypermods:
params.extend(mod.parameters())
return params
def forward(self, v, e):
'''
Take initial embeddings from the select labeled data.
Return predicted cls.
'''
v = self.vtx_lin(v)
if self.args.predict_edge:
e = self.edge_lin(e)
for mod in self.hypermods:
v, e = mod(v, e)
pred = self.cls(v)
return v, e, pred
class Hypertrain:
def __init__(self, args):
#cross entropy between predicted and actual labels
self.loss_fn = nn.CrossEntropyLoss() #consider logits
self.hypergraph = Hypergraph(args.vidx, args.eidx, args.nv, args.ne, args.v_weight, args.e_weight, args)
#optim.Adam([self.P, self.Ly], lr=.4)
self.optim = optim.Adam(self.hypergraph.all_params(), lr=.04)
#'''
milestones = [100*i for i in range(1, 4)] #[100, 200, 300]
self.scheduler = optim.lr_scheduler.MultiStepLR(self.optim, milestones=milestones, gamma=0.51)
#'''
self.args = args
def train(self, v, e, label_idx, labels):
self.hypergraph = self.hypergraph.to_device(device)
#v = v.to(device)
#e = e.to(device)
#label_idx = label_idx.to(device)
#labels = labels.to(device)
v_init = v
e_init = e
best_err = sys.maxsize
for i in range(self.args.n_epoch):
args.cur_epoch = i
v, e, pred_all = self.hypergraph(v_init, e_init)
pred = pred_all[label_idx]
loss = self.loss_fn(pred, labels)
if i % 30 == 0:
test_err =self.eval(pred_all)
if test_err < best_err:
best_err = test_err
if i % 50 == 0:
sys.stdout.write(' loss {} \t'.format(loss))
self.optim.zero_grad()
loss.backward()
self.optim.step()
self.scheduler.step()
e_loss = self.eval(pred_all)
return pred_all, loss, best_err
def eval(self, all_pred):
if self.args.val_idx is None:
ones = torch.ones(len(all_pred))
ones[self.args.label_idx] = -1
else:
ones = -torch.ones(len(all_pred))
ones[self.args.val_idx] = 1
tgt = self.args.all_labels
tgt[ones==-1] = -1
fn = nn.CrossEntropyLoss(ignore_index=-1)
loss = fn(all_pred, tgt)
#print(' ~~ eval loss ~~ ', loss)
tgt = self.args.all_labels[ones>-1]
#tgt[self.args.label_idx] = -1
pred = torch.argmax(all_pred, -1)[ones>-1]
acc = torch.eq(pred, tgt).sum().item()/len(tgt)
if args.verbose:
print('TEST ERR ', 1-acc, ' ~~ eval loss ~~ ', loss)
return 1-acc
def train(args):
'''
args.vidx, args.eidx, args.nv, args.ne, args = s
args.e_weight = s
args.v_weight = s
label_idx, labels = s
'''
#args.e = torch.randn(args.ne, args.n_hidden)
if args.predict_edge:
args.e = args.edge_X
else:
args.e = torch.zeros(args.ne, args.n_hidden).to(device)
#args.v = torch.randn(self.args.nv, args.n_hidden)
hypertrain = Hypertrain(args)
pred_all, loss, test_err = hypertrain.train(args.v, args.e, args.label_idx, args.labels)
return test_err
def gen_data_cora(args, data_path='data/cora_author.pt', flip_edge_node=True, do_val=False):
'''
Retrieve and process data, can be used generically for any dataset with predefined data format, eg cora, citeseer, etc.
flip_edge_node: whether to flip edge and node in case of relation prediction.
'''
data_dict = torch.load(data_path)
paper_author = torch.LongTensor(data_dict['paper_author'])
author_paper = torch.LongTensor(data_dict['author_paper'])
n_author = data_dict['n_author']
n_paper = data_dict['n_paper']
classes = data_dict['classes']
#in sparse np array format
paper_X = data_dict['paper_X']
if args.predict_edge: #'author_X' in data_dict:
#edge representations
author_X = data_dict['author_X']
author_classes = data_dict['author_classes']
paperwt = data_dict['paperwt']
authorwt = data_dict['authorwt']
#n_cls = data_dict['n_cls']
cls_l = list(set(classes))
#can flip nodes and edges here for e.g. learning hyperedge representations
#can flip due to symmetry in HNHN
if args.predict_edge:
if flip_edge_node:
temp = paper_author
paper_author = author_paper
author_paper = temp
temp = n_author
n_author = n_paper
n_paper = temp
args.edge_X = torch.from_numpy(paper_X).to(torch.float32).to(device) #paper_X.to(device)
args.edge_classes = classes
temp = paper_X
paper_X = author_X
#author_X = temp
classes = author_classes
temp = paperwt
paperwt = authorwt
authorwt = temp
else:
args.edge_X = torch.from_numpy(author_X).to(torch.float32).to(device)
args.edge_classes = torch.LongTensor(author_classes).to(device)
cls2int = {k:i for (i, k) in enumerate(cls_l)}
classes = [cls2int[c] for c in classes]
args.input_dim = paper_X.shape[-1] #300 if args.dataset_name == 'citeseer' else 300
args.n_hidden = 800 if args.predict_edge else 400
args.final_edge_dim = 100
args.n_epoch = 140 if args.n_layers == 1 else 230 #130 #120
args.ne = n_author
args.nv = n_paper
#args.n_layers = 1 #2 #2
ne = args.ne
nv = args.nv
args.n_cls = len(cls_l)
#no replacement!
#n_labels = max(1, math.ceil(nv*.052))
n_labels = max(1, math.ceil(nv*utils.get_label_percent(args.dataset_name)))
args.all_labels = torch.LongTensor(classes)
proportional_select = False
if proportional_select:
n_labels = int(math.ceil(n_labels/args.n_cls)*args.n_cls)
all_cls_idx = []
n_label_per_cls = n_labels//args.n_cls
for i in range(args.n_cls):
cur_idx = torch.LongTensor(list(range(nv)))[args.all_labels == i]
rand_idx = torch.from_numpy(np.random.choice(len(cur_idx), size=(n_label_per_cls,), replace=False )).to(torch.int64)
cur_idx = cur_idx[rand_idx]
all_cls_idx.append(cur_idx)
args.label_idx = torch.cat(all_cls_idx, 0)
else:
args.label_idx = torch.from_numpy(np.random.choice(nv, size=(n_labels,), replace=False )).to(torch.int64)
if do_val:
#eg for cross validation on training set
val_idx = torch.from_numpy(np.random.choice(len(args.label_idx), size=len(args.label_idx)//args.kfold ))
args.val_idx = args.label_idx[val_idx]
ones = torch.ones(len(args.label_idx))
ones[val_idx] = -1
args.label_idx = args.label_idx[ones>-1]
else:
args.val_idx = None
args.labels = args.all_labels[args.label_idx].to(device) #torch.ones(n_labels, dtype=torch.int64)
args.all_labels = args.all_labels.to(device)
#isinstance(paper_X, scipy.sparse.csr.csr_matrix)
if isinstance(paper_X, np.ndarray):
args.v = torch.from_numpy(paper_X.astype(np.float32)).to(device)
else:
args.v = torch.from_numpy(np.array(paper_X.astype(np.float32).todense())).to(device)
#labeled
#args.vidx, args.eidx, args.nv, args.ne, args.v_weight, args.e_weight
#vidx has shape (ne, max n)
#generate edges
#edge weights. ensure labels.
#args.vidx = torch.zeros((ne+1,), dtype=torch.int64).random_(0, nv-1) + 1 #np.random.randint(nv, (ne, 3))
#args.eidx = torch.zeros((nv+1,), dtype=torch.int64).random_(0, ne-1) + 1 #torch.random.randint(ne, (nv, 2))
args.vidx = paper_author[:, 0].to(device)
args.eidx = paper_author[:, 1].to(device)
args.paper_author = paper_author
#pdb.set_trace()
args.v_weight = torch.Tensor([(1/w if w > 0 else 1) for w in paperwt]).unsqueeze(-1).to(device) #torch.ones((nv, 1)) / 2 #####
args.e_weight = torch.Tensor([(1/w if w > 0 else 1) for w in authorwt]).unsqueeze(-1).to(device) # 1)) / 2 #####torch.ones(ne, 1) / 3
assert len(args.v_weight) == nv and len(args.e_weight) == ne
#args.alpha = 0.15 #.1 #-.1
#
#weights for regularization
#'''
paper2sum = defaultdict(list)
author2sum = defaultdict(list)
e_reg_weight = torch.zeros(len(paper_author)) ###
v_reg_weight = torch.zeros(len(paper_author)) ###
#a switch to determine whether to have wt in exponent or base
use_exp_wt = args.use_exp_wt #True #False
for i, (paper_idx, author_idx) in enumerate(paper_author.tolist()):
e_wt = args.e_weight[author_idx]
e_reg_wt = torch.exp(args.alpha_e*e_wt) if use_exp_wt else e_wt**args.alpha_e
e_reg_weight[i] = e_reg_wt
paper2sum[paper_idx].append(e_reg_wt) ###
v_wt = args.v_weight[paper_idx]
v_reg_wt = torch.exp(args.alpha_v*v_wt) if use_exp_wt else v_wt**args.alpha_v
v_reg_weight[i] = v_reg_wt
author2sum[author_idx].append(v_reg_wt) ###
#'''
v_reg_sum = torch.zeros(nv) ###
e_reg_sum = torch.zeros(ne) ###
for paper_idx, wt_l in paper2sum.items():
v_reg_sum[paper_idx] = sum(wt_l)
for author_idx, wt_l in author2sum.items():
e_reg_sum[author_idx] = sum(wt_l)
pdb.set_trace()
#this is used in denominator only
e_reg_sum[e_reg_sum==0] = 1
v_reg_sum[v_reg_sum==0] = 1
args.e_reg_weight = torch.Tensor(e_reg_weight).unsqueeze(-1).to(device)
args.v_reg_sum = torch.Tensor(v_reg_sum).unsqueeze(-1).to(device)
args.v_reg_weight = torch.Tensor(v_reg_weight).unsqueeze(-1).to(device)
args.e_reg_sum = torch.Tensor(e_reg_sum).unsqueeze(-1).to(device)
return args
def gen_data_dblp(args, data_path='data/dblp_data.pt', do_val=False):
"""
Exact same data as generated by hypergcn.
"""
#paper_idx':paper_idx, 'author':author_idx, 'paper_X':dataset['features'], 'train_idx':train, 'test_idx':test
data = torch.load(data_path)
args.n_hidden = 800 if args.dataset_name == 'pubmed' else 400
args.final_edge_dim = 100
args.n_epoch = 200
#args.n_layers = 1 #2
paperwt = data['paperwt']
authorwt = data['authorwt']
train_idx = torch.LongTensor(data['train_idx'])
args.label_idx = torch.from_numpy(np.random.choice(len(paperwt), size=len(train_idx) ))
if do_val:
#eg for cross validation on training set
val_idx = torch.from_numpy(np.random.choice(len(args.label_idx), size=len(args.label_idx)//args.kfold ))
args.val_idx = args.label_idx[val_idx]
ones = torch.ones(len(train_idx))
ones[val_idx] = -1
args.label_idx = args.label_idx[ones>-1]
else:
args.val_idx = None
args.all_labels = torch.from_numpy(np.where(data['labels'])[1]).to(torch.int64).to(device)
args.labels = args.all_labels[args.label_idx].to(device)
if args.do_svd:
svd = TruncatedSVD(n_components=300, n_iter=7)
X = svd.fit_transform(data['paper_X'])
else:
X = data['paper_X']
args.input_dim = X.shape[-1] #300 ###
args.v = torch.from_numpy(X).to(device)
args.n_cls = int(args.all_labels.max()) + 1 #len(cls_l)
'''
#no replacement!
n_labels = max(1, int(nv*.052))
args.all_labels = torch.LongTensor(classes)
if False:
n_labels = int(math.ceil(n_labels/args.n_cls)*args.n_cls)
all_cls_idx = []
n_label_per_cls = n_labels//args.n_cls
for i in range(args.n_cls):
#pdb.set_trace()
cur_idx = torch.LongTensor(list(range(nv)))[args.all_labels == i]
rand_idx = torch.from_numpy(np.random.choice(len(cur_idx), size=(n_label_per_cls,), replace=False )).to(torch.int64)
cur_idx = cur_idx[rand_idx]
all_cls_idx.append(cur_idx)
args.label_idx = torch.cat(all_cls_idx, 0)
else:
args.label_idx = torch.from_numpy(np.random.choice(nv, size=(n_labels,), replace=False )).to(torch.int64)
args.labels = args.all_labels[args.label_idx] #torch.ones(n_labels, dtype=torch.int64)
#args.v = torch.from_numpy(paper_X.astype(np.float32))
'''
###
args.ne = len(authorwt)
args.nv = len(paperwt)
ne = args.ne
nv = args.nv
args.vidx = torch.from_numpy(data['paper_idx']).to(torch.int64).to(device) # paper_author[:, 0]
if 'author' in data:
data['author_idx'] = data['author']
args.eidx = torch.from_numpy(data['author_idx']).to(torch.int64).to(device) #paper_author[:, 1]
args.paper_author = torch.stack([args.vidx, args.eidx], -1)
paper_author = args.paper_author
args.v_weight = torch.Tensor([(1/w if w > 0 else 1) for w in paperwt]).unsqueeze(-1).to(device) #torch.ones((nv, 1)) / 2 #####
args.e_weight = torch.Tensor([(1/w if w > 0 else 1) for w in authorwt]).unsqueeze(-1).to(device) # 1)) / 2 #####torch.ones(ne, 1) / 3
#weights for regularization
#v_reg_wt = torch.exp(args.alpha*v_wt) if use_exp_wt else v_wt**args.alpha
#v_reg_weight[i] = v_reg_wt
paper2sum = defaultdict(list)
author2sum = defaultdict(list)
e_reg_weight = torch.zeros(len(paper_author)) ###
v_reg_weight = torch.zeros(len(paper_author)) ###
#a switch to determine whether to have wt in exponent or base
use_exp_wt = args.use_exp_wt #True #False
for i, (paper_idx, author_idx) in enumerate(paper_author.tolist()):
e_wt = args.e_weight[author_idx]
e_reg_wt = torch.exp(args.alpha_e*e_wt) if use_exp_wt else e_wt**args.alpha_e
e_reg_weight[i] = e_reg_wt
paper2sum[paper_idx].append(e_reg_wt) ###
v_wt = args.v_weight[paper_idx]
v_reg_wt = torch.exp(args.alpha_v*v_wt) if use_exp_wt else v_wt**args.alpha_v
v_reg_weight[i] = v_reg_wt
author2sum[author_idx].append(v_reg_wt) ###
#'''
v_reg_sum = torch.zeros(nv) ###
e_reg_sum = torch.zeros(ne) ###
for paper_idx, wt_l in paper2sum.items():
v_reg_sum[paper_idx] = sum(wt_l)
for author_idx, wt_l in author2sum.items():
e_reg_sum[author_idx] = sum(wt_l)
#this is used in denominator only
e_reg_sum[e_reg_sum==0] = 1
v_reg_sum[v_reg_sum==0] = 1
args.e_reg_weight = torch.Tensor(e_reg_weight).unsqueeze(-1).to(device)
args.v_reg_sum = torch.Tensor(v_reg_sum).unsqueeze(-1).to(device)
args.v_reg_weight = torch.Tensor(v_reg_weight).unsqueeze(-1).to(device)
args.e_reg_sum = torch.Tensor(e_reg_sum).unsqueeze(-1).to(device)
#pdb.set_trace()
'''
paper2sum = defaultdict(list)
e_reg_weight = torch.zeros() ###
for paper_idx, author_idx in paper_author:
wt = args.e_weight[author_idx]
reg_wt = torch.exp(args.alpha*wt)
e_reg_weight[author_idx] = reg_wt
paper2sum[paper_idx].append(reg_wt) ###
'''
return args
def gen_synthetic_data(args):
'''
Generate synthetic data.
'''
args.n_hidden = 50
args.n_epoch = 200
args.ne = 2
args.nv = 3
ne = args.ne
nv = args.nv
#no replacement!
n_labels = max(1, int(nv*.1))
#numpy.random.choice(a, size=None, replace=True, p=None)
args.label_idx = torch.from_numpy(np.random.choice(nv, size=(n_labels,), replace=False )).to(torch.int64)
#args.label_idx = torch.zeros(n_labels, dtype=torch.int64).random_(0, nv) #torch.randint(torch.int64, nv, (int(nv*.1), ))
args.labels = torch.ones(n_labels, dtype=torch.int64)
args.labels[:n_labels//2] = 0
args.n_cls = 2
#labeled
#args.vidx, args.eidx, args.nv, args.ne, args.v_weight, args.e_weight
#vidx has shape (ne, max n)
#generate edges
#check upper bound!
#args.vidx = torch.zeros((ne+1, 3), dtype=torch.int64).random_(0, nv) + 1 #np.random.randint(nv, (ne, 3))
#args.eidx = torch.zeros((nv+1, 2), dtype=torch.int64).random_(0, ne) + 1 #torch.random.randint(ne, (nv, 2))
args.vidx = torch.zeros((ne,), dtype=torch.int64).random_(0, nv-1) + 1 #np.random.randint(nv, (ne, 3))
args.eidx = torch.zeros((nv,), dtype=torch.int64).random_(0, ne-1) + 1 #torch.random.randint(ne, (nv, 2))
args.v_weight = torch.ones((nv, 1)) / 2
args.e_weight = torch.ones(ne, 1) / 3
train(args)
def compare_normalization(data_path, args):
"""
Studying the effects of normalization hyperparameters on test accuracy.
"""
best_err = sys.maxsize
best_err_std = sys.maxsize
best_alpha_v = sys.maxsize
best_alpha_e = sys.maxsize
print('ARGS {}'.format(args))
mean_err_l = []
mean_err_std_l = []
time_l = []
time_std_l = []
n_runs = 1 #5
n_runs = 3 #5
a_list = [0] #
a_list = range(-2, 2, 1)
#a_list = range(-20, -5, 3)
a_list = range(-1, 1, 1)
a_list1 = []
test_alpha = False #True
for av in a_list:
if test_alpha:
#a_list1.append([av, 0]) #test beta
a_list1.append([0, av]) #test alpha
else:
for ae in a_list:
a_list1.append([av, ae])
sys.stdout.write('alpha beta list {}'.format(a_list1))
for av, ae in a_list1:
args.alpha_v = av/10
args.alpha_e = ae/10
print('ALPHA ', args.alpha_v)
err_ar = np.zeros(n_runs)
time_ar = np.zeros(n_runs)
for i in range(n_runs):
if args.dataset_name in ['dblp', 'pubmed']:
data_path = 'data/pubmed_data.pt' if args.dataset_name == 'pubmed' else 'data/dblp_data.pt'
args = gen_data_dblp(args, data_path=data_path)
else:
args = gen_data_cora(args, data_path=data_path)
#pred_all, loss, test_err = hypertrain.train(args.v, args.e, args.label_idx, args.labels)
time0 = time.time()
test_err = train(args)
time_ar[i] = time.time() - time0
err_ar[i] = test_err
sys.stdout.write(' test err {}\t'.format(test_err))
mean_err = err_ar.mean()
err_std = err_ar.std()
mean_err_l.append(mean_err)
mean_err_std_l.append(err_std)
dur = time_ar.mean()
time_l.append(dur)
time_std_l.append(time_ar.std())
sys.stdout.write('\n ~~~Mean test err {}+-{} for alpha {} {} time {}~~~\n'.format(np.round(mean_err, 2), np.round(err_std, 2), args.alpha_v, args.alpha_e, dur ))
if mean_err < best_err:
best_err = mean_err
best_err_std = err_std
best_alpha_v = args.alpha_v
best_alpha_e = args.alpha_e
best_time = np.round(dur, 3)
best_time_std = time_ar.std()
print('mean errs {} mean err std {}'.format(mean_err_l, mean_err_std_l))
print('best err {}+-{} best alpha_v {} alpha_e {} for dataset {}'.format(np.round(best_err*100, 2), np.round(best_err_std*100, 2), best_alpha_v, best_alpha_e, args.dataset_name))
print('best ACC {}+-{} time {}+-{}'.format(np.round((1-best_err)*100, 2), np.round(best_err_std*100, 2), best_time, best_time_std ))
#train(args)
def select_params(data_path, args):
#find best hyperparameters with by splitting training set into train + validation set
best_err = sys.maxsize
best_err_std = sys.maxsize
best_alpha_v = sys.maxsize
best_alpha_e = sys.maxsize
print('ARGS {}'.format(args))
mean_err_l = []
mean_err_std_l = []
time_l = []
time_std_l = []
args.kfold = 1 #5
args.kfold = 5 #5
kfold = args.kfold
a_list = [0] #
a_list = range(-2, 2, 1)
#a_list = range(-20, -5, 3)
a_list = range(-1, 2, 1)
a_list1 = []
test_alpha = False #True
for av in a_list:
if test_alpha:
#a_list1.append([av, 0]) #test beta
a_list1.append([0, av]) #test alpha
else:
for ae in a_list:
a_list1.append([av, ae])
sys.stdout.write('alpha beta list {}'.format(a_list1))
for av, ae in a_list1:
args.alpha_v = av/10
args.alpha_e = ae/10
print('ALPHA ', args.alpha_v)
err_ar = np.zeros(kfold)
time_ar = np.zeros(kfold)
for i in range(kfold):
if args.dataset_name in ['dblp', 'pubmed']:
data_path = 'data/pubmed_data.pt' if args.dataset_name == 'pubmed' else 'data/dblp_data.pt'
args = gen_data_dblp(args, data_path=data_path, do_val=True)
else:
args = gen_data_cora(args, data_path=data_path, do_val=True)
#pred_all, loss, test_err = hypertrain.train(args.v, args.e, args.label_idx, args.labels)
time0 = time.time()
test_err = train(args)
time_ar[i] = time.time() - time0
err_ar[i] = test_err
sys.stdout.write(' Validation err {}\t'.format(test_err))
mean_err = err_ar.mean()
err_std = err_ar.std()
mean_err_l.append(mean_err)
mean_err_std_l.append(err_std)
dur = time_ar.mean()
time_l.append(dur)
time_std_l.append(time_ar.std())
sys.stdout.write('\n ~~~Mean VAL err {}+-{} for alpha {} {} time {}~~~\n'.format(np.round(mean_err, 2), np.round(err_std, 2), args.alpha_v, args.alpha_e, dur ))
if mean_err < best_err:
best_err = mean_err
best_err_std = err_std
best_alpha_v = args.alpha_v
best_alpha_e = args.alpha_e
best_time = np.round(dur, 3)
best_time_std = time_ar.std()
print('mean validation errs {} mean err std {}'.format(mean_err_l, mean_err_std_l))
print('best err {}+-{} best alpha_v {} alpha_e {} for dataset {}'.format(np.round(best_err*100, 2), np.round(best_err_std*100, 2), best_alpha_v, best_alpha_e, args.dataset_name))
print('best validation ACC {}+-{} time {}+-{}'.format(np.round((1-best_err)*100, 2), np.round(best_err_std*100, 2), best_time, best_time_std ))
return best_alpha_v, best_alpha_e
if __name__ =='__main__':
args = utils.parse_args()
dataset_name = args.dataset_name #'citeseer' #'cora'
data_path = None
if dataset_name == 'cora':
if args.do_svd:
data_path = 'data/cora_author_10cls300.pt'
else:
data_path = 'data/cora_author_10cls1000.pt'
elif dataset_name == 'citeseer':
if args.do_svd:
data_path = 'data/citeseer.pt'
else:
data_path = 'data/citeseer6cls3703.pt'
elif dataset_name not in ['dblp', 'pubmed']:
#args = gen_data_dl(args)
raise Exception('dataset {} not supported!'.format(dataset_name))
if args.fix_seed:
np.random.seed(0)
torch.manual_seed(0)
#if doing cross validate
do_cross_val = True
if do_cross_val:
select_params(data_path, args)
#if studying the effects of hyperparameters
study_normalization = False
if study_normalization:
compare_normalization(data_path, args)