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utils.py
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utils.py
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import os
import argparse
import logging
import json
import torch
# import tensorflow as tf
import numpy as np
import scipy.sparse as sp
import pdb
from core import SoftmaxClassifier, GCN, EmbLookUp
FALSY_STRINGS = {'off', 'false', '0'}
TRUTHY_STRINGS = {'on', 'true', '1'}
MAIN_DIR = os.path.relpath(os.path.dirname(os.path.abspath(__file__)))
DATA_PATH = os.path.join(MAIN_DIR, 'data/FB15K237')
TRAIN_DATA_PATH = os.path.join(DATA_PATH, 'train2id.txt')
VALID_DATA_PATH = os.path.join(DATA_PATH, 'valid2id.txt')
TEST_DATA_PATH = os.path.join(DATA_PATH, 'test2id.txt')
def csr_zero_rows(csr, rows_to_zero):
"""Set rows given by rows_to_zero in a sparse csr matrix to zero.
NOTE: Inplace operation! Does not return a copy of sparse matrix."""
rows, cols = csr.shape
mask = np.ones((rows,), dtype=np.bool)
mask[rows_to_zero] = False
nnz_per_row = np.diff(csr.indptr)
mask = np.repeat(mask, nnz_per_row)
nnz_per_row[rows_to_zero] = 0
csr.data = csr.data[mask]
csr.indices = csr.indices[mask]
csr.indptr[1:] = np.cumsum(nnz_per_row)
csr.eliminate_zeros()
return csr
def csc_zero_cols(csc, cols_to_zero):
"""Set rows given by cols_to_zero in a sparse csc matrix to zero.
NOTE: Inplace operation! Does not return a copy of sparse matrix."""
rows, cols = csc.shape
mask = np.ones((cols,), dtype=np.bool)
mask[cols_to_zero] = False
nnz_per_row = np.diff(csc.indptr)
mask = np.repeat(mask, nnz_per_row)
nnz_per_row[cols_to_zero] = 0
csc.data = csc.data[mask]
csc.indices = csc.indices[mask]
csc.indptr[1:] = np.cumsum(nnz_per_row)
csc.eliminate_zeros()
return csc
def sp_vec_from_idx_list(idx_list, dim):
"""Create sparse vector of dimensionality dim from a list of indices."""
shape = (dim, 1)
data = np.ones(len(idx_list))
row_ind = list(idx_list)
col_ind = np.zeros(len(idx_list))
return sp.csr_matrix((data, (row_ind, col_ind)), shape=shape)
def sp_row_vec_from_idx_list(idx_list, dim):
"""Create sparse vector of dimensionality dim from a list of indices."""
shape = (1, dim)
data = np.ones(len(idx_list))
row_ind = np.zeros(len(idx_list))
col_ind = list(idx_list)
return sp.csr_matrix((data, (row_ind, col_ind)), shape=shape)
def get_neighbors(adj, nodes):
"""Takes a set of nodes and a graph adjacency matrix and returns a set of neighbors."""
sp_nodes = sp_row_vec_from_idx_list(list(nodes), adj.shape[1])
sp_neighbors = sp_nodes.dot(adj)
neighbors = set(sp.find(sp_neighbors)[1]) # convert to set of indices
return neighbors
def bfs(adj, roots):
"""
Perform BFS on a graph given by an adjaceny matrix adj.
Can take a set of multiple root nodes.
Root nodes have level 0, first-order neighors have level 1, and so on.]
"""
visited = set()
current_lvl = set(roots)
while current_lvl:
for v in current_lvl:
visited.add(v)
next_lvl = get_neighbors(adj, current_lvl)
next_lvl -= visited # set difference
yield next_lvl
current_lvl = next_lvl
def bfs_relational(adj_list, roots):
"""
BFS for graphs with multiple edge types. Returns list of level sets.
Each entry in list corresponds to relation specified by adj_list.
"""
visited = set()
current_lvl = set(roots)
next_lvl = list()
for rel in range(len(adj_list)):
next_lvl.append(set())
while current_lvl:
for v in current_lvl:
visited.add(v)
for rel in range(len(adj_list)):
next_lvl[rel] = get_neighbors(adj_list[rel], current_lvl)
next_lvl[rel] -= visited # set difference
yield next_lvl
current_lvl = set.union(*next_lvl)
def bfs_sample(adj, roots, max_lvl_size):
"""
BFS with node dropout. Only keeps random subset of nodes per level up to max_lvl_size.
'roots' should be a mini-batch of nodes (set of node indices).
NOTE: In this implementation, not every node in the mini-batch is guaranteed to have
the same number of neighbors, as we're sampling for the whole batch at the same time.
"""
visited = set(roots)
current_lvl = set(roots)
while current_lvl:
next_lvl = get_neighbors(adj, current_lvl)
next_lvl -= visited # set difference
for v in next_lvl:
visited.add(v)
yield next_lvl
current_lvl = next_lvl
def get_splits(y, train_idx, test_idx, validation=True):
# Make dataset splits
# np.random.shuffle(train_idx)
if validation:
idx_train = train_idx[len(train_idx) / 5:]
idx_val = train_idx[:len(train_idx) / 5]
idx_test = idx_val # report final score on validation set for hyperparameter optimization
else:
idx_train = train_idx
idx_val = train_idx # no validation
idx_test = test_idx
y_train = np.zeros(y.shape)
y_val = np.zeros(y.shape)
y_test = np.zeros(y.shape)
y_train[idx_train] = np.array(y[idx_train].todense())
y_val[idx_val] = np.array(y[idx_val].todense())
y_test[idx_test] = np.array(y[idx_test].todense())
return y_train, y_val, y_test, idx_train, idx_val, idx_test
def normalize_adj(adj, symmetric=True):
if symmetric:
d = sp.diags(np.power(np.array(adj.sum(1)), -0.5).flatten())
a_norm = adj.dot(d).transpose().dot(d).tocsr()
else:
d = sp.diags(np.power(np.array(adj.sum(1)), -1).flatten())
a_norm = d.dot(adj).tocsr()
return a_norm
def preprocess_adj(adj, symmetric=True):
adj = normalize_adj(adj, symmetric)
return adj
def sample_mask(idx, l):
mask = np.zeros(l)
mask[idx] = 1
return np.array(mask, dtype=np.bool)
def categorical_crossentropy(preds, labels):
return np.mean(-np.log(np.extract(labels, preds)))
def binary_crossentropy(preds, labels):
return np.mean(-labels * np.log(preds) - (1 - labels) * np.log(1 - preds))
def two_class_accuracy(preds, labels, threshold=0.5):
return np.mean(np.equal(labels, preds > 0.5))
def accuracy(preds, labels):
return np.mean(np.equal(np.argmax(labels, 1), np.argmax(preds, 1)))
def evaluate_preds(preds, labels, indices):
split_loss = list()
split_acc = list()
for y_split, idx_split in zip(labels, indices):
split_loss.append(categorical_crossentropy(preds[idx_split], y_split[idx_split]))
split_acc.append(accuracy(preds[idx_split], y_split[idx_split]))
return split_loss, split_acc
def evaluate_preds_sigmoid(preds, labels, indices):
split_loss = list()
split_acc = list()
for y_split, idx_split in zip(labels, indices):
split_loss.append(binary_crossentropy(preds[idx_split], y_split[idx_split]))
split_acc.append(two_class_accuracy(preds[idx_split], y_split[idx_split]))
return split_loss, split_acc
def bool_flag(s):
"""
Parse boolean arguments from the command line.
"""
if s.lower() in FALSY_STRINGS:
return False
elif s.lower() in TRUTHY_STRINGS:
return True
else:
raise argparse.ArgumentTypeError("invalid value for a boolean flag. use 0 or 1")
def initialize_experiment(params):
exps_dir = os.path.join(MAIN_DIR, 'experiments')
if not os.path.exists(exps_dir):
os.makedirs(exps_dir)
params.exp_dir = os.path.join(exps_dir, params.experiment_name)
if not os.path.exists(params.exp_dir):
os.makedirs(params.exp_dir)
file_handler = logging.FileHandler(os.path.join(params.exp_dir, "log.txt"))
logger = logging.getLogger()
logger.addHandler(file_handler)
logger.info('============ Initialized logger ============')
logger.info('\n'.join('%s: %s' % (k, str(v)) for k, v
in sorted(dict(vars(params)).items())))
logger.info('============================================')
with open(os.path.join(params.exp_dir, "params.json"), 'w') as fout:
json.dump(vars(params), fout)
def initialize_model(params, classifier_data, fresh=True):
if not fresh and os.path.exists(os.path.join(params.exp_dir, 'best_gcn.pth')):
logging.info('Loading existing model from %s' % os.path.join(params.exp_dir, 'best_gcn.pth'))
enc = torch.load(os.path.join(params.exp_dir, 'best_gcn.pth')).to(device=params.device) # Update these
logging.info('Loading existing model from %s' % os.path.join(params.exp_dir, 'best_classifier.pth'))
sm_classifier = torch.load(os.path.join(params.exp_dir, 'best_classifier.pth')).to(device=params.device) # Update these
else:
logging.info('No existing model found. Initializing new model..')
if params.no_encoder:
enc = EmbLookUp(params, params.total_ent).to(device=params.device)
else:
enc = GCN(params).to(device=params.device)
sm_classifier = SoftmaxClassifier(params, classifier_data['y'].shape[1]).to(device=params.device)
return enc, sm_classifier
# class Logger(object):
# def __init__(self, log_dir):
# """Create a summary writer logging to log_dir."""
# self.writer = tf.summary.FileWriter(log_dir)
# def scalar_summary(self, tag, value, step):
# """Log a scalar variable."""
# summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
# self.writer.add_summary(summary, step)
# def image_summary(self, tag, images, step):
# """Log a list of images."""
# img_summaries = []
# for i, img in enumerate(images):
# s = BytesIO()
# scipy.misc.toimage(img).save(s, format="png")
# # Create an Image object
# img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
# height=img.shape[0],
# width=img.shape[1])
# # Create a Summary value
# img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))
# # Create and write Summary
# summary = tf.Summary(value=img_summaries)
# self.writer.add_summary(summary, step)
# def histo_summary(self, tag, values, step, bins=1000):
# """Log a histogram of the tensor of values."""
# # Create a histogram using numpy
# counts, bin_edges = np.histogram(values, bins=bins)
# # Fill the fields of the histogram proto
# hist = tf.HistogramProto()
# hist.min = float(np.min(values))
# hist.max = float(np.max(values))
# hist.num = int(np.prod(values.shape))
# hist.sum = float(np.sum(values))
# hist.sum_squares = float(np.sum(values**2))
# # Drop the start of the first bin
# bin_edges = bin_edges[1:]
# # Add bin edges and counts
# for edge in bin_edges:
# hist.bucket_limit.append(edge)
# for c in counts:
# hist.bucket.append(c)
# # Create and write Summary
# summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
# self.writer.add_summary(summary, step)
# self.writer.flush()
def get_torch_sparse_matrix(A, dev):
'''
A : list of sparse adjacency matrices
'''
idx = torch.LongTensor([A.tocoo().row, A.tocoo().col])
dat = torch.FloatTensor(A.tocoo().data)
return torch.sparse.FloatTensor(idx, dat, torch.Size([A.shape[0], A.shape[1]])).to(device=dev)