import argparse
import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.svm import LinearSVC
from data.AuthorshipDataset import AuthorshipDataset
from data.fetch_blogs import Blogs
from data.fetch_imdb62 import Imdb62
from data.fetch_enron_mail import EnronMail
from index import Index
from model.classifiers import AuthorshipAttributionClassifier #, SameAuthorClassifier, FullAuthorClassifier
from data.fetch_victorian import Victorian
from evaluation import evaluation
import torch
import torch.nn as nn
from model.layers import *
from util import create_path_if_not_exists
import os
import sys
def load_dataset(opt):
# dataset load
if opt.dataset == 'enron':
loader = EnronMail
data_path = '../../authorship_analysis/data/enron_mail_20150507/maildir/*'
elif opt.dataset == 'imdb62':
loader = Imdb62
data_path = '../../authorship_analysis/data/imdb62/imdb62.txt'
elif opt.dataset == 'victorian':
loader = Victorian
data_path='../../authorship_analysis/data/victoria'
elif opt.dataset == 'blogs':
loader = Blogs
data_path = '../../authorship_analysis/data/blogs'
dataset_name = f'{loader.__name__}_A{opt.authors}_D{opt.documents}_S{opt.seed}'
pickle_path = None
if opt.pickle:
pickle_path = f'{opt.pickle}/{dataset_name}.pickle'
dataset = AuthorshipDataset.load(
loader,
pickle_path=pickle_path,
data_path=data_path,
n_authors=opt.authors,
docs_by_author=opt.documents,
random_state=opt.seed
)
return dataset_name, dataset
def main(opt):
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print(f'running on {device}')
dataset_name, dataset = load_dataset(opt)
# dataset indexing
Xtr, ytr = dataset.train.data, dataset.train.target
Xte, yte = dataset.test.data, dataset.test.target
A = np.unique(ytr)
print(f'num authors={len(A)}')
print(f'ntr = {len(Xtr)} nte = {len(Xte)}')
bigrams = False
index = Index(analyzer='char', ngram_range=(2,2) if bigrams else (1,1))
Xtr = index.fit_transform(Xtr)
Xte = index.transform(Xte)
pad_index = index.add_word('PADTOKEN')
print(f'vocabulary size={index.vocabulary_size()}')
# attribution
print('Attribution')
phi = Phi(
cnn=CNNProjection(
vocabulary_size=index.vocabulary_size(),
embedding_dim=opt.hidden,
channels_out=opt.chout,
kernel_sizes=opt.kernelsizes),
ff=FFProjection(input_size=len(opt.kernelsizes) * opt.chout,
hidden_sizes=[1024],
output_size=opt.repr,
activation=nn.functional.relu,
dropout=0.5,
activate_last=True),
).to(device)
cls = AuthorshipAttributionClassifier(
phi, num_authors=A.size, pad_index=pad_index, pad_length=opt.pad, device=device
)
cls.xavier_uniform()
print(cls)
if opt.name == 'auto':
method = f'{phi.__class__.__name__}_alpha{opt.alpha}'
else:
method = opt.name
if opt.mode=='savlin':
Xtr_, Xval_, ytr_, yval_ = train_test_split(Xtr, ytr, test_size=0.1, stratify=ytr)
cls.supervised_contrastive_learning(Xtr_, ytr_, Xval_, yval_,
batch_size=opt.batchsize, epochs=opt.epochs, lr=opt.lr,
log=f'{opt.log}/{method}-{dataset_name}.csv',
checkpointpath=opt.checkpoint)
val_microf1 = cls.train_linear_classifier(Xtr_, ytr_, Xval_, yval_,
batch_size=opt.batchsize, epochs=opt.epochs, lr=opt.lr,
log=f'{opt.log}/{method}-{dataset_name}.csv',
checkpointpath=opt.checkpoint)
svm = GridSearchCV(LinearSVC(), param_grid={'C':np.logspace(-2,3,6), 'class_weight':['balanced',None]}, n_jobs=-1)
svm.fit(cls.project(Xtr), ytr)
yte_ = svm.predict(cls.project(Xte))
acc, macrof1, microf1 = evaluation(yte, yte_)
print(f'svm: acc={acc:.3f} macrof1={macrof1:.3f} microf1={microf1:.3f}')
elif opt.mode=='attr':
# train
val_microf1 = cls.fit(Xtr, ytr,
batch_size=opt.batchsize, epochs=opt.epochs, alpha=opt.alpha, lr=opt.lr,
log=f'{opt.log}/{method}-{dataset_name}.csv',
checkpointpath=opt.checkpoint
)
# test
yte_ = cls.predict(Xte)
print('network prediction')
acc, macrof1, microf1 = evaluation(yte, yte_)
results = Results(opt.output)
results.add(dataset_name, method, acc, macrof1, microf1, val_microf1)
# verification
#print('Verification')
#phi = RNNProjection(vocab_size=index.vocabulary_size(), hidden_size=hidden_size, output_size=output_size, device=device)
#cls = SameAuthorClassifier(phi, num_authors=A.size, pad_index=pad_index, pad_length=pad_length, device=device)
#cls.fit(Xtr, ytr, batch_size=batch_size, epochs=n_epochs)
#paired_y_ = cls.predict(x1,x2)
#eval(paired_y, paired_y_)
# attribution & verification
#print('Attribution & Verification')
#phi = RNNProjection(vocab_size=index.vocabulary_size(), hidden_size=hidden_size, output_size=output_size, device=device)
#cls = FullAuthorClassifier(phi, num_authors=A.size, pad_index=pad_index, pad_length=pad_length, device=device)
#cls.fit(Xtr, ytr, batch_size=batch_size, epochs=n_epochs)
#yte_ = cls.predict_labels(Xte)
#eval(yte, yte_)
#paired_y_ = cls.predict_sav(x1,x2)
#eval(paired_y, paired_y_)
class Results:
def __init__(self, path):
addheader = not os.path.exists(path)
self.foo = open(path, 'at')
if addheader:
self.add('Dataset', 'Method', 'Accuracy', 'MacroF1', 'microF1', 'val_microF1')
def add(self, dataset, method, acc, macrof1, microf1, val_microF1):
self.foo.write(f'{dataset}\t{method}\t{acc}\t{macrof1}\t{microf1}\t{val_microF1}\n')
self.foo.flush()
def close(self):
self.foo.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='CNN with KTA regularization')
parser.add_argument('-H', '--hidden', help='Hidden/embedding size', type=int, default=32)
parser.add_argument('-c', '--chout', help='Channels output size', type=int, default=128)
parser.add_argument('-r', '--repr', help='Projection size (phi)', type=int, default=1024)
parser.add_argument('-k', '--kernelsizes', help='Size of the convolutional kernels', nargs='+', default=[6,7,8])
parser.add_argument('-p', '--pad', help='Pad length', type=int, default=3000)
parser.add_argument('-b', '--batchsize', help='Batch size', type=int, default=50)
parser.add_argument('-e', '--epochs', help='Max number of epochs', type=int, default=250)
parser.add_argument('-A', '--authors', help='Number of authors (-1 to select all)', type=int, default=-1)
parser.add_argument('-D', '--documents', help='Number of documents per author (-1 to select all)', type=int, default=-1)
parser.add_argument('-s', '--seed', help='Random seed', type=int, default=0)
parser.add_argument('-o', '--output', help='File where to write test results', default='../results.csv')
parser.add_argument('-l', '--log', help='Log dir where to output training an validation losses', default='../log')
parser.add_argument('-P', '--pickle', help='If specified, pickles a copy of the dataset for faster reload. '
'This parameter indicates a directory, the name of the pickle is '
'derived automatically.', default='../pickles')
parser.add_argument('-a', '--alpha', help='Controls the loss as attr-loss(alpha) + sav-loss(1-alpha)', type=float, default=1.)
parser.add_argument('--lr', help='Learning rate', type=float, default=0.001)
parser.add_argument('--checkpoint', help='Path where to dump model parameters', default='../checkpoint/model.dat')
parser.add_argument('-n', '--name', help='Name of the model', default='auto')
requiredNamed = parser.add_argument_group('required named arguments')
requiredNamed.add_argument('-d', '--dataset', help='Name of the dataset', required=True, type=str)
requiredNamed.add_argument('-m', '--mode', help='training mode', choices=['attr', 'savlin'], required=True, type=str)
opt = parser.parse_args()
assert opt.dataset in ['enron', 'imdb62', 'blogs', 'victorian'], 'unknown dataset'
create_path_if_not_exists(opt.output)
create_path_if_not_exists(opt.log)
create_path_if_not_exists(opt.checkpoint)
if opt.pickle is not None:
create_path_if_not_exists(opt.pickle)
main(opt)