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iqla_authorship_attribution.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# File : core.classifiers.RCNLPTextClassifier.py
# Description : Echo State Network for text classification.
# Auteur : Nils Schaetti <nils.schaetti@unine.ch>
# Date : 01.02.2017 17:59:05
# Lieu : Nyon, Suisse
#
# This file is part of the Reservoir Computing NLP Project.
# The Reservoir Computing Memory Project is a set of free software:
# you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Foobar is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Foobar. If not, see <http://www.gnu.org/licenses/>.
#
import os
import argparse
import pickle
import numpy as np
import itertools
from random import shuffle
import json
import logging as log
from core.converters.RCNLPPosConverter import RCNLPPosConverter
from core.converters.RCNLPTagConverter import RCNLPTagConverter
from core.converters.RCNLPWordVectorConverter import RCNLPWordVectorConverter
from core.converters.RCNLPFuncWordConverter import RCNLPFuncWordConverter
from core.classifiers.RCNLPEchoWordClassifier import RCNLPEchoWordClassifier
from core.tools.RCNLPLogging import RCNLPLogging
#########################################################################
# Experience settings
#########################################################################
# Exp. info
ex_name = "Authorship Attribution Experience"
ex_instance = "Author Attribution Multiple Authors"
# Reservoir Properties
rc_leak_rate = 0.1 # Leak rate
rc_input_scaling = 0.25 # Input scaling
rc_size = 100 # Reservoir size
rc_spectral_radius = 0.99 # Spectral radius
rc_w_sparsity = 0.1
rc_input_sparsity = 0.1
####################################################
# Functions
####################################################
def get_combinations(n_authors, n_samples):
"""
Return n_samples random combinations of n_authors
:param n_authors:
:param n_samples:
:return:
"""
comb = itertools.combinations(np.arange(1, 51), n_authors)
combl = list()
for c in comb:
combl.append(c)
# end for
return shuffle(combl)[:n_samples]
# end get_combinations
####################################################
# Main function
####################################################
if __name__ == "__main__":
# Argument parser
parser = argparse.ArgumentParser(description="RCNLP - Authorship attribution witn ESN on the IQLA dataset")
# Argument
parser.add_argument("--dataset", type=str, help="Dataset's directory")
parser.add_argument("--lang", type=str, help="Language (ar, en, es, pt)", default='it')
parser.add_argument("--converter", type=str, help="The text converter to use (fw, pos, tag, wv)", default='pos')
parser.add_argument("--pca-model", type=str, help="PCA model to load", default="")
parser.add_argument("--in-components", type=int, help="Number of principal component to reduce inputs to",
default=-1)
parser.add_argument("--k", type=int, help="n-Fold Cross Validation.", default=10)
args = parser.parse_args()
# Logging
logging = RCNLPLogging(exp_name=ex_name, exp_inst=ex_instance,
exp_value=RCNLPLogging.generate_experience_name(locals()))
logging.save_globals()
logging.save_variables(locals())
# PCA model
pca_model = None
if args.pca_model != "":
pca_model = pickle.load(open(args.pca_model, 'r'))
# end if
# >> Choose a text to symbol converter.
if args.converter == "pos":
converter = RCNLPPosConverter(resize=args.in_components, pca_model=pca_model)
elif args.converter == "tag":
converter = RCNLPTagConverter(resize=args.in_components, pca_model=pca_model)
elif args.converter == "fw":
converter = RCNLPFuncWordConverter(resize=args.in_components, pca_model=pca_model)
else:
converter = RCNLPWordVectorConverter(resize=args.in_components, pca_model=pca_model)
# end if
# Load texts information
with open(os.path.join(args.dataset, "texts.json"), 'r') as f:
texts_data = json.load(f)
# end with
text_codes = texts_data.keys()
# Load author data
with open(os.path.join(args.dataset, "authors.json"), 'r') as f:
authors_data = json.load(f)
# end with
# Prepare training and test set indexes.
n_texts = len(texts_data)
n_fold_samples = int(n_texts / args.k)
indexes = np.arange(0, n_texts, 1)
indexes.shape = (args.k, n_fold_samples)
# Array for results
average_success_rate = np.array([])
# n-Fold cross validation
for k in range(0, args.k):
# Info
print(u"K-Fold {}".format(k))
# Prepare training and test set
test_set_indexes = indexes[k]
training_set_indexes = indexes
training_set_indexes = np.delete(training_set_indexes, k, axis=0)
training_set_indexes.shape = (n_texts - n_fold_samples)
# Create Echo Word Classifier
classifier = RCNLPEchoWordClassifier(size=rc_size, input_scaling=rc_input_scaling, leak_rate=rc_leak_rate,
input_sparsity=rc_input_sparsity, converter=converter, n_classes=2,
spectral_radius=rc_spectral_radius, w_sparsity=rc_w_sparsity)
# Add examples
print(u"Adding examples...")
for training_index in training_set_indexes:
training_text_path = os.path.join(args.dataset, text_codes[training_index] + ".txt")
training_text_author = texts_data[text_codes[training_index]]
classifier.add_example(training_text_path, training_text_author)
# end for
# Train model
print(u"Training...")
classifier.train()
# Test model performance
success = 0.0
count = 1.0
for test_index in test_set_indexes:
test_text_path = os.path.join(args.dataset, text_codes[test_index] + ".txt")
observed_author = texts_data[text_codes[test_index]]
predicted_author = classifier.pred(test_text_path)
if observed_author == predicted_author:
success += 1
# end if
count += 1
# end for
# >> 11. Save results
average_success_rate = np.append(average_success_rate, [(success / count) * 100.0])
# Delete variables
del classifier
# end for
# Log results
logging.save_results(u"Average success rate ", np.average(average_success_rate), display=True)
logging.save_results(u"Success rate std ", np.std(average_success_rate), display=True)
# end if