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authorship_attribution_two_authors_debugging.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 io
import os
import argparse
import pickle
import numpy as np
import spacy
from core.converters.WVConverter import WVConverter
from core.converters.ReverseConverter import ReverseConverter
from core.classifiers.EchoWordClassifier import EchoWordClassifier
from core.tools.RCNLPLogging import RCNLPLogging
from core.tools.Metrics import Metrics
#########################################################################
# Experience settings
#########################################################################
# Exp. info
ex_name = "Authorship Attribution"
ex_instance = "Two Authors Two Sided Inputs"
# Reservoir Properties
rc_leak_rate = 0.01 # Leak rate
rc_input_scaling = 0.25 # Input scaling
rc_size = 2000 # Reservoir size
rc_spectral_radius = 0.1 # Spectral radius
rc_w_sparsity = 0.1
rc_input_sparsity = 0.01
####################################################
# Functions
####################################################
def get_n_token(text_file):
t_nlp = spacy.load(args.lang)
doc = t_nlp(io.open(text_file, 'r').read())
count = 0
# For each token
for index, word in enumerate(doc):
count += 1
# end for
return count
# end get_n_token
####################################################
# Main function
####################################################
if __name__ == "__main__":
# Argument parser
parser = argparse.ArgumentParser(description="RCNLP - Authorship clustering with Echo State Network")
# Argument
parser.add_argument("--dataset", type=str, help="Dataset's directory.")
parser.add_argument("--author1", type=str, help="Author 1' ID.")
parser.add_argument("--author2", type=str, help="Author 2's ID.")
parser.add_argument("--lang", type=str, help="Language (ar, en, es, pt)", default='en')
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=None)
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)
parser.add_argument("--k", type=int, help="Fold position to use", default=0)
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 is not None:
pca_model = pickle.load(open(args.pca_model, 'r'))
# end if
# Base converter
base_converter = ReverseConverter()
# Reverse WV converter
reverse_wv_converter = WVConverter(pca_model=pca_model, upper_level=base_converter)
# WV converter
wv_converter = WVConverter(pca_model=pca_model)
# Prepare training and test set indexes.
n_fold_samples = int(100 / args.K)
indexes = np.arange(0, 100, 1)
indexes.shape = (args.K, n_fold_samples)
# Prepare training and test set.
test_set_indexes = indexes[args.k]
training_set_indexes = indexes
training_set_indexes = np.delete(training_set_indexes, args.k, axis=0)
training_set_indexes.shape = (100 - n_fold_samples)
# Classifier
classifier = EchoWordClassifier(classes=[0, 1], size=rc_size, input_scaling=rc_input_scaling,
leak_rate=rc_leak_rate,
input_sparsity=rc_input_sparsity, converter=wv_converter,
spectral_radius=rc_spectral_radius, w_sparsity=rc_w_sparsity)
# Add examples
for author_index, author_id in enumerate((args.author1, args.author2)):
author_path = os.path.join(args.dataset, "total", author_id)
for file_index in training_set_indexes:
file_path = os.path.join(author_path, str(file_index) + ".txt")
classifier.train(io.open(file_path, 'r').read(), author_index)
# end for
# end for
# Finalize model training
classifier.finalize(verbose=True)
# Init test epoch
test_set = list()
# Get text
for author_index, author_id in enumerate((args.author1, args.author2)):
author_path = os.path.join(args.dataset, "total", str(author_id))
for file_index in test_set_indexes:
file_path = os.path.join(author_path, str(file_index) + ".txt")
test_set.append((io.open(file_path, 'r').read(), author_index))
# end for
# end for
# Success rate
success_rate = Metrics.success_rate(classifier, test_set, verbose=True, debug=True)
print(u"Success rate : {}".format(success_rate))
# end if