def optimize(corpusdir, outputdir):
parameters = fit_parameters()
acc, n, L = max(parameters, key=lambda r: r[0])
logging.info("Choose parameters: n=%d, l=%d", n, L)
logging.disable(logging.DEBUG)
authors, scores = create_ranking(n, L)
jsonhandler.storeJson(outputdir, jsonhandler.unknowns, authors, scores)
def optimize(corpusdir, outputdir):
parameters = fit_parameters()
acc, n, L = max(parameters, key=lambda r: r[0])
logging.info("Choose parameters: n=%d, l=%d", n, L)
logging.disable(logging.DEBUG)
authors, scores = create_ranking(n, L)
jsonhandler.storeJson(outputdir, jsonhandler.unknowns, authors, scores)
def main():
parser = argparse.ArgumentParser(
description="Tira submission for PPM approach (teahan03)")
parser.add_argument("-i", action="store", help="path to corpus directory")
parser.add_argument("-o", action="store", help="path to output directory")
args = vars(parser.parse_args())
corpusdir = args["i"]
outputdir = args["o"]
if corpusdir == None or outputdir == None:
parser.print_help()
return
jsonhandler.loadJson(corpusdir)
global modeldir
modeldir = os.path.join(outputdir, MODEL_DIR)
if not os.path.exists(modeldir):
# os.makedirs(modeldir)
createModels()
else:
loadModels()
createAnswers()
jsonhandler.storeJson(outputdir, unknowns, authors, scores)
def tira(corpusdir, outputdir):
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
stopwords = open("stopwords.txt")
logger.info("Reads in stopwords")
for line in stopwords:
stopwords = line.split()
authors = jsonhandler.candidates
tests = jsonhandler.unknowns
raw = {}
raw_test = {}
C = {}
C_test = {}
Gauthors = {}
Gtests = {}
for author in authors:
logger.info("Reads in text " + str(author) + "...")
# raw[author] = open(author,encoding='iso-8859-1')
for training in jsonhandler.trainings[author]:
newtext = jsonhandler.getTrainingText(author, training)
if author in raw.keys():
if len(newtext) > len(raw[author]):
raw[author] = newtext
else:
raw[author] = newtext
C[author] = prepareCorpus(raw[author])
logger.info("Calculates Stopword Graph of " + str(author) + "...")
Gauthors[author] = getStopWordGraph(C[author], stopwords)
for author in tests:
logger.info("Reads in test document " + str(author) + "...")
# raw[author] = open(author,encoding='iso-8859-1')
raw_test[author] = jsonhandler.getUnknownText(author)
C_test[author] = prepareCorpus(raw_test[author])
logger.info("Calculates Stopword Graph " + str(author) + "...")
Gtests[author] = getStopWordGraph(C_test[author], stopwords)
results = []
for testcase in tests:
print(testcase)
KL = {}
Gtst = deepcopy(Gtests[testcase])
Gtst = normalizeWeights(Gtst)
for author in authors:
logger.info("Calculates KL Divergence of " + str(author) + "...")
KL[author] = authorKLdiv(Gauthors[author], Gtst)
print(KL)
# m = np.argmin(KL)
m = min(KL, key=KL.get)
results.append((testcase, m))
texts = [text for (text, cand) in results]
cands = [cand for (text, cand) in results]
jsonhandler.storeJson(outputdir, texts, cands)
def tira(corpusdir, outputdir):
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
stopwords = open("stopwords.txt")
logger.info("Reads in stopwords")
for line in stopwords:
stopwords = line.split()
authors = jsonhandler.candidates
tests = jsonhandler.unknowns
raw = {}
raw_test = {}
C = {}
C_test = {}
Gauthors = {}
Gtests = {}
for author in authors:
logger.info("Reads in text " + str(author) + "...")
# raw[author] = open(author,encoding='iso-8859-1')
for training in jsonhandler.trainings[author]:
newtext = jsonhandler.getTrainingText(author, training)
if author in raw.keys():
if len(newtext) > len(raw[author]):
raw[author] = newtext
else:
raw[author] = newtext
C[author] = prepareCorpus(raw[author])
logger.info("Calculates Stopword Graph of " + str(author) + "...")
Gauthors[author] = getStopWordGraph(C[author], stopwords)
for author in tests:
logger.info("Reads in test document " + str(author) + "...")
# raw[author] = open(author,encoding='iso-8859-1')
raw_test[author] = jsonhandler.getUnknownText(author)
C_test[author] = prepareCorpus(raw_test[author])
logger.info("Calculates Stopword Graph " + str(author) + "...")
Gtests[author] = getStopWordGraph(C_test[author], stopwords)
results = []
for testcase in tests:
print(testcase)
KL = {}
Gtst = deepcopy(Gtests[testcase])
Gtst = normalizeWeights(Gtst)
for author in authors:
logger.info("Calculates KL Divergence of " + str(author) + "...")
KL[author] = authorKLdiv(Gauthors[author], Gtst)
print(KL)
# m = np.argmin(KL)
m = min(KL, key=KL.get)
results.append((testcase, m))
texts = [text for (text, cand) in results]
cands = [cand for (text, cand) in results]
jsonhandler.storeJson(outputdir, texts, cands)
def main():
parser = argparse.ArgumentParser(description='keselj03')
parser.add_argument('-i', action='store', help='Path to input directory')
parser.add_argument('-o', action='store', help='Path to output directory')
args = vars(parser.parse_args())
corpusdir = args['i']
outputdir = args['o']
jsonhandler.loadJson(corpusdir)
parameters = fit_parameters()
acc, n, L = max(parameters, key=lambda r: r[0])
logging.info("Choose parameters: n=%d, l=%d", n, L)
#jsonhandler.loadTesting()
authors, scores = create_ranking(n, L)
jsonhandler.storeJson(outputdir, jsonhandler.unknowns, authors, scores)
def main(corpusdir,
outputdir,
n_max_feature_number=1000,
m_subspace_width=2,
mySolver='svd',
shrinkage=None):
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
authors = jsonhandler.candidates
tests = jsonhandler.unknowns
encoding_setting = jsonhandler.encoding
global trainSet, testSet
texts = []
text_authors = []
test_texts = []
ergebnisList = []
prob_list = []
for i in range(len(authors)):
author = authors[i]
for text in jsonhandler.trainings[author]:
texts.append(jsonhandler.getTrainingText(author, text))
text_authors.append(i)
trainSet = getBunchOutRawTrain(texts, text_authors, authors)
classifier_set = exhaustiv_disjoint_subspacing(trainSet,
n_max_feature_number,
m_subspace_width,
encoding_setting, mySolver,
shrinkage)
for t_text in tests:
# run classifier for every test text
test_texts.append(jsonhandler.getUnknownText(t_text))
proMatrx = mp(n_max_feature_number, m_subspace_width, classifier_set,
test_texts)
ergebnis = argmax(proMatrx, 1)
result_author_list = []
for i in range(len(ergebnis)):
result_author_list.append(authors[ergebnis[i]])
prob_list.append(proMatrx[i][ergebnis[i]])
jsonhandler.storeJson(outputdir, tests, result_author_list, prob_list)
def main():
parser = argparse.ArgumentParser(description='keselj03')
parser.add_argument('-i',
action='store',
help='Path to input directory')
parser.add_argument('-o',
action='store',
help='Path to output directory')
args = vars(parser.parse_args())
corpusdir = args['i']
outputdir = args['o']
jsonhandler.loadJson(corpusdir)
parameters = fit_parameters()
acc, n, L = max(parameters, key=lambda r: r[0])
logging.info("Choose parameters: n=%d, l=%d", n, L)
#jsonhandler.loadTesting()
authors, scores = create_ranking(n, L)
jsonhandler.storeJson(outputdir, jsonhandler.unknowns, authors, scores)
def tira(corpusdir, outputdir):
"""
Keyword arguments:
corpusdir -- Path to a tira corpus
outputdir -- Output directory
"""
candidates = jsonhandler.candidates
unknowns = jsonhandler.unknowns
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
authors = []
scores = []
for filename in unknowns:
ranking = create_author_ranking(candidates, filename,
method=dict_entropy)
# ranking = create_simple_ranking(candidates, filename,
# method=relative_zlib_entropy)
author = ranking[0][0]
score = 0.5
authors.append(author)
scores.append(score)
jsonhandler.storeJson(outputdir, unknowns, authors, scores)
def tira(corpusdir, outputdir):
"""
Keyword arguments:
corpusdir -- Path to a tira corpus
outputdir -- Output directory
"""
candidates = jsonhandler.candidates
unknowns = jsonhandler.unknowns
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
authors = []
scores = []
for filename in unknowns:
ranking = create_author_ranking(candidates, filename,
method=dict_entropy)
# ranking = create_simple_ranking(candidates, filename,
# method=relative_zlib_entropy)
author = ranking[0][0]
score = 0.5
authors.append(author)
scores.append(score)
jsonhandler.storeJson(outputdir, unknowns, authors, scores)
def test_method(corpusdir, outputdir, method="d1", n=3, L=2000):
logging.info("Test method %s with L=%d", method, L)
authors, scores = create_ranking(n, L, method)
jsonhandler.storeJson(outputdir, jsonhandler.unknowns, authors, scores)
def tira(corpusdir, outputdir):
"""
Keyword arguments:
corpusdir -- Path to a tira corpus
outputdir -- Output directory
"""
pos_tag = False
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
# creating training data
logging.info("Load the training data...")
database = Database(150, real_words=True)
for candidate in jsonhandler.candidates:
author = Author(candidate)
for training in jsonhandler.trainings[candidate]:
logging.info(
"Author '%s': Loading training '%s'", candidate, training)
text = Text(jsonhandler.getTrainingText(candidate, training),
candidate + " " + training,
pos_tag=pos_tag)
author.add_text(text)
database.add_author(author)
database.process()
for author in database.authors:
author.calc_cmsz(database)
# do some training, i.e. check which parameter is best
logging.info("Start training...")
results = {}
for length in range(150, 301, 50):
logging.info("Check for %s words.", length)
database.considered_words = length
database.process()
results[length] = 0
for correct_author in database.authors:
for training in jsonhandler.trainings[correct_author.name]:
trainingcase = Text(
jsonhandler.getTrainingText(correct_author.name, training),
"Trainingcase",
pos_tag=pos_tag)
trainingcase.calc_zscores(database)
deltas = {}
for author in database.authors:
deltas[author] = trainingcase.calc_delta(database, author)
if min(deltas, key=deltas.get) == correct_author:
results[length] += 1
length = max(results, key=results.get)
logging.info("Choose %s as length.", str(length))
# reconfigure the database with length
database.considered_words = length
database.process()
# run the testcases
results = []
for unknown in jsonhandler.unknowns:
testcase = Text(jsonhandler.getUnknownText(unknown),
unknown,
pos_tag=pos_tag)
testcase.calc_zscores(database)
deltas = {}
for author in database.authors:
deltas[author] = testcase.calc_delta(database, author)
results.append((unknown, min(deltas, key=deltas.get).name))
texts = [text for (text, candidate) in results]
cands = [candidate for (text, candidate) in results]
jsonhandler.storeJson(outputdir, texts, cands)
def main():
#
parser = argparse.ArgumentParser(
description="Tira submission for PPM approach (koppel11)")
parser.add_argument("-i", action="store", help="path to corpus directory")
parser.add_argument("-o", action="store", help="path to output directory")
args = vars(parser.parse_args())
corpusdir = args["i"]
outputdir = args["o"]
if corpusdir == None or outputdir == None:
parser.print_help()
return
candidates = jsonhandler.candidates
unknowns = jsonhandler.unknowns
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
texts = {}
# texts = frozenset() would this work??
corpus = ""
print("loading texts for training")
deletes = []
for cand in candidates:
texts[cand] = ""
for file in jsonhandler.trainings[cand]:
texts[cand] += jsonhandler.getTrainingText(cand, file)
# if frozenset() is used:
# texts.add(jsonhandler.getTrainingText(cand, file))
print("text " + file + " read")
if len(texts[cand].split()) < mintrainlen:
del texts[cand]
deletes.append(cand)
else:
corpus += texts[cand]
newcands = []
for cand in candidates:
if cand not in deletes:
newcands.append(cand)
candidates = newcands
words = [len(texts[cand].split()) for cand in texts]
minwords = min(words)
print(minwords)
fl = training(corpus)
authors = []
scores = []
for file in unknowns:
print("testing " + file)
utext = jsonhandler.getUnknownText(file)
ulen = len(utext.split())
if ulen < minlen:
authors.append("None")
scores.append(0)
else:
wins = [0] * len(candidates)
textlen = min(ulen, minwords)
print(textlen)
ustring = "".join(utext.split()[:textlen])
for i in range(repetitions):
rfl = random.sample(fl, len(fl) // 2)
sims = []
for cand in candidates:
candstring = getRandomString(texts[cand], textlen)
sims.append(testSim(candstring, ustring, rfl, 1))
wins[sims.index(max(sims))] += 1
score = max(wins) / float(repetitions)
if score >= threshold:
authors.append(candidates[wins.index(max(wins))])
scores.append(score)
else:
authors.append("None")
scores.append(score)
print("storing answers")
jsonhandler.storeJson(outputdir, unknowns, authors, scores)
def tira(corpusdir, outputdir):
# load training data
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
database = Database()
for candidate in jsonhandler.candidates:
database.add_author(candidate)
for training in jsonhandler.trainings[candidate]:
logging.info(
"Reading training text '%s' of '%s'", training, candidate)
text = Text(jsonhandler.getTrainingText(candidate, training),
candidate + " " + training)
try:
text.create_chunks()
database.add_text(candidate, text)
except:
# logging.info("Text size too small. Skip this text.")
logging.warning("Text too small. Exit.")
sys.exit()
database.calc_initial_feature_set()
candidates = [] # this list shall contain the most likely candidates
# We use the unmasking procedure to compare all unknown texts to all
# enumerated texts of known authorship and then decide which fit best.
# runtime could surely be optimized
for unknown in jsonhandler.unknowns:
try:
results = {}
# dictionary containing the maximum difference (first and
# last iteration) for every author
# load the unknown text and create the chunks which are used
# for the unmasking process
unknown_text = Text(jsonhandler.getUnknownText(unknown), unknown)
unknown_text.create_chunks()
for candidate in jsonhandler.candidates:
results[candidate] = float("inf")
for known_text in database.texts[candidate]:
# reset the feature list, i.e. create a copy of the initial
# list
features = list(database.initial_feature_set)
# randomly select equally many chunks from each text
select_chunks(unknown_text, known_text)
# create label vector
# (0 -> chunks of unknown texts, 1 -> chunks of known texts)
label = [0 for i in range(0, len(unknown_text.selected_chunks))] + \
[1 for i in range(
0, len(known_text.selected_chunks))]
label = numpy.array(label)
# the reshape is necessary for the classifier
label.reshape(
len(unknown_text.selected_chunks) + len(known_text.selected_chunks), 1)
# loop
global NUMBER_ITERATIONS
global NUMBER_ELIMINATE_FEATURES
scores = []
for i in range(0, NUMBER_ITERATIONS):
logging.info("Iteration #%s for texts '%s' and '%s'",
str(i + 1), unknown, known_text.name)
# Create the matrix containing the relative word counts
# in each chunk (for the selected features)
matrix = [[chunk.count(word) / CHUNK_LENGTH
for word in features]
for chunk
in (unknown_text.selected_chunks + known_text.selected_chunks)]
matrix = numpy.array(matrix)
# Get a LinearSVC classifier and its score (i.e. accuracy
# in the training data). Save this score as a point in the
# scores curve. (We want to select the curve with the
# steepest decrease)
classifier = svm.LinearSVC()
classifier.fit(matrix, label)
scores.append(classifier.score(matrix, label))
# a list of all feature weights
flist = classifier.coef_[0]
# Now, we have to delete the strongest weighted features
# (NUMBER_ELIMINATE_FEATURES) from each side.
# indices of maximum 3 values and minimum 3 values
delete = list(numpy.argsort(flist)[-NUMBER_ELIMINATE_FEATURES:]) \
+ list(numpy.argsort(flist)[
:NUMBER_ELIMINATE_FEATURES])
# We cannot directly use the delete list to eliminate from
# the features list since peu-a-peu elimination changes
# the indices.
delete_features = []
for i in delete:
delete_features.append(features[i])
logging.info("Delete %s", str(delete_features))
for feature in delete_features:
# a single feature could appear twice in the delete
# list
if feature in features:
features.remove(feature)
# The scores list is now the graph we use to get our results
# Therefore, compare with previous scores.
score = curve_score(scores)
logging.info("Calculated a score of %s", str(score))
if score < results[candidate]:
results[candidate] = score
# Which author has the biggest score?
most_likely_author = min(results, key=results.get)
logging.info("Most likely author is '%s' with a score of %s",
most_likely_author, results[most_likely_author])
candidates.append(most_likely_author)
except:
candidates.append("FILE_TO_SMALL")
# save everything in the specified directory
jsonhandler.storeJson(outputdir, jsonhandler.unknowns, candidates)
def test_method(corpusdir, outputdir, method="d1", n=3, L=2000):
logging.info("Test method %s with L=%d", method, L)
authors, scores = create_ranking(n, L, method)
jsonhandler.storeJson(outputdir, jsonhandler.unknowns, authors, scores)
def tira(corpusdir, outputdir):
"""
Keyword arguments:
corpusdir -- Path to a tira corpus
outputdir -- Output directory
"""
pos_tag = False
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
# creating training data
logging.info("Load the training data...")
database = Database(150, real_words=True)
for candidate in jsonhandler.candidates:
author = Author(candidate)
for training in jsonhandler.trainings[candidate]:
logging.info("Author '%s': Loading training '%s'", candidate,
training)
text = Text(jsonhandler.getTrainingText(candidate, training),
candidate + " " + training,
pos_tag=pos_tag)
author.add_text(text)
database.add_author(author)
database.process()
for author in database.authors:
author.calc_cmsz(database)
# do some training, i.e. check which parameter is best
logging.info("Start training...")
results = {}
for length in range(150, 301, 50):
logging.info("Check for %s words.", length)
database.considered_words = length
database.process()
results[length] = 0
for correct_author in database.authors:
for training in jsonhandler.trainings[correct_author.name]:
trainingcase = Text(jsonhandler.getTrainingText(
correct_author.name, training),
"Trainingcase",
pos_tag=pos_tag)
trainingcase.calc_zscores(database)
deltas = {}
for author in database.authors:
deltas[author] = trainingcase.calc_delta(database, author)
if min(deltas, key=deltas.get) == correct_author:
results[length] += 1
length = max(results, key=results.get)
logging.info("Choose %s as length.", str(length))
# reconfigure the database with length
database.considered_words = length
database.process()
# run the testcases
results = []
for unknown in jsonhandler.unknowns:
testcase = Text(jsonhandler.getUnknownText(unknown),
unknown,
pos_tag=pos_tag)
testcase.calc_zscores(database)
deltas = {}
for author in database.authors:
deltas[author] = testcase.calc_delta(database, author)
results.append((unknown, min(deltas, key=deltas.get).name))
texts = [text for (text, candidate) in results]
cands = [candidate for (text, candidate) in results]
jsonhandler.storeJson(outputdir, texts, cands)
def tira(corpusdir, outputdir):
# load training data
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
database = Database()
for candidate in jsonhandler.candidates:
database.add_author(candidate)
for training in jsonhandler.trainings[candidate]:
logging.info("Reading training text '%s' of '%s'", training,
candidate)
text = Text(jsonhandler.getTrainingText(candidate, training),
candidate + " " + training)
try:
text.create_chunks()
database.add_text(candidate, text)
except:
# logging.info("Text size too small. Skip this text.")
logging.warning("Text too small. Exit.")
sys.exit()
database.calc_initial_feature_set()
candidates = [] # this list shall contain the most likely candidates
# We use the unmasking procedure to compare all unknown texts to all
# enumerated texts of known authorship and then decide which fit best.
# runtime could surely be optimized
for unknown in jsonhandler.unknowns:
try:
results = {}
# dictionary containing the maximum difference (first and
# last iteration) for every author
# load the unknown text and create the chunks which are used
# for the unmasking process
unknown_text = Text(jsonhandler.getUnknownText(unknown), unknown)
unknown_text.create_chunks()
for candidate in jsonhandler.candidates:
results[candidate] = float("inf")
for known_text in database.texts[candidate]:
# reset the feature list, i.e. create a copy of the initial
# list
features = list(database.initial_feature_set)
# randomly select equally many chunks from each text
select_chunks(unknown_text, known_text)
# create label vector
# (0 -> chunks of unknown texts, 1 -> chunks of known texts)
label = [0 for i in range(0, len(unknown_text.selected_chunks))] + \
[1 for i in range(
0, len(known_text.selected_chunks))]
label = numpy.array(label)
# the reshape is necessary for the classifier
label.reshape(
len(unknown_text.selected_chunks) +
len(known_text.selected_chunks), 1)
# loop
global NUMBER_ITERATIONS
global NUMBER_ELIMINATE_FEATURES
scores = []
for i in range(0, NUMBER_ITERATIONS):
logging.info("Iteration #%s for texts '%s' and '%s'",
str(i + 1), unknown, known_text.name)
# Create the matrix containing the relative word counts
# in each chunk (for the selected features)
matrix = [[
chunk.count(word) / CHUNK_LENGTH
for word in features
] for chunk in (unknown_text.selected_chunks +
known_text.selected_chunks)]
matrix = numpy.array(matrix)
# Get a LinearSVC classifier and its score (i.e. accuracy
# in the training data). Save this score as a point in the
# scores curve. (We want to select the curve with the
# steepest decrease)
classifier = svm.LinearSVC()
classifier.fit(matrix, label)
scores.append(classifier.score(matrix, label))
# a list of all feature weights
flist = classifier.coef_[0]
# Now, we have to delete the strongest weighted features
# (NUMBER_ELIMINATE_FEATURES) from each side.
# indices of maximum 3 values and minimum 3 values
delete = list(numpy.argsort(flist)[-NUMBER_ELIMINATE_FEATURES:]) \
+ list(numpy.argsort(flist)[
:NUMBER_ELIMINATE_FEATURES])
# We cannot directly use the delete list to eliminate from
# the features list since peu-a-peu elimination changes
# the indices.
delete_features = []
for i in delete:
delete_features.append(features[i])
logging.info("Delete %s", str(delete_features))
for feature in delete_features:
# a single feature could appear twice in the delete
# list
if feature in features:
features.remove(feature)
# The scores list is now the graph we use to get our results
# Therefore, compare with previous scores.
score = curve_score(scores)
logging.info("Calculated a score of %s", str(score))
if score < results[candidate]:
results[candidate] = score
# Which author has the biggest score?
most_likely_author = min(results, key=results.get)
logging.info("Most likely author is '%s' with a score of %s",
most_likely_author, results[most_likely_author])
candidates.append(most_likely_author)
except:
candidates.append("FILE_TO_SMALL")
# save everything in the specified directory
jsonhandler.storeJson(outputdir, jsonhandler.unknowns, candidates)
def heavy_lifter(corpusdir, outputdir):
# load training data
jsonhandler.loadJson(corpusdir)
# 现在吃进meta-file.json: corpusdir, upath, candidates, unknowns, encoding, language
# corpusdir是meta-file.json所在文件夹 ,upath是"unknown"文件夹的位置，也就是放unknown texts的地方
# unknowns是unknowns-texts的文件名，ie. "unknown00011.txt"
jsonhandler.loadTraining()
# 现在trainings（dict）有内容了，ie:{...'candidate00010': ['known00001.txt', 'known00002.txt']}
database = Database(
) # database.authors = [], database.texts = {}, database.features = {}
for candidate in jsonhandler.candidates:
database.add_author(
candidate
) # database.authors = [cand1, cand2...]// database.texts = {author:[]...}
for training in jsonhandler.trainings[
candidate]: # 对于某个candidate的training text的文件名
# logging.info("Reading training text '%s' of '%s'", training, candidate)
text = Text(
jsonhandler.getTrainingText(
candidate, training), # 创建Text实例 text(raw, name)
candidate + " " +
training) # name类似于 "candidate00001 known00002.txt"
try:
text.chunks_generator(
) # Text.chunks是一个list of lists（由许多chunks组成，每个chunk里是tokens列表）
text.ngrams_generator() # 生成text实例的ngram
database.add_text(
candidate, text
) # 为database实例加上texts属性 {candidate00001: [text, text,...]}
except:
# logging.info("Text size too small. Skip this text.")
# logging.warning("Text too small. Exit.")
# sys.exit()
print('Something went wrong.')
database.feature_generator()
# We use the unmasking procedure to compare all unknown texts to all
# enumerated texts of known authorship and then decide which fit best.
for unknown in jsonhandler.unknowns:
results = {
} # dictionary containing the average ten folds accuracy score of each candidates
dropped_features = {}
# load the unknown text and create the chunks which are used for the unmasking process
unknown_text = Text(jsonhandler.getUnknownText(unknown), unknown)
unknown_text.chunks_generator(
) # 得到unknown_text.tokens (list) 和 .chunks (list of lists)
for candidate in jsonhandler.candidates:
# print(candidate)
# results是总的dict，每个key所对应的value是个list，包含了5个子list
# 实验要进行5轮，每轮都是10折
results[candidate] = []
dropped_features[candidate] = []
# features = (database.features[candidate]).copy()
for known_text in database.texts[candidate]:
# experiment_scores = []
# randomly select equally many chunks from each text
# 重复5次实验，每次都随机删掉多余的chunk
for experiment in range(0, NUMBER_EXPERIMENTS):
features = (database.features[candidate]).copy()
select_chunks(unknown_text, known_text)
# (dropped_features[candidate])[experiment] = []
# create label vector
# (0 for chunks of unknown texts, 1 for chunks of known texts)
label = [
0 for i in range(0, len(unknown_text.selected_chunks))
] + [1 for i in range(0, len(known_text.selected_chunks))]
label = np.array(label)
# the reshape is necessary for the classifier
label.reshape(
len(unknown_text.selected_chunks) +
len(known_text.selected_chunks), 1)
global NUMBER_ITERATIONS, FOLD_ELIMINATION
fold_scores = []
for i in range(0, NUMBER_ITERATIONS):
# logging.info("Iteration #%s for texts '%s' and '%s'",
# str(i + 1), unknown, known_text.name)
# (dropped_features[candidate])[i] = []
matrix = [[
chunk[0].count(token) / len(chunk2tokens(chunk[0]))
for token in features
] for chunk in (unknown_text.selected_chunks +
known_text.selected_chunks)]
matrix = np.array(matrix)
print(
"进行", candidate, "的第" + str(experiment + 1) +
"个实验的第" + str(i + 1) + "轮。", '\n')
classifier = svm.LinearSVC()
# classifier = svm.SVC(kernel='linear')
classifier.fit(matrix, label)
fold_scores.append(classifier.score(matrix, label))
weights = classifier.coef_[0]
delete = list(np.argsort(weights)[-FOLD_ELIMINATION:]) \
+ list(np.argsort(weights)[:FOLD_ELIMINATION])
# We cannot directly use the delete list to eliminate from
# the features list since peu-a-peu elimination changes the indices.
delete_features = []
for x in delete:
delete_features.append(features[x])
# ((dropped_features[candidate])[experiment]).append(delete_features)
(dropped_features[candidate]).append(delete_features)
# logging.info("Delete %s", str(delete_features))
for feature in delete_features:
if feature in features:
features.remove(feature)
# experiment_scores.append(fold_scores)
#
# experiment_scores = np.array(experiment_scores)
# experiment_scores = np.mean(experiment_scores, axis=0)
(results[candidate]).append(fold_scores)
# save everything in the specified directory
jsonhandler.storeJson(outputdir, results, dropped_features)
def main():
"""The main function."""
parser = argparse.ArgumentParser(
description="PPM approach according to Koppel11"
)
parser.add_argument("-i", action="store", help="path to corpus directory")
parser.add_argument("-o", action="store", help="path to output directory")
args = vars(parser.parse_args())
corpusdir = args["i"]
outputdir = args["o"]
if corpusdir is None or outputdir is None:
parser.print_help()
return
candidates = jsonhandler.candidates
unknowns = jsonhandler.unknowns
jsonhandler.loadJson(corpusdir)
jsonhandler.loadTraining()
texts = {}
corpus = ""
print("Loading texts for training...")
deletes = []
for cand in candidates:
texts[cand] = ""
for file in jsonhandler.trainings[cand]:
texts[cand] += jsonhandler.getTrainingText(cand, file)
print(f"Text {file} read")
if len(texts[cand].split()) < MINTRAINLEN:
del texts[cand]
deletes.append(cand)
else:
corpus += texts[cand]
newcands = []
for cand in candidates:
if cand not in deletes:
newcands.append(cand)
candidates = newcands
words = [len(texts[cand].split()) for cand in texts]
minwords = min(words)
print(minwords)
feature_list = training(corpus)
authors = []
scores = []
for file in unknowns:
print(f"Testing {file}")
utext = jsonhandler.getUnknownText(file)
ulen = len(utext.split())
if ulen < MINLEN:
authors.append("None")
scores.append(0)
else:
wins = [0] * len(candidates)
textlen = min(ulen, minwords)
print(textlen)
ustring = "".join(utext.split()[:textlen])
for _ in range(REPETITIONS):
rfl = random.sample(feature_list, len(feature_list) // 2)
sims = []
for cand in candidates:
candstring = get_random_string(texts[cand], textlen)
sims.append(test_sim(candstring, ustring, rfl, 1))
wins[sims.index(max(sims))] += 1
score = max(wins) / float(REPETITIONS)
if score >= THRESHOLD:
authors.append(candidates[wins.index(max(wins))])
scores.append(score)
else:
authors.append("None")
scores.append(score)
print("Storing answers...")
jsonhandler.storeJson(outputdir, unknowns, authors, scores)
print("Done!")