def main():
"""
processing text
"""
from reader import PickledCorpusReader
# Classification model
model_file = 'model-SGDClassifier.pickle'
# Initialize model from pickle
with open(model_file, 'rb') as f:
model = pickle.load(f)
# Initialize a corpus reader
corpus = PickledCorpusReader('./pickle_corpus/')
# Call the model to classify text
y_pred, x_pred = predict_model(model,
corpus,
categories=['artistic_event'])
# Print result
count = nartistics = 0
for result in y_pred:
if result != 'artistic_event':
for doc in x_pred[count]:
print_event(doc)
print('======================')
nartistics += 1
count += 1
print('{:d} artistic events found/ {:d} events'.format(nartistics, count))
def train_model(path, model, saveto=None, cv=12):
"""
Trains model from corpus at specified path; constructing cross-validation
scores using the cv parameter, then fitting the model on the full data and
writing it to disk at the saveto path if specified. Returns the scores.
"""
# Load the corpus data and labels for classification
corpus = PickledCorpusReader(path)
X = documents(corpus)
y = labels(corpus)
# Compute cross validation scores
scores = cross_val_score(model, X, y, cv=cv)
# Fit the model on entire data set
model.fit(X, y)
# Write to disk if specified
if saveto:
joblib.dump(model, saveto)
# Return scores as well as training time via decorator
return scores
from reader import PickledCorpusReader
reader = PickledCorpusReader('../corpus')
for category in reader.categories():
n_docs = len(reader.fileids(categories=[category]))
n_words = sum(1 for word in reader.words(categories=[category]))
print("- '{}' contains {:,} docs and {:,} words".format(
category, n_docs, n_words))
table.append(row)
table.sort(key=lambda r: r[-1], reverse=True)
print(tabulate.tabulate(table, headers=fields))
if __name__ == '__main__':
results_file = "results.json"
labels = [
"artistic_event",
"other_event",
]
# Initialzing corpus reader and loader (generates K-Folds)
reader = PickledCorpusReader('./pickle_corpus')
loader = CorpusLoader(reader, 5, shuffle=True, categories=labels)
txt = TextNormalizer()
txt.lemmatize("qu'", "")
# Initalizing models
models = []
for form in (LogisticRegression, SGDClassifier):
models.append(create_pipeline(form(), True))
models.append(create_pipeline(form(), False))
models.append(create_pipeline(MultinomialNB(), False))
models.append(create_pipeline(GaussianNB(), True))
# Running all models
from reader import PickledCorpusReader
reader = PickledCorpusReader('../corpus')
for category in reader.categories():
n_docs = len(reader.fileids(categories=[category]))
n_words = sum(1 for word in reader.words(categories=[category]))
print("- '{}' contains {:,} docs and {:,} words".format(category, n_docs, n_words))
def graph(docs):
G = nx.Graph()
for doc in docs:
for pair in pairs(doc):
if (pair[0][0], pair[1][0]) in G.edges():
G.edges[(pair[0][0], pair[1][0])]['weight'] += 1
else:
G.add_edge(pair[0][0], pair[1][0], weight=1)
return G
def nbest_centrality(G, metric, n=10, attr="centrality", **kwargs):
# Compute the centrality scores for each vertex
scores = metric(G, **kwargs)
# Set the score as a property on each node
nx.set_node_attributes(G, name=attr, values=scores)
# Find the top n scores and print them along with their index
topn = heapq.nlargest(n, scores.items(), key=itemgetter(1))
for idx, item in enumerate(topn):
print("{}. {}: {:0.4f}".format(idx + 1, *item))
return G
if __name__ == '__main__':
corpus = PickledCorpusReader("../corpus")
docs = corpus.docs()
G = graph(docs)
self.estimator = lsimodel.LsiTransformer(num_topics=self.n_topics)
else:
self.estimator = ldamodel.LdaTransformer(num_topics=self.n_topics)
self.model = Pipeline([('norm', TextNormalizer()),
('vect', GensimTfidfVectorizer()),
('model', self.estimator)])
def fit(self, documents):
self.model.fit(documents)
return self.model
if __name__ == '__main__':
corpus = PickledCorpusReader('../corpus')
# With Sklearn
skmodel = SklearnTopicModels(estimator='NMF')
documents = corpus.docs()
skmodel.fit_transform(documents)
topics = skmodel.get_topics()
for topic, terms in topics.items():
print("Topic #{}:".format(topic + 1))
print(terms)
# # With Gensim
# gmodel = GensimTopicModels(estimator='LSA')
#
# docs = [
trees = ne_chunk(sentence)
for tree in trees:
if hasattr(tree, 'label'):
if tree.label() in self.labels:
entities.append(
' '.join([child[0].lower() for child in tree])
)
return entities
def fit(self, documents, labels=None):
return self
def transform(self, documents):
for document in documents:
yield self.get_entities(document)
if __name__ == '__main__':
from reader import PickledCorpusReader
corpus = PickledCorpusReader('../corpus')
docs = corpus.docs()
phrase_extractor = KeyphraseExtractor()
keyphrases = list(phrase_extractor.fit_transform(docs))
print(keyphrases[0])
entity_extractor = EntityExtractor()
entities = list(entity_extractor.fit_transform(docs))
print(entities[0])
self.save()
return self
def transform(self, documents):
def generator():
for document in documents:
vec = self.tfidf[self.lexicon.doc2bow(document)]
if self.tofull:
yield sparse2full(vec)
else:
yield vec
return list(generator())
if __name__ == '__main__':
from reader import PickledCorpusReader
corpus = PickledCorpusReader('../corpus')
docs = [
list(corpus.docs(fileids=fileid))[0] for fileid in corpus.fileids()
]
model = Pipeline([('norm', TextNormalizer()),
('vect', GensimTfidfVectorizer()),
('lda', ldamodel.LdaTransformer())])
model.fit_transform(docs)
print(model.named_steps['norm'])
from sklearn.feature_extraction.text import TfidfVectorizer
from reader import PickledCorpusReader
from normalizer import TextNormalizer
def identity(words):
return words
corpus = PickledCorpusReader('../../corpora/Pickled_Corpus_Sample')
normalizer = TextNormalizer()
docs = normalizer.fit_transform(corpus.docs())
vectorizer = TfidfVectorizer(tokenizer=identity,
preprocessor=None,
lowercase=False)
vectors = vectorizer.fit_transform(docs)
print(vectors.shape)
self.estimator = ldamodel.LdaTransformer(num_topics=self.n_topics)
self.model = Pipeline([
('norm', TextNormalizer()),
('vect', GensimTfidfVectorizer()),
('model', self.estimator)
])
def fit(self, documents):
self.model.fit(documents)
return self.model
if __name__ == '__main__':
corpus = PickledCorpusReader('../corpus')
# With Sklearn
skmodel = SklearnTopicModels(estimator='NMF')
documents = corpus.docs()
skmodel.fit_transform(documents)
topics = skmodel.get_topics()
for topic, terms in topics.items():
print("Topic #{}:".format(topic+1))
print(terms)
# # With Gensim
# gmodel = GensimTopicModels(estimator='LSA')
#
# docs = [
def __iter__(self):
for train_index, test_index in self.folds.split(self.files):
X_train = self.documents(train_index)
y_train = self.labels(train_index)
X_test = self.documents(test_index)
y_test = self.labels(test_index)
yield X_train, X_test, y_train, y_test
if __name__ == '__main__':
from reader import PickledCorpusReader
corpus = PickledCorpusReader('../pickle_corpus')
loader = CorpusLoader(
corpus,
12,
categories=["expositions", "expositions_galeries", 'marches_salons'])
fcount = dcount = pcount = scount = tcount = 0
for x_train, x_test, y_train, x_test in loader:
for file in x_train:
fcount += 1
for doc in file:
dcount += 1
for para in doc:
#print(para)
#key=input('>>')
pcount += 1
#if pcount >= 1 :
def score(self, word, context):
"""
Use KneserNeyProbDist from NLTK to get score
"""
trigram = tuple((context[0], context[1], word))
return self.model.prob(trigram)
def samples(self):
return self.model.samples()
def prob(self, sample):
return self.model.prob(sample)
if __name__ == '__main__':
corpus = PickledCorpusReader('../corpus')
tokens = [''.join(word) for word in corpus.words()]
vocab = Counter(tokens)
sents = list([word[0] for word in sent] for sent in corpus.sents())
counter = count_ngrams(3, vocab, sents)
knm = KneserNeyModel(counter)
def complete(input_text):
tokenized = nltk.word_tokenize(input_text)
if len(tokenized) < 2:
response = "Say more."
else:
completions = {}
for sample in knm.samples():
self.model = KMeansClusterer(self.k,
distance=cosine,
avoid_empty_clusters=True)
self.model.cluster([
self.vectorize(corpus.words(fileid))
for fileid in corpus.fileids(categories=['news'])
])
def classify(self, document):
"""
Pass through to the internal model classify
"""
return self.model.classify(self.vectorize(document))
if __name__ == '__main__':
corpus = PickledCorpusReader('../corpus')
clusterer = KMeansTopics(corpus, k=7)
clusterer.cluster(corpus)
# Classify documents in the new corpus by cluster affinity
groups = [(clusterer.classify(corpus.words(fileid)), fileid)
for fileid in corpus.fileids(categories=['news'])]
# Group documents in corpus by cluster and display them
groups.sort(key=itemgetter(0))
for group, items in groupby(groups, key=itemgetter(0)):
for cluster, fname in items:
print("Cluster {}: {}".format(cluster + 1, fname))
# Compute the centrality scores for each vertex
scores = metric(G, **kwargs)
# Set the score as a property on each node
nx.set_node_attributes(G, name=attr, values=scores)
# Find the top n scores and print them along with their index
topn = heapq.nlargest(n, scores.items(), key=itemgetter(1))
for idx, item in enumerate(topn):
print("{}. {}: {:0.4f}".format(idx + 1, *item))
return G
if __name__ == '__main__':
corpus = PickledCorpusReader(
'/Users/dd/PycharmProjects/atap/resources/sample/')
G = graph(corpus)
# # Write the graph to disk, if needed
nx.write_graphml(G, "entities.graphml")
# # Get summary stats for the full graph
print(nx.info(G))
# # find the most central entities in the social network
print("Degree centrality")
nbest_centrality(G, nx.degree_centrality)
print("Betweenness centrality")
nbest_centrality(G,
nx.betweenness_centrality,
10,
# Create the TF-IDF Model and compute the scores
model = gensim.models.TfidfModel(vectors)
scores = model[vectors]
for doc in scores:
yield [
(lexicon[vec], score) for vec, score in doc
]
if __name__ == '__main__':
import heapq
from reader import PickledCorpusReader
from collections import Counter
corpus = PickledCorpusReader('../corpus')
scores = scored_document_phrases([
list(corpus.sents(fileids=fileid)) for fileid in corpus.fileids(categories=["politics", "news"])
], True)
tfidfs = Counter()
for phrases in scores:
for phrase, score in phrases:
tfidfs[phrase] += score
print(
tabulate(tfidfs.most_common(50), headers=["keyphrase", "cumulative tfidf"])
)
indices = train_idx if train else test_idx
return [
fileid for doc_idx, fileid in enumerate(self.corpus.fileids())
if doc_idx in indices
]
def documents(self, fold=None, train=False, test=False):
for fileid in self.fileids(fold, train, test):
yield list(self.corpus.docs(fileids=fileid))
def labels(self, fold=None, train=False, test=False):
return [
self.corpus.categories(fileids=fileid)[0]
for fileid in self.fileids(fold, train, test)
]
if __name__ == '__main__':
from reader import PickledCorpusReader
corpus = PickledCorpusReader('corpus/tagcorpusoracle')
for para in corpus.fileids(categories='281550031684823'):
print(para)
loader = CorpusLoader(corpus,12)
for fid in loader.fileids(0, test=True):
print(fid)
print(loader.labels(0, test=True))
preprocessor=None,
lowercase=False))]
if reduction:
steps.append(('reduction', TruncatedSVD(n_components=1000)))
# Add the estimator
steps.append(('classifier', estimator))
return Pipeline(steps)
labels = [
"281571400036367", "281585707268948", "281723051068305", "281981394899011",
"281988872632944", "281723051068312"
]
reader = PickledCorpusReader('corpus/tagcorpusoracle_test')
loader = CorpusLoader(reader, 5, shuffle=True, categories=labels)
models = []
for form in (LogisticRegression, SGDClassifier, DecisionTreeClassifier):
models.append(create_pipeline(form(), True))
models.append(create_pipeline(form(), False))
models.append(create_pipeline(MultinomialNB(), False))
models.append(create_pipeline(GaussianNB(), True))
import time
import json
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
yield list(self.reader.docs(fileids=[fileid]))
def labels(self, idx=None):
return [
self.reader.categories(fileids=[fileid])[0]
for fileid in self.fileids(idx)
]
def __iter__(self):
for train_index, test_index in self.folds.split(self.files):
X_train = self.documents(train_index)
y_train = self.labels(train_index)
X_test = self.documents(test_index)
y_test = self.labels(test_index)
yield X_train, X_test, y_train, y_test
if __name__ == '__main__':
from reader import PickledCorpusReader
reader = PickledCorpusReader('../../corpora/Pickled_Corpus_Sample')
loader = CorpusLoader(reader, 10)
X_train, X_test, y_train, y_test = next(loader.__iter__())
for i in range(2):
print('\nDOC {} LABEL: "{}"'.format(i+1, y_train[i]))
print(next(X_train))
(
"vectorize",
TfidfVectorizer(tokenizer=identity, preprocessor=None, lowercase=False),
),
]
if reduction:
steps.append(("reduction", TruncatedSVD(n_components=10000)))
# Add the estimator
steps.append(("classifier", estimator))
return Pipeline(steps)
labels = ["books", "cinema", "cooking", "gaming", "sports", "tech"]
reader = PickledCorpusReader("./corpus")
loader = CorpusLoader(reader, 5, shuffle=True, categories=labels)
models = []
for form in (LogisticRegression, SGDClassifier):
models.append(create_pipeline(form(), True))
models.append(create_pipeline(form(), False))
models.append(create_pipeline(MultinomialNB(), False))
models.append(create_pipeline(GaussianNB(), True))
import json
import time
from sklearn.metrics import (accuracy_score, f1_score, precision_score,
recall_score)
# Compute the centrality scores for each vertex
nbest = {}
for name, metric in metrics.items():
scores = metric(G)
# Set the score as a property on each node
nx.set_node_attributes(G, name=name, values=scores)
# Find the top n scores and print them along with their index
topn = heapq.nlargest(n, scores.items(), key=itemgetter(1))
nbest[name] = topn
return nbest
if __name__ == '__main__':
from reader import PickledCorpusReader
corpus = PickledCorpusReader(root='../../corpora/politics_pickled')
docs = corpus.docs()
entity_extractor = EntityExtractor()
entities = entity_extractor.fit_transform(docs)
entity_pairing = EntityPairs()
pairs = entity_pairing.fit_transform(entities)
graph = GraphExtractor()
G = graph.fit_transform(pairs)
print(nx.info(G))
centralities = {
"Degree Centrality": nx.degree_centrality,
"Betweenness Centrality": nx.betweenness_centrality
from sklearn.model_selection import train_test_split as tts
from reader import PickledCorpusReader
reader = PickledCorpusReader('../corpus')
labels = ["books", "cinema", "cooking", "gaming", "sports", "tech"]
docs = reader.fileids(categories=labels)
X = list(reader.docs(fileids=docs))
y = [reader.categories(fileids=[fileid])[0] for fileid in docs]
def score(self, word, context):
"""
Use KneserNeyProbDist from NLTK to get score
"""
trigram = tuple((context[0], context[1], word))
return self.model.prob(trigram)
def samples(self):
return self.model.samples()
def prob(self, sample):
return self.model.prob(sample)
if __name__ == '__main__':
corpus = PickledCorpusReader('../corpus')
tokens = [''.join(word) for word in corpus.words()]
vocab = Counter(tokens)
sents = list([word for word in sent] for sent in corpus.sents())
counter = count_ngrams(3, vocab, sents)
knm = KneserNeyModel(counter)
def complete(input_text):
tokenized = nltk.word_tokenize(input_text)
if len(tokenized) < 2:
response = "Say more."
else:
completions = {}
for sample in knm.samples():
def documents(self, idx=None):
for fileid in self.fileids(idx):
yield list(self.reader.docs(fileids=[fileid]))
def labels(self, idx=None):
return [
self.reader.categories(fileids=[fileid])[0]
for fileid in self.fileids(idx)
]
def __iter__(self):
for train_index, test_index in self.folds.split(self.files):
X_train = self.documents(train_index)
y_train = self.labels(train_index)
X_test = self.documents(test_index)
y_test = self.labels(test_index)
yield X_train, X_test, y_train, y_test
if __name__ == '__main__':
from reader import PickledCorpusReader
corpus = PickledCorpusReader('corpus/tagcorpusoracle')
loader = CorpusLoader(corpus, 12)
#for X_train, X_test, y_train, y_test in loader:
#for f in y_train:
#print(f)
self.model = KMeansClusterer(
self.k, distance=cosine, avoid_empty_clusters=True)
self.model.cluster([
self.vectorize(
corpus.words(fileid)
) for fileid in corpus.fileids(categories=['news'])
])
def classify(self, document):
"""
Pass through to the internal model classify
"""
return self.model.classify(self.vectorize(document))
if __name__ == '__main__':
corpus = PickledCorpusReader('../corpus')
clusterer = KMeansTopics(corpus, k=7)
clusterer.cluster(corpus)
# Classify documents in the new corpus by cluster affinity
groups = [
(clusterer.classify(corpus.words(fileid)), fileid)
for fileid in corpus.fileids(categories=['news'])
]
# Group documents in corpus by cluster and display them
groups.sort(key=itemgetter(0))
for group, items in groupby(groups, key=itemgetter(0)):
for cluster, fname in items:
print("Cluster {}: {}".format(cluster+1,fname))
return self
def transform(self, documents):
def generator():
for document in documents:
vec = self.tfidf[self.lexicon.doc2bow(document)]
if self.tofull:
yield sparse2full(vec)
else:
yield vec
return list(generator())
if __name__ == '__main__':
from reader import PickledCorpusReader
corpus = PickledCorpusReader('../corpus')
docs = [
list(corpus.docs(fileids=fileid))[0]
for fileid in corpus.fileids()
]
model = Pipeline([
('norm', TextNormalizer()),
('vect', GensimTfidfVectorizer()),
('lda', ldamodel.LdaTransformer())])
model.fit_transform(docs)
print(model.named_steps['norm'])
'id': 1,
'name': 'Exposition de peinture',
'description':
'ceci est une exposition de Claude Monet.\nVernissage le 23 mai 2020',
'eventcat': [200]
}
event2 = {
'id': 2,
'name': 'Exposition de sculpture',
'description': 'Ceci est une exposition de Rodin',
'eventcat': [200],
}
events = [event1, event2]
fileid = 'artistic_event/0b7944a1a37ff80d982f0e18abeab26d.pickle'
corpus1 = PickledCorpusReader('./pickle_corpus',
fileids=[fileid
]) #categories='artistic_event')
corpus2 = EventCorpusReader(events, categories=[300])
for corpus in [corpus1, corpus2]:
print('------- words --------')
count = 0
for word in corpus.words(categories=[200, 300]):
print(word)
count += 1
if count > 10:
break
print('------- sents --------')
count = 0
for sent in corpus.sents(categories=[200, 300]):
print(sent)
# Now determine if we're in train or test mode.
if not (test or train) or (test and train):
raise ValueError("Please specify either train or test flag")
# Select only the indices to filter upon.
indices = train_idx if train else test_idx
return [
fileid for doc_idx, fileid in enumerate(self.corpus.fileids())
if doc_idx in indices
]
def documents(self, fold=None, train=False, test=False):
for fileid in self.fileids(fold, train, test):
yield list(self.corpus.docs(fileids=fileid))
def labels(self, fold=None, train=False, test=False):
return [
self.corpus.categories(fileids=fileid)[0]
for fileid in self.fileids(fold, train, test)
]
if __name__ == '__main__':
from reader import PickledCorpusReader
corpus = PickledCorpusReader('corpus')
loader = CorpusLoader(corpus, 12)
for fid in loader.fileids(0, test=True):
print(fid)
def labels(self, idx=None):
return [
self.reader.categories(fileids=[fileid])[0]
for fileid in self.fileids(idx)
]
def __iter__(self):
for train_index, test_index in self.folds.split(self.files):
X_train = self.documents(train_index)
y_train = self.labels(train_index)
X_test = self.documents(test_index)
y_test = self.labels(test_index)
yield X_train, X_test, y_train, y_test
if __name__ == '__main__':
from reader import PickledCorpusReader
corpus = PickledCorpusReader('path\\to\\preprocessed')
loader = CorpusLoader(corpus, 12)
for X_train, X_test, y_train, y_test in loader:
inputDocs = []
for doc in X_train:
inputDocs.append(doc)
print(len(inputDocs))
print(len(y_train))
break
if idx is None:
return self.files
return self.files[idx]
def documents(self, idx=None):
for fileid in self.fileids(idx):
yield list(self.reader.docs(fileids=[fileid]))
def labels(self, idx=None):
return [
self.reader.categories(fileids=[fileid])[0]
for fileid in self.fileids(idx)
]
def __iter__(self):
for train_index, test_index in self.folds.split(self.files):
X_train = self.documents(train_index)
y_train = self.labels(train_index)
X_test = self.documents(test_index)
y_test = self.labels(test_index)
yield X_train, X_test, y_train, y_test
if __name__ == '__main__':
from reader import PickledCorpusReader
corpus = PickledCorpusReader('../corpus')
loader = CorpusLoader(corpus, folds=12)
def create_pipeline(estimator, reduction=False):
steps = [('normalize', TextNormalizer()),
('vectorize',
GensimVectorizer(path="path\\to\\gensimModels\\doc2vec.d2v"))]
if reduction:
steps.append(('reduction', TruncatedSVD(n_components=1000)))
# Add the estimator
steps.append(('classifier', estimator))
return Pipeline(steps)
labels = ["label1", "label2", "RRR", "CAP"]
reader = PickledCorpusReader('path\\to\\preprocessed_min')
loader = CorpusLoader(reader, 12, shuffle=True, categories=labels)
models = []
for form in (LogisticRegression, SGDClassifier):
# models.append(create_pipeline(form(), True))
models.append(create_pipeline(form(), False))
# models.append(create_pipeline(MultinomialNB(), False))
models.append(create_pipeline(LinearSVC(), False))
# models.append(create_pipeline(PartialSGDEstimator(), False))
import time
import json