def load(self, model_name='kyubyong', df_data=None):
'''
Available pre-trained models: lwvlib, kyubyong, wikicorpus
'''
if model_name == 'lwvlib':
# http://dl.turkunlp.org/finnish-embeddings/
# https://turkunlp.org/finnish_nlp.html --> Finnish internet parsebank
# http://bionlp-www.utu.fi/wv_demo/ --> online interface
# https://github.com/fginter/wvlib_light/tree/3471a8db66883769c4e5398806876d5be3e3df24 --> library
import lwvlib
self.wv = lwvlib.load("finnish_4B_parsebank_skgram.bin") #
#self.wv=lwvlib.load("finnish_s24_skgram.bin") #,10000,500000)
elif model_name == 'kyubyong':
#import gensim
#https://github.com/Kyubyong/wordvectors
self.wv = gensim.models.Word2Vec.load(
'kyubyong_fin_word2vec.bin').wv
elif model_name == 'wikicorpus':
wiki_corpus = WikiDumpToCorpus(
'fiwiki-latest-pages-articles.xml.bz2')
if os.path.exists('wikicorpus_word2vec.bin'): # load model
self.mv = gensim.models.Word2Vec.load(
'wikicorpus_word2vec.bin').wv
elif wiki_corpus.check_corpus(): # train model
# model = gensim.models.Word2Vec(['testi'], size=200, window=10, min_count=5,
# workers=multiprocessing.cpu_count(), iter=2000)
if not df_data.empty:
# sentences = []
# for i, row in df_data.iterrows():
# sentence = gensim.utils.simple_preprocess(row['Nimi'])
# if len(sentence) > 0:
# sentences.append(sentence)
# model.build_vocab(sentences)
def train_func(train_data):
model = gensim.models.Word2Vec(
gensim.utils.simple_preprocess(train_data),
size=200,
window=10,
min_count=5,
workers=multiprocessing.cpu_count(),
iter=1)
model.save("wikicorpus_word2vec.model")
model.wv.save_word2vec_format(
"wikicorpus_word2vec.bin", binary=True)
#data = gensim.utils.simple_preprocess(train_data)
#model.train(data, total_examples=len(data), epochs=100)
wiki_corpus.open_corpus(train_func)
elif os.path.exists(
'fiwiki-latest-pages-articles.xml.bz2'): # make corpus
wiki_corpus.make_corpus()
else: #train
print("Error in 'wikicorpus'")
def read_pretrained_vectors(vocabulary, vector_model, max_words=1000000):
print("Loading pretrained word embeddings from " + vector_model)
model = lwvlib.load(vector_model, max_words, max_words)
vectors = np.zeros((len(vocabulary), model.vectors.shape[1]), np.float)
for word, idx in vocabulary.items():
if idx == 0 or idx == 1 or idx == 2:
continue # mask and unk and end
midx = model.get(word)
if midx is not None:
vectors[idx] = model.vectors[midx] # TODO: normalize?
return vectors
def read_vocab(self,fname,vsize=10000):
""" read vocabulary from wvlib model """
model=lwvlib.load(fname,vsize,vsize)
self.words=model.words
self.words.insert(0,"<MASK>")
self.words.insert(1,"<UNK>")
self.shape=(len(self.words),model.vectors.shape[1])
self.word_to_dim=dict((w,i) for i,w in enumerate(self.words))
self.vectors=np.zeros((vsize+2,model.vectors.shape[1]),np.float)
for i,row in enumerate(model.vectors):
self.vectors[i+2]=row
self.vectors[1]=self.vectors[np.random.randint(2,len(self.words))] # take a random vector for unk # TODO: average of something...
def read_vocab(self, fname, vsize=10000):
""" read vocabulary from wvlib model """
model = lwvlib.load(fname, vsize, vsize)
self.words = model.words
self.words.insert(0, "<MASK>")
self.words.insert(1, "<UNK>")
self.shape = (len(self.words), model.vectors.shape[1])
self.word_to_dim = dict((w, i) for i, w in enumerate(self.words))
self.vectors = np.zeros((vsize + 2, model.vectors.shape[1]), np.float)
for i, row in enumerate(model.vectors):
self.vectors[i + 2] = row
self.vectors[1] = self.vectors[np.random.randint(
2, len(self.words)
)] # take a random vector for unk # TODO: average of something...
def main(args):
print("Reading",args.char_model,"model",file=sys.stderr)
model=lwvlib.load(args.char_model)
vocabulary=read_vocab(args.vocab)
# TODO normalize?
# words: the words themselves
all_features=[]
# data: the vector matrix
data=np.zeros((len(vocabulary),model.vsize),np.float32)
for feature,wcount in vocabulary:
vector=np.zeros(model.vsize,np.float32)
norm=0
postag,feats=feature.split("|",1)
for feat in feats.split("|"):
vidx=model.get(postag+"|"+feat)
if vidx!=None:
vector=np.add(vector,model.vectors[vidx])
norm+=1
if norm==0:
print("No vector for feature",feature,"with pos tag",postag,file=sys.stderr)
continue
vector=np.divide(vector,norm)
data[len(all_features),:]=vector
all_features.append(feature)
# reshape data to len(words)
data=data[:len(all_features),:]
print("Original vector model:",len(model.words),model.vsize,file=sys.stderr)
print("New vector model:",data.shape,file=sys.stderr)
assert len(all_features)==data.shape[0]
save_txt(all_features,data,args.output)
def replace_embeddings(host, donor, out):
"""
Replaces donor embeddings in host, writes to out.
host: hnmt/bnas Embeddings submodel file name in
donor: word2vec bin file name
out: hnmt/bnas Embeddings submodel file name out
"""
with open(host, "rb") as hostf:
host_config = pickle.load(hostf)
host_data = pickle.load(hostf)
host_matrix = host_data[("w", )]
print("...{} loaded".format(host), file=sys.stderr, flush=True)
donor_model = lwvlib.load(donor)
donor_matrix = donor_model.vectors.astype(host_matrix.dtype)
assert host_matrix.shape[1] == donor_matrix.shape[1], (
host_matrix.shape[1], donor_matrix.shape[1])
print("...{} loaded".format(donor), file=sys.stderr, flush=True)
print("Sum before exchange",
np.sum(host_data[("w", )]),
file=sys.stderr,
flush=True)
#Replace
for w, host_dim in host_config["src_encoder"].index.items():
donor_dim = donor_model.get(w)
if donor_dim is None:
print("Cannot map {}".format(w), file=sys.stderr, flush=True)
continue
host_matrix[host_dim] = donor_matrix[donor_dim]
#Done replacing now
with open(out, "wb") as outf:
pickle.dump(host_config, outf)
pickle.dump(host_data, outf)
print("Sum after exchange",
np.sum(host_data[("w", )]),
file=sys.stderr,
flush=True)
random.shuffle(shuffle)
data_matrix=data_matrix[shuffle]
class_indices=class_indices[shuffle].astype(np.int32)
return class_indices, data_matrix
if __name__=="__main__":
import argparse
parser = argparse.ArgumentParser(description='Sclassifier')
parser.add_argument('--embeddings', help='embeddings file')
parser.add_argument('--edim', type=int, default=200000, help='Embedding dimension')
parser.add_argument('--tr-data', help='training data')
parser.add_argument('--max-seq-len', type=int, default=30, help='training data')
args = parser.parse_args()
#source of embeddings
wv_model=lwvlib.load(args.embeddings,args.edim,args.edim)
d=Data(wv_model)
with open(args.tr_data) as tr_f:
class_indices, data_matrix=d.read(tr_f,args)
class_indices_1hot=keras.utils.to_categorical(class_indices)
inp_seq=Input(shape=(args.max_seq_len,), name="words", dtype='int32')
inp_embeddings=Embedding(*wv_model.vectors.shape, input_length=args.max_seq_len, mask_zero=False, weights=[wv_model.vectors])
inp_embeddings.trainable=False
text_src=inp_embeddings(inp_seq)
#gru1_out=GRU(100,name="gru1")(text_src)
cnn1_out=Conv1D(100,2,padding="same")(text_src)
"""Matrix words x dimensions, returns"""
vectors = sklearn.preprocessing.normalize(vectors)
top_n = np.argpartition(-np.abs(vectors), max_count, 0)
top_per_dim = (top_n.T)[:, :max_count] #dims x max_count
for dim in range(top_per_dim.shape[0]):
print(dim, " ", end=' ')
for w in top_per_dim[dim]:
print(model.words[w], end=' ')
print()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='ICA postprocessing for w2v')
parser.add_argument('--w2v-model',
required=True,
action="store",
help='W2V model')
parser.add_argument('--ica-model',
required=True,
action="store",
help='ICA model')
parser.add_argument('--max-rank',
type=int,
default=200000,
help='Max rank default %(default)d')
args = parser.parse_args()
model = lwvlib.load(args.w2v_model, args.max_rank, args.max_rank)
ica_matrix = np.load(args.ica_model)
dimension_extremes(ica_matrix, 15, model)
parser.add_argument("--max-rank-mem",
type=int,
default=None,
help='Max vectors in memory')
parser.add_argument("--max-rank",
type=int,
default=None,
help='Max vectors total')
parser.add_argument("--interactive",
default=False,
action="store_true",
help='Read interactive')
args = parser.parse_args()
models = [
lwvlib.load(m, max_rank_mem=args.max_rank_mem, max_rank=args.max_rank)
for m in args.models
]
while True:
if args.interactive:
w = input("> ")
else:
w = sys.stdin.readline().strip()
if not w:
break
nearest = [m.nearest(w, 200) for m in models]
if any(n == None for n in nearest):
print("Out of vocab")
continue
for nn in zip(*nearest):
for sim, neighb in nn:
import finmeter, random, lwvlib, json, re
from uralicNLP import semfi
from uralicNLP import uralicApi
from nltk.tokenize import RegexpTokenizer
from collections import Counter
import numpy as np
from numpy.random import choice
from markovchain import JsonStorage
from markovchain.text import MarkovText, ReplyMode
wv = lwvlib.load("fin-word2vec-lemma.bin", 10000, 500000)
vowels = ['a', 'e', 'i', 'o', 'u', 'y', 'ä', 'ö']
markov = MarkovText.from_file('kalevala_and_others_markov.json')
fallback_markov = MarkovText.from_file('fallback_markov.json')
def count_syllables(verse):
''' Count syllables in a verse '''
tokenizer = RegexpTokenizer(r'\w+')
n_syllables = 0
for word in tokenizer.tokenize(verse):
try:
n_syllables += len(finmeter.hyphenate(word).split("-"))
except Exception as e:
pass
# print(e)
# print(verse)
# print("Error täällä: count_syllables")
import sys, json, gzip
import lwvlib
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
def check_nearest_words(vector, vector_i, wv):
wv_vectors = wv.vectors
wv_words = wv.words
sims = cosine_similarity(np.array(vector).reshape(1, -1), wv_vectors)[0]
indexes = np.argsort(sims)[::-1]
for index in indexes[:10]:
print(wv_words[index])
print()
#print(vector)
#print(wv.nearest(vector))
if __name__ == "__main__":
wv = lwvlib.load("/home/ginter/w2v/pb34_wf_200_v2.bin", max_rank=400000)
for line in sys.stdin:
vectors = json.loads(line)
for vector_i, vector in enumerate(vectors):
check_nearest_words(vector, vector_i, wv)
ordered = [] #(major,minor)
sents = {} #(major,minor) -> Sentiment()
for line in inp:
line = line.strip().lower()
if not line:
continue
if line.startswith("## "):
minor = line.split()[1]
ordered.append((major, minor))
elif line.startswith("# "):
major = line.split()[1]
else:
words = set(commaRe.split(line))
sents[(major, minor)] = Sentiment(major, minor, words)
return ordered, sents
if __name__ == "__main__":
wv = lwvlib.load("../w2v/pb34_wf_200_v2_skgram.bin", 1000000, 1000000)
ordered, sents = get_sents(sys.stdin)
for s in sents.values():
s.expand(wv, 100)
m = None
for (major, minor) in ordered:
if major != m:
print("#", major)
print()
m = major
print(sents[(major, minor)])
print()
import lwvlib
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='Train')
parser.add_argument('--orig',
required=True,
help='A (.bin) embedding file before training.')
parser.add_argument(
'--trained',
required=True,
help=
'A (.bin) embedding file after training (produced by tools/extract_embeddings).'
)
args = parser.parse_args()
orig_m = lwvlib.load(args.orig)
trained_m = lwvlib.load(args.trained)
df = orig_m.vectors - trained_m.vectors
print(df)
