def test_tfidf_corpus():
from nose.tools import assert_almost_equals
from microtc.textmodel import TextModel
from microtc.weighting import TFIDF
from microtc.utils import Counter
from microtc.utils import tweet_iterator
import os
import numpy as np
fname = join(os.path.dirname(__file__), 'text.json')
tw = list(tweet_iterator(fname))
docs = [x['text'] for x in tw]
text = TextModel(token_list=[-1, 3])
docs = [text.tokenize(d) for d in docs]
counter = Counter()
[counter.update(set(x))for x in docs]
tfidf = TFIDF(docs)
tfidf2 = TFIDF.counter(counter)
assert tfidf.num_terms == tfidf2.num_terms
assert tfidf._ndocs == tfidf2._ndocs
for k in tfidf2.word2id.keys():
assert k in tfidf2.word2id
for k, v in tfidf.word2id.items():
id2 = tfidf2.word2id[k]
v = tfidf.wordWeight[v]
v2 = tfidf2.wordWeight[id2]
print(v, v2, k)
assert_almost_equals(v, v2)
def test_lang():
from microtc.textmodel import TextModel
text = [
"Hi :) :P XD",
"excelente dia xc",
"el alma de la fiesta XD"
]
model = TextModel(text, **{
"del_dup1": True,
"emo_option": "group",
"lc": True,
"num_option": "group",
"strip_diac": False,
"token_list": [
(2, 1),
(2, 2),
-1,
# 5,
],
"url_option": "group",
"usr_option": "group",
})
text = "El alma de la fiesta :) conociendo la maquinaria @user bebiendo nunca manches que onda"
a = model.tokenize(text)
b = ['el~de', 'alma~la', 'de~fiesta', 'la~_pos', 'fiesta~conociendo', '_pos~la', 'conociendo~maquinaria', 'la~_usr', 'maquinaria~bebiendo', '_usr~nunca',
'bebiendo~manches', 'nunca~que', 'manches~onda', 'el~la', 'alma~fiesta', 'de~_pos', 'la~conociendo', 'fiesta~la', '_pos~maquinaria', 'conociendo~_usr',
'la~bebiendo', 'maquinaria~nunca', '_usr~manches', 'bebiendo~que', 'nunca~onda', 'el', 'alma', 'de', 'la', 'fiesta', '_pos',
'conociendo', 'la', 'maquinaria', '_usr', 'bebiendo', 'nunca', 'manches', 'que', 'onda']
print(text)
assert a == b, "got: {0}, expected: {1}".format(a, b)
def fit_predict(self, X):
#fit
#Corpus
textmodel = TextModel().fit(X) #Modelo de texto
X = textmodel.transform(X)
nmf = NMF(n_components=6, max_iter=500).fit(X) #reduccion de dimension
nmf_features = nmf.transform(X)
#Topics
topics = self.topics
X_topics = textmodel.transform(topics) #al modelo de texto
nmf_topics = nmf.transform(X_topics) #reduccion de dimension
K = cosine_similarity(nmf_features, nmf_topics) #similaridades
n, p = K.shape
#predict
prob = K
cat = self.categories
labels = []
for i in range(prob.shape[0]):
xx = prob[i, :]
indx = self.maxin(xx)
lab = cat[indx]
labels.append(lab)
return np.array(labels)
def test_textmodel_num_terms():
from microtc.textmodel import TextModel
from microtc.utils import tweet_iterator
import os
fname = os.path.dirname(__file__) + '/text.json'
tw = list(tweet_iterator(fname))
text = TextModel(token_list=[-2, -1, 3, 4]).fit(tw)
assert text.num_terms is not None
text.transform(["buenos"])
print(text.num_terms)
assert text.num_terms == text.model.num_terms
def test_doc2weight():
from microtc.textmodel import TextModel
from microtc.weighting import TFIDF
from microtc.utils import tweet_iterator
import os
fname = join(os.path.dirname(__file__), 'text.json')
tw = list(tweet_iterator(fname))
docs = [x['text'] for x in tw]
text = TextModel(docs, token_list=[-1, 3])
# print(text['buenos dias'])
docs = [text.tokenize(d) for d in docs]
sp = TFIDF(docs)
assert len(sp.doc2weight(text.tokenize('odio odio los los'))) == 3
def test_space():
from microtc.textmodel import TextModel
from microtc.weighting import TFIDF
from microtc.utils import tweet_iterator
import os
fname = os.path.dirname(__file__) + '/text.json'
tw = list(tweet_iterator(fname))
docs = [x['text'] for x in tw]
text = TextModel(docs, token_list=[-1, 3])
# print(text['buenos dias'])
docs = [text.tokenize(d) for d in docs]
sp = TFIDF(docs)
assert len(sp.wordWeight) == len(sp._w2id)
def test_textmodel_compute_tokens():
from microtc.textmodel import TextModel
from microtc.utils import tweet_iterator
import os
fname = os.path.dirname(__file__) + '/text.json'
tw = list(tweet_iterator(fname))
tm = TextModel(token_list=[-2, -1])
text = tm.text_transformations(tw[0]['text'])
L = tm.compute_tokens(text)
assert len(L) == 2
r = []
[r.__iadd__(x) for x in L]
for a, b in zip(tm.tokenize(tw[0]), r):
assert a == b
def test_textmodel_transform_tonp():
from microtc.textmodel import TextModel
from microtc.utils import tweet_iterator
from sklearn.svm import LinearSVC
from sklearn.preprocessing import LabelEncoder
import os
fname = os.path.dirname(__file__) + '/text.json'
tw = list(tweet_iterator(fname))
text = TextModel().fit(tw)
X = text.transform(tw)
le = LabelEncoder().fit([x['klass'] for x in tw])
y = le.transform([x['klass'] for x in tw])
m = LinearSVC().fit(text.tonp(X), y)
assert len(m.predict(text.tonp(X))) == len(y)
def test_textmodel_token_min_filter():
from microtc.textmodel import TextModel
from microtc.utils import tweet_iterator
import os
fname = os.path.dirname(__file__) + '/text.json'
tw = list(tweet_iterator(fname))
text = TextModel(tw, token_min_filter=1, token_list=[-2, -1, 3, 4])
print(len(text.model._w2id), 'hh', text.token_min_filter,
text.token_max_filter)
assert len(text.model._w2id) == 28
text = TextModel(tw, token_min_filter=0.01, token_list=[-2, -1, 3, 4])
print(len(text.model._w2id))
assert len(text.model._w2id) == 28
text = TextModel(tw, token_min_filter=1)
def test_textmodel():
from microtc.textmodel import TextModel
from microtc.utils import tweet_iterator
import os
fname = os.path.dirname(__file__) + '/text.json'
tw = list(tweet_iterator(fname))
text = TextModel([x['text'] for x in tw])
# print(text.tokenize("hola amiguitos gracias por venir :) http://hello.com @chanfle"))
# assert False
assert isinstance(text[tw[0]['text']], list)
assert len(text[tw[0]]) == 3
text = TextModel(token_list=[3]).fit(tw)
print(text.model.word2id)
for k, _ in text.model.word2id.items():
assert len(k) == 5
def __call__(self, conf_code):
conf, code = conf_code
st = time()
predY = np.zeros(len(self.y))
# X = np.array(self.X)
for train, test in self.kfolds.split(self.X, self.y):
# A = X[train]
A = [self.X[i] for i in train]
if len(self.Xstatic) > 0:
A.extend(self.Xstatic)
trainY = self.y[train]
if len(self.ystatic) > 0:
trainY = np.hstack((trainY, self.ystatic))
textmodel = TextModel(A, **conf)
# textmodel = TextModel([X[i] for i in train], **conf)
trainX = [textmodel[x] for x in A]
c = self.create_classifier()
try:
c.fit(trainX, trainY)
except ValueError:
conf["_error"] = "this configuration produces an empty matrix"
conf["_score"] = 0.0
return conf
testX = [textmodel[self.X[i]] for i in test]
predY[test] = c.predict(testX)
self.compute_score(conf, predY)
conf['_time'] = (time() - st) / self.nfolds
return conf
def test_getitem():
from microtc.textmodel import TextModel
from microtc.weighting import TFIDF
from microtc.utils import tweet_iterator
import os
fname = join(os.path.dirname(__file__), 'text.json')
tw = list(tweet_iterator(fname))
docs = [x['text'] for x in tw]
text = TextModel(docs, token_list=[-1, 3])
# print(text['buenos dias'])
docs = [text.tokenize(d) for d in docs]
sp = TFIDF(docs)
tok = text.tokenize('buenos dias')
bow = sp.doc2weight(tok)
ids = bow[0]
assert len(ids) == len(sp[tok])
def __call__(self, conf_code):
conf, code = conf_code
st = time()
predY = np.zeros(len(self.y))
# X = np.array(self.X)
for train, test in self.kfolds:
# A = X[train]
A = [self.X[i] for i in train]
if len(self.Xstatic) > 0:
A.extend(self.Xstatic)
textmodel = TextModel(A, **conf)
# textmodel = TextModel([X[i] for i in train], **conf)
trainX = [textmodel[x] for x in A]
trainY = self.y[train]
if len(self.ystatic) > 0:
trainY = np.hstack((trainY, self.ystatic))
c = self.create_classifier()
c.fit(trainX, trainY)
testX = [textmodel[self.X[i]] for i in test]
predY[test] = c.predict(testX)
self.compute_score(conf, predY)
conf['_time'] = (time() - st) / self.nfolds
return conf
def test_textmodel_weighting_key():
from microtc.textmodel import TextModel
from microtc.utils import tweet_iterator
import os
fname = os.path.dirname(__file__) + '/text.json'
tw = list(tweet_iterator(fname))
for w in ['tfidf', 'tf', 'entropy']:
TextModel(token_list=[-2, -1], weighting=w).fit(tw)
def test_textmodel_save_load():
import os
from microtc.textmodel import TextModel
from microtc.utils import tweet_iterator, save_model, load_model
fname = os.path.dirname(__file__) + '/text.json'
tw = list(tweet_iterator(fname))
tm = TextModel().fit(tw)
save_model(tm, 't.model')
assert isinstance(load_model('t.model'), TextModel)
os.unlink('t.model')
def test_entropy():
from microtc.textmodel import TextModel
from microtc.weighting import Entropy, TFIDF
from microtc.utils import tweet_iterator
import os
fname = join(os.path.dirname(__file__), 'text.json')
tw = list(tweet_iterator(fname))
docs = [x['text'] for x in tw]
text = TextModel(token_list=[-1, 3])
# print(text['buenos dias'])
docs = [text.tokenize(d) for d in docs]
sp = Entropy(docs, X=tw)
print(sp.wordWeight)
tfidf = TFIDF(docs)
for k in sp.wordWeight.keys():
if sp.wordWeight[k] != tfidf.wordWeight[k]:
return
# print(sp.w)
assert False
def __call__(self, conf_code):
conf, code = conf_code
st = time()
textmodel = TextModel(self.train_corpus, **conf)
train_X = [textmodel[doc] for doc in self.train_corpus]
c = self.create_classifier()
c.fit(train_X, self.train_y)
test_X = [textmodel[doc] for doc in self.test_corpus]
pred_y = c.predict(test_X)
self.compute_score(conf, pred_y)
conf['_time'] = (time() - st)
return conf
def params(cls):
"""
Parameters
>>> from b4msa.textmodel import TextModel
>>> TextModel.params()
['docs', 'threshold', 'lang', 'negation', 'stemming', 'stopwords', 'kwargs', 'docs', 'text', 'num_option', 'usr_option', 'url_option', 'emo_option', 'hashtag_option', 'ent_option', 'lc', 'del_dup', 'del_punc', 'del_diac', 'token_list', 'token_min_filter', 'token_max_filter', 'select_ent', 'select_suff', 'select_conn', 'weighting']
"""
import inspect
r = mTCTextModel.params()
sig = inspect.signature(cls)
params = sig.parameters.keys()
return list(params) + list(r)
def params(cls):
"""
Parameters
>>> from b4msa.textmodel import TextModel
>>> TextModel.params()
['docs', 'threshold', 'lang', 'negation', 'stemming', 'stopwords', 'kwargs', 'docs', 'text', 'num_option', 'usr_option', 'url_option', 'emo_option', 'hashtag_option', 'ent_option', 'lc', 'del_dup', 'del_punc', 'del_diac', 'token_list', 'token_min_filter', 'token_max_filter', 'select_ent', 'select_suff', 'select_conn', 'weighting']
"""
import inspect
r = mTCTextModel.params()
sig = inspect.signature(cls)
params = sig.parameters.keys()
return list(params) + list(r)
def test_tfidf_corpus2():
from nose.tools import assert_almost_equals
from microtc.textmodel import TextModel
from microtc.weighting import TFIDF
from microtc.utils import Counter
from microtc.utils import tweet_iterator
import os
import numpy as np
fname = join(os.path.dirname(__file__), 'text.json')
tw = list(tweet_iterator(fname))
docs = [x['text'] for x in tw]
tm = TextModel(token_list=[-1, 3])
docs = [tm.tokenize(d) for d in docs]
counter = Counter()
[counter.update(set(x))for x in docs]
tfidf = TFIDF(docs, token_min_filter=1)
tfidf2 = TFIDF.counter(counter, token_min_filter=1)
id2w2 = {v: k for k, v in tfidf2.word2id.items()}
for text in docs:
tokens = tm.tokenize(text)
fm = {k: v for k, v in tfidf[tokens]}
for k, v in tfidf2[tokens]:
assert_almost_equals(fm[tfidf.word2id[id2w2[k]]], v)
def __call__(self, conf_code):
conf, code = conf_code
st = time()
model_klass = os.environ.get("TEXTMODEL_KLASSES", None)
if model_klass:
model_klass = self.le.transform(model_klass.split(','))
_train = [
self.train_corpus[i] for i in len(self.train_corpus)
if self.train_y[i] in model_klass
]
textmodel = TextModel(_train, **conf)
if conf['dist_vector'] != OPTION_NONE:
_train_y = [
self.train_y[i] for i in len(self.train_corpus)
if self.train_y[i] in model_klass
]
textmodel = DistTextModel(textmodel, _train, _train_y,
self.le.classes_.shape[0],
conf['dist_vector'])
else:
textmodel = TextModel(self.train_corpus, **conf)
if conf['dist_vector'] != OPTION_NONE:
textmodel = DistTextModel(textmodel, self.train_corpus,
self.train_y,
self.le.classes_.shape[0],
conf['dist_vector'])
train_X = [textmodel[doc] for doc in self.train_corpus]
c = self.create_classifier()
c.fit(train_X, self.train_y)
test_X = [textmodel[doc] for doc in self.test_corpus]
pred_y = c.predict(test_X)
self.compute_score(conf, pred_y)
conf['_time'] = (time() - st)
return conf
def test_textmodel_entropy():
from microtc.textmodel import TextModel
from microtc.utils import tweet_iterator
import os
fname = os.path.dirname(__file__) + '/text.json'
tw = list(tweet_iterator(fname))
text = TextModel(tw,
weighting='microtc.weighting.Entropy',
token_list=[-1, 3])
# print(text.tokenize("hola amiguitos gracias por venir :) http://hello.com @chanfle"))
# assert False
assert isinstance(text[tw[0]['text']], list)
_ = text[tw[0]]
print(_)
for k, v in _:
assert text.model.wordWeight[k] == v
def test_params():
import os
import itertools
from microtc.params import BASIC_OPTIONS
from microtc.textmodel import TextModel
from microtc.utils import tweet_iterator
params = dict(strip_diac=[True, False], usr_option=BASIC_OPTIONS,
url_option=BASIC_OPTIONS)
params = sorted(params.items())
fname = os.path.dirname(__file__) + '/text.json'
tw = [x for x in tweet_iterator(fname)]
text = [x['text'] for x in tw]
for x in itertools.product(*[x[1] for x in params]):
args = dict(zip([x[0] for x in params], x))
ins = TextModel(text, **args)
assert isinstance(ins[text[0]], list)
def test_predict_from_file():
from microtc.wrappers import ClassifierWrapper
from microtc.textmodel import TextModel
from microtc.utils import read_data_labels
from sklearn.preprocessing import LabelEncoder
import os
fname = os.path.dirname(__file__) + '/text.json'
corpus, labels = read_data_labels(fname)
t = TextModel(corpus)
le = LabelEncoder()
le.fit(labels)
y = le.transform(labels)
c = ClassifierWrapper()
X = [t[x] for x in corpus]
c.fit(X, y)
hy = le.inverse_transform(c.predict(X))
for i in hy:
assert i in ['POS', 'NEU', 'NEG']
def __call__(self, conf_code):
conf, code = conf_code
st = time()
textmodel = TextModel(self.train_corpus, **conf)
train_X = [textmodel[doc] for doc in self.train_corpus]
c = self.create_classifier()
# c.fit(train_X, self.train_y)
try:
c.fit(train_X, self.train_y)
except ValueError:
conf["_error"] = "this configuration produces an empty matrix"
conf["_score"] = 0.0
return conf
test_X = [textmodel[doc] for doc in self.test_corpus]
pred_y = c.predict(test_X)
self.compute_score(conf, pred_y)
conf['_time'] = (time() - st)
return conf
from microtc.textmodel import TextModel
from microtc.params import OPTION_NONE
from glob import glob
from collections import Counter
import numpy as np
from scipy.optimize import minimize
from matplotlib import pylab as plt
from nltk.stem.porter import PorterStemmer
from typing import Callable, Iterable
tm = TextModel(num_option=OPTION_NONE,
usr_option=OPTION_NONE,
url_option=OPTION_NONE,
emo_option=OPTION_NONE,
hashtag_option=OPTION_NONE,
ent_option=OPTION_NONE,
lc=False,
del_dup=False,
del_punc=False,
del_diac=False,
token_list=[-1])
tm.tokenize("Hello good morning")
# Count the number of words
def N_tokens_types(fname: str, counter: Counter, tm: Callable[[str],
Iterable[str]]):
txt = open(fname).read()
tokens = tm(txt)
def __create_models(self):
models = []
models_fit = []
#for _params in self.model_params:
_params = {}
for k, v in self.params.items():
if k.startswith('_'):
continue
_params[k] = v
self.textModels = dict(
mtc=TextModel(_params).fit(self.train),
#charEmb=DocumentPoolEmbeddings([CharacterEmbeddings()]),
#charLangEmb=DocumentPoolEmbeddings([CharacterEmbeddings(),BytePairEmbeddings(self.lang)]),
##charMultiEmb=DocumentPoolEmbeddings([CharacterEmbeddings(),BytePairEmbeddings('multi')]),
langEmb=DocumentPoolEmbeddings([BytePairEmbeddings(self.lang)]),
charLangMultiEmb=DocumentPoolEmbeddings([
CharacterEmbeddings(),
BytePairEmbeddings(self.lang),
BytePairEmbeddings('multi')
]),
langMultiEmb=DocumentPoolEmbeddings(
[BytePairEmbeddings(self.lang),
BytePairEmbeddings('multi')]),
bytePairEMB=DocumentPoolEmbeddings([BytePairEmbeddings('multi')]),
#flairEmbF=DocumentPoolEmbeddings([FlairEmbeddings('multi-forward')]),
#flairEmbB=DocumentPoolEmbeddings([FlairEmbeddings('multi-backward')]),
#bertEMB=DocumentPoolEmbeddings([TransformerWordEmbeddings('bert-base-uncased', layers='-1')])
)
for km, tmodel in self.textModels.items():
models.append({'name': km})
models_fit.append({'name': km})
if km == 'mtc':
xt = tmodel.transform(self.train)
xv = tmodel.transform(self.validation)
X = tmodel.transform(self.data)
else:
sentences_train = [Sentence(txt) for txt in self.train]
tmodel.embed(sentences_train)
xt = np.array([
e.get_embedding().cpu().detach().numpy()
for e in sentences_train
])
sentences_val = [Sentence(txt) for txt in self.validation]
tmodel.embed(sentences_val)
xv = np.array([
e.get_embedding().cpu().detach().numpy()
for e in sentences_val
])
sentences = [Sentence(txt) for txt in self.data]
tmodel.embed(sentences)
X = np.array([
e.get_embedding().cpu().detach().numpy() for e in sentences
])
models[-1]['xv'] = xv
models[-1]['xt'] = xt
models_fit[-1]['xt'] = X
#max_iter=5000
#if km=='mtc': max_iter=1000
#if km=='langMulti': max_iter=5000
#self.models[-1]['clf']=LinearSVC(max_iter=max_iter).fit(xt,self.yt)
#yp=self.models[-1]['clf'].decision_function(xv)
#scaler=Normalizer().fit(yp)
#self.models[-1]['macroF1']=f1_score(self.yv,np.argmax(scaler.transform(yp),axis=1),average='weighted')
#self.models[-1]['weightedF1']=f1_score(self.yv,np.argmax(scaler.transform(yp),axis=1),average='weighted')
#self.models[-1]['score']=f1_score(self.yv,np.argmax(yp,axis=1),average='weighted')
#self.models[-1]['probas']=scaler.transform(yp)
### Fit model with all avaliable data
#self.models_fit[-1]['clf']=LinearSVC(max_iter=max_iter).fit(X,self.y)
print('Fitting Ensemble')
#self.models = Parallel(n_jobs=5)(delayed(self._train_model)(md) for md in models)
#self.models_fit = Parallel(n_jobs=5)(delayed(self._train_model)(md) for md in models_fit)
self.models, self.models_fit = [], []
for md, mdf in zip(models, models_fit):
self.models.append(self._train_model(
md)) # = [self._train_model(md) for md in models]
self.models_fit.append(self._train_model(md))
from EvoMSA.model import Bernoulli from EvoMSA.utils import LabelEncoder, bootstrap_confidence_interval from microtc.textmodel import TextModel from microtc.utils import tweet_iterator from os.path import join, dirname from sklearn.model_selection import train_test_split import numpy as np from sklearn.model_selection import StratifiedKFold tweets = join(dirname(base.__file__), 'tests', 'tweets.json') D = list(tweet_iterator(tweets)) y = [x['klass'] for x in D] le = LabelEncoder().fit(y) y = le.transform(y) tm = TextModel(token_list=[-1]).fit(D) X = tm.transform(D) m = Bernoulli().fit(X, y) print((y == m.predict(X)).mean()) # 0.724 _ = train_test_split(D, y, test_size=0.2) Xtrain, Xtest, ytrain, ytest = _ tm = TextModel(token_list=[-1]).fit(Xtrain) m = Bernoulli().fit(tm.transform(Xtrain), ytrain) hy = m.predict(tm.transform(Xtest)) print((ytest == hy).mean()) # 0.55 folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=0) hy = np.empty_like(y)
