class CRFModel(object):
def __init__(self,
algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_possible_transitions=False):
self.model = CRF(algorithm=algorithm,
c1=c1,
c2=c2,
max_iterations=max_iterations,
all_possible_transitions=all_possible_transitions)
def train(self, sentences, tag_lists, tagged=False):
if tagged:
features = [sent2features_tagged(s) for s in sentences]
else:
features = [sent2features(s) for s in sentences]
self.model.fit(features, tag_lists)
def test(self, sentences, tagged=False):
if tagged:
features = [sent2features_tagged(s) for s in sentences]
pred_tag_lists = self.model.predict(features)
else:
features = [sent2features(s) for s in sentences]
pred_tag_lists = self.model.predict(features)
return pred_tag_lists
def crf_tag():
brown_tagged_sents = brown.tagged_sents(categories='news')
#print(brown_tagged_sents[0])
train_len = int(len(brown_tagged_sents) * 0.9)
training_sentences = brown_tagged_sents[:train_len]
test_sentences = brown_tagged_sents[train_len:]
X_train, y_train = transform_to_dataset(training_sentences)
X_test, y_test = transform_to_dataset(test_sentences)
#print(len(X_train))
#print(len(X_test))
print(X_train[0])
print(y_train[0])
model = CRF()
model.fit(X_train, y_train)
raw_sent = ['I', 'am', 'a', 'student']
sent_feat = [
feature_extract(raw_sent, index) for index in range(len(raw_sent))
]
print(list(zip(raw_sent, model.predict([sent_feat])[0])))
y_pred = model.predict(X_test)
print(metrics.flat_accuracy_score(y_test, y_pred))
class CRFNER(object):
""" A class to get reviews for products on Amazon """
def __init__(self, gazetteer, fraction=0.7):
self.gazateer = gazetteer
self.fraction = fraction
def train(self, documents):
self.data = ner_processing.NERFormatter(self.gazateer, documents)
d_train, d_test = ner_processing.train_test_NER(self.data)
self.X_train, self.X_test, self.y_train, self.y_test = crf_processing.feature_extraction(
d_train, d_test)
self.model = CRF(algorithm='lbfgs',
c1=0.31,
c2=0.02,
max_iterations=100,
all_possible_transitions=True)
self.model.fit(self.X_train, self.y_train)
def predict(self, sentence):
"""Transforms a single sentence (for NER testing) into a CRF-suite format"""
sentence_split = nltk.word_tokenize(sentence)
n_words = [0] * len(sentence_split)
df_pred = pd.DataFrame({
'word':
sentence_split,
'sentence_no':
n_words,
'category':
n_words,
'POS': [x[-1] for x in nltk.pos_tag(sentence_split)],
})
getter = crf_processing.SentenceGetter(df_pred)
sent = getter.get_next()
sentences = getter.sentences
self.X = [crf_processing.sent2features(s) for s in sentences]
return self.model.predict(self.X)
def report(self):
labels = list(self.model.classes_)
y_pred = self.model.predict(self.X_test)
print('F1 score {}'.format(
metrics.flat_f1_score(self.y_test,
y_pred,
average='weighted',
labels=labels)))
sorted_labels = sorted(labels, key=lambda name: (name[1:], name[0]))
print(
metrics.flat_classification_report(self.y_test,
y_pred,
labels=sorted_labels,
digits=3))
class CRFNerModel(object):
def __init__(self, is_save=False):
self.crf = CRF(algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_possible_transitions=True)
self.is_save = is_save
self.save_model = "crf.model"
def fit(self, train_x, train_y):
self.crf.fit(train_x, train_y)
if self.is_save:
self.dump_model()
def predict(self, input_x):
input_x = list(input_x)
input_feature = [sent2features(input_x)]
return self.crf.predict(input_feature)
def dump_model(self):
model_data = pickle.dumps(self.crf)
with open(self.save_model, "wb") as f:
f.write(model_data)
def load_model(self):
with open(self.save_model, "rb") as f:
model_data = f.read()
self.crf = pickle.loads(model_data)
def predict_list(self, input_list):
return self.crf.predict(input_list)
def extract_ner(self, input_x):
extract_ner = []
res = self.predict(input_x)
start = None
label = None
for i, x in enumerate(res[0]):
if x == "O":
if start is not None:
extract_ner.append((start, i, label, input_x[start:i]))
start = None
label = None
else:
xindex, xlabel = x.split("-")
if xindex == "B":
if start is not None:
extract_ner.append((start, i, label, input_x[start:i]))
start = i
label = xlabel
else:
if label != xlabel:
start = None
label = None
return extract_ner
def test_accuracy(training_dir_path, test_dir_path, is_Convo_label):
global labels, type_labels
curr_labels = {}
if is_Convo_label:
curr_labels = labels
else:
curr_labels = type_labels
# Get the training Data
x_train, y_train = get_conversation_data(training_dir_path, True, is_Convo_label)
print("Loaded Training Data")
# Get Testing Data
x_test, y_test = get_conversation_data(test_dir_path, False, is_Convo_label)
print("Loaded Testing Data")
crf = CRF(algorithm='l2sgd',
c2=0.001,
max_iterations=100,
all_possible_transitions=False)
crf.fit(x_train, y_train)
y_prediction = crf.predict(x_test)
predictions = np.array([curr_labels[tag] for row in y_prediction for tag in row])
truths = np.array([curr_labels[tag] for row in y_test for tag in row])
# Print Metrics
if is_Convo_label:
print(classification_report(
truths, predictions,
target_names=['REQ', 'ANSW', 'COMPLIM', 'ANNOU', 'THK', 'RESPOS', 'APOL', 'RCPT']))
# Get test accuracy
test_ = str(accuracy_score(truths, predictions))
# for w in sorted(crf.transition_features_, key=crf.transition_features_.get, reverse=True):
# print(str(w) + ":" + str(crf.transition_features_[w]))
# Testing on training data without label
x_test, y_test = get_conversation_data(training_dir_path, False, is_Convo_label)
y_prediction = crf.predict(x_test)
predictions = np.array([curr_labels[tag] for row in y_prediction for tag in row])
truths = np.array([curr_labels[tag] for row in y_test for tag in row])
sf = crf.state_features_
print(type(sf))
# Get train accuracy
train_ = str(accuracy_score(truths, predictions))
return test_, train_
def test_crf(xseq, yseq, algorithm):
crf = CRF(algorithm)
crf.fit([xseq], [yseq])
y_pred = crf.predict([xseq])
if algorithm != "ap": # Averaged Perceptron is regularized too much
assert y_pred == [yseq]
def train(file_path: str):
"""
Training CRF model from a given ``file_path``
"""
addresses = []
with jsonlines.open(file_path) as reader:
for obj in reader:
addresses.append(obj)
addresses_train, addresses_val = train_test_split(addresses,
test_size=0.25,
random_state=42)
X_train, y_train = addresses_to_features(addresses_train)
X_val, y_val = addresses_to_features(addresses_val)
crf = CRF(c1=0.2,
c2=0.2,
max_iterations=100,
all_possible_transitions=True)
crf.fit(X_train, y_train)
# prediction score on validation set
y_pred = crf.predict(X_val)
metrics.flat_f1_score(y_val,
y_pred,
average='weighted',
labels=[l for l in LABELS if l != 'O'])
return crf
def parameter_tuning(args, dataset):
c1s = experiment_util.get_param_list(args.c1)
c2s = experiment_util.get_param_list(args.c2)
best_valid_f1_score = -np.inf
best_c1 = -np.inf
best_c2 = -np.inf
best_model = None
for c1 in c1s:
for c2 in c2s:
crf = CRF(algorithm='lbfgs',
c1=c1,
c2=c2,
max_iterations=500,
all_possible_transitions=True,
verbose=args.debug)
crf.fit(dataset.training.list_of_feature_dicts,
dataset.training.list_of_labels)
preds = crf.predict(dataset.validation.list_of_feature_dicts)
valid_f1_score = metrics.flat_f1_score(
dataset.validation.list_of_labels, preds, average='micro')
if valid_f1_score > best_valid_f1_score:
best_valid_f1_score = valid_f1_score
best_c1 = c1
best_c2 = c2
best_model = crf
print('Best validation F1 score:', best_valid_f1_score, 'Best c1:',
best_c1, 'Best c2:', best_c2)
return best_model
def train1(self, data, y, tag):
#tagged_data = a.fit(a.tag(),y,tag)
# Features as conditional random field accepts
feaobj = Features(data, self.num_features)
x_train, y_train = feaobj.get
print("labelled data")
# Using conditional random field as features
crf = CRF(algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_possible_transitions=False)
print(crf)
crf.fit(x_train, y_train)
# Saving the model which is trained
filename = 'finalized_model.sav'
pickle.dump(crf, open(filename, 'wb'))
# Prediction on train
pred = crf.predict(x_train)
# printing classification report and Accuracy
print('\n \n Prediction On Trained Data:\n \n',
flat_classification_report(y_train, pred))
print('Accuracy:', flat_accuracy_score(y_train, pred))
def train(train_file, test_file, min_freq, model_file):
'''Train a CRF tagger based'''
# Read in initial training data
conll_data_train = read_conll_data(train_file)
train_sents = [[line[0] for line in doc] for doc in conll_data_train]
train_labels = [[line[2] for line in doc] for doc in conll_data_train]
# Featurize and create instance from list of sentences
feat_sent_train = build_dataset(train_sents)
print("Training on {0} inst".format(len(feat_sent_train)))
# Train and test loop for parameter settings
# Create and train CRF model
# For different parameter options, see:
# https://sklearn-crfsuite.readthedocs.io/en/latest/_modules/sklearn_crfsuite/estimator.html
model = CRF(min_freq=min_freq)
model.fit(feat_sent_train, train_labels)
# Test the model on held out test set if wanted
if args.test_file:
conll_data_test = read_conll_data(test_file)
test_sents = [[line[0] for line in doc] for doc in conll_data_test]
test_labels = [[line[2] for line in doc] for doc in conll_data_test]
feat_sent_test = build_dataset(test_sents)
# Predicting and printing accuracy
pred = model.predict(feat_sent_test)
acc = metrics.flat_accuracy_score(test_labels, pred)
print("Accuracy: {0}%".format(float(round(acc, 3)) * 100))
# Save model to disk if wanted
if args.model:
print("Saving model to {0}".format(model_file))
joblib.dump(model, model_file)
def main(path_train, path_test, path_pred, path_crf, take_first, dev_size):
print("loading train corpus..")
_, X_raw, y = load_corpus(path_train, take_first=take_first)
print("extracting features from train corpus..")
fe = TaggerFeatureExtractor()
X = fe.fit_transform(tqdm(X_raw))
print("training..")
crf = CRF(algorithm='ap', verbose=True, max_iterations=10)
if dev_size:
X, X_dev, y, y_dev = train_test_split(X, y, test_size=dev_size)
else:
X_dev, y_dev = None, None
crf.fit(X, y, X_dev, y_dev)
print("saving..")
joblib.dump({'fe': fe, 'crf': crf}, path_crf, compress=2)
print("loading test corpus..")
corpus, X_test_raw, y_test = load_corpus(path_test)
print("extracting features from test corpus..")
X_test = fe.transform(X_test_raw)
print("predicting..")
y_pred = crf.predict(tqdm(X_test))
print("saving results..")
sents_pred = y_pred_to_sents_pred(corpus, y_pred)
conll.write_sents(sents_pred, path_pred)
def train_crf(labelled_files, save=True, eval=True):
x, y, _ = format_labelled_data(labelled_files)
crf = CRF(algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_possible_transitions=False)
if eval:
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.1,
random_state=42)
crf.fit(x_train, y_train)
pred = crf.predict(x_test)
report = classification_report(y_test, pred)
print("Test Results:\n")
line(60)
print(report)
line(60)
log_results(y_test, pred)
line(60)
else:
crf.fit(x, y)
if save:
save_crf(crf)
return crf
class CRFBased:
'''CRF based information retrieval.
The model is similar to the Default model
used in homework 2 and 3'''
def __init__(self, load, n_train, n_test):
self.load = load
self.crf = CRF(algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_possible_transitions=False)
self.n_train = int(n_train)
self.n_test = int(n_test)
def load_data(self):
X_train, y_train, X_test, y_test = prepare_crf_dataset(
self.load, self.n_train, self.n_test)
return X_train, y_train, X_test, y_test
def fit(self, X, y):
self.crf.fit(X, y)
self.labels = list(self.crf.classes_)
self.labels.remove('O')
def predict(self, X): #dataset = Train or Test
pred = self.crf.predict(X)
return pred
def evaluate(self, y_true, y_pred):
print("Final Scores for CRF Based Modes:")
print(
flat_classification_report(y_pred=y_pred,
y_true=y_true,
labels=self.labels))
def train(self, model_name, tagged_sentences):
# Split the dataset for training and testing
cutoff = int(.75 * len(tagged_sentences))
training_sentences = tagged_sentences[:cutoff]
test_sentences = tagged_sentences[cutoff:]
X_train, y_train = transform_to_dataset(training_sentences)
X_test, y_test = transform_to_dataset(test_sentences)
print(len(X_train))
print(len(X_test))
print("Training Started........")
print("it will take time according to your dataset size..")
model = CRF()
model.fit(X_train, y_train)
print("Training Finished!")
print("Evaluating with Test Data...")
y_pred = model.predict(X_test)
print("Accuracy is: ")
print(metrics.flat_accuracy_score(y_test, y_pred))
pickle.dump(model, open(model_name, 'wb'))
print("Model Saved!")
def test_crf(train_file,test_file,model_name=""):
valores = []
data=pandas.read_csv(train_file,sep="\t",header=None)
X_dataset=fromListToTuple(data.iloc[:,[0,1,2,3]].values)
useful_features=[True,True]
X_train,y_train=prepareData([X_dataset],'train',useful_features)
data2=pandas.read_csv(test_file,sep="\t",header=None)
X_test=fromListToTuple(data2.iloc[:,[0,1,2]].values)
X_teste,y_teste=prepareData([X_test],'predict',useful_features)
crf = CRF(
algorithm='lbfgs',
c1=0.0625,
c2=0.5,
max_iterations=100,
all_possible_transitions=False,
all_possible_states=True,
verbose=True
)
crf.fit(X_train, y_train)
if(model_name!=""):
save_model(model_name + ".pickle",crf)
useful_features=[True,True]
data2=pandas.read_csv(test_file,sep="\t",header=None)
X_test=fromListToTuple(data2.iloc[:,[0,1,2]].values)
X_teste,y_teste=prepareData([X_test],'predict',useful_features)
y_pred=crf.predict(X_teste)
resultados = []
for index,elem in enumerate(y_pred[0]):
resultados.append(str(y_pred[0][index]))
return resultados
def write_to_CoNLL(mdl_file_name, sentence2features, test_sentences, write_path):
X_test_local = []
cond_rand_mdl = CRF(algorithm='lbfgs',
c1=0.0001,
c2=0.0001,
max_iterations=100,
all_possible_transitions=False,
model_filename=mdl_file_name)
if mdl_file_name[(len(mdl_file_name) - 1)] == '2':
old_crf = CRF(algorithm='lbfgs',
c1=0.0001,
c2=0.0001,
max_iterations=100,
all_possible_transitions=False,
model_filename=(mdl_file_name[:(len(mdl_file_name) - 1)]) + '1')
X_test_local = [sent2features_second_guess(s, sentence2features, old_crf) for s in test_sentences]
else:
X_test_local = [sentence2features(s) for s in test_sentences]
predictions = cond_rand_mdl.predict(X_test_local)
with open(write_path, 'a') as f:
for i in range(0, len(predictions)):
sent = test_sentences[i]
preds = predictions[i]
for j in range(0, len(sent)):
str_to_write = '{}\t{}\n'.format(sent[j][0], preds[j])
f.write(str_to_write)
f.write('\n')
def train(file_path: str, model_path: str = None):
"""
Training CRF model from a given ``file_path``
"""
addresses = read_file(file_path)
addresses_train, addresses_val = train_test_split(addresses,
test_size=0.25,
random_state=42)
X_train, y_train = addresses_to_features(addresses_train)
X_val, y_val = addresses_to_features(addresses_val)
crf = CRF(c1=0.2,
c2=0.2,
max_iterations=100,
all_possible_transitions=True)
crf.fit(X_train, y_train)
# prediction score on validation set
y_pred = crf.predict(X_val)
f1_score = metrics.flat_f1_score(y_val,
y_pred,
average="weighted",
labels=[l for l in LABELS if l != "O"])
print("Flat F1-Score on validation set = {}".format(f1_score))
if model_path:
joblib.dump(crf, model_path)
print("Save model to {}".format(model_path))
return crf
def test_crf(xseq, yseq, algorithm):
crf = CRF(algorithm=algorithm)
crf.fit([xseq], [yseq])
y_pred = crf.predict([xseq])
if algorithm != 'ap': # Averaged Perceptron is regularized too much
assert y_pred == [yseq]
def test_crf_verbose(xseq, yseq, algorithm, use_dev):
crf = CRF(algorithm=algorithm, verbose=True)
if use_dev:
X_dev, y_dev = [xseq], [yseq]
else:
X_dev, y_dev = None, None
crf.fit(X=[xseq, xseq], y=[yseq, yseq], X_dev=X_dev, y_dev=y_dev)
y_pred = crf.predict([xseq])
if algorithm != 'ap': # Averaged Perceptron is regularized too much
assert y_pred == [yseq]
def test_crf_verbose(xseq, yseq, algorithm, use_dev):
crf = CRF(algorithm, verbose=True)
if use_dev:
X_dev, y_dev = [xseq], [yseq]
else:
X_dev, y_dev = None, None
crf.fit(X=[xseq, xseq], y=[yseq, yseq], X_dev=X_dev, y_dev=y_dev)
y_pred = crf.predict([xseq])
if algorithm != "ap": # Averaged Perceptron is regularized too much
assert y_pred == [yseq]
class CRFmodel(ModelBase):
def __init__(self):
self.model = CRF(algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=500,
all_possible_transitions=True)
def fit(self, X, Y):
if self.debug:
print("training CRF...")
self.model.fit(X, Y)
def predict(self, X):
return self.model.predict(X)
class CRFEvaluateStep(Step):
"""
Step to evaluate testing data against a CRF model,
stored on file
"""
def __init__(self, model_file_path):
self.model_file_path = path.abspath(path.expanduser(model_file_path))
self.model = CRF(algorithm='l2sgd',
c2=0.1,
max_iterations=1000,
all_possible_transitions=True,
model_filename=self.model_file_path)
def run(self, batches: Generator) -> None:
"""
Runs the CRF model, storing to pickle in the end
"""
st = time.time()
x = []
y = []
# For prediction, CRF does not implement batching, so we pass a list
for batch in batches:
b = list(batch)
x.extend(b[0])
y.extend(b[1])
accuracy = self.model.score(x, y)
y_pred = self.model.predict(x)
f1_score = metrics.flat_f1_score(y, y_pred, average='weighted')
accuracy_sentence = metrics.sequence_accuracy_score(y, y_pred)
classification_report = metrics.flat_classification_report(
y, y_pred, labels=self.model.classes_)
print("*" * 80)
print("MODEL EVALUATION")
print("*" * 80)
print("Token-wise accuracy score on Test Data:")
print(round(accuracy, 3))
print("F1 score on Test Data:")
print(round(f1_score, 3))
print(
"Sequence accurancy score (% of sentences scored 100% correctly):")
print(round(accuracy_sentence, 3))
print("Class-wise classification report:")
print(classification_report)
et = time.time()
print(f"Evaluation finished in {round(et-st, 2)} seconds.")
def main():
X, y = load_dataset(DATA_PATH)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
crf = CRF(algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_possible_transitions=False)
crf.fit(X_train, y_train)
y_pred = crf.predict(X_test)
f1_score = flat_f1_score(y_test, y_pred, average='weighted')
print(f1_score)
report = flat_classification_report(y_test, y_pred)
print(report)
def test_model(
model: sklearn_crfsuite.CRF,
test_path: typing.Union[str, Path],
out_file: typing.Optional[typing.TextIO] = None,
):
"""Print an accuracy report for a model to a file"""
try:
import conllu
except ImportError as e:
_LOGGER.fatal("conllu package is required for testing")
_LOGGER.fatal("pip install 'conllu>=4.4'")
raise e
_LOGGER.debug("Loading test file (%s)", test_path)
with open(test_path, "r") as test_file:
test_sents = conllu.parse(test_file.read())
_LOGGER.debug("Getting features for %s test sentence(s)", len(test_sents))
x_test = [sent2features(s) for s in test_sents]
y_test = [sent2labels(s) for s in test_sents]
labels = list(model.classes_)
y_pred = model.predict(x_test)
print(
"F1 score on the test set = {}".format(
metrics.flat_f1_score(y_test,
y_pred,
average="weighted",
labels=labels)),
file=out_file,
)
print(
"Accuracy on the test set = {}".format(
metrics.flat_accuracy_score(y_test, y_pred)),
file=out_file,
)
sorted_labels = sorted(labels, key=lambda name: (name[1:], name[0]))
print(
"Test set classification report: {}".format(
metrics.flat_classification_report(y_test,
y_pred,
labels=sorted_labels,
digits=3)),
file=out_file,
)
class CRFModel(object):
def __init__(self,algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_possible_transitions=False):
self.model=CRF(algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_possible_transitions=False)
def train(self,sentences,tagLists):
features=[utils.sent2feature(sent)for sent in sentences]
self.model.fit(features,tagLists)
def test(self,sentences):
features=[utils.sent2feature(sent)for sent in sentences]
predictLists=self.model.predict(features)
return predictLists
def train_crf_pos(corpus, corpus_name):
# Required corpus structure:
# [[(w1,t1), (w2,t2),...(wn,tn)], [(w1,t1)(w2,t2),...(wm,tm)],...]
#feat_all = {} # common features (baseline set)
#feat_en = {} # extra features for English
#features = {**feat_all, **feat_en}
train_frac = 0.9 # fraction of data for the training set
split_idx = int(train_frac * len(corpus))
# Extract the feautures and separate labels from features
X = [get_crf_features([pair[0] for pair in sent]) for sent in corpus]
y = [[pair[1] for pair in sent] for sent in corpus]
# Create the training and the test sets
X_train = X[:split_idx]
y_train = y[:split_idx]
X_test = X[split_idx:]
y_test = y[split_idx:]
# Create the CRF model
model = CRF(
algorithm='lbfgs', # gradient descent using the L-BFGS method
c1=0.1, # coeff. for L1 regularization
c2=0.1, # coeff. for L2 regularization
max_iterations=100,
)
# Train the model
model.fit(X_train, y_train)
# Save the model
with open(os.path.join('data', 'models', corpus_name + '_crf.pkl'),
'wb') as f:
pickle.dump(model, f, 4)
# Evaluate the model
y_pred = model.predict(X_test)
print("Test accuracy: %.4f" % metrics.flat_accuracy_score(y_test, y_pred))
return model
def test_crf(train_file, test_file, model_name=""):
l1 = [0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5, 1]
l2 = [0.03125, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16]
valores = []
useful_features = [True, True]
data = pandas.read_csv(train_file, sep="\t", header=None)
X_dataset = fromListToTuple(data.iloc[:, [0, 1, 2, 3]].values)
X_teste, y_teste = prepareData([X_dataset], 'test', useful_features)
#X_teste, test = train_test_split(X_dataset, test_size=0.1)
X_teste = pd.DataFrame(X_teste).transpose()
y_teste = pd.DataFrame(y_teste).transpose()
print(X_teste.shape)
print(y_teste.shape)
X_teste_2 = X_teste
y_teste_2 = y_teste
crf = CRF(
algorithm='lbfgs',
#c1=0.0625,
c1=1.0,
#c2=0.5,
c2=1.0,
max_iterations=100,
all_possible_transitions=False,
all_possible_states=True,
verbose=True)
crf.fit(X_teste.values.tolist(), y_teste.values.tolist())
y_pred = crf.predict(X_teste.values.tolist())
labels = list(crf.classes_)
save_model("NP_Final_Macro.pickle", crf)
string = " "
string += str(
metrics.flat_classification_report(y_teste.values.tolist(),
y_pred,
labels=labels,
digits=3))
filename = "Results _.txt"
print("$$$$$$$$$$$")
print(filename)
print("$$$$$$$$$$$")
with open(filename, 'a') as f:
f.write(string)
def train_seq(X_train, Y_train, X_dev, Y_dev):
# crf = CRF(algorithm='lbfgs', c1=0.1, c2=0.1, max_iterations=50, all_possible_states=True)
crf = CRF(algorithm='lbfgs', c1=0.1, c2=10,
max_iterations=50) #, all_possible_states=True)
#Just to fit on training data
crf.fit(X_train, Y_train)
labels = list(crf.classes_)
#testing:
y_pred = crf.predict(X_dev)
sorted_labels = sorted(labels, key=lambda name: (name[1:], name[0]))
print(
metrics.flat_f1_score(Y_dev, y_pred, average='weighted',
labels=labels))
print(
metrics.flat_classification_report(Y_dev,
y_pred,
labels=sorted_labels,
digits=3))
print(metrics.sequence_accuracy_score(Y_dev, y_pred))
get_confusion_matrix(Y_dev, y_pred, labels=sorted_labels)
class CRFModel(object):
def __init__(self,
solver="lbfgs",
c1=0.1,
c2=0.1,
max_iter=100,
all_possible_transitions=False):
self.model = CRF(algorithm=solver,
c1=c1,
c2=c2,
max_iterations=max_iter,
all_possible_transitions=all_possible_transitions)
def train(self, sentences, tag_lists):
features = [sent2features(s) for s in sentences]
self.model.fit(features, tag_lists)
def test(self, sentences):
features = [sent2features(s) for s in sentences]
pred_tag_lists = self.model.predict(features)
return pred_tag_lists
class TrainCRF():
def __init__(self,
char2idx_path,
tag2idx_path,
algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_possible_transitions=False):
# 载入一些字典
# char2idx: 字 转换为 token
self.char2idx = load_dict(char2idx_path)
# tag2idx: 标签转换为 token
self.tag2idx = load_dict(tag2idx_path)
# idx2tag: token转换为标签
self.idx2tag = {v: k for k, v in self.tag2idx.items()}
# 初始化隐状态数量(实体标签数)和观测数量(字数)
self.tag_size = len(self.tag2idx)
self.vocab_size = max([v for _, v in self.char2idx.items()]) + 1
self.model = CRF(algorithm=algorithm,
c1=c1,
c2=c2,
max_iterations=max_iterations,
all_possible_transitions=all_possible_transitions)
def train_crf(self, train_dic_path):
train_dic = load_data(train_dic_path)
features = []
labels = []
for dic in tqdm(train_dic):
features.append(sent2features(dic["text"]))
labels.append(dic["label"])
self.model.fit(features, labels)
def predict(self, setence):
features = [sent2features(s) for s in setence]
pred_tag_lists = self.model.predict(features)
print(pred_tag_lists)
class GenericRetriever(Retriever):
def learn(self, config):
texts = config.getTexts()
sentences = []
for text in texts:
for sent in ET.fromstring(text).findall('sentence'):
stemp = []
for wrd in sent.findall('word'):
stemp.append(
[wrd.text, wrd.attrib['pos'], wrd.attrib['tag']])
sentences.append(stemp)
X = [sent2features(s) for s in sentences]
y = [sent2labels(s) for s in sentences]
self.clf = CRF(algorithm='lbfgs',
c1=10,
c2=0.1,
max_iterations=100,
all_possible_transitions=False)
self.clf.fit(X, y)
def retrieve(self, text):
text = nltk.pos_tag(nltk.word_tokenize(text.lower()))
X = sent2features(text)
resp = []
pred = self.clf.predict([X])[0]
acum = None
for i in range(len(pred)):
if pred[i][0] == 'B':
acum = text[i][0]
elif pred[i][0] == 'I':
acum = acum + " " + text[i][0]
else:
if acum != None: resp.append(acum)
acum = None
return resp
class CRFPredictStep(Step):
"""
Step to get predictions from features using a CRF model, for specific sentences.
"""
def __init__(self, model_file_path):
self.model_file_path = path.abspath(path.expanduser(model_file_path))
self.model = CRF(algorithm='l2sgd',
c2=0.1,
max_iterations=1000,
all_possible_transitions=True,
model_filename=self.model_file_path)
def run(self, batches: Generator) -> None:
"""
Runs the step
"""
features = list(batches)
pred = self.model.predict(features)
for index, feature in enumerate(features):
print(' '.join(map(lambda x: x['word'], feature)), end='')
print(' => ', end='')
print(pred[index])
def test_predict_without_fit(xseq, algorithm):
crf = CRF(algorithm)
with pytest.raises(Exception):
crf.predict([xseq])