def load_tagger(model_path):
"""Loads tagger from a CRFSUITE binary model file.
:param str model_path: path to the binary model file.
"""
tagger = Tagger()
tagger.open(model_path)
return tagger
def _load_tagger(self):
# In pycrfsuite, you have to save the model first, then load it as a tagger
self.model_name = 'model_{}'.format(self.task_obj.unique_id)
file_path = os.path.join(MODELS_DIR, self.task_type, self.model_name)
try:
tagger = Tagger()
tagger.open(file_path)
except Exception as e:
print(e)
logging.getLogger(ERROR_LOGGER).error('Failed to load crf model from the filesystem.', exc_info=True, extra={
'model_name': self.model_name,
'file_path': file_path})
self.tagger = tagger
return self.tagger
def gen(corpus=test, model='m.model', indir=INDIR, outdir=''):
tagger = Tagger()
tagger.open(model)
for doc in corpus.documents:
path = setup_newdir(doc.filepath, olddir=indir, newdir=outdir,
suffix='--', renew=True)
if not path:
continue
mkparentdirs(path)
task = etree.Element(TASK_ROOT)
tags = etree.Element(TAGS_ROOT)
tokens = etree.Element(TOKENS_ROOT)
task.append(tags)
task.append(tokens)
sents = doc.sentences
seqs = doc.sequence_list()
tagged_seqs = [tagger.tag(seq) for seq in seqs]
freq_dict = defaultdict(int)
for (sent, seq, tagged_seq) in zip(sents, seqs, tagged_seqs):
s = etree.Element('s')
for (lex, feat, label) in zip(sent.getchildren(), seq, tagged_seq):
lex_tag = etree.Element(lex.tag, lex.attrib)
lex_tag.text = lex.text
s.append(lex_tag)
if label != 'None':
iso_tag = etree.Element(label)
if label in attribs:
for key in attribs[label]:
iso_tag.attrib[key] = attribs[label][key]
iso_tag.attrib['text'] = lex.text
iso_tag.attrib['id'] = ids[label] + str(freq_dict[label])
lex_tag.attrib['id'] = iso_tag.attrib['id']
freq_dict[label] += 1
tags.append(iso_tag)
tokens.append(s)
s = etree.tostring(task, pretty_print=True)
with open(path, 'w') as f:
print>>f, HEADER
print>>f, s
def test_open_close_labels(model_filename, yseq):
tagger = Tagger()
with pytest.raises(ValueError):
# tagger should be closed, so labels() method should fail here
labels = tagger.labels()
with tagger.open(model_filename):
labels = tagger.labels()
assert set(labels) == set(yseq)
with pytest.raises(ValueError):
# tagger should be closed, so labels() method should fail here
labels = tagger.labels()
class PassageTagger(object):
def __init__(self, do_train=False, trained_model_name="passage_crf_model", algorithm="crf"):
self.trained_model_name = trained_model_name
self.fp = FeatureProcessing()
self.do_train = do_train
self.algorithm = algorithm
if algorithm == "crf":
if do_train:
self.trainer = Trainer()
else:
self.tagger = Tagger()
else:
if do_train:
model = ChainCRF()
self.trainer = FrankWolfeSSVM(model=model)
self.feat_index = {}
self.label_index = {}
else:
self.tagger = pickle.load(open(self.trained_model_name, "rb"))
self.feat_index = pickle.load(open("ssvm_feat_index.pkl", "rb"))
label_index = pickle.load(open("ssvm_label_index.pkl", "rb"))
self.rev_label_index = {i: x for x, i in label_index.items()}
def read_input(self, filename):
str_seqs = []
str_seq = []
feat_seqs = []
feat_seq = []
label_seqs = []
label_seq = []
for line in codecs.open(filename, "r", "utf-8"):
lnstrp = line.strip()
if lnstrp == "":
if len(str_seq) != 0:
str_seqs.append(str_seq)
str_seq = []
feat_seqs.append(feat_seq)
feat_seq = []
label_seqs.append(label_seq)
label_seq = []
else:
if self.do_train:
clause, label = lnstrp.split("\t")
label_seq.append(label)
else:
clause = lnstrp
str_seq.append(clause)
feats = self.fp.get_features(clause)
feat_dict = {}
for f in feats:
if f in feat_dict:
feat_dict[f] += 1
else:
feat_dict[f] = 1
#feat_dict = {i: v for i, v in enumerate(feats)}
feat_seq.append(feat_dict)
if len(str_seq) != 0:
str_seqs.append(str_seq)
str_seq = []
feat_seqs.append(feat_seq)
feat_seq = []
label_seqs.append(label_seq)
label_seq = []
return str_seqs, feat_seqs, label_seqs
def predict(self, feat_seqs):
print >>sys.stderr, "Tagging %d sequences"%len(feat_seqs)
if self.algorithm == "crf":
self.tagger.open(self.trained_model_name)
preds = [self.tagger.tag(ItemSequence(feat_seq)) for feat_seq in feat_seqs]
else:
Xs = []
for fs in feat_seqs:
X = []
for feat_dict in fs:
x = [0] * len(self.feat_index)
for f in feat_dict:
if f in self.feat_index:
x[self.feat_index[f]] = feat_dict[f]
X.append(x)
Xs.append(numpy.asarray(X))
pred_ind_seqs = self.tagger.predict(Xs)
preds = []
for ps in pred_ind_seqs:
pred = []
for pred_ind in ps:
pred.append(self.rev_label_index[pred_ind])
preds.append(pred)
return preds
def train(self, feat_seqs, label_seqs):
print >>sys.stderr, "Training on %d sequences"%len(feat_seqs)
if self.algorithm == "crf":
for feat_seq, label_seq in zip(feat_seqs, label_seqs):
self.trainer.append(ItemSequence(feat_seq), label_seq)
self.trainer.train(self.trained_model_name)
else:
for fs in feat_seqs:
for feat_dict in fs:
for f in feat_dict:
if f not in self.feat_index:
self.feat_index[f] = len(self.feat_index)
Xs = []
for fs in feat_seqs:
X = []
for feat_dict in fs:
x = [0] * len(self.feat_index)
for f in feat_dict:
x[self.feat_index[f]] = feat_dict[f]
X.append(x)
Xs.append(numpy.asarray(X))
for ls in label_seqs:
for label in ls:
if label not in self.label_index:
self.label_index[label] = len(self.label_index)
Ys = []
for ls in label_seqs:
Y = []
for label in ls:
Y.append(self.label_index[label])
Ys.append(numpy.asarray(Y))
self.trainer.fit(Xs, Ys)
pickle.dump(self.trainer, open(self.trained_model_name, "wb"))
pickle.dump(self.feat_index, open("ssvm_feat_index.pkl", "wb"))
pickle.dump(self.label_index, open("ssvm_label_index.pkl", "wb"))
def test_open_invalid_with_correct_signature(tmpdir):
tmp = tmpdir.join('tmp.txt')
tmp.write(b"lCRFfoo"*100)
tagger = Tagger()
with pytest.raises(ValueError):
tagger.open(str(tmp))
def test_open_invalid_small(tmpdir):
tmp = tmpdir.join('tmp.txt')
tmp.write(b'foo')
tagger = Tagger()
with pytest.raises(ValueError):
tagger.open(str(tmp))
def test_open_invalid():
tagger = Tagger()
with pytest.raises(ValueError):
tagger.open(__file__)
def test_open_non_existing():
tagger = Tagger()
with pytest.raises(IOError):
tagger.open('foo')
def evaluate_model_by_story(model_name, test_samples):
model = Tagger()
model.open(model_name)
story_fps = dict()
for sample in test_samples:
model.set(build_model_features(sample, 17, True))
predicted_labels = model.tag()
chars = list(sample.sentence)
predicted_fps = []
fp = ''
for index, word in enumerate(predicted_labels):
if word == 'E' or word == 'S':
fp += chars[index]
predicted_fps.append(fp)
fp = ''
if word == 'B' or word == 'I':
fp += chars[index]
actual_fps = [fp for fp in sample.fps if fp != '' and fp != 'null' and fp in sample.sentence]
filtered_predicted_fps = predicted_fps
# for predicted_fp in predicted_fps:
# lan_confidence_temp = lmmodel.score(predicted_fp, bos=True, eos=True) / len(predicted_fp)
# if len(re.findall('[a-zA-Z0-9+]+', predicted_fp)) > 0:
# lan_confidence_temp += 5
# if lan_confidence_temp > -2.4:
# filtered_predicted_fps.append(predicted_fp)
if sample.story_id not in story_fps:
story_fps[sample.story_id] = [set(actual_fps), set(filtered_predicted_fps)]
else:
story_fps[sample.story_id][0].update(actual_fps)
story_fps[sample.story_id][1].update(filtered_predicted_fps)
# print(len(story_fps))
global sim_t
sim_threshold = sim_t
TP_precision = 0
TP_recall = 0
all_actual_fps = 0
all_predicted_fps = 0
for story_id, (actual_fps, predicted_fps) in story_fps.items():
story_precision = 0.0
story_recall = 0.0
all_actual_fps += len(actual_fps)
all_predicted_fps += len(predicted_fps)
# for actual_fp in actual_fps:
story = samples_dao.read_story_by_story_id(int(story_id))
data = [story_id,
story[0] if story is not None else '',
story[1] if story is not None else '',
story[2] if story is not None else '',
story[3] if story is not None else '',
story[4] if story is not None else '',
actual_fps,
predicted_fps]
with open('../Archive/date_performance/resultsIterRes_by_story_details.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(data)
for predicted_fp in predicted_fps:
sim = []
for actual_fp in actual_fps:
similarity = 1-distance.nlevenshtein(actual_fp, predicted_fp, method=1)
# if actual_fp in predicted_fp:
# similarity = 1
sim.append(similarity)
# print(sim)
if len(sim) == 0:
sim = [0]
if max(sim) >= sim_threshold:
TP_precision += 1
story_precision += 1
for actual_fp in actual_fps:
sim = []
for predicted_fp in predicted_fps:
similarity = 1-distance.nlevenshtein(actual_fp, predicted_fp, method=1)
sim.append(similarity)
# print(sim)
if len(sim) == 0:
sim = [0]
if max(sim) >= sim_threshold:
TP_recall += 1
story_recall += 1
# 每个故事的详情
story_precision = 0 if len(filtered_predicted_fps) == 0 else story_precision/len(filtered_predicted_fps)
story_recall = 0 if len(actual_fps) == 0 else story_recall/len(actual_fps)
data = ["STORY " + story_id, story_precision, story_recall]
with open('../Archive/date_performance/results/story_details.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(data)
with open('../Archive/date_performance/results/story_details.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(["THE END!!!"])
# 整体的详情
precision = TP_precision/all_predicted_fps
recall = TP_recall/all_actual_fps
f1 = 2 * precision * recall / (precision + recall)
print("By Story: Iteration: %s\n\tPrecision: %f\n\tRecall: %f\n\tF1: %f\n\n\n"
% (model_name.split('_')[2], precision, recall, f1))
data = ["BY STORY: Iteration " + model_name.split('_')[2], precision, recall, f1]
with open('../Archive/date_performance/results/IterRes_by_story.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(data)
return precision, recall, f1
def evaluate_model(model_name, test_samples):
'''
最后一次迭代训练模型，并输出测试结果
:param test_samples:
:param model_name:
:return:
'''
model = Tagger()
model.open(model_name)
accuracy = 0.0
recall = 0.0
f1 = 0.0
# sample_accuracy = 0.0
iteration_test_details = []
for sample in test_samples:
model.set(build_model_features(sample, 17, True))
predicted_labels = model.tag()
true_labels = sample.char_label
predicted_label_index = []
for predicted_label in predicted_labels:
if predicted_label == 'N':
predicted_label_index.append(0)
else:
predicted_label_index.append(1)
true_label_index = []
for true_label in true_labels:
if true_label == 'N':
true_label_index.append(0)
else:
true_label_index.append(1)
iteration_test_details = []
chars = list(sample.sentence)
# sen_words = sample.sen_words
iteration_test_details.append(sample.sentence)
predicted_fps = ''
actual_fps = ''
for index, word in enumerate(predicted_labels):
if word != 'N':
predicted_fps += chars[index]
if len(predicted_fps) == 0:
predicted_fps = '-----'
for index, word in enumerate(true_labels):
if word != 'N':
actual_fps += chars[index]
iteration_test_details.append(actual_fps)
iteration_test_details.append(predicted_fps)
with open('../Archive/date_performance/results/Iteration_Test_Details.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(iteration_test_details)
# print(sample.sen_words)
# print(predicted_labels)
# print(true_labels)
accuracy += metrics.accuracy_score(true_label_index, predicted_label_index)
recall += metrics.recall_score(true_label_index, predicted_label_index, average='binary', pos_label=1)
f1 += 2*accuracy*recall/(accuracy+recall)
# sample_accuracy += metrics.sequence_accuracy_score(true_labels, predicted_labels)
print("Iteration: %s\n\tAccuracy: %f\n\tRecall: %f\n\tF1: %f\n\n\n"
% (
model_name.split('_')[2], accuracy / len(test_samples), recall / len(test_samples),
f1 / len(test_samples)))
data = ["Iteration " + model_name.split('_')[2], accuracy / len(test_samples), recall / len(test_samples),
f1 / len(test_samples)]
with open('../Archive/date_performance/results/IterRes.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(data)
with open('../Archive/date_performance/results/Iteration_Test_Details.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(data)
return accuracy / len(test_samples), recall / len(test_samples), f1 / len(test_samples)
def __init__(self, tagger=None):
if not tagger:
tagger = Tagger()
tagger.open(TOKENIZATION_MODEL_PATH)
self.tagger = tagger
class CRFSTagger:
def __init__(self,
cfg=None,
mp=None,
fnx=None,
win_fnx=None,
cols=None,
verbose=False):
"""Creates an instance of CRFSTagger
:param cfg: configuration
:type cfg: ConfigParser.ConfigParser
:param mp: model path
:type mp: str
"""
# configuration
self.cfg = None
# feature template
self.ft_tmpl = None
# list of resources used by features, e.g. word clusters, embeddings
self.resources = None
# data
self.train_data = None
self.test_data = None
# instance of pycrfsuite.Tagger
self.tagger = None
self.verbose = verbose
# attempt to import cannonical replacements
try:
import canonical
self.canonical = canonical.REPLACEMENTS
except ImportError:
self.canonical = None
self.fnx = fnx
self.win_fnx = win_fnx
self.ft_tmpl_cols = cols
# load data and resources if configuration is provided
if cfg:
self.cfg = cfg
expandpaths(self.cfg)
# loading resources (clusters, embeddings, etc.)
self._load_resources()
# loading data
self._load_data()
# load model
elif mp:
m = pickle.load(open(mp, 'r'))
self.cfg = m.cfg
self.cfg.set('tagger', 'model', mp)
self.resources = m.resources
self.fnx = [self._load_function(n, f)
for n, f in m.fnx.items()] if m.fnx else None
self.win_fnx = [self._load_function(n, f)
for n, f in m.win_fnx] if m.win_fnx else None
self.ft_tmpl_cols = m.cols
else:
raise RuntimeError(
'Configuration initialisation failed. Please, provide either '
'a configuration or a model.')
# parsing feature template
self.ft_tmpl = FeatureTemplate(fnx=self.fnx,
win_fnx=self.win_fnx,
cols=self.ft_tmpl_cols)
self.ft_tmpl.parse_ftvec_templ(self.cfg_tag.get('ftvec'),
self.resources)
@property
def cfg_tag(self):
"""Configuration parameters of this tagger. Returns a section from a
ConfigParser object.
:return: tagger configuration
:rtype: dict
"""
return dict(self.cfg.items('tagger'))
@property
def cfg_crf(self):
"""Configuration parameters for CRFSuite. These are passed to the tagger
instance when training is done. Note, these are not necessarily the
same as the ones in self.tagger.params.
:return: CRFSuite configuration
:rtype: dict
"""
return dict(self.cfg.items('crfsuite'))
@property
def cfg_res(self):
"""Resources configuration. Essentially a list of name and file path
pairs.
:return: list of resources
:rtype: dict
"""
return dict(self.cfg.items('resources'))
############################################################################
### A group of properties mapped to configuration values of the tagger. ###
############################################################################
############################################################################
@property
def ts(self):
tss = {'\\t': '\t', '\\s': ' '}
return tss.get(self.cfg_tag['tab_sep'], self.cfg_tag['tab_sep'])
@property
def cols(self):
return self.cfg_tag['cols']
@property
def form_col(self):
return self.cfg_tag.get('form_col', 'form')
@property
def lbl_col(self):
return self.cfg_tag['label_col']
@property
def ilbl_col(self):
return self.cfg_tag.get('guess_label_col', 'guesstag')
@property
def model_path(self):
return self.cfg_tag['model']
@property
def eval_func(self):
return getattr(eval, '%s' % self.cfg_tag['eval_func'])
@property
def info(self):
return self.tagger.info if self.tagger else None
############################################################################
############################################################################
def _load_resources(self):
"""Loads resources listed in the `resources` section of the
configuration. Resources are generally needed for feature generation.
However, note that for a resource to be loaded a `reader` method
is needed. For example, to load a clusters resource `cls`, there needs
to be a method called `read_cls` in `readers.py` that takes a file path
parameter and returns a resource data structure.
"""
self.resources = {}
for n, p in self.cfg_res.items():
self.resources[n] = getattr(readers, 'read_%s' % n)(p)
def _load_data(self):
"""Loads training and testing data if provided in the initial
configuration.
"""
if 'train' in self.cfg_tag and self.cfg_tag['train']:
self.train_data = parse_tsv(self.cfg_tag['train'],
cols=self.cols,
ts=self.ts)
if 'test' in self.cfg_tag and self.cfg_tag['test']:
self.test_data = parse_tsv(fp=self.cfg_tag['test'],
cols=self.cols,
ts=self.ts)
def _load_function(self, name, code_string):
code = marshal.loads(code_string)
return types.FunctionType(code, globals(), name)
def _extract_features(self, doc, form_col='form'):
"""A generator methof that extracts features from the data using a
feature set template. Yields the feature vector of each sequence in the
data.
:param doc: data
:type doc: np.recarray
"""
d = copy.deepcopy(doc)
# replace tokens with canonical forms
if self.canonical:
for t in d:
for r in self.canonical.keys():
if re.match(r, t['form']):
t['form'] = self.canonical[r]
# number of features
nft = len(self.ft_tmpl.vec)
# record count
rc = len(d)
# recarray data types (60 >= char string, [30 >= char string] * nft)
dt = 'a60,{}'.format(','.join('a30' for _ in range(nft)))
# constructing empty recarray
fts = np.zeros(rc, dtype=dt)
# sequence start and end indices
s, e = 0, 0
sc = 0
# extracting features sequences by sequence
while 0 <= s < len(d):
# index of the end of a sequence is recorded at the beginning
e = d[s]['eos']
# slicing a sequence
seq = d[s:e]
ft_seq = np.zeros(len(seq), dtype=dt)
# extracting the features
for i in range(len(seq)):
ft_seq[i] = tuple(
self.ft_tmpl.make_fts(seq, i, form_col=form_col))
# moving the start index
s = e
sc += 1
# yielding a feature sequence
yield ft_seq
def train(self,
data=None,
form_col=None,
lbl_col=None,
ilbl_col=None,
data_cols=None,
data_sep=None,
dump=True):
"""Trains a model based on provided data and features. The default
behaviour is to load training parameters from the global configuration,
unless they are passed to this method.
IMPORTANT: there are two ways to pass data directly through the `data`
parameter:
-- np.recarray `data` needs to be a recarray with column names that
match what the feature extractor expects.
-- csv str `data` needs to contain a TSV/CSV formatted string.
Column names and separator should be provided in the
`data_cols` and `data_sep` parameters. They should still
match what is expected by the feature extractor.
The observation, label, and inference column names can be set through
the global configuration using the following parameter names:
`form_col`, `label_col`, `guess_label_col`. The default observation
column name is `fc`, and the inference column name is `guesstag`.
All three names can be passed to this method to override global
configuration. Any other column names need to match their respective
feature extractor functions, e.g. part-of-speech tags need to be placed
in `postag` column. See `ftex.FeatureTemplate` for others.
RECOMMENDED: use `utils.parse_tsv` to parse input data to avoid column
configuration errors.
NOTE: Due to the way `pycrfsuite` works, the crfsuite model needs to be
dumped on the hard drive, however, the CRFSuiteTagger model does not
NEED to be dumped. That process is controlled through the `dump`
parameter.
:param data: training data
:type data: np.recarray or str
:param form_col: fc column name
:type form_col: str
:param lbl_col: label column name
:type lbl_col: str
:param ilbl_col: inference label column name
:type ilbl_col: str
:param data_cols: list of columns in the data
:type data_cols: str
:param data_sep: data tab separator
:type data_sep: str
:param dump: dumps the model at specified location if True
:type dump: bool
"""
# overriding parameters
fc = form_col if form_col else self.form_col
c = data_cols if data_cols else self.cols
sep = data_sep if data_sep else self.ts
lc = lbl_col if lbl_col else self.lbl_col
ilc = ilbl_col if ilbl_col else self.ilbl_col
if type(data) in [np.core.records.recarray, np.ndarray]:
d = data
elif type(data) == str:
d = parse_tsv(s=data, cols=c, ts=sep, inference_col=ilc)
elif data is None:
d = self.train_data
else:
raise ValueError('Invalid input type.')
# extract features
X = self._extract_features(d, fc)
# extract labels
y = gsequences(d, [lc])
trainer = Trainer(verbose=self.verbose)
# setting CRFSuite parameters
trainer.set_params(self.cfg_crf)
for x_seq, y_seq in zip(X, y):
trainer.append(x_seq, [l[0] for l in y_seq])
crfs_mp = '%s.crfs' % self.model_path
try:
makedirs(dirname(crfs_mp))
except OSError:
pass
trainer.train(crfs_mp)
self.tagger = Tagger()
self.tagger.open(crfs_mp)
# dumps the model
if dump:
self.dump_model(self.model_path)
pickle.dump(self.cfg, open('%s.cfg.pcl' % self.model_path, 'w'))
def tag(self,
data,
form_col=None,
ilbl_col=None,
tagger=None,
cols=None,
ts=None):
"""Tags TSV/CSV or np.recarray data using the loaded CRFSuite model.
See documentation for `train` for more details on requirements for the
data passed to this method.
:param data: data
:type data: str or recarray
:param form_col: form column name
:type form_col: str
:param ilbl_col: inference label column name
:type ilbl_col: str
:param tagger: CRFS tagger
:type tagger: Tagger
:param cols: TSV column names
:type cols: str or list of str
:param ts: tab separator for TSV
:type ts: str
:return: tagged data
:rtype: recarray
"""
fc = form_col if form_col else self.form_col
c = cols if cols else self.cols
sep = ts if ts else self.ts
ilc = ilbl_col if ilbl_col else self.ilbl_col
if type(data) in [np.core.records.recarray, np.ndarray]:
d = data
elif type(data) == str:
d = parse_tsv(s=data, cols=c, ts=sep)
else:
raise ValueError('Invalid input type.')
tgr = tagger
if tgr is None and self.tagger:
tgr = self.tagger
elif tgr is None:
tgr = Tagger()
tgr.open('%s.crfs' % self.model_path)
# extracting features
X = self._extract_features(d, form_col=fc)
# tagging sentences
idx = 0
for fts in X:
for l in tgr.tag(fts):
d[idx][ilc] = l
idx += 1
return d
def test(self,
data=None,
form_col=None,
ilbl_col=None,
tagger=None,
cols=None,
ts=None,
eval_func=None):
"""Tags TSV/CSV or np.recarray data using the loaded CRFSuite model and
evaluates the results.
See documentation for `train` for more details on requirements for the
data passed to this method.
:param data: data
:type data: str or recarray
:param form_col: form column name
:type form_col: str
:param ilbl_col: inference label column name
:type ilbl_col: str
:param tagger: CRFS tagger
:type tagger: Tagger
:param cols: TSV column names
:type cols: str or list of str
:param ts: tab separator for TSV
:type ts: str
:param eval_func: evaluation function name [pos, conll, bio]
:type eval_func: str
:return: results and tagged data pair
:rtype: AccuracyResults, np.recarray
"""
# use provided data or testing data from config file
d = self.test_data if data is None else data
# setting inference label column name
ilc = self.ilbl_col if ilbl_col is None else ilbl_col
# tagging
d = self.tag(d,
form_col=form_col,
ilbl_col=ilbl_col,
tagger=tagger,
cols=cols,
ts=ts)
# evaluating
f = eval_func if eval_func else self.eval_func
r = f(d, label_col=self.lbl_col, inference_col=self.ilbl_col)
# returnning AccuracyResults and np.recarray tagged data
return r, d
def dump_model(self, fp):
"""Dumps the CRFSuiteTagger model in provided file path `fp`.
The dumping consists of two files: <fp> and <fp>.crfs. The first
contains the configuration and all feature extraction resources needed
by a CRFSuiteTagger object to replicate this one. The second one is the
pycrfsuite model that needsto be dumped separately as it is always read
from the file system.
:param fp: model file path
:type fp: str
"""
md = Model()
md.cfg = clean_cfg(self.cfg)
md.resources = self.resources
md.fnx = {f.__name__: marshal.dumps(f.func_code)
for f in self.fnx} if self.fnx else None
md.win_fnx = {
f.__name__: marshal.dumps(f.func_code)
for f in self.win_fnx
} if self.win_fnx else None
md.cols = self.ft_tmpl_cols
fpx = expanduser(fp)
try:
makedirs(dirname(fpx))
except OSError:
pass
pickle.dump(md, open(fpx, 'w'))
if fpx != self.model_path:
src = '%s.crfs' % self.model_path
trg = '%s.crfs' % fpx
try:
makedirs(dirname(trg))
except OSError:
pass
shutil.copy(src, trg)
def test(features):
print("Testing..")
tagger = Tagger()
tagger.open('crf.model')
y_pred = [tagger.tag(xseq) for xseq in features]
return y_pred