def __init__(self,
algorithm=None,
train_params=None,
verbose=False,
model_filename=None,
keep_tempfiles=False,
trainer_cls=None):
self.algorithm = algorithm
self.train_params = train_params
self.modelfile = FileResource(filename=model_filename,
keep_tempfiles=keep_tempfiles,
suffix=".crfsuite",
prefix="model")
self.verbose = verbose
self._tagger = None
if trainer_cls is None:
self.trainer_cls = pycrfsuite.Trainer
else:
self.trainer_cls = trainer_cls
self.training_log_ = None
def __init__(self, algorithm=None, train_params=None, verbose=False,
model_filename=None, keep_tempfiles=False, trainer_cls=None):
self.algorithm = algorithm
self.train_params = train_params
self.modelfile = FileResource(
filename =model_filename,
keep_tempfiles=keep_tempfiles,
suffix=".crfsuite",
prefix="model"
)
self.verbose = verbose
self._tagger = None
if trainer_cls is None:
self.trainer_cls = pycrfsuite.Trainer
else:
self.trainer_cls = trainer_cls
self.training_log_ = None
class CRF(object):
def __init__(self, algorithm=None, train_params=None, verbose=False,
model_filename=None, keep_tempfiles=False, trainer_cls=None):
self.algorithm = algorithm
self.train_params = train_params
self.modelfile = FileResource(
filename =model_filename,
keep_tempfiles=keep_tempfiles,
suffix=".crfsuite",
prefix="model"
)
self.verbose = verbose
self._tagger = None
if trainer_cls is None:
self.trainer_cls = pycrfsuite.Trainer
else:
self.trainer_cls = trainer_cls
self.training_log_ = None
def fit(self, X, y, X_dev=None, y_dev=None):
"""
Train a model.
Parameters
----------
X : list of lists of dicts
Feature dicts for several documents (in a python-crfsuite format).
y : list of lists of strings
Labels for several documents.
X_dev : (optional) list of lists of dicts
Feature dicts used for testing.
y_dev : (optional) list of lists of strings
Labels corresponding to X_dev.
"""
if (X_dev is None and y_dev is not None) or (X_dev is not None and y_dev is None):
raise ValueError("Pass both X_dev and y_dev to use the holdout data")
if self._tagger is not None:
self._tagger.close()
self._tagger = None
self.modelfile.refresh()
trainer = self._get_trainer()
train_data = zip(X, y)
if self.verbose:
train_data = tqdm(train_data, "loading training data to CRFsuite", len(X), leave=True)
for xseq, yseq in train_data:
trainer.append(xseq, yseq)
if self.verbose:
print("")
if X_dev is not None:
test_data = zip(X_dev, y_dev)
if self.verbose:
test_data = tqdm(test_data, "loading dev data to CRFsuite", len(X_dev), leave=True)
for xseq, yseq in test_data:
trainer.append(xseq, yseq, 1)
if self.verbose:
print("")
trainer.train(self.modelfile.name, holdout=-1 if X_dev is None else 1)
self.training_log_ = trainer.logparser
return self
def predict(self, X):
"""
Make a prediction.
Parameters
----------
X : list of lists of dicts
feature dicts in python-crfsuite format
Returns
-------
y : list of lists of strings
predicted labels
"""
return list(map(self.predict_single, X))
def predict_single(self, xseq):
"""
Make a prediction.
Parameters
----------
X : list of dicts
feature dicts in python-crfsuite format
Returns
-------
y : list of strings
predicted labels
"""
return self.tagger.tag(xseq)
def predict_marginals(self, X):
"""
Make a prediction.
Parameters
----------
X : list of lists of dicts
feature dicts in python-crfsuite format
Returns
-------
y : list of lists of dicts
predicted probabilities for each label at each position
"""
return list(map(self.predict_marginals_single, X))
def predict_marginals_single(self, xseq):
"""
Make a prediction.
Parameters
----------
X : list of dicts
feature dicts in python-crfsuite format
Returns
-------
y : list of dicts
predicted probabilities for each label at each position
"""
labels = self.tagger.labels()
self.tagger.set(xseq)
return [
{label: self.tagger.marginal(label, i) for label in labels}
for i in range(len(xseq))
]
@property
def tagger(self):
if self._tagger is None:
if self.modelfile.name is None:
raise Exception("Can't load model. Is the model trained?")
tagger = pycrfsuite.Tagger()
tagger.open(self.modelfile.name)
self._tagger = tagger
return self._tagger
def _get_trainer(self):
return self.trainer_cls(
algorithm=self.algorithm,
params=self.train_params,
verbose=self.verbose,
)
def __getstate__(self):
dct = self.__dict__.copy()
dct['_tagger'] = None
return dct
class CRF(object):
def __init__(self,
algorithm=None,
train_params=None,
verbose=False,
model_filename=None,
keep_tempfiles=False,
trainer_cls=None):
self.algorithm = algorithm
self.train_params = train_params
self.modelfile = FileResource(filename=model_filename,
keep_tempfiles=keep_tempfiles,
suffix=".crfsuite",
prefix="model")
self.verbose = verbose
self._tagger = None
if trainer_cls is None:
self.trainer_cls = pycrfsuite.Trainer
else:
self.trainer_cls = trainer_cls
self.training_log_ = None
def fit(self, X, y, X_dev=None, y_dev=None):
"""
Train a model.
Parameters
----------
X : list of lists of dicts
Feature dicts for several documents (in a python-crfsuite format).
y : list of lists of strings
Labels for several documents.
X_dev : (optional) list of lists of dicts
Feature dicts used for testing.
y_dev : (optional) list of lists of strings
Labels corresponding to X_dev.
"""
if (X_dev is None and y_dev is not None) or (X_dev is not None
and y_dev is None):
raise ValueError(
"Pass both X_dev and y_dev to use the holdout data")
if self._tagger is not None:
self._tagger.close()
self._tagger = None
self.modelfile.refresh()
trainer = self._get_trainer()
train_data = zip(X, y)
if self.verbose:
train_data = tqdm(train_data,
"loading training data to CRFsuite",
len(X),
leave=True)
for xseq, yseq in train_data:
trainer.append(xseq, yseq)
if self.verbose:
print("")
if X_dev is not None:
test_data = zip(X_dev, y_dev)
if self.verbose:
test_data = tqdm(test_data,
"loading dev data to CRFsuite",
len(X_dev),
leave=True)
for xseq, yseq in test_data:
trainer.append(xseq, yseq, 1)
if self.verbose:
print("")
trainer.train(self.modelfile.name, holdout=-1 if X_dev is None else 1)
self.training_log_ = trainer.logparser
return self
def predict(self, X):
"""
Make a prediction.
Parameters
----------
X : list of lists of dicts
feature dicts in python-crfsuite format
Returns
-------
y : list of lists of strings
predicted labels
"""
return list(map(self.predict_single, X))
def predict_single(self, xseq):
"""
Make a prediction.
Parameters
----------
X : list of dicts
feature dicts in python-crfsuite format
Returns
-------
y : list of strings
predicted labels
"""
return self.tagger.tag(xseq)
def predict_marginals(self, X):
"""
Make a prediction.
Parameters
----------
X : list of lists of dicts
feature dicts in python-crfsuite format
Returns
-------
y : list of lists of dicts
predicted probabilities for each label at each position
"""
return list(map(self.predict_marginals_single, X))
def predict_marginals_single(self, xseq):
"""
Make a prediction.
Parameters
----------
X : list of dicts
feature dicts in python-crfsuite format
Returns
-------
y : list of dicts
predicted probabilities for each label at each position
"""
labels = self.tagger.labels()
self.tagger.set(xseq)
return [{label: self.tagger.marginal(label, i)
for label in labels} for i in range(len(xseq))]
@property
def tagger(self):
if self._tagger is None:
if self.modelfile.name is None:
raise Exception("Can't load model. Is the model trained?")
tagger = pycrfsuite.Tagger()
tagger.open(self.modelfile.name)
self._tagger = tagger
return self._tagger
def _get_trainer(self):
return self.trainer_cls(
algorithm=self.algorithm,
params=self.train_params,
verbose=self.verbose,
)
def __getstate__(self):
dct = self.__dict__.copy()
dct['_tagger'] = None
return dct
