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A wrapper for crfsuite ItemSequence - a class for storing
features for all items in a single sequence.
Using this class is an alternative to passing data to Trainer
and Tagger directly. By using this class it is possible to
save some time if the same input sequence is passed to trainers/taggers
more than once - features won’t be processed multiple times.
It also allows to get “processed” features/attributes that are sent
to CRFsuite - they could be helpful e.g. to check which attributes
(returned by info()) are active for a given observation.
Initialize ItemSequence with a list of item features:
>>> ItemSequence([{'foo':1,'bar':0},{'foo':1.5,'baz':2}])<ItemSequence of size 2>
Item features could be in one of the following formats:
{“string_key”: float_weight, …} dict where keys are
observed features and values are their weights;
{“string_key”: bool, …} dict; True is converted to 1.0 weight,
False - to 0.0;
{“string_key”: “string_value”, …} dict; that’s the same as
{“string_key=string_value”: 1.0, …}
[“string_key1”, “string_key2”, …] list; that’s the same as
{“string_key1”: 1.0, “string_key2”: 1.0, …}
{“string_prefix”: {…}} dicts: nested dict is processed and
“string_prefix” s prepended to each key.
{“string_prefix”: […]} dicts: nested list is processed and
“string_prefix” s prepended to each key.
{“string_prefix”: set([…])} dicts: nested list is processed and
“string_prefix” s prepended to each key.
Dict-based features can be mixed, i.e. this is allowed:
Append an instance (item/label sequence) to the data set.
Parameters:
xseq:a sequence of item features
The item sequence of the instance. xseq should be a list
of item features or an ItemSequence instance.
Allowed item features formats are the same as described
in ItemSequence docs.
yseq:a sequence of strings
The label sequence of the instance. The number
of elements in yseq must be identical to that
in xseq.
group:int, optional
The group number of the instance. Group numbers are used to
select subset of data for heldout evaluation.
Get the value of a training parameter.
This function gets a parameter value for the graphical model and
training algorithm specified by Trainer.select() method.
Get the description of a training parameter.
This function obtains the help message for the parameter specified
by the name. The graphical model and training algorithm must be
selected by Trainer.select() method before calling this method.
This function returns the list of parameter names available for the
graphical model and training algorithm specified in Trainer constructor
or by Trainer.select() method.
Returns:
list of strings
The list of parameters available for the current
graphical model and training algorithm.
Run the training algorithm.
This function starts the training algorithm with the data set given
by Trainer.append() method.
Parameters:
model:string
The filename to which the trained model is stored.
If this value is empty, this function does not
write out a model file.
holdout:int, optional
The group number of holdout evaluation. The
instances with this group number will not be used
for training, but for holdout evaluation.
Default value is -1, meaning “use all instances for training”.
Compute the marginal probability of the label y at position pos
for the current input sequence (i.e. a sequence set using
Tagger.set() method or a sequence used in a previous
Tagger.tag() call).
Parameters:
y:string
The label.
t:int
The position of the label.
Returns:
float
The marginal probability of the label y at position t.
Compute the probability of the label sequence for the current input
sequence (a sequence set using Tagger.set() method or
a sequence used in a previous Tagger.tag() call).
The item sequence of the instance. xseq should be a list of
item features or an ItemSequence instance.
Allowed item features formats are the same as described
in ItemSequence docs.
The item sequence. If omitted, the current sequence is used
(a sequence set using Tagger.set() method or
a sequence used in a previous Tagger.tag() call).
xseq should be a list of item features or
an ItemSequence instance. Allowed item features formats
are the same as described in ItemSequence docs.
More data formats for xseq: {"prefix":{feature_dict}} and
{"key":set(["key1",...])} feature dicts are now accepted by
pycrfsuite.Trainer and pycrfsuite.Tagger;
feature separator changed from “=” to “:” (it looks better in case of
multi-level features);
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