pyliblinear package¶
Module contents¶
Copyright: | Copyright 2015 - 2017 André Malo or his licensors, as applicable |
---|---|
License: | Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
pyliblinear - a liblinear python API¶
pyliblinear - a liblinear python API
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class
pyliblinear.
FeatureMatrix
¶ Bases:
object
Feature matrix to be used for training or prediction.
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features
(self)¶ Return the features as iterator of dicts.
Return: The feature vectors Rtype: iterable
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from_iterables
(cls, labels, features)¶ Create FeatureMatrix instance from a two separated iterables - labels and features.
Parameters: - labels : iterable
Iterable providing the labels per feature vector (assigned by order)
- features : iterable
Iterable providing the feature vector per label (assigned by order)
Return: New feature matrix instance
Rtype: FeatureMatrix
Exceptions: - ValueError : The lengths of the iterables differ
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height
¶ The matrix height (number of labels and vectors).
Type: int
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labels
(self)¶ Return the labels as iterator.
Return: The labels Rtype: iterable
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load
(cls, file)¶ Create FeatureMatrix instance from a file.
Each line of the file contains the label and the accompanying sparse feature vector, separated by a space/tab sequence. The feature vector consists of index/value pairs. The index and the value are separated by a colon (
:
). The pairs are separated by space/tab sequences. Accepted line endings are\r
,\n
and\r\n
.All numbers are represented as strings parsable either as ints (for indexes) or doubles (for values and labels).
Note that the exact I/O exceptions depend on the stream passed in.
Parameters: - file :
file
orstr
Either a readable stream or a filename. If the passed object provides a
read
attribute/method, it’s treated as readable file stream, as a filename otherwise. If it’s a stream, the stream is read from the current position and remains open after hitting EOF. In case of a filename, the accompanying file is opened in text mode, read from the beginning and closed afterwards.
Return: New feature matrix instance
Rtype: FeatureMatrix
Exceptions: - IOError : Error reading the file
- ValueError : Error parsing the file
- file :
-
save
(self, file)¶ Save FeatureMatrix instance to a file.
Each line of the file contains the label and the accompanying sparse feature vector, separated by a space. The feature vector consists of index/value pairs. The index and the value are separated by a colon (
:
). The pairs are separated by a space again. The line ending is\n
.All numbers are represented as strings parsable either as ints (for indexes) or doubles (for values and labels).
Note that the exact I/O exceptions depend on the stream passed in.
Parameters: - file :
file
orstr
Either a writeable stream or a filename. If the passed object provides a
write
attribute/method, it’s treated as writeable stream, as a filename otherwise. If it’s a stream, the stream is written to the current position and remains open when done. In case of a filename, the accompanying file is opened in text mode, truncated, written from the beginning and closed afterwards.
Exceptions: - IOError : Error writing the file
- file :
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width
¶ The matrix width (number of features).
Type: int
-
-
class
pyliblinear.
Solver
¶ Bases:
object
Solver container
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C
¶ The configured C parameter.
Type: float
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eps
¶ The configured eps parameter.
Type: float
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p
¶ The configured p parameter.
Type: float
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type
¶ The configured solver type.
Type: str
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weights
(self)¶ Return the configured weights as a dict (label -> weight).
Return: The weights (maybe empty) Rtype: dict
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class
pyliblinear.
Model
¶ Bases:
object
Classification model. Use its Model.load or Model.train methods to construct a new instance
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bias
¶ Bias used to create the model
None
if no bias was applied.Type: double
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is_probability
¶ Is model a probability model?
Type: bool
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is_regression
¶ Is model a regression model?
Type: bool
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load
(cls, file, mmap=False)¶ Create Model instance from a file (previously created by Model.save())
Note that the exact I/O exceptions depend on the stream passed in.
Parameters: - file :
file
orstr
Either a readable stream or a filename. If the passed object provides a
read
attribute/method, it’s treated as readable file stream, as a filename otherwise. If it’s a stream, the stream is read from the current position and remains open after hitting EOF. In case of a filename, the accompanying file is opened in text mode, read from the beginning and closed afterwards.- mmap :
bool
Load the model into a file-backed memory area? Default: false
Return: New model instance
Rtype: Model
Exceptions: - IOError : Error reading the file
- ValueError : Error parsing the file
- file :
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predict
(self, matrix, label_only=True, probability=False)¶ Run the model on matrix and predict labels.
Parameters: - matrix : pyliblinear.FeatureMatrix or iterable
Either a feature matrix or a simple iterator over feature vectors to inspect and predict upon.
- label_only :
bool
Return the label only? If false, the decision dict for all labels is returned as well.
- probability :
bool
Use probability estimates?
Return: Result iterator. Either over labels or over label/decision dict tuples.
Rtype: iterable
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save
(self, file)¶ Save Model instance to a file.
After some basic information about solver type, dimensions and labels the model matrix is stored as a sequence of doubles per line. The matrix is transposed, so the height is the number of features (including the bias feature) and the width is the number of classes.
All numbers are represented as strings parsable either as ints (for dimensions and labels) or doubles (other values).
Note that the exact I/O exceptions depend on the stream passed in.
Parameters: - file :
file
orstr
Either a writeable stream or a filename. If the passed object provides a
write
attribute/method, it’s treated as writeable stream, as a filename otherwise. If it’s a stream, the stream is written to the current position and remains open when done. In case of a filename, the accompanying file is opened in text mode, truncated, written from the beginning and closed afterwards.
Exceptions: - IOError : Error writing the file
- file :
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solver_type
¶ Solver type used to create the model
Type: str
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train
(cls, matrix, solver=None, bias=None)¶ Create model instance from a training run
Parameters: - matrix : pyliblinear.FeatureMatrix
Feature matrix to use for training
- solver : pyliblinear.Solver
Solver instance. If omitted or
None
, a default solver is picked.- bias :
float
Bias to the hyperplane. Of omitted or
None
, no bias is applied.bias >= 0
.
Return: New model instance
Rtype: Model
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