| | |
- classify_example(x, sm, sparm)
- Given a pattern x, return the predicted label.
- eval_prediction(exnum, (x, y), ypred, sm, sparm, teststats)
- Accumulate statistics about a single training example.
Allows accumulated statistics regarding how well the predicted
label ypred for pattern x matches the true label y. The first
time this function is called teststats is None. This function's
return value will be passed along to the next call to
eval_prediction. After all test predictions are made, the last
value returned will be passed along to print_testing_stats.
On the first call, that is, when exnum==0, teststats==None. The
default behavior is that the function does nothing.
- find_most_violated_constraint(x, y, sm, sparm)
- Return ybar associated with x's most violated constraint.
Returns the label ybar for pattern x corresponding to the most
violated constraint according to SVM^struct cost function. To
find which cost function you should use, check sparm.loss_type for
whether this is slack or margin rescaling (1 or 2 respectively),
and check sparm.slack_norm for whether the slack vector is in an
L1-norm or L2-norm in the QP (1 or 2 respectively).
If this function is not implemented, this function is equivalent
to 'classify(x, sm, sparm)'. The optimality guarantees of
Tsochantaridis et al. no longer hold, since this doesn't take the
loss into account at all, but it isn't always a terrible
approximation. One still technically maintains the empirical
risk bound condition, but without any regularization.
- find_most_violated_constraint_margin(x, y, sm, sparm)
- Return ybar associated with x's most violated constraint.
The find most violated constraint function for margin rescaling.
The default behavior is that this returns the value from the
general find_most_violated_constraint function.
- find_most_violated_constraint_slack(x, y, sm, sparm)
- Return ybar associated with x's most violated constraint.
The find most violated constraint function for slack rescaling.
The default behavior is that this returns the value from the
general find_most_violated_constraint function.
- init_constraints(sample, sm, sparm)
- Initializes special constraints.
Returns a sequence of initial constraints. Each constraint in the
returned sequence is itself a sequence with two items (the
intention is to be a tuple). The first item of the tuple is a
document object. The second item is a number, indicating that the
inner product of the feature vector of the document object with
the linear weights must be greater than or equal to the number
(or, in the nonlinear case, the evaluation of the kernel on the
feature vector with the current model must be greater). This
initializes the optimization problem by allowing the introduction
of special constraints. Typically no special constraints are
necessary. A typical constraint may be to ensure that all feature
weights are positive.
Note that the slack id must be set. The slack IDs 1 through
len(sample) (or just 1 in the combined constraint option) are used
by the training examples in the sample, so do not use these if you
do not intend to share slack with the constraints inferred from
the training data.
The default behavior is equivalent to returning an empty list,
i.e., no constraints.
- init_model(sample, sm, sparm)
- Initializes the learning model.
Initialize the structure model sm. The sm.size_psi must be set to
the number of features. The ancillary purpose is to add any
information to sm that is necessary from the user code
perspective. This function returns nothing.
- loss(y, ybar, sparm)
- Return the loss of ybar relative to the true labeling y.
Returns the loss for the correct label y and the predicted label
ybar. In the event that y and ybar are identical loss must be 0.
Presumably as y and ybar grow more and more dissimilar the
returned value will increase from that point. sparm.loss_function
holds the loss function option specified on the command line via
the -l option.
The default behavior is to perform 0/1 loss based on the truth of
y==ybar.
- parse_parameters(sparm)
- Sets attributes of sparm based on command line arguments.
This gives the user code a chance to change sparm based on the
custom command line arguments. The custom command line arguments
are stored in sparm.argv as a list of strings. The custom command
lines are stored in '--option', then 'value' sequence.
If this function is not implemented, any custom command line
arguments are ignored and sparm remains unchanged.
- parse_parameters_classify(attribute, value)
- Process a single custom command line argument for the classifier.
This gives the user code a chance to change the state of the
classifier based on a single custom command line argument, e.g.,
one that begins with two dashes. This function will be called
multiple times if there are multiple custom command line
arguments.
If this function is not implemented, any custom command line
arguments are ignored.
- print_help()
- Help printed for badly formed CL-arguments when learning.
If this function is not implemented, the program prints the
default SVM^struct help string as well as a note about the use of
the --m option to load a Python module.
- print_help_classify()
- Help printed for badly formed CL-arguments when classifying.
If this function is not implemented, the program prints the
default SVM^struct help string as well as a note about the use of
the --m option to load a Python module.
- print_iteration_stats(ceps, cached_constraint, sample, sm, cset, alpha, sparm)
- Called just before the end of each cutting plane iteration.
This is called just before the end of each cutting plane
iteration, primarily to print statistics. The 'ceps' argument is
how much the most violated constraint was violated by. The
'cached_constraint' argument is true if this constraint was
constructed from the cache.
The default behavior is that nothing is printed.
- print_learning_stats(sample, sm, cset, alpha, sparm)
- Print statistics once learning has finished.
This is called after training primarily to compute and print any
statistics regarding the learning (e.g., training error) of the
model on the training sample. You may also use it to make final
changes to sm before it is written out to a file. For example, if
you defined any non-pickle-able attributes in sm, this is a good
time to turn them into a pickle-able object before it is written
out. Also passed in is the set of constraints cset as a sequence
of (left-hand-side, right-hand-side) two-element tuples, and an
alpha of the same length holding the Lagrange multipliers for each
constraint.
The default behavior is that nothing is printed.
- print_testing_stats(sample, sm, sparm, teststats)
- Print statistics once classification has finished.
This is called after all test predictions are made to allow the
display of any summary statistics that have been accumulated in
the teststats object through use of the eval_prediction function.
The default behavior is that nothing is printed.
- psi(x, y, sm, sparm)
- Return a feature vector representing pattern x and label y.
This is the combined feature function, which this returns either a
svmapi.Sparse object, or sequence of svmapi.Sparse objects (useful
during kernel evaluations, as all components undergo kernel
evaluation separately). There is no default behavior.
- read_examples(filename, sparm)
- Reads and returns x,y example pairs from a file.
This reads the examples contained at the file at path filename and
returns them as a sequence. Each element of the sequence should
be an object 'e' where e[0] and e[1] is the pattern (x) and label
(y) respectively. Specifically, the intention is that the element
be a two-element tuple containing an x-y pair.
- read_model(filename, sparm)
- Load the structure model from a file.
Return the structmodel stored in the file at path filename, or
None if the file could not be read for some reason.
The default behavior is equivalent to
'return cPickle.load(bz2.BZ2File(filename))'.
- write_label(fileptr, y)
- Write a predicted label to an open file.
Called during classification, this function is called for every
example in the input test file. In the default behavior, the
label is written to the already open fileptr. (Note that this
object is a file, not a string. Attempts to close the file are
ignored.) The default behavior is equivalent to
'print>>fileptr,y'
- write_model(filename, sm, sparm)
- Dump the structmodel sm to a file.
Write the structmodel sm to a file at path filename.
The default behavior is equivalent to
'cPickle.dump(sm,bz2.BZ2File(filename,'w'))'.
|