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LinearSupportVectorClassification

Use linear support vector machines to classify data instances.

This is the linear version of the support vector classification. As such, it will not be able to exploit non-linear structure, but on data that has little to no dominant non-linear features, it will perform just as well as non-linear ("kernel") SVMs, and do so in a computationally more efficient way. Linear SVMs are quite similar to logistic regression, and can be more robust in the presence of outliers (when using the default hinge loss), though on the other hand the probability estimates produced by SVM are not as theoretically straightforward and well-motivated as those of logistic regression. This implementation uses regularization by default, which allows it to handle large numbers of features very well. Importantly, there are two types of regularization that one can choose from: the default l2 regularization is a fine choice for most data (it is closely related to shrinkage in LDA or a Gaussian prior in Bayesian methods). The alternative l1 regularization is unique in that it can learn to identify a sparse subset of features that is relevant while pruning out all other features as irrelevant. This sparsity regularization is statistically very efficient and can deal with an extremely large number of irrelevant features. To determine the optimal regularization strength, a list of candidate parameter settings can be given, which is then searched by the method using an internal cross-validation on the data to find the best value. If there are very few trials, or some extensive stretches of the data exhibit only one class, this cross-validation can fail with an error that there were too few or no trials of a given class present. Also, the default search grid for regularization (i.e., the list of candidate values) is deliberately rather coarse to keep the method fast. For higher-quality results, use a more fine grained list of values (which will be correspondingly slower). This method can be implemented using a number of different numerical approaches which have different running times depending on the number of data points and features. If you are re-solving the problem a lot, it can make sense to try out the various solvers to find the fastest one. Like all machine learning methods, this method needs to be calibrated ("trained") before it can make any predictions on data. For this, the method requires training instances and associated training labels. The typical way to get such labels associated with time-series data is to make sure that a marker stream is included in the data, which is usually imported together with the data using one of the Import nodes, or received over the network alongside with the data, e.g., using the LSL Input node (with a non-empty marker query). These markers are then annotated with target labels using the Assign Targets node. To generate instances of training data for each of the training markers, one usually uses the Segmentation node to extract segments from the continuous time series around each marker. Since this machine learning method is not capable of being trained incrementally on streaming data, the method requires a data packet that contains the entire training data; this training data packet can either be accumulated online and then released in one shot using the Accumulate Calibration Data node, or it can be imported from a separate calibration recording and then spliced into the processing pipeline using the Inject Calibration Data, where it passes through the same nodes as the regular data until it reaches the machine learning node, where it is used for calibration. Once this node is calibrated, the trainable state of this node can be saved to a model file and later loaded for continued use. More Info... Version 1.0.1

Ports/Properties

data

Data to process.

• verbose name: Data
• default value: None
• port type: DataPort
• value type: Packet (can be None)
• data direction: INOUT

class_weights

Per-class weights. Optionally this is a mapping from class label to weight. The weights represent the a priori ("prior") probability of encountering a specific class that the model shall assume. The weights will be renormalized so that they add up to one. Example syntax: {'0': 0.5, '1': 0.5} (note the quotes before the colons).

• verbose name: Per-Class Weight
• default value: None
• port type: Port
• value type: object (can be None)

loss

Loss function to use. This selects the data term, i.e., what assumptions are being imposed on the data. l1 is the hinge loss (standard SVM), which has a certain level of robustness to outliers. l2 is the squared hinge loss which is like hinge but is quadratically penalized and therefore in some sense more regression-like.

• verbose name: Data Term
• default value: l1
• port type: EnumPort
• value type: str (can be None)

regularizer

Regularization type. The default l2 regularization is a good choice for most data. The alternative l1 regularization results in a type of "feature selection", that is, only a sparse set of features will have a non-zero weight, and all other features will remain effectively unused. This sparsity regularization is very useful if only a small number of features are relevant. It is nevertheless a good idea to compare to the performance of using l2 regularization, since excessive sparsity can sometimes degrade performance.

• verbose name: Regularization Type
• default value: l2
• port type: EnumPort
• value type: str (can be None)

include_bias

Include a bias term. If false, your features need to be centered, or include a dummy feature set to 1.

• verbose name: Include Bias Term
• default value: True
• port type: BoolPort
• value type: bool (can be None)

cost

SVM cost parameter. This value determines the degree to which solutions are penalized that would mis-classify data points. Higher values result in models that are less likely to mis-classify the training data, but at the expense of potentially worse generalization to new data (less margin for error when slightly different trials are encountered in future test data). This is a list of candidate values, the best of which is found via an exhaustive search (i.e., each value is tested one by one, and therefore the total running time is proportional to the number of values listed here). The details of the parameter search can be controlled via the search metric and number of folds parameters. Larger values cause stronger regularization, that is, less risk of the method over-fitting to random details of the data, and thus better generalization to new data. A very small value means effectively no penalty, and there is no upper limit to how large the values that can be given here may be -- however, depending on the scale of the data and the number of trials. Often one covers a range between 0.1 and 10, and at times 0.01 to 100. Typically the values here are not linearly spaced, but follow an exponential progression (e.g., 0.25, 0.5, 1, 2, 4, 8, ... etc). The default search range is intentionally coarse for quick running times; refine it to smaller steps to obtain potentially better solutions, but do not expect massive gains from refining.

• verbose name: Cost
• default value: [0.01, 0.1, 1.0, 10.0, 100]
• port type: Port
• value type: list (can be None)

search_metric

Parameter search metric. When the regularization parameter is given as a list of values, then the method will run a cross-validation for each possible parameter value and use this metric to score how well the method performs in each case, in order to select the best parameter. While 'accuracy' is usually a good default, some other metrics can be useful under special circumstances, e.g., roc_auc for highly imbalanced ratios of trials from different classes.

• verbose name: Scoring Metric
• default value: accuracy
• port type: EnumPort
• value type: str (can be None)

num_folds

Number of cross-validation folds for parameter search. Cross-validation proceeds by splitting up the data into this many blocks of trials, and then tests the method on each block. For each fold, the method is re-trained on all the other blocks, excluding the test block (therefore, the total running time is proportional to the number of folds). This is not a randomized cross-validation, but a blockwise cross-validation, which is usually the correct choice if the data stem from a time series. If there are few trials in the data, one can use a higher number here (e.g., 10) to ensure that more data is available for training.

• verbose name: Number Of Cross-Validation Folds
• default value: 5
• port type: IntPort
• value type: int (can be None)

cv_group_field

Optionally a field indicating the group from which each trial is sourced. If given, then data will be split such that test sets contain unseen groups. Examples groups are SubjectID, SessionID, etc.

• verbose name: Grouping Field (Cross-Validation)
• default value:
• port type: StringPort
• value type: str (can be None)

cv_stratified

Optionally perform stratified cross-validation. This means that all the folds have the same relative percentage of trials in each class.

• verbose name: Stratified Cross-Validation
• default value: False
• port type: BoolPort
• value type: bool (can be None)

max_iter

Maximum number of iterations. This is one of the stopping criteria to limit the compute time. The default is usually fine, and gains from increasing the number of iterations will be minimal (it can be worth experimenting with lower iteration numbers if the algorithm must finish in a fixed time budget, at a cost of potentially less accurate solutions).

• verbose name: Maximum Number Of Iterations
• default value: 1000
• port type: IntPort
• value type: int (can be None)

tolerance

Convergence tolerance. This is the desired errors tolerance or acceptable inaccuracy in the solution. Using larger values gives less accurate results, but will lead to faster compute times. Note that, for biosignal-driven machine learning systems, one often does not need very small tolerances.

• verbose name: Tolerance
• default value: 0.0001
• port type: FloatPort
• value type: float (can be None)

dual_formulation

Use dual formulation. This is an alternative way to solve the problem. If enabled, it can be faster when the number of trials is larger than the number of features, but it is not supported with l1 regularization.

• verbose name: Use Alternative Dual Formulation
• default value: True
• port type: BoolPort
• value type: bool (can be None)

initialize_once

Calibrate the model only once. If set to False, then this node will recalibrate itself whenever a non-streaming data chunk is received that has both training labels and associated training instances.

• verbose name: Calibrate Only Once
• default value: True
• port type: BoolPort
• value type: bool (can be None)

dont_reset_model

Do not reset the model when the preceding graph is changed. Normally, when certain parameters of preceding nodes are being changed, the model will be reset. If this is enabled, the model will persist, but there is a chance that the model is incompatible when input data format to this node has changed.

• verbose name: Do Not Reset Model
• default value: False
• port type: BoolPort
• value type: bool (can be None)

bias_scaling

Scale for bias term. Since this implementation applies the regularization to the bias term too (which is usually not ideal, although rarely a significant issue), you can use this scale to counter the effect.

• verbose name: Bias Scaling
• default value: 1.0
• port type: FloatPort
• value type: float (can be None)

random_seed

Random seed. Different values may give slightly different outcomes. It is not recommended to ever touch this since manipulation the random seed does generally not result in robust improvements.

• verbose name: Random Seed
• default value: 12345
• port type: IntPort
• value type: int (can be None)

verbosity

Verbosity level. Higher numbers will produce more extensive diagnostic output.

• verbose name: Verbosity Level
• default value: 0
• port type: IntPort
• value type: int (can be None)

set_breakpoint

Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.

• verbose name: Set Breakpoint (Debug Only)
• default value: False
• port type: BoolPort
• value type: bool (can be None)

metadata

User-definable meta-data associated with the node. Usually reserved for technical purposes.

• verbose name: Metadata
• default value: {}
• port type: DictPort
• value type: dict (can be None)