IncrementalPrincipalComponentAnalysis¶
Reduce dimensionality using Incremental Principal Component Analysis (PCA).
PCA is one of the most commonly-used dimensionality reduction techniques, and will produce components, that is linear combinations of input features, such that the first component captures the direction of largest variance in the data, the second component the next-largest (orthogonal) direction, and so on. Note that, since PCA is not aware of any "labels" of the data, and as such only performs what is known as unsupervised learning, there is no guarantee that PCA will not remove data dimensions that would have been informative about those labels, that is, useful to a subsequent supervised learning method. Nevertheless, dimensionality reduction can greatly speed up subsequent data processing, or make it tractable in the first place. Also, since the components are sorted by the amount of variance in the data that they explain, in some settings they may yield individually interpretable or otherwise meaningful features. Important:This node is adaptive to the data, that is, it will learn a transformation of the data that depends on the input data. In contrast to basic PCA, this node can update itself incrementally on streaming data as well as in one shot on offline data. When applying this node on streaming data, keep in mind that, as the node keeps updating its model, the output space will keep changing, especially in the beginning on the first few data points. If you use subsequent processing that assumes that the data space remains fixed, that can cause problems. The most common 'abuse' would be to follow this node by any node that buffers calibration data for a period of time and then does a one-shot calibration of some state. A better setup would be instead to perform the buffering prior to the incremental PCA node, or to use a regular static PCA, to avoid changing the data 'under the feet' of some other adaptive node. Once this node is calibrated, the trainable state of this node can be saved to a model file and later loaded for continued use. Like most other feature extraction nodes, this node can compute features between elements of an axis of your choice while treating elements of another axis as the observations, trials, or samples. It can also optionally compute multiple separate models on different slices of the data along some axis of choice. It is also possible to pool multiple axes for any of these roles. More Info... Version 1.0.0
Ports/Properties¶
domain_axes¶
Axes which form the input domain of the transformation. Features are computed between elements along these axes (or in other words, elements along these axes will be combined with each other to yield features). This is a comma-separated list of axis names (for example: "space, frequency"), possibly empty, or the special string "(all others)", which stands for all axes that are not listed in the other two lists of axes. For time-series data, this is usually the space axis, and if features have already been extracted from the data through some other method, it would be the features axis. In rare cases it can also include other axes, such as frequency, lag, and time. This axis drops out of the data and is replaced by a feature axis that has a number of elements that corresponds to the output feature dimension of the model.
- verbose name: Compute Features Between Axes
- default value: (all others)
- port type: ComboPort
- value type: str (can be None)
aggregate_axes¶
Axes that have the statistical observations in them. The elements along these axes are treated as the "trials", "samples", or, equivalently, "observations". Adaptive feature extractors will aggregate statistics along the elements of these axes during training. When the transformer applies to data, this axis remains in the data (if multiple, they will be flattened into one axis, however). This is a comma-separated list of axis names (for example: "time, instance"), possibly empty, or the special string "(all others)", which stands for all axes that are not listed in the other two lists of axes. This is almost always the instance axis (especially if the data has already been segmented, i.e., if the Segmentation node was used), but in some cases it may also be the time axis, or occasionally other axes.
- verbose name: Treat Elements As Trials/samples Along Axes
- default value: instance
- port type: ComboPort
- value type: str (can be None)
separate_axes¶
Axes along which to learn separate models. It is possible to use multiple separate feature-extraction models, each of which operates on a different slice of the data. This node does not combine data between elements along these axes in any way (although features between these elements may of course be combined in later stages, for instance in a classifier node). This is a comma-separated list of axis names (for example: "time, frequency"), possibly empty, or the special string "(all others)", which stands for all axes that are not listed in the other two lists of axes. This axis, if any, will also persist in the data.
- verbose name: Compute Separate Models Along Axes
- default value:
- port type: ComboPort
- value type: str (can be None)
data¶
Data to process.
- verbose name: Data
- default value: None
- port type: DataPort
- value type: Packet (can be None)
- data direction: INOUT
num_components¶
Number of components to keep. If left unspecified, all components are kept, that is, the number of output features will correspond to the number of input dimensions.
- verbose name: Number Of Components
- default value: None
- port type: IntPort
- value type: int (can be None)
whiten¶
Normalize (whiten) outputs. This will decorrelate the features.
- verbose name: Whiten
- default value: False
- port type: BoolPort
- value type: bool (can be None)
batch_size¶
Batch size. Optionally the number of samples to use for each mini-batch. This is a tradeoff between high performance if enough data is available during a given update (using larger batches) and low memory use (using smaller batches).
- verbose name: Batch Size
- default value: None
- 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)