An API to VFML's engine for learning decision trees from high-speed data streams. The vfdt program is basically a wrapper around this interface. You can use this interface to learn decision trees from a stream of data as it arrives, and use the tree in a parallel to make predictions.
To use this interface you generate a VFDTPtr using the VFDTNew function, set any parameters you want to use to control the learning using the functions that are described below, and then repeatedly feed examples to the VFDTPtr using the VFDTProcessExample function. You can call VFDTGetLearnedTree at anytime to get a copy of the current learned tree. Note that vfdt-engine will take over the memory of any examples feed to it, and you should not free them or your program will crash.
Go to the source code of this file.
Data Structures | |
| struct | _VFDT_ |
| Holds the information needed to learn decision trees from data streams. More... | |
Typedefs | |
| typedef _VFDT_ | VFDT |
| Holds the information needed to learn decision trees from data streams. | |
| typedef _VFDT_ * | VFDTPtr |
| Holds the information needed to learn decision trees from data streams. | |
Functions | |
| VFDTPtr | VFDTNew (ExampleSpecPtr spec, float splitConfidence, float tieConfidence) |
| Allocate memory to learn a decision tree from a data stream. | |
| void | VFDTFree (VFDTPtr vfdt) |
| frees all the memory being used by the learning process. | |
| void | VFDTSetMessageLevel (VFDTPtr vfdt, int value) |
| Higher message levels print more output to the console. | |
| void | VFDTSetMaxAllocationMegs (VFDTPtr vfdt, int value) |
| Put a limit on the dynamic memory used by the program. | |
| void | VFDTSetProcessChunkSize (VFDTPtr vfdt, int value) |
| Sets the number of examples before checks for tree growth. | |
| void | VFDTSetUseGini (VFDTPtr vfdt, int value) |
| Set the evaluation function to Gini (default is Entropy). | |
| void | VFDTSetRestartLeaves (VFDTPtr vfdt, int value) |
| Consider reactivating leaves where growing was stopped to save RAM. | |
| void | VFDTSetCacheTrainingExamples (VFDTPtr vfdt, int value) |
| Use extra RAM to cache training examples. | |
| void | VFDTSetPrePruneTau (VFDTPtr vfdt, float value) |
| Set the pre-prune parameter. | |
| void | VFDTSetLaplace (VFDTPtr vfdt, int value) |
| Set how many examples worth of smoothing to do in class probability estimates. | |
| void | VFDTProcessExamples (VFDTPtr vfdt, FILE *input) |
| Read as many examples as possible from the file and learn from them. | |
| void | VFDTProcessExamplesBatch (VFDTPtr vfdt, FILE *input) |
| Learn from the examples in batch mode. | |
| void | VFDTProcessExampleBatch (VFDTPtr vfdt, ExamplePtr e) |
| Add the example to the learner without checking for splits. | |
| void | VFDTBatchExamplesDone (VFDTPtr vfdt) |
| Forces vfdt-engine to make as many splits as possible using traditional methods. | |
| void | VFDTProcessExample (VFDTPtr vfdt, ExamplePtr e) |
| Adds another example to the learning process. | |
| int | VFDTIsDoneLearning (VFDTPtr vfdt) |
| Returns 1 if no nodes in the tree are still active. | |
| long | VFDTGetNumGrowing (VFDTPtr vfdt) |
| Returns the number of nodes that are growing. | |
| long | VFDTGetNumBoundsUsed (VFDTPtr vfdt) |
| Returns the number of statistical tests made. | |
| void | VFDTPrintStats (VFDTPtr vfdt, FILE *out) |
| Prints some information about the growing nodes to the file. | |
| DecisionTreePtr | VFDTGetLearnedTree (VFDTPtr vfdt) |
| Returns the current tree. | |
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Holds the information needed to learn decision trees from data streams.
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Holds the information needed to learn decision trees from data streams.
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Forces vfdt-engine to make as many splits as possible using traditional methods.
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frees all the memory being used by the learning process.
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Returns the current tree. This will guess on which class to predict at the growing leaves (smoothing with strength of the lapace paramterer towards the distribution seen at the parent). Returns a copy of the internally growing decision tree, and so vfdt-engine can continue to learn on the internal tree unaffected. |
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Returns the number of statistical tests made.
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Returns the number of nodes that are growing.
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Returns 1 if no nodes in the tree are still active. This may happen because RAM runs out, or because everything was pre-pruned. |
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Allocate memory to learn a decision tree from a data stream. Sets up the learning, makes an initial tree with a single node. splitConfidence is the delta parameter from our paper (the probability of making a mistake with the sampling we use) and the tieConfidence is tau (the minimum difference in gain that you care about). splitConfidence should be a small non-zero number, maybe 10^-7. And tieConfidence should be something in the range of 0 - .1 (although slightly bigger values may be useful). |
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Prints some information about the growing nodes to the file.
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Adds another example to the learning process. Check for splits as needed (according to the chunk size). |
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Add the example to the learner without checking for splits. When you have added all the examples you want call VFDTBatchExamplesDone to tell vfdt-engine it is time to make splits. |
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Read as many examples as possible from the file and learn from them. This will repeatedly read and learn from examples in the file until it can not read any more. Note that this function will block until that time. |
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Learn from the examples in batch mode. That is, read them all into RAM and use every example to make every learning decision. |
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Use extra RAM to cache training examples. Default is to cache. This keeps examples in memory to possibly use them to help make several decisions, speeding up the induction. When RAM is full vfdt-engine starts deactivating example caches at the least promising leaves. All caches will be deactivated before any leaves are deactived. |
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Set how many examples worth of smoothing to do in class probability estimates.
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Put a limit on the dynamic memory used by the program. This requires that DEBUGMEMORY is defined in memory.h (which is the default). By setting this you limit the amount of memory allocated with calls to MemNewPtr by either your program and by vfdt-engine. This means any other calls you make to VFML functions (e.g. reading examples from disk) will be counted against vfdt's total. (you can use MSetActivePool with pool id 0 to get around this). If this memory threshold is crossed vfdt-engine starts purging its allocations by first throwing away cahed examples and then disabling learning at the least promising leaves. |
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Higher message levels print more output to the console. Levels above 2 print a lot of output. More than you want. I promise. |
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Set the pre-prune parameter. The default is 0.0, which means no pre-pruning. If the gain of all attributes is less than this value then pre-prune. Also, do not call a tie unless an attribute beats another by at least this much. |
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Sets the number of examples before checks for tree growth. Check for growth at a leaf once every time it accumulates this many examples. Default is 300. |
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Consider reactivating leaves where growing was stopped to save RAM. The default value for this is true, so periodically vfdt-engine looks at all the deactivated leaves to see if any of them are more promising than any of the currently active ones, and makes adjustments. If set to false any leaf that is deactivated is effectively pre-pruned. |
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Set the evaluation function to Gini (default is Entropy).
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