AdaptiveRandomForestClassifier¶
Adaptive Random Forest classifier.
The 3 most important aspects of Adaptive Random Forest 1 are:
-
inducing diversity through re-sampling
-
inducing diversity through randomly selecting subsets of features for node splits
-
drift detectors per base tree, which cause selective resets in response to drifts
It also allows training background trees, which start training if a warning is detected and replace the active tree if the warning escalates to a drift.
Parameters¶
-
n_models (int) – defaults to
10
Number of trees in the ensemble.
-
max_features (Union[bool, str, int]) – defaults to
sqrt
Max number of attributes for each node split.
- Ifint
, then considermax_features
at each split.
- Iffloat
, thenmax_features
is a percentage andint(max_features * n_features)
features are considered per split.
- If "sqrt", thenmax_features=sqrt(n_features)
.
- If "log2", thenmax_features=log2(n_features)
.
- If None, thenmax_features=n_features
. -
lambda_value (int) – defaults to
6
The lambda value for bagging (lambda=6 corresponds to Leveraging Bagging).
-
metric (river.metrics.base.MultiClassMetric) – defaults to
Accuracy: 0.00%
Metric used to track trees performance within the ensemble.
-
disable_weighted_vote – defaults to
False
If
True
, disables the weighted vote prediction. -
drift_detector (Union[base.DriftDetector, NoneType]) – defaults to
ADWIN
Drift Detection method. Set to None to disable Drift detection.
-
warning_detector (Union[base.DriftDetector, NoneType]) – defaults to
ADWIN
Warning Detection method. Set to None to disable warning detection.
-
grace_period (int) – defaults to
50
[Tree parameter] Number of instances a leaf should observe between split attempts.
-
max_depth (int) – defaults to
None
[Tree parameter] The maximum depth a tree can reach. If
None
, the tree will grow indefinitely. -
split_criterion (str) – defaults to
info_gain
[Tree parameter] Split criterion to use.
- 'gini' - Gini
- 'info_gain' - Information Gain
- 'hellinger' - Hellinger Distance -
split_confidence (float) – defaults to
0.01
[Tree parameter] Allowed error in split decision, a value closer to 0 takes longer to decide.
-
tie_threshold (float) – defaults to
0.05
[Tree parameter] Threshold below which a split will be forced to break ties.
-
leaf_prediction (str) – defaults to
nba
[Tree parameter] Prediction mechanism used at leafs.
- 'mc' - Majority Class
- 'nb' - Naive Bayes
- 'nba' - Naive Bayes Adaptive -
nb_threshold (int) – defaults to
0
[Tree parameter] Number of instances a leaf should observe before allowing Naive Bayes.
-
nominal_attributes (list) – defaults to
None
[Tree parameter] List of Nominal attributes. If empty, then assume that all attributes are numerical.
-
splitter (river.tree.splitter.base.Splitter) – defaults to
None
[Tree parameter] The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the
tree.splitter
module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their propertyis_target_class
. This is an advanced option. Special care must be taken when choosing different splitters. By default,tree.splitter.GaussianSplitter
is used ifsplitter
isNone
. -
binary_split (bool) – defaults to
False
[Tree parameter] If True, only allow binary splits.
-
max_size (int) – defaults to
32
[Tree parameter] Maximum memory (MB) consumed by the tree.
-
memory_estimate_period (int) – defaults to
2000000
[Tree parameter] Number of instances between memory consumption checks.
-
stop_mem_management (bool) – defaults to
False
[Tree parameter] If True, stop growing as soon as memory limit is hit.
-
remove_poor_attrs (bool) – defaults to
False
[Tree parameter] If True, disable poor attributes to reduce memory usage.
-
merit_preprune (bool) – defaults to
True
[Tree parameter] If True, enable merit-based tree pre-pruning.
-
seed (int) – defaults to
None
If
int
,seed
is used to seed the random number generator; IfRandomState
,seed
is the random number generator; IfNone
, the random number generator is theRandomState
instance used bynp.random
.
Attributes¶
- models
Examples¶
>>> from river import synth
>>> from river import ensemble
>>> from river import evaluate
>>> from river import metrics
>>> dataset = synth.ConceptDriftStream(seed=42, position=500,
... width=40).take(1000)
>>> model = ensemble.AdaptiveRandomForestClassifier(
... n_models=3,
... seed=42
... )
>>> metric = metrics.Accuracy()
>>> evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 70.47%
Methods¶
append
S.append(value) -- append value to the end of the sequence
Parameters
- item
clear
S.clear() -> None -- remove all items from S
clone
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy
if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
copy
count
S.count(value) -> integer -- return number of occurrences of value
Parameters
- item
extend
S.extend(iterable) -- extend sequence by appending elements from the iterable
Parameters
- other
index
S.index(value, [start, [stop]]) -> integer -- return first index of value. Raises ValueError if the value is not present.
Supporting start and stop arguments is optional, but recommended.
Parameters
- item
- args
insert
S.insert(index, value) -- insert value before index
Parameters
- i
- item
learn_one
pop
S.pop([index]) -> item -- remove and return item at index (default last). Raise IndexError if list is empty or index is out of range.
Parameters
- i – defaults to
-1
predict_one
Predict the label of a set of features x
.
Parameters
- x (dict)
Returns
typing.Union[bool, str, int]: The predicted label.
predict_proba_one
Predict the probability of each label for a dictionary of features x
.
Parameters
- x (dict)
Returns
typing.Dict[typing.Union[bool, str, int], float]: A dictionary that associates a probability which each label.
remove
S.remove(value) -- remove first occurrence of value. Raise ValueError if the value is not present.
Parameters
- item
reset
Reset the forest.
reverse
S.reverse() -- reverse IN PLACE
sort
References¶
-
Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabricio Enembreck, Bernhard Pfharinger, Geoff Holmes, Talel Abdessalem. Adaptive random forests for evolving data stream classification. In Machine Learning, DOI: 10.1007/s10994-017-5642-8, Springer, 2017. ↩