imblearn.ensemble
.BalancedRandomForestClassifier¶

class
imblearn.ensemble.
BalancedRandomForestClassifier
(n_estimators=100, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=2, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, sampling_strategy='auto', replacement=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)[source]¶ A balanced random forest classifier.
A balanced random forest randomly undersamples each boostrap sample to balance it.
Read more in the User Guide.
 Parameters
 n_estimatorsint, default=100
The number of trees in the forest.
 criterionstr, default=”gini”
The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. Note: this parameter is treespecific.
 max_depthint, default=None
The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.
 min_samples_splitint, float, default=2
The minimum number of samples required to split an internal node:
If int, then consider
min_samples_split
as the minimum number.If float, then
min_samples_split
is a percentage andceil(min_samples_split * n_samples)
are the minimum number of samples for each split.
 min_samples_leafint, float, default=1
The minimum number of samples required to be at a leaf node:
If int, then consider
min_samples_leaf
as the minimum number.If float, then
min_samples_leaf
is a fraction andceil(min_samples_leaf * n_samples)
are the minimum number of samples for each node.
 min_weight_fraction_leaffloat, default=0.0
The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.
 max_features{“auto”, “sqrt”, “log2”}, int, float, or None, default=”auto”
The number of features to consider when looking for the best split:
If int, then consider
max_features
features at each split.If float, then
max_features
is a percentage andint(max_features * n_features)
features are considered at each split.If “auto”, then
max_features=sqrt(n_features)
.If “sqrt”, then
max_features=sqrt(n_features)
(same as “auto”).If “log2”, then
max_features=log2(n_features)
.If None, then
max_features=n_features
.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features. max_leaf_nodesint, default=None
Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_decreasefloat, default=0.0
A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:
N_t / N * (impurity  N_t_R / N_t * right_impurity  N_t_L / N_t * left_impurity)
where
N
is the total number of samples,N_t
is the number of samples at the current node,N_t_L
is the number of samples in the left child, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed. bootstrapbool, default=True
Whether bootstrap samples are used when building trees.
 oob_scorebool, default=False
Whether to use outofbag samples to estimate the generalization accuracy.
 sampling_strategyfloat, str, dict, callable, default=’auto’
Sampling information to sample the data set.
When
float
, it corresponds to the desired ratio of the number of samples in the minority class over the number of samples in the majority class after resampling. Therefore, the ratio is expressed as where is the number of samples in the minority class and is the number of samples in the majority class after resampling.Warning
float
is only available for binary classification. An error is raised for multiclass classification.When
str
, specify the class targeted by the resampling. The number of samples in the different classes will be equalized. Possible choices are:'majority'
: resample only the majority class;'not minority'
: resample all classes but the minority class;'not majority'
: resample all classes but the majority class;'all'
: resample all classes;'auto'
: equivalent to'not minority'
.When
dict
, the keys correspond to the targeted classes. The values correspond to the desired number of samples for each targeted class.When callable, function taking
y
and returns adict
. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.
 replacementbool, default=False
Whether or not to sample randomly with replacement or not.
 n_jobsint, default=None
Number of CPU cores used during the crossvalidation loop.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. random_stateint, RandomState instance, default=None
Control the randomization of the algorithm.
If int,
random_state
is the seed used by the random number generator;If
RandomState
instance, random_state is the random number generator;If
None
, the random number generator is theRandomState
instance used bynp.random
.
 verboseint, default=0
Controls the verbosity of the tree building process.
 warm_startbool, default=False
When set to
True
, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. class_weightdict, list of dicts, {“balanced”, “balanced_subsample”}, default=None
Weights associated with classes in the form dictionary with the key being the class_label and the value the weight. If not given, all classes are supposed to have weight one. For multioutput problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for fourclass multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
The “balanced_subsample” mode is the same as “balanced” except that weights are computed based on the bootstrap sample for every tree grown. For multioutput, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. ccp_alphanonnegative float, default=0.0
Complexity parameter used for Minimal CostComplexity Pruning. The subtree with the largest cost complexity that is smaller than
ccp_alpha
will be chosen. By default, no pruning is performed.New in version 0.6: Added in
scikitlearn
in 0.22 max_samplesint or float, default=None
If bootstrap is True, the number of samples to draw from X to train each base estimator.
If None (default), then draw
X.shape[0]
samples.If int, then draw
max_samples
samples.If float, then draw
max_samples * X.shape[0]
samples. Thus,max_samples
should be in the interval(0, 1)
.
Be aware that the final number samples used will be the minimum between the number of samples given in
max_samples
and the number of samples obtained after resampling.New in version 0.6: Added in
scikitlearn
in 0.22
See also
BalancedBaggingClassifier
Bagging classifier for which each base estimator is trained on a balanced bootstrap.
EasyEnsembleClassifier
Ensemble of AdaBoost classifier trained on balanced bootstraps.
RUSBoostClassifier
AdaBoost classifier were each bootstrap is balanced using randomunder sampling at each round of boosting.
References
 R2d8f3e873ec31
Chen, Chao, Andy Liaw, and Leo Breiman. “Using random forest to learn imbalanced data.” University of California, Berkeley 110 (2004): 112.
Examples
>>> from imblearn.ensemble import BalancedRandomForestClassifier >>> from sklearn.datasets import make_classification >>> >>> X, y = make_classification(n_samples=1000, n_classes=3, ... n_informative=4, weights=[0.2, 0.3, 0.5], ... random_state=0) >>> clf = BalancedRandomForestClassifier(max_depth=2, random_state=0) >>> clf.fit(X, y) BalancedRandomForestClassifier(...) >>> print(clf.feature_importances_) [...] >>> print(clf.predict([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])) [1]
 Attributes
 estimators_list of DecisionTreeClassifier
The collection of fitted subestimators.
 samplers_list of RandomUnderSampler
The collection of fitted samplers.
 pipelines_list of Pipeline.
The collection of fitted pipelines (samplers + trees).
 classes_ndarray of shape (n_classes,) or a list of such arrays
The classes labels (single output problem), or a list of arrays of class labels (multioutput problem).
 n_classes_int or list
The number of classes (single output problem), or a list containing the number of classes for each output (multioutput problem).
 n_features_int
The number of features when
fit
is performed. n_outputs_int
The number of outputs when
fit
is performed.feature_importances_
ndarray of shape (n_features,)The impuritybased feature importances.
 oob_score_float
Score of the training dataset obtained using an outofbag estimate.
 oob_decision_function_ndarray of shape (n_samples, n_classes)
Decision function computed with outofbag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case,
oob_decision_function_
might contain NaN.

__init__
(self, n_estimators=100, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=2, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, sampling_strategy='auto', replacement=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

apply
(self, X)[source]¶ Apply trees in the forest to X, return leaf indices.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
.
 Returns
 X_leavesndarray of shape (n_samples, n_estimators)
For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in.

decision_path
(self, X)[source]¶ Return the decision path in the forest.
New in version 0.18.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
.
 Returns
 indicatorsparse matrix of shape (n_samples, n_nodes)
Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. The matrix is of CSR format.
 n_nodes_ptrndarray of shape (n_estimators + 1,)
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the ith estimator.

property
feature_importances_
¶ The impuritybased feature importances.
The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.
Warning: impuritybased feature importances can be misleading for high cardinality features (many unique values). See
sklearn.inspection.permutation_importance
as an alternative. Returns
 feature_importances_ndarray of shape (n_features,)
The values of this array sum to 1, unless all trees are single node trees consisting of only the root node, in which case it will be an array of zeros.

fit
(self, X, y, sample_weight=None)[source]¶ Build a forest of trees from the training set (X, y).
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsc_matrix
. yarraylike of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in regression).
 sample_weightarraylike of shape (n_samples,)
Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.
 Returns
 selfobject
The fitted instance.

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(self, X)[source]¶ Predict class for X.
The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
.
 Returns
 yndarray of shape (n_samples,) or (n_samples, n_outputs)
The predicted classes.

predict_log_proba
(self, X)[source]¶ Predict class logprobabilities for X.
The predicted class logprobabilities of an input sample is computed as the log of the mean predicted class probabilities of the trees in the forest.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
.
 Returns
 pndarray of shape (n_samples, n_classes), or a list of n_outputs
such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

predict_proba
(self, X)[source]¶ Predict class probabilities for X.
The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
.
 Returns
 pndarray of shape (n_samples, n_classes), or a list of n_outputs
such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

score
(self, X, y, sample_weight=None)[source]¶ Return the mean accuracy on the given test data and labels.
In multilabel classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of self.predict(X) wrt. y.

set_params
(self, **params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.