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=1, random_state=None, verbose=0, warm_start=False, class_weight=None)[source][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_estimators : integer, optional (default=100)
The number of trees in the forest.
 criterion : string, optional (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_depth : integer or None, optional (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_split : int, float, optional (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 and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.
 min_samples_leaf : int, float, optional (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 and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.
 If int, then consider
 min_weight_fraction_leaf : float, optional (default=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 : int, float, string or None, optional (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 and int(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_nodes : int or None, optional (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_decrease : float, optional (default=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. bootstrap : boolean, optional (default=True)
Whether bootstrap samples are used when building trees.
 oob_score : bool (default=False)
Whether to use outofbag samples to estimate the generalization accuracy.
 sampling_strategy : float, 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 majority class over the number of samples in the minority class after resampling. Therefore, the ratio is expressed as where and are the number of samples in the majority class after resampling and the number of samples in the minority class, respectively.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.
 replacement : bool, optional (default=False)
Whether or not to sample randomly with replacement or not.
 n_jobs : int, optional (default=1)
The number of jobs to run in parallel for both fit and predict. If 1, then the number of jobs is set to the number of cores.
 random_state : int, RandomState instance or None, optional (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
.
 If int,
 verbose : int, optional (default=0)
Controls the verbosity of the tree building process.
 warm_start : bool, optional (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_weight : dict, list of dicts, “balanced”,
“balanced_subsample” or None, optional (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.
References
[1] 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) # doctest: +ELLIPSIS BalancedRandomForestClassifier(...) >>> print(clf.feature_importances_) [ 0.21506735 0.0104961 0.00706549 0.17414694 0.00556422 0.00704686 0.19779549 0.01865445 0.00608294 0.00490484 0.00866699 0.00251414 0.00339721 0.01174379 0.09380596 0.05049964 0.0033278 0.01008566 0.15534173 0.01379241] >>> 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_ : ndaray, 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, shape (n_features,)Return the feature importances (the higher, the more important the feature).
 oob_score_ : float
Score of the training dataset obtained using an outofbag estimate.
 oob_decision_function_ : ndarray, 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__
(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=1, random_state=None, verbose=0, warm_start=False, class_weight=None)[source][source]¶ Initialize self. See help(type(self)) for accurate signature.

apply
(X)[source]¶ Apply trees in the forest to X, return leaf indices.
Parameters:  X : arraylike or sparse matrix, 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_leaves : array_like, 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
(X)[source]¶ Return the decision path in the forest
New in version 0.18.
Parameters:  X : arraylike or sparse matrix, 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:  indicator : sparse csr array, shape = [n_samples, n_nodes]
Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes.
 n_nodes_ptr : array of size (n_estimators + 1, )
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the ith estimator.

feature_importances_
¶  Return the feature importances (the higher, the more important the
 feature).
Returns:  feature_importances_ : array, shape = [n_features]

fit
(X, y, sample_weight=None)[source][source]¶ Build a forest of trees from the training set (X, y).
Parameters:  X : {arraylike, sparse matrix}, 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
. y : arraylike, shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in regression).
 sample_weight : arraylike, 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:  self : object

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

predict
(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 or 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:  y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted classes.

predict_log_proba
(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 or 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:  p : array 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
(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 or 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:  p : array 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
(X, y, sample_weight=None)[source]¶ Returns 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:  X : arraylike, shape = (n_samples, n_features)
Test samples.
 y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
 sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns:  score : float
Mean accuracy of self.predict(X) wrt. y.

set_params
(**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.Returns:  self