skopt.learning.ExtraTreesRegressor

class skopt.learning.ExtraTreesRegressor(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, min_variance=0.0)

ExtraTreesRegressor that supports conditional standard deviation.

Parameters

n_estimatorsinteger, optional (default=10)

The number of trees in the forest.

criterionstring, optional (default=”mse”)

The function to measure the quality of a split. Supported criteria are “mse” for the mean squared error, which is equal to variance reduction as feature selection criterion, and “mae” for the mean absolute error.

max_featuresint, 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=n_features.

• If “sqrt”, then max_features=sqrt(n_features).

• 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_depthinteger 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_splitint, 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_leafint, 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 percentage and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

min_weight_fraction_leaffloat, 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_leaf_nodesint or None, optional (default=None)

Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

min_impurity_decreasefloat, 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, and N_t_R is the number of samples in the right child. N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

bootstrapboolean, optional (default=True)

Whether bootstrap samples are used when building trees.

oob_scorebool, optional (default=False)

whether to use out-of-bag samples to estimate the R^2 on unseen data.

n_jobsinteger, 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_stateint, RandomState instance or None, optional (default=None)

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 the RandomState instance used by np.random.

verboseint, optional (default=0)

Controls the verbosity of the tree building process.

warm_startbool, 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.

Attributes

estimators_list of DecisionTreeRegressor

The collection of fitted sub-estimators.

feature_importances_array of shape = [n_features]

The impurity-based feature importances.

n_features_int

DEPRECATED: Attribute n_features_ was deprecated in version 1.0 and will be removed in 1.2.

n_outputs_int

The number of outputs when fit is performed.

oob_score_float

Score of the training dataset obtained using an out-of-bag estimate.

oob_prediction_array of shape = [n_samples]

Prediction computed with out-of-bag estimate on the training set.

Notes

The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data, max_features=n_features and bootstrap=False, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, random_state has to be fixed.

References

1

12. Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001.

Methods

apply(X)	Apply trees in the forest to X, return leaf indices.
decision_path(X)	Return the decision path in the forest.
fit(X, y[, sample_weight])	Build a forest of trees from the training set (X, y).
get_params([deep])	Get parameters for this estimator.
predict(X[, return_std])	Predict continuous output for X.
score(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
set_params(**params)	Set the parameters of this estimator.

__init__(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, min_variance=0.0)

apply(X)

Apply trees in the forest to X, return leaf indices.

Parameters

X{array-like, 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 sparse csr_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(X)

Return the decision path in the forest.

New in version 0.18.

Parameters

X{array-like, 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 sparse csr_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 i-th estimator.

property feature_importances_

The impurity-based 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: impurity-based 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(X, y, sample_weight=None)

Build a forest of trees from the training set (X, y).

Parameters

X{array-like, 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 sparse csc_matrix.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

The target values (class labels in classification, real numbers in regression).

sample_weightarray-like of shape (n_samples,), default=None

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

Fitted estimator.

get_params(deep=True)

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

paramsdict

Parameter names mapped to their values.

property n_features_

Attribute n_features_ was deprecated in version 1.0 and will be removed in 1.2. Use n_features_in_ instead.

Number of features when fitting the estimator.

Type

DEPRECATED

predict(X, return_std=False)

Predict continuous output for X.

Parameters

Xarray-like of shape=(n_samples, n_features)

Input data.

return_stdboolean

Whether or not to return the standard deviation.

Returns

predictionsarray-like of shape=(n_samples,)

Predicted values for X. If criterion is set to “mse”, then predictions[i] ~= mean(y | X[i]).

stdarray-like of shape=(n_samples,)

Standard deviation of y at X. If criterion is set to “mse”, then std[i] ~= std(y | X[i]).

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns

scorefloat

\(R^2\) of self.predict(X) wrt. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). 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

selfestimator instance

Estimator instance.