Forest Classifier

class skgrf.ensemble.GRFForestClassifier(n_estimators=100, *, equalize_cluster_weights=False, sample_fraction=0.5, mtry=None, min_node_size=5, honesty=True, honesty_fraction=0.5, honesty_prune_leaves=True, alpha=0.05, imbalance_penalty=0.0, ci_group_size=2, n_jobs=- 1, seed=42, enable_tree_details=False)[source]

GRF Classification implementation for sci-kit learn.

Provides a sklearn classifier interface to the GRF C++ library using Cython.

Parameters

  • n_estimators (int) – The number of tree classifiers to train

  • equalize_cluster_weights (bool) – Weight the samples such that clusters have equally weight. If False, larger clusters will have more weight. If True, the number of samples drawn from each cluster is equal to the size of the smallest cluster. If True, sample weights should not be passed on fitting.

  • sample_fraction (float) – Fraction of samples used in each tree. If ci_group_size > 1, the max allowed fraction is 0.5

  • mtry (int) – The number of features to split on each node. The default is sqrt(p) + 20 where p is the number of features.

  • min_node_size (int) – The minimum number of observations in each tree leaf.

  • honesty (bool) – Use honest splitting (subsample splitting).

  • honesty_fraction (float) – The fraction of data used for subsample splitting.

  • honesty_prune_leaves (bool) – Prune estimation sample tree such that no leaves are empty. If False, trees with empty leaves are skipped.

  • alpha (float) – The maximum imbalance of a split.

  • imbalance_penalty (float) – Penalty applied to imbalanced splits.

  • ci_group_size (int) – The quantity of trees grown on each subsample. At least 2 is required to provide confidence intervals.

  • n_jobs (int) – The number of threads. Default is number of CPU cores.

  • seed (int) – Random seed value.

  • enable_tree_details (bool) – When True, perform additional calculations for detailing the underlying decision trees. Must be enabled for estimators_ and get_estimator to work. Very slow.

Variables

  • estimators_ (list) – A list of tree objects from the forest.

  • n_features_in_ (int) – The number of features (columns) from the fit input X.

  • grf_forest_ (dict) – The returned result object from calling C++ grf.

  • mtry_ (int) – The mtry value determined by validation.

  • outcome_index_ (int) – The index of the grf train matrix holding the outcomes.

  • samples_per_cluster_ (list) – The number of samples to train per cluster.

  • clusters_ (list) – The cluster labels determined from the fit input cluster.

  • n_clusters_ (int) – The number of unique cluster labels from the fit input cluster.

  • classes_ (list) – The class labels determined from the fit input y.

  • n_classes_ (int) – The number of unique class labels from the fit input y.

  • criterion (str) – The criterion used for splitting: gini

fit(X, y, sample_weight=None, cluster=None, compute_oob_predictions=False)[source]

Fit the grf forest using training data.

Parameters

  • X (array2d) – training input features

  • y (array1d) – training input targets

  • sample_weight (array1d) – optional weights for input samples

  • cluster (array1d) – optional cluster assignments for input samples

get_estimator(idx)[source]

Extract a single estimator tree from the forest.

Parameters

idx (int) – The index of the tree to extract.

get_feature_importances(decay_exponent=2, max_depth=4)

Get the feature importances.

Parameters

  • decay_exponent (int) – Exponential decay of importance by split depth

  • max_depth (int) – The maximum depth of splits to consider

get_kernel_weights(X, oob_prediction=False)

Get training sample weights for test data.

Given a trained forest and test data, compute the kernel weights for each test point.

Creates a sparse matrix in which the value at (i, j) gives the weight of training sample j for test sample i. Use oob_prediction=True if using training set.

Parameters

  • X (array2d) – input features

  • oob_prediction (bool) – whether to calculate weights out of bag

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

get_split_frequencies(max_depth=4)

Get the split frequencies of feature indexes at various depths.

Parameters

max_depth (int) – The maximum depth of splits to consider

predict(X)[source]

Predict classes from X.

Parameters

X (array2d) – prediction input features

predict_log_proba(X)[source]

Predict log probabilities for classes from X.

Parameters

X (array2d) – prediction input features

predict_proba(X)[source]

Predict probabilities for classes from X.

Parameters

X (array2d) – prediction input features

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label 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 (array-like of shape (n_samples, n_features)) – Test samples.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – Mean accuracy of self.predict(X) wrt. y.

Return type

float

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

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance