mojolearn.RandomForestRegressor#

class mojolearn.RandomForestRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None, max_leaves=-1, n_bins=128, n_streams=4, max_batch_size=4096, device='gpu', inference_engine='auto', numeric_mode=None)[source]#

Bases: _RandomForestBase

Random forest regressor with cuML’s defaults.

An ensemble of bootstrapped regression trees whose split thresholds are searched over at most n_bins per-feature quantiles, as in cuML, rather than scikit-learn’s exact thresholds. Defaults follow cuML, including max_features=1.0. Every scikit-learn-shaped parameter is either honoured or refused by name with NotImplementedError.

X and y are converted to float32; NaN or infinity in X raises ValueError. random_state=None means seed 0 (deterministic), unlike scikit-learn. Supports the fast, deterministic and identical numeric modes through numeric_mode.

Parameters:

  • n_estimators (int, default 100) – Number of trees.

  • criterion ({‘squared_error’, ‘mse’, ‘poisson’, ‘gamma’, ‘inverse_gaussian’}, default ‘squared_error’) – Split criterion; ‘mse’ is cuML’s spelling of ‘squared_error’. ‘poisson’ requires finite y >= 0 with at least one positive value; ‘gamma’ and ‘inverse_gaussian’ require finite y > 0; otherwise fit raises ValueError. ‘absolute_error’, ‘mae’ and ‘friedman_mse’ raise NotImplementedError.

  • max_depth (int or None, default None) – Maximum tree depth. None means unlimited.

  • min_samples_split (int, default 2) – Minimum rows needed to split a node.

  • min_samples_leaf (int, default 1) – Minimum rows in a leaf.

  • min_weight_fraction_leaf (float, default 0.0) – Only 0.0 is accepted.

  • max_features ({‘sqrt’, ‘log2’}, int, float or None, default 1.0) – Features considered per split: a name, a count, a fraction in (0, 1], or None for all features.

  • max_leaf_nodes (None, default None) – Refused by name. Use max_leaves for cuML’s level-order leaf cap, or mojolearn.ExtraTreesRegressor, whose max_leaf_nodes is scikit-learn’s best-first budget.

  • min_impurity_decrease (float, default 0.0) – Minimum impurity decrease required to split.

  • bootstrap (bool, default True) – Sample rows with replacement for each tree.

  • oob_score (bool, default False) – Compute the out-of-bag score during fit; requires bootstrap=True.

  • n_jobs (None, default None) – Only None is accepted.

  • random_state (int or None, default None) – Seed; None means 0.

  • verbose (int, default 0) – Only 0 is accepted.

  • warm_start (bool, default False) – Only False is accepted.

  • ccp_alpha (float, default 0.0) – Only 0.0 is accepted.

  • max_samples (float or None, default None) – Fraction of rows drawn per tree, in (0, 1]. None means all rows.

  • monotonic_cst (None, default None) – Only None is accepted.

  • max_leaves (int, default -1) – cuML’s leaf cap on a level-order grower; -1 (or None) means unlimited.

  • n_bins (int, default 128) – Number of per-feature quantile bins searched for splits.

  • n_streams (int, default 4) – cuML’s stream count parameter.

  • max_batch_size (int, default 4096) – cuML’s node batch size for the tree builder.

  • device ({‘gpu’}, default ‘gpu’) – Only ‘gpu’ is accepted. On a CPU-only install the same setting runs the host build of the forest.

  • inference_engine ({‘auto’, ‘sequential’, ‘parallel_groves’}, default ‘auto’) – How predict reduces over trees. ‘auto’ uses ‘parallel_groves’ in fast mode and ‘sequential’ otherwise.

  • numeric_mode ({‘fast’, ‘deterministic’, ‘identical’} or None, default None) – Numeric mode for this estimator; None uses the package default.

Variables:

  • n_features_in_ (int) – Number of features seen in fit.

  • oob_score_ (float) – Out-of-bag score; present when oob_score=True.

  • oob_prediction_ (Array) – Out-of-bag predictions, float64; present when oob_score=True.

  • feature_importances_ (Array) – Impurity importances, float32; present when fit was called with feature_importances=True.

Examples

>>> import numpy as np, mojolearn
>>> rng = np.random.default_rng(0)
>>> X = rng.random((1000, 8), dtype=np.float32)
>>> y = X[:, 0] * 2.0 + X[:, 1]
>>> model = mojolearn.RandomForestRegressor(n_estimators=50).fit(X, y)
>>> pred = model.predict(X)
__init__(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None, max_leaves=-1, n_bins=128, n_streams=4, max_batch_size=4096, device='gpu', inference_engine='auto', numeric_mode=None)[source]#

Methods

apply(X)

Return tree-relative leaf IDs, shape (rows, trees), from the native walk.

fit(X, y, *[, feature_importances])

Fit the forest.

get_params([deep])

load(path)

Load a forest saved by save.

numeric_mode_used()

The numeric mode this estimator runs in.

predict(X)

Predicted values: the mean of the trees' predictions.

predict_with_leaves(X)

Return ordered raw mean scores (rows, outputs) and leaf IDs (rows, trees).

save(path)

Write the fitted forest to path as an npz file.

score(X, y[, sample_weight])

Mode-aware GPU accuracy or Float32 R², weighted when sample_weight is given (scikit-learn's weighted accuracy and R² on the pinned-sum path of metrics.accuracy_score and metrics.r2_score).

set_params(**params)

Set constructor parameters.

staged_predict(X)

Return eager mean prefixes, shape (rows, trees, outputs), in tree order.

vendor_used()

The GPU backend this estimator runs on.
fit(X, y, *, feature_importances=False)[source]#

Fit the forest.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Training data, converted to float32.

  • y (array-like of shape (n_samples,)) – Targets.

  • feature_importances (bool, default False) – Also compute feature_importances_ during this fit.

Returns:

self

predict(X)[source]#

Predicted values: the mean of the trees’ predictions.

Returns:

Array of shape (n_samples,), float32