RandomForestRegressor

class cuml.ensemble.RandomForestRegressor(

*,

n_estimators=100,

split_criterion='mse',

bootstrap=True,

max_samples=1.0,

max_depth=None,

max_leaves=-1,

max_features=1.0,

n_bins=128,

min_samples_leaf=1,

min_samples_split=2,

min_impurity_decrease=0.0,

max_batch_size=4096,

random_state=None,

n_streams=4,

oob_score=False,

verbose=False,

output_type=None,

)

Implements a Random Forest regressor model which fits multiple decision trees in an ensemble.

Note

Note that the underlying algorithm for tree node splits differs from that used in scikit-learn. By default, the cuML Random Forest uses a quantile-based algorithm to determine splits, rather than an exact count. You can tune the size of the quantiles with the n_bins parameter

Note

You can export cuML Random Forest models and run predictions with them on machines without an NVIDIA GPUs. See https://docs.rapids.ai/api/cuml/nightly/pickling_cuml_models.html for more details.

Parameters:

n_estimatorsint (default = 100)

Number of trees in the forest. (Default changed to 100 in cuML 0.11)

split_criterionstr or int (default = 'mse')

The criterion used to split nodes.

• 'mse' or 2 for mean squared error

• 'poisson' or 4 for poisson half deviance

• 'gamma' or 5 for gamma half deviance

• 'inverse_gaussian' or 6 for inverse gaussian deviance

bootstrapboolean (default = True)

Control bootstrapping.

• If True, each tree in the forest is built on a bootstrapped sample with replacement.

• If False, the whole dataset is used to build each tree.

max_samplesfloat (default = 1.0)

Ratio of dataset rows used while fitting each tree.

max_depthint or None (default = None)

Maximum tree depth. Use None for unlimited depth (trees grow until all leaves are pure). Must be a positive integer or None.

Changed in version 26.08: The default of max_depth changed from 16 to None.

max_leavesint (default = -1)

Maximum leaf nodes per tree. Soft constraint. Unlimited, If -1.

max_features{‘sqrt’, ‘log2’, None}, int or float (default = 1.0)

The number of features to consider per node split:

• If an int then max_features is the absolute count of features to be used.

• If a float then max_features is used as a fraction.

• If 'sqrt' then max_features=1/sqrt(n_features).

• If 'log2' then max_features=log2(n_features)/n_features.

• If None then max_features=n_features

Changed in version 24.06: The default of max_features changed from "auto" to 1.0.

n_binsint (default = 128)

Maximum number of bins used by the split algorithm per feature. For large problems, particularly those with highly-skewed input data, increasing the number of bins may improve accuracy.

n_streamsint (default = 4 )

Number of parallel streams used for forest building

min_samples_leafint or float (default = 1)

The minimum number of samples (rows) in each leaf node.

• If type int, then min_samples_leaf represents the minimum number.

• If float, then min_samples_leaf represents a fraction and ceil(min_samples_leaf * n_rows) is the minimum number of samples for each leaf node.

min_samples_splitint or float (default = 2)

The minimum number of samples required to split an internal node.

• If type int, then min_samples_split represents the minimum number.

• If type float, then min_samples_split represents a fraction and max(2, ceil(min_samples_split * n_rows)) is the minimum number of samples for each split.

min_impurity_decreasefloat (default = 0.0)

The minimum decrease in impurity required for node to be split

max_batch_sizeint (default = 4096)

Maximum number of nodes that can be processed in a given batch.

random_stateint (default = None)

Seed for the random number generator. Unseeded by default.

oob_scorebool (default = False)

Whether to use out-of-bag samples to estimate the generalization performance. Only available if bootstrap=True. The out-of-bag estimate provides a way to evaluate the model without requiring a separate validation set. The OOB score is computed using R² (coefficient of determination).

verboseint or boolean, default=False

Sets logging level. It must be one of cuml.common.logger.level_*. See Verbosity Levels for more info.

output_type{None, ‘input’, ‘cupy’, ‘numpy’, ‘cudf’, ‘pandas’}, default=None

Return results and set estimator attributes to the indicated output type. If None, the output type set at the module level (cuml.global_settings.output_type) will be used. See Output Data Type Configuration for more info.

Attributes:

oob_score_float

Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when oob_score is True.

oob_prediction_ndarray of shape (n_samples,) or (n_samples, n_outputs)

Prediction computed with out-of-bag estimate on the training set. This attribute exists only when oob_score is True.

feature_importances_ndarray of shape (n_features,)

The impurity-based feature importances.

Methods

fit(X, y[, sample_weight])	Perform Random Forest Regression on the input data
predict(X, *[, layout, default_chunk_size, ...])	Predicts the values for X.
score(X, y[, sample_weight, layout, ...])	Calculates the r2 score of the model on test data.

Notes

For additional docs, see scikitlearn’s RandomForestRegressor.

When converting to sklearn using as_sklearn(), the feature_importances_ attribute will return NaN values. If you need feature importances, save them before conversion: importances = cuml_model.feature_importances_

Examples

>>> import cupy as cp
>>> from cuml.ensemble import RandomForestRegressor as curfr
>>> X = cp.asarray([[0,10],[0,20],[0,30],[0,40]], dtype=cp.float32)
>>> y = cp.asarray([0.0,1.0,2.0,3.0], dtype=cp.float32)
>>> cuml_model = curfr(max_features=1.0, n_bins=128,
...                    min_samples_leaf=1,
...                    min_samples_split=2,
...                    n_estimators=40)
>>> cuml_model.fit(X,y)
RandomForestRegressor(n_estimators=40)
>>> cuml_score = cuml_model.score(X,y)
>>> print("R2 score of cuml : ", cuml_score)
R2 score of cuml :  0.9076250195503235

fit(

X,

y,

sample_weight=None,

) → RandomForestRegressor

Perform Random Forest Regression on the input data

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features)

Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

yarray-like (device or host) shape = (n_samples, 1)

Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

sample_weightarray-like (device or host) shape = (n_samples,), default=None

The weights for each observation in X. If None, all observations are assigned equal weight. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

predict(

X,

*,

layout='depth_first',

default_chunk_size=None,

align_bytes=None,

)

Predicts the values for X.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features)

Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

layoutstring (default = ‘depth_first’)

Specifies the in-memory layout of nodes in FIL forests. Options: ‘depth_first’, ‘layered’, ‘breadth_first’.

default_chunk_sizeint, optional (default = None)

Determines how batches are further subdivided for parallel processing. The optimal value depends on hardware, model, and batch size. If None, will be automatically determined.

align_bytesint, optional (default = None)

If specified, trees will be padded such that their in-memory size is a multiple of this value. This can improve performance by guaranteeing that memory reads from trees begin on a cache line boundary. Typical values are 0 or 128.

Returns:

ycuDF, CuPy or NumPy object depending on cuML’s output typeconfiguration, shape =(n_samples, 1)

score(

X,

y,

sample_weight=None,

*,

layout='depth_first',

default_chunk_size=None,

align_bytes=None,

)

Calculates the r2 score of the model on test data.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features)

Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

yarray-like (device or host) shape = (n_samples, 1)

Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

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

Sample weights for weighted R^2.

layoutstring (default = ‘depth_first’)

Specifies the in-memory layout of nodes in FIL forests. Options: ‘depth_first’, ‘layered’, ‘breadth_first’.

default_chunk_sizeint, optional (default = None)

Determines how batches are further subdivided for parallel processing. The optimal value depends on hardware, model, and batch size. If None, will be automatically determined.

align_bytesint, optional (default = None)

If specified, trees will be padded such that their in-memory size is a multiple of this value. This can improve performance by guaranteeing that memory reads from trees begin on a cache line boundary. Typical values are 0 or 128.

Returns:

r2_scorefloat