RandomForestClassifier#

class cuml.ensemble.RandomForestClassifier( *, n_estimators=100, split_criterion='gini', bootstrap=True, max_samples=1.0, max_depth=None, max_leaves=-1, max_features='sqrt', 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, class_weight=None, verbose=False, output_type=None, )[source]#

Implements a Random Forest classifier model which fits multiple decision tree classifiers 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 i 0.11)

split_criterionstr or int (default = 'gini')

The criterion used to split nodes.

'gini' or 0 for gini impurity

'entropy' or 1 for information gain (entropy)

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 = ‘sqrt’)

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 "sqrt".

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)

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 accuracy. 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 accuracy.

class_weightdict, ‘balanced’, or None, default=None

Weights associated with classes. If 'balanced', class weights are computed from the training labels.

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:

classes_np.ndarray, shape=(n_classes,): A sorted array of the class labels.
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_decision_function_ndarray of shape (n_samples, n_classes): Decision function computed with out-of-bag 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. 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, convert_dtype])	Perform Random Forest Classification on the input data
`predict`(X, *[, threshold, convert_dtype, ...])	Predicts the labels for X.
`predict_log_proba`(X, *[, convert_dtype, ...])	Predicts log class probabilities for X.
`predict_proba`(X, *[, convert_dtype, layout, ...])	Predicts class probabilities for X.
`score`(X, y[, sample_weight, threshold, ...])	Calculates the accuracy score of the model on test data.

Notes

While training the model for multi class classification problems, using deep trees or max_features=1.0 provides better performance.

For additional docs, see scikitlearn’s RandomForestClassifier.

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 RandomForestClassifier as cuRFC

>>> X = cp.random.normal(size=(10,4)).astype(cp.float32)
>>> y = cp.asarray([0,1]*5, dtype=cp.int32)

>>> cuml_model = cuRFC(max_features=1.0,
...                    n_bins=8,
...                    n_estimators=40)
>>> cuml_model.fit(X,y)
RandomForestClassifier()
>>> cuml_predict = cuml_model.predict(X)

>>> print("Predicted labels : ", cuml_predict)
Predicted labels :  [0. 1. 0. 1. 0. 1. 0. 1. 0. 1.]

fit( X, y, sample_weight=None, *, convert_dtype='deprecated', ) → RandomForestClassifier[source]#

Perform Random Forest Classification 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 int32. 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.
convert_dtypebool, optional (default = ‘deprecated’): Deprecated since version 26.08: convert_dtype was deprecated in version 26.08 and will be removed in version 26.10. cuML only copies input arrays when necessary (e.g. to unify dtypes), there is no reason to provide this keyword going forward.

predict( X, *, threshold=0.5, convert_dtype='deprecated', layout='depth_first', default_chunk_size=None, align_bytes=None, )[source]#

Predicts the labels 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.
thresholdfloat (default = 0.5): Threshold used for classification.
convert_dtypebool, default=”deprecated”: Deprecated since version 26.08: convert_dtype was deprecated in version 26.08 and will be removed in version 26.10. cuML only copies input arrays when necessary (e.g. to unify dtypes), there is no reason to provide this keyword going forward.
layoutstring (default = ‘depth_first’): Forest layout for GPU inference. Options: ‘depth_first’, ‘layered’, ‘breadth_first’.
default_chunk_sizeint, optional (default = None): Controls batch subdivision for parallel processing. Optimal value depends on hardware, model and batch size. If None, determined automatically.
align_bytesint, optional (default = None): If specified, trees will be padded to this byte alignment, which can improve performance. Typical values are 0 or 128 on GPU.

Returns:

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

predict_log_proba( X, *, convert_dtype='deprecated', layout='depth_first', default_chunk_size=None, align_bytes=None, )[source]#

Predicts log class probabilities 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.
convert_dtypebool, default=”deprecated”: Deprecated since version 26.08: convert_dtype was deprecated in version 26.08 and will be removed in version 26.10. cuML only copies input arrays when necessary (e.g. to unify dtypes), there is no reason to provide this keyword going forward.
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 on GPU and 0 or 64 on CPU.

Returns:

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

predict_proba( X, *, convert_dtype='deprecated', layout='depth_first', default_chunk_size=None, align_bytes=None, )[source]#

Predicts class probabilities 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.
convert_dtypebool, default=”deprecated”: Deprecated since version 26.08: convert_dtype was deprecated in version 26.08 and will be removed in version 26.10. cuML only copies input arrays when necessary (e.g. to unify dtypes), there is no reason to provide this keyword going forward.
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 on GPU and 0 or 64 on CPU.

Returns:

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

score( X, y, sample_weight=None, *, threshold=0.5, convert_dtype='deprecated', layout='depth_first', default_chunk_size=None, align_bytes=None, )[source]#

Calculates the accuracy 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 int32. 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 mean accuracy.
thresholdfloat (default = 0.5): Threshold used for classification predictions
convert_dtypebool, default=”deprecated”: Deprecated since version 26.08: convert_dtype was deprecated in version 26.08 and will be removed in version 26.10. cuML only copies input arrays when necessary (e.g. to unify dtypes), there is no reason to provide this keyword going forward.
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 on GPU and 0 or 64 on CPU.

Returns:

accuracyfloat: Accuracy of the model [0.0 - 1.0]