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CategoricalNB#

class cuml.naive_bayes.CategoricalNB( *, alpha=1.0, fit_prior=True, class_prior=None, output_type=None, verbose=False, )[source]#

Naive Bayes classifier for categorical features.

The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution.

Parameters:

alphafloat, default=1.0: Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
fit_priorbool, default=True: Whether to learn class prior probabilities or not. If false, a uniform prior will be used.
class_priorarray-like of shape (n_classes,), default=None: Prior probabilities of the classes. If specified the priors are not adjusted according to the data.
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.
verboseint or boolean, default=False: Sets logging level. It must be one of cuml.common.logger.level_*. See Verbosity Levels for more info.

Attributes:

category_count_ndarray of shape (n_features, n_classes, n_categories): With n_categories being the highest category of all the features. This array provides the number of samples encountered for each feature, class and category of the specific feature.
class_count_ndarray of shape (n_classes,): Number of samples encountered for each class during fitting.
class_log_prior_ndarray of shape (n_classes,): Smoothed empirical log probability for each class.
classes_ndarray of shape (n_classes,): Class labels known to the classifier
feature_log_prob_ndarray of shape (n_features, n_classes, n_categories): With n_categories being the highest category of all the features. Each array of shape (n_classes, n_categories) provides the empirical log probability of categories given the respective feature and class, P(x_i|y). This attribute is not available when the model has been trained with sparse data.
n_features_int: Number of features of each sample.

Examples

>>> import cupy as cp
>>> from cuml.naive_bayes import CategoricalNB
>>> rng = cp.random.RandomState(1)
>>> X = rng.randint(5, size=(6, 100), dtype=cp.int32)
>>> y = cp.array([1, 2, 3, 4, 5, 6])
>>> clf = CategoricalNB()
>>> clf.fit(X, y)
CategoricalNB()
>>> print(clf.predict(X[2:3]))
[3]

fit( X, y, ) → _BaseDiscreteNB[source]#

Fit Naive Bayes classifier according to X, y

Parameters:

X{array-like, cupy sparse matrix} of shape (n_samples, n_features): Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like shape (n_samples): Target values.

get_params(deep=True)[source]#: Returns a dict of all params owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is there in this method, then it doesn’t have to override this method

partial_fit( X, y, classes=None, ) → _BaseDiscreteNB[source]#

Incremental fit on a batch of samples.

This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning.

This is especially useful when the whole dataset is too big to fit in memory at once.

This method has some performance overhead hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead.

Parameters:

X{array-like, cupy sparse matrix} of shape (n_samples, n_features): Training vectors, where n_samples is the number of samples and n_features is the number of features
yarray-like of shape (n_samples): Target values.
classesarray-like of shape (n_classes): List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls.

Returns:

selfobject

predict(X)[source]#

Perform classification on an array of test vectors X.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features): Dense or sparse matrix with dtype float32 or float64. Acceptable dense formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

y_hatcuDF, CuPy or NumPy object depending on cuML’s output type configuration, shape = (n_rows, 1)

Predicted values

For more information on how to configure cuML’s output type, refer to: Output Data Type Configuration.

predict_log_proba(X)[source]#

Return log-probability estimates for the test vector X.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features): Dense or sparse matrix with dtype float32 or float64. Acceptable dense formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

CcuDF, CuPy or NumPy object depending on cuML’s output type configuration, shape = (n_rows, 1)

Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

For more information on how to configure cuML’s output type, refer to: Output Data Type Configuration.

predict_proba(X)[source]#

Return probability estimates for the test vector X.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features): Dense or sparse matrix with dtype float32 or float64. Acceptable dense formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

CcuDF, CuPy or NumPy object depending on cuML’s output type configuration, shape = (n_rows, 1)

Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

For more information on how to configure cuML’s output type, refer to: Output Data Type Configuration.

score(X, y, sample_weight=None, **kwargs)[source]#

Scoring function for classifier estimators based on mean accuracy.

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.

Returns:

scorefloat: Accuracy of self.predict(X) wrt. y (fraction where y == pred_y)

set_params(**params)[source]#: Accepts a dict of params and updates the corresponding ones owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is, there in this method, then it doesn’t have to override this method

update_log_probs()[source]#: Updates the log probabilities. This enables lazy update for applications like distributed Naive Bayes, so that the model can be updated incrementally without incurring this cost each time.