Python cuml.dask.naive_bayes.MultinomialNB用法及代碼示例

用法:
class cuml.dask.naive_bayes.MultinomialNB(*, client=None, verbose=False, **kwargs)

多項式模型的分布式樸素貝葉斯分類器

例子：

從 Scikit-learn 加載 20 個新聞組數據集並訓練一個樸素貝葉斯分類器。

import cupy as cp

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer

from dask_cuda import LocalCUDACluster
from dask.distributed import Client

from cuml.dask.common import to_sparse_dask_array

from cuml.dask.naive_bayes import MultinomialNB

# Create a local CUDA cluster

cluster = LocalCUDACluster()
client = Client(cluster)

# Load corpus

twenty_train = fetch_20newsgroups(subset='train',
                          shuffle=True, random_state=42)

cv = CountVectorizer()
xformed = cv.fit_transform(twenty_train.data).astype(cp.float32)

X = to_sparse_dask_array(xformed, client)
y = dask.array.from_array(twenty_train.target, asarray=False,
                      fancy=False).astype(cp.int32)

# Train model

model = MultinomialNB()
model.fit(X, y)

# Compute accuracy on training set

model.score(X, y)

輸出：

0.9244298934936523

相關用法

注：本文由純淨天空篩選整理自rapids.ai大神的英文原創作品 cuml.dask.naive_bayes.MultinomialNB。非經特殊聲明，原始代碼版權歸原作者所有，本譯文未經允許或授權，請勿轉載或複製。