朴素贝叶斯文本分类流预测

功能介绍

• 朴素贝叶斯文本分类是一个多分类算法
• 朴素贝叶斯文本分类组件支持稀疏、稠密两种数据格式
• 朴素贝叶斯文本分类组件支持带样本权重的训练

参数说明

脚本示例

运行脚本

data =  np.array([
    ["$31$0:1.0 1:1.0 2:1.0 30:1.0","1.0  1.0  1.0  1.0", '1'],
    ["$31$0:1.0 1:1.0 2:0.0 30:1.0","1.0  1.0  0.0  1.0", '1'],
    ["$31$0:1.0 1:0.0 2:1.0 30:1.0","1.0  0.0  1.0  1.0", '1'],
    ["$31$0:1.0 1:0.0 2:1.0 30:1.0","1.0  0.0  1.0  1.0", '1'],
    ["$31$0:0.0 1:1.0 2:1.0 30:0.0","0.0  1.0  1.0  0.0", '0'],
    ["$31$0:0.0 1:1.0 2:1.0 30:0.0","0.0  1.0  1.0  0.0", '0'],
    ["$31$0:0.0 1:1.0 2:1.0 30:0.0","0.0  1.0  1.0  0.0", '0']])
dataSchema = ["sv", "dv", "label"]
df = pd.DataFrame({"sv": data[:, 0], "dv": data[:, 1], "label": data[:, 2]})
batchData = dataframeToOperator(df, schemaStr='sv string, dv string, label string', op_type='batch')
streamData = dataframeToOperator(df, schemaStr='sv string, dv string, label string', op_type='stream')
ns = NaiveBayesTextTrainBatchOp().setVectorCol("sv").setLabelCol("label")
model = batchData.link(ns)
predictor = NaiveBayesTextPredictStreamOp(model).setVectorCol("sv").setReservedCols(["sv", "label"]).setPredictionCol("pred")
predictor.linkFrom(streamData).print()
StreamOperator.execute()

运行结果