java调用python数据分析模型_pmml:java调用Python训练的模型之xgboost
使用sklearn2pmml 保存Python的模型
第一步：Python端安装sklearn2pmml,这里安装的是PMML最新版本，4.4 ，这里的4.4和java的1.5.x.jar对应
pip install sklearn2pmml
第二步：Python端修改代码
pipeline = PMMLPipeline([('classifier', clf)])
pipeline.fit(X_train, Y_train)
sklearn2pmml(pipeline, 'output/XGboost1.pmml', with_repr=True, debug=True)from xgboost.sklearn import XGBClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
from sklearn2pmml import PMMLPipeline, sklearn2pmml
def trian_xgboost(df, fwmodel):
'''
训练模型，并测试结果
:param arr: 数据
:param samplemore: 是否往多了采样
:param fwmodel: 模型的保存路径
:return:
'''
y = df['lable'].values
df.drop(['lable'], axis=1, inplace=True)
X = df.values
print(df.columns)
print('=============xgboost=============')
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.25, random_state=0)
clf = XGBClassifier(
silent=0, # 设置成1则没有运行信息输出，最好是设置为0.是否在运行升级时打印消息。
# nthread=4,# cpu 线程数 默认最大
learning_rate=0.07, # 如同学习率
min_child_weight=3,
# 这个参数默认是 1，是每个叶子里面 h 的和至少是多少，对正负样本不均衡时的 0-1 分类而言
# ，假设 h 在 0.01 附近，min_child_weight 为 1 意味着叶子节点中最少需要包含 100 个样本。
# 这个参数非常影响结果，控制叶子节点中二阶导的和的最小值，该参数值越小，越容易 overfitting。
max_depth=12, # 构建树的深度，越大越容易过拟合
gamma=0, # 树的叶子节点上作进一步分区所需的最小损失减少,越大越保守，一般0.1、0.2这样子。
subsample=1, # 随机采样训练样本 训练实例的子采样比
max_delta_step=0, # 最大增量步长，我们允许每个树的权重估计。
# colsample_bytree=1, # 生成树时进行的列采样
reg_lambda=1, # 控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合。
# objective='multi:softmax', # 多分类的问题 指定学习任务和相应的学习目标
n_estimators=100, # 树的个数
seed=1000
)
pipeline = PMMLPipeline([('classifier', clf)])
pipeline.fit(X_train, Y_train)
y_pred = pipeline.predict(X_test)
print("================================")
print(y_pred)
print('=================================')
print(Y_test)
print('=========================')
# print(X_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(Y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
xx = precision_score(Y_test, predictions, average='macro')
yy = recall_score(Y_test, predictions, average='macro')
print("精确率: %.2f%%" % (xx * 100.0))
print("召回率: %.2f%%" % (yy * 100.0))
mm = f1_score(Y_test, predictions, average='weighted')
print("f1 %.2f%%" % (mm * 100.0))
# 混淆矩阵
nn = confusion_matrix(Y_test, predictions)
print("混淆矩阵", nn)
# 分类报告：precision / recall / fi - score / 均值 / 分类个数
target_names = ['class 0', 'class 1']
print(classification_report(Y_test, predictions, target_names=target_names))
sklearn2pmml(pipeline, 'output/XGboost1.pmml', with_repr=True, debug=True)
第三步：生成pmml模型
第四步：java端引入maven需要的jar包
注意：这里pmml相关的包必须是1.5.x以上，因为Python端生成的模型是4.4的，否则就会包版本不对应的错误
java.lang.IllegalArgumentException
org.jpmml
pmml-evaluator
1.5.11
org.jpmml
pmml-evaluator-extension
1.5.11
javax.xml.bind
jaxb-api
2.3.0
com.sun.xml.bind
jaxb-core
2.3.0
com.sun.xml.bind
jaxb-impl
2.3.0
第五步：java端代码，因为导入的是1.5.x的jar包所以代码跟1.4.x的写的不一样
import org.dmg.pmml.FieldName;
import org.dmg.pmml.PMML;
import org.jpmml.evaluator.Evaluator;
import org.jpmml.evaluator.FieldValue;
import org.jpmml.evaluator.InputField;
import org.jpmml.evaluator.ModelEvaluator;
import org.jpmml.evaluator.ModelEvaluatorBuilder;
import org.jpmml.evaluator.ModelEvaluatorFactory;
import org.jpmml.evaluator.TargetField;
import org.jpmml.evaluator.ReportFactory;
public class Rmd_prod {
public static void predictLrHeart(Map irismap,String pathxml)throws Exception {
PMML pmml;
// 模型导入
File file = new File(pathxml);
InputStream inputStream = new FileInputStream(file);
try (InputStream is = inputStream) {
pmml = org.jpmml.model.PMMLUtil.unmarshal(is);
//pmml = org.jpmml.model.PMMLUtil.unmarshal(is);
ModelEvaluatorFactory modelEvaluatorFactory = ModelEvaluatorFactory
.newInstance();
//1.5.x版本
ModelEvaluatorBuilder modelEvaluatorBuilder = new ModelEvaluatorBuilder(pmml);
Evaluator evaluator = modelEvaluatorBuilder.build();
// Activate the generation of MathML prediction reports
modelEvaluatorBuilder.setModelEvaluatorFactory(modelEvaluatorFactory);
Evaluator reportingEvaluator = modelEvaluatorBuilder.build();
//1.4.x版本
//ModelEvaluator> modelEvaluator = modelEvaluatorFactory.newModelEvaluator(pmml);
//Evaluator evaluator = (Evaluator) modelEvaluator;
List inputFields = evaluator.getInputFields();
// 过模型的原始特征，从画像中获取数据，作为模型输入
Map arguments = new LinkedHashMap<>();
for (InputField inputField : inputFields) {
FieldName inputFieldName = inputField.getName();
Object rawValue = irismap
.get(inputFieldName.getValue());
FieldValue inputFieldValue = inputField.prepare(rawValue);
arguments.put(inputFieldName, inputFieldValue);
}
Map results = evaluator.evaluate(arguments);
List targetFields = evaluator.getTargetFields();
//对于分类问题等有多个输出。
for (TargetField targetField : targetFields) {
FieldName targetFieldName = targetField.getName();
Object targetFieldValue = results.get(targetFieldName);
System.err.println("target: " + targetFieldName.getValue()
+ " value: " + targetFieldValue);
}
}
}
public static void main(String[] args) throws Exception{
String pathxml="/Users/wl/Documents/Pycharm/output/XGboost1.pmml";
Map map=new HashMap();
map.put("x1",0.7);
map.put("x2",0.2);
map.put("x3",(double)1);
map.put("x4",(double)0);
map.put("x5",(double)1);
map.put("x6",(double)0);
map.put("x7",(double)0);
map.put("x8",(double)0);
map.put("x9",(double)0);
map.put("x3",(double)1);
map.put("x4",(double)0);
map.put("x5",(double)1);
map.put("x6",(double)0);
map.put("x7",(double)0);
map.put("x8",(double)0);
map.put("x9",(double)0);
map.put("x10",(double)0);
map.put("x11",(double)1);
map.put("x12",(double)1);
map.put("x13",(double)1);
map.put("x14",(double)0);
map.put("x15",(double)1);
map.put("x16",(double)0);
map.put("x17",(double)0);
map.put("x18",(double)0);
map.put("x19",(double)1);
map.put("x20",(double)1);
map.put("x21",(double)0);
map.put("x22",(double)0);
map.put("x23",(double)0);
map.put("x24",(double)0);
map.put("x25",(double)0);
map.put("x26",(double)0);
map.put("x27",(double)0);
map.put("x28",(double)0);
map.put("x29",(double)1);
map.put("x30",(double)1);
map.put("x31",(double)1);
map.put("x32",(double)0);
map.put("x33",(double)1);
map.put("x34",(double)0);
map.put("x35",(double)0);
map.put("x36",(double)1);
map.put("x37",(double)0);
map.put("x38",(double)0);
map.put("x39",(double)1);
map.put("x40",(double)0);
map.put("x41",(double)0);
map.put("x42",(double)0);
map.put("x43",(double)1);
map.put("x44",(double)1);
map.put("x45",(double)1);
map.put("x46",(double)0);
map.put("x47",(double)0);
map.put("x48",(double)0);
map.put("x49",(double)0);
map.put("x50",(double)0);
map.put("x51",(double)0);
map.put("x52",(double)0);
map.put("x53",(double)1);
map.put("x54",(double)1);
rmd.predictLrHeart(map, pathxml);
}
}