PyMLpipe ML run

with mlp.run():
    mlp.set_tags(["Classification","test run","dtree"])
    model=DecisionTreeClassifier()
    model.fit(trainx, trainy)
    predictions=model.predict(testx)
    
    mlp.log_metrics({"Accuracy":accuracy_score(testy,predictions),"Precision": precision_score(testy,predictions,average='macro')})
    
    mlp.log_metric("Recall", recall_score(testy,predictions,average='macro'))
    mlp.log_metric("F1", f1_score(testy,predictions,average='macro'))
    
  
    mlp.register_artifact("train.csv", trainx)
    mlp.register_artifact("test.csv", testx,artifact_type="testing")
    mlp.scikit_learn.register_model("dtree", model)