【阿里天池-医学影像报告异常检测】4 机器学习模型调参

引言

（1）先对idtdf提取特征的ngram大小和feature调参，最终ngram=(1,2)feature=500，最佳
（2）对LogisticRegression、XGBClassifier、LGBMClassifier三个模型单独调参，本人仅仅对XGB的几个参数进行了调整，工作量太庞大，就没有所有参数调整对比分析。这里仅仅提出调参的例子，提供模型调参的思路学习
（3）开源源码https://github.com/823316627bandeng/TIANCHI-2021-AI-Compition

实现

（1）导入包

import os
import numpy as np
import pandas as pd
from sklearn.decomposition import NMF, TruncatedSVD, PCA
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import StandardScaler
from xgboost import XGBClassifier
from utils import *
from lightgbm import LGBMClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import roc_auc_score
#加载数据
label= np.array(pd.read_csv('./data/label.csv'))
train = pd.read_csv('./temp/train.csv',header = None,names=['id','text','label'])
adjust_model()

        

          
        
        
        

          

          AI 代码解读
        
      
      

（2）模型的调参

def adjust_model():
    Tdf = TfidfVectorizer(ngram_range=(1,2),max_features=500)
    tdf_data = Tdf.fit_transform(train['text'])
    X_train,X_test,y_train,y_test = train_test_split(tdf_data,label,test_size=0.3)
    paralist = []
    score_dict = {"list_n":[],"list_f":[],"loss":[]}
    # for n in paralist
    param_test1 = {'estimator__max_depth':range(2,8,2)}
    '''
    model = OneVsRestClassifier(XGBClassifier(eval_metric= 'mlogloss',
                                            max_depth = 11,
                                            min_child_weight =1,
                                            use_label_encoder=False,
                                            learning_rate =0.01,
                                            n_estimators=150,
                                            gamma=0,
                                            subsample=0.8,
                                            colsample_bytree=0.8,
                                            nthread=100,
                                            scale_pos_weight=1,
                                            seed=27,
                                            verbose=True
                                            ))
    '''
    '''
    model = OneVsRestClassifier(LGBMClassifier(is_unbalance = True,
                                                metric = 'binary_logloss,auc',
                                                # max_depth = 6,
                                                num_leaves = 40,
                                                learning_rate = 0.1,
                                                feature_fraction = 0.7,
                                                min_child_samples=21,
                                                min_child_weight=0.001,
                                                bagging_fraction = 1,
                                                bagging_freq = 2,
                                                reg_alpha = 0.001,
                                                reg_lambda = 8,
                                                cat_smooth = 0,
                                                # num_iterations = 200
                                                ))
    '''
    # model = OneVsRestClassifier(LGBMClassifier())
    model = OneVsRestClassifier(XGBClassifier(eval_metric= 'mlogloss',use_label_encoder=False,n_estimators=150))
    model.fit(X_train, y_train)

    predict = model.predict_proba(X_test)
    score = roc_auc_score(y_test,predict)
    print(score)

        

          
        
        
        

          

          AI 代码解读
        
      
      

XGB
{‘estimator__max_depth’: 9, ‘estimator__min_child_weight’: 1}
{‘estimator__max_depth’: 11}0.9812110365828264
{‘estimator__n_estimators’: 150} 0.9834881407453535
调参后：0.9726861215062805
LGB
{‘estimator__max_depth’: 6}最佳得分 0.9811430144134826

（3）idtdf提取特征调参

#list_ngram = [1,2,3,4]
#list_feature = [100,200,300,400]
def adjust_idtdf():
    list_ngram = [1,2,3,4,5]
    list_feature = [100,200,300,400,500]
    #分数记录字典
    score_dict = {"list_n":[],"list_f":[],"loss":[]}
    #创建方法进行验证
    def para_Tdf(data_x):
        for n in list_ngram:
            for fea in list_feature:
                Tdf = TfidfVectorizer(ngram_range=(1,n),max_features=fea)
                tdf_data = Tdf.fit_transform(data_x)
                # tdf_data = tdf_data.toarray()
                X_train,X_test,y_train,y_test = train_test_split(tdf_data,label,test_size=0.3)
                model = OneVsRestClassifier(XGBClassifier(eval_metric= 'mlogloss',use_label_encoder=False,n_estimators=50))
                model.fit(X_train, y_train)
                predict = model.predict_proba(X_test)
                loss = Mutilogloss(y_test,predict)
                score_dict["list_n"].append(n)
                score_dict['list_f'].append(fea)
                score_dict['loss'].append(loss)
                print("n={0},feature={1},loss={2}".format(n,fea,loss))
    #方法调用
    para_Tdf(train['text'])
    #以DataFrame形式显示分数
    print(score_dict)

        

          
        
        
        

          

          AI 代码解读
        
      
      

最佳是n=2,features = 500
n=1,feature=100,loss=0.09694388171340544
n=1,feature=200,loss=0.07941648607131963
n=1,feature=300,loss=0.0780516995282797
n=1,feature=400,loss=0.07654529189186797
n=1,feature=500,loss=0.07875673493941672
n=2,feature=100,loss=0.10700796997032506
n=2,feature=200,loss=0.0872626769884241
n=2,feature=300,loss=0.08134605319231948
n=2,feature=400,loss=0.07927331816025636
n=2,feature=500,loss=0.07391725763363112
n=3,feature=100,loss=0.10642417486808319
n=3,feature=200,loss=0.0932806660865527
n=3,feature=300,loss=0.0821267581008504
n=3,feature=400,loss=0.08258777666414407
n=3,feature=500,loss=0.07525704598697901
n=4,feature=100,loss=0.10395870861632356
n=4,feature=200,loss=0.09252871191998951
n=4,feature=300,loss=0.08208295772650118
n=4,feature=400,loss=0.08249975725295985
n=4,feature=500,loss=0.07920155662551372
n=5,feature=100,loss=0.10649166642764825
n=5,feature=200,loss=0.09238465463657325
n=5,feature=300,loss=0.08104836900458223
n=5,feature=400,loss=0.07833574743241475
n=5,feature=500,loss=0.07796380784547806