- 比赛报名
- 查看训练、测试集的包含字段及字段类型，代码如下：
import pandas as pd train_df = pd.read_csv('./糖尿病遗传风险预测挑战赛公开数据/比赛训练集.csv', encoding='gbk') test_df = pd.read_csv('./糖尿病遗传风险预测挑战赛公开数据/比赛测试集.csv', encoding='gbk') print(train_df.shape, test_df.shape) print(train_df.dtypes, test_df.dtypes)
- 查看训练、测试集的包含字段及字段类型，代码如下：
- 比赛数据分析
- 缺失值统计
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def do_nan(train_df,test_df,method = None): #口服耐糖量测试为-1，也应该为null train_df['口服耐糖量测试'] = train_df['口服耐糖量测试'].replace(-1, np.nan) test_df['口服耐糖量测试'] = test_df['口服耐糖量测试'].replace(-1, np.nan) train_df['口服耐糖量测试'] = train_df['口服耐糖量测试'].replace(0, np.nan) test_df['口服耐糖量测试'] = test_df['口服耐糖量测试'].replace(0, np.nan) if method is not None: train_df['胰岛素释放实验'] = train_df['胰岛素释放实验'].replace(0, np.nan) test_df['胰岛素释放实验'] = test_df['胰岛素释放实验'].replace(0, np.nan) train_df['肱三头肌皮褶厚度'] = train_df['肱三头肌皮褶厚度'].replace(0, np.nan) test_df['肱三头肌皮褶厚度'] = test_df['肱三头肌皮褶厚度'].replace(0, np.nan) train_df['体重指数'] = train_df['体重指数'].replace(0, np.nan) test_df['体重指数'] = test_df['体重指数'].replace(0, np.nan) # 训练集缺失值情况 print(train_df.isnull().mean(0)) # 训练集缺失值情况 print(test_df.isnull().mean(0)) #将糖尿病家族史转换为类别变量 dict_糖尿病家族史 = { '无记录': 0, '叔叔或姑姑有一方患有糖尿病': 1, '叔叔或者姑姑有一方患有糖尿病': 1, '父母有一方患有糖尿病': 2 } train_df['糖尿病家族史'] = train_df['糖尿病家族史'].map(dict_糖尿病家族史) test_df['糖尿病家族史'] = test_df['糖尿病家族史'].map(dict_糖尿病家族史) #出生年份转换 train_df['年龄'] = 2022 - train_df['出生年份'] test_df['年龄'] = 2022 - test_df['出生年份'] #填充缺失值处理 #下列三个字段比例较低，可以直接填充 train_df['舒张压'].fillna(89, inplace=True) test_df['舒张压'].fillna(89, inplace=True) train_df['口服耐糖量测试'].fillna(6,inplace = True) test_df['口服耐糖量测试'].fillna(6,inplace = True) train_df['体重指数'].fillna(38,inplace = True) test_df['体重指数'].fillna(38,inplace = True) #胰岛素释放实验 肱三头肌皮褶厚度 字段缺失比例较高，试试统计填充（多元线性回归） all_df = pd.concat([train_df,test_df]) all_df.drop(['患有糖尿病标识'],axis=1,inplace=True) all_df.dropna(inplace=True) if method == 'lg': lg_g3 = LinearRegression() lg_yd = LinearRegression() lg_features = [f for f in all_df.columns if f not in ['编号','肱三头肌皮褶厚度','胰岛素释放实验']] lg_g3.fit(all_df[lg_features],all_df['肱三头肌皮褶厚度']) lg_yd.fit(all_df[lg_features],all_df['胰岛素释放实验']) for index,bool in enumerate (train_df['胰岛素释放实验'].isnull()): if bool: train_df.loc[index,'胰岛素释放实验'] = lg_yd.predict(np.array(train_df.loc[index,lg_features]).reshape(1, -1)) for index,bool in enumerate (train_df['肱三头肌皮褶厚度'].isnull()): if bool: train_df.loc[index,'肱三头肌皮褶厚度'] = lg_g3.predict(np.array(train_df.loc[index,lg_features]).reshape(1, -1)) for index,bool in enumerate (test_df['胰岛素释放实验'].isnull()): if bool: test_df.loc[index,'胰岛素释放实验'] = lg_yd.predict(np.array(test_df.loc[index,lg_features]).reshape(1, -1)) for index,bool in enumerate (test_df['肱三头肌皮褶厚度'].isnull()): if bool: test_df.loc[index,'肱三头肌皮褶厚度'] = lg_g3.predict(np.array(test_df.loc[index,lg_features]).reshape(1, -1)) #试试knn拟合 if method == 'knn': train_df = pd.DataFrame(KNN(k=5).fit_transform(train_df),columns= train_df.columns) test_df = pd.DataFrame(KNN(k=5).fit_transform(test_df),columns= test_df.columns) return train_df,test_df - 口服耐糖量测试为-1，0，也认为缺失
- 体重指数为0，认为缺失
- 胰岛素释放实验 为0 ，认为缺失
- 肱三头肌皮褶厚度为0，认为缺失
- 缺失值（左：测试，右：验证）
- 舒张压分布一致， 缺失比例约为4.9%
- 口服耐糖量测试，缺失比例不同，测试集：1.6%；训练集：4.9%
- 体重指数，缺失比例不同，测试集：0.2%；训练集：0.15%
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测试了将胰岛素释放实验和肱三头肌皮褶厚度为0认为缺失，用KNN和线性拟合，模型结果不好，所以最终还是只用平均值填充了 口服耐糖量、舒张压和体重指数
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后面再想想辙吧，这个就搞了两三个小时。
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- 字段类型分析
- 结合比赛报名中的字段类型（int、float、object）
- 数值类型：
- 体重指数，舒张压，口服耐糖量测试、胰岛素释放试验，肱三头肌皮褶厚度，肱三头肌皮褶厚度
- 类别类型：
- 性别、糖尿病家族史、患有糖尿病标识、性别、出生年份（后续处理为年龄）
- 相关性计算
- 相关性
#将糖尿病家族史转换为类别变量 dict_糖尿病家族史 = { '无记录': 0, '叔叔或姑姑有一方患有糖尿病': 1, '叔叔或者姑姑有一方患有糖尿病': 1, '父母有一方患有糖尿病': 2 } train_df['糖尿病家族史'] = train_df['糖尿病家族史'].map(dict_糖尿病家族史) test_df['糖尿病家族史'] = test_df['糖尿病家族史'].map(dict_糖尿病家族史) #出生年份转换 train_df['年龄'] = 2022 - train_df['出生年份'] test_df['年龄'] = 2022 - test_df['出生年份'] train_df.corr().loc['患有糖尿病标识',:]- 肱三头肌皮喆厚度、体重指数、口服耐糖量测试、舒张压、胰岛素释放实验、相关性较高
- 查看不同标签下，各字段分布
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for column in train_df.columns: if column == '编号': continue if column == '患有糖尿病标识': continue plt.figure() for i in [0,1]: train_df[column][train_df['患有糖尿病标识']==i].hist() plt.title("{}".format(column)) plt.savefig("fig/{}.png".format(column))
- 相关性
- 缺失值统计
- 尝试逻辑回归
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from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import MinMaxScaler from sklearn.pipeline import make_pipeline from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV from sklearn.metrics import accuracy_score #填充空值 train_df['舒张压'].fillna(89.42, inplace=True) test_df['舒张压'].fillna(89.42, inplace=True) train_df['口服耐糖量测试'].fillna(5.95,inplace = True) test_df['口服耐糖量测试'].fillna(5.95,inplace = True) #划分训练集、验证集 features = [f for f in train_df.columns if f not in ['编号','出生年份','患有糖尿病标识']] x_train, x_test, y_train, y_test = train_test_split(train_df[features],train_df['患有糖尿病标识'],test_size=0.2, random_state=2022) #构建模型 lr_l1 = make_pipeline( MinMaxScaler(), LogisticRegression(penalty="l1", C=1, solver="liblinear") ) lr_l2 = make_pipeline( MinMaxScaler(), LogisticRegression(penalty="l2", C=1, solver="liblinear") ) # 训练集表现 l1_train_predict = [] l2_train_predict = [] # 测试集表现 l1_test_predict = [] l2_test_predict = [] for c in np.linspace(0.01, 10, 100) : lr_l1 = LogisticRegression(penalty="l1", C=c, solver="liblinear", max_iter=2000) lr_l2 = LogisticRegression(penalty='l2', C=c, solver='liblinear', max_iter=2000) # 训练模型，记录L1正则化模型在训练集测试集上的表现 lr_l1.fit(x_train, y_train) l1_train_predict.append(accuracy_score(lr_l1.predict(x_train), y_train)) l1_test_predict.append(accuracy_score(lr_l1.predict(x_test), y_test)) # 记录L2正则化模型的表现 lr_l2.fit(x_train, y_train) l2_train_predict.append(accuracy_score(lr_l2.predict(x_train), y_train)) l2_test_predict.append(accuracy_score(lr_l2.predict(x_test), y_test)) data = [l1_train_predict, l2_train_predict, l1_test_predict, l2_test_predict] label = ['l1_train', 'l2_train', 'l1_test', "l2_test"] color = ['red', 'green', 'orange', 'blue'] plt.figure(figsize=(12, 6)) for i in range(4) : plt.plot(np.linspace(0.01, 10, 100), data[i], label=label[i], color=color[i]) plt.legend(loc="best") plt.show() #取出最优参数，进行训练、预测 best_model = LogisticRegression(penalty="l1", C=0.01, solver="liblinear", max_iter=2000) best_model.fit(train_df[features],train_df['患有糖尿病标识']) test_df['label'] = best_model.predict(test_df.drop(['编号'], axis=1)) test_df.rename({'编号': 'uuid'}, axis=1)[['uuid', 'label']].to_csv('submit.csv', index=None) -
基于训练集验证集的准确率：82.05%
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- 特征工程
- 基于性别的新特征（考虑到体重指数和舒张压与性别有关）
df_sex = train_df[['体重指数','舒张压']].groupby(train_df['性别']).mean() train_df['体重指数_diff'] = [train_df.loc[i,'体重指数'] - df_sex.loc[train_df.loc[i,'性别'],'体重指数'] for i in range(len(train_df))] train_df['舒张压_diff'] = [train_df.loc[i,'舒张压'] - df_sex.loc[train_df.loc[i,'性别'],'舒张压'] for i in range(len(train_df))]
- 基于训练集验证集的准确率：82.35%
- 基于糖尿病家族史的新特征（考虑到部分因素与糖尿病家族史相关）
df_family = train_df[['口服耐糖量测试','胰岛素释放实验','肱三头肌皮褶厚度']].groupby(train_df['糖尿病家族史']).mean() for column in ['口服耐糖量测试','胰岛素释放实验','肱三头肌皮褶厚度']: train_df[column +'_diff'] = [train_df.loc[i,column] - df_family.loc[train_df.loc[i,'糖尿病家族史'],column] for i in range(len(train_df))]- 基于训练集验证集的准确率：82.05%
- 基于性别的新特征（考虑到体重指数和舒张压与性别有关）
- 特征筛选
# 构建模型 model = make_pipeline( MinMaxScaler(), DecisionTreeClassifier() ) #拟合结果 model.fit(x_train,y_train) #查看特征重要度 for index, importance in enumerate(model.named_steps['decisiontreeclassifier'].feature_importances_): print(index, importance)- top5
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['体重指数','舒张压','口服耐糖量测试', '胰岛素释放实验','肱三头肌皮褶厚度',]
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10个指标重要度：[0.0030843 , 0.31595439, 0.00559351, 0.11861977, 0.16319968, 0.08296783, 0.25307058, 0.03212695, 0.01597175, 0.00941124]
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没有明显提高：模型的达到瓶颈，不能接受更复杂的参数特征？
- top5
- 高阶树模型
#构建模型 clf = lgb.LGBMClassifier( max_depth=3, n_estimators=4000, n_jobs=-1, verbose=-1, verbosity=-1, learning_rate=0.1, ) #模型训练 clf.fit(x_train,y_train) print(accuracy_score(y_test,clf.predict(x_test))) test_df['label'] = clf.predict(test_df[features]) test_df.rename({'编号': 'uuid'}, axis=1)[['uuid', 'label']].to_csv('submit.csv', index=None)-
在验证集上准确度：95.95%，test上：
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调整超参数：具体代码不写了，参考了一位大神的代码：
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白话机器学习算法理论+实战番外篇之LightGBM_翻滚的小@强的博客-CSDN博客_互斥特征捆绑
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结果：
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看样子过拟合了
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- 多折训练与集成
# 模型交叉验证 def run_model_cv(model, kf, X_tr, y, X_te, cate_col=None): train_pred = np.zeros( (len(X_tr), len(np.unique(y))) ) test_pred = np.zeros( (len(X_te), len(np.unique(y))) ) cv_clf = [] for tr_idx, val_idx in kf.split(X_tr, y): x_tr = X_tr.iloc[tr_idx]; y_tr = y.iloc[tr_idx] x_val = X_tr.iloc[val_idx]; y_val = y.iloc[val_idx] call_back = [ lgb.early_stopping(50), ] eval_set = [(x_val, y_val)] model.fit(x_tr, y_tr, eval_set=eval_set, callbacks=call_back, verbose=-1) cv_clf.append(model) train_pred[val_idx] = model.predict_proba(x_val) test_pred += model.predict_proba(X_te) test_pred /= kf.n_splits return train_pred, test_pred, cv_clf #定义模型 clf = lgb.LGBMClassifier(boosting_type='gbdt',objective='binary',metrics='binary_logloss',learning_rate=0.01, n_estimators=94, max_depth=3, num_leaves=10,max_bin=175,min_data_in_leaf=101,bagging_fraction=0.6,bagging_freq= 0, feature_fraction= 0.8, lambda_l1=0.7,lambda_l2=0.7,min_split_gain=0.1,num_iterations =10000) #训练KFold train_pred, test_pred, cv_clf = run_model_cv( clf, KFold(n_splits=5), x_train, y_train, x_test) test_pred_1 = [ i.argmax() for i in test_pred] accuracy_score(test_pred_1,y_test) #训练StratifiedKfold train_pred, test_pred, cv_clf = run_model_cv( clf, StratifiedKFold(n_splits=5), x_train, y_train, x_test) test_pred_2 = [ i.argmax() for i in test_pred] accuracy_score(test_pred_2,y_test)-
使用了KFold 和 StratifiedKFold，尝试3-5折，发现4折StratifiedKFold效果最好，在验证集上为95.85%，好像也没有优于单个的gbm模型。
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test上结果：
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试一下stacking：
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参考代码：Introduction to Ensembling/Stacking in Python | Kaggle
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第一层选用了，gbdt，adaboost、rf、extratrees、svm；第二层lightgbm
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应该过拟合了，在我的验证集96.15%效果更好
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test结果：
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- 后面再试试特征工程，调调参吧！就这样！
