年薪预测任务(50k)

心已赠人 2023-06-27 05:58 27阅读 0赞

年薪预测任务

# 1.数据下载并设置格式 #

在官网上下载数据，并设置数据位置并加标签

import numpy as np
    import pandas as pd
    train_path = "./data/adult.data"
    test_path = "./data/adult.test"
    train_set = pd.read_csv(train_path, header=None)
    test_set = pd.read_csv(test_path, skiprows = 1, header = None)
    col_labels = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status',
                                'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss',
                                'hours_per_week', 'native_country', 'wage_class']
    train_set.columns = col_labels
    test_set.columns = col_labels

# 2.替换缺失数据 #

train_set.replace(' ?', np.nan).dropna().shape
    test_set.replace(' ?', np.nan).dropna().shape
    
    train_nomissing = train_set.replace(' ?', np.nan).dropna()
    test_nomissing = test_set.replace(' ?', np.nan).dropna()

# 3.参数优化过程 #

## 3.1 max\_depth和min\_child\_weight参数优化过程 ##

cv_params = {'max_depth': [3,5,7], 'min_child_weight': [1,3,5]}
    ind_params = {'learning_rate': 0.1, 'n_estimators': 1000, 'seed':0, 'subsample': 0.8, 'colsample_bytree': 0.8,
                 'objective': 'binary:logistic'}
    optimized_GBM = GridSearchCV(xgb.XGBClassifier(**ind_params),
                                cv_params,
                                scoring = 'accuracy', cv = 5, n_jobs = -1)
    # Optimize for accuracy since that is the metric used in
    optimized_GBM.fit(final_train, y_train)

输出结果

print (optimized_GBM.best_params_)
    print (optimized_GBM.cv_results_['mean_test_score'])
    {'max_depth': 3, 'min_child_weight': 5}
    [0.86648763 0.86668656 0.86675287 0.86075194 0.86144818 0.86164711
    0.85485047 0.85465155 0.85683973]

## 3.2 learning\_rate和subsample优化过程 ##

cv_params = {'learning_rate': [0.1, 0.01], 'subsample': [0.7,0.8,0.9]}
    ind_params = {'n_estimators': 1000, 'seed':0, 'colsample_bytree': 0.8,
                 'objective': 'binary:logistic', 'max_depth': 3, 'min_child_weight': 5}
    optimized_GBM = GridSearchCV(xgb.XGBClassifier(**ind_params),
                                cv_params,
                                 scoring = 'accuracy', cv = 5, n_jobs = -1)
    optimized_GBM.fit(final_train, y_train)

输出结果

print (optimized_GBM.best_params_)
    print (optimized_GBM.cv_results_['mean_test_score'])
    {'learning_rate': 0.1, 'subsample': 0.8}
    [0.86559247 0.86675287 0.86595716 0.86018832 0.86038724 0.85992308]

# 4. 最优num\_boost\_round #

xgdmat = xgb.DMatrix(final_train, y_train) # Create our DMatrix to make XGBoost more efficient
    our_params = {'eta': 0.1, 'seed':0, 'subsample': 0.8, 'colsample_bytree': 0.8,
                 'objective': 'binary:logistic', 'max_depth':3, 'min_child_weight':5}
    # Grid Search CV optimized settings
    cv_xgb = xgb.cv(params = our_params, dtrain = xgdmat, num_boost_round = 3000, nfold = 5,
                    metrics = ['error'], # Make sure you enter metrics inside a list or you may encounter issues!
                    early_stopping_rounds = 100) # Look for early stopping that minimizes error

输出结果

print (cv_xgb.tail(5))
    429          0.119190         0.001485         0.131556        0.004630
    430          0.119090         0.001430         0.131457        0.004759
    431          0.119123         0.001423         0.131457        0.004752
    432          0.119049         0.001482         0.131291        0.004626
    433          0.119065         0.001603         0.131059        0.004543

参考设置

our_params = {'eta': 0.1, 'seed':0, 'subsample': 0.8, 'colsample_bytree': 0.8,
                 'objective': 'binary:logistic', 'max_depth':3, 'min_child_weight':5}
    final_gb = xgb.train(our_params, xgdmat, num_boost_round = 433)

# 数据可视化分析 #

#matplotlib inline
    import seaborn as sns
    from numpy.random import randn
    import matplotlib.pyplot as plt
    sns.set(font_scale = 1.5)
    xgb.plot_importance(final_gb)
    #显示重要性
    #plt.show()
    importances = final_gb.get_fscore()
    
    importance_frame = pd.DataFrame({'Importance': list(importances.values()), 'Feature': list(importances.keys())})
    importance_frame.sort_values(by = 'Importance', inplace = True)
    importance_frame.plot(kind = 'barh', x = 'Feature', figsize = (8,8), color = 'orange')
    #plt.show()

重要性结果显示

print(importances)
    {'relationship': 146, 'education_num': 158, 'capital_gain': 329, 'hours_per_week': 237,
    'education': 76, 'age': 325, 'capital_loss': 234, 'marital_status': 105, 'fnlwgt': 488,
    'occupation': 234, 'sex': 42, 'workclass': 103, 'race': 53, 'native_country': 94}

# 实验结果 #

testdmat = xgb.DMatrix(final_test)
    from sklearn.metrics import accuracy_score
    y_pred = final_gb.predict(testdmat) # Predict using our testdmat
    #print (y_pred)
    y_pred[y_pred > 0.5] = 1
    y_pred[y_pred <= 0.5] = 0
    #print (y_pred)

准确度

print (accuracy_score(y_pred, y_test), 1-accuracy_score(y_pred, y_test))
    # 0.8678618857901726 0.13213811420982735

# 随机森林对比 #

参考代码

import warnings
    warnings.filterwarnings("ignore", category=FutureWarning, module="sklearn", lineno=196)
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import classification_report
    rf = RandomForestClassifier(n_jobs=4)
    rf.fit(final_train, y_train)
    Y_pred = rf.predict(final_test)

参考结果  
准确率

print (accuracy_score(Y_pred, y_test), 1-accuracy_score(Y_pred, y_test))
    #0.8415670650730411 0.15843293492695887