task5-模型调优

【模型调优（2天）】 使用网格搜索法对5个模型进行调优（调参时采用五折交叉验证的方式），并进行模型评估，记得展示代码的运行结果。 

使用网格搜索法对5个模型进行调优

import pandas as pd 
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV

params_lr = {'solver': ['newton-cg', 'lbfgs', 'sag'], 'C': [0.1, 1, 10]}
lr_model = GridSearchCV(LogisticRegression(class_weight='balanced', max_iter=1000), params_lr, cv=5, scoring='roc_auc')

params_svm = {'kernel': ('linear', 'rbf'), 'C': [0.1, 1, 10]}
svm_model = GridSearchCV(SVC(class_weight='balanced', gamma='auto', probability=True), params_svm, cv=5, scoring='roc_auc')

params_dt = {'criterion': ['gini', 'entropy'], 'max_features': ['sqrt', 'log2', None]}
dt_model = GridSearchCV(DecisionTreeClassifier(class_weight='balanced'), params_dt, cv=5, scoring='roc_auc')

params_rf = {'n_estimators': range(10, 80, 10), 'criterion': ['gini', 'entropy'], 'max_features': ['sqrt', 'log2', None]}
rf_model = GridSearchCV(RandomForestClassifier(class_weight='balanced'), params_rf, cv=5, scoring='roc_auc')

params_en = {'n_estimators': range(10, 80, 10)}
xg_model = GridSearchCV(XGBClassifier(), params_en, cv=5, scoring='roc_auc')

models = {'LR': lr_model,
          'SVM': svm_model,
          'DT': dt_model,
          'RF': rf_model,
          'XGBoost': xg_model}

def get_metric(clf, x, y_true):
    y_pred = clf.predict(x)
    y_proba = clf.predict_proba(x)

    acc = metrics.accuracy_score(y_true, y_pred)
    p = metrics.precision_score(y_true, y_pred)
    r = metrics.recall_score(y_true, y_pred)
    f1 = metrics.f1_score(y_true, y_pred)
    fpr, tpr, thresholds = metrics.roc_curve(y_true, y_proba[:, 1])
    auc = metrics.auc(fpr, tpr)
    return acc, p, r, f1, fpr, tpr, auc


def plot_roc(fprs, tprs, aucs, title):
    plt.figure()
    lw = 2
    for i, name in enumerate(models):
        plt.plot(fprs[i], tprs[i], lw=lw,
                 label='{0} (AUC:{1:0.2f})'.format(name, aucs[i]))
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic of '+title)
    plt.legend(loc="lower right")
    plt.savefig(title + '.jpg')
    plt.show()

df_result = pd.DataFrame(columns=('Model', 'dataset', 'Accuracy', 'Precision', 'Recall', 'F1 score', 'AUC'))
row = 0
fprs_train = []
tprs_train = []
aucs_train = []
fprs_test = []
tprs_test = []
aucs_test = []
for name, clf in models.items():
    clf.fit(x_train, y_train)
    print(name)
    print(clf.best_params_)
    acc, p, r, f1, fpr_train, tpr_train, auc_train = get_metric(clf, x_train, y_train)
    fprs_train.append(fpr_train)
    tprs_train.append(tpr_train)
    aucs_train.append(auc_train)
    df_result.loc[row] = [name, 'train', acc, p, r, f1, auc_train]
    row += 1

    acc, p, r, f1, fpr_test, tpr_test, auc_test = get_metric(clf, x_test, y_test)
    fprs_test.append(fpr_test)
    tprs_test.append(tpr_test)
    aucs_test.append(auc_test)
    df_result.loc[row] = [name, 'test', acc, p, r, f1, auc_test]
    row += 1

    plt.figure()
    lw = 2
    plt.plot(fpr_train, tpr_train, color='darkorange', lw=lw, label='train (AUC:%0.2f)' % auc_train)
    plt.plot(fpr_test, tpr_test, color='cornflowerblue', lw=lw, label='test (AUC:%0.2f)' % auc_test)
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic of '+name)
    plt.legend(loc="lower right")
    plt.savefig(name + '.jpg')
    plt.show()


print(df_result)
df_result.to_csv("df_result.csv")

plot_roc(fprs_train, tprs_train, aucs_train, 'train')
plot_roc(fprs_test, tprs_test, aucs_test, 'test')

参考：https://github.com/ireneqiyiguo/datamining/blob/master/data5.ipynb

最优：https://github.com/Hirotransfer/DataMining/blob/master/Task_05_Financial_Data_Analysis%E2%80%94Model_Tuning.ipynb