（python源码）完成基于sns预测心脏病算法实现

# coding=utf-8 import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from pylab import mpl from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import roc_curve from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score def plot_roc_curve(fprs,tprs): plt.figure(figsize=(8,6),dpi=80) plt.plot(fprs,tprs) plt.plot([0,1],linestyle='--') plt.xticks(fontsize=13) plt.yticks(fontsize=13) plt.ylabel('TP rate',fontsize=15) plt.xlabel('FP rate',fontsize=15) plt.title('ROC曲线',fontsize=17) plt.show() mpl.rcParams['font.sans-serif'] = ['SimHei'] mpl.rcParams['axes.unicode_minus'] = False df = pd.read_csv("../data/heart.csv") print(df.info()) print(df.target.value_counts()) sns.countplot(x='target', data=df, palette="muted") plt.xlabel("得病/未得病比例") plt.show() print(df.describe()) first = pd.get_dummies(df['cp'], prefix="cp") second = pd.get_dummies(df['slope'], prefix="slope") thrid = pd.get_dummies(df['thal'], prefix="thal") df = pd.concat([df, first, second, thrid], axis=1) df = df.drop(columns=['cp', 'slope', 'thal']) print(df.head(3)) y = df.target.values X = df.drop(['target'], axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=6) standardScaler = StandardScaler() standardScaler.fit(X_train) X_train = standardScaler.transform(X_train) X_test = standardScaler.transform(X_test) rf_clf = RandomForestClassifier(n_estimators=500, random_state=666, oob_score=True, n_jobs=-1) rf_clf.fit(X, y) RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1, oob_score=True, random_state=666, verbose=0, warm_start=False) print(rf_clf.oob_score_) print(rf_clf.score(X_test, y_test)) y_probabilities_rf = rf_clf.predict_proba(X_test)[:, 1] print(roc_auc_score(y_test, y_probabilities_rf)) y_probabilities = rf_clf.predict_proba(X)[:, 1] print(roc_auc_score(y, y_probabilities)) fprs4, tprs4, thresholds4 = roc_curve(y_test, y_probabilities_rf) # 此处调用前面的绘制函数 plot_roc_curve(fprs4, tprs4) rf_clf2 = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, random_state=666, oob_score=True, n_jobs=-1) rf_clf2.fit(X,y) RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=16, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1, oob_score=True, random_state=666, verbose=0, warm_start=False)