心脏病预测模型（基于Python的数据挖据）

#导入依赖库import pandas as pndimport numpy as npfrom sklearn import preprocessingfrom sklearn import neighbors, datasetsfrom sklearn import cross_validationfrom sklearn.linear_model import SGDClassifierfrom sklearn import svmimport operatorfrom sklearn.cross_validation import KFold
import itertoolsimport numpy as npimport matplotlib.pyplot as plt
from sklearn import metricsfrom sklearn.metrics import confusion_matrix
from sklearn import treeimport seaborn as sns
from IPython.display import Image
%matplotlib inline

# 添加列名header_row = ['age','sex','chest_pain','blood pressure','serum_cholestoral','fasting_blood_sugar',\               'electrocardiographic','max_heart_rate','induced_angina','ST_depression','slope','vessels','thal','diagnosis']
# 载入数据heart = pnd.read_csv('processed.cleveland.data.csv', names=header_row)heart[:5]

#查看数据维度heart.shape

(303, 14)   #303行人的数据，13个连续观察不同症状。

# 计算统计值heart.describe()

names_descr = dict()categorical_columns = ["sex", "chest_pain", "fasting_blood_sugar", "electrocardiographic", "induced_angina", "slope", "vessels", \                       "thal", "diagnosis"]for c in categorical_columns:    print  (heart.groupby([c])["age"].count())

for c in heart.columns[:-1]:    heart[c] = heart[c].apply(lambda x:heart[heart[c]!='?'][c].astype(float).mean() if x == "?" else x)    heart[c] = heart[c].astype(float)

set(heart.loc[:, "diagnosis"].values)

vecs_1 = heart[heart["diagnosis"] == 1 ].median().values[:-2]vecs_2 = heart[heart["diagnosis"] == 2 ].median().values[:-2]vecs_3 = heart[heart["diagnosis"] == 3 ].median().values[:-2]vecs_4 = heart[heart["diagnosis"] == 4 ].median().values[:-2]

print ("Similarity between type 1 and type 2 is ", np.linalg.norm(vecs_1-vecs_2))print ("Similarity between type 1 and type 3 is ", np.linalg.norm(vecs_1-vecs_3))print ("Similarity between type 1 and type 4 is ", np.linalg.norm(vecs_1-vecs_4))print ("Similarity between type 2 and type 3 is ", np.linalg.norm(vecs_2-vecs_3))print ("Similarity between type 2 and type 4 is ", np.linalg.norm(vecs_2-vecs_4))print ("Similarity between type 3 and type 4 is ", np.linalg.norm(vecs_3-vecs_4))

sim = {"(1,2)": np.linalg.norm(vecs_1-vecs_2), \       "(1,3)": np.linalg.norm(vecs_1-vecs_3),\       "(1,4)": np.linalg.norm(vecs_1-vecs_4),\       "(2,3)": np.linalg.norm(vecs_2-vecs_3),\       "(2,4)": np.linalg.norm(vecs_2-vecs_4),\       "(3,4)": np.linalg.norm(vecs_3-vecs_4)          }

# 根据相近值排序sorted_sim = sorted(sim.items(), key=operator.itemgetter(1))sorted_sim

heart_d = heart[heart["diagnosis"] >= 1 ]heart_d[:5]

# if "diagnosis" == 0, 没有疾病# if "diagnosis" >= 1, 有疾病heart.loc[:, "diag_int"] = heart.loc[:, "diagnosis"].apply(lambda x: 1 if x >= 1 else 0)

#数据标准化preprocessing.Normalizer().fit_transform(heart)

#划分数据集heart_train, heart_test, goal_train, goal_test = cross_validation.train_test_split(heart.loc[:,'age':'thal'], \                                                 heart.loc[:,'diag_int'], test_size=0.33, random_state=0)  

#计算相关系数corr = heart.corr()heart.corr()

#绘制热图cmap = sns.diverging_palette(250, 10, n=3, as_cmap=True)
def magnify():    return [dict(selector="th",                 props=[("font-size", "7pt")]),            dict(selector="td",                 props=[('padding', "0em 0em")]),            dict(selector="th:hover",                 props=[("font-size", "12pt")]),            dict(selector="tr:hover td:hover",                 props=[('max-width', '200px'),                        ('font-size', '12pt')])]
corr.style.background_gradient(cmap, axis=1)\    .set_properties(**{'max-width': '80px', 'font-size': '10pt'})\    .set_caption("Hover to magify")\    .set_precision(2)\    .set_table_styles(magnify())

#年龄与血压关系import matplotlib.pyplot as plt%matplotlib inline
plt.xlabel("age")plt.ylabel("blood pressure")
# define titleplt.title("Relationship between age and blood pressure")
# plotplt.scatter(heart['age'], heart['blood pressure'])plt.show()

# add parameters for grid searchloss = ["hinge", "log"]penalty = ["l1", "l2"]alpha = [0.1, 0.05, 0.01]n_iter = [500, 1000]
# build the models with different parameters and select the best combination for the highest Accuracybest_score = 0best_param = (0,0,0,0)for l in loss:    for p in penalty:        for a in alpha:            for n in n_iter:                print("Parameters for model", (l,p,a,n))                lss = SGDClassifier(loss=l, penalty=p, alpha=a, n_iter=n)                lss.fit(heart_train, goal_train)                print("Linear regression SGD Cross-Validation scores:")                scores = cross_validation.cross_val_score(lss, heart.loc[:,'age':'thal'], heart.loc[:,'diag_int'], cv=10)                print (scores)                print("Mean Linear regression SGD Cross-Validation score = ", np.mean(scores))
                if np.mean(scores) > best_score:                    best_score = np.mean(scores)                    best_param = (l,p,a,n)

print("The best parameters for model are ", best_param)print("The Cross-Validation score = ", best_score)

lss_best = SGDClassifier(alpha=0.05, fit_intercept=True, loss='log', n_iter=1000,penalty='l1')lss_best.fit(heart_train, goal_train)print("Linear regression SGD Test score:")print(lss_best.score(heart_test, goal_test))   

# Compute confusion matrixcnf_matrix = confusion_matrix(goal_test, lss_best.predict(heart_test))np.set_printoptions(precision=2)
# Plot non-normalized confusion matrixplt.figure()plot_confusion_matrix(cnf_matrix, classes=["Heart disease", "No heart disease"],                      title='Confusion matrix, without normalization')plt.show()