Python数据分析实战之：特征重要性分析

提醒

• pandas 读取 excel 文件，需要 xlrd >= 1.1.0

代码

import sklearn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# 混淆矩阵
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_score
from sklearn.metrics import roc_curve, f1_score, precision_score, recall_score
from sklearn.svm import SVC

## pandas 显示全部单元格

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', -1)

数据处理

path = "./2019-2020年.xlsx"

# df = pd.read_csv(path, error_bad_lines=False)
df = pd.read_excel(path)

# 因为 OTT, TOAST 空值太多，暂不用于分析
df = df.drop(['OTT', 'TOAST subtypes'],axis=1)


# 其余的缺失值比较少，直接删除空值
df = df.dropna(axis=0, how='any')

df = df.reset_index()

# 有些列中的数据是字符串，要转成 int 或者 float 才能训练
# 看哪些数据是 object 类型的，全部转成 int 或者 float64 型

# 只有这一个数据有问题，转成 0 即可
df['Coronary heart disease'][74] = 0

# 数据很干净
df.head(10)

	index	Sex	Medication before thrombolytic therapy	Age	Age.1	Periventricular White Matter	Deep White Matter	The degree of WMH	Smoking	Drinking	AtrialFibrillation	Hypertension	Diabetes	Hyperlipidemia	Coronary heart disease	Heart failure	Stroke	TIA	WBC	N	L	NLR	HB	PLT	PCV	PT	INR	APTT	TT	Fibrinogen	Emergency blood sugar	Fasting blood glucose	Creatinine	HDL	LDL	HDL/LDL比值	Cholesterol	Triglyceride	HBLAC	HCY	DNT	sBP	dBP	Baseline NIHSS score	Hemorrhagic Transformation(HT)	Early neurological deterioration (END)	Prognosis&0 (mRS0-2：0；3-6：1）	90dmRS	Prognosis&1(mRS0-1：0；2-6：1）
0	5	0	2	58.0	0.0	2	1	2	1.0	1.0	0.0	1	0	0	0	0	0	0.0	8.4	5.44	2.35	2.310000	141	266	38	10.7	0.92	34.5	14.9	3.52	5.68	5.11	66.2	0.92	3.15	0.290000	4.27	1.99	4.6	11.0	25.0	177.0	90.0	8	0	0	1	5	1
1	6	0	2	53.0	0.0	2	1	2	1.0	1.0	0.0	1	0	0	0	0	0	0.0	11.6	6.5	4.04	1.608911	145	259	45	9.9	0.86	30.6	15.3	3.34	6.38	6.13	74.0	1.46	4.54	0.321586	6.11	2.39	5.5	11.3	37.0	166.0	98.0	4	0	0	0	1	0
2	10	1	2	77.0	1.0	3	3	3	0.0	0.0	0.0	0	0	0	0	0	1	0.0	7.83	6.63	0.59	11.237288	103	225	30	9.2	0.80	28	14.9	4.82	1.76	7.11	107.9	1.19	1.03	1.155340	2.28	0.41	5.0	13.0	30.0	150.0	90.0	9	0	0	0	0	0
3	13	0	2	65.0	0.0	1	0	1	1.0	0.0	0.0	0	0	0	0	0	0	0.0	13.84	10.3	2.33	4.420601	156	327	47	11.8	1.02	38.3	12.4	2.69	8.22	4.98	85.0	0.72	2.27	0.317181	3.51	1.34	5.7	12.0	43.0	150.0	102.0	9	0	0	0	2	1
4	26	1	2	66.0	0.0	2	3	3	0.0	0.0	0.0	1	1	0	0	0	1	0.0	4.73	11.6	2.56	4.531250	142	281	40	12.5	1.08	26.1	18.2	2.49	6.36	4.02	95.0	0.82	2.94	0.278912	4.14	1.67	5.9	22.0	17.0	147.0	75.0	3	0	0	0	2	1
5	27	0	2	74.0	1.0	3	3	3	0.0	0.0	0.0	1	0	0	1	0	0	0.0	6.18	5.07	0.54	9.388889	110	152	32	12.3	1.06	26.7	16.1	3.33	10.40	7.07	80.3	1.11	2.71	0.409594	4.20	0.97	6.7	19.0	22.0	125.0	80.0	5	0	0	0	0	0
6	30	1	2	70.0	1.0	1	1	1	1.0	1.0	0.0	0	0	0	0	0	0	0.0	8.5	6.19	1.61	3.844720	136	258	40	11.3	0.97	31.4	14.1	4.29	5.98	4.40	54.3	1.13	3.59	0.314763	5.28	1.00	5.4	15.0	15.0	147.0	89.0	2	0	0	0	0	0
7	34	0	1	58.0	0.0	1	1	1	1.0	1.0	0.0	1	1	0	0	0	0	0.0	9.48	6.85	1.73	3.959538	152	203	47	10.1	0.87	31.2	12.7	4.43	10.46	8.37	72.8	1.42	3.55	0.400000	4.96	1.51	8.6	13.4	43.0	160.0	105.0	4	0	0	0	1	0
8	36	1	2	65.0	0.0	1	1	1	0.0	0.0	1.0	0	0	0	0	0	1	0.0	15.6	11.52	3.06	3.764706	142	256	43	11.2	0.96	31.4	11.6	4.63	7.32	4.75	50.6	1.08	2.02	0.534653	3.52	1.02	6.5	21.0	15.0	110.0	76.0	11	0	0	1	3	1
9	38	0	2	82.0	1.0	3	3	3	1.0	0.0	0.0	1	0	0	0	0	0	0.0	10.31	8.12	1.24	6.548387	125	247	37	11.3	0.97	31.4	12.5	4.79	6.25	4.88	72.2	1.65	2.56	0.644531	4.21	0.77	5.6	18.7	25.0	220.0	104.0	4	0	0	0	1	0

分离 data 和 label

# 看分布
label_1 = df.columns[-3]
label_2 = df.columns[-1]

# df[label_1].hist()

# df[label_2].hist()

# 得到数据集 Data 是训练数据；label1_data 是第一种标签的标签数据; label2_data 是第二种标签的标签数据

label1_data = df[label_1]
label2_data = df[label_2]


data = df[[column for column in df.columns if column not in [label_1, label_2]]]
# 去除 index 这一列，这列数据没有意义
data.drop(['index'], axis=1, inplace=True)

data.drop(['90dmRS'], axis=1, inplace=True)

data.head(10)

	Sex	Medication before thrombolytic therapy	Age	Age.1	Periventricular White Matter	Deep White Matter	The degree of WMH	Smoking	Drinking	AtrialFibrillation	Hypertension	Diabetes	Hyperlipidemia	Coronary heart disease	Heart failure	Stroke	TIA	WBC	N	L	NLR	HB	PLT	PCV	PT	INR	APTT	TT	Fibrinogen	Emergency blood sugar	Fasting blood glucose	Creatinine	HDL	LDL	HDL/LDL比值	Cholesterol	Triglyceride	HBLAC	HCY	DNT	sBP	dBP	Baseline NIHSS score	Hemorrhagic Transformation(HT)	Early neurological deterioration (END)
0	0	2	58.0	0.0	2	1	2	1.0	1.0	0.0	1	0	0	0	0	0	0.0	8.4	5.44	2.35	2.310000	141	266	38	10.7	0.92	34.5	14.9	3.52	5.68	5.11	66.2	0.92	3.15	0.290000	4.27	1.99	4.6	11.0	25.0	177.0	90.0	8	0	0
1	0	2	53.0	0.0	2	1	2	1.0	1.0	0.0	1	0	0	0	0	0	0.0	11.6	6.5	4.04	1.608911	145	259	45	9.9	0.86	30.6	15.3	3.34	6.38	6.13	74.0	1.46	4.54	0.321586	6.11	2.39	5.5	11.3	37.0	166.0	98.0	4	0	0
2	1	2	77.0	1.0	3	3	3	0.0	0.0	0.0	0	0	0	0	0	1	0.0	7.83	6.63	0.59	11.237288	103	225	30	9.2	0.80	28	14.9	4.82	1.76	7.11	107.9	1.19	1.03	1.155340	2.28	0.41	5.0	13.0	30.0	150.0	90.0	9	0	0
3	0	2	65.0	0.0	1	0	1	1.0	0.0	0.0	0	0	0	0	0	0	0.0	13.84	10.3	2.33	4.420601	156	327	47	11.8	1.02	38.3	12.4	2.69	8.22	4.98	85.0	0.72	2.27	0.317181	3.51	1.34	5.7	12.0	43.0	150.0	102.0	9	0	0
4	1	2	66.0	0.0	2	3	3	0.0	0.0	0.0	1	1	0	0	0	1	0.0	4.73	11.6	2.56	4.531250	142	281	40	12.5	1.08	26.1	18.2	2.49	6.36	4.02	95.0	0.82	2.94	0.278912	4.14	1.67	5.9	22.0	17.0	147.0	75.0	3	0	0
5	0	2	74.0	1.0	3	3	3	0.0	0.0	0.0	1	0	0	1	0	0	0.0	6.18	5.07	0.54	9.388889	110	152	32	12.3	1.06	26.7	16.1	3.33	10.40	7.07	80.3	1.11	2.71	0.409594	4.20	0.97	6.7	19.0	22.0	125.0	80.0	5	0	0
6	1	2	70.0	1.0	1	1	1	1.0	1.0	0.0	0	0	0	0	0	0	0.0	8.5	6.19	1.61	3.844720	136	258	40	11.3	0.97	31.4	14.1	4.29	5.98	4.40	54.3	1.13	3.59	0.314763	5.28	1.00	5.4	15.0	15.0	147.0	89.0	2	0	0
7	0	1	58.0	0.0	1	1	1	1.0	1.0	0.0	1	1	0	0	0	0	0.0	9.48	6.85	1.73	3.959538	152	203	47	10.1	0.87	31.2	12.7	4.43	10.46	8.37	72.8	1.42	3.55	0.400000	4.96	1.51	8.6	13.4	43.0	160.0	105.0	4	0	0
8	1	2	65.0	0.0	1	1	1	0.0	0.0	1.0	0	0	0	0	0	1	0.0	15.6	11.52	3.06	3.764706	142	256	43	11.2	0.96	31.4	11.6	4.63	7.32	4.75	50.6	1.08	2.02	0.534653	3.52	1.02	6.5	21.0	15.0	110.0	76.0	11	0	0
9	0	2	82.0	1.0	3	3	3	1.0	0.0	0.0	1	0	0	0	0	0	0.0	10.31	8.12	1.24	6.548387	125	247	37	11.3	0.97	31.4	12.5	4.79	6.25	4.88	72.2	1.65	2.56	0.644531	4.21	0.77	5.6	18.7	25.0	220.0	104.0	4	0	0

训练

def train(model, dataset, labelset):
    x_train, x_test, y_train, y_test = train_test_split(dataset.values
                                                        , labelset.values
                                                        , test_size=0.2
                                                        , train_size=0.8
                                                        , shuffle=True
                                                        , stratify=labelset)
    model.fit(x_train, y_train)
    score = model.score(x_test, y_test)
    accs = cross_val_score(model, dataset.values, labelset.values, verbose=0)
    print(f'validation acc is: {score}')
    print(f'cross validation accs are: {accs}')
    
    y_pre = model.predict(x_test)
    metri = confusion_matrix(y_test, y_pre)
    sns.heatmap(metri, annot=True)
    plt.show()
    

训练结果 & 混淆矩阵

svc1 = SVC(class_weight='balanced', kernel='linear')
train(svc1, data, label1_data)

validation acc is: 0.8863636363636364
cross validation accs are: [0.90909091 0.69767442 0.76744186 0.88372093 0.88372093]

svc2 = SVC(class_weight='balanced', kernel='linear')
train(svc2, data, label2_data)

validation acc is: 0.8863636363636364
cross validation accs are: [0.81818182 0.6744186  0.72093023 0.76744186 0.76744186]

lr1 = LogisticRegression(class_weight='balanced', max_iter=10000)
train(lr1, data, label1_data)

validation acc is: 0.9090909090909091
cross validation accs are: [0.81818182 0.6744186  0.76744186 0.88372093 0.90697674]

lr2 = LogisticRegression(class_weight='balanced', max_iter=10000)
train(lr2, data, label2_data)

validation acc is: 0.8863636363636364
cross validation accs are: [0.79545455 0.69767442 0.74418605 0.6744186  0.81395349]

各种 feature 的重要性

def make_coef_dictNdf(data_columns, coef):
    name_influence_dic = {string: imp for string, imp in zip(data_columns, coef.squeeze())}
    name_influence_df = pd.DataFrame(data=name_influence_dic, index=['influence']).T
    return name_influence_dic, name_influence_df
## label2 结果各个特征的重要性

def write(filename, name_df_dic):
    writer = pd.ExcelWriter(filename)
    for k,v in name_df_dic.items():
        v.to_excel(writer, sheet_name=k)
    writer.save()
    writer.close()

# writer = pd.ExcelWriter("逻辑回归.xlsx")
# label1_df.to_excel(writer, sheet_name="label1")
# label2_df.to_excel(writer, sheet_name="label2")
# writer.save()
# writer.close()

def plot(figsize, name_influence_df_lst, img_label_lst, title):
    plt.figure(figsize=figsize)
    for i in range(len(name_influence_df_lst)):
        df = name_influence_df_lst[i]
        plt.bar(x=df.index, height=df['influence'],label=img_label_lst[i])
    plt.title(title)
    plt.legend()
    plt.xticks(rotation=90)
# plt.figure(figsize=(20,10))
# plt.bar(x=label1_df.index, height=label1_df['influence'],label='label1')
# plt.bar(x=label1_df.index, height=label2_df['influence'], label='label2')
# plt.legend()
# plt.xticks(rotation=90) # 旋转90度

lr1_dic, lr1_df = make_coef_dictNdf(data.columns, lr1.coef_)
lr2_dic, lr2_df = make_coef_dictNdf(data.columns, lr2.coef_)

svc1_dic, svc1_df = make_coef_dictNdf(data.columns, svc1.coef_)
svc2_dic, svc2_df = make_coef_dictNdf(data.columns, svc2.coef_)

write("逻辑回归.xlsx", {'label1': lr1_df, 'label2': lr2_df})

write("SVM.xlsx", {'label1': svc1_df, 'label2': svc2_df})

plot((20,10), [lr1_df, lr2_df], ['label1', 'label2'], 'lr')

plot((20,10), [svc1_df, svc2_df], ['label1', 'label2'], 'svm')