from interval import interval
a = interval[1,5]
# interval([1.0, 5.0])
print(3 in a)
# True
此外，你还可以构建一个多区间：
from interval import interval
a = interval([0, 1], [2, 3], [10, 15])
print(2.5 in a)
# True
interval.hall 方法还可以将多个区间合并，取其最小及最大值为边界：
from interval import interval
a = interval.hull((interval[1, 3], interval[10, 15], interval[16, 2222]))
# interval([1.0, 2222.0])
print(1231 in a)
# True
区间并集计算：
from interval import interval
a = interval.union([interval([1, 3], [4, 6]), interval([2, 5], 9)])
# interval([1.0, 6.0], [9.0])
print(5 in a)
# True
print(8 in a)
# False
url = 'http://map.amap.com/service/subway?_1615466846985&srhdata=1100_drw_beijing.json'
response = requests.get(url)
result = json.loads(response.text)
stations = []
for i in result['l']:
station = []
for a in i['st']:
station.append([float(b) for b in a['sl'].split(',')])
stations.append(station)
pprint.pprint(stations)
import matplotlib.pyplot as plt import seaborn as sns sns.set(style='darkgrid',font_scale=1.3) plt.rcParams['font.family']='SimHei' plt.rcParams['axes.unicode_minus']=False
6.1.3 特征工程
import sklearn from sklearn import preprocessing #数据预处理模块 from sklearn.preprocessing import LabelEncoder #编码转换 from sklearn.preprocessing import StandardScaler #归一化 from sklearn.model_selection import StratifiedShuffleSplit #分层抽样 from sklearn.model_selection import train_test_split #数据分区 from sklearn.decomposition import PCA #主成分分析 （降维）
6.1.4 分类算法
from sklearn.ensemble import RandomForestClassifier #随机森林 from sklearn.svm import SVC,LinearSVC #支持向量机 from sklearn.linear_model import LogisticRegression #逻辑回归 from sklearn.neighbors import KNeighborsClassifier #KNN算法 from sklearn.cluster import KMeans #K-Means 聚类算法 from sklearn.naive_bayes import GaussianNB #朴素贝叶斯 from sklearn.tree import DecisionTreeClassifier #决策树
6.1.5 分类算法–集成学习
import xgboost as xgb from xgboost import XGBClassifier from catboost import CatBoostClassifier from sklearn.ensemble import AdaBoostClassifier from sklearn.ensemble import GradientBoostingClassifier
6.1.6 模型评估
from sklearn.metrics import classification_report,precision_score,recall_score,f1_score #分类报告 from sklearn.metrics import confusion_matrix #混淆矩阵 from sklearn.metrics import silhouette_score #轮廓系数（评价k-mean聚类效果） from sklearn.model_selection import GridSearchCV #交叉验证 from sklearn.metrics import make_scorer from sklearn.ensemble import VotingClassifier #投票
plt.figure(figsize=(15,6)) df_onehot.corr()['Churn'].sort_values(ascending=False).plot(kind='bar') plt.title('Correlation between Churn and variables ')
print('ka_var列表中的维度与Churn交叉分析结果如下：','\n') for i in kf_var: print('................Churn BY {}...............'.format(i)) print(pd.crosstab(df['Churn'],df[i],normalize=0),'\n') #交叉分析，同行百分比
#自定义齐性检验 & 方差分析 函数 defANOVA(x): li_index=list(df['Churn'].value_counts().keys()) args=[] for i in li_index: args.append(df[df['Churn']==i][x]) w,p=stats.levene(*args) #齐性检验 if p<0.05: print('警告：Churn BY {}的P值为{:.2f}，小于0.05，表明齐性检验不通过，不可作方差分析'.format(x,p),'\n') else: f,p_value=stats.f_oneway(*args) #方差分析 print('Churn BY {} 的f值是{},p_value值是{}'.format(x,f,p_value),'\n') if p_value<0.05: print('Churn BY {}的均值有显著性差异，可进行均值比较'.format(x),'\n') else: print('Churn BY {}的均值无显著性差异，不可进行均值比较'.format(x),'\n')
deflabelencode(x): churn_var[x] = LabelEncoder().fit_transform(churn_var[x]) for i in range(0,len(df_object.columns)): labelencode(df_object.columns[i]) print(list(map(Label,df_object.columns)))
Churn by MonthlyCharges 的卡方临界值是0.00，小于0.05，表明MonthlyCharges组间有显著性差异,可进行【交叉分析】
Churn by TotalCharges 的卡方临界值是0.00，小于0.05，表明TotalCharges组间有显著性差异,可进行【交叉分析】
交叉分析
for i in ['MonthlyCharges','TotalCharges']: print('................Churn BY {}...............'.format(i)) print(pd.crosstab(df['Churn'],df[i],normalize=0),'\n')
# my global config
global:
scrape_interval: 15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
# scrape_timeout is set to the global default (10s).
# Alertmanager configuration
alerting:
alertmanagers:
- static_configs:
- targets:
# - alertmanager:9093
# Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
rule_files:
# - "first_rules.yml"
# - "second_rules.yml"
# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
# The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
- job_name: 'prometheus'
# metrics_path defaults to '/metrics'
# scheme defaults to 'http'.
static_configs:
- targets: ['localhost:9090']
# 主要是新增了node_exporter的job，如果有多个node_exporter，在targets数组后面加即可
- job_name: 'node_exporter'
static_configs:
- targets: ['localhost:9100']
#(CentOS) vim /data/prometheus/conf/prometheus.yaml
vim /data/prometheus/conf/prometheus.yml # ubuntu
配置如下：
# my global config
global:
scrape_interval: 15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
# scrape_timeout is set to the global default (10s).
# Alertmanager configuration
alerting:
alertmanagers:
- static_configs:
- targets:
# - alertmanager:9093
# Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
rule_files:
# - "first_rules.yml"
# - "second_rules.yml"
# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
# The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
- job_name: 'prometheus'
# metrics_path defaults to '/metrics'
# scheme defaults to 'http'.
static_configs:
- targets: ['localhost:9090']
# 主要是新增了node_exporter的job，如果有多个node_exporter，在targets数组后面加即可
- job_name: 'node_exporter'
static_configs:
- targets: ['localhost:9100']
# 新增我们的Python股票采集脚本
- job_name: 'hot_list'
static_configs:
- targets: ['localhost:8000']
