File name
Commit message
Commit date
import pandas as pd
from datetime import datetime, date
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score ,confusion_matrix# 정확도 함수
def taking_TF(user_id):
df= pd.read_csv(r'D:\takensoft\project7\약상자송신데이터.csv',encoding='euc-kr')
df['humantype']=df['type1'].astype(str)+df['type2'].astype(str)+df['type3'].astype(str)+df['type4'].astype(str)+df['type5'].astype(str)
df['taketype']=df['take1'].astype(str)+df['take2'].astype(str)+df['take3'].astype(str)+df['take4'].astype(str)+df['take5'].astype(str)
li_type = []
li_taketype = []
for i in range(0,len(df)):
li_type.append(int(df['humantype'][i],2))
li_taketype.append(int(df['taketype'][i],2))
humantype=pd.DataFrame({'humantypes':li_type,'taketypes':li_taketype})
df=pd.concat([humantype,df],axis=1)
df=df[df['user_id']==user_id]
if len(df) <30:
print("분석불가")
return "분석불가"
df=df.reset_index()
df['date_info']=pd.to_datetime(df['date_info'])
li_weekday=[]
for i in range(0,len(df)):
li_weekday.append(df['date_info'][i].weekday())
day=pd.DataFrame({'weekday':li_weekday})
df=pd.concat([df,day],axis=1)
df['error']=df['humantypes']!=df['taketypes']
df['prev_humantypes']=df['humantypes'].shift()
df['prev_taketypes']=df['taketypes'].shift()
df=df[1:len(df)-1].reset_index()
X= df[['temp1','temp2','temp3','temp4','weekday','prev_humantypes','prev_taketypes']]
y = df['error']
train_x, test_x, train_y, test_y = train_test_split(X, y, test_size = 0.2, random_state = 42) # 학습데이터와 평가데이터의 비율을 8:2 로 분할|
clf = RandomForestClassifier(n_estimators=20, max_depth=5,random_state=0)
clf.fit(train_x,train_y)
#print(len(train_x))
predict1 = clf.predict(test_x)
print(accuracy_score(test_y,predict1))
#print(predict1)
#print(confusion_matrix(test_y, predict1))
#feature_scores = pd.Series(clf.feature_importances_, index=train_x.columns).sort_values(ascending=False)
#print(feature_scores[:10])
def taking_class(user_id):
df= pd.read_csv(r'D:\takensoft\project7\약상자송신데이터.csv',encoding='euc-kr')
df['humantype']=df['type1'].astype(str)+df['type2'].astype(str)+df['type3'].astype(str)+df['type4'].astype(str)+df['type5'].astype(str)
df['taketype']=df['take1'].astype(str)+df['take2'].astype(str)+df['take3'].astype(str)+df['take4'].astype(str)+df['take5'].astype(str)
li_type = []
li_taketype = []
for i in range(0,len(df)):
li_type.append(int(df['humantype'][i],2))
li_taketype.append(int(df['taketype'][i],2))
humantype=pd.DataFrame({'humantypes':li_type,'taketypes':li_taketype})
df=pd.concat([humantype,df],axis=1)
df=df[df['user_id']==user_id]
if len(df) <30:
print("분석불가")
return "분석불가"
df=df.reset_index()
df['date_info']=pd.to_datetime(df['date_info'])
li_weekday=[]
for i in range(0,len(df)):
li_weekday.append(df['date_info'][i].weekday())
day=pd.DataFrame({'weekday':li_weekday})
df=pd.concat([df,day],axis=1)
df['error']=df['humantypes']!=df['taketypes']
df['prev_humantypes']=df['humantypes'].shift()
df['prev_taketypes']=df['taketypes'].shift()
df=df[1:len(df)-1].reset_index()
X= df[['temp1','temp2','temp3','temp4','weekday','prev_humantypes','prev_taketypes']]
y = df['taketypes']
train_x, test_x, train_y, test_y = train_test_split(X, y, test_size = 0.2, random_state = 42) # 학습데이터와 평가데이터의 비율을 8:2 로 분할|
clf = RandomForestClassifier(n_estimators=20, max_depth=5,random_state=0)
clf.fit(train_x,train_y)
#print(len(train_x))
predict1 = clf.predict(test_x)
print(accuracy_score(test_y,predict1))
#print(confusion_matrix(test_y, predict1))
#feature_scores = pd.Series(clf.feature_importances_, index=train_x.columns).sort_values(ascending=False)
#print(feature_scores[:10])
taking_TF('MEMBER_000000000000584')
taking_class('MEMBER_000000000000584')