机器学习处理流程、特征工程，模型设计实例

 

作者：大树

更新时间:01.14

email:59888745@qq.com

数据处理，机器学习

回主目录：2017 年学习记录和总结

阿里天池 大航杯“智造扬中”电力AI大赛 的案例分析实现

今天我来实现大航杯“智造扬中”电力AI大赛的案例实现,按照工业界流程来一一呈现:

  1. 业务场景定义 包括:核心目标定义,关键场景描述.
  2. 业务规则梳理 包括:业务规则提炼,规则联动分析
  3. 数据定量分析 包括:数据多维分析,数据异常处理
  4. 模型设计研究 包括:应用场景定制,模型参数调优设置
  5. 运算和结果分析 包括:模型运算输出,业务回归验证

电力AI大赛大赛介绍请参考: https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.333.2.mnbu1L&raceId=231602

1.业务场景定义
a.电力AI大赛大赛介绍请参考URL.
b.通过分析，我们得知，业务需求是通分析江苏镇江扬中市的高新区企业历史近2年的用电量，
希望能够根据历史数据去精准预测未来一个月每一天的用电量,如10月份。
c.高新技术产业开发区,高薪区，上班族（工作日，休息日，节假日，夏天还是冬天等
和用电量相关的关键场景。
2.业务规则梳理
a.通过分析，这是一个典型的回归类问题，和我们的流量预测非常相似，
我们来看看如何用数据驱动的方式去完成这样一个预测。
3 .数据定量分析
3.1.载入数据,数据一览

 
In [2]:
import numpy as np
import pandas as pd

_df = pd.read_csv("tianchi_powerdata/zhenjiang_power.csv")
_df.head()
Out[2]:
 
 record_dateuser_idpower_consumption
0 2015/1/1 1 1135
1 2015/1/2 1 570
2 2015/1/3 1 3418
3 2015/1/4 1 3968
4 2015/1/5 1 3986
In [ ]:
3.2 数据清洗处理（包括异常，缺省值，空值，重复值，日期格式等）
处理na的方法有这些，具体业务具体看：
dropna(),dropna(axis=0,how='all',thresh=None) #thresh =3,
fillna(0)填充d.mean()
isnull(),
notnull(),
drop_duplicates(),重复值_df.drop_duplicates(['user_id','record_date'])
In [11]:
import numpy as np
import pandas as pd

_df = pd.read_csv("tianchi_powerdata/zhenjiang_power.csv")
_df.head()
#_df.shape

_df.dropna(axis=0,how='all',thresh=None) 
_df.drop_duplicates(['user_id','record_date'])

_df['record_date']=pd.to_datetime(_df['record_date'])
_df.head()
Out[11]:
 
 record_dateuser_idpower_consumption
0 2015-01-01 1 1135
1 2015-01-02 1 570
2 2015-01-03 1 3418
3 2015-01-04 1 3968
4 2015-01-05 1 3986
 

构造和时间相关的强特征

In [7]:
import numpy as np
import pandas as pd

_df = pd.read_csv("tianchi_powerdata/zhenjiang_power.csv")
_df.head()
#_df.shape

_df.dropna(axis=0,how='all',thresh=None) 
_df.drop_duplicates(['user_id','record_date'])

_df['record_date']=pd.to_datetime(_df['record_date'])
_df.head()

test_df=pd.date_range('2016-10-1',periods=31,freq='D')#create very data for 10.1--10.31
 
test_df=pd.DataFrame(test_df,columns=['record_date'])

test_df['power_consumption']=0.0
test_df
total_df=pd.concat([_df,test_df])
#total_df.fillna(0)
total_df.dropna()
#total_df.head()
total_df.tail()

#时间相关的特征
total_df['day_of_week']=total_df['record_date'].apply(lambda x:x.dayofweek)
total_df['day_of_month']=total_df['record_date'].apply(lambda x:x.day)
total_df['day_of_year']=total_df['record_date'].apply(lambda x:x.dayofyear)
total_df['month_of_year']=total_df['record_date'].apply(lambda x:x.month)
total_df['year']=total_df['record_date'].apply(lambda x:x.year)

#添加工作日还是周末的信息，周六周日和工作日的用电量显然是不一样
total_df['holiday']=0
total_df['holiday_sat']=0
total_df['holiday_sun']=0

#周末特征信息
total_df.loc[total_df.day_of_week ==5,'holiday']=1
total_df.loc[total_df.day_of_week ==5,'holiday_sat']=1

total_df.loc[total_df.day_of_week ==6,'holiday']=1
total_df.loc[total_df.day_of_week ==6,'holiday_sun']=1

#一个月4周的周信息,属于第几周
def week_of_month(day):
    if day in range(1,8):return 1
    if day in range(8,15):return 2
    if day in range(15,22):return 3
    if day in range(22,32):return 4

total_df['week_of_month']=total_df['day_of_month'].apply(lambda x:week_of_month(x))
total_df.head()

#属于上中下旬信息,有些企业的任务是按照月份的上中下旬来安排的，同样可能对用电量会有影响
def period_of_month(day):
    if day in range(1,11):return 1
    if day in range(11,21):return 2
    if day in range(21,32):return 3
    
total_df['period_of_month'] =total_df['day_of_month'].apply(lambda x:period_of_month(x))
total_df.head()

#上半月下半月信息
def period2_of_month(day):
    if day in range(1,16):return 1
    if day in range(16,32):return 2
total_df['period2_of_month'] =total_df['day_of_month'].apply(lambda x:period2_of_month(x))
total_df.head()

# 手动填充节日信息 另外一个对用电量非常大的影响是节假日，法定节假日大部分企业会放假，
# 电量会有大程度的下滑。我们通过查日历的方式去手动填充一个特征/字段，表明这一天是否是节日。
def day_of_festival(day):
    l_festival=['2016-10-01','2016-10-02','2016-10-03','2016-10-04','2016-10-05','2016-10-06','2016-10-07']
    if day in l_festival:return 1
    else:return 0
    
total_df['festival_pc']=0
total_df['festival']=0


total_df['festival']=total_df['festival'].apply(lambda x:day_of_festival(x))

total_df.head(20)
Out[7]:
 
 power_consumptionrecord_dateuser_idday_of_weekday_of_monthday_of_yearmonth_of_yearyearholidayholiday_satholiday_sunweek_of_monthperiod_of_monthperiod2_of_monthfestival_pcfestival
0 1135.0 2015-01-01 1.0 3 1 1 1 2015 0 0 0 1 1 1 0 0
1 570.0 2015-01-02 1.0 4 2 2 1 2015 0 0 0 1 1 1 0 0
2 3418.0 2015-01-03 1.0 5 3 3 1 2015 1 1 0 1 1 1 0 0
3 3968.0 2015-01-04 1.0 6 4 4 1 2015 1 0 1 1 1 1 0 0
4 3986.0 2015-01-05 1.0 0 5 5 1 2015 0 0 0 1 1 1 0 0
5 4082.0 2015-01-06 1.0 1 6 6 1 2015 0 0 0 1 1 1 0 0
6 4172.0 2015-01-07 1.0 2 7 7 1 2015 0 0 0 1 1 1 0 0
7 4022.0 2015-01-08 1.0 3 8 8 1 2015 0 0 0 2 1 1 0 0
8 4025.0 2015-01-09 1.0 4 9 9 1 2015 0 0 0 2 1 1 0 0
9 4047.0 2015-01-10 1.0 5 10 10 1 2015 1 1 0 2 1 1 0 0
10 4135.0 2015-01-11 1.0 6 11 11 1 2015 1 0 1 2 2 1 0 0
11 4111.0 2015-01-12 1.0 0 12 12 1 2015 0 0 0 2 2 1 0 0
12 3926.0 2015-01-13 1.0 1 13 13 1 2015 0 0 0 2 2 1 0 0
13 4244.0 2015-01-14 1.0 2 14 14 1 2015 0 0 0 2 2 1 0 0
14 4144.0 2015-01-15 1.0 3 15 15 1 2015 0 0 0 3 2 1 0 0
15 4269.0 2015-01-16 1.0 4 16 16 1 2015 0 0 0 3 2 2 0 0
16 4262.0 2015-01-17 1.0 5 17 17 1 2015 1 1 0 3 2 2 0 0
17 2782.0 2015-01-18 1.0 6 18 18 1 2015 1 0 1 3 2 2 0 0
18 3327.0 2015-01-19 1.0 0 19 19 1 2015 0 0 0 3 2 2 0 0
19 4002.0 2015-01-20 1.0 1 20 20 1 2015 0 0 0 3 2 2 0 0
In [6]:
#已经有的数据特征字段
    # 可以看到有
    # 日期
    # 用电量
    # 星期几
    # 一个月第几天
    # 一年第几天
    # 一年第几个月
    # 年
    # 是否节假日
    # 月中第几周
    # 一个月上中下旬哪个旬
    # 上半月还是下半月
    # 是否节日
col_names=total_df.columns.values
col_names

#确认一下训练数据没有缺省值
counts={}
for name in col_names:
    count=total_df[name].isnull().sum()
    counts[name]=[count]

is_null_filds = pd.DataFrame(counts)
is_null_filds
Out[6]:
 
 day_of_monthday_of_weekday_of_yearfestivalfestival_pcholidayholiday_satholiday_sunmonth_of_yearperiod2_of_monthperiod_of_monthpower_consumptionrecord_dateuser_idweek_of_monthyear
0 0 0 0 0 0 0 0 0 0 0 0 0 0 31 0 0
In [ ]:
# 4. 模型设计研究
包括:应用场景定制,模型参数设置
分离训练集和测试集
我们根据日期分割训练集和测试集，用于后续的建模
In [15]:
## 非十月份的是训练集
train_X = total_df[~((total_df.year==2016)&(total_df.month_of_year==10))]
test_X = total_df[((total_df.year==2016)&(total_df.month_of_year==10))]
train_y = train_X.power_consumption
train_X = train_X.drop(['power_consumption','record_date','year'],axis=1)
test_X = test_X.drop(['power_consumption','record_date','year'],axis=1)
train_X.head()
Out[15]:
 
 user_idday_of_weekday_of_monthday_of_yearmonth_of_yearholidayholiday_satholiday_sunweek_of_monthperiod_of_monthperiod2_of_monthfestival_pcfestival
0 1.0 3 1 1 1 0 0 0 1 1 1 0 0
1 1.0 4 2 2 1 0 0 0 1 1 1 0 0
2 1.0 5 3 3 1 1 1 0 1 1 1 0 0
3 1.0 6 4 4 1 1 0 1 1 1 1 0 0
4 1.0 0 5 5 1 0 0 0 1 1 1 0 0
In [16]:
train_X.shape
Out[16]:
(885468, 13)
In [ ]:
# 5 建模与调参,利用网格搜索交叉验证去查找最好的参数，
# DecisionTree
In [17]:
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import GridSearchCV

param_grid = {'max_features': [0.7, 0.8, 0.9, 1],
              'max_depth':  [3, 5, 7, 9, 12]
             }

dt = DecisionTreeRegressor()

grid = GridSearchCV(dt, param_grid=param_grid, cv=5, n_jobs=8, refit=True)
grid.fit(train_X, train_y)
best_dt_reg = grid.best_estimator_
print(best_dt_reg)
print(best_dt_reg.score(train_X,train_y))
 
DecisionTreeRegressor(criterion='mse', max_depth=3, max_features=0.9,
           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,
           presort=False, random_state=None, splitter='best')
0.919640509654
In [ ]:
#考察一下训练集上的拟合程度
In [18]:
best_dt_reg.score(train_X, train_y)
Out[18]:
0.91964050965438204
In [ ]:
#进行结果预测
In [28]:
from datetime import datetime 

#完成提交日期格式的转换
def dataprocess(t):
    t = str(t)[0:10]
    time = datetime.strptime(t, '%Y-%m-%d')
    res = time.strftime('%Y%m%d')
    return res

#生成10月份31天的时间段
commit_df = pd.date_range('2016/10/1', periods=31, freq='D')
commit_df = pd.DataFrame(commit_df)
commit_df.columns = ['predict_date']

#用模型进行预测
test_X['user_id']=test_X['day_of_month'].apply(lambda x:x)
test_X
prediction = best_dt_reg.predict(test_X.values)
commit_df['predict_power_consumption'] = pd.DataFrame(prediction).astype('int')
commit_df['predict_date'] = commit_df['predict_date'].apply(dataprocess)
commit_df.head()
Out[28]:
 
 predict_datepredict_power_consumption
0 20161001 3820886
1 20161002 3845830
2 20161003 3845830
3 20161004 3845830
4 20161005 3845830
In [ ]:
RandomForest 模型融合
In [ ]:
# RandomForestRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor

### 多少颗树，树有多深（一般不超过10），建树的时候不用全部属性（具体看多少属性）， 采样
param_grid = {
              'n_estimators': [5, 8, 10, 15, 20, 50, 100, 200],
              'max_depth': [3, 5, 7, 9],
              'max_features': [0.6, 0.7, 0.8, 0.9],
             }

rf = RandomForestRegressor()

grid = GridSearchCV(rf, param_grid=param_grid, cv=3, n_jobs=8, refit=True)
grid.fit(train_X, train_y)

breg = grid.best_estimator_
print(breg)
print(breg.score(train_X, train_y))
In [ ]:
用模型进行预测
In [ ]:
from datetime import datetime 

def dataprocess(t):
    t = str(t)[0:10]
    time = datetime.strptime(t, '%Y-%m-%d')
    res = time.strftime('%Y%m%d')
    return res


#用模型进行预测
test_X['user_id']=test_X['day_of_month'].apply(lambda x:x)

commit_df = pd.date_range('2016/10/1', periods=31, freq='D')
commit_df = pd.DataFrame(commit_df)
commit_df.columns = ['predict_date']
prediction = breg.predict(test_X)
commit_df['predict_power_consumption'] = pd.DataFrame(prediction).astype('int')
commit_df['predict_date'] = commit_df['predict_date'].apply(dataprocess)

commit_df.head()
 

总结：

通过上面这个用电量分析预测未来用电量例子，我们可以发现，在建摸前对业务数据的分析， 特征提取很重要，它直接决定了你预测的准确度的高低，所以好的特征提取很重要。 只有尽可能全面准确的对业务场景的了解，才能比较好的做特征提取， 在加上合适的算法模型，才能作出好的效果.

 

完整版代码

In [ ]:
import numpy as np
import pandas as pd

_df = pd.read_csv("tianchi_powerdata/zhenjiang_power.csv")
train_df = _df 
_df.head()
#_df.shape
#df_201609
#train_df.head(5)
_df['record_date']=pd.to_datetime(_df['record_date'])
_df.head()
train_df=_df[['record_date','power_consumption']].groupby(by='record_date').agg('sum')

train_df=train_df.reset_index()
train_df.head()

test_df=pd.date_range('2016-10-1',periods=31,freq='D')#create very data for 10.1--10.31
 
test_df=pd.DataFrame(test_df,columns=['record_date'])

test_df['power_consumption']=0.0
test_df
total_df=pd.concat([_df,test_df])
#total_df.fillna(np.random.randint(100,10000))
total_df.dropna()
#total_df.head()
total_df.tail()

#时间相关的特征
total_df['day_of_week']=total_df['record_date'].apply(lambda x:x.dayofweek)
total_df['day_of_month']=total_df['record_date'].apply(lambda x:x.day)
total_df['day_of_year']=total_df['record_date'].apply(lambda x:x.dayofyear)
total_df['month_of_year']=total_df['record_date'].apply(lambda x:x.month)
total_df['year']=total_df['record_date'].apply(lambda x:x.year)

#添加工作日还是周末的信息，周六周日和工作日的用电量显然是不一样
total_df['holiday']=0
total_df['holiday_sat']=0
total_df['holiday_sun']=0

#周末特征信息
total_df.loc[total_df.day_of_week ==5,'holiday']=1
total_df.loc[total_df.day_of_week ==5,'holiday_sat']=1

total_df.loc[total_df.day_of_week ==6,'holiday']=1
total_df.loc[total_df.day_of_week ==6,'holiday_sun']=1

#一个月4周的周信息,属于第几周
def week_of_month(day):
    if day in range(1,8):return 1
    if day in range(8,15):return 2
    if day in range(15,22):return 3
    if day in range(22,32):return 4

total_df['week_of_month']=total_df['day_of_month'].apply(lambda x:week_of_month(x))
total_df.head()

#属于第上中下旬信息
def period_of_month(day):
    if day in range(1,11):return 1
    if day in range(11,21):return 2
    if day in range(21,32):return 3
    
total_df['period_of_month'] =total_df['day_of_month'].apply(lambda x:period_of_month(x))
total_df.head()

#上半月下半月信息
def period2_of_month(day):
    if day in range(1,16):return 1
    if day in range(16,32):return 2
total_df['period2_of_month'] =total_df['day_of_month'].apply(lambda x:period2_of_month(x))
total_df.head()

# 手动填充节日信息 另外一个对用电量非常大的影响是节假日，法定节假日大部分企业会放假，
# 电量会有大程度的下滑。我们通过查日历的方式去手动填充一个特征/字段，表明这一天是否是节日。
def day_of_festival(day):
    l_festival=['2016-10-01','2016-10-02','2016-10-03','2016-10-04','2016-10-05','2016-10-06','2016-10-07']
    if day in l_festival:return 1
    else:return 0
    
total_df['festival_pc']=0
total_df['festival']=0


total_df['festival']=total_df['festival'].apply(lambda x:day_of_festival(x))

total_df.head(20)

#已经有的数据特征字段
    # 可以看到有
    # 日期
    # 用电量
    # 星期几
    # 一个月第几天
    # 一年第几天
    # 一年第几个月
    # 年
    # 是否节假日
    # 月中第几周
    # 一个月上中下旬哪个旬
    # 上半月还是下半月
    # 是否节日
col_names=total_df.columns.values
col_names

#确认一下训练数据没有缺省值
counts={}
for name in col_names:
    count=total_df[name].isnull().sum()
    counts[name]=[count]

is_null_filds = pd.DataFrame(counts)
is_null_filds

#添加独热向量编码/one-hot encoding  ;针对星期几这个特征，初始化一个长度为7的向量[0,0,0,0,0,0,0]
    #对于类别型特征，我们经常在特征工程的时候会对他们做一些特殊的处理
    # 星期一会被填充成[1,0,0,0,0,0,0]
    # 星期二会被填充成[0,1,0,0,0,0,0]
    # 星期三会被填充成[0,0,1,0,0,0,0]
    # 星期四会被填充成[0,0,0,1,0,0,0]
    # 以此类推...


# 树状模型建模 树状模型是工业界最常用的机器学习算法之一，我们在训练集上去学习出来一个最好的决策路径，而每条决策路径的根节点是我们预测的结果;
# 1.分离训练集和测试集
## 非十月份的是训练集
train_X = total_df[~((total_df.year==2016)&(total_df.month_of_year==10))]
test_X = total_df[((total_df.year==2016)&(total_df.month_of_year==10))]
#print(train_X.shape)
#print(test_X.shape)

train_y = train_X.power_consumption
train_X = train_X.drop(['power_consumption','record_date','year'],axis=1)
test_X = test_X.drop(['power_consumption','record_date','year'],axis=1)

train_X.head()

#建模与调参;我们利用网格搜索交叉验证去查找最好的参数
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import GridSearchCV

param_grid = {'max_features': [0.7, 0.8, 0.9, 1],
              'max_depth':  [3, 5, 7, 9, 12]
             }

dt = DecisionTreeRegressor()

grid = GridSearchCV(dt, param_grid=param_grid, cv=5, n_jobs=8, refit=True)
grid.fit(train_X, train_y)
best_dt_reg = grid.best_estimator_
print(best_dt_reg)
print(best_dt_reg.score(train_X,train_y))

from datetime import datetime 

#完成提交日期格式的转换
def dataprocess(t):
    t = str(t)[0:10]
    time = datetime.strptime(t, '%Y-%m-%d')
    res = time.strftime('%Y%m%d')
    return res

#生成10月份31天的时间段
commit_df = pd.date_range('2016/10/1', periods=31, freq='D')
commit_df = pd.DataFrame(commit_df)
commit_df.columns = ['predict_date']

#用模型进行预测
prediction = best_dt_reg.predict(test_X.values)
commit_df['predict_power_consumption'] = pd.DataFrame(prediction).astype('int')
commit_df['predict_date'] = commit_df['predict_date'].apply(dataprocess)
commit_df.head()
特征重要度
%matplotlib inline
import matplotlib.pyplot as plt
print("Feature ranking:")
feature_names = [u'day_of_week', u'day_of_month', u'day_of_year', u'month_of_year',
       u'holiday', u'holiday_sat', u'holiday_sun', u'week_of_month',
       u'period_of_month', u'period2_of_month', u'festival_pc', u'festival']
feature_importances = breg.feature_importances_
indices = np.argsort(feature_importances)[::-1]

for f in indices:
    print("feature %s (%f)" % (feature_names[f], feature_importances[f]))

plt.figure(figsize=(20,8))
plt.title("Feature importances")
plt.bar(range(len(feature_importances)), feature_importances[indices],
       color="b",align="center")
plt.xticks(range(len(feature_importances)), np.array(feature_names)[indices])
plt.xlim([-1, train_X.shape[1]])
plt.show()

remark:

说明:

模型设计:

load data

交叉验证

classer

model=classer.fit(x,y)

predict = model.transforam(x,y)

predict.filter()

predict.count()

sklearn:

from sklearn.tree import DecisionTreeRegressor

from sklearn.model_selection import GridSearchCV

from sklearn.ensemble import RandomForestRegressor

import xgboost as xgb

 

回归问题,对连续值进行预测,如上面的用电量预测:

DecisionTreeRegressor()

XGBRegressor()

RandomForestRegressor()

xgb.XGBRegressor()

GridSearchCV(xgb_model, param_grid, n_jobs=8)

param_grid = {'max_features': [0.7, 0.8, 0.9, 1],               'max_depth':  [3, 5, 7, 9, 12]              }

dt = DecisionTreeRegressor()

grid = GridSearchCV(dt, param_grid=param_grid, cv=5, n_jobs=8, refit=True)

grid.fit(train_X, train_y)

best_dt_reg = grid.best_estimator_

best_dt_reg.predict(test_X.values)

rf = RandomForestRegressor()

grid = GridSearchCV(rf, param_grid=param_grid, cv=3, n_jobs=8, refit=True)

grid.fit(train_X, train_y)

best_dt_reg = grid.best_estimator_

best_dt_reg.score(train_X, train_y)

best_dt_reg.predict(test_X.values)

param_grid = {               'max_depth': [3, 4, 5, 7, 9],               'n_estimators': [20, 40, 50, 80, 100, 200, 400, 800, 1000, 1200],               'learning_rate': [0.05, 0.1, 0.2, 0.3],               'subsample': [0.8, 1],               'colsample_bylevel':[0.8, 1]              }

# 使用xgboost的regressor完成回归

xgb_model = xgb.XGBRegressor()

# 数据拟合

rgs = GridSearchCV(xgb_model, param_grid, n_jobs=8)

rgs.fit(X, y)

print(rgs.best_score_)

print(rgs.best_params_)

 rgs.predict(test_X.values)

LogisticRegression逻辑回归      被用来解决分类问题（二元分类），但多类的分类（所谓的一对多方法）也适用;优点是对于每一个输出的对象都有一个对应类别的概率

GaussianNB朴素贝叶斯              在多类的分类问题上表现的很好;

kNN（k-最近邻）方法                                      通常用于一个更复杂分类算法的一部分,用它的估计值做为一个对象的特征;

DecisionTree决策树分类和回归树（CART）    适用于多类分类 支持向量机SVM                用于分类问题;逻辑回归

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原文地址：https://www.cnblogs.com/csj007523/p/8310763.html