00

.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.5.2 (D:\Program Files\Anaconda3\python.exe)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/tuia-alg-engineering-py.iml" filepath="$PROJECT_DIR$/.idea/tuia-alg-engineering-py.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

.idea/tuia-alg-engineering-py.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

.idea/tuia-alg-engineering-python.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="projectConfiguration" value="Nosetests" />
+    <option name="PROJECT_TEST_RUNNER" value="Nosetests" />
+  </component>
+</module>
\ No newline at end of file

.idea/vcs.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_day/.idea/alg_report_day.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.5.2 (D:\Program Files\Anaconda3\python.exe)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="projectConfiguration" value="Nosetests" />
+    <option name="PROJECT_TEST_RUNNER" value="Nosetests" />
+  </component>
+</module>
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_day/.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.5.2 (D:\Program Files\Anaconda3\python.exe)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_day/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/alg_report_day.iml" filepath="$PROJECT_DIR$/.idea/alg_report_day.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_day/.idea/vcs.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_day/arpu_fix_day.py

+# -*- coding: utf-8 -*-
+import matplotlib
+matplotlib.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+
+#data.shape
+#data.columns
+def arpu_fix_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    day_fixd=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0),
+                         '日期',['策略'],'修正计费金额',np.sum)
+    day_lunch=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0),
+                          '日期', ['策略'], '发券量', np.sum)
+
+    ARPU_fix_day=pd.DataFrame(day_fixd.astype('float').values/day_lunch.values,
+                    index=day_lunch.index.values,
+               columns=[day_lunch.columns[i][2] for i in range(len(day_lunch.columns))])
+
+
+    #8个子图块-近8天数据
+    #alg=alg+['manually-list']
+    ARPU_fix=ARPU_fix_day.drop(['manually-app-whitelist'],axis=1)
+    ARPU_fix_day_8=ARPU_fix[-8:]
+    index =ARPU_fix.index
+    columns=ARPU_fix.columns
+    part=pd.Series([(0,0),(0,1),(0,2),(1,0),(1,1),(1,2),(2,0),(2,1)],index=range(8))
+    plt.figure(figsize=(17,15))
+    plt.style.use('ggplot')
+    for i in range(8):
+        plt.subplot2grid((3, 3), part[i])
+        x = range(len(columns))
+        y = ARPU_fix.ix[-(-i + 8), :]
+        plt.bar(x, y, alpha=0.8, align='center',color='sandybrown')
+        plt.ylim(0, 20)
+        plt.title(u'%s arpu_fixed' % ARPU_fix_day.index[-(-i + 8)])
+        plt.xticks(x, columns, rotation=90)
+        plt.tight_layout()  #调整子图间距
+        if part[i][1]==0:
+            plt.ylabel(u'arpu_fixed')
+    #plt.show()
+    plt.savefig("arpu_fix_day.png")
+
+    # 修正的arpu
+    arpu_fix_day_info = ARPU_fix_day_8.T.round(4)
+    arpu_fix_day_info['均值'] = ARPU_fix_day_8.mean().round(2)  # 均值
+    arpu_fix_day_info['标准差'] = ARPU_fix_day_8.std().round(2)  # 标准差
+    arpu_fix_day_info['同比'] = ((ARPU_fix_day_8.ix[-1] / ARPU_fix_day_8.ix[1] - 1) * 100).round(2)  # 同比
+    arpu_fix_day_info['环比'] = ((ARPU_fix_day_8.ix[-1] / ARPU_fix_day_8.ix[-2] - 1) * 100).round(2)  # 环比
+    arpu_fix_day_info['成长率'] = ((ARPU_fix_day_8.ix[-1] / ARPU_fix_day_8[:-1].mean() - 1) * 100).round(2)  # 成长率
+    arpu_fix_day_info['排序'] = ARPU_fix_day_8.ix[-1].rank(ascending=False)  # 排序（昨天值）
+    return arpu_fix_day_info
+

alg-evaluat-system/alg_report/alg_report_day/arpu_ocpc_bias_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import  pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+
+
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+
+def arpu_ocpc_bias_day():
+    data = pd.read_csv('data.csv')
+    df = data
+
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    #计算ARPU
+    charge_fee=pivot((df['出价区间']!=0) & (df['是否OCPC']==1),
+                         '日期',['策略'],'计费金额',np.sum)
+    charge_fee=pd.DataFrame(charge_fee.values,
+                            index=charge_fee.index.values,
+                            columns=[charge_fee.columns[i][2] for i in range(len(charge_fee.columns))])
+    lunch_cnt=pivot((df['出价区间']!=0) & (df['是否OCPC']==1),
+                         '日期',['策略'],'发券量',np.sum)
+    lunch_cnt=pd.DataFrame(lunch_cnt.values,
+                            index=lunch_cnt.index.values,
+                            columns=[lunch_cnt.columns[i][2] for i in range(len(lunch_cnt.columns))])
+    arpu=charge_fee/lunch_cnt
+    #计算ARPU2
+    charge_fee2=pivot((df['出价区间']!=0) & (df['是否OCPC']==1),
+                         '日期',['策略'],'修正计费金额',np.sum)
+    charge_fee2=pd.DataFrame(charge_fee2.values,
+                            index=charge_fee2.index.values,
+                            columns=[charge_fee2.columns[i][2] for i in range(len(charge_fee2.columns))])
+    arpu2=charge_fee2/lunch_cnt
+    #arpu偏差
+    arpu_bias=arpu/arpu2-1
+
+    #绘图
+
+    arpu_bias = arpu_bias.ix[-8:,:]
+    arpu_bias_day_8 = arpu_bias[-8:]
+    index = arpu_bias.index
+    columns = arpu_bias.columns
+    x = range(len(index))
+
+
+    plt.figure(figsize=(16, 18))
+    plt.style.use('ggplot')
+    for i in range(len(columns)):
+        part = (math.floor(i / 5), i % 5)
+        plt.subplot2grid((math.ceil(len(columns)/5), 5), part)
+        y = list(arpu_bias.ix[:, columns[i]])
+        plt.hlines(0, 0, len(index), colors="r", linestyles="dashed")
+        plt.plot(x, y, marker="o")
+        plt.ylim(-0.2, 0.8)
+        plt.xticks(x, index, rotation=90)
+        plt.title(columns[i])
+        plt.tight_layout()  # 调整子图间距
+        if part[1] == 0:
+            plt.ylabel(u'arpu_dev_rate')
+    # plt.show()
+    plt.savefig(u"arpu_ocpc_bias_day.png")
+
+    arpu_ocpc_bias_day_info = arpu_bias_day_8.T.round(4)
+    arpu_ocpc_bias_day_info['均值'] = arpu_bias_day_8.mean().round(2)  # 均值
+    arpu_ocpc_bias_day_info['标准差'] = arpu_bias_day_8.std().round(2)  # 标准差
+    arpu_ocpc_bias_day_info['同比'] = ((arpu_bias_day_8.ix[-1] / arpu_bias_day_8.ix[1] - 1) * 100).round(2)  # 同比
+    arpu_ocpc_bias_day_info['环比'] = ((arpu_bias_day_8.ix[-1] / arpu_bias_day_8.ix[-2] - 1) * 100).round(2)  # 环比
+    arpu_ocpc_bias_day_info['成长率'] = ((arpu_bias_day_8.ix[-1] / arpu_bias_day_8[:-1].mean() - 1) * 100).round(2)  # 成长率
+    arpu_ocpc_bias_day_info['排序'] = arpu_bias_day_8.ix[-1].map(lambda x:abs(x)).rank()  # 排序（昨天值）
+
+    return arpu_ocpc_bias_day_info
+

alg-evaluat-system/alg_report/alg_report_day/arpu_orig_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+#from get_data import *
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+def arpu_orig_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    day_fee=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0),
+                         '日期',['策略'],'计费金额',np.sum)
+    day_lunch=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0),
+                          '日期', ['策略'], '发券量', np.sum)
+
+    ARPU_arig_day=pd.DataFrame(day_fee.astype('float').values/day_lunch.values,
+                    index=day_lunch.index.values,
+               columns=[day_lunch.columns[i][2] for i in range(len(day_lunch.columns))])
+
+    #绘图#8个子图块
+    #alg = alg + ['manually-list']
+    ARPU_arig=ARPU_arig_day.drop(['manually-app-whitelist'],axis=1)
+    arpu_orig_day_8=ARPU_arig[-8:]
+    index =ARPU_arig.index
+    columns=ARPU_arig.columns
+    part = pd.Series([(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1)], index=range(8))
+
+
+    mpl.rcParams["font.sans-serif"] = ["Microsoft YaHei"]
+    mpl.rcParams['axes.unicode_minus'] = False
+    plt.figure(figsize=(17,15))
+    plt.style.use('ggplot')
+    for i in range(8):
+        plt.subplot2grid((3, 3), part[i])
+        x = range(len(columns))
+        y = ARPU_arig.ix[-(-i + 8), :]
+        plt.bar(x, y, alpha=0.8, align='center',color='sandybrown')
+        plt.ylim(0, 20)
+        plt.title(u'%s arpu_notfix' % index[-(-i + 8)])
+        plt.xticks(x, columns, rotation=90)
+        plt.tight_layout()  #调整子图间距
+        if part[i][1]==0:
+            plt.ylabel(u'arpu_notfix')
+    #plt.show()
+    plt.savefig("arpu_orig_day.png")
+
+    arpu_orig_day_info = arpu_orig_day_8.T.round(4)
+    arpu_orig_day_info['均值'] = arpu_orig_day_8.mean().round(2)  # 均值
+    arpu_orig_day_info['标准差'] = arpu_orig_day_8.std().round(2)  # 标准差
+    arpu_orig_day_info['同比'] = ((arpu_orig_day_8.ix[-1] / arpu_orig_day_8.ix[1] - 1) * 100).round(2)  # 同比
+    arpu_orig_day_info['环比'] = ((arpu_orig_day_8.ix[-1] / arpu_orig_day_8.ix[-2] - 1) * 100).round(2)  # 环比
+    arpu_orig_day_info['成长率'] = ((arpu_orig_day_8.ix[-1] / arpu_orig_day_8[:-1].mean() - 1) * 100).round(2)  # 成长率
+    arpu_orig_day_info['排序'] = arpu_orig_day_8.ix[-1].rank(ascending=False) # 排序（昨天值）
+    return arpu_orig_day_info
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_day/ctr_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+#from get_data import *
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+
+def ctr_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    day_alg_charge=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0),
+                         '日期',['策略'],'计费量',np.sum)
+    day_alg_lunch=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0),
+                          '日期', ['策略'], '发券量', np.sum)
+
+    CTR_day=pd.DataFrame(day_alg_charge.astype('float').values/day_alg_lunch.values,
+                    index=day_alg_charge.index.values,
+               columns=[day_alg_charge.columns[i][2] for i in range(len(day_alg_charge.columns))])
+
+    #绘图#8个子图块
+    #alg = alg + ['manually-list']
+    CTR=CTR_day.drop(['manually-app-whitelist'],axis=1)
+    CTR_day_8=CTR[-8:]
+    index =CTR.index
+    columns=CTR.columns
+    part = pd.Series([(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1)], index=range(8))
+
+    plt.figure(figsize=(17,15))
+    plt.style.use('ggplot')
+    for i in range(8):
+        plt.subplot2grid((3, 3), part[i])
+        x = range(len(columns))
+        y = CTR.ix[-(-i + 8), :]
+        plt.bar(x, y, alpha=0.8, align='center',color='sandybrown')
+        plt.ylim(0.0,0.5)
+        plt.title('%s CTR' % index[-(-i + 8)])
+        plt.xticks(x, columns, rotation=90)
+        plt.tight_layout()  #调整子图间距
+        if part[i][1]==0:
+            plt.ylabel('CTR')
+    #plt.show()
+    plt.savefig("ctr_day.png")
+
+    ctr_day_info = CTR_day_8.T.round(4)
+    ctr_day_info['均值'] = CTR_day_8.mean().round(2)  # 均值
+    ctr_day_info['标准差'] = CTR_day_8.std().round(2)  # 标准差
+    ctr_day_info['同比'] = ((CTR_day_8.ix[-1] / CTR_day_8.ix[1] - 1) * 100).round(2)  # 同比
+    ctr_day_info['环比'] = ((CTR_day_8.ix[-1] / CTR_day_8.ix[-2] - 1) * 100).round(2)  # 环比
+    ctr_day_info['成长率'] = ((CTR_day_8.ix[-1] / CTR_day_8[:-1].mean() - 1) * 100).round(2)  # 成长率
+    ctr_day_info['排序'] = CTR_day_8.ix[-1].rank(ascending=False)  # 排序（昨天值）
+    return ctr_day_info
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_day/cvr_cpc_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+#from get_data import *
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+def cvr_cpc_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    day_chan=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0) & (df['是否OCPC']==0),
+                         '日期',['策略'],'转化量',np.sum)
+    day_cpc_charge=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0) & (df['是否OCPC']==0),
+                          '日期', ['策略'], 'OCPC计费量', np.sum)
+
+    CPC_CVR_day=pd.DataFrame(day_chan.astype('float').values/day_cpc_charge.values,
+                    index=day_chan.index.values,
+               columns=[day_chan.columns[i][2] for i in range(len(day_chan.columns))])
+
+    #绘图
+    #绘图#8个子图块
+    #alg = alg + ['manually-list']
+    CVR=CPC_CVR_day.drop(['manually-app-whitelist'],axis=1)
+    cvr_cpc_day_8=CVR[-8:]
+    index =CVR.index
+    columns=CVR.columns
+    part = pd.Series([(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1)], index=range(8))
+
+    plt.figure(figsize=(17,15))
+    plt.style.use('ggplot')
+    for i in range(8):
+        plt.subplot2grid((3, 3), part[i])
+        x = range(len(columns))
+        y = CVR.ix[-(-i + 8), :]
+        plt.bar(x, y, alpha=0.8, align='center',color='sandybrown')
+        plt.ylim(0,0.35)
+        plt.title('%s CPC_CVR' % index[-(-i + 8)])
+        plt.xticks(x, columns, rotation=90)
+        plt.tight_layout()  #调整子图间距
+        if part[i][1]==0:
+            plt.ylabel('CPC_CVR')
+    #plt.show()
+    plt.savefig("cvr_cpc_day.png")
+
+    cvr_cpc_day_info = cvr_cpc_day_8.T.round(4)
+    cvr_cpc_day_info['均值'] = cvr_cpc_day_8.mean().round(2)  # 均值
+    cvr_cpc_day_info['标准差'] = cvr_cpc_day_8.std().round(2)  # 标准差
+    cvr_cpc_day_info['同比'] = ((cvr_cpc_day_8.ix[-1] / cvr_cpc_day_8.ix[1] - 1) * 100).round(2)  # 同比
+    cvr_cpc_day_info['环比'] = ((cvr_cpc_day_8.ix[-1] / cvr_cpc_day_8.ix[-2] - 1) * 100).round(2)  # 环比
+    cvr_cpc_day_info['成长率'] = ((cvr_cpc_day_8.ix[-1] / cvr_cpc_day_8[:-1].mean() - 1) * 100).round(2)  # 成长率
+    cvr_cpc_day_info['排序'] = cvr_cpc_day_8.ix[-1].rank(ascending=False)  # 排序（昨天值）
+    return cvr_cpc_day_info
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_day/cvr_ocpc_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+#from get_data import *
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+def cvr_ocpc_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    day_chan=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0) & (df['是否OCPC']==1),
+                         '日期',['策略'],'转化量',np.sum)
+    day_ocpc_charge=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0) & (df['是否OCPC']==1),
+                          '日期', ['策略'], 'OCPC计费量', np.sum)
+
+    OCPC_CVR_day=pd.DataFrame(day_chan.astype('float').values/day_ocpc_charge.values,
+                    index=day_chan.index.values,
+               columns=[day_chan.columns[i][2] for i in range(len(day_chan.columns))])
+
+    #绘图
+    #绘图#8个子图块
+
+    CVR=OCPC_CVR_day
+    cvr_ocpc_day_8=CVR[-8:]
+    index =CVR.index
+    columns=CVR.columns
+    part = pd.Series([(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1)], index=range(8))
+
+    plt.figure(figsize=(17,15))
+    plt.style.use('ggplot')
+    for i in range(8):
+        plt.subplot2grid((3, 3), part[i])
+        x = range(len(columns))
+        y = CVR.ix[-(-i + 8), :]
+        plt.bar(x, y, alpha=0.8, align='center',color='sandybrown')
+        plt.ylim(0,0.35)
+        plt.title('%s OCPC_CVR' % index[-(-i + 8)])
+        plt.xticks(x, columns, rotation=90)
+        plt.tight_layout()  #调整子图间距
+        if part[i][1]==0:
+            plt.ylabel('OCPC_CVR')
+    #plt.show()
+    plt.savefig("cvr_ocpc_day.png")
+    '''
+    #近五日每个策略CPC_CVR_日变化
+    alg=['alg-2.5','alg-2.6','alg-3.0','alg-3.0.2','alg-3.1','alg-3.2.1',
+     'alg-3.2.2','alg-4.0','alg-4.0.1','alg-4.0.2','alg-4.0.3','alg-4.0.4','alg-4.1','alg-4.2.1']
+    OCPC_CVR=OCPC_CVR详情_day[alg]
+    plt.style.use('ggplot')
+    for i in range(5):
+        x=np.arange(0,len(alg)*6,6)+i
+        y=OCPC_CVR.ix[-(-i+5),:]
+        plt.bar(x, y, alpha = 0.8, align = 'center',color=['b','m','g','y','r'][i],label=OCPC_CVR.index[-(-i+5)])
+    plt.xticks(x,alg)
+    plt.ylim(0,0.35)
+    plt.legend()
+    plt.xlabel('策略—近五日')
+    plt.ylabel('ocpc_cvr')
+    plt.title('近5日每个策略ocpc_cvr变化')
+    plt.show()
+    
+    #CPC_CVR详情_日变化
+    plt.style.use('ggplot')
+    index = OCPC_CVR详情_day.index
+    columns=OCPC_CVR详情_day.columns
+    x = range(len(index))
+    fig = plt.figure(figsize=(8,4))
+    for i in ["alg-2.5",	"alg-2.6","alg-3.0","alg-3.0.2","alg-3.1","alg-3.2.1","alg-3.2.2","alg-4.0",
+                     "alg-4.0.1","alg-4.0.2","alg-4.0.3","alg-4.0.4","alg-4.1","alg-4.2.1"]:
+        y=list(OCPC_CVR详情_day.ix[:,i])
+        plt.plot(x, y, label=i)
+    plt.xticks(x, index,rotation = 45)
+    plt.legend(loc='center left', bbox_to_anchor=(1, 0.5),fontsize='x-small')  # 让图例生效
+    plt.title('OCPC_CVR详情_日变化')
+    plt.xlabel('日期')
+    plt.ylabel('cvr')
+    plt.show()
+    '''
+    cvr_ocpc_day_info = cvr_ocpc_day_8.T.round(4)
+    cvr_ocpc_day_info['均值'] = cvr_ocpc_day_8.mean().round(2)  # 均值
+    cvr_ocpc_day_info['标准差'] = cvr_ocpc_day_8.std().round(2)  # 标准差
+    cvr_ocpc_day_info['同比'] = ((cvr_ocpc_day_8.ix[-1] / cvr_ocpc_day_8.ix[1] - 1) * 100).round(2)  # 同比
+    cvr_ocpc_day_info['环比'] = ((cvr_ocpc_day_8.ix[-1] / cvr_ocpc_day_8.ix[-2] - 1) * 100).round(2)  # 环比
+    cvr_ocpc_day_info['成长率'] = ((cvr_ocpc_day_8.ix[-1] / cvr_ocpc_day_8[:-1].mean() - 1) * 100).round(2)  # 成长率
+    cvr_ocpc_day_info['排序'] = cvr_ocpc_day_8.ix[-1].rank(ascending=False)  # 排序（昨天值）
+    return cvr_ocpc_day_info

alg-evaluat-system/alg_report/alg_report_day/demotion_distribution.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+from pyhive import hive
+import datetime
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+now = datetime.datetime.now()
+delta = datetime.timedelta(days=1)
+yestoday = (now - delta).strftime('%Y-%m-%d')
+dt=yestoday
+cursor=hive.connect(host='10.50.10.11',port=10000, username='mengxiangxuan',database='default').cursor()
+
+sql_dm='''select advert_id,match_tag_nums,
+case when rcmd_type=5 then 'alg-demotion' else 'alg-normal' end as rmcd_name,
+count(1) as launch_cnt
+from  advert.dws_advert_order_wide_v4_level_3_di
+where dt='{0}'
+and floor((fee+9)/10)*10>0
+group by advert_id,match_tag_nums,
+case when rcmd_type=5 then 'alg-demotion' else 'alg-normal' end
+'''.format(dt)
+cursor.execute(sql_dm)
+df=pd.DataFrame(cursor.fetchall(),
+        columns=['advert_id','match_tag_nums','rmcd_name','launch_cnt'])
+
+#降级分布
+launch_advert=pd.DataFrame(pd.pivot_table(df, index='advert_id', columns='rmcd_name',
+               values='launch_cnt',aggfunc=np.sum, fill_value=0)).sort_index(by=['alg-normal'])
+if ['alg-demotion'] not in list(launch_advert.columns):
+    launch_advert['alg-demotion']=0
+launch_tag=pd.DataFrame(pd.pivot_table(df, index='match_tag_nums', columns='rmcd_name',
+               values='launch_cnt',aggfunc=np.sum, fill_value=0)).sort_index(by=['alg-normal'])
+if ['alg-demotion'] not in list(launch_tag.columns):
+    launch_tag['alg-demotion']=0
+advert_demotion_top10=launch_advert.sort_index(by=['alg-demotion'],ascending=False).head(10)
+advert_demotion_top10.index=advert_demotion_top10.index.astype('str')
+tag_demotion_top10=launch_tag.sort_index(by=['alg-demotion'],ascending=False).head(10)
+
+# 绘图
+# #降级分布advert
+fig = plt.figure(figsize=(16, 9))
+plt.subplot2grid((1, 2), (0,0))
+plt.style.use('ggplot')
+x = range(len(launch_advert.index))
+y1 = launch_advert['alg-demotion'].values
+y2 = launch_advert['alg-normal'].values
+plt.plot(x, y1,label='demotion')
+plt.plot(x, y2,label='alg')
+#plt.xticks(x, launch_app.index, rotation=90)
+plt.ylabel('cnt')
+plt.xlabel('advert_id')
+plt.legend()
+plt.ylim(0, 400000)
+plt.title('advert_demotion_distribution')
+#plt.show()
+
+# #降级分布advert
+plt.subplot2grid((1, 2), (0,1))
+plt.style.use('ggplot')
+x = range(len(launch_tag.index))
+y1 = launch_tag['alg-demotion'].values
+y2 = launch_tag['alg-normal'].values
+plt.plot(x, y1,label='demotion')
+plt.plot(x, y2,label='alg')
+plt.ylim(0, 600000)
+#plt.xticks(x, launch_app.index, rotation=90)
+plt.ylabel('cnt')
+plt.xlabel('match_tag_nums')
+plt.legend()
+plt.title('advert_tag_demotion_distribution')
+# plt.show()
+plt.savefig("demotion_distribution.png")
+
+
+
+
+
+
+
+
+
+

alg-evaluat-system/alg_report/alg_report_day/emile_fun.py

+
+#######################################################
+#发送邮件 Simple Mail Transfer Protocol
+import matplotlib as mpl
+mpl.use('Agg')
+import smtplib
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from email.mime.image import MIMEImage
+from html_merge import *
+
+html =get_html()
+
+mail_host = "smtp.exmail.qq.com"  # 设置服务器
+mail_user = "mengxiangxuan@duiba.com.cn"  # 用户名
+mail_pass = "Mxx880903"  # 口令
+
+
+def send_mail(to_list):
+    def addimg(src, imgid):  # 文件路径、图片id
+        fp = open(src, 'rb')  # 打开文件
+        msgImage = MIMEImage(fp.read())  # 读入 msgImage 中
+        fp.close()  # 关闭文件
+        msgImage.add_header('Content-ID', imgid)
+        return msgImage
+
+    msg = MIMEMultipart('related')
+    # HTML代码
+    msgtext = MIMEText(html, "html", "utf-8")
+    msg.attach(msgtext)
+    msg.attach(addimg("arpu_fix_day.png", "arpu_fix_day"))  # 全文件路径，后者为ID 根据ID在HTML中插入的位置
+    msg.attach(addimg("arpu_ocpc_bias_day.png","arpu_ocpc_bias_day")) #同上
+    msg.attach(addimg("arpu_orig_day.png","arpu_orig_day"))#同上
+    msg.attach(addimg("ctr_day.png","ctr_day"))#同上
+    msg.attach(addimg("cvr_cpc_day.png", "cvr_cpc_day"))  # 同上
+    msg.attach(addimg("cvr_ocpc_day.png", "cvr_ocpc_day"))  # 同上
+
+    msg.attach(addimg("lunch_alg_ratio_day.png", "lunch_alg_ratio_day"))  # 同上
+    msg.attach(addimg("lunch_arithmetic_demotion_day.png", "lunch_arithmetic_demotion_day"))  # 同上
+    msg.attach(addimg("demotion_distribution.png", "demotion_distribution"))  # 同上
+    msg.attach(addimg("lunch_OCPC_alg_ratio_day.png", "lunch_OCPC_alg_ratio_day"))  # 同上
+    msg.attach(addimg("lunch_OCPC_ratio_day.png", "lunch_OCPC_ratio_day"))  # 同上
+    msg.attach(addimg("lunch_price_distribution_day.png", "lunch_price_distribution_day"))  # 同上
+    msg.attach(addimg("lunch_price_ratio_day.png", "lunch_price_ratio_day"))  # 同上
+
+    me = "孟祥轩"
+    msg['Subject'] = '策略效果日报'  # 主题
+    msg['From'] = me  # 发件人
+    msg['To'] = ";".join(to_list)  # 收件人列表
+
+    server = smtplib.SMTP()  # SMTP
+    server.connect(mail_host)  # 连接
+    server.login(mail_user, mail_pass)  # 登录
+    server.sendmail(mail_user, to_list, msg.as_string())  # 发送邮件
+    server.close()  # 关闭
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

alg-evaluat-system/alg_report/alg_report_day/emile_send.py

+import matplotlib as mpl
+mpl.use('Agg')
+import os
+os.chdir('/home/mengxiangxuan/alg_report_day')
+from emile_fun import *
+mailto_list=['rd_suanfa@tuia.cn']
+
+#mailto_list=['mengxiangxuan@duiba.com.cn','jiali@duiba.com.cn',
+#            'lwj@duiba.com.cn','zhangbo@duiba.com.cn','zhangshiyao@duiba.com.cn','houyawei@duiba.com.cn']
+
+send_mail(mailto_list)
+
+##调整import
+
+#os.chdir(r'D:\duiba_code\alg_report_day')
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_day/get_data.py

+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+from pyhive import hive
+import datetime
+
+def get_data():
+    cursor = hive.connect(host='10.50.10.11', port=10000, username='mengxiangxuan', database='default').cursor()
+    now = datetime.datetime.now()
+    today=now.strftime('%Y-%m-%d')
+    delta = datetime.timedelta(days=31)
+    n_days = now - delta
+    last_month_today=n_days.strftime('%Y-%m-%d')
+    sql="select * from advert.dws_advert_effect_stat_v4_di where dt>='{}' and dt<'{}'".format(last_month_today,today)
+    cursor.execute(sql)
+    data=pd.DataFrame(cursor.fetchall())
+    data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',\
+         'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+    data.to_csv('data.csv',encoding='utf-8',index=False)
+
+##########################################

alg-evaluat-system/alg_report/alg_report_day/lunch_OCPC_alg_ratio_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+
+def lunch_OCPC_alg_ratio_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+
+    def pivot(filter, index, columns, values, aggfunc):
+        result = pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                                aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    lunch_OCPC_alg_ratio = pivot(~pd.isnull(df['类型']),
+                                 '日期', ['策略', '是否OCPC'], '发券量', np.sum)
+
+    columns = [i[2] + '_' + str(i[3]) for i in list(lunch_OCPC_alg_ratio.columns)]
+    lunch_OCPC_alg = pd.DataFrame(lunch_OCPC_alg_ratio.values,
+                                  index=lunch_OCPC_alg_ratio.index.values, columns=columns)
+
+    colname = [i[2] for i in list(lunch_OCPC_alg_ratio.columns) if i[3] == 0]
+    # colname.remove('manually-list')
+    colname.remove('manually-app-whitelist')
+
+    columns_alg_0 = [i + '_0' for i in colname]
+    columns_alg_1 = [i + '_1' for i in colname]
+
+    col_na0 = [i for i in set(columns_alg_0) - set(columns)]
+    col_na1 = [i for i in set(columns_alg_1) - set(columns)]
+    for i in range(len(col_na0)):
+        lunch_OCPC_alg[col_na0[i]] = 0
+
+    for i in range(len(col_na1)):
+        lunch_OCPC_alg[col_na1[i]] = 0
+
+    l0 = lunch_OCPC_alg[columns_alg_0].astype('float').values
+    l1 = lunch_OCPC_alg[columns_alg_1].astype('float').values
+    l = lunch_OCPC_alg[columns_alg_0].values + lunch_OCPC_alg[columns_alg_1].values
+    y_0 = pd.DataFrame(l0 / l, index=lunch_OCPC_alg.index, columns=columns_alg_0).fillna(value=0)
+    y_1 = pd.DataFrame(l1 / l, index=lunch_OCPC_alg.index, columns=columns_alg_1).fillna(value=0)
+
+    # 绘图#8个子图块
+    part = pd.Series([(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1)], index=range(8))
+    plt.figure(figsize=(17, 15))
+    plt.style.use('ggplot')
+    for i in range(8):
+        plt.subplot2grid((3, 3), part[i])
+        x = range(len(colname))
+        y0 = y_0.ix[-(-i + 8), :]
+        y1 = y_1.ix[-(-i + 8), :]
+        plt.bar(x, list(y0), color='sandybrown', alpha=0.8, label='CPC', align='center')
+        plt.bar(x, list(y1), bottom=y0, color='thistle', alpha=0.8, label='OCPC', align='center')
+        plt.ylim(0, 1)
+        plt.title(u'%s cpc-ocpc' % lunch_OCPC_alg_ratio.index[-(-i + 8)])
+        plt.xticks(x, colname, rotation=90)
+        plt.tight_layout()  # 调整子图间距
+        if part[i][1] == 0:
+            plt.ylabel(u'launch')
+        if part[i][1] == 3:
+            plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize='small', title=u'CPC or OCPC')
+    plt.show()
+    plt.savefig("lunch_OCPC_alg_ratio_day.png")
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_day/lunch_OCPC_ratio_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+def lunch_OCPC_ratio_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    lunch_OCPC_ratio=pivot(~pd.isnull(df['类型']),
+                         '日期',['是否OCPC'],'发券量',np.sum)
+    lunch_OCPC_ratio=pd.DataFrame(lunch_OCPC_ratio.values,
+                                  index=lunch_OCPC_ratio.index.values,columns=[0,1])
+    #绘图-面积图
+    x=range(len(lunch_OCPC_ratio.index))
+    y0=lunch_OCPC_ratio[0]/(lunch_OCPC_ratio[0]+lunch_OCPC_ratio[1])
+    y1=lunch_OCPC_ratio[1]/(lunch_OCPC_ratio[0]+lunch_OCPC_ratio[1])
+    plt.figure(figsize=(16,9))
+    plt.stackplot(x, # x轴
+                  y0,y1, # 可变参数，接受多个y
+                  labels=['cpc','ocpc']
+                 )
+    plt.xticks(x, lunch_OCPC_ratio.index,rotation = 90)
+    plt.xlabel('date')
+    plt.ylabel('distribution')
+    plt.legend(bbox_to_anchor=(1, 0.5),fontsize='small',title='CPC or OCPC')
+    #plt.show()
+    plt.savefig("lunch_OCPC_ratio_day.png")

alg-evaluat-system/alg_report/alg_report_day/lunch_alg_ratio_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+def lunch_alg_ratio_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    temp=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0),
+                         '日期',['策略'],'发券量',np.sum)
+    temp=pd.DataFrame(temp.values,index=temp.index.values,columns=[temp.columns[i][2]
+                                 for i in range(len(temp.columns))])
+    lunch_alg=temp
+    lunch_alg_ratio=temp.apply(lambda x:x/x.sum(),axis=1)
+
+    #绘图
+    plt.figure(figsize=(16,9))
+    x=range(len(lunch_alg_ratio.index))
+
+    #y0,y1,y2,y3,y4,y5,y6,y7,y8,y9,y10,y11,y12,y13,y14,y15,y16=lunch_alg_ratio.T.values
+
+    a=plt.stackplot(x, # x轴
+                    lunch_alg_ratio.T.values # 可变参数，接受多个y
+                    ,labels=list(lunch_alg_ratio.columns)
+                 )
+
+    plt.xticks(x, lunch_alg_ratio.index,rotation = 90)
+    plt.ylim(0,1)
+    plt.xlabel('date')
+    plt.ylabel('Percent')
+    plt.legend(a[::-1],list(lunch_alg_ratio.columns)[::-1],loc='center left', bbox_to_anchor=(1, 0.5),fontsize='x-small')
+    #plt.show()
+    plt.savefig("lunch_alg_ratio_day.png")
+    #分布图
+    plt.figure(figsize=(16, 9))
+    x = range(len(lunch_alg.index))
+
+    #y0, y1, y2, y3, y4, y5, y6, y7, y8, y9, y10, y11, y12, y13, y14, y15, y16 = lunch_alg.T.values
+
+    b = plt.stackplot(x,  # x轴
+                      lunch_alg.T.values  # 可变参数，接受多个y
+                      , labels=list(lunch_alg.columns)
+                      )
+
+    plt.xticks(x, lunch_alg.index, rotation=90)
+    plt.xlabel('date')
+    plt.ylabel('launch_count')
+    plt.legend(b[::-1], list(lunch_alg.columns)[::-1], loc='center left', bbox_to_anchor=(1, 0.5),
+               fontsize='x-small')
+    # plt.show()
+    plt.savefig("lunch_alg_distribution_day.png")
+    # #总发券量及发券占比
+    # lunch_alg['总发券量'] = lunch_alg.sum(axis=1)
+    # lunch_alg_day = lunch_alg.T
+    #每日流量分配占比
+    lunch_alg_ratio_day=lunch_alg_ratio[-8:].T.round(3)
+
+    return lunch_alg_ratio_day
+
+
+
+
+

alg-evaluat-system/alg_report/alg_report_day/lunch_arithmetic_demotion_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+
+def lunch_arithmetic_demotion_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    lunch_alg_day=pivot((~pd.isnull(df['类型'])) & (df['出价区间']!=0),
+                         '日期',['策略'],'发券量',np.sum)
+    lunch_alg_day=pd.DataFrame(lunch_alg_day.values,
+                                  index=lunch_alg_day.index.values,
+                               columns=[lunch_alg_day.columns[i][2] for i in range(len(lunch_alg_day.columns))])
+    #每日各策略发券概率分布
+    lunch_alg_day_ratio=lunch_alg_day.drop(['manually-app-whitelist'],axis=1).apply(lambda x:x/x.sum(),axis=1)
+    #每日降级率
+    lunch_demotion_day_ratio=lunch_alg_day_ratio['alg-demotion']*100
+   
+    #绘图
+    fig = plt.figure(figsize=(16,9))
+    plt.style.use('ggplot')
+    x=range(len(lunch_demotion_day_ratio.index))
+    y=lunch_demotion_day_ratio.values
+    plt.plot(x,y)
+    plt.hlines(1,0,len(lunch_demotion_day_ratio.index),linestyles = "dashed")
+    plt.ylim(0,10)
+    plt.xticks(x,lunch_demotion_day_ratio.index,rotation = 90)
+    plt.ylabel('Percent')
+    #plt.show()
+    plt.savefig("lunch_arithmetic_demotion_day.png")
+
+
+
+
+
+
+
+
+
+
+

alg-evaluat-system/alg_report/alg_report_day/lunch_price_distribution_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+def lunch_price_distribution_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    #(~pd.isnull(df['类型'])) &
+    lunch_price_day=pivot( df['出价区间']<300,
+                         '日期',['出价区间'],'发券量',np.sum)
+    lunch_price_day=pd.DataFrame(lunch_price_day.values,
+                                  index=lunch_price_day.index.values,columns=range(0,300,10))
+
+
+    #绘图
+    x=range(len(lunch_price_day.index))
+    # y0,y10,y20,y30,y40,y50,y60,y70,y80,y90,y100,y110,y120,y130,y140,y150,y160,\
+    # y170,y180,y190,y200,y210,y220,y230,y240,y250,y260,y270,y280,y290=lunch_price_day.T.values
+    fig = plt.figure(figsize=(16,9))
+    a=plt.stackplot(x, # x轴
+                    lunch_price_day.T.values # 可变参数，接受多个y
+                    ,labels=list(lunch_price_day.columns)
+                 )
+
+    plt.xticks(x, lunch_price_day.index,rotation = 90)
+    plt.xlabel('date')
+    plt.ylabel('distribution')
+    plt.legend(a[::-1],list(lunch_price_day.columns)[::-1],loc='center left',
+               bbox_to_anchor=(1, 0.5),fontsize='small',title=u'price_section')
+    #plt.show()
+    plt.savefig("lunch_price_distribution_day.png")
+

alg-evaluat-system/alg_report/alg_report_day/lunch_price_ratio_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+#data=pd.read_csv(r'D:\duiba_code\week_report\data.csv')
+#data.columns=['策略ID', '是否OCPC', '出价区间', '策略', '类型', '发券量', 'OCPC发券量', '点击量', '计费量',
+#       'OCPC计费量', '计费金额', '修正计费金额', 'OCPC计费金额', '转化曝光量', '转化量', '周几', '周', '月','日期']
+# data=read_data()
+# df = data
+#data.shape
+#data.columns
+def lunch_price_ratio_day():
+    data = pd.read_csv('data.csv')
+    df = data
+    '''
+    CTR	=计费量/发券量
+    CVR	=转化量/OCPC计费量
+    ARPU	=计费金额/发券量
+    1/ROI	=OCPC计费金额/转化量
+    OCPC_RATIO	=SUM(IF(是否OCPC=1,1,0))/发券量
+    ARPU2	=修正计费金额/发券量
+    OCPC-ARPU-目标比率	=(ARPU/ARPU2)-1
+    '''
+
+    def pivot(filter,index,columns,values,aggfunc):
+        result=pd.pivot_table(df.ix[filter], index=[index], columns=columns, values=[values],
+                       aggfunc=[aggfunc], fill_value=0)
+        return result
+
+    #(~pd.isnull(df['类型'])) &
+    lunch_price_day=pivot( df['出价区间']<300,
+                         '日期',['出价区间'],'发券量',np.sum)
+    lunch_price_day=pd.DataFrame(lunch_price_day.values,
+                                  index=lunch_price_day.index.values,columns=range(0,300,10))
+    lunch_price_day_ratio=lunch_price_day.apply(lambda x:x/x.sum(),axis=1)
+
+    #绘图
+    x=range(len(lunch_price_day_ratio.index))
+    # y0,y10,y20,y30,y40,y50,y60,y70,y80,y90,y100,y110,y120,y130,y140,y150,y160,\
+    # y170,y180,y190,y200,y210,y220,y230,y240,y250,y260,y270,y280,y290=lunch_price_day_ratio.T.values
+    plt.figure(figsize=(16,9))
+    a=plt.stackplot(x, # x轴
+                    lunch_price_day_ratio.T.values # 可变参数，接受多个y
+                    ,labels=list(lunch_price_day_ratio.columns)
+                 )
+
+    plt.xticks(x, lunch_price_day_ratio.index,rotation = 90)
+    plt.xlabel('date')
+    plt.ylabel('distribution')
+    plt.ylim(0, 1)
+    plt.legend(a[::-1],list(lunch_price_day_ratio.columns)[::-1],loc='center left',
+               bbox_to_anchor=(1, 0.5),fontsize='small',title=u'price_section')
+    #plt.show()
+    plt.savefig("lunch_price_ratio_day.png")
+

alg-evaluat-system/alg_report/alg_report_day/result_info_day.py

+# -*- coding: utf-8 -*-
+import matplotlib as mpl
+mpl.use('Agg')
+import pandas as pd
+from arpu_fix_day import *
+from arpu_ocpc_bias_day import *
+from arpu_orig_day import *
+from ctr_day import *
+from cvr_cpc_day import *
+from cvr_ocpc_day import *
+from lunch_OCPC_alg_ratio_day import *
+from lunch_OCPC_ratio_day import *
+from lunch_alg_ratio_day import *
+from lunch_arithmetic_demotion_day import *
+from lunch_price_distribution_day import *
+from lunch_price_ratio_day import *
+from get_data import *
+
+# alg = ['alg-2.5', 'alg-2.6', 'alg-3.0', 'alg-3.0.2', 'alg-3.1',
+#         'alg-4.0', 'alg-4.0.1', 'alg-4.0.2', 'alg-4.0.3', 'alg-4.0.4', 'alg-4.1', 'alg-4.2.1',
+#         'alg-600', 'alg-601','alg-602','alg-610','alg-tab-filter','alg-act-tab',
+#        'alg-online-learn','alg-online-stat','alg-online-base','alg-online-act',
+#        'alg-online-weight','alg-app-optimize','alg-act-count']
+def result_info():
+    get_data()
+    #修正的arpu
+    arpu_fix_day_info=arpu_fix_day()
+    #未修正的arpu
+    arpu_orig_day_info=arpu_orig_day()
+    #ocpc计费arpu偏差率
+    arpu_ocpc_bias_day_info=arpu_ocpc_bias_day()
+    #ctr
+    ctr_day_info=ctr_day()
+    #cpc计费cvr
+    cvr_cpc_day_info=cvr_cpc_day()
+    #ocpc计费cvr
+    cvr_ocpc_day_info=cvr_ocpc_day()
+    #发券
+    lunch_alg_ratio_day_info=lunch_alg_ratio_day()
+    lunch_arithmetic_demotion_day()
+    lunch_OCPC_alg_ratio_day()
+    lunch_OCPC_ratio_day()
+    lunch_price_distribution_day()
+    lunch_price_ratio_day()
+
+   #  name_list=['算法-统计模型-灵敏度优化',
+   #  '算法-统计模型-素材优选',
+   #  '算法-LR模型',
+   #  '算法-LR模型-素材标签对比',
+   #  '算法-LR模型-素材优选',
+   #  '算法-FM模型',
+   #  '算法-FM模型-纠偏',
+   #  '算法-FM模型-纠偏&分布重构',
+   #  '算法-FM模型-素材标签对比',
+   #  '算法-FM模型-交叉统计特征',
+   #  '算法-FM模型-素材优选',
+   #  '算法-FM模型-CVR提升优化',
+   #  '算法-FM模型-基础模型',
+   #  '算法-FM模型-添加app安装列表信息',
+   #  '算法-FM模型-添加社会标签',
+   #  '算法-FM模型-添加app聚类特征',
+   #  '算法-行为标签',
+   #  '在线学习 - 用户行为特征',
+   #  '用户行为-计数特征',
+   # 'app安装列表优化模型',
+   # '降级'
+   # '智能匹配'
+   # '算法-在线学习-基础特征',
+   # '算法-在线学习',
+   # '算法-在线学习-统计特征',
+   # '在线学习-预估值权重优化',
+   # '算法-标签过滤',
+   #  '人工-列表投放']
+
+    cl_id=list(lunch_alg_ratio_day_info.index)
+    cl_id.remove('manually-app-whitelist')
+    cl_id.remove('alg-demotion')
+
+    rank_result=pd.DataFrame({'策略ID':cl_id})
+    rank_result['ctr排序']=ctr_day_info.ix[cl_id,'排序'].values
+    rank_result['arpu_未修正排序']=arpu_orig_day_info.ix[cl_id,'排序'].values
+    rank_result['arpu_修正排序']=arpu_fix_day_info.ix[cl_id,'排序'].values
+    rank_result['OCPC计费偏差排序']=list(arpu_ocpc_bias_day_info.ix[cl_id,'排序'])
+    rank_result['cvr_ocpc排序']=list(cvr_ocpc_day_info.ix[cl_id,'排序'])
+    rank_result['cvr_cpc排序']=cvr_cpc_day_info.ix[cl_id,'排序'].values
+    rank_result['流量分配占比%'] = list(lunch_alg_ratio_day_info.ix[cl_id, -1])
+    rank_result.ix[:, 1:-1] = rank_result.ix[:, 1:-1].astype('str').apply(lambda y:y.map(lambda x:x.replace('.0','')))
+    rank_result=rank_result.ix[rank_result['策略ID']!='manually-list']
+    info=pd.DataFrame([[arpu_fix_day_info,arpu_orig_day_info,arpu_ocpc_bias_day_info,ctr_day_info,
+     cvr_cpc_day_info,cvr_ocpc_day_info],
+    ['arpu_fix_day','arpu_orig_day','arpu_ocpc_bias_day',
+     'ctr_day','cvr_cpc_day','cvr_ocpc_day']],
+     columns=['修正后arpu','修正前arpu','ocpc计费下arpu修正前后偏移率','ctr','cpc_cvr','ocpc_cvr'])
+    return rank_result,info,lunch_alg_ratio_day_info
+

alg-evaluat-system/alg_report/alg_report_week/.idea/.name

+_report_week
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_week/.idea/_report_week.iml

+<?xml version="1.0" encoding="UTF-8"?>
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+  <component name="TestRunnerService">
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+    <option name="PROJECT_TEST_RUNNER" value="Nosetests" />
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+</module>
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_week/.idea/alg_report_week.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
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+    <orderEntry type="jdk" jdkName="Python 3.5.2 (D:\Program Files\anaconda3\python.exe)" jdkType="Python SDK" />
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+  <component name="TestRunnerService">
+    <option name="projectConfiguration" value="Nosetests" />
+    <option name="PROJECT_TEST_RUNNER" value="Nosetests" />
+  </component>
+</module>
\ No newline at end of file

alg-evaluat-system/alg_report/alg_report_week/.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.5.2 (D:\Program Files\anaconda3\python.exe)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file