CDA数据分析师 出品

作者：CDA教研组

编辑：Mika

案例介绍

背景：以某大型电商平台的用户行为数据为数据集，使用大数据处理技术分析海量数据下的用户行为特征，并通过建立逻辑回归模型、随机森林对用户行为做出预测;

案例思路:

使用大数据处理技术读取海量数据海量数据预处理抽取部分数据调试模型使用海量数据搭建模型

#全部行输出

from IPython.core.interactiveshell import InteractiveShell

InteractiveShell.ast_node_interactivity = "all"

数据字典:

U_Id:the serialized ID that represents a user

T_Id:the serialized ID that represents an item

C_Id:the serialized ID that represents the category which the corresponding item belongs to Ts:the timestamp of the behavior

Be_type:enum-type from (‘pv’, ‘buy’, ‘cart’, ‘fav’)

pv: Page view of an item's detail page, equivalent to an item click

buy: Purchase an item

cart: Add an item to shopping cart

fav: Favor an item

读取数据

这里关键是使用dask库来处理海量数据，它的大多数操作的运行速度比常规pandas等库快十倍左右。

pandas在分析结构化数据方面非常的流行和强大，但是它最大的限制就在于设计时没有考虑到可伸缩性。pandas特别适合处理小型结构化数据，并且经过高度优化，可以对存储在内存中的数据执行快速高 效的操作。然而随着数据量的大幅度增加，单机肯定会读取不下的，通过集群的方式来处理是最好的选 择。这就是Dask DataFrame API发挥作用的地方:通过为pandas提供一个包装器，可以智能的将巨大的DataFrame分隔成更小的片段，并将它们分散到多个worker(帧)中，并存储在磁盘中而不是RAM中。

Dask DataFrame会被分割成多个部门，每个部分称之为一个分区，每个分区都是一个相对较小的 DataFrame，可以分配给任意的worker，并在需要复制时维护其完整数据。具体操作就是对每个分区并 行或单独操作(多个机器的话也可以并行)，然后再将结果合并，其实从直观上也能推出Dask肯定是这么做的。

# 安装库(清华镜像)

# pip install dask -i

import os

import gc # 垃圾回收接口

from tqdm import tqdm # 进度条库

import dask # 并行计算接口

from dask.diagnostics import ProgressBar

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import time

import dask.dataframe as dd # dask中的数表处理库 import sys # 外部参数获取接口

面对海量数据，跑完一个模块的代码就可以加一行gc.collect()来做内存碎片回收，Dask Dataframes与Pandas Dataframes具有相同的API

gc.collect()

42

# 加载数据

data = dd.read_csv('UserBehavior_all.csv')# 需要时可以设置blocksize=参数来手工指定划分方法，默认是64MB(需要设置为总线的倍数，否则会放慢速度)

data.head()

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data

Dask DataFrame Structure :

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Dask Name: read-csv, 58 tasks

与pandas不同，这里我们仅获取数据框的结构，而不是实际数据框。Dask已将数据帧分为几块加载，这些块存在 于磁盘上，而不存在于RAM中。如果必须输出数据帧，则首先需要将所有数据帧都放入RAM，将它们缝合在一 起，然后展示最终的数据帧。使用.compute()强迫它这样做，否则它不.compute() 。其实dask使用了一种延迟数 据加载机制，这种延迟机制类似于python的迭代器组件，只有当需要使用数据的时候才会去真正加载数据。

# 真正加载数据 data.compute()

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# 可视化工作进程，58个分区任务 data.visualize()

数据预处理

数据压缩

# 查看现在的数据类型 data.dtypes

U_Id int64

T_Id int64

C_Id int64

Be_type object

Ts int64

dtype: object

# 压缩成32位uint，无符号整型，因为交易数据没有负数 dtypes = {

'U_Id': 'uint32',

'T_Id': 'uint32',

'C_Id': 'uint32',

'Be_type': 'object',

'Ts': 'int64'

}

data = data.astype(dtypes)

data.dtypes

U_Id uint32

T_Id uint32

C_Id uint32

Be_type object

Ts int64

dtype: object

缺失值

# 以dask接口读取的数据，无法直接用.isnull()等pandas常用函数筛查缺失值

data.isnull()

Dask DataFrame Structure :

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columns1 = [ 'U_Id', 'T_Id', 'C_Id', 'Be_type', 'Ts']

tmpDf1 = pd.DataFrame(columns=columns1)

tmpDf1

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s = data["U_Id"].isna()

s.loc[s == True]

Dask Series Structure:

npartitions=58

bool ...

... ...

...

Name: U_Id, dtype: bool

Dask Name: loc-series, 348 tasks

U_Id列缺失值数目为0

T_Id列缺失值数目为0

C_Id列缺失值数目为0

Be_type列缺失值数目为0

Ts列缺失值数目为0

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无缺失值

数据探索与可视化

这里我们使用pyecharts库。pyecharts是一款将python与百度开源的echarts结合的数据可视化工具。新版的1.X和旧版的0.5.X版本代码规则大 不相同，新版详见官方文档

# pip install pyecharts -i https://pypi.tuna.tsinghua.edu.cn/simple

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple

Requirement already satisfied: pyecharts in d:\anaconda\lib\site-packages (0.1.9.4)

Requirement already satisfied: jinja2 in d:\anaconda\lib\site-packages (from pyecharts)

(3.0.2)

Requirement already satisfied: future in d:\anaconda\lib\site-packages (from pyecharts)

(0.18.2)

Requirement already satisfied: pillow in d:\anaconda\lib\site-packages (from pyecharts)

(8.3.2)

Requirement already satisfied: MarkupSafe>=2.0 in d:\anaconda\lib\site-packages (from

jinja2->pyecharts) (2.0.1)

Note: you may need to restart the kernel to use updated packages.

U_Id列缺失值数目为0 T_Id列缺失值数目为0 C_Id列缺失值数目为0 Be_type列缺失值数目为0 Ts列缺失值数目为0

WARNING: Ignoring invalid distribution -umpy (d:\anaconda\lib\site-packages)

WARNING: Ignoring invalid distribution -ip (d:\anaconda\lib\site-packages)

WARNING: Ignoring invalid distribution -umpy (d:\anaconda\lib\site-packages)

WARNING: Ignoring invalid distribution -ip (d:\anaconda\lib\site-packages)

WARNING: Ignoring invalid distribution -umpy (d:\anaconda\lib\site-packages)

WARNING: Ignoring invalid distribution -ip (d:\anaconda\lib\site-packages)

WARNING: Ignoring invalid distribution -umpy (d:\anaconda\lib\site-packages)

WARNING: Ignoring invalid distribution -ip (d:\anaconda\lib\site-packages)

WARNING: Ignoring invalid distribution -umpy (d:\anaconda\lib\site-packages)

WARNING: Ignoring invalid distribution -ip (d:\anaconda\lib\site-packages)

饼图

# 例如，我们想画一张漂亮的饼图来看各种用户行为的占比 data["Be_type"]

# 使用dask的时候，所有支持的原pandas的函数后面需加.compute()才能最终执行

Be_counts = data["Be_type"].value_counts().compute()

Be_counts

pv 89716264

cart 5530446

fav 2888258

buy 2015839

Name: Be_type, dtype: int64

Be_index = Be_counts.index.tolist() # 提取标签

Be_index

['pv', 'cart', 'fav', 'buy']

Be_values = Be_counts.values.tolist() # 提取数值

Be_values

[89716264, 5530446, 2888258, 2015839]

from pyecharts import options as opts

from pyecharts.charts import Pie

#pie这个包里的数据必须传入由元组组成的列表

c = Pie()

c.add("", [list(z) for z in zip(Be_index, Be_values)]) # zip函数的作用是将可迭代对象打包成一 个个元组，然后返回这些元组组成的列表 c.set_global_opts(title_opts=opts.TitleOpts(title="用户行为")) # 全局参数(图命名) c.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))

c.render_notebook() # 输出到当前notebook环境

# c.render("pie_base.html") # 若需要可以将图输出到本机

<pyecharts.charts.basic_charts.pie.Pie at 0x1b2da75ae48>

<div id="490361952ca944fcab93351482e4b254" style="width:900px; height:500px;"></div>

漏斗图

from pyecharts.charts import Funnel # 旧版的pyecharts不需要.charts即可import import pyecharts.options as opts

from IPython.display import Image as IMG

from pyecharts import options as opts

from pyecharts.charts import Pie

<pyecharts.charts.basic_charts.funnel.Funnel at 0x1b2939d50c8>

<div id="071b3b906c27405aaf6bc7a686e36aaa" style="width:800px; height:400px;"></div>

数据分析

时间戳转换

dask对于时间戳的支持非常不友好

type(data)

dask.dataframe.core.DataFrame

data['Ts1']=data['Ts'].apply(lambda x: time.strftime("%Y-%m-%d %H:%M:%S",

time.localtime(x)))

data['Ts2']=data['Ts'].apply(lambda x: time.strftime("%Y-%m-%d", time.localtime(x)))

data['Ts3']=data['Ts'].apply(lambda x: time.strftime("%H:%M:%S", time.localtime(x)))

D:\anaconda\lib\site-packages\dask\dataframe\core.py:3701: UserWarning:

You did not provide metadata, so Dask is running your function on a small dataset to

guess output types. It is possible that Dask will guess incorrectly.

To provide an explicit output types or to silence this message, please provide the

`meta=` keyword, as described in the map or apply function that you are using.

Before: .apply(func)

After: .apply(func, meta=('Ts', 'object'))

warnings.warn(meta_warning(meta))

data.head(1)

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data.dtypes

U_Id uint32

T_Id uint32

C_Id uint32

Be_type object

Ts int64

Ts1 object

Ts2 object

Ts3 object

dtype: object

抽取一部分数据来调试代码

df = data.head(1000000)

df.head(1)

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用户流量和购买时间情况分析

用户行为统计表

describe = df.loc[:,["U_Id","Be_type"]]

ids = pd.DataFrame(np.zeros(len(set(list(df["U_Id"])))),index=set(list(df["U_Id"])))

pv_class=describe[describe["Be_type"]=="pv"].groupby("U_Id").count()

pv_class.columns = ["pv"]

buy_class=describe[describe["Be_type"]=="buy"].groupby("U_Id").count()

buy_class.columns = ["buy"]

fav_class=describe[describe["Be_type"]=="fav"].groupby("U_Id").count()

fav_class.columns = ["fav"]

cart_class=describe[describe["Be_type"]=="cart"].groupby("U_Id").count()

cart_class.columns = ["cart"]

user_behavior_counts=ids.join(pv_class).join(fav_class).join(cart_class).join(buy_class).

iloc[:,1:]

user_behavior_counts.head()

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总访问量成交量时间变化分析(天)

from matplotlib import font_manager

# 解决坐标轴刻度负号乱码

# 解决负号'-'显示为方块的问题 plt.rcParams['axes.unicode_minus'] = False # 解决中文乱码问题 plt.rcParams['font.sans-serif'] = ['Simhei']

由总访问量、成交量时间变化分析知，从17年11月25日至17年12月1日访问量和成交量存在小幅波动，2017年12 月2日访问量和成交量均出现大幅上升，2日、3日两天保持高访问量和高成交量。此现象原因之一为12月2日和3 日为周末，同时考虑2日3日可能存在某些促销活动，可结合实际业务情况进行具体分析。(图中周五访问量有上 升，但成交量出现下降，推测此现象可能与周末活动导致周五推迟成交有关。)

总访问量成交量时间变化分析(小时)

# 数据准备 df_pv_timestamp=df[df["Be_type"]=="pv"][["Be_type","Ts1"]] df_pv_timestamp["Ts1"] = pd.to_datetime(df_pv_timestamp["Ts1"])

df_pv_timestamp=df_pv_timestamp.set_index("Ts1")

df_pv_timestamp=df_pv_timestamp.resample("H").count()["Be_type"]

df_pv_timestamp

df_buy_timestamp=df[df["Be_type"]=="buy"][["Be_type","Ts1"]]

df_buy_timestamp["Ts1"] = pd.to_datetime(df_buy_timestamp["Ts1"])

df_buy_timestamp=df_buy_timestamp.set_index("Ts1")

df_buy_timestamp=df_buy_timestamp.resample("H").count()["Be_type"]

df_buy_timestamp

Ts1

2017-09-11 16:00:00 1

2017-09-11 17:00:00 0

2017-09-11 18:00:00 0

2017-09-11 19:00:00 0

2017-09-11 20:00:00 0

...

2017-12-03 20:00:00 8587

2017-12-03 21:00:00 10413

2017-12-03 22:00:00 9862

2017-12-03 23:00:00 7226

2017-12-04 00:00:00 1

Freq: H, Name: Be_type, Length: 2001, dtype: int64

Ts1

2017-11-25 00:00:00 64

2017-11-25 01:00:00 29

2017-11-25 02:00:00 18

2017-11-25 03:00:00 8

2017-11-25 04:00:00 3

...

2017-12-03 19:00:00 141

2017-12-03 20:00:00 159

2017-12-03 21:00:00 154

2017-12-03 22:00:00 154

2017-12-03 23:00:00 123

Freq: H, Name: Be_type, Length: 216, dtype: int64

#绘图

plt.figure(figsize=(20,6),dpi =70)

x2= df_buy_timestamp.index plt.plot(range(len(x2)),df_buy_timestamp.values,label="成交量",color="blue",linewidth=2) plt.title("总成交量变化折现图(小时)")

x2 = [i.strftime("%Y-%m-%d %H:%M") for i in x2]

plt.xticks(range(len(x2))[::4],x2[::4],rotation=90)

plt.xlabel("Ts2")

plt.ylabel("Ts3")

plt.grid(alpha=0.4);

特征工程

思路:不考虑时间窗口，只以用户的点击和收藏等行为来预测是否购买 流程:以用户ID(U_Id)为分组键，将每位用户的点击、收藏、加购物车的行为统计出来，分别为

是否点击，点击次数;是否收藏，收藏次数;是否加购物车，加购物车次数

以此来预测最终是否购买

# 去掉时间戳

df = df[["U_Id", "T_Id", "C_Id", "Be_type"]] df

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行为类型

U_Id

1 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,...

100 [1,1,1,1,1,1,1,1,1,3,1,1,3,1,3,...

115 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,...

117 [4,1,1,1,1,1,1,4,1,1,1,1,1,1,1,...

118 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,...

Name: Be_type1, dtype: object

最后创建一个DataFrame用来存储等下计算出的用户行为。

df_new = pd.DataFrame()

点击次数

df_new['pv_much'] = df_Be.apply(lambda x: Counter(x)['1'])

df_new

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加购次数

#是否加购

df_new['is_cart'] = df_Be.apply(lambda x: 1 if '2' in x else 0)

df_new

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#加购了几次

df_new['cart_much'] = df_Be.apply(lambda x: 0 if '2' not in x else Counter(x)['2'])

df_new

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收藏次数

#是否收藏

df_new['is_fav'] = df_Be.apply(lambda x: 1 if '3' in x else 0)

df_new

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#收藏了几次

df_new['fav_much'] = df_Be.apply(lambda x: 0 if '3' not in x else Counter(x)['3'])

df_new

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相关分析

#部分数据 df_new.corr('spearman')

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是否加购与加购次数、是否收藏与收藏次数之间存在一定相关性，但经验证剔除其中之一与纳入全部变量效果基本一致，故之后使用全部变量建模。

数据标签

import seaborn as sns

#是否购买

df_new['is_buy'] = df_Be.apply(lambda x: 1 if '4' in x else 0)

df_new

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df_new.is_buy.value_counts()

1 6689

0 3050

Name: is_buy, dtype: int64

df_new['label'] = df_new['is_buy']

del df_new['is_buy']

df_new.head()

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f,ax=plt.subplots(1,2,figsize=(12,5))

sns.set_palette(["#9b59b6","#3498db",]) #设置所有图的颜色，使用hls色彩空间

sns.distplot(df_new['fav_much'],bins=30,kde=True,label='123',ax=ax[0]);

sns.distplot(df_new['cart_much'],bins=30,kde=True,label='12',ax=ax[1]);

C:\Users\CDA\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt

your code to use either `displot` (a figure-level function with similar flexibility) or

`histplot` (an axes-level function for histograms).

warnings.warn(msg, FutureWarning)

C:\Users\CDA\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt

your code to use either `displot` (a figure-level function with similar flexibility) or

`histplot` (an axes-level function for histograms).

warnings.warn(msg, FutureWarning)

建立模型

划分数据集

from sklearn.model_selection import train_test_split

X = df_new.iloc[:,:-1]

Y = df_new.iloc[:,-1]

X.head()

Y.head()

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U_Id

10

100 1

115 0

117 1

118 0

Name: label, dtype: int64

Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,Y,test_size= 0.3,random_state= 42)

逻辑回归

模型建立

from sklearn.linear_model import LogisticRegression

LR_1 = LogisticRegression().fit(Xtrain,Ytrain)

#简单测试 LR_1.score(Xtest,Ytest)

0.6741957563312799

模型评估

from sklearn import metrics

from sklearn.metrics import classification_report

from sklearn.metrics import auc,roc_curve

#混淆矩阵

print(metrics.confusion_matrix(Ytest, LR_1.predict(Xtest)))

[[ 0 952]

[ 0 1970]]

print(classification_report(Ytest,LR_1.predict(Xtest)))

D:\anaconda\lib\site-packages\sklearn\metrics\_classification.py:1308:

UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in

labels with no predicted samples. Use `zero_division` parameter to control this behavior.

_warn_prf(average, modifier, msg_start, len(result))

D:\anaconda\lib\site-packages\sklearn\metrics\_classification.py:1308:

UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in

labels with no predicted samples. Use `zero_division` parameter to control this behavior.

_warn_prf(average, modifier, msg_start, len(result))

D:\anaconda\lib\site-packages\sklearn\metrics\_classification.py:1308:

UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in

labels with no predicted samples. Use `zero_division` parameter to control this behavior.

_warn_prf(average, modifier, msg_start, len(result))

fpr,tpr,threshold = roc_curve(Ytest,LR_1.predict_proba(Xtest)[:,1])

roc_auc = auc(fpr,tpr)

print(roc_auc)

0.6379193682549162

随机森林

模型建立

from sklearn.ensemble import RandomForestClassifier

rfc = RandomForestClassifier(n_estimators=200, max_depth=1)

rfc.fit(Xtrain, Ytrain)

RandomForestClassifier(max_depth=1, n_estimators=200)

模型评估

#混淆矩阵

print(metrics.confusion_matrix(Ytest, rfc.predict(Xtest)))

[[ 0 952]

[ 0 1970]]

#分类报告

print(metrics.classification_report(Ytest, rfc.predict(Xtest)))

D:\anaconda\lib\site-packages\sklearn\metrics\_classification.py:1308:

UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in

labels with no predicted samples. Use `zero_division` parameter to control this behavior.

_warn_prf(average, modifier, msg_start, len(result))

D:\anaconda\lib\site-packages\sklearn\metrics\_classification.py:1308:

UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in

labels with no predicted samples. Use `zero_division` parameter to control this behavior.

_warn_prf(average, modifier, msg_start, len(result))

D:\anaconda\lib\site-packages\sklearn\metrics\_classification.py:1308:

UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in

labels with no predicted samples. Use `zero_division` parameter to control this behavior.

_warn_prf(average, modifier, msg_start, len(result))