1.數(shù)據(jù)挖掘簡介(略)

2.使用Python和IPython Notebook

2.1.安裝Python

2.2.安裝IPython

2.3.安裝scikit-learn

scikit-learn是用Python開發(fā)的機(jī)器學(xué)習(xí)庫，它包含大量機(jī)器學(xué)習(xí)算法、數(shù)據(jù)集、工具和框架。它以Python科學(xué)計(jì)算的相關(guān)工具集為基礎(chǔ)，其中numpy和scipy等都針對數(shù)據(jù)處理任務(wù)進(jìn)行過優(yōu)化，因此scikit-learn速度快、擴(kuò)展性強(qiáng)，為此做數(shù)據(jù)挖掘很實(shí)用。

scikit-learn可以用Python3提供的pip工具進(jìn)行安裝，之前沒有安裝Numpy和Scipy的話也會(huì)順便安裝。安裝命令如下：

pip install scikit-learn

3.親和性分析示例

3.1什么是親和性分析

親和性分析根據(jù)樣本個(gè)體(物體)之間的相似度，確定它們關(guān)系的親疏。親和性分析的應(yīng)用場景如下。

(1)向網(wǎng)站用戶提供多樣化的服務(wù)或投放定向廣告

(2)為了向用戶推薦電影或商品，而賣給他們一些與之相關(guān)的小玩意。

(3)根據(jù)基因?qū)ふ矣杏H緣關(guān)系的人。

......

親和性有哪些測量方法？

(1)統(tǒng)計(jì)兩件商品一起出售的頻率，或者統(tǒng)計(jì)顧客購買商品1后再買商品2的比率。

(2)計(jì)算兩個(gè)體之間的相似度

......

3.2商品推薦

商品銷售從線下搬到線上后，很多之前靠人工完成的工作只有實(shí)現(xiàn)自動(dòng)化，才有望將生意做大，向上銷售出自英文up-selling，指的是向已經(jīng)購買商品的顧客推銷另一種商品。原來線下由人工完成的商品推薦工作，現(xiàn)在依靠數(shù)據(jù)挖掘技術(shù)就能完成，而且利潤大，助推電子商務(wù)革命的發(fā)展！

我們一起看一個(gè)簡單的推薦服務(wù)：人們之前經(jīng)常購買的兩件商品，以后也很可能同時(shí)購買。

作為數(shù)據(jù)挖掘入門性質(zhì)的例子，我們希望得到下面的規(guī)則：

如果一個(gè)人買了商品X，那么他很有可能購買商品Y

3.3在Numpy中加載數(shù)據(jù)集

import numpy as np

dataset_filename = 'affinity_dataset.txt'

X = np.loadtxt(dataset_filename)

3.4實(shí)現(xiàn)簡單的排序規(guī)則

規(guī)則的優(yōu)劣有多種衡量方法，常用的是支持度(support)和置信度(confidence)。

支持度指數(shù)據(jù)集中應(yīng)驗(yàn)的次數(shù)，有時(shí)候需要對支持度進(jìn)行規(guī)范化。

支持度衡量給定規(guī)則應(yīng)驗(yàn)的比例，置信度衡量規(guī)則準(zhǔn)確率如何，即符合給定條件的所有規(guī)則里，跟當(dāng)前規(guī)則結(jié)論一致的比例有多大，計(jì)算方法為首先統(tǒng)計(jì)當(dāng)前規(guī)則的出現(xiàn)次數(shù)，再用它除以條件相同的規(guī)則數(shù)量

如果顧客買了蘋果，他們也會(huì)購買香蕉的支持度和置信度

num_apple_purchases = 0

for sample in X:

if sample[3] ==1: #This person bought apples

num_apple_purchases += 1

# print('{0} people bought apples'.format(num_apple_purchases)) #ou can try the print way to find difference

print('{0} people bought apples'.format(num_apple_purchases))

image.png

同理，檢測sample[4]的值是否為1，就可以確定顧客是否也買了香蕉，進(jìn)而可以計(jì)算支持度和置信度。

我們需要統(tǒng)計(jì)數(shù)據(jù)集中所有規(guī)則的相關(guān)數(shù)據(jù)，首先分別為規(guī)則應(yīng)驗(yàn)和規(guī)則無效兩種情況構(gòu)建字典。字典的鍵是由條件和結(jié)論組成的元組，元組元素為特征在特征列表中的索引值，不要用實(shí)際特證名，比如“顧客如果購買了蘋果，也買了香蕉”就用(3，4)表示。如果某個(gè)個(gè)體的條件和結(jié)論均與給定規(guī)則相符，則表示給定規(guī)則對該個(gè)體適用，反之無效。

為了計(jì)算所有規(guī)則的置信度和支持度，首先創(chuàng)建幾個(gè)字典，用來存儲(chǔ)計(jì)算結(jié)果。這里使用defaultdict，好處是如果查找的鍵不存在，則返回默認(rèn)值。需要統(tǒng)計(jì)的量有規(guī)則應(yīng)驗(yàn)、規(guī)則無效、條件相同的規(guī)則數(shù)量。

from collections import defaultdict

vaild_rules = defaultdict(int)

invaild_rules = defaultdict(int)

num_occurances = defaultdict(int)

#依次對樣本的每個(gè)個(gè)體及個(gè)體的每個(gè)特征值進(jìn)行處理。第一個(gè)特征為規(guī)則的前提條件-----顧客購買了某種商品

for sample in X:

for premise in range(5):

#檢測個(gè)體是否滿足條件，如果不滿足則檢測下一個(gè)條件

if sample[premise] ==0:continue

#如果條件滿足(即值為1)，該條件的出現(xiàn)次數(shù)加1，在遍歷過程中跳過條件和結(jié)論相同的情況，比如“如果顧客購買了蘋果，他們也買蘋果”，這樣的規(guī)則無用

num_occurances[premise] += 1

n_sample,n_features = X.shape

for conclusion in range(n_features):

if premise ==conclusion:continue

#如果規(guī)則適用于個(gè)體，規(guī)則應(yīng)驗(yàn)這種情況(vaild_rules字典中，鍵為由條件和結(jié)論組成的元組)增加一次，反之，違反規(guī)則情況(invaild_rules字典中)就增加一次

https://github.com/datawhalechina/joyful-pandas datawhale pandas教程

https://space.bilibili.com/631186842?from=search&seid=16882960572917617056 Rachel's english

https://www.liulishuo.com/liulishuo.html 流利說英語 有app直接下

https://github.com/fengdu78/lihang-code 李航Python實(shí)現(xiàn)

[ch1_affinity_create]

X = np.zeros((100, 5), dtype='bool')

#dtype can change,such as int,float

X.shape[0]

#0 is row,1 is col

#數(shù)組的索引方式是和列表一樣的

np.savetxt("affinity_dataset.txt", X, fmt='%d')

#parameters

fmt : str or sequence of strs, optional

A single format (%10.5f), a sequence of formats, or a

multi-format string, e.g. 'Iteration %d -- %10.5f', in which

case `delimiter` is ignored. For complex `X`, the legal options

for `fmt` are:

#create a random float from 0 to 1

a = np.random.random()

print(X[:5].astype(np.int))

[ch1_affinity]

n_samples, n_features = X.shape

print("This dataset has {0} samples and {1} features".format(n_samples, n_features))

#這種print格式用print('{0},{1}'.format(a,b))

#count the people who bought apples

num_apple_purchases = 0

for sample in X:

if sample[3] = 1:

num_apple_purchases += 1

print('{0} people bought apples'.format(num_apples_purchases))

####################################################################################################

##bought 3 but not bought 4

rule_valid = 0

rule_invalid = 0

for sample in X:

if sample[3] == 1: # This person bought Apples

if sample[4] == 1:

# This person bought both Apples and Bananas

rule_valid += 1

else:

# This person bought Apples, but not Bananas

rule_invalid += 1

print("{0} cases of the rule being valid were discovered".format(rule_valid))

print("{0} cases of the rule being invalid were discovered".format(rule_invalid))

####################################################################################################

## not bought 3

rule_valid = 0

rule_invalid = 0

for sample in X:

if sample[3] == 1:

if sample[4] == 1:

rule_valid += 1

else:

rule_invalid += 1

print('{0} rule_valid'.format(rule_valid))

print('{0} rule_invalid'.format(rule_invalid))

####################################################################################################

規(guī)則是 如果買了蘋果，可能也買了香蕉。

規(guī)則無效是 如果買了蘋果，但沒買香蕉

print("The support is {0} and the confidence is {1:.3f}.".format(support, confidence))

# Confidence can be thought of as a percentage using the following:

print("As a percentage, that is {0:.1f}%.".format(100 * confidence))

####################################################################################################

from collections import defaultdict

# Now compute for all possible rules

valid_rules = defaultdict(int)

invalid_rules = defaultdict(int)

num_occurences = defaultdict(int)

for sample in X:

for premise in range(n_features):

if sample[premise] == 0: continue

# Record that the premise was bought in another transaction

num_occurences[premise] += 1

for conclusion in range(n_features):

if premise == conclusion: # It makes little sense to measure if X -> X.

continue

if sample[conclusion] == 1:

# This person also bought the conclusion item

valid_rules[(premise, conclusion)] += 1

else:

# This person bought the premise, but not the conclusion

invalid_rules[(premise, conclusion)] += 1

support = valid_rules

confidence = defaultdict(float)

for premise, conclusion in valid_rules.keys():

confidence[(premise, conclusion)] = valid_rules[(premise, conclusion)] / num_occurences[premise]

####################################################################################################

from collections import defaultdict

rule_valid = defaultdict(int)

rule_invalid = defaultdict(int)

num_premise = defaultdict(int)

n_features = X.shape[1]

for sample in X:

for premise in range(n_features):

if sample[premise] == 0:continue

if sample[premise] == 1:

num_premise[premise] += 1

for conclusion in range(n_features):

if premise == conclusion:continue

if sample[conclusion] == 1:

rule_valid[(premise,conclusion)] += 1

else:

rule_invalid[(premise,conclusion)] += 1

support = rule_valid

confidence = defaultdict(float)

for premise,conclusion in rule_valid.keys():

confidence[(premise,conclusion)] = rule_valid[(premise,conclusion)] / num_premise[premise]

####################################################################################################

for premise, conclusion in confidence:

premise_name = features[premise]

conclusion_name = features[conclusion]

print("Rule: If a person buys {0} they will also buy {1}".format(premise_name, conclusion_name))

print(" - Confidence: {0:.3f}".format(confidence[(premise, conclusion)]))

print(" - Support: {0}".format(support[(premise, conclusion)]))

print("")

####################################################################################################

for premise,conclusion in confidence:

features = ["bread", "milk", "cheese", "apples", "bananas"]

premise_name = features[premise]

conclusion_name = features[conclusion]

print('If someone buy {0} then they may buy {1}'.format(premise_name,conclusion_name))

print('confidence is {0:.3f}'.format(confidence[(premise,conclusion)]))

print('support is {0}'.format(support[(premise,conclusion)]))

#用于打印特定的數(shù)據(jù)結(jié)構(gòu)，整齊好看

from pprint import pprint

pprint(list(support.items()))

#example

import pprint

data = ("test", [1, 2, 3,'test', 4, 5], "This is a string!",

{'age':23, 'gender':'F'})

print(data)

pprint.pprint(data)

image.png

注意

# [Python: 字典列表: itemgetter 函數(shù): 根據(jù)某個(gè)或某幾個(gè)字典字段來排序列表](https://www.cnblogs.com/baxianhua/p/8182627.html)

from operator import itemgetter

sorted_support = sorted(support.items(), key=itemgetter(1), reverse=True)

# [python中sorted和sort 、reversed和reverse的使用](https://www.cnblogs.com/shengguorui/p/10863988.html)

[OneR]

[ch1_oner_application]

#iris.describe

print(dataset.DESCR)

在進(jìn)行OneR算法分類前需要將數(shù)據(jù)進(jìn)行離散化

# Compute the mean for each attribute

attribute_means = X.mean(axis=0)

assert attribute_means.shape == (n_features,)#assert:斷言

X_d = np.array(X >= attribute_means, dtype='int')

#X.means(axis):axis = 0 is symbol take col

#assert 1==1 # 條件為 true 正常執(zhí)行

#assert 1==2 # 條件為 false 觸發(fā)異常

#sklearn中已經(jīng)廢棄cross_validation,將其中的內(nèi)容整合到#model_selection中

#將sklearn.cross_validation 替換為 sklearn.model_selection

##origin

# Now, we split into a training and test set

from sklearn.cross_validation import train_test_split

# Set the random state to the same number to get the same results as in the book

random_state = 14

X_train, X_test, y_train, y_test = train_test_split(X_d, y, random_state=random_state)

print("There are {} training samples".format(y_train.shape))

print("There are {} testing samples".format(y_test.shape))

##new

# Now, we split into a training and test set

from sklearn.model_selection import train_test_split

# Set the random state to the same number to get the same results as in the book

random_state = 14

X_train, X_test, y_train, y_test = train_test_split(X_d, y, random_state=random_state)

print("There are {} training samples".format(y_train.shape))

print("There are {} testing samples".format(y_test.shape))

#train_X,test_X,train_y,test_y = train_test_split(train_data,train_target,test_size=0.3,random_state=5)

#train_test_split()函數(shù)是用來隨機(jī)劃分樣本數(shù)據(jù)為訓(xùn)練集和測試集的，當(dāng)然也可以人為的切片劃分

#優(yōu)點(diǎn)：隨機(jī)客觀的劃分?jǐn)?shù)據(jù)，減少人為因素

#test_size：測試數(shù)據(jù)占樣本數(shù)據(jù)的比例，若整數(shù)則樣本數(shù)量

#zip() 函數(shù)用于將可迭代的對象作為參數(shù)，將對象中對應(yīng)的元素打包成一個(gè)個(gè)元組，然后返回由這些元組組成的列表。

#如果各個(gè)迭代器的元素個(gè)數(shù)不一致，則返回列表長度與最短的對象相同，利用 * 號(hào)操作符，可以將元組解壓為列表

>>>a = [1,2,3]

>>>b = [4,5,6]

>>>c = [4,5,6,7,8]

>>>zipped = zip(a,b) # 打包為元組的列表

[(1, 4), (2, 5), (3, 6)]

>>>zip(a,c) # 元素個(gè)數(shù)與最短的列表一致

[(1, 4), (2, 5), (3, 6)]

>>>zip(*zipped) # 與 zip 相反，*zipped 可理解為解壓，返回二維矩陣式

[(1, 2, 3), (4, 5, 6)]

class_counts = defaultdict(int)

#Iterate through each sample and count the frequency of each class/value pair

for sample, y in zip(X, y_true):

if sample[feature] == value:

class_counts[y] += 1

a = zip(X, y)

for b in a:

print(b)

image.png

a = zip(X, y)

for b,c in a:

print(b)

image.png

a = zip(X, y)

for b,c in a:

print(c)

image.png

error = sum([class_count for class_value, class_count in class_counts.items()

if class_value != most_frequent_class])

總結(jié)

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