Q&A
集合地プログラミングを読んで質問があります。
第２章のアイテムベースのレコメンドエンジンを作る場合
critics={'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
'The Night Listener': 3.0},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 3.5},
'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
'Superman Returns': 3.5, 'The Night Listener': 4.0},
critics['Lisa Rose']['Lady in the Water']
2.5
critics['Toby']['Snakes on a Plane']=4.5
critics['Toby']
{'Snakes on a Plane':4.5,'You, Me and Dupree':1.0}
これがこういう風に数値がでるのもわかっていないです。←--------
この配列についても教えてください。
{'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}}
これを次のように変換する。
{'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0},
'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc..
def transformPrefs(prefs):
result={}
for person in prefs:
for item in prefs[person]:
result.setdefault(item,{})
itemとpersonを入れ替える
result[item][person]=prefs[person][item] ←------------------------
return result
なぜresult[item][person]=prefs[person][item]をひっくり返しただけで
key値がかわるのでしょうか？
この辞書型がよくわかりません。
また
critics['Lisa Rose']['Lady in the Water']
2.5
critics['Toby']['Snakes on a Plane']=4.5
critics['Toby']
{'Snakes on a Plane':4.5,'You, Me and Dupree':1.0}
これがこういう風に数値がでるのもわかっていないです。←--------
この配列についても教えてください。
教えてください。
よろしくお願いします。
またこれと同じことはphpやjavaでもできるのでしょうか。
本のソースコード↓
ftp://ftp.oreilly.co.jp/9784873113647/PCI_sample.pdf
ここにもあります。
A dictionary of movie critics and their ratings of a small
set of movies
critics={'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
'The Night Listener': 3.0},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 3.5},
'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
'The Night Listener': 4.5, 'Superman Returns': 4.0,
'You, Me and Dupree': 2.5},
'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 2.0},
'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
'Toby': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0}}
from math import sqrt
Returns a distance-based similarity score for person1 and person2
def sim_distance(prefs,person1,person2):
Get the list of shared_items
si={}
for item in prefs[person1]:
if item in prefs[person2]: si[item]=1
if they have no ratings in common, return 0
if len(si)==0: return 0
Add up the squares of all the differences
sum_of_squares=sum([pow(prefs[person1][item]-prefs[person2][item],2)
for item in prefs[person1] if item in prefs[person2]])
return 1/(1+sum_of_squares)
Returns the Pearson correlation coefficient for p1 and p2
def sim_pearson(prefs,p1,p2):
Get the list of mutually rated items
si={}
for item in prefs[p1]:
if item in prefs[p2]: si[item]=1
if they are no ratings in common, return 0
if len(si)==0: return 0
Sum calculations
n=len(si)
Sums of all the preferences
sum1=sum([prefs[p1][it] for it in si])
sum2=sum([prefs[p2][it] for it in si])
Sums of the squares
sum1Sq=sum([pow(prefs[p1][it],2) for it in si])
sum2Sq=sum([pow(prefs[p2][it],2) for it in si])
Sum of the products
pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])
Calculate r (Pearson score)
num=pSum-(sum1sum2/n)
den=sqrt((sum1Sq-pow(sum1,2)/n)(sum2Sq-pow(sum2,2)/n))
if den==0: return 0
r=num/den
return r
Returns the best matches for person from the prefs dictionary.
Number of results and similarity function are optional params.
def topMatches(prefs,person,n=5,similarity=sim_pearson):
scores=[(similarity(prefs,person,other),other)
for other in prefs if other!=person]
scores.sort()
scores.reverse()
return scores[0:n]
Gets recommendations for a person by using a weighted average
of every other user's rankings
def getRecommendations(prefs,person,similarity=sim_pearson):
totals={}
simSums={}
for other in prefs:
# don't compare me to myself
if other==person: continue
sim=similarity(prefs,person,other)
# ignore scores of zero or lower if sim<=0: continue for item in prefs[other]: # only score movies I haven't seen yet if item not in prefs[person] or prefs[person][item]==0: # Similarity * Score totals.setdefault(item,0) totals[item]+=prefs[other][item]*sim # Sum of similarities simSums.setdefault(item,0) simSums[item]+=sim
Create the normalized list
rankings=[(total/simSums[item],item) for item,total in totals.items()]
Return the sorted list
rankings.sort()
rankings.reverse()
return rankings
def transformPrefs(prefs):
result={}
for person in prefs:
for item in prefs[person]:
result.setdefault(item,{})
# Flip item and person result[item][person]=prefs[person][item]
return result
def calculateSimilarItems(prefs,n=10):
Create a dictionary of items showing which other items they
are most similar to.
result={}
Invert the preference matrix to be item-centric
itemPrefs=transformPrefs(prefs)
c=0
for item in itemPrefs:
# Status updates for large datasets
c+=1
if c%100==0: print "%d / %d" % (c,len(itemPrefs))
# Find the most similar items to this one
scores=topMatches(itemPrefs,item,n=n,similarity=sim_distance)
result[item]=scores
return result
def getRecommendedItems(prefs,itemMatch,user):
userRatings=prefs[user]
scores={}
totalSim={}
Loop over items rated by this user
for (item,rating) in userRatings.items( ):
# Loop over items similar to this one for (similarity,item2) in itemMatch[item]: # Ignore if this user has already rated this item if item2 in userRatings: continue # Weighted sum of rating times similarity scores.setdefault(item2,0) scores[item2]+=similarity*rating # Sum of all the similarities totalSim.setdefault(item2,0) totalSim[item2]+=similarity
Divide each total score by total weighting to get an average
rankings=[(score/totalSim[item],item) for item,score in scores.items( )]
Return the rankings from highest to lowest
rankings.sort( )
rankings.reverse( )
return rankings
def loadMovieLens(path='/data/movielens'):
Get movie titles
movies={}
for line in open(path+'/u.item'):
(id,title)=line.split('|')[0:2]
movies[id]=title
Load data
prefs={}
for line in open(path+'/u.data'):
(user,movieid,rating,ts)=line.split('\t')
prefs.setdefault(user,{})
prefs[user][movies[movieid]]=float(rating)
return prefs
回答1件
0
ベストアンサー
なぜresult[item][person]=prefs[person][item]をひっくり返しただけでkey値がかわるのでしょうか？
ひっくり返しただけではないのでは。
ソースにコメントを入れてみました。
Python
1def transformPrefs(prefs): 2 result={} 3 for person in prefs: 4 for item in prefs[person]: 5 result.setdefault(item,{}) # <= この行は if item not in result: result[item] = {}と同義 6 result[item][person]=prefs[person][item] # <= この行で result[item][person]に値をセット 7 return result 8
ご参考になれば。
投稿2016/03/24 10:06
編集2016/03/24 16:36
総合スコア2425
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