import os
import pandas as pd
''
''
data_folder=os.path.join( os.path.expanduser("~"),"ml-100k")
ratings_filename=os.path.join( data_folder,"u.data")
all_ratings=pd.read_csv( ratings_filename, delimiter="\t",header=None, names=["UserID","MovieID","Rating","Datetime"])
all_ratings[:1]
''
all_ratings["Datetime"]=pd.to_datetime(all_ratings["Datetime"],unit='s')
all_ratings[:1]
all_ratings["Favorable"]=all_ratings["Rating"]>3
all_ratings[:10]
''
ratings=all_ratings[ all_ratings['UserID'].isin(range(200))]
favorable_ratings=ratings[ratings["Favorable"]]
favorable_ratings[:5]
from collections import defaultdict
favorable_reviews_by_users=dict((k,frozenset(v.values))
for k,v in favorable_ratings.groupby("UserID")["MovieID"])
print("length: {0}".format( len(favorable_reviews_by_users) ) )
num_favorable_by_movie=ratings[["MovieID","Favorable"]].groupby("MovieID").sum()
num_favorable_by_movie
num_favorable_by_movie.sort( "Favorable",ascending=False)[:5]
''
def find_frequent_itemsets( favorable_reviews_by_users, k_1_itemsets, min_support):
counts=defaultdict( int )
for user,reviews in favorable_reviews_by_users.items():
for itemset in k_1_itemsets:
if itemset.issubset( reviews):
for other_reviewed_movie in reviews-itemset:
current_superset=itemset|frozenset( (other_reviewed_movie,))
counts[current_superset]+=1
return dict( [(itemset,frequency) for itemset,frequency in counts.items() if frequency>=min_support ] )
import sys
frequent_itemsets={}
min_support=50
frequent_itemsets[1]= dict((frozenset((movie_id,)),row["Favorable"]) for movie_id,row in num_favorable_by_movie.iterrows() if row["Favorable"]>min_support)
frequent_itemsets[1]
print("there are {0} movie with more than {1} favorable reviews".format( len(frequent_itemsets[1]), min_support))
sys.stdout.flush()
max_length=20
for k in range(2, max_length):
cur_frequent_itemsets=find_frequent_itemsets( favorable_reviews_by_users, frequent_itemsets[k-1], min_support )
if len(cur_frequent_itemsets)==0:
print("can not find any frequent itemsets of length {0}".format( k ))
sys.stdout.flush()
break
else:
print(" find {0} frequent itemsets of length {1}".format(len(cur_frequent_itemsets), k))
print("\t data as following:")
sys.stdout.flush()
frequent_itemsets[k]=cur_frequent_itemsets
candidate_rules=[]
for itemset_length,itemset_counts in frequent_itemsets.items():
for itemset in itemset_counts.keys():
for conclusion in itemset:
premise=itemset-set((conclusion,))
candidate_rules.append((premise,conclusion))
print("there are {0} candidate rules".format( len(candidate_rules)))
correct_counts=defaultdict(int)
incorrect_counts=defaultdict(int)
for user, reviews in favorable_reviews_by_users.items():
for candidate_rule in candidate_rules:
premise,conclusion=candidate_rule
if premise.issubset(reviews):
if conclusion in reviews:
correct_counts[candidate_rule]+=1
else:
incorrect_counts[candidate_rule]+=1
rule_confidence={candidate_rule: correct_counts[candidate_rule]/ float(correct_counts[candidate_rule]+incorrect_counts[candidate_rule]) for candidate_rule in candidate_rules}
min_confidence=0.9
rule_confidence={candidate_rule: confidence for candidate_rule,confidence in rule_confidence.items() if confidence>min_confidence}
print( "the total of rules which bigger than min_confidence is {}".format( len(rule_confidence )) )
from operator import itemgetter
sorted_confidence=sorted( rule_confidence.items(),key=itemgetter(1),reverse=True)
for index in range(5):
print("Rule #{0}".format(index+1))
(premise,conclusion)=sorted_confidence[index][0]
print("Rule: if a person recommends {0} they will also recommend {1}".format( premise, conclusion))
print( " - Confidence: {0:.3f}".format( rule_confidence[(premise,conclusion)]))
print("")
movie_name_filename=os.path.join( data_folder,"u.item")
movie_name_data=pd.read_csv(movie_name_filename,delimiter="|",header=None,encoding="mac-roman")
movie_name_data.columns=["MovieID", "Title", "Release Date", "Video Release", "IMDB", "<UNK>", "Action", "Adventure",
"Animation", "Children's", "Comedy", "Crime", "Documentary", "Drama", "Fantasy", "Film-Noir",
"Horror", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western"]
def get_movie_name(movie_id):
title_object=movie_name_data[movie_name_data["MovieID"]==movie_id]["Title"]
title=title_object.values[0]
return title
get_movie_name(4)
for index in range(5):
print("Rule #{0}".format(index+1))
(premise,conclusion)=sorted_confidence[index][0]
premise_names=",".join( get_movie_name(idx) for idx in premise )
conclusion_name=get_movie_name( conclusion)
print("Rule: if a person recommends {0} they will also recommend {1}".format( premise_names, conclusion_name))
print( " - Confidence: {0:.3f}".format( rule_confidence[(premise,conclusion)]))
print("")
test_dataset= all_ratings[~all_ratings['UserID'].isin(range(200))]
test_favorable_ratings=test_dataset[test_dataset["Favorable"]]
test_favorable_reviews_by_users=dict((k,frozenset(v.values))
for k,v in test_favorable_ratings.groupby("UserID")["MovieID"])
test_correct_counts=defaultdict(int)
test_incorrect_counts=defaultdict(int)
for user, reviews in test_favorable_reviews_by_users.items():
for candidate_rule in candidate_rules:
premise,conclusion=candidate_rule
if premise.issubset(reviews):
if conclusion in reviews:
test_correct_counts[candidate_rule]+=1
else:
test_incorrect_counts[candidate_rule]+=1
test_rule_confidence={candidate_rule: test_correct_counts[candidate_rule]/ float(test_correct_counts[candidate_rule]+test_incorrect_counts[candidate_rule]) for candidate_rule in candidate_rules}
print( len(test_rule_confidence))
sorted_test_confidence=sorted( test_rule_confidence.items(),key=itemgetter(1),reverse=True )
print( sorted_test_confidence[:5])
for index in range(10):
print("Rule #{0}".format(index+1))
(premise,conclusion)=sorted_confidence[index][0]
premise_names=",".join( get_movie_name(idx) for idx in premise )
conclusion_name=get_movie_name( conclusion)
print("Rule: if a person recommends {0} they will also recommend {1}".format( premise_names, conclusion_name))
print( " - Train Confidence: {0:.3f}".format( rule_confidence[(premise,conclusion)]))
print( " - Test Confidence: {0:.3f}".format( test_rule_confidence[(premise,conclusion)]))
print("")
posted @
2017-06-28 12:51
专注it
阅读(
1858 )
评论()
编辑
收藏
举报