def base_demo():
# 基础数据-测试数据
from scikits.crab import datasets
movies = datasets.load_sample_movies()
#print movies.data
#print movies.user_ids
#print movies.item_ids

#Build the model  
from scikits.crab.models import MatrixPreferenceDataModel  
model = MatrixPreferenceDataModel(movies.data)  

#Build the similarity  
# 选用算法 pearson_correlation  
from scikits.crab.metrics import pearson_correlation  
from scikits.crab.similarities import UserSimilarity  
similarity = UserSimilarity(model, pearson_correlation)  

# 选择 基于User的推荐  
from scikits.crab.recommenders.knn import UserBasedRecommender  
recommender = UserBasedRecommender(model, similarity, with_preference=True)  
print (recommender.recommend(5)) # 输出个结果看看效果 Recommend items for the user 5 (Toby)  

# 选择 基于Item 的推荐(同样的基础数据，选择角度不同)  
from scikits.crab.recommenders.knn import ItemBasedRecommender  
recommender = ItemBasedRecommender(model, similarity, with_preference=True)  
print (recommender.recommend(5)) # 输出个结果看看效果 Recommen

def itembase_demo():
from scikits.crab.models.classes import MatrixPreferenceDataModel
from scikits.crab.recommenders.knn.classes import ItemBasedRecommender
from scikits.crab.similarities.basic_similarities import ItemSimilarity
from scikits.crab.recommenders.knn.item_strategies import ItemsNeighborhoodStrategy
from scikits.crab.metrics.pairwise import euclidean_distances
movies = {'Marcel Caraciolo': {'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},
'Paola Pow':{'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},
'Leopoldo Pires': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0, 'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Lorena Abreu': {'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},
'Steve Gates': {'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}}
model = MatrixPreferenceDataModel(movies)
items_strategy = ItemsNeighborhoodStrategy()
similarity = ItemSimilarity(model, euclidean_distances)
recsys = ItemBasedRecommender(model, similarity, items_strategy)

print (recsys.most_similar_items('Lady in the Water') ) 
#Return the recommendations for the given user.  
print (recsys.recommend('Leopoldo Pires') ) 
#Return the 2 explanations for the given recommendation.  
print (recsys.recommended_because('Leopoldo Pires', 'Just My Luck', 2))
#Return the similar recommends  
print (recsys.most_similar_items('Lady in the Water')) 
#估算评分  
print (recsys.estimate_preference('Leopoldo Pires','Lady in the Water'))      

base_demo()
itembase_demo()