crab-源码-细节和示例
数据
* 测试用的数据都在 scikits/crab/datasets下
* 数据以 自定义容器 Bunch 的形式提供(对数据格式基本无限制)
示例代码
#找到的示例代码有个小错误 #from crab.recommenders.knn import UserBasedRecommender #应加上scikits前缀 from scikits.crab.recommenders.knn import UserBasedRecommender
#!/usr/bin/env python #coding=utf-8 # 基础数据-测试数据 from scikits.crab import datasets movies = datasets.load_sample_movies() # 构建 model from scikits.crab.models import MatrixPreferenceDataModel model = MatrixPreferenceDataModel(movies.data) # 选定算法，计算 similarity from scikits.crab.metrics import pearson_correlation # 选用算法 pearson_correlation from scikits.crab.similarities import UserSimilarity similarity = UserSimilarity(model, pearson_correlation) # 选择了 基于User的推荐 from scikits.crab.recommenders.knn import UserBasedRecommender recommender = UserBasedRecommender(model, similarity, with_preference=True) # 输出个结果看看效果 Recommend items for the user 5 (Toby) print recommender.recommend(5)
示例代码2
#!/usr/bin/env python
#coding=utf-8
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}, \
'Sheldom':\
{'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}, \
'Penny Frewman': \
{'Snakes on a Plane':4.5,'You, Me and Dupree':1.0, 'Superman Returns':4.0}, 'Maria Gabriela': {}
}
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')
itembase_demo()