def api_import1():
movies = datasets.load_sample_movies()
import pprint ## to make printed items clearer
pprint.pprint(movies.data)
pprint.pprint(movies.user_ids)
pprint.pprint(movies.item_ids)
from scikits.crab.models import MatrixPreferenceDataModel
#Build the model
model = MatrixPreferenceDataModel(movies.data)
from scikits.crab.metrics import pearson_correlation
from scikits.crab.similarities import UserSimilarity
#Build the similarity
similarity = UserSimilarity(model, pearson_correlation)
from sklearn.base import BaseEstimator
from scikits.crab.recommenders.knn import UserBasedRecommender
#build the user Based recommender
recommender = UserBasedRecommender(model, similarity, with_preference=True)
#recommend item for the user 5 (Toby)
recomendations = recommender.recommend(5)
pprint.pprint(recomendations)
return 'List of ' + url_for('api_articles')
from scikits.crab import datasets
from scikits.crab.recommenders.knn.item_strategies import AllPossibleItemsStrategy, ItemsNeighborhoodStrategy
from scikits.crab.recommenders.svd.classes import MatrixFactorBasedRecommender
from scikits.crab.models.classes import MatrixPreferenceDataModel, MatrixBooleanPrefDataModel
movies = datasets.load_sample_movies()
data = movies['data']
def recommend(data, users, items):
# print 'data = ', data
matrix_model = MatrixPreferenceDataModel(data)
items_strategy = AllPossibleItemsStrategy()
# items_strategy = ItemsNeighborhoodStrategy()
recsys = MatrixFactorBasedRecommender(
model=matrix_model,
items_selection_strategy=items_strategy,
n_features=2)
score = dict()
for user in users:
row = dict()
for item in items:
try:
if data[user][item] != 0: # the user has already rated.
row[item] = data[user][item]
else:
row[item] = recsys.estimate_preference(user, item)
except:
row[item] = 0
# This code depicts the snippets appeared in Chapter 2 of the Crab's Official Tutorial: # # http://muricoca.github.io/crab/tutorial.html#introducing-recommendation-engines # from scikits.crab import datasets from scikits.crab.models import MatrixPreferenceDataModel from scikits.crab.metrics import pearson_correlation from scikits.crab.similarities import UserSimilarity from scikits.crab.recommenders.knn import UserBasedRecommender # input movies = datasets.load_sample_movies() print movies.data # build the model model = MatrixPreferenceDataModel(movies.data) print model # build the similarity similarity = UserSimilarity(model, pearson_correlation) # build the user based recommender recommender = UserBasedRecommender(model, similarity, with_preference=True) # recommend items for the user 5 result = recommender.recommend(5) print result
def get_movie_dataset():
movies = datasets.load_sample_movies()
pprint.pprint(movies.data)
pprint.pprint(movies.user_ids)
pprint.pprint(movies.item_ids)
return movies
from scikits.crab.datasets import load_sample_movies movies = load_sample_movies() from scikits.crab.models import MatrixPreferenceDataModel model = MatrixPreferenceDataModel(movies.data) from scikits.crab.metrics import pearson_correlation from scikits.crab.similarities import UserSimilarity similarity = UserSimilarity(model, pearson_correlation) from scikits.crab.recommenders.knn import UserBasedRecommender recommender = UserBasedRecommender(model, similarity, with_preference=True) print recommender.recommend(5)
def test_load_sample_movies(self):
movies = load_sample_movies()
self.assertEquals(len(movies['data']), 7)
self.assertEquals(len(movies['item_ids']), 6)
def test_load_sample_movies(self):
movies = load_sample_movies()
self.assertEquals(len(movies["data"]), 7)
self.assertEquals(len(movies["item_ids"]), 6)
