本文整理汇总了Python中recsys.algorithm.factorize.SVD类的典型用法代码示例。如果您正苦于以下问题：Python SVD类的具体用法？Python SVD怎么用？Python SVD使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了SVD类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: SVDloadData
def SVDloadData():
svd = SVD()
recsys.algorithm.VERBOSE = True
dat_file = '/home/commons/RecSys/MOVIEDATA/MOVIEDATA/ml-1m/ratings.dat'
svd.load_data(filename=dat_file, sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
print svd.get_matrix()
return svd示例2: SVDtrain2
def SVDtrain2(data,pct_train):
train, test = data.split_train_test(percent=pct_train)
K=100
svd = SVD()
svd.set_data(train)
svd.compute(k=K, min_values=5, pre_normalize=None, mean_center=True,
post_normalize=True)
return svd,train,test示例3: getSimilarityMatrix
def getSimilarityMatrix(svd_model_file):
""" Returns similarity matrix from svd_model_file
"""
#Import SVD from file
svd=SVD()
svd.load_model(svd_model_file)
return svd.get_matrix_similarity()示例4: setup
def setup():
global users, items, svd
print 'Reading items...'
items = _read_items(os.path.join(MOVIELENS_DATA_PATH, 'movies.dat'))
users = []
svd = SVD()
svd.load_data(filename=os.path.join(MOVIELENS_DATA_PATH, 'ratings.dat'), sep='::', format={'col':0, 'row':1, 'value':2, 'ids':int})示例5: build_model
def build_model(self,uids,kn):
data = Data()
for uid,songs in uids.items():
for song in songs:
data.add_tuple((1,song,uid))
svd = SVD()
svd.set_data(data)
svd.compute(k=kn,min_values=1)
self.model = svd示例6: train_svd
def train_svd(data):
"""
This method load processed data and modelling data using Singular Value Decomposition
:return: SVD model
"""
svd = SVD()
svd.set_data(get_data_model_matrix(data))
k = 30
svd.compute(k=k, min_values=0, pre_normalize=None, mean_center=True, post_normalize=True)
return svd示例7: calculate_SVD_features
def calculate_SVD_features():
print "Thanks for input, calculating..."
svd = SVD()
recsys.algorithm.VERBOSE = True
dat_file = 'feature_matrix.csv'
svd.load_data(filename=dat_file, sep=',',
format = {'col':0, 'row':1, 'value': 2, 'ids': int})
svd.compute(k=100, min_values=0, pre_normalize=None,
mean_center=False, post_normalize=True)
return svd 示例8: getSVD
def getSVD():
filename = "/home/udaysagar/Documents/Classes/239/recsys/model/movielens.zip"
if os.path.exists(filename):
return SVD("./model/movielens")
else:
svd = SVD()
svd.load_data(filename='./data/movielens/ratings.dat', sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
k = 100
svd.compute(k=k, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True, savefile='./model/movielens')
return svd示例9: get_model
def get_model(model_name,datasource_name,start,end,model_params):
if not model_name in model_data:
model_data[model_name] = (datasource_name,start,end,model_params)
if not os.path.exists(model_dir+model_name):
#initialize model with new data
svd = SVD()
svd.load_data(filename=data_dir+datasource_name+'.csv', sep=',', format={'col':0, 'row':1, 'value':2, 'ids': int})
models[model_name] = svd
else:
if not model_name in models:
models[model_name] = SVD(filename=model_dir+model_name)示例10: calculate_SVD_users
def calculate_SVD_users():
print "Thanks for input, calculating..."
svd = SVD()
recsys.algorithm.VERBOSE = True
dat_file = 'user_data_working.csv'
svd.load_data(filename=dat_file, sep=',',
format = {'col':0, 'row':1, 'value': 2, 'ids': int})
svd.compute(k=100, min_values=2, pre_normalize=None,
mean_center=True, post_normalize=True)
shutil.copy('user_data_original.csv','user_data_working.csv')
return svd示例11: calculate_stats_features
def calculate_stats_features(pct_train):
dat_file='feature_matrix.csv'
data = Data()
data.load(dat_file, sep=',', format={'col':0, 'row':1, 'value':2,'ids':int})
train, test = data.split_train_test(percent=pct_train)
K=100
svd = SVD()
svd.set_data(train)
svd.compute(k=K, min_values=0, pre_normalize=None, mean_center=False,
post_normalize=False)
return svd,train,test示例12: impute_to_file
def impute_to_file(self, tastings, k=100, min_values=2, verbose=True):
# create a data file in Movielens format with the tastings data
self.save_tastings_to_movielens_format_file(tastings)
# for logging/testing purposes we may like this verbose
if verbose:
recsys.algorithm.VERBOSE = True
svd = SVD()
# load source data, perform SVD, save to zip file
source_file = self.file_location(self.tastings_movielens_format)
svd.load_data(filename=source_file, sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
outfile = self.file_location(self.tastings_recsys_svd)
svd.compute(k=k, min_values=min_values, pre_normalize=None, mean_center=True, post_normalize=True, savefile=outfile)
return svd示例13: create_svd_model
def create_svd_model(train):
""" Build SVD model
"""
svd = SVD()
svd.set_data(train)
svd.compute(k=100,
min_values=0,
pre_normalize=None,
mean_center=True,
post_normalize=True)
return svd示例14: __init__
def __init__(self):
#Dataset
data = Data()
self.filename = "emag"
if False and os.path.isfile(self.filename + ".zip"):
svd = SVD(filename=self.filename)
else:
svd = SVD()
svd.set_data(data)
#svd.compute(k=K, min_values=5, pre_normalize=None, mean_center=True, post_normalize=True, savefile="svd")
self.svd = svd
self.iterations = 0示例15: process_svd
def process_svd(preload):
if preload:
svd = SVD(filename='./data/svd-all') # Loading already computed SVD model
else:
print "Reading data..."
svdlibc = SVDLIBC('./data/behavior-ml-score.csv')
svdlibc.to_sparse_matrix(sep=',', format={'col':0, 'row':1, 'value':2, 'ids': str})
k=100
print "Computing SVD..."
svdlibc.compute(k)
svd = svdlibc.export()
svd.save_model('./data/svd-all', options={'k': k})
#svd.predict('TV268', 9, 1, 3)
return svd