def compute_svd(self):
'''
ratings = pd.read_csv("/home/sourabhkondapaka/Desktop/ratingsss.csv",index_col= False)
ratings = ratings.ix[1:]
ratings.to_csv("/home/sourabhkondapaka/Desktop/ratingsss.csv",index = False)
self.data = Data()
self.data.load(self.ratings_file, sep=',', format={'col':0, 'row':1 ,'value':2, 'ids':float})
self.train , self.test = self.data.split_train_test(percent=self.PERCENT_TRAIN)
self.svd = SVD()
self.svd.set_data(self.train)
self.svd.compute(k=self.K, min_values=1, pre_normalize=None, mean_center=True, post_normalize=True)'''
self.data = Data()
self.data.load(self.ratings_file,
sep=',',
format={
'col': 0,
'row': 1,
'value': 2,
'ids': float
})
self.train, self.test = self.data.split_train_test(percent=85)
self.svd = SVDNeighbourhood()
self.svd.set_data(self.train)
self.svd.compute(k=100,
min_values=1,
pre_normalize=None,
mean_center=False,
post_normalize=True)
# recommender = Recommender()
# recommender.load_web_data('dataset',
# [{'Запах женщины': 9, 'The Usual Suspects': 8, 'The Departed': 8,
# 'Тутси': 7, 'Выпускник': 10, 'Залечь на дно в Брюгге': 4, 'Евротур': 7,
# 'Goodfellas': 6, 'Донни Браско': 8, 'Амели': 3, 'Идиократия': 7}],
# 100, 0, 10, 10)
# recommender.load_local_data('dataset', K=100, min_values=0)
# m = recommender.matrix.get_rating_matrix()
#
# m1 = recommender.get_predictions_for_all_users()
from recsys.algorithm.factorize import SVDNeighbourhood
svd = SVDNeighbourhood()
svd.load_data('test_dataset',
sep=' ',
format={
'col': 1,
'row': 0,
'value': 2,
'ids': int
})
svd.compute(100, 0)
print svd.predict(108, 698)
# svd.load_data(filename=sys.argv[1], sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
# K=100
# svd.compute(k=K, min_values=5, pre_normalize=None, mean_center=True, post_normalize=True)
import sys
from numpy import nan, mean
#To show some messages:
import recsys.algorithm
recsys.algorithm.VERBOSE = True
from recsys.algorithm.factorize import SVD, SVDNeighbourhood
from recsys.datamodel.data import Data
from recsys.evaluation.prediction import RMSE, MAE
# Create SVD
K = 100
svd = SVD()
svd_neig = SVDNeighbourhood()
#Dataset
PERCENT_TRAIN = int(sys.argv[2])
data = Data()
data.load(sys.argv[1],
sep='::',
format={
'col': 0,
'row': 1,
'value': 2,
'ids': int
})
rmse_svd_all = []
mae_svd_all = []
rmse_svd_neig_all = []
