#!/usr/bin/python
"""MovieLense Tutorial
see doc/tutorial.rst for more information.
"""
import numpy as np
from rsvd import MovieLensDataset
dataset = MovieLensDataset.loadDat("data/movielense/ratings.dat")
ratings = dataset.ratings()
# make sure that the ratings a properly shuffled
np.random.shuffle(ratings)
# create train, validation and test sets.
n = int(ratings.shape[0] * 0.8)
train = ratings[:n]
test = ratings[n:]
v = int(train.shape[0] * 0.9)
val = train[v:]
train = train[:v]
from rsvd import RSVD
dims = (dataset.movieIDs().shape[0], dataset.userIDs().shape[0])
model = RSVD.train(20, train, dims, probeArray=val, maxEpochs=100, learnRate=0.0005, regularization=0.005)
sqerr = 0.0
for movieID, userID, rating in test:
#!/usr/bin/python
"""MovieLense Tutorial
see doc/tutorial.rst for more information.
"""
import numpy as np
from rsvd import MovieLensDataset
dataset = MovieLensDataset.loadDat('data/movielense/ratings.dat')
ratings = dataset.ratings()
# make sure that the ratings a properly shuffled
np.random.shuffle(ratings)
# create train, validation and test sets.
n = int(ratings.shape[0] * 0.8)
train = ratings[:n]
test = ratings[n:]
v = int(train.shape[0] * 0.9)
val = train[v:]
train = train[:v]
from rsvd import RSVD
dims = (dataset.movieIDs().shape[0], dataset.userIDs().shape[0])
model = RSVD.train(20,
train,
dims,
probeArray=val,
maxEpochs=100,
learnRate=0.0005,
#!/usr/bin/python2.7
import numpy as np
import matplotlib.pyplot as plt
from rsvd import RSVD, rating_t, MovieLensDataset
ratingsDataset = MovieLensDataset.loadDat('data_movilens1m/ratings.dat')
ratings=ratingsDataset.ratings()
# make sure that the ratings a properly shuffled
np.random.shuffle(ratings)
# create train, validation and test sets.
n = int(ratings.shape[0]*0.8)
train = ratings[:n]
test = ratings[n:]
v = int(train.shape[0]*0.9)
val = train[v:]
train = train[:v]
dims = (ratingsDataset.movieIDs().shape[0], ratingsDataset.userIDs().shape[0])
factor = 40
lambdas = []
errors = []
# lambda_f ne doit pas depasser 1
# maxEpochs = 1000
for lambda_f in np.arange(0.0, 0.05, 0.0005):
# Bash needed
# rm first line
# tail -n +2 "data/20150701094451-Behavior_training.csv" > data/behavior.csv
# ml conversion
# awk -F',' '{gsub("TV", "", $3) ; gsub("-", "", $1) ; gsub("T", "", $1) ; print $2"::"$3"::"$5}' data/behavior.csv > data/behavior-ml.csv
#!/usr/bin/python
"""MovieLense Tutorial
see doc/tutorial.rst for more information.
"""
import numpy as np
from rsvd import MovieLensDataset
dataset = MovieLensDataset.loadDat('./data/behavior-ml.csv')
ratings=dataset.ratings()
# make sure that the ratings a properly shuffled
np.random.shuffle(ratings)
# create train, validation and test sets.
n = int(ratings.shape[0]*0.8)
train = ratings[:n]
test = ratings[n:]
v = int(train.shape[0]*0.9)
val = train[v:]
train = train[:v]
from rsvd import RSVD
dims = (dataset.movieIDs().shape[0], dataset.userIDs().shape[0])
model = RSVD.train(20, train, dims, probeArray=val,
learnRate=0.0005, regularization=0.005)
# define rating array (itemID,userID,rating)
ratings=np.empty((n,),dtype=rating_t)
for i,row in enumerate(rows):
ratings[i]=(row[1],row[0]-1,row[2])
movieIDs=np.unique(ratings['f0'])
userIDs=np.unique(ratings['f1'])
movieIDs.sort()
userIDs.sort()
#map movieIDs
for i,rec in enumerate(ratings):
ratings[i]['f0']=movieIDs.searchsorted(rec['f0'])+1
# correspondance entre les ids du fichier movies.dat et les ids utilisés dans l'objet ratings
original_movieIDs=movieIDs
movieIDs=np.unique(ratings['f0'])
movieIDs.sort()
ratingsDataset = MovieLensDataset(movieIDs,userIDs,ratings)
finally:
f.close()
ratings=ratingsDataset.ratings()
# make sure that the ratings a properly shuffled
np.random.shuffle(ratings)
# create train, validation and test sets.
n = int(ratings.shape[0]*0.8)
train = ratings[:n]
