def __init__(self):
self._model= RSVD.load("objects/")
self._original_movieIDs=np.load("objects/original_movieIDs")
self._ratings= np.load("objects/ratings")
self._moviesInformations = functions.loadMoviesInformations('data_movilens1m/movies.dat')
self._usersInformations = functions.loadUsersInformations('data_movilens1m/users.dat')
self._dissim = functions.getSimilaritiesFromModel(self._model.u)
self._movieMean=np.load("objects/movieMean")
self._userMean=np.load("objects/userMean")
# PCA pour les films les plus notes
self._mostRated = functions.getNMostRatedMovies(self._ratings,2000)
j = 0
self._subModel=np.empty((len(self._mostRated),self._model.u.shape[1])) #subRatings doit etre du meme type que model
self._subMovieMean = np.empty((len(self._mostRated),2))
for i,row in enumerate(self._model.u):
if i in self._mostRated:
self._subMovieMean[j] = self._movieMean[i]
self._subModel[j]=row
j= j +1
# PCA pour les films les plus notes
self._mostRating = functions.getNMostRatingUsers(self._ratings)
j = 0
self._subModel2=np.empty((len(self._mostRating),self._model.v.shape[1])) #subRatings doit etre du meme type que model
self._subMovieMean2 = np.empty((len(self._mostRating),2))
for i,row in enumerate(self._model.v):
if i in self._mostRating:
self._subMovieMean2[j] = self._userMean[i]
self._subModel2[j]=row
j= j +1
def makeModel(itemIdMap, userIdMap, ratings, factors=20, learnRate=0.001, regularization=0.011):
"""
Makes a RSVD model from ratings
"""
ratings = np.array(ratings, rating_t)
np.random.shuffle(ratings)
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]
# Increasing training data
v = int(ratings.shape[0] * 0.9)
val = ratings[v:]
train = ratings[:v]
dims = (len(itemIdMap), len(userIdMap))
model = RSVD.train(factors, train, dims, probeArray=val,
learnRate=learnRate, regularization=regularization,
maxEpochs=1000)
sqerr=0.0
for itemID,userID,rating in test:
err = rating - model(itemID,userID)
sqerr += err * err
sqerr /= test.shape[0]
print "Test RMSE: ", np.sqrt(sqerr)
return model
def findFactorsAndErrors(ratingsDataset):
ratings=ratingsDataset.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])
factors = []
errors = []
# lambda_f ne doit pas depasser 1
# default values
#probeArray=None
#maxEpochs=100
#minImprovement=0.000001
#learnRate=0.001
#regularization=0.011
#randomize=False
#randomNoise=0.005
for factor in range(1, 100):
model = RSVD.train(factor, train, dims, probeArray=val, maxEpochs = 1000, regularization=0.011)
sqerr=0.0
for movieID,userID,rating in test:
err = rating - model(movieID,userID)
sqerr += err * err
sqerr /= test.shape[0]
factors.append(factor)
errors.append(np.sqrt(sqerr))
# returns a dict, do result['best_factor'] to get the corresponding value
return {'factors':factors, 'errors':errors}
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:
err = rating - model(movieID, userID)
sqerr += err * err
sqerr /= test.shape[0]
print "Test RMSE: ", np.sqrt(sqerr)
import IPython
IPython.embed()
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:
err = rating - model(movieID, userID)
sqerr += err * err
sqerr /= test.shape[0]
print "Test RMSE: ", np.sqrt(sqerr)
import IPython
IPython.embed()
"""
Test
----
This is a test
"""
#log# Automatic Logger file. *** THIS MUST BE THE FIRST LINE ***
#log# DO NOT CHANGE THIS LINE OR THE TWO BELOW
#log# opts = Struct({'__allownew': True, 'logfile': 'ipython_log.py'})
#log# args = []
#log# It is safe to make manual edits below here.
#log#-----------------------------------------------------------------------
import numpy as np
from rsvd import RSVD
print 'load ratings'
ratings = np.load('data/ratings_float.arr')
probeRatings = np.load('data/probe_ratings_float.arr')
model = RSVD.train(20,
ratings, (17770, 480189),
probeRatings,
100,
randomize=False)
print "model trained..."
model.save("models/t_20_001_011_100")
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): model = RSVD.train(factor, train, dims, probeArray=val, maxEpochs = 1000, regularization=lambda_f) sqerr=0.0 for movieID,userID,rating in test: err = rating - model(movieID,userID) sqerr += err * err sqerr /= test.shape[0] print "-------------------------------------------------" print "Pour lambda = ",lambda_f, " Test RMSE: ", np.sqrt(sqerr) print "-------------------------------------------------" lambdas.append(lambda_f) errors.append(np.sqrt(sqerr)) # print the lamdas and errors vectors
print "ok"
with open(ARTISTS_LUT_PATH, 'w') as outfile:
json.dump(ARTISTS_LUT, outfile)
with open(INV_ARTISTS_LUT_PATH, 'w') as outfile:
json.dump(INV_ARTISTS_LUT, outfile)
else:
with open(ARTISTS_LUT_PATH,'r') as jsonStream:
ARTISTS_LUT = json.load(jsonStream)
with open(INV_ARTISTS_LUT_PATH,'r') as jsonStream:
INV_ARTISTS_LUT = json.load(jsonStream)
f_corpus = formatData(CORPUS,LEXICON,PLAYLISTS_LUT,ARTISTS_LUT)
CORPUS.clear() #no need to keep corpus in memory
print "ok"
print "\nNumber of playlists:",len(PLAYLISTS_LUT),"\nNumber of artists:",len(ARTISTS_LUT)
### Split the dataset
print "\nGenerating the training/validation and test set ...",
sys.stdout.flush()
trainSet,validSet,testSet = splitDataset(f_corpus)
print "ok"
### train our MF model
"""prototype: train(factors,ratingsArray,dims,probeArray=None,\
maxEpochs=100,minImprovement=0.000001,\
learnRate=0.001,regularization=0.011,\
randomize=False, randomNoise=0.005)"""
model = RSVD.train(25,trainSet,(len(ARTISTS_LUT),len(PLAYLISTS_LUT)),validSet,learnRate=0.001,regularization=0.00000,minImprovement=0.0000001,maxEpochs=700)
model.save("./")
f_testing = open(TESTING_FILENAME, 'r')
f_out = open(OUTPUT_FILENAME + fn, 'w')
print "Making %d predictions..." % NUM_TESTING
start_time = time.time()
i = 0
j = 0
for line in f_testing:
user, movie, date = line.strip().split()
f_out.write(str(model(int(user), int(movie))) + '\n')
i += 1
if i % (NUM_TESTING / INCR) == 0:
j += 100.0 / INCR
sys.stdout.write("\r%.1f%% done (elapsed time: %f s)." %
(j, time.time() - start_time))
sys.stdout.flush()
f_testing.close()
print "Predictions complete (elapsed time: %f s)." % (time.time() -
start_time)
f_out.close()
if __name__ == '__main__':
print "Importing ratings..."
ratings = np.fromfile(TRAINING_FILENAME, dtype=rating_t)
print "Importing probes..."
probeRatings = np.fromfile(PROBE_FILENAME, dtype=rating_t)
print "running svd..."
model = RSVD.train(10, ratings, (17770, 458292), probeRatings)
print "predicting..."
predict(model)
def predict(model, fn=""):
f_testing = open(TESTING_FILENAME, "r")
f_out = open(OUTPUT_FILENAME + fn, "w")
print "Making %d predictions..." % NUM_TESTING
start_time = time.time()
i = 0
j = 0
for line in f_testing:
user, movie, date = line.strip().split()
f_out.write(str(model(int(user), int(movie))) + "\n")
i += 1
if i % (NUM_TESTING / INCR) == 0:
j += 100.0 / INCR
sys.stdout.write("\r%.1f%% done (elapsed time: %f s)." % (j, time.time() - start_time))
sys.stdout.flush()
f_testing.close()
print "Predictions complete (elapsed time: %f s)." % (time.time() - start_time)
f_out.close()
if __name__ == "__main__":
print "Importing ratings..."
ratings = np.fromfile(TRAINING_FILENAME, dtype=rating_t)
print "Importing probes..."
probeRatings = np.fromfile(PROBE_FILENAME, dtype=rating_t)
print "running svd..."
model = RSVD.train(10, ratings, (17770, 458292), probeRatings)
print "predicting..."
predict(model)