def learn(maxRating, nUsers, nItems, X, Y, testX):
assert (len(X) == len(Y))
movieMean = centerMean(X, Y, nItems)
trainSize = int(len(X) * trainRatio)
trainX, trainY = X[:trainSize], Y[:trainSize]
crossvalX, crossvalY = X[trainSize:], Y[trainSize:]
learningMethod = methodByName(methodName)
recs = [
learningMethod(maxRating, nUsers, nItems, trainX, trainY, L)
for L in lRange
]
trainErrors = [errors.rmse(recommend(rec, trainX), trainY) for rec in recs]
crossvalErrors = [
errors.rmse(recommend(rec, crossvalX), crossvalY) for rec in recs
]
plotErrors(lRange, trainErrors, crossvalErrors)
bestError, bestL, bestRec = min(zip(crossvalErrors, lRange, recs))
testY = recommend(bestRec, testX)
for i in range(len(testX)):
testY[i] += movieMean[testX[i][1]]
return bestL, testY
def solveOne(setN, setName, ansName):
startTime = time()
maxRating, nUsers, nItems, trainX, trainY, testX, testY = loadData(setName, ansName)
print("Set #{0}: read {1} users, {2} items, {3} train set ratings and {4} test set ratings".format(setN, nUsers,
nItems,
len(trainX),
len(testX)))
bestL, predictedRs = learn(maxRating, nUsers, nItems, trainX, trainY, testX)
print("Set #{0}: RMSE = {1}, best λ = {2}".format(setN, errors.rmse(predictedRs, testY), bestL))
endTime = time()
print("Set #{0}: Leaning done in {1} seconds".format(setN, endTime - startTime))
def learn(maxRating, nUsers, nItems, X, Y, testX):
assert (len(X) == len(Y))
movieMean = centerMean(X, Y, nItems)
trainSize = int(len(X) * trainRatio)
trainX, trainY = X[:trainSize], Y[:trainSize]
crossvalX, crossvalY = X[trainSize:], Y[trainSize:]
learningMethod = methodByName(methodName)
recs = [learningMethod(maxRating, nUsers, nItems, trainX, trainY, L) for L in lRange]
trainErrors = [errors.rmse(recommend(rec, trainX), trainY) for rec in recs]
crossvalErrors = [errors.rmse(recommend(rec, crossvalX), crossvalY) for rec in recs]
plotErrors(lRange, trainErrors, crossvalErrors)
bestError, bestL, bestRec = min(zip(crossvalErrors, lRange, recs))
testY = recommend(bestRec, testX)
for i in range(len(testX)):
testY[i] += movieMean[testX[i][1]]
return bestL, testY
def solveOne(setN, setName, ansName):
startTime = time()
maxRating, nUsers, nItems, trainX, trainY, testX, testY = loadData(
setName, ansName)
print(
"Set #{0}: read {1} users, {2} items, {3} train set ratings and {4} test set ratings"
.format(setN, nUsers, nItems, len(trainX), len(testX)))
bestL, predictedRs = learn(maxRating, nUsers, nItems, trainX, trainY,
testX)
print("Set #{0}: RMSE = {1}, best λ = {2}".format(
setN, errors.rmse(predictedRs, testY), bestL))
endTime = time()
print("Set #{0}: Leaning done in {1} seconds".format(
setN, endTime - startTime))
