/// <summary>
/// Ordinal Matrix Factorization.
/// </summary>
/// <param name="R_train">The matrix contains training ratings</param>
/// <param name="R_unknown">The matrix contains ones indicating unknown ratings</param>
/// <param name="R_scorer">This matrix contains ratings predicted by the scorer on
/// both the R_train and R_unknown sets</param>
/// <returns>The predicted ratings on R_unknown</returns>
#region PredictRatings
public static string PredictRatings(SparseMatrix R_train, SparseMatrix R_unknown,
SparseMatrix R_scorer, List <double> quantizer, out DataMatrix R_predicted,
out Dictionary <Tuple <int, int>, List <double> > OMFDistributionByUserItem)
{
StringBuilder log = new StringBuilder();
/************************************************************
* Parameterization and Initialization
************************************************************/
#region Parameterization and Initialization
// This matrix stores predictions
SparseMatrix R_predicted_out = (SparseMatrix)Matrix.Build.Sparse(R_unknown.RowCount, R_unknown.ColumnCount);
Dictionary <Tuple <int, int>, List <double> > OMFDistributionByUserItem_out =
new Dictionary <Tuple <int, int>, List <double> >(R_unknown.NonZerosCount);
// User specified parameters
double maxEpoch = Config.OMF.MaxEpoch;
double learnRate = Config.OMF.LearnRate;
double regularization = Config.OMF.Regularization;
int intervalCount = quantizer.Count;
int userCount = R_train.RowCount;
// Parameters for each user
Dictionary <int, ParametersOfUser> paramtersByUser = new Dictionary <int, ParametersOfUser>(R_train.RowCount);
// Compute initial values of t1 and betas
// that will be used for all users, Eq. 5
double t1_initial = (double)(quantizer[0] + quantizer[1]) / 2;
Vector <double> betas_initial = Vector.Build.Dense(quantizer.Count - 2);
for (int i = 1; i <= betas_initial.Count; i++)
{
double t_r = t1_initial;
double t_r_plus_1 = (quantizer[i] + quantizer[i + 1]) * 0.5f;
betas_initial[i - 1] = Math.Log(t_r_plus_1 - t_r); // natural base
t_r = t_r_plus_1;
}
// Initialize parameters (t1, betas) for each user
for (int indexOfUser = 0; indexOfUser < R_train.RowCount; indexOfUser++)
{
paramtersByUser[indexOfUser] = new ParametersOfUser(t1_initial, betas_initial);
}
#endregion
/************************************************************
* Learn parameters from training data R_train and R_score
************************************************************/
#region Learn parameters from training data R_train and R_score
// Learn parameters for each user, note that each user has his own model
Object lockMe = new Object();
Parallel.ForEach(R_train.EnumerateRowsIndexed(), row =>
{
int indexOfUser = row.Item1;
SparseVector ratingsOfUser = (SparseVector)row.Item2;
// Store this user's ratings from R_train and correpsonding ratings from scorer
List <double> ratingsFromScorer = new List <double>(ratingsOfUser.NonZerosCount);
List <double> ratingsFromRTrain = new List <double>(ratingsOfUser.NonZerosCount);
foreach (var element in ratingsOfUser.EnumerateIndexed(Zeros.AllowSkip))
{
int indexOfItem = element.Item1;
double rating = element.Item2;
// Ratings need to be added in the same order
ratingsFromScorer.Add(R_scorer[indexOfUser, indexOfItem]);
ratingsFromRTrain.Add(rating);
}
Debug.Assert(ratingsFromScorer.Count == ratingsOfUser.NonZerosCount);
Debug.Assert(ratingsFromRTrain.Count == ratingsOfUser.NonZerosCount);
// Parameters for the current user are estimated by
// maximizing the log likelihood (Eq. 21) using stochastic gradient ascent
// Eq. 22
double t1 = paramtersByUser[indexOfUser].t1;
Vector <double> betas = paramtersByUser[indexOfUser].betas;
for (int epoch = 0; epoch < maxEpoch; epoch++)
{
for (int i = 0; i < ratingsFromRTrain.Count; i++)
{
double ratingFromRTrain = ratingsFromRTrain[i];
double ratingFromScorer = ratingsFromScorer[i];
int r = quantizer.IndexOf(ratingFromRTrain); // r is the interval that the rating falls into
double probLE_r = ComputeProbLE(ratingFromScorer, r, t1, betas); // Eq. 9
double probLE_r_minus_1 = ComputeProbLE(ratingFromScorer, r - 1, t1, betas);
double probE_r = probLE_r - probLE_r_minus_1; // Eq. 10
// Compute derivatives/gradients
double derivativeOft1 = learnRate / probE_r * (probLE_r * (1 - probLE_r) * DerivativeOfBeta(r, 0, t1)
- probLE_r_minus_1 * (1 - probLE_r_minus_1) * DerivativeOfBeta(r - 1, 0, t1)
- Config.OMF.Regularization * t1);
Vector <double> derivativesOfbetas = Vector.Build.Dense(betas.Count);
for (int k = 0; k < betas.Count; k++)
{
derivativesOfbetas[k] = learnRate / probE_r * (probLE_r * (1 - probLE_r) *
DerivativeOfBeta(r, k + 1, betas[k]) - probLE_r_minus_1 * (1 - probLE_r_minus_1) *
DerivativeOfBeta(r - 1, k + 1, betas[k]) - regularization * betas[k]);
}
// Update parameters
t1 += derivativeOft1;
betas += derivativesOfbetas;
}
}
// Store the leanred paramemters
lock (lockMe)
{
paramtersByUser[indexOfUser].t1 = t1;
paramtersByUser[indexOfUser].betas = betas;
}
log.AppendLine(Utils.PrintEpoch("user/total", indexOfUser, userCount, "Learned params",
String.Format("t1={0:0.000},betas={1}", t1, string.Concat(
betas.Select(i => string.Format("{0:0.00},", i))))));
});
#endregion
/************************************************************
* Make predictions using learned parameters
************************************************************/
#region Make predictions using learned parameters
lockMe = new Object();
Parallel.ForEach(R_unknown.EnumerateIndexed(Zeros.AllowSkip), element =>
{
int indexOfUser = element.Item1;
int indexOfItem = element.Item2;
// This is the ordinal distribution of the current user
// given the internal score by MF
// e.g. what is the probability of each rating 1-5
List <double> probabilitiesByInterval = new List <double>(quantizer.Count);
double scoreFromScorer = R_scorer[indexOfUser, indexOfItem];
double pre = ComputeProbLE(scoreFromScorer, 0, paramtersByUser[indexOfUser].t1, paramtersByUser[indexOfUser].betas);
probabilitiesByInterval.Add(pre);
for (int i = 1; i < intervalCount; i++)
{
double pro = ComputeProbLE(scoreFromScorer, i, paramtersByUser[indexOfUser].t1, paramtersByUser[indexOfUser].betas);
probabilitiesByInterval.Add(pro - pre);
pre = pro;
}
// Compute smoothed expectation for RMSE metric
double expectationRating = 0.0;
for (int i = 0; i < probabilitiesByInterval.Count; i++)
{
expectationRating += (i + 1) * probabilitiesByInterval[i];
}
// TODO: Compute most likely value for MAE metric
lock (lockMe)
{
// Keep OMF Distribution
OMFDistributionByUserItem_out[new Tuple <int, int>(indexOfUser, indexOfItem)] = probabilitiesByInterval;
// Keep the numerical prediction
R_predicted_out[indexOfUser, indexOfItem] = expectationRating;
}
});
#endregion
/************************************************************
* Generate OMF distributions for R_train as well
************************************************************/
#region Generate OMF distributions for R_train as well
lockMe = new Object();
Parallel.ForEach(R_train.EnumerateIndexed(Zeros.AllowSkip), element =>
{
int indexOfUser = element.Item1;
int indexOfItem = element.Item2;;
// This is the ordinal distribution of the current user
// given the internal score by MF
// e.g. what is the probability of each rating 1-5
List <double> probabilitiesByInterval = new List <double>(quantizer.Count);
double scoreFromScorer = R_scorer[indexOfUser, indexOfItem];
double pre = ComputeProbLE(scoreFromScorer, 0, paramtersByUser[indexOfUser].t1, paramtersByUser[indexOfUser].betas);
probabilitiesByInterval.Add(pre);
for (int i = 1; i < intervalCount; i++)
{
double pro = ComputeProbLE(scoreFromScorer, i, paramtersByUser[indexOfUser].t1, paramtersByUser[indexOfUser].betas);
probabilitiesByInterval.Add(pro - pre);
pre = pro;
}
lock (lockMe)
{
// Keep OMF Distribution
OMFDistributionByUserItem_out[new Tuple <int, int>(indexOfUser, indexOfItem)] = probabilitiesByInterval;
}
});
#endregion
R_predicted = new DataMatrix(R_predicted_out);
OMFDistributionByUserItem = OMFDistributionByUserItem_out;
return(log.ToString());
}
/// <summary>
/// Ordinal Matrix Factorization.
/// </summary>
/// <param name="R_train">The matrix contains training ratings</param>
/// <param name="R_unknown">The matrix contains ones indicating unknown ratings</param>
/// <param name="R_scorer">This matrix contains ratings predicted by the scorer on
/// both the R_train and R_unknown sets</param>
/// <returns>The predicted ratings on R_unknown</returns>
#region PredictRatings
public static string PredictRatings(SparseMatrix R_train, SparseMatrix R_unknown,
SparseMatrix R_scorer, List<double> quantizer, out DataMatrix R_predicted,
out Dictionary<Tuple<int, int>, List<double>> OMFDistributionByUserItem)
{
StringBuilder log = new StringBuilder();
/************************************************************
* Parameterization and Initialization
************************************************************/
#region Parameterization and Initialization
// This matrix stores predictions
SparseMatrix R_predicted_out = (SparseMatrix)Matrix.Build.Sparse(R_unknown.RowCount, R_unknown.ColumnCount);
Dictionary<Tuple<int, int>, List<double>> OMFDistributionByUserItem_out =
new Dictionary<Tuple<int, int>, List<double>>(R_unknown.NonZerosCount);
// User specified parameters
double maxEpoch = Config.OMF.MaxEpoch;
double learnRate = Config.OMF.LearnRate;
double regularization = Config.OMF.Regularization;
int intervalCount = quantizer.Count;
int userCount = R_train.RowCount;
// Parameters for each user
Dictionary<int, ParametersOfUser> paramtersByUser = new Dictionary<int, ParametersOfUser>(R_train.RowCount);
// Compute initial values of t1 and betas
// that will be used for all users, Eq. 5
double t1_initial = (double)(quantizer[0] + quantizer[1]) / 2;
Vector<double> betas_initial = Vector.Build.Dense(quantizer.Count - 2);
for (int i = 1; i <= betas_initial.Count; i++)
{
double t_r = t1_initial;
double t_r_plus_1 = (quantizer[i] + quantizer[i + 1]) * 0.5f;
betas_initial[i - 1] = Math.Log(t_r_plus_1 - t_r); // natural base
t_r = t_r_plus_1;
}
// Initialize parameters (t1, betas) for each user
for (int indexOfUser = 0; indexOfUser < R_train.RowCount; indexOfUser++)
{
paramtersByUser[indexOfUser] = new ParametersOfUser(t1_initial, betas_initial);
}
#endregion
/************************************************************
* Learn parameters from training data R_train and R_score
************************************************************/
#region Learn parameters from training data R_train and R_score
// Learn parameters for each user, note that each user has his own model
Object lockMe = new Object();
Parallel.ForEach(R_train.EnumerateRowsIndexed(), row =>
{
int indexOfUser = row.Item1;
SparseVector ratingsOfUser = (SparseVector)row.Item2;
// Store this user's ratings from R_train and correpsonding ratings from scorer
List<double> ratingsFromScorer = new List<double>(ratingsOfUser.NonZerosCount);
List<double> ratingsFromRTrain = new List<double>(ratingsOfUser.NonZerosCount);
foreach (var element in ratingsOfUser.EnumerateIndexed(Zeros.AllowSkip))
{
int indexOfItem = element.Item1;
double rating = element.Item2;
// Ratings need to be added in the same order
ratingsFromScorer.Add(R_scorer[indexOfUser, indexOfItem]);
ratingsFromRTrain.Add(rating);
}
Debug.Assert(ratingsFromScorer.Count == ratingsOfUser.NonZerosCount);
Debug.Assert(ratingsFromRTrain.Count == ratingsOfUser.NonZerosCount);
// Parameters for the current user are estimated by
// maximizing the log likelihood (Eq. 21) using stochastic gradient ascent
// Eq. 22
double t1 = paramtersByUser[indexOfUser].t1;
Vector<double> betas = paramtersByUser[indexOfUser].betas;
for (int epoch = 0; epoch < maxEpoch; epoch++)
{
for (int i = 0; i < ratingsFromRTrain.Count; i++)
{
double ratingFromRTrain = ratingsFromRTrain[i];
double ratingFromScorer = ratingsFromScorer[i];
int r = quantizer.IndexOf(ratingFromRTrain); // r is the interval that the rating falls into
double probLE_r = ComputeProbLE(ratingFromScorer, r, t1, betas); // Eq. 9
double probLE_r_minus_1 = ComputeProbLE(ratingFromScorer, r - 1, t1, betas);
double probE_r = probLE_r - probLE_r_minus_1; // Eq. 10
// Compute derivatives/gradients
double derivativeOft1 = learnRate / probE_r * (probLE_r * (1 - probLE_r) * DerivativeOfBeta(r, 0, t1)
- probLE_r_minus_1 * (1 - probLE_r_minus_1) * DerivativeOfBeta(r - 1, 0, t1)
- Config.OMF.Regularization * t1);
Vector<double> derivativesOfbetas = Vector.Build.Dense(betas.Count);
for (int k = 0; k < betas.Count; k++)
{
derivativesOfbetas[k] = learnRate / probE_r * (probLE_r * (1 - probLE_r) *
DerivativeOfBeta(r, k + 1, betas[k]) - probLE_r_minus_1 * (1 - probLE_r_minus_1) *
DerivativeOfBeta(r - 1, k + 1, betas[k]) - regularization * betas[k]);
}
// Update parameters
t1 += derivativeOft1;
betas += derivativesOfbetas;
}
}
// Store the leanred paramemters
lock (lockMe)
{
paramtersByUser[indexOfUser].t1 = t1;
paramtersByUser[indexOfUser].betas = betas;
}
log.AppendLine(Utils.PrintEpoch("user/total", indexOfUser, userCount, "Learned params",
String.Format("t1={0:0.000},betas={1}", t1, string.Concat(
betas.Select(i => string.Format("{0:0.00},", i))))));
});
#endregion
/************************************************************
* Make predictions using learned parameters
************************************************************/
#region Make predictions using learned parameters
lockMe = new Object();
Parallel.ForEach(R_unknown.EnumerateIndexed(Zeros.AllowSkip), element =>
{
int indexOfUser = element.Item1;
int indexOfItem = element.Item2;
// This is the ordinal distribution of the current user
// given the internal score by MF
// e.g. what is the probability of each rating 1-5
List<double> probabilitiesByInterval = new List<double>(quantizer.Count);
double scoreFromScorer = R_scorer[indexOfUser, indexOfItem];
double pre = ComputeProbLE(scoreFromScorer, 0, paramtersByUser[indexOfUser].t1, paramtersByUser[indexOfUser].betas);
probabilitiesByInterval.Add( pre);
for (int i = 1; i < intervalCount; i++)
{
double pro = ComputeProbLE(scoreFromScorer, i, paramtersByUser[indexOfUser].t1, paramtersByUser[indexOfUser].betas);
probabilitiesByInterval.Add( pro - pre);
pre = pro;
}
// Compute smoothed expectation for RMSE metric
double expectationRating = 0.0;
for (int i = 0; i < probabilitiesByInterval.Count; i++)
{
expectationRating += (i + 1) * probabilitiesByInterval[i];
}
// TODO: Compute most likely value for MAE metric
lock (lockMe)
{
// Keep OMF Distribution
OMFDistributionByUserItem_out[new Tuple<int, int>(indexOfUser, indexOfItem)] = probabilitiesByInterval;
// Keep the numerical prediction
R_predicted_out[indexOfUser, indexOfItem] = expectationRating;
}
});
#endregion
/************************************************************
* Generate OMF distributions for R_train as well
************************************************************/
#region Generate OMF distributions for R_train as well
lockMe = new Object();
Parallel.ForEach(R_train.EnumerateIndexed(Zeros.AllowSkip), element =>
{
int indexOfUser = element.Item1;
int indexOfItem = element.Item2; ;
// This is the ordinal distribution of the current user
// given the internal score by MF
// e.g. what is the probability of each rating 1-5
List<double> probabilitiesByInterval = new List<double>(quantizer.Count);
double scoreFromScorer = R_scorer[indexOfUser, indexOfItem];
double pre = ComputeProbLE(scoreFromScorer, 0, paramtersByUser[indexOfUser].t1, paramtersByUser[indexOfUser].betas);
probabilitiesByInterval.Add(pre);
for (int i = 1; i < intervalCount; i++)
{
double pro = ComputeProbLE(scoreFromScorer, i, paramtersByUser[indexOfUser].t1, paramtersByUser[indexOfUser].betas);
probabilitiesByInterval.Add(pro - pre);
pre = pro;
}
lock (lockMe)
{
// Keep OMF Distribution
OMFDistributionByUserItem_out[new Tuple<int, int>(indexOfUser, indexOfItem)] = probabilitiesByInterval;
}
});
#endregion
R_predicted = new DataMatrix(R_predicted_out);
OMFDistributionByUserItem = OMFDistributionByUserItem_out;
return log.ToString();
}
