public async Task RunAsync(CancellationToken token)
{
var sourceRows = await _tableStore.GetRowsAsync(_sourceTableName, CancellationToken.None);
var targetRows = new List <DataRow>();
var targetSchema = new RowSchema(
new FieldDefinition("ContactId", FieldKind.Key, FieldDataType.Guid),
new FieldDefinition("MovieId", FieldKind.Key, FieldDataType.String),
new FieldDefinition("Title", FieldKind.Attribute, FieldDataType.String),
new FieldDefinition("Overview", FieldKind.Attribute, FieldDataType.String),
new FieldDefinition("Image", FieldKind.Attribute, FieldDataType.String),
new FieldDefinition("ImdbRating", FieldKind.Attribute, FieldDataType.String)
);
while (await sourceRows.MoveNext())
{
foreach (var row in sourceRows.Current)
{
var movieIds = row["LastMoviesId"].ToString().Split(new[] { "," }, StringSplitOptions.RemoveEmptyEntries);
//grabbing only unique movie ID's
movieIds = movieIds.Distinct().ToArray();
if (!movieIds.Any())
{
continue;
}
var recommendations = _movieRecommendationsProvider.GetRecommendations(movieIds);
//clean recommendations to not have any dups
recommendations = recommendations.GroupBy(m => m.netflixid).Select(g => g.FirstOrDefault()).ToList();
if (recommendations.Any())
{
for (var i = 0; i < _limit; i++)
{
if (i < recommendations.Count)
{
var targetRow = new DataRow(targetSchema);
targetRow.SetGuid(0, row.GetGuid(0));
targetRow.SetString(1, recommendations[i].netflixid);
targetRow.SetString(2, recommendations[i].title);
targetRow.SetString(3, recommendations[i].synopsis);
targetRow.SetString(4, recommendations[i].image);
targetRow.SetString(5, recommendations[i].rating);
targetRows.Add(targetRow);
}
}
}
}
}
var tableDefinition = new TableDefinition(_targetTableName, targetSchema);
var targetTable = new InMemoryTableData(tableDefinition, targetRows);
await _tableStore.PutTableAsync(targetTable, TimeSpan.FromMinutes(30), CancellationToken.None);
}
public async Task RunAsync(CancellationToken token)
{
var sourceRows = await _tableStore.GetRowsAsync(_sourceTableName, CancellationToken.None);
var targetRows = new List <DataRow>();
var targetSchema = new RowSchema(
new FieldDefinition("ContactId", FieldKind.Key, FieldDataType.Guid),
new FieldDefinition("MovieId", FieldKind.Key, FieldDataType.Int64),
new FieldDefinition("Title", FieldKind.Attribute, FieldDataType.String),
new FieldDefinition("Overview", FieldKind.Attribute, FieldDataType.String),
new FieldDefinition("PosterPath", FieldKind.Attribute, FieldDataType.String)
);
//ML context
MLContext mlContext = new MLContext();
ITransformer mlModel = mlContext.Model.Load(GetAbsolutePath(MODEL_FILEPATH), out DataViewSchema inputSchema);
var predEngine = mlContext.Model.CreatePredictionEngine <TacoFlixML.Model.DataModels.ModelInput, ModelOutput>(mlModel);
while (await sourceRows.MoveNext())
{
foreach (var row in sourceRows.Current)
{
var movieId = int.Parse(row["LastMovieId"].ToString());
var recommendations = _movieRecommendationsProvider.GetRecommendations(movieId);
if (recommendations.Any())
{
for (var i = 0; i < _limit; i++)
{
if (i < recommendations.Count)
{
//ML
ModelInput input = new ModelInput();
input.Movieid = recommendations[i].Id;
ModelOutput predictionResult = predEngine.Predict(input);
var targetRow = new DataRow(targetSchema);
targetRow.SetGuid(0, row.GetGuid(0));
targetRow.SetInt64(1, recommendations[i].Id);
targetRow.SetString(2, recommendations[i].Title + "score=" + predictionResult.Score);
targetRow.SetString(3, recommendations[i].Overview);
targetRow.SetString(4, recommendations[i].PosterPath);
targetRows.Add(targetRow);
}
}
}
}
}
var tableDefinition = new TableDefinition(_targetTableName, targetSchema);
var targetTable = new InMemoryTableData(tableDefinition, targetRows);
await _tableStore.PutTableAsync(targetTable, TimeSpan.FromMinutes(30), CancellationToken.None);
}