public int calculateFinishTime(string userName, float commentLength, float noteLength, float workFollowRowCount)
{
string connectionString = "Data Source=.;Initial Catalog=Trello_Clone;Integrated Security=True";
var loaderColumns = new DatabaseLoader.Column[]
{
new DatabaseLoader.Column()
{
Name = "taskCommentLength", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "taskNoteLength", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "taskWorkFollowRowCount", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "taskFinishTime", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "userName", Type = DbType.String
}
};
var connection = new SqlConnection(connectionString);
var factory = DbProviderFactories.GetFactory(connection);
var context = new MLContext();
var loader = context.Data.CreateDatabaseLoader(loaderColumns);
var dbSource = new DatabaseSource(factory, connectionString,
"select U.userName,F.taskCommentLength,F.taskNoteLength,F.taskWorkFollowRowCount,F.taskFinishTime from userTable U inner join FinishTime F on U.userID=F.userID");
var data = loader.Load(dbSource);
var preview = data.Preview();
var testTrainSplit = context.Data.TrainTestSplit(data, testFraction: 0.9);
var pipeline = context.Transforms.Categorical.OneHotEncoding(outputColumnName: "userNameEncoded", inputColumnName: "userName")
.Append(context.Transforms.Concatenate("Features", "taskCommentLength", "taskNoteLength", "taskWorkFollowRowCount", "userNameEncoded"))
.Append(context.Regression.Trainers.LbfgsPoissonRegression(labelColumnName: "taskFinishTime"));
var model = pipeline.Fit(testTrainSplit.TestSet);
var predictionFunc = context.Model.CreatePredictionEngine <HousingData, HousingPrediction>(model);
var prediction = predictionFunc.Predict(new HousingData
{
userName = userName,
taskCommentLength = commentLength,
taskNoteLength = noteLength,
taskWorkFollowRowCount = workFollowRowCount
});
return(Convert.ToInt32(prediction.taskFinishTime));
}
private void IrisLightGbmImpl(DatabaseSource dbs)
{
var mlContext = new MLContext(seed: 1);
var loaderColumns = new DatabaseLoader.Column[]
{
new DatabaseLoader.Column()
{
Name = "Label", Type = DbType.Int32
},
new DatabaseLoader.Column()
{
Name = "SepalLength", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "SepalWidth", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "PetalLength", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "PetalWidth", Type = DbType.Single
}
};
var loader = mlContext.Data.CreateDatabaseLoader(loaderColumns);
var trainingData = loader.Load(dbs);
IEstimator <ITransformer> pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
.Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.MulticlassClassification.Trainers.LightGbm())
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
var model = pipeline.Fit(trainingData);
var engine = mlContext.Model.CreatePredictionEngine <IrisData, IrisPrediction>(model);
Assert.Equal(0, engine.Predict(new IrisData()
{
SepalLength = 4.5f,
SepalWidth = 5.6f,
PetalLength = 0.5f,
PetalWidth = 0.5f,
}).PredictedLabel);
Assert.Equal(1, engine.Predict(new IrisData()
{
SepalLength = 4.9f,
SepalWidth = 2.4f,
PetalLength = 3.3f,
PetalWidth = 1.0f,
}).PredictedLabel);
}
static void Main(string[] args)
{
var builder = new ConfigurationBuilder()
.SetBasePath(Directory.GetCurrentDirectory())
.AddJsonFile("config.json");
var configuration = builder.Build();
var connectionString = configuration["connectionString"];
var loaderColumns = new DatabaseLoader.Column[]
{
new DatabaseLoader.Column()
{
Name = "YearsOfExperience", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "Salary", Type = DbType.Single
}
};
var connection = new SqlConnection(connectionString);
var factory = DbProviderFactories.GetFactory(connection);
var context = new MLContext();
var loader = context.Data.CreateDatabaseLoader(loaderColumns);
var dbSource = new DatabaseSource(factory, connectionString,
"SELECT YearsOfExperience, Salary FROM salarydb.dbo.SalaryData");
var data = loader.Load(dbSource);
var preview = data.Preview();
var testTrainData = context.Data.TrainTestSplit(data, testFraction: 0.2);
var pipeline = context.Transforms.Concatenate("Features", "YearsOfExperience")
.Append(context.Transforms.CopyColumns("Label", "Salary"))
.Append(context.Regression.Trainers.Sdca());
var model = pipeline.Fit(testTrainData.TrainSet);
var prediction = model.Transform(testTrainData.TestSet);
var metrics = context.Regression.Evaluate(prediction);
}
public void IrisLightGbm()
{
if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
// https://github.com/dotnet/machinelearning/issues/4156
return;
}
var mlContext = new MLContext(seed: 1);
var connectionString = GetConnectionString(TestDatasets.irisDb.name);
var commandText = $@"SELECT * FROM ""{TestDatasets.irisDb.trainFilename}""";
var loaderColumns = new DatabaseLoader.Column[]
{
new DatabaseLoader.Column()
{
Name = "Label", Type = DbType.Int32
},
new DatabaseLoader.Column()
{
Name = "SepalLength", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "SepalWidth", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "PetalLength", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "PetalWidth", Type = DbType.Single
}
};
var loader = mlContext.Data.CreateDatabaseLoader(loaderColumns);
var databaseSource = new DatabaseSource(SqlClientFactory.Instance, connectionString, commandText);
var trainingData = loader.Load(databaseSource);
IEstimator <ITransformer> pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
.Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
.Append(mlContext.MulticlassClassification.Trainers.LightGbm())
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
var model = pipeline.Fit(trainingData);
var engine = mlContext.Model.CreatePredictionEngine <IrisData, IrisPrediction>(model);
Assert.Equal(0, engine.Predict(new IrisData()
{
SepalLength = 4.5f,
SepalWidth = 5.6f,
PetalLength = 0.5f,
PetalWidth = 0.5f,
}).PredictedLabel);
Assert.Equal(1, engine.Predict(new IrisData()
{
SepalLength = 4.9f,
SepalWidth = 2.4f,
PetalLength = 3.3f,
PetalWidth = 1.0f,
}).PredictedLabel);
}
static void Main(string[] args)
{
var builder = new ConfigurationBuilder()
.SetBasePath(Directory.GetCurrentDirectory())
.AddJsonFile("config.json");
var configuration = builder.Build();
var connectionString = configuration["connectionString"];
var loaderColumns = new DatabaseLoader.Column[]
{
new DatabaseLoader.Column()
{
Name = "Longitude", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "Latitude", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "HousingMedianAge", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "TotalRooms", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "TotalBedrooms", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "Population", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "Households", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "MedianIncome", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "MedianHouseValue", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "OceanProximity", Type = DbType.String
}
};
var connection = new SqlConnection(connectionString);
var factory = DbProviderFactories.GetFactory(connection);
var context = new MLContext();
var loader = context.Data.CreateDatabaseLoader(loaderColumns);
var dbSource = new DatabaseSource(factory, connectionString,
"SELECT * FROM salarydb.dbo.Housing");
var data = loader.Load(dbSource);
var preview = data.Preview();
var testTrainSplit = context.Data.TrainTestSplit(data, testFraction: 0.2);
var features = data.Schema
.Select(col => col.Name)
.Where(colName => colName != "MedianHouseValue" && colName != "OceanProximity")
.ToArray();
var pipeline = context.Transforms.Text.FeaturizeText("Text", "OceanProximity")
.Append(context.Transforms.Concatenate("Features", features))
.Append(context.Transforms.Concatenate("Features", "Text"))
.Append(context.Regression.Trainers.LbfgsPoissonRegression(featureColumnName: "Features", labelColumnName: "MedianHouseValue"));
var model = pipeline.Fit(testTrainSplit.TestSet);
var predictionFunc = context.Model.CreatePredictionEngine <HousingData, HousingPrediction>(model);
var prediction = predictionFunc.Predict(new HousingData
{
Longitude = -122.25f,
Latitude = 37.85f,
HousingMedianAge = 55.0f,
TotalRooms = 1627.0f,
TotalBedrooms = 235.0f,
Population = 322.0f,
Households = 120.0f,
MedianIncome = 8.3014f,
OceanProximity = "NEAR BAY"
});
Console.WriteLine($"Prediction - {prediction.PredictedHouseValue}");
Console.ReadLine();
}
public void IrisSdcaMaximumEntropy()
{
var mlContext = new MLContext(seed: 1);
var connectionString = GetDataPath(TestDatasets.iris.trainFilename);
var commandText = "Label;SepalLength;SepalWidth;PetalLength;PetalWidth";
var loaderColumns = new DatabaseLoader.Column[]
{
new DatabaseLoader.Column()
{
Name = "Label", Type = DbType.Int32
},
new DatabaseLoader.Column()
{
Name = "SepalLength", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "SepalWidth", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "PetalLength", Type = DbType.Single
},
new DatabaseLoader.Column()
{
Name = "PetalWidth", Type = DbType.Single
}
};
var loader = mlContext.Data.CreateDatabaseLoader(loaderColumns);
var mockProviderFactory = new MockProviderFactory(mlContext, loaderColumns);
var databaseSource = new DatabaseSource(mockProviderFactory, connectionString, commandText);
var trainingData = loader.Load(databaseSource);
var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
.Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
.Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy())
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
var model = pipeline.Fit(trainingData);
var engine = mlContext.Model.CreatePredictionEngine <IrisData, IrisPrediction>(model);
Assert.Equal(0, engine.Predict(new IrisData()
{
SepalLength = 4.5f,
SepalWidth = 5.6f,
PetalLength = 0.5f,
PetalWidth = 0.5f,
}).PredictedLabel);
Assert.Equal(1, engine.Predict(new IrisData()
{
SepalLength = 4.9f,
SepalWidth = 2.4f,
PetalLength = 3.3f,
PetalWidth = 1.0f,
}).PredictedLabel);
}
