Daany Library version 0.7.0 – brings new set of high performance routines for Linear Algebra

The last years the Daany library got lot of attention from the community since it contains the implementation which is crucial for Machine Learning and Data transformation on .NET. There are plenty of great .NET libraries on GitHub but the Daany contains very specific set of components which make it special.

Today I am happy to announce the new package within the Daany library called Daany.LinA – the package for linear algebra. The package is .NET wrapper about MKL LAPACK and BLASS routines. With combination of DataFrame, Daany.Stat and Daany.LinA you can build high performance code for data analytics. For more inforamtion please visit http://github.com/bhrnjica/daany and check Developer Guide, and unit tests.


MagmaSharp – .NET High Level API for MAGMA


Few weeks ago, I was doing research and I needed a fast program for Singular Value Decomposition. I have SVD implementation in my open source project called Daany which is using the SVD implementation of Accord.NET great Machine Learning Framework. However, the decomposition is working fine and smooth for small matrices with few hundreds rows/cols but for matrices with more than 500 rows and columns it is pretty slow. So I was forced to think about of using different library in order to speed up the SVD calculation. I could use some of python libraries eg. TensorFlow, PyTorch or SciPy or similar libraries from R and c++. I have used such libraries and I know how they are fast. But I still wanted to have approximately same speed on .NET as well.

Then I decided to look how can I use some of available c++ based libraries. Once I switch to c++ based project I would not be able to use .NET framework where other parts of my research are implemented. So only solution was to implement a wrapper around a c++ library and use pInvoke in order to expose required methods in C# code.

The first idea was to use LAPACK/BLAS numerical library to calculate not only SVD but whole set of Linear Algebra routines. LAPACK/BLAS libraries have long history back to 70s of the 20th century. They are proved to be very fast and reliable. However they are not supported for GPU.

Then I came to MAGMA which is nothing but LAPACK for GPU. MAGMA is very complex and fast library which requires CUDA. However if the machine has no CUDA, the library cannot be used.

The I decided to use hybrid approach and use MAGMA whenever the machine has CUDA, otherwise use LAPACK as computation engine. This approach is the most complex and required advance skills in C++ and C#. So after a more than a month of the implementation the MagmaSharp is published as GitHub open source project with the fist public release MagmaSharp 0.02.01 at Nuget.org.

MagmaSharp v0.02.01

The first release of MagmaSharp supports MAGMA Device routines: Currently the library supports MAGMA driver routines for general rectangular matrix:

  1. gesv – solve linear system, AX = B, A is general non-symetric matrix,
  2. gels – least square solve, AX = B, A is rectangular,
  3. geev – eigen value solver for non-symetric matrix, AX = X \lambda
  4. gesvd– singular value decomposition (SVD), A = U \sigma V^T .

The library supports float and double value types.

Software requirements

The project is build on .NET Core 3.1 and .NET Standard 2.1. It is built and tested on Windows 10 1909 only.

Software (Native Libraries) requirements

In order to compile, build and use the library the following native libraries are needed to be installed.

However, if you install the MagmaSharp as Nuget package, both libraries are included, so you don’t have to install it.

How to use MagmaSharp

MagmaSharp is packed as Nuget and can be added to your .NET project as ordinary .NET component. You don’t have to worry about native libraries and dependencies. Everything is included in the package. The package can be installed from this link, or just search for MagmaSharp.

How to Build MagmaSharp from the source

  1. Download the MagmaSharp source code from the GitHub page.

  2. Reference Magma static library and put it to folder MagmaLib. Magma static library can be downloaded and built from the Official site.

  3. Open ‘MagmaSharp.sln’ with Visual Studio 2019.

  4. Make sure the building architecture is x64.

  5. Restore Nuget packages.

  6. Build and run the Solution.

How to start with MagmaSharp

The best way to start with MahmaSharp is to take a look at the MagmaSharp.XUnit project, there is a small example how to use each of the implemented method with or without CUDA device.

Building Predictive Maintenance Model Using ML.NET


This C# notebook is a continuation from the previous blog post Predictive Maintenance on .NET Platform.

The notebook is completely implemented on .NET platform using C# Jupyter Notebook and Daany – C# data analytics library. There are small differences between this notebook and the notebooks at the official azure gallery portal, but in most cases, the code follows the steps defined there.

The notebook shows how to use .NET Jupyter Notebook with Daany.DataFrame and ML.NET in order to prepare the data and build the Predictive Maintenance Model on .NET platform.


In the previous post, we analyzed 5 data sets with information about telemetry, data, errors and maintenance as well as failure for 100 machines. The data were transformed and analyzed in order to create the final data set for building a machine learning model for Predictive maintenance.

Once we created all features from the data sets, as a final step we created the label column so that it describes if a certain machine will fail in the next 24 hours due to failure a component1, component2, component3, component4 or it will continue to work. . In this part, we are going to perform a part of the machine learning task and start training a machine learning model for predicting if a certain machine will fail in the next 24 hours due to failure, or it will be in functioning normal in that time period.

The model which we are going to build is multi-class classification model sice it has 5 values to predict:

  • component1,
  • component2,
  • component3,
  • component4 or
  • none – means it will continue to work.

ML.NET framework as library for training

In order to train the model, we are going to use ML.NET – Microsoft open source framework for Machine Learning on .NET Platform. First we need to put some preparation codes like:

  • Required Nuget packages,
  • Set of using statements and code for formatting the output:

At the beggining of this notebook, we installed the several NugetPackages in order to complete this notebook. The following code shows using statements, and method for formatting the data from the DataFrame.

//using Microsoft.ML.Data;
using XPlot.Plotly;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;

using Microsoft.ML;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;
using Microsoft.ML.Trainers.LightGbm;
using Daany;
using Daany.Ext;
//DataFrame formatter
using Microsoft.AspNetCore.Html;
Formatter.Register((df, writer) =>
    var headers = new List();
    headers.AddRange(df.Columns.Select(c => (IHtmlContent) th(c)));
    //renders the rows
    var rows = new List<List>();
    var take = 20;
    for (var i = 0; i < Math.Min(take, df.RowCount()); i++)
        var cells = new List();
        foreach (var obj in df[i]){
    var t = table(
                r => tr(r)))); 
}, "text/html");

Once we install the Nuget packages and define using statements we are going to define a class we need to create an ML.NET pipeline.

The class PrMaintenanceClass – contains the features (properties) we build in the previous post. We need them to define features in the ML.NET pipeline. The second class we defined is PrMaintenancePrediction we used for prediction and model evaluation.

class PrMaintenancePrediction
    public string failure { get; set; }
class PrMaintenanceClass
    public DateTime datetime { get; set; }
    public int machineID { get; set; }
    public float voltmean_3hrs { get; set; }
    public float rotatemean_3hrs { get; set; }
    public float pressuremean_3hrs { get; set; }
    public float vibrationmean_3hrs { get; set; }
    public float voltstd_3hrs { get; set; }
    public float rotatestd_3hrs { get; set; }
    public float pressurestd_3hrs { get; set; }
    public float vibrationstd_3hrs { get; set; }
    public float voltmean_24hrs { get; set; }
    public float rotatemean_24hrs { get; set; }
    public float pressuremean_24hrs { get; set; }
    public float vibrationmean_24hrs { get; set; }
    public float voltstd_24hrs { get; set; }
    public float rotatestd_24hrs { get; set; }
    public float pressurestd_24hrs { get; set; }
    public float vibrationstd_24hrs { get; set; }
    public float error1count { get; set; }
    public float error2count { get; set; }
    public float error3count { get; set; }
    public float error4count { get; set; }
    public float error5count { get; set; }
    public float sincelastcomp1 { get; set; }
    public float sincelastcomp2 { get; set; }
    public float sincelastcomp3 { get; set; }
    public float sincelastcomp4 { get; set; }
    public string model { get; set; }
    public float age { get; set; }
    public string failure { get; set; }

Now that we have defined a class type, we are going to implement the pipeline for this ml model.First, we create MLContext with constant seed, so that the model can be reproduced by any user running this notebook. Then we load the data and split the data into train and test set.

MLContext mlContext= new MLContext(seed:88888);
var strPath="data/final_dataFrame.csv";
var mlDF= DataFrame.FromCsv(strPath);
//split data frame on training and testing part
//split at 2015-08-01 00:00:00, to train on the first 8 months and test on last 4 months
var trainDF = mlDF.Filter("datetime", new DateTime(2015, 08, 1, 1, 0, 0), FilterOperator.LessOrEqual);
var testDF = mlDF.Filter("datetime", new DateTime(2015, 08, 1, 1, 0, 0), FilterOperator.Greather);

The summary for the training set is show in the following tables:

Similarly the testing set has the following summary:

Once we have data into application memory, we can prepare the ML.NET pipeline. The pipeline consists of data transformation from the Daany.DataFrame type into collection IDataView. For this task, the LoadFromEnumerable method is used.

//Load daany:DataFrame into ML.NET pipeline
public static IDataView loadFromDataFrame(MLContext mlContext,Daany.DataFrame df)
    IDataView dataView = mlContext.Data.LoadFromEnumerable(df.GetEnumerator(oRow =>
        //convert row object array into PrManitenance row
        var ooRow = oRow;
        var prRow = new PrMaintenanceClass();
        prRow.datetime = (DateTime)ooRow["datetime"];
        prRow.machineID = (int)ooRow["machineID"];
        prRow.voltmean_3hrs = Convert.ToSingle(ooRow["voltmean_3hrs"]);
        prRow.rotatemean_3hrs = Convert.ToSingle(ooRow["rotatemean_3hrs"]);
        prRow.pressuremean_3hrs = Convert.ToSingle(ooRow["pressuremean_3hrs"]);
        prRow.vibrationmean_3hrs = Convert.ToSingle(ooRow["vibrationmean_3hrs"]);
        prRow.voltstd_3hrs = Convert.ToSingle(ooRow["voltsd_3hrs"]);
        prRow.rotatestd_3hrs = Convert.ToSingle(ooRow["rotatesd_3hrs"]);
        prRow.pressurestd_3hrs = Convert.ToSingle(ooRow["pressuresd_3hrs"]);
        prRow.vibrationstd_3hrs = Convert.ToSingle(ooRow["vibrationsd_3hrs"]);
        prRow.voltmean_24hrs = Convert.ToSingle(ooRow["voltmean_24hrs"]);
        prRow.rotatemean_24hrs = Convert.ToSingle(ooRow["rotatemean_24hrs"]);
        prRow.pressuremean_24hrs = Convert.ToSingle(ooRow["pressuremean_24hrs"]);
        prRow.vibrationmean_24hrs = Convert.ToSingle(ooRow["vibrationmean_24hrs"]);
        prRow.voltstd_24hrs = Convert.ToSingle(ooRow["voltsd_24hrs"]);
        prRow.rotatestd_24hrs = Convert.ToSingle(ooRow["rotatesd_24hrs"]);
        prRow.pressurestd_24hrs = Convert.ToSingle(ooRow["pressuresd_24hrs"]);
        prRow.vibrationstd_24hrs = Convert.ToSingle(ooRow["vibrationsd_24hrs"]);
        prRow.error1count = Convert.ToSingle(ooRow["error1count"]);
        prRow.error2count = Convert.ToSingle(ooRow["error2count"]);
        prRow.error3count = Convert.ToSingle(ooRow["error3count"]);
        prRow.error4count = Convert.ToSingle(ooRow["error4count"]);
        prRow.error5count = Convert.ToSingle(ooRow["error5count"]);
        prRow.sincelastcomp1 = Convert.ToSingle(ooRow["sincelastcomp1"]);
        prRow.sincelastcomp2 = Convert.ToSingle(ooRow["sincelastcomp2"]);
        prRow.sincelastcomp3 = Convert.ToSingle(ooRow["sincelastcomp3"]);
        prRow.sincelastcomp4 = Convert.ToSingle(ooRow["sincelastcomp4"]);
        prRow.model = (string)ooRow["model"];
        prRow.age = Convert.ToSingle(ooRow["age"]);
        prRow.failure = (string)ooRow["failure"];
        return prRow;
    return dataView;

Load the data sets into the app memory:

//Split dataset in two parts: TrainingDataset  and TestDataset          
var trainData = loadFromDataFrame(mlContext, trainDF);
var testData = loadFromDataFrame(mlContext, testDF);

Prior to start training we need to process that data, so that we encoded all non-numerical columns into numerical columns. Also we need to define which columns are going to be part of the Featuresand which one will be label. For this reason we define PrepareData method.

public static IEstimator PrepareData(MLContext mlContext)
    //one hot encoding category column
    IEstimator dataPipeline =

    mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: "Label", inputColumnName: nameof(PrMaintenanceClass.failure))
    //encode model column
    .Append(mlContext.Transforms.Categorical.OneHotEncoding("model",outputKind: OneHotEncodingEstimator.OutputKind.Indicator))

    //define features column
    nameof(PrMaintenanceClass.voltmean_3hrs), nameof(PrMaintenanceClass.rotatemean_3hrs),
    nameof(PrMaintenanceClass.voltstd_3hrs), nameof(PrMaintenanceClass.rotatestd_3hrs), 
    nameof(PrMaintenanceClass.pressurestd_3hrs), nameof(PrMaintenanceClass.vibrationstd_3hrs), 
    nameof(PrMaintenanceClass.error1count), nameof(PrMaintenanceClass.error2count),
    nameof(PrMaintenanceClass.error3count), nameof(PrMaintenanceClass.error4count), 
    nameof(PrMaintenanceClass.error5count), nameof(PrMaintenanceClass.sincelastcomp1),
    nameof(PrMaintenanceClass.sincelastcomp4),nameof(PrMaintenanceClass.model), nameof(PrMaintenanceClass.age) ));

    return dataPipeline;

As can be seen, the method converts the label column failure which is a simple textual column into categorical columns containing numerical representation for each different category called Keys.

Now that we have finished with data transformation, we are going to define the Train method which is going to implement ML algorithm, hyper-parameters for it and training process. Once we call this method the method will return the trained model.

//train method
static public TransformerChain Train(MLContext mlContext, IDataView preparedData)
    var transformationPipeline=PrepareData(mlContext);
    //settings hyper parameters
    var options = new LightGbmMulticlassTrainer.Options();
    options.FeatureColumnName = "Features";
    options.LearningRate = 0.005;
    options.NumberOfLeaves = 70;
    options.NumberOfIterations = 2000;
    options.NumberOfLeaves = 50;
    options.UnbalancedSets = true;
    var boost = new DartBooster.Options();
    boost.XgboostDartMode = true;
    boost.MaximumTreeDepth = 25;
    options.Booster = boost;
    // Define LightGbm algorithm estimator
    IEstimator lightGbm = mlContext.MulticlassClassification.Trainers.LightGbm(options);

    //train the ML model
    TransformerChain model = transformationPipeline.Append(lightGbm).Fit(preparedData);

    //return trained model for evaluation
    return model;

Training process and model evaluation

Since we have all required methods, the main program structure looks like:

//prepare data transformation pipeline
var dataPipeline = PrepareData(mlContext);

//print prepared data
var pp = dataPipeline.Fit(trainData);
var transformedData = pp.Transform(trainData);

//train the model
var model = Train(mlContext, trainData);

Once the Train method returns the model, the evaluation phase started. In order to evaluate model, we perform full evaluation with training and testing data.

Model Evaluation with train data set

The evaluation of the model will be performed for training and testing data sets:

//evaluate train set
var predictions = model.Transform(trainData);
var metricsTrain = mlContext.MulticlassClassification.Evaluate(predictions);

ConsoleHelper.PrintMultiClassClassificationMetrics("TRAIN DataSet", metricsTrain);
ConsoleHelper.ConsoleWriteHeader("Train DataSet Confusion Matrix ");

The model evaluation output:

*    Metrics for TRAIN DataSet multi-class classification model   
    AccuracyMacro = 0.9603, a value between 0 and 1, the closer to 1, the better
    AccuracyMicro = 0.999, a value between 0 and 1, the closer to 1, the better
    LogLoss = 0.0015, the closer to 0, the better
    LogLoss for class 1 = 0, the closer to 0, the better
    LogLoss for class 2 = 0.088, the closer to 0, the better
    LogLoss for class 3 = 0.0606, the closer to 0, the better
Train DataSet Confusion Matrix 

Confusion table
PREDICTED ||  none | comp4 | comp1 | comp2 | comp3 | Recall
TRUTH     ||========================================
     none || 165 371 |     0 |     0 |     0 |     0 | 1.0000
    comp4 ||     0 |   772 |    16 |    25 |    11 | 0.9369
    comp1 ||     0 |     8 |   884 |    26 |     4 | 0.9588
    comp2 ||     0 |    31 |    22 | 1 097 |     8 | 0.9473
    comp3 ||     0 |    13 |     4 |     8 |   576 | 0.9584
Precision ||1.0000 |0.9369 |0.9546 |0.9490 |0.9616 |

As can be seen the model predict the values correctly in most cases in the train data set. Now lets see how the model predict the data which have not been part of the raining process.

Model evaluation with test data set

//evaluate test set
var testPrediction = model.Transform(testData);
var metricsTest = mlContext.MulticlassClassification.Evaluate(testPrediction);
ConsoleHelper.PrintMultiClassClassificationMetrics("Test Dataset", metricsTest);

ConsoleHelper.ConsoleWriteHeader("Test DataSet Confusion Matrix ");
*    Metrics for Test Dataset multi-class classification model   
    AccuracyMacro = 0.9505, a value between 0 and 1, the closer to 1, the better
    AccuracyMicro = 0.9986, a value between 0 and 1, the closer to 1, the better
    LogLoss = 0.0033, the closer to 0, the better
    LogLoss for class 1 = 0.0012, the closer to 0, the better
    LogLoss for class 2 = 0.1075, the closer to 0, the better
    LogLoss for class 3 = 0.1886, the closer to 0, the better
Test DataSet Confusion Matrix 

Confusion table
PREDICTED ||  none | comp4 | comp1 | comp2 | comp3 | Recall
TRUTH     ||========================================
     none || 120 313 |     6 |    15 |     0 |     0 | 0.9998
    comp4 ||     1 |   552 |    10 |    17 |     4 | 0.9452
    comp1 ||     2 |    14 |   464 |    24 |    24 | 0.8788
    comp2 ||     0 |    39 |     0 |   835 |    16 | 0.9382
    comp3 ||     0 |     4 |     0 |     0 |   412 | 0.9904
Precision ||1.0000 |0.8976 |0.9489 |0.9532 |0.9035 |

We can see, that the model has overall accuracy 99%, and 95% average per class accuracy. The complete nptebook of this blog post can be found here.

Your first data analysis with .NET Jupyter Notebook and Daany.DataFrame

Note: The .NET Jupyter notebook for this blog post can be found here.

The Structure of Daany.DataFrame

The main part of Daany project is Daany.DataFrame – an c# implementation of a data frame. A data frame is a software component used for handling tabular data, especially for data preparation, feature engineering, and analysis during the development of machine learning models. The concept of Daany.DataFrame implementation is based on simplicity and .NET coding standard. It represents tabular data consisting of columns and rows. Each column has name and type and each row has its index and label.

Usually, rows indicate a zero axis, while columns indicate axis one.

The following image shows a data frame structure:

data frame structure

The basic components of the data frame are:

  • header – list of column names,
  • index – list of object representing each row,
  • data – list of values in the data frame,
  • missing value – data with no values in data frame.

The image above shows the data frame components visually, and how they are positioned in the data frame.

Create Data Frame from a text based file

The data we used are stored in files, and they must be load into application memory in order to be analyzed and transformed. Loading data from files by using Daany.DataFrame is as easy as calling one method.

By using static method DataFrame.FromCsv a user can create data frame object from the csv file. Otherwise, data frame can be persisted on disk by calling static method DataFrame.ToCsv.

The following code shows how to use static methods ToCsv and FromCsv to show persisting and loading data to data frame:

string filename = "df_file.txt";
//define a dictionary of data
var dict = new Dictionary<string, List>
    { "ID",new List() { 1,2,3} },
    { "City",new List() { "Sarajevo", "Seattle", "Berlin" } },
    { "Zip Code",new List() { 71000,98101,10115 } },
    { "State",new List() {"BiH","USA","GER" } },
    { "IsHome",new List() { true, false, false} },
    { "Values",new List() { 3.14, 3.21, 4.55 } },
    { "Date",new List() { DateTime.Now.AddDays(-20) , DateTime.Now.AddDays(-10) , DateTime.Now.AddDays(-5) } },


//create data frame with 3 rows and 7 columns
var df = new DataFrame(dict);

//first Save data frame on disk and load it
DataFrame.ToCsv(filename, df);

//create data frame with 3 rows and 7 columns
var dfFromFile = DataFrame.FromCsv(filename, sep:',');

//show dataframe

First, we created a data frame from the dictionary collection. Then we store the data frame to file. After successfully saving, we load the same data frame from the CSV file. The end of the code snippet put asserts in order to prove everything is correctly implemented. The output of the code cell is:

data frame structure

In case the performance is important, you should pass column types to the FromCSV method in order to achieve up to 50% of loading time. For example the following code loads the data from the file, by passing predefined column types:

//defined types of the column 
var colTypes1 = new ColType[] { ColType.I32, ColType.IN, ColType.I32, ColType.STR, ColType.I2, ColType.F32, ColType.DT };
//create data frame with 3 rows and 7 columns
var dfFromFile = DataFrame.FromCsv(filename, sep: ',', colTypes: colTypes1);

And we got the same result: data frame structure

Loading Real Data from the Web

Data can be loaded directly from the web storage by using FromWebstatic method. The following code shows how to load the Concrete Slump Test data from the web. The data set includes 103 data points. There are 7 input variables, and 3 output variables in the data set: Cement, Slag, Fly ash, Water, SP, Coarse Aggr.,Fine Aggr., SLUMP (cm), FLOW (cm), Strength (Mpa). The following code load the Concrete Slump Test data set into Daany DataFrame:

//define web url where the data is stored
var url = "https://archive.ics.uci.edu/ml/machine-learning-databases/concrete/slump/slump_test.data";
var df = DataFrame.FromWeb(url);

data frame structure

Once we have the data into the application memory, we can perform some statistical calculations. First, let’s see the structure of the data by calling the Describe method:


data frame structure

Now, we see we have a data frame with 103 rows and all columns are of numerical type. The frequency of the data indicated that values are mostly not repeated. From the maximum and minimum values, we can see the data have no outlines since distributions of the values are tends to be normal.

Data Visualization

Let’s perform some visualization just to see how visually data look like. As first let’s see the Slump distribution with respect of SP and Fly ash:

var chart = Chart.Plot(
    new Graph.Scatter()
        x = df["SP"],
        y = df["Fly ash"],
        mode = "markers",
        marker = new Graph.Marker()
            color = df["SLUMP(cm)"].Select(x=>x),
            colorscale = "Jet"

var layout = new Layout.Layout(){title="Slump vs. Cement and Slag"};


data frame structure

From the chart above, we cannot see any relation between those two columns. Let’s see the chart made between Slump and Flow:

var chart = Chart.Plot(
    new Graph.Scatter()
        x = df["SLUMP(cm)"],
        y = df["FLOW(cm)"],
        mode = "markers",

var layout = new Layout.Layout(){title="Slump vs. Cement and Slag"};


data frame structure

We can see some relation in the chart and the relation is positive. This means as Slupm is growing, Flow value grows as well. If we want to measure the relation between the columns we can do that with the following code:

var x1= df["SLUMP(cm)"].Select(x=>Convert.ToDouble(x)).ToArray();
var x2= df["FLOW(cm)"].Select(x=>Convert.ToDouble(x)).ToArray();

//The Pearson coefficient is calculated by
var r=x1.R(x2);

The correlation is 0.90 which indicates a strong relationship between those two columns.

The complete .NET Jupyter Notebook for this blog post can be found here