This project proposes some extension to machinelearning written in C#. Work in progress.
On windows: build.cmd or build ml to force rebuilding machinelearning.
On Linux: build.sh.
The documentation can be build with: doxygen conf.dox.
- machinelearning
- EntryPoints
- Data View ansd Cursors
- IDV File Format
- machinelearningext
This example relies on the inner API, mostly used inside components of ML.net.
var env = new TlcEnvironment();
var iris = "iris.txt";
// We read the text data and create a dataframe / dataview.
var df = DataFrameIO.ReadCsv(iris, sep: '\t',
dtypes: new DataKind?[] { DataKind.R4 });
// We add a transform to concatenate two features in one vector columns.
var conc = env.CreateTransform("Concat{col=Feature:Sepal_length,Sepal_width}", df);
// We create training data by mapping roles to columns.
var trainingData = env.CreateExamples(conc, "Feature", label: "Label");
// We create a trainer, here a One Versus Rest with a logistic regression as inner model.
var trainer = env.CreateTrainer("ova{p=lr}");
using (var ch = env.Start("test"))
{
// We train the model.
var pred = trainer.Train(env, ch, trainingData);
// We compute the prediction (here with the same training data but it should not be the same).
var scorer = ScoreUtils.GetScorer(pred, trainingData, env, null);
// We store the predictions on a file.
DataFrame.ViewToCsv(scorer, "iris_predictions.txt", host: env);
// Or we could put the predictions into a dataframe.
var dfout = DataFrameIO.ReadView(scorer);
// And access one value...
var v = dfout.iloc[0, 7];
Console.WriteLine("PredictedLabel: {0}", v);
}The current interface of DataFrame is not rich. It will improve in the future.
This is the same example but with a ScikitPipeline which looks like scikit-learn.
var env = new TlcEnvironment();
var iris = "iris.txt";
// We read the text data and create a dataframe / dataview.
var df = DataFrameIO.ReadCsv(iris, sep: '\t',
dtypes: new DataKind?[] { DataKind.R4 });
var pipe = new ScikitPipeline(new[] { "Concat{col=Feature:Sepal_length,Sepal_width}" }, "ova{p=lr}");
pipe.Train(df, feature: "Feature", label: "Label");
var scorer = pipe.Predict(df);
var dfout = DataFrameIO.ReadView(scorer);
// And access one value...
var v = dfout.iloc[0, 7];
Console.WriteLine("PredictedLabel: {0}", v);The class DataFrame replicates some functionalities
datascientist are used to in others languages such as
Python or R. It is possible to do basic operations
on columns:
var text = "AA,BB,CC\n0,1,text\n1,1.1,text2";
var df = DataFrameIO.ReadStr(text);
df["AA+BB"] = df["AA"] + df["BB"];
Console.WriteLine(df.ToString());AA,BB,CC,AA+BB
0,1,text,1
1,1.1,text2,2.1
Or:
df["AA2"] = df["AA"] + 10;
Console.WriteLine(df.ToString());AA,BB,CC,AA+BB,AA2
0,1,text,1,10
1,1.1,text2,2.1,11
The next instructions change one value based on a condition.
df.loc[df["AA"].Filter<DvInt4>(c => (int)c == 1), "CC"] = "changed";
Console.WriteLine(df.ToString());AA,BB,CC,AA+BB,AA2
0,1,text,1,10
1,1.1,changed,2.1,11
A specific set of columns or rows can be extracted:
var view = df[df.ALL, new [] {"AA", "CC"}];
Console.WriteLine(view.ToString());AA,CC
0,text
1,changed
The dataframe also allows basic filtering:
var view = df[df["AA"] == 0];
Console.WriteLine(view.ToString());AA,BB,CC,AA+BB,AA2
0,1,text,1,10