public void TokenizeWithSeparators()
{
string dataPath = GetDataPath("wikipedia-detox-250-line-data.tsv");
var data = TextLoader.CreateReader(Env, ctx => (
label: ctx.LoadBool(0),
text: ctx.LoadText(1)), hasHeader: true)
.Read(dataPath).AsDynamic;
var est = new WordTokenizeEstimator(Env, "text", "words", separators: new[] { ' ', '?', '!', '.', ',' });
var outdata = TakeFilter.Create(Env, est.Fit(data).Transform(data), 4);
var savedData = new ChooseColumnsTransform(Env, outdata, "words");
var saver = new TextSaver(Env, new TextSaver.Arguments {
Silent = true
});
var outputPath = GetOutputPath("Text", "tokenizedWithSeparators.tsv");
using (var ch = Env.Start("save"))
{
using (var fs = File.Create(outputPath))
DataSaverUtils.SaveDataView(ch, saver, savedData, fs, keepHidden: true);
}
CheckEquality("Text", "tokenizedWithSeparators.tsv");
Done();
}
public void NgramWorkout()
{
string sentimentDataPath = GetDataPath("wikipedia-detox-250-line-data.tsv");
var data = TextLoader.CreateReader(Env, ctx => (
label: ctx.LoadBool(0),
text: ctx.LoadText(1)), hasHeader: true)
.Read(sentimentDataPath);
var invalidData = TextLoader.CreateReader(Env, ctx => (
label: ctx.LoadBool(0),
text: ctx.LoadFloat(1)), hasHeader: true)
.Read(sentimentDataPath);
var est = new WordTokenizeEstimator(Env, "text", "text")
.Append(new TermEstimator(Env, "text", "terms"))
.Append(new NgramEstimator(Env, "terms", "ngrams"))
.Append(new NgramHashEstimator(Env, "terms", "ngramshash"));
// The following call fails because of the following issue
// https://github.com/dotnet/machinelearning/issues/969
// TestEstimatorCore(est, data.AsDynamic, invalidInput: invalidData.AsDynamic);
var outputPath = GetOutputPath("Text", "ngrams.tsv");
using (var ch = Env.Start("save"))
{
var saver = new TextSaver(Env, new TextSaver.Arguments {
Silent = true
});
IDataView savedData = TakeFilter.Create(Env, est.Fit(data.AsDynamic).Transform(data.AsDynamic), 4);
savedData = new ChooseColumnsTransform(Env, savedData, "text", "terms", "ngrams", "ngramshash");
using (var fs = File.Create(outputPath))
DataSaverUtils.SaveDataView(ch, saver, savedData, fs, keepHidden: true);
}
CheckEquality("Text", "ngrams.tsv");
Done();
}
public void TextTokenizationWorkout()
{
string sentimentDataPath = GetDataPath("wikipedia-detox-250-line-data.tsv");
var data = TextLoader.CreateReader(Env, ctx => (
label: ctx.LoadBool(0),
text: ctx.LoadText(1)), hasHeader: true)
.Read(sentimentDataPath);
var invalidData = TextLoader.CreateReader(Env, ctx => (
label: ctx.LoadBool(0),
text: ctx.LoadFloat(1)), hasHeader: true)
.Read(sentimentDataPath);
var est = new WordTokenizeEstimator(Env, "text", "words")
.Append(new CharacterTokenizeEstimator(Env, "text", "chars"))
.Append(new KeyToValueEstimator(Env, "chars"));
TestEstimatorCore(est, data.AsDynamic, invalidInput: invalidData.AsDynamic);
var outputPath = GetOutputPath("Text", "tokenized.tsv");
using (var ch = Env.Start("save"))
{
var saver = new TextSaver(Env, new TextSaver.Arguments {
Silent = true
});
IDataView savedData = TakeFilter.Create(Env, est.Fit(data.AsDynamic).Transform(data.AsDynamic), 4);
savedData = new ChooseColumnsTransform(Env, savedData, "text", "words", "chars");
using (var fs = File.Create(outputPath))
DataSaverUtils.SaveDataView(ch, saver, savedData, fs, keepHidden: true);
}
CheckEquality("Text", "tokenized.tsv");
Done();
}
public void TestOldSavingAndLoading()
{
var data = new[] { new TestClass()
{
A = "This is a good sentence.", B = new string[2] {
"Much words", "Wow So Cool"
}
} };
var dataView = ComponentCreation.CreateDataView(Env, data);
var pipe = new WordTokenizeEstimator(Env, new[] {
new WordTokenizeTransform.ColumnInfo("A", "TokenizeA"),
new WordTokenizeTransform.ColumnInfo("B", "TokenizeB"),
});
var result = pipe.Fit(dataView).Transform(dataView);
var resultRoles = new RoleMappedData(result);
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(Env, Env.Start("saving"), ms, null, resultRoles);
ms.Position = 0;
var loadedView = ModelFileUtils.LoadTransforms(Env, dataView, ms);
}
}