public void GetDataNotSameTest()
{
var provider = new ExcelDataProvider(file);
var data1 = provider.GetData();
var data2 = provider.GetData();
Console.WriteLine(JsonConvert.SerializeObject(data1));
Console.WriteLine(JsonConvert.SerializeObject(data2));
Assert.AreNotSame(data1, data2);
}
public void GetDataNotNullTest()
{
var provider = new ExcelDataProvider(file);
var data = provider.GetData();
Console.WriteLine(JsonConvert.SerializeObject(data));
Assert.AreNotEqual(null, data);
}
public static void Ex()
{
LearningPipeline pipeline = new LearningPipeline();
pipeline.Add(new TextLoader(@"C:\Users\Дарья\Desktop\AdverbNoun.csv").CreateFrom <Model>(useHeader: true, separator: ','));
pipeline.Add(new Dictionarizer(("Category", "Label")));
pipeline.Add(new TextFeaturizer("Features", "Word"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
//Vec < R4, 472 >
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
var model = pipeline.Train <Model, CategoryPrediction>();
model.WriteAsync(@"C:\Users\Дарья\Desktop\NewModel.zip");
model = PredictionModel.ReadAsync <Model, CategoryPrediction>(@"C:\Users\Дарья\Desktop\NewModel.zip").Result;
var testData = new TextLoader(@"C:\Users\Дарья\Desktop\TestAdverbNoun.csv").CreateFrom <Model>(useHeader: true, separator: ',');
var evaluator = new ClassificationEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"AccuracyMicro: {metrics.AccuracyMicro}\nAccuracyMacro: {metrics.AccuracyMacro}");
var data = ExcelDataProvider.GetData(@"C:\Users\Дарья\Desktop\TestAdverbNoun.xlsx", 0);
List <Model> models = new List <Model>();
data.ForEach(x => models.Add(new Model()
{
Category = x.Category,
Word = x.Word
}));
foreach (var sample in models)
{
var prediction = model.Predict(sample);
Console.WriteLine($"Word: {sample.Word}");
Console.WriteLine($"\nActual Category: {sample.Category} \nPredicted Category: {prediction.Category}\nPredicted Category scores: [{String.Join(",", prediction.Score)}]\n\n");
}
Console.ReadLine();
}
static void Main(string[] args)
{
DataTable data = new DataTable("Categories of words");
data.Columns.Add("Category", "Word");
List <InputData> words = ExcelDataProvider.GetData(@"C:\Users\Дарья\Desktop\AdverbNoun.xlsx", 0);
foreach (var word in words)
{
data.Rows.Add(word.Category, word.Word);
}
Codification codebook = new Codification(data, "Category", "Word");
DataTable symbols = codebook.Apply(data);
int[][] inputs = symbols.ToJagged <int>("Category");
int[] outputs = symbols.ToArray <int>("Word");
var learner = new NaiveBayesLearning();
NaiveBayes nb = learner.Learn(inputs, outputs);
data = new DataTable("Categories of words");
data.Columns.Add("Category", "Word");
words = ExcelDataProvider.GetData(@"C:\Users\Дарья\Desktop\TestAdverbNoun.xlsx", 0);
foreach (var word in words)
{
data.Rows.Add(word.Category, word.Word);
}
int[] instance = codebook.Translate("helpful");
int c = nb.Decide(instance);
string result = codebook.Translate("Category", c);
double[] probs = nb.Probabilities(instance);
Console.WriteLine(0);
}
public static void ShowPredictResults(string dataPath, string modelPath, int predictDataSheetNumber)
{
MLContext mlContext = new MLContext();
List <InputData> inputs = ExcelDataProvider.GetData(dataPath, predictDataSheetNumber);
List <Input> input = new List <Input>();
inputs.ForEach(x => input.Add(new Input(x.Category, x.Word)));
IDataView dataView = mlContext.Data.LoadFromEnumerable <Input>(input);
IEnumerable <Input> samples = mlContext.Data.CreateEnumerable <Input>(dataView, false);
Console.WriteLine("Predictions");
foreach (var sample in samples)
{
Output predictionResult = Predict(sample, modelPath);
Console.WriteLine($"Word: {sample.Word}");
//Console.WriteLine($"\nActual Category: {sample.Category} \nPredicted Category: {predictionResult.Category}\nPredicted Category scores: [{String.Join(",", predictionResult.Score)}]\n\n");
Console.WriteLine($"\nActual Category: {sample.Category} \nPredicted Category: {predictionResult.Prediction}\nPredicted Category scores: [{String.Join(",", predictionResult.Score)}]\n\n");
}
Console.ReadKey();
}
private List <InputData> GetDataFromExcel(int worksheetIndex)
{
return(ExcelDataProvider.GetData(data.DataPath, worksheetIndex));
}