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将“数据”从新的TextLoader添加到现有的管道(版本。0.6)

ml.net c#

0

michasaucer · 技术社区 · 8 年前

我从中复制粘贴示例 ML.NET Cookbook 上面写着:

// Create a new environment for ML.NET operations. It can be used for 
exception tracking and logging, 
// as well as the source of randomness.
var env = new LocalEnvironment();

// Create the reader: define the data columns and where to find them in the 
 text file.
 var reader = TextLoader.CreateReader(env, ctx => (
    // We read the first 11 values as a single float vector.
    FeatureVector: ctx.LoadFloat(0, 10),
    // Separately, read the target variable.
    Target: ctx.LoadFloat(11)
    ),
    // Default separator is tab, but we need a comma.
    separator: ',');


// Now read the file (remember though, readers are lazy, so the actual 
reading will happen when the data is accessed).
var data = reader.Read(new MultiFileSource(dataPath));

所以我开始在我的模型中实现它:

using System;
using Microsoft.ML.Legacy;
using Microsoft.ML.Legacy.Data;
using Microsoft.ML.Legacy.Transforms;
using Microsoft.ML.Legacy.Trainers;
using Microsoft.ML.Legacy.Models;
using Microsoft.ML.Runtime.Data;

public static PredictionModel<CancerData, CancerPrediction> Train()
    {
        var pipeline = new LearningPipeline();
        //0.6 way to upload data into model
        var env = new LocalEnvironment();
            var reader = Microsoft.ML.Runtime.Data.TextLoader.CreateReader(env, ctx => (
            FeatureVector: ctx.LoadFloat(0, 30),
            Target: ctx.LoadText(31)
                ),
            separator: ';');

        var data = reader.Read(new MultiFileSource("Cancer-Train.csv"));

        //pipeline.Add(new TextLoader("Cancer-Train.csv").CreateFrom<CancerData>(useHeader: true, separator: ';'));
        pipeline.Add(new Dictionarizer(("Diagnosis", "Label")));
        pipeline.Add(data); //dont work, i just write it to show you what i want to do

        //below the 0.5 way to load data into pipeline!
        //pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
        //    "RadiusMean",
        //    "TextureMean",
        // .. and so on...
        //    "SymmetryWorst",
        //    "FractalDimensionWorst"));
        pipeline.Add(new StochasticDualCoordinateAscentBinaryClassifier());
        pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" });
        PredictionModel<CancerData, CancerPrediction> model = pipeline.Train<CancerData, CancerPrediction>();

        model.WriteAsync(modelPath);
        return model;

    }

var data 进入我的存在 pipeline ? 我需要做的是 var数据 从0.6版到0.5版 管道 ?

1 回复 | 直到 8 年前

1

Gal Oshri 8 年前

我不认为 LearningPipeline API与新的静态类型API兼容(例如。 TextLoader.CreateReader ). 这个 cookbook This 测试也可能有助于二进制分类。

var env = new LocalEnvironment();
var reader = Microsoft.ML.Runtime.Data.TextLoader.CreateReader(env, ctx => (
FeatureVector: ctx.LoadFloat(0, 30),
Target: ctx.LoadBool(31)
    ),
separator: ';');

var data = reader.Read(new MultiFileSource("Cancer-Train.csv"));

BinaryClassificationContext bcc = new BinaryClassificationContext(env);

var estimator = reader.MakeNewEstimator()
    .Append(row => (
        label: row.Target,
        features: row.FeatureVector.Normalize()))
    .Append(row => (
        row.label,
        score: bcc.Trainers.Sdca(row.label, row.features)))
    .Append(row => (
        row.label,
        row.score,
        predictedLabel: row.score.predictedLabel));

var model = estimator.Fit(data);