internal FieldAwareFactorizationMachinePredictionTransformer(IHostEnvironment host, FieldAwareFactorizationMachineModelParameters model, DataViewSchema trainSchema,
string[] featureColumns, float threshold = 0f, string thresholdColumn = DefaultColumnNames.Score)
: base(Contracts.CheckRef(host, nameof(host)).Register(nameof(FieldAwareFactorizationMachinePredictionTransformer)), model, trainSchema)
{
Host.CheckNonEmpty(thresholdColumn, nameof(thresholdColumn));
Host.CheckNonEmpty(featureColumns, nameof(featureColumns));
_threshold = threshold;
_thresholdColumn = thresholdColumn;
FeatureColumns = featureColumns;
var featureColumnTypes = new DataViewType[featureColumns.Length];
int i = 0;
foreach (var feat in featureColumns)
{
if (!trainSchema.TryGetColumnIndex(feat, out int col))
{
throw Host.ExceptSchemaMismatch(nameof(featureColumns), "feature", feat);
}
featureColumnTypes[i++] = trainSchema[col].Type;
}
FeatureColumnTypes = featureColumnTypes;
BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, model);
var schema = GetSchema();
var args = new BinaryClassifierScorer.Arguments {
Threshold = _threshold, ThresholdColumn = _thresholdColumn
};
Scorer = new BinaryClassifierScorer(Host, args, new EmptyDataView(Host, trainSchema), BindableMapper.Bind(Host, schema), schema);
}
void ReconfigurablePrediction()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline
var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath));
var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader);
// Train
var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments
{
NumThreads = 1
});
var cached = new CacheDataView(env, trans, prefetch: null);
var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features");
IPredictor predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
using (var ch = env.Start("Calibrator training"))
{
predictor = CalibratorUtils.TrainCalibrator(env, ch, new PlattCalibratorTrainer(env), int.MaxValue, predictor, trainRoles);
}
var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
var dataEval = new RoleMappedData(scorer, label: "Label", feature: "Features", opt: true);
var evaluator = new BinaryClassifierMamlEvaluator(env, new BinaryClassifierMamlEvaluator.Arguments()
{
});
var metricsDict = evaluator.Evaluate(dataEval);
var metrics = BinaryClassificationMetrics.FromMetrics(env, metricsDict["OverallMetrics"], metricsDict["ConfusionMatrix"])[0];
var bindable = ScoreUtils.GetSchemaBindableMapper(env, predictor, null);
var mapper = bindable.Bind(env, trainRoles.Schema);
var newScorer = new BinaryClassifierScorer(env, new BinaryClassifierScorer.Arguments {
Threshold = 0.01f, ThresholdColumn = DefaultColumnNames.Probability
},
scoreRoles.Data, mapper, trainRoles.Schema);
dataEval = new RoleMappedData(newScorer, label: "Label", feature: "Features", opt: true);
var newEvaluator = new BinaryClassifierMamlEvaluator(env, new BinaryClassifierMamlEvaluator.Arguments()
{
Threshold = 0.01f, UseRawScoreThreshold = false
});
metricsDict = newEvaluator.Evaluate(dataEval);
var newMetrics = BinaryClassificationMetrics.FromMetrics(env, metricsDict["OverallMetrics"], metricsDict["ConfusionMatrix"])[0];
}
}
internal FieldAwareFactorizationMachinePredictionTransformer(IHostEnvironment host, ModelLoadContext ctx)
: base(Contracts.CheckRef(host, nameof(host)).Register(nameof(FieldAwareFactorizationMachinePredictionTransformer)), ctx)
{
// *** Binary format ***
// <base info>
// ids of strings: feature columns.
// float: scorer threshold
// id of string: scorer threshold column
// count of feature columns. FAFM uses more than one.
int featCount = Model.FieldCount;
var featureColumns = new string[featCount];
var featureColumnTypes = new DataViewType[featCount];
for (int i = 0; i < featCount; i++)
{
featureColumns[i] = ctx.LoadString();
if (!TrainSchema.TryGetColumnIndex(featureColumns[i], out int col))
{
throw Host.ExceptSchemaMismatch(nameof(FeatureColumns), "feature", featureColumns[i]);
}
featureColumnTypes[i] = TrainSchema[col].Type;
}
FeatureColumns = featureColumns;
FeatureColumnTypes = featureColumnTypes;
_threshold = ctx.Reader.ReadSingle();
_thresholdColumn = ctx.LoadString();
BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, Model);
var schema = GetSchema();
var args = new BinaryClassifierScorer.Arguments {
Threshold = _threshold, ThresholdColumn = _thresholdColumn
};
Scorer = new BinaryClassifierScorer(Host, args, new EmptyDataView(Host, TrainSchema), BindableMapper.Bind(Host, schema), schema);
}
public FieldAwareFactorizationMachinePredictionTransformer(IHostEnvironment host, FieldAwareFactorizationMachinePredictor model, ISchema trainSchema,
string[] featureColumns, float threshold = 0f, string thresholdColumn = DefaultColumnNames.Score)
: base(Contracts.CheckRef(host, nameof(host)).Register(nameof(FieldAwareFactorizationMachinePredictionTransformer)), model, trainSchema)
{
Host.CheckNonEmpty(thresholdColumn, nameof(thresholdColumn));
_threshold = threshold;
_thresholdColumn = thresholdColumn;
Host.CheckValue(featureColumns, nameof(featureColumns));
int featCount = featureColumns.Length;
Host.Check(featCount >= 0, "Empty features column.");
FeatureColumns = featureColumns;
FeatureColumnTypes = new ColumnType[featCount];
int i = 0;
foreach (var feat in featureColumns)
{
if (!trainSchema.TryGetColumnIndex(feat, out int col))
{
throw Host.ExceptSchemaMismatch(nameof(featureColumns), RoleMappedSchema.ColumnRole.Feature.Value, feat);
}
FeatureColumnTypes[i++] = trainSchema.GetColumnType(col);
}
BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, model);
var schema = GetSchema();
var args = new BinaryClassifierScorer.Arguments {
Threshold = _threshold, ThresholdColumn = _thresholdColumn
};
_scorer = new BinaryClassifierScorer(Host, args, new EmptyDataView(Host, trainSchema), BindableMapper.Bind(Host, schema), schema);
}
