IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input, out IDataView sourceCtx, IDataScorerTransform scorer)
{
sourceCtx = input;
Contracts.CheckValue(env, "env");
env.CheckValue(args, "args");
env.CheckValue(input, "input");
env.CheckUserArg(!string.IsNullOrWhiteSpace(args.taggedPredictor), "taggedPredictor",
"The input tag is required.");
if (scorer != null)
{
_scorer = scorer;
return(scorer);
}
else
{
var predictor = TagHelper.GetTaggedPredictor(env, input, args.taggedPredictor);
string feat = TrainUtils.MatchNameOrDefaultOrNull(env, input.Schema,
"featureColumn", args.featureColumn, DefaultColumnNames.Features);
string group = TrainUtils.MatchNameOrDefaultOrNull(env, input.Schema,
"groupColumn", args.groupColumn, DefaultColumnNames.GroupId);
var customCols = TrainUtils.CheckAndGenerateCustomColumns(env, args.customColumnPair);
_scorer = PredictorHelper.CreateDefaultScorer(_host, input, feat, group, predictor);
return(_scorer);
}
}
protected void DebugChecking2(RoleMappedData td, ITrainer trainer)
{
var scorer = PredictorHelper.CreateDefaultScorer(Host, td, trainer.CreatePredictor());
if (trainer.PredictionKind == PredictionKind.Ranking)
{
string schemas = SchemaHelper.ToString(scorer.Schema);
if (!schemas.Contains("Score"))
{
throw Host.Except("Issue with the schema: {0}", schemas);
}
}
using (var cursor = scorer.GetRowCursor(i => true))
{
int ilab, ipred, ifeat;
cursor.Schema.TryGetColumnIndex(td.Schema.Label.Name, out ilab);
if (trainer.PredictionKind == PredictionKind.Ranking)
{
cursor.Schema.TryGetColumnIndex("Score", out ipred);
cursor.Schema.TryGetColumnIndex(td.Schema.Feature.Name, out ifeat);
var getter = cursor.GetGetter <uint>(ilab);
var fgetter = cursor.GetGetter <VBuffer <float> >(ifeat);
var pgetter = cursor.GetGetter <float>(ipred);
if (pgetter == null)
{
throw Host.Except("Issue with the schema: {0}", SchemaHelper.ToString(cursor.Schema));
}
uint lab = 0;
var counts = new Dictionary <uint, int>();
var counts_pred = new Dictionary <float, int>();
float pre = 0;
VBuffer <float> features = default(VBuffer <float>);
int nbrows = 0;
int err = 0;
while (cursor.MoveNext())
{
getter(ref lab);
pgetter(ref pre);
fgetter(ref features);
counts[lab] = counts.ContainsKey(lab) ? counts[lab] + 1 : 0;
counts_pred[pre] = counts_pred.ContainsKey(pre) ? counts_pred[pre] + 1 : 0;
if (trainer.PredictionKind == PredictionKind.Ranking)
{
var elab = features.Values[features.Count - 1];
if (pre > 0 && lab < 0)
{
++err;
}
}
else if (!lab.Equals(pre))
{
++err;
}
++nbrows;
}
if (nbrows == 0)
{
throw Host.Except("No results.");
}
if (err * 2 > nbrows)
{
throw Host.Except("No training.");
}
}
else
{
cursor.Schema.TryGetColumnIndex("PredictedLabel", out ipred);
var getter = cursor.GetGetter <bool>(ilab);
var pgetter = cursor.GetGetter <bool>(ipred);
var counts = new Dictionary <bool, int>();
var counts_pred = new Dictionary <bool, int>();
bool lab = false;
bool pre = false;
int nbrows = 0;
int err = 0;
while (cursor.MoveNext())
{
getter(ref lab);
pgetter(ref pre);
counts[lab] = counts.ContainsKey(lab) ? counts[lab] + 1 : 0;
counts_pred[pre] = counts_pred.ContainsKey(pre) ? counts_pred[pre] + 1 : 0;
if (!lab.Equals(pre))
{
++err;
}
++nbrows;
}
if (nbrows == 0)
{
throw Host.Except("No results.");
}
if (err * 2 > nbrows)
{
throw Host.Except("No training.");
}
}
}
}
/// <summary>
/// Finalizes the test on a predictor, calls the predictor with a scorer,
/// saves the data, saves the models, loads it back, saves the data again,
/// checks the output is the same.
/// </summary>
/// <param name="env">environment</param>
/// <param name="outModelFilePath">output filename</param>
/// <param name="predictor">predictor</param>
/// <param name="roles">label, feature, ...</param>
/// <param name="outData">first output data</param>
/// <param name="outData2">second output data</param>
/// <param name="kind">prediction kind</param>
/// <param name="checkError">checks errors</param>
/// <param name="ratio">check the error is below that threshold (if checkError is true)</param>
/// <param name="ratioReadSave">check the predictions difference after reloading the model are below this threshold</param>
public static void FinalizeSerializationTest(IHostEnvironment env,
string outModelFilePath, IPredictor predictor,
RoleMappedData roles, string outData, string outData2,
PredictionKind kind, bool checkError = true,
float ratio = 0.8f, float ratioReadSave = 0.06f,
bool checkType = true)
{
string labelColumn = kind != PredictionKind.Clustering ? roles.Schema.Label.Value.Name : null;
#region save, reading, running
// Saves model.
using (var ch = env.Start("Save"))
using (var fs = File.Create(outModelFilePath))
TrainUtils.SaveModel(env, ch, fs, predictor, roles);
if (!File.Exists(outModelFilePath))
{
throw new FileNotFoundException(outModelFilePath);
}
// Loads the model back.
using (var fs = File.OpenRead(outModelFilePath))
{
var pred_local = ModelFileUtils.LoadPredictorOrNull(env, fs);
if (pred_local == null)
{
throw new Exception(string.Format("Unable to load '{0}'", outModelFilePath));
}
if (checkType && predictor.GetType() != pred_local.GetType())
{
throw new Exception(string.Format("Type mismatch {0} != {1}", predictor.GetType(), pred_local.GetType()));
}
}
// Checks the outputs.
var sch1 = SchemaHelper.ToString(roles.Schema.Schema);
var scorer = PredictorHelper.CreateDefaultScorer(env, roles, predictor);
var sch2 = SchemaHelper.ToString(scorer.Schema);
if (string.IsNullOrEmpty(sch1) || string.IsNullOrEmpty(sch2))
{
throw new Exception("Empty schemas");
}
var saver = env.CreateSaver("Text");
var columns = new int[scorer.Schema.Count];
for (int i = 0; i < columns.Length; ++i)
{
columns[i] = saver.IsColumnSavable(scorer.Schema[i].Type) ? i : -1;
}
columns = columns.Where(c => c >= 0).ToArray();
using (var fs2 = File.Create(outData))
saver.SaveData(fs2, scorer, columns);
if (!File.Exists(outModelFilePath))
{
throw new FileNotFoundException(outData);
}
// Check we have the same output.
using (var fs = File.OpenRead(outModelFilePath))
{
var model = ModelFileUtils.LoadPredictorOrNull(env, fs);
scorer = PredictorHelper.CreateDefaultScorer(env, roles, model);
saver = env.CreateSaver("Text");
using (var fs2 = File.Create(outData2))
saver.SaveData(fs2, scorer, columns);
}
var t1 = File.ReadAllLines(outData);
var t2 = File.ReadAllLines(outData2);
if (t1.Length != t2.Length)
{
throw new Exception(string.Format("Not the same number of lines: {0} != {1}", t1.Length, t2.Length));
}
var linesN = new List <int>();
for (int i = 0; i < t1.Length; ++i)
{
if (t1[i] != t2[i])
{
linesN.Add(i);
}
}
if (linesN.Count > (int)(t1.Length * ratioReadSave))
{
var rows = linesN.Select(i => string.Format("1-Mismatch on line {0}/{3}:\n{1}\n{2}", i, t1[i], t2[i], t1.Length)).ToList();
rows.Add($"Number of differences: {linesN.Count}/{t1.Length}");
throw new Exception(string.Join("\n", rows));
}
#endregion
#region clustering
if (kind == PredictionKind.Clustering)
{
// Nothing to do here.
return;
}
#endregion
#region supervized
string expectedOuput = kind == PredictionKind.Regression ? "Score" : "PredictedLabel";
// Get label and basic checking about performance.
using (var cursor = scorer.GetRowCursor(scorer.Schema))
{
int ilabel, ipred;
ilabel = SchemaHelper.GetColumnIndex(cursor.Schema, labelColumn);
ipred = SchemaHelper.GetColumnIndex(cursor.Schema, expectedOuput);
var ty1 = cursor.Schema[ilabel].Type;
var ty2 = cursor.Schema[ipred].Type;
var dist1 = new Dictionary <int, int>();
var dist2 = new Dictionary <int, int>();
var conf = new Dictionary <Tuple <int, int>, long>();
if (kind == PredictionKind.MulticlassClassification)
{
#region Multiclass
if (!ty2.IsKey())
{
throw new Exception(string.Format("Label='{0}' Predicted={1}'\nSchema: {2}", ty1, ty2, SchemaHelper.ToString(cursor.Schema)));
}
if (ty1.RawKind() == DataKind.Single)
{
var lgetter = cursor.GetGetter <float>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <uint>(SchemaHelper._dc(ipred, cursor));
float ans = 0;
uint pre = 0;
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
// The scorer +1 to the argmax.
++ans;
var key = new Tuple <int, int>((int)pre, (int)ans);
if (!conf.ContainsKey(key))
{
conf[key] = 1;
}
else
{
++conf[key];
}
if (!dist1.ContainsKey((int)ans))
{
dist1[(int)ans] = 1;
}
else
{
++dist1[(int)ans];
}
if (!dist2.ContainsKey((int)pre))
{
dist2[(int)pre] = 1;
}
else
{
++dist2[(int)pre];
}
}
}
else if (ty1.RawKind() == DataKind.UInt32 && ty1.IsKey())
{
var lgetter = cursor.GetGetter <uint>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <uint>(SchemaHelper._dc(ipred, cursor));
uint ans = 0;
uint pre = 0;
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
var key = new Tuple <int, int>((int)pre, (int)ans);
if (!conf.ContainsKey(key))
{
conf[key] = 1;
}
else
{
++conf[key];
}
if (!dist1.ContainsKey((int)ans))
{
dist1[(int)ans] = 1;
}
else
{
++dist1[(int)ans];
}
if (!dist2.ContainsKey((int)pre))
{
dist2[(int)pre] = 1;
}
else
{
++dist2[(int)pre];
}
}
}
else
{
throw new NotImplementedException(string.Format("Not implemented for type {0}", ty1.ToString()));
}
#endregion
}
else if (kind == PredictionKind.BinaryClassification)
{
#region binary classification
if (ty2.RawKind() != DataKind.Boolean)
{
throw new Exception(string.Format("Label='{0}' Predicted={1}'\nSchema: {2}", ty1, ty2, SchemaHelper.ToString(cursor.Schema)));
}
if (ty1.RawKind() == DataKind.Single)
{
var lgetter = cursor.GetGetter <float>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <bool>(SchemaHelper._dc(ipred, cursor));
float ans = 0;
bool pre = default(bool);
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
if (ans != 0 && ans != 1)
{
throw Contracts.Except("The problem is not binary, expected answer is {0}", ans);
}
var key = new Tuple <int, int>(pre ? 1 : 0, (int)ans);
if (!conf.ContainsKey(key))
{
conf[key] = 1;
}
else
{
++conf[key];
}
if (!dist1.ContainsKey((int)ans))
{
dist1[(int)ans] = 1;
}
else
{
++dist1[(int)ans];
}
if (!dist2.ContainsKey(pre ? 1 : 0))
{
dist2[pre ? 1 : 0] = 1;
}
else
{
++dist2[pre ? 1 : 0];
}
}
}
else if (ty1.RawKind() == DataKind.UInt32)
{
var lgetter = cursor.GetGetter <uint>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <bool>(SchemaHelper._dc(ipred, cursor));
uint ans = 0;
bool pre = default(bool);
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
if (ty1.IsKey())
{
--ans;
}
if (ans != 0 && ans != 1)
{
throw Contracts.Except("The problem is not binary, expected answer is {0}", ans);
}
var key = new Tuple <int, int>(pre ? 1 : 0, (int)ans);
if (!conf.ContainsKey(key))
{
conf[key] = 1;
}
else
{
++conf[key];
}
if (!dist1.ContainsKey((int)ans))
{
dist1[(int)ans] = 1;
}
else
{
++dist1[(int)ans];
}
if (!dist2.ContainsKey(pre ? 1 : 0))
{
dist2[pre ? 1 : 0] = 1;
}
else
{
++dist2[pre ? 1 : 0];
}
}
}
else if (ty1.RawKind() == DataKind.Boolean)
{
var lgetter = cursor.GetGetter <bool>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <bool>(SchemaHelper._dc(ipred, cursor));
bool ans = default(bool);
bool pre = default(bool);
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
var key = new Tuple <int, int>(pre ? 1 : 0, ans ? 1 : 0);
if (!conf.ContainsKey(key))
{
conf[key] = 1;
}
else
{
++conf[key];
}
if (!dist1.ContainsKey(ans ? 1 : 0))
{
dist1[ans ? 1 : 0] = 1;
}
else
{
++dist1[ans ? 1 : 0];
}
if (!dist2.ContainsKey(pre ? 1 : 0))
{
dist2[pre ? 1 : 0] = 1;
}
else
{
++dist2[pre ? 1 : 0];
}
}
}
else
{
throw new NotImplementedException(string.Format("Not implemented for type {0}", ty1));
}
#endregion
}
else if (kind == PredictionKind.Regression)
{
#region regression
if (ty1.RawKind() != DataKind.Single)
{
throw new Exception(string.Format("Label='{0}' Predicted={1}'\nSchema: {2}", ty1, ty2, SchemaHelper.ToString(cursor.Schema)));
}
if (ty2.RawKind() != DataKind.Single)
{
throw new Exception(string.Format("Label='{0}' Predicted={1}'\nSchema: {2}", ty1, ty2, SchemaHelper.ToString(cursor.Schema)));
}
var lgetter = cursor.GetGetter <float>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <float>(SchemaHelper._dc(ipred, cursor));
float ans = 0;
float pre = 0f;
float error = 0f;
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
error += (ans - pre) * (ans - pre);
if (!dist1.ContainsKey((int)ans))
{
dist1[(int)ans] = 1;
}
else
{
++dist1[(int)ans];
}
if (!dist2.ContainsKey((int)pre))
{
dist2[(int)pre] = 1;
}
else
{
++dist2[(int)pre];
}
}
if (float.IsNaN(error) || float.IsInfinity(error))
{
throw new Exception("Regression wen wrong. Error is infinite.");
}
#endregion
}
else
{
throw new NotImplementedException(string.Format("Not implemented for kind {0}", kind));
}
var nbError = conf.Where(c => c.Key.Item1 != c.Key.Item2).Select(c => c.Value).Sum();
var nbTotal = conf.Select(c => c.Value).Sum();
if (checkError && (nbError * 1.0 > nbTotal * ratio || dist2.Count <= 1))
{
var sconf = string.Join("\n", conf.OrderBy(c => c.Key)
.Select(c => string.Format("pred={0} exp={1} count={2}", c.Key.Item1, c.Key.Item2, c.Value)));
var sdist2 = string.Join("\n", dist1.OrderBy(c => c.Key)
.Select(c => string.Format("label={0} count={1}", c.Key, c.Value)));
var sdist1 = string.Join("\n", dist2.OrderBy(c => c.Key).Take(20)
.Select(c => string.Format("label={0} count={1}", c.Key, c.Value)));
throw new Exception(string.Format("Too many errors {0}/{1}={7}\n###########\nConfusion:\n{2}\n########\nDIST1\n{3}\n###########\nDIST2\n{4}\nOutput:\n{5}\n...\n{6}",
nbError, nbTotal,
sconf, sdist1, sdist2,
string.Join("\n", t1.Take(Math.Min(30, t1.Length))),
string.Join("\n", t1.Skip(Math.Max(0, t1.Length - 30))),
nbError * 1.0 / nbTotal));
}
}
#endregion
}
IDataScorerTransform GetScorer()
{
return(PredictorHelper.CreateDefaultScorer(_env, _predictor.roleMapData, _predictor.predictor, null));
}
IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input, out IDataView sourceCtx)
{
sourceCtx = input;
Contracts.CheckValue(env, "env");
env.CheckValue(args, "args");
env.CheckValue(input, "input");
env.CheckValue(args.tag, "tag is empty");
env.CheckValue(args.trainer, "trainer",
"Trainer cannot be null. If your model is already trained, please use ScoreTransform instead.");
var views = TagHelper.EnumerateTaggedView(true, input).Where(c => c.Item1 == args.tag);
if (views.Any())
{
throw env.Except("Tag '{0}' is already used.", args.tag);
}
var host = env.Register("TagTrainOrScoreTransform");
using (var ch = host.Start("Train"))
{
ch.Trace("Constructing trainer");
var trainerSett = ScikitSubComponent <ITrainer, SignatureTrainer> .AsSubComponent(args.trainer);
ITrainer trainer = trainerSett.CreateInstance(host);
var customCols = TrainUtils.CheckAndGenerateCustomColumns(env, args.CustomColumn);
string feat;
string group;
var data = CreateDataFromArgs(_host, ch, new OpaqueDataView(input), args, out feat, out group);
ICalibratorTrainer calibrator = args.calibrator == null
? null
: ScikitSubComponent <ICalibratorTrainer, SignatureCalibrator> .AsSubComponent(args.calibrator).CreateInstance(host);
var nameTrainer = args.trainer.ToString().Replace("{", "").Replace("}", "").Replace(" ", "").Replace("=", "").Replace("+", "Y").Replace("-", "N");
var extTrainer = new ExtendedTrainer(trainer, nameTrainer);
_predictor = extTrainer.Train(host, ch, data, null, calibrator, args.maxCalibrationExamples);
if (!string.IsNullOrEmpty(args.outputModel))
{
ch.Info("Saving model into '{0}'", args.outputModel);
using (var fs = File.Create(args.outputModel))
TrainUtils.SaveModel(env, ch, fs, _predictor, data);
ch.Info("Done.");
}
if (_cali != null)
{
throw ch.ExceptNotImpl("Calibrator is not implemented yet.");
}
ch.Trace("Scoring");
if (_args.scorer != null)
{
var mapper = new SchemaBindablePredictorWrapper(_predictor);
var roles = new RoleMappedSchema(input.Schema, null, feat, group: group);
var bound = mapper.Bind(_host, roles);
var scorPars = ScikitSubComponent <IDataScorerTransform, SignatureDataScorer> .AsSubComponent(_args.scorer);
_scorer = scorPars.CreateInstance(_host, input, bound, roles);
}
else
{
_scorer = PredictorHelper.CreateDefaultScorer(_host, input, feat, group, _predictor);
}
ch.Info("Tagging with tag '{0}'.", args.tag);
var ar = new TagViewTransform.Arguments {
tag = args.tag
};
var res = new TagViewTransform(env, ar, _scorer, _predictor);
return(res);
}
}