ML.net Add New Data to Exist Generated Model
i generated a ML Model Using ML.Net V 0.7.0 last version
i need to add a new learning data to this existing Model without regenerated it with the new and old data
as i have a large data set that exceed 100 Million Record
and i need to add 100 record without reload all last data set to generate the new model
any ideas please
this is critical for me
best regards
ml.net
asked Nov 15 '18 at 11:50
NobDevNobDev
113
add a comment |
i generated a ML Model Using ML.Net V 0.7.0 last version
i need to add a new learning data to this existing Model without regenerated it with the new and old data
as i have a large data set that exceed 100 Million Record
and i need to add 100 record without reload all last data set to generate the new model
any ideas please
this is critical for me
best regards
ml.net
asked Nov 15 '18 at 11:50
NobDevNobDev
113
add a comment |
i generated a ML Model Using ML.Net V 0.7.0 last version
i need to add a new learning data to this existing Model without regenerated it with the new and old data
as i have a large data set that exceed 100 Million Record
and i need to add 100 record without reload all last data set to generate the new model
any ideas please
this is critical for me
best regards
ml.net
asked Nov 15 '18 at 11:50
NobDevNobDev
113
i generated a ML Model Using ML.Net V 0.7.0 last version
i need to add a new learning data to this existing Model without regenerated it with the new and old data
as i have a large data set that exceed 100 Million Record
and i need to add 100 record without reload all last data set to generate the new model
any ideas please
this is critical for me
best regards
ml.net
ml.net
asked Nov 15 '18 at 11:50
NobDevNobDev
113
asked Nov 15 '18 at 11:50
NobDevNobDev
113
asked Nov 15 '18 at 11:50
NobDevNobDev
113
asked Nov 15 '18 at 11:50
NobDevNobDev
113
asked Nov 15 '18 at 11:50
NobDevNobDev
113
113
add a comment |
add a comment |
1 Answer
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Some trainers in ML.NET support training with an initial predictor, which means you can use an existing predictor as the starting point for training with new data.
A test showing this can be found here with the relevant code being:
// Train the first predictor.
var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features",advancedSettings: s => s.NumThreads = 1);
var firstModel = trainer.Fit(trainData);
// Train the second predictor on the same data.
var secondTrainer = ml.BinaryClassification.Trainers.AveragedPerceptron("Label","Features");
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
var finalModel = secondTrainer.Train(new TrainContext(trainRoles, initialPredictor: firstModel.Model));
answered Nov 15 '18 at 22:34
Gal OshriGal Oshri
3182
I think the OP is interested in adding more data to the first predictor, not creating a second type of predictor. I'm interested in this as well.
– Webjedi
Jan 4 at 15:55
Is it as simple as train model1 and model2 and then do finalmodel=model1.append(model2)? If both models are trained the same way?
– Webjedi
Jan 4 at 17:27
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Some trainers in ML.NET support training with an initial predictor, which means you can use an existing predictor as the starting point for training with new data.
A test showing this can be found here with the relevant code being:
// Train the first predictor.
var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features",advancedSettings: s => s.NumThreads = 1);
var firstModel = trainer.Fit(trainData);
// Train the second predictor on the same data.
var secondTrainer = ml.BinaryClassification.Trainers.AveragedPerceptron("Label","Features");
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
var finalModel = secondTrainer.Train(new TrainContext(trainRoles, initialPredictor: firstModel.Model));
answered Nov 15 '18 at 22:34
Gal OshriGal Oshri
3182
I think the OP is interested in adding more data to the first predictor, not creating a second type of predictor. I'm interested in this as well.
– Webjedi
Jan 4 at 15:55
Is it as simple as train model1 and model2 and then do finalmodel=model1.append(model2)? If both models are trained the same way?
– Webjedi
Jan 4 at 17:27
add a comment |
Some trainers in ML.NET support training with an initial predictor, which means you can use an existing predictor as the starting point for training with new data.
A test showing this can be found here with the relevant code being:
// Train the first predictor.
var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features",advancedSettings: s => s.NumThreads = 1);
var firstModel = trainer.Fit(trainData);
// Train the second predictor on the same data.
var secondTrainer = ml.BinaryClassification.Trainers.AveragedPerceptron("Label","Features");
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
var finalModel = secondTrainer.Train(new TrainContext(trainRoles, initialPredictor: firstModel.Model));
answered Nov 15 '18 at 22:34
Gal OshriGal Oshri
3182
I think the OP is interested in adding more data to the first predictor, not creating a second type of predictor. I'm interested in this as well.
– Webjedi
Jan 4 at 15:55
Is it as simple as train model1 and model2 and then do finalmodel=model1.append(model2)? If both models are trained the same way?
– Webjedi
Jan 4 at 17:27
add a comment |
Some trainers in ML.NET support training with an initial predictor, which means you can use an existing predictor as the starting point for training with new data.
A test showing this can be found here with the relevant code being:
// Train the first predictor.
var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features",advancedSettings: s => s.NumThreads = 1);
var firstModel = trainer.Fit(trainData);
// Train the second predictor on the same data.
var secondTrainer = ml.BinaryClassification.Trainers.AveragedPerceptron("Label","Features");
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
var finalModel = secondTrainer.Train(new TrainContext(trainRoles, initialPredictor: firstModel.Model));
answered Nov 15 '18 at 22:34
Gal OshriGal Oshri
3182
Some trainers in ML.NET support training with an initial predictor, which means you can use an existing predictor as the starting point for training with new data.
A test showing this can be found here with the relevant code being:
// Train the first predictor.
var trainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features",advancedSettings: s => s.NumThreads = 1);
var firstModel = trainer.Fit(trainData);
// Train the second predictor on the same data.
var secondTrainer = ml.BinaryClassification.Trainers.AveragedPerceptron("Label","Features");
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
var finalModel = secondTrainer.Train(new TrainContext(trainRoles, initialPredictor: firstModel.Model));
answered Nov 15 '18 at 22:34
Gal OshriGal Oshri
3182
answered Nov 15 '18 at 22:34
Gal OshriGal Oshri
3182
answered Nov 15 '18 at 22:34
Gal OshriGal Oshri
3182
answered Nov 15 '18 at 22:34
Gal OshriGal Oshri
3182
3182
I think the OP is interested in adding more data to the first predictor, not creating a second type of predictor. I'm interested in this as well.
– Webjedi
Jan 4 at 15:55
Is it as simple as train model1 and model2 and then do finalmodel=model1.append(model2)? If both models are trained the same way?
– Webjedi
Jan 4 at 17:27
add a comment |
I think the OP is interested in adding more data to the first predictor, not creating a second type of predictor. I'm interested in this as well.
– Webjedi
Jan 4 at 15:55
Is it as simple as train model1 and model2 and then do finalmodel=model1.append(model2)? If both models are trained the same way?
– Webjedi
Jan 4 at 17:27
I think the OP is interested in adding more data to the first predictor, not creating a second type of predictor. I'm interested in this as well.
– Webjedi
Jan 4 at 15:55
I think the OP is interested in adding more data to the first predictor, not creating a second type of predictor. I'm interested in this as well.
– Webjedi
Jan 4 at 15:55
Is it as simple as train model1 and model2 and then do finalmodel=model1.append(model2)? If both models are trained the same way?
– Webjedi
Jan 4 at 17:27
Is it as simple as train model1 and model2 and then do finalmodel=model1.append(model2)? If both models are trained the same way?
– Webjedi
Jan 4 at 17:27
add a comment |
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