/Ampep_Python

Primary LanguagePython

Ampep_Python

This project is for the Ampep project of using Python

Cross Validation Testing Case

Parameters

py cv.py [Feature] [Machine Learning Model] [Cross Validation Method] [Fold] [Trees] [Step]

Example

py cv.py CTDD RandomForestClassifier ShuffleSplit 10 100 30

Feature

The feature method is base on the iFeature. For example in this program, we use 'CTDD' feature.

Machine Learning Model

We support six training model and the default model is RandomForestClassifier.

  • RandomForestClassifier
  • BaggingClassifier
  • ExtraTreesClassifier
  • RandomTreesEmbedding
  • AdaBoostClassifier
  • GradientBoostingClassifier

Cross Validation Method

There are four cross-validation methods and the default method is ShuffleSplit.

  • ShuffleSplit
  • StratifiedKFold
  • StratifiedShuffleSplit
  • RepeatedStratifiedKFold

Fold

This parameter is for the cross-validation fold and the default value is 10.

Trees

Amount of trees. The default trees are 100.

Step

The number of looping for the training. Each loop will increases 100 trees. The default of step is 30.

Training Case

Parameters

py train.py [Feature] [Machine Learning Model] [Trees]

Example

py train.py CTDD RandomForestClassifier 800

Feature

The feature method is base on the iFeature. For example in this program, we use 'CTDD' feature.

Machine Learning Model

We support six training model and the default model is RandomForestClassifier.

  • RandomForestClassifier
  • BaggingClassifier
  • ExtraTreesClassifier
  • RandomTreesEmbedding
  • AdaBoostClassifier
  • GradientBoostingClassifier

Trees

Amount of trees. The default trees are 100.

Testing Case

Parameters

py test.py [Feature] [Fasta Path] [Model Path]

Example

py test.py CTDD './data/input.fasta' './model/RandomForestClassifier_800.pkl'

Feature

The feature method is base on the iFeature. For example in this program, we use 'CTDD' feature.

Fasta Path

The path of the input .fasta file.

Model Path

The path of the model file.