An integrated machine learning API for scikit-learn.

Support function:

• train_valid.py: training + N-fold validation (multi-thread)
• train_pred.py: training + prediction
• build_data.py: generate svm-format data from data list
• merge_svmfeature.py: merge svm-format features

Support learning model(abbreviation):

• SVC(svm)
• Linear SVC(linearsvm)
• Linear model with Stochastic Gradient Descent(SGD)
• Logistic Regression(LR)
• Ridge Regression(ridge)
• Random Forest(RF)
• AdaBoost(adaboost)
• Gradient Boosting(GB)
• K-Nearest neighbors(KNN)
• Gaussian Naive Bayesian(GNB)
• Linear Discriminant Analysis(LDA)

(Specific options for each model are described later)

Train by SVM with 5-fold cross validation and 8-thread processing:

  ./train_valid.py -i TRAIN_FILE -m svm -f 5 -th 8

Train and predict by SVM and output predicted label:

  ./train_pred.py -i TRAIN_FILE -t TEST_FILE -m svm -o PREDICT_FILE

Generate svm-format feature from list:

  ./build_data.py -l LIST -i INPUT_DIR -e EXT -o OUTPUT_FILE

Merge 3 svm-format features:

  ./merge_svmfeature.py -i FILE1 FILE2 FILE3 -o OUTPUT_FILE

Sample 100 training data and 50 testing data from a list:

  ./sample_list.py -i INPUT_LIST -n 100 500 -o TRAIN_LIST TEST_LIST