Exemplo de código da construção de um experimento de Sistema de Recomendação Híbrido

recommend-system.java

// Algoritmos de filtragem colaborativa
Task BMF_F1 = new Task("BiasedMatrixFactorization-F1.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F1/BiasedMatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample2345.txt --test-file=BD/Sample1.txt --recommender=BiasedMatrixFactorization --recommender-options=\"num_factors=14 bias_reg=0.042 frequency_regularization=False learn_rate=0.064 num_iter=32 bold_driver=True\" > ../run/out/bc/predictionsCF/BiasedMatrixFactorization-F1.out");
Task BMF_F2 = new Task("BiasedMatrixFactorization-F2.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F2/BiasedMatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1345.txt --test-file=BD/Sample2.txt --recommender=BiasedMatrixFactorization --recommender-options=\"num_factors=14 bias_reg=0.042 frequency_regularization=False learn_rate=0.064 num_iter=32 bold_driver=True\" > ../run/out/bc/predictionsCF/BiasedMatrixFactorization-F2.out");
Task BMF_F3 = new Task("BiasedMatrixFactorization-F3.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F3/BiasedMatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1245.txt --test-file=BD/Sample3.txt --recommender=BiasedMatrixFactorization --recommender-options=\"num_factors=14 bias_reg=0.042 frequency_regularization=False learn_rate=0.064 num_iter=32 bold_driver=True\" > ../run/out/bc/predictionsCF/BiasedMatrixFactorization-F3.out");
Task BMF_F4 = new Task("BiasedMatrixFactorization-F4.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F4/BiasedMatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1235.txt --test-file=BD/Sample4.txt --recommender=BiasedMatrixFactorization --recommender-options=\"num_factors=14 bias_reg=0.042 frequency_regularization=False learn_rate=0.064 num_iter=32 bold_driver=True\" > ../run/out/bc/predictionsCF/BiasedMatrixFactorization-F4.out");
Task BMF_F5 = new Task("BiasedMatrixFactorization-F5.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F5/BiasedMatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1234.txt --test-file=BD/Sample5.txt --recommender=BiasedMatrixFactorization --recommender-options=\"num_factors=14 bias_reg=0.042 frequency_regularization=False learn_rate=0.064 num_iter=32 bold_driver=True\" > ../run/out/bc/predictionsCF/BiasedMatrixFactorization-F5.out");

Task FWMF_F1 = new Task("FactorWiseMatrixFactorization-F1.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F1/FactorWiseMatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample2345.txt --test-file=BD/Sample1.txt --recommender=FactorWiseMatrixFactorization --recommender-options=\"num_factors=1 shrinkage=100 num_iter=52\" > ../run/out/bc/predictionsCF/FactorWiseMatrixFactorization-F1.out");
Task FWMF_F2 = new Task("FactorWiseMatrixFactorization-F2.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F2/FactorWiseMatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1345.txt --test-file=BD/Sample2.txt --recommender=FactorWiseMatrixFactorization --recommender-options=\"num_factors=1 shrinkage=100 num_iter=52\" > ../run/out/bc/predictionsCF/FactorWiseMatrixFactorization-F2.out");
Task FWMF_F3 = new Task("FactorWiseMatrixFactorization-F3.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F3/FactorWiseMatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1245.txt --test-file=BD/Sample3.txt --recommender=FactorWiseMatrixFactorization --recommender-options=\"num_factors=1 shrinkage=100 num_iter=52\" > ../run/out/bc/predictionsCF/FactorWiseMatrixFactorization-F3.out");
Task FWMF_F4 = new Task("FactorWiseMatrixFactorization-F4.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F4/FactorWiseMatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1235.txt --test-file=BD/Sample4.txt --recommender=FactorWiseMatrixFactorization --recommender-options=\"num_factors=1 shrinkage=100 num_iter=52\" > ../run/out/bc/predictionsCF/FactorWiseMatrixFactorization-F4.out");
Task FWMF_F5 = new Task("FactorWiseMatrixFactorization-F5.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F5/FactorWiseMatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1234.txt --test-file=BD/Sample5.txt --recommender=FactorWiseMatrixFactorization --recommender-options=\"num_factors=1 shrinkage=100 num_iter=52\" > ../run/out/bc/predictionsCF/FactorWiseMatrixFactorization-F5.out");

Task LFLLM_F1 = new Task("LatentFeatureLogLinearModel-F1.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F1/LatentFeatureLogLinearModel.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample2345.txt --test-file=BD/Sample1.txt --recommender=LatentFeatureLogLinearModel --recommender-options=\"num_factors=1 bias_reg=0.231 reg_u=0.015 reg_i=0.015 frequency_regularization=False learn_rate=0.099 bias_learn_rate=1 num_iter=60 loss=RMSE\" > ../run/out/bc/predictionsCF/LatentFeatureLogLinearModel-F1.out");
Task LFLLM_F2 = new Task("LatentFeatureLogLinearModel-F2.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F2/LatentFeatureLogLinearModel.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1345.txt --test-file=BD/Sample2.txt --recommender=LatentFeatureLogLinearModel --recommender-options=\"num_factors=1 bias_reg=0.231 reg_u=0.015 reg_i=0.015 frequency_regularization=False learn_rate=0.099 bias_learn_rate=1 num_iter=60 loss=RMSE\" > ../run/out/bc/predictionsCF/LatentFeatureLogLinearModel-F2.out");
Task LFLLM_F3 = new Task("LatentFeatureLogLinearModel-F3.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F3/LatentFeatureLogLinearModel.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1245.txt --test-file=BD/Sample3.txt --recommender=LatentFeatureLogLinearModel --recommender-options=\"num_factors=1 bias_reg=0.231 reg_u=0.015 reg_i=0.015 frequency_regularization=False learn_rate=0.099 bias_learn_rate=1 num_iter=60 loss=RMSE\" > ../run/out/bc/predictionsCF/LatentFeatureLogLinearModel-F3.out");
Task LFLLM_F4 = new Task("LatentFeatureLogLinearModel-F4.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F4/LatentFeatureLogLinearModel.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1235.txt --test-file=BD/Sample4.txt --recommender=LatentFeatureLogLinearModel --recommender-options=\"num_factors=1 bias_reg=0.231 reg_u=0.015 reg_i=0.015 frequency_regularization=False learn_rate=0.099 bias_learn_rate=1 num_iter=60 loss=RMSE\" > ../run/out/bc/predictionsCF/LatentFeatureLogLinearModel-F4.out");
Task LFLLM_F5 = new Task("LatentFeatureLogLinearModel-F5.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F5/LatentFeatureLogLinearModel.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1234.txt --test-file=BD/Sample5.txt --recommender=LatentFeatureLogLinearModel --recommender-options=\"num_factors=1 bias_reg=0.231 reg_u=0.015 reg_i=0.015 frequency_regularization=False learn_rate=0.099 bias_learn_rate=1 num_iter=60 loss=RMSE\" > ../run/out/bc/predictionsCF/LatentFeatureLogLinearModel-F5.out");

Task MF_F1 = new Task("MatrixFactorization-F1.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F1/MatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample2345.txt --test-file=BD/Sample1.txt --recommender=MatrixFactorization --recommender-options=\"num_factors=4 regularization=0.266 learn_rate=0.098 num_iter=30\" > ../run/out/bc/predictionsCF/MatrixFactorization-F1.out");
Task MF_F2 = new Task("MatrixFactorization-F2.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F2/MatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1345.txt --test-file=BD/Sample2.txt --recommender=MatrixFactorization --recommender-options=\"num_factors=4 regularization=0.266 learn_rate=0.098 num_iter=30\" > ../run/out/bc/predictionsCF/MatrixFactorization-F2.out");
Task MF_F3 = new Task("MatrixFactorization-F3.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F3/MatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1245.txt --test-file=BD/Sample3.txt --recommender=MatrixFactorization --recommender-options=\"num_factors=4 regularization=0.266 learn_rate=0.098 num_iter=30\" > ../run/out/bc/predictionsCF/MatrixFactorization-F3.out");
Task MF_F4 = new Task("MatrixFactorization-F4.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F4/MatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1235.txt --test-file=BD/Sample4.txt --recommender=MatrixFactorization --recommender-options=\"num_factors=4 regularization=0.266 learn_rate=0.098 num_iter=30\" > ../run/out/bc/predictionsCF/MatrixFactorization-F4.out");
Task MF_F5 = new Task("MatrixFactorization-F5.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F5/MatrixFactorization.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1234.txt --test-file=BD/Sample5.txt --recommender=MatrixFactorization --recommender-options=\"num_factors=4 regularization=0.266 learn_rate=0.098 num_iter=30\" > ../run/out/bc/predictionsCF/MatrixFactorization-F5.out");

Task SCAFM_F1 = new Task("SigmoidCombinedAsymmetricFactorModel-F1.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F1/SigmoidCombinedAsymmetricFactorModel.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample2345.txt --test-file=BD/Sample1.txt --recommender=SigmoidCombinedAsymmetricFactorModel --recommender-options=\"num_factors=34 regularization=0.007 bias_reg=0.226 frequency_regularization=True learn_rate=0.088 bias_learn_rate=0.088 learn_rate_decay=1 num_iter=58 loss=RMSE\" > ../run/out/bc/predictionsCF/SigmoidCombinedAsymmetricFactorModel-F1.out");
Task SCAFM_F2 = new Task("SigmoidCombinedAsymmetricFactorModel-F2.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F2/SigmoidCombinedAsymmetricFactorModel.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1345.txt --test-file=BD/Sample2.txt --recommender=SigmoidCombinedAsymmetricFactorModel --recommender-options=\"num_factors=34 regularization=0.007 bias_reg=0.226 frequency_regularization=True learn_rate=0.088 bias_learn_rate=0.088 learn_rate_decay=1 num_iter=58 loss=RMSE\" > ../run/out/bc/predictionsCF/SigmoidCombinedAsymmetricFactorModel-F2.out");
Task SCAFM_F3 = new Task("SigmoidCombinedAsymmetricFactorModel-F3.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F3/SigmoidCombinedAsymmetricFactorModel.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1245.txt --test-file=BD/Sample3.txt --recommender=SigmoidCombinedAsymmetricFactorModel --recommender-options=\"num_factors=34 regularization=0.007 bias_reg=0.226 frequency_regularization=True learn_rate=0.088 bias_learn_rate=0.088 learn_rate_decay=1 num_iter=58 loss=RMSE\" > ../run/out/bc/predictionsCF/SigmoidCombinedAsymmetricFactorModel-F3.out");
Task SCAFM_F4 = new Task("SigmoidCombinedAsymmetricFactorModel-F4.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F4/SigmoidCombinedAsymmetricFactorModel.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1235.txt --test-file=BD/Sample4.txt --recommender=SigmoidCombinedAsymmetricFactorModel --recommender-options=\"num_factors=34 regularization=0.007 bias_reg=0.226 frequency_regularization=True learn_rate=0.088 bias_learn_rate=0.088 learn_rate_decay=1 num_iter=58 loss=RMSE\" > ../run/out/bc/predictionsCF/SigmoidCombinedAsymmetricFactorModel-F4.out");
Task SCAFM_F5 = new Task("SigmoidCombinedAsymmetricFactorModel-F5.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F5/SigmoidCombinedAsymmetricFactorModel.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1234.txt --test-file=BD/Sample5.txt --recommender=SigmoidCombinedAsymmetricFactorModel --recommender-options=\"num_factors=34 regularization=0.007 bias_reg=0.226 frequency_regularization=True learn_rate=0.088 bias_learn_rate=0.088 learn_rate_decay=1 num_iter=58 loss=RMSE\" > ../run/out/bc/predictionsCF/SigmoidCombinedAsymmetricFactorModel-F5.out");

Task SCDPP_F1 = new Task("SVDPlusPlus-F1.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F1/SVDPlusPlus.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample2345.txt --test-file=BD/Sample1.txt --recommender=SVDPlusPlus --recommender-options=\"num_factors=1 regularization=0.131 bias_reg=0.091 frequency_regularization=False learn_rate=0.006 num_iter=45\" > ../run/out/bc/predictionsCF/SVDPlusPlus-F1.out");
Task SCDPP_F2 = new Task("SVDPlusPlus-F2.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F2/SVDPlusPlus.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1345.txt --test-file=BD/Sample2.txt --recommender=SVDPlusPlus --recommender-options=\"num_factors=1 regularization=0.131 bias_reg=0.091 frequency_regularization=False learn_rate=0.006 num_iter=45\" > ../run/out/bc/predictionsCF/SVDPlusPlus-F2.out");
Task SCDPP_F3 = new Task("SVDPlusPlus-F3.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F3/SVDPlusPlus.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1245.txt --test-file=BD/Sample3.txt --recommender=SVDPlusPlus --recommender-options=\"num_factors=1 regularization=0.131 bias_reg=0.091 frequency_regularization=False learn_rate=0.006 num_iter=45\" > ../run/out/bc/predictionsCF/SVDPlusPlus-F3.out");
Task SCDPP_F4 = new Task("SVDPlusPlus-F4.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F4/SVDPlusPlus.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1235.txt --test-file=BD/Sample4.txt --recommender=SVDPlusPlus --recommender-options=\"num_factors=1 regularization=0.131 bias_reg=0.091 frequency_regularization=False learn_rate=0.006 num_iter=45\" > ../run/out/bc/predictionsCF/SVDPlusPlus-F4.out");
Task SCDPP_F5 = new Task("SVDPlusPlus-F5.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F5/SVDPlusPlus.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1234.txt --test-file=BD/Sample5.txt --recommender=SVDPlusPlus --recommender-options=\"num_factors=1 regularization=0.131 bias_reg=0.091 frequency_regularization=False learn_rate=0.006 num_iter=45\" > ../run/out/bc/predictionsCF/SVDPlusPlus-F5.out");

Task UKNN_F1 = new Task("UserKNN-F1.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F1/UserKNN.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample2345.txt --test-file=BD/Sample1.txt --recommender=UserKNN --recommender-options=\"k=103 correlation=Pearson weighted_binary=True\" > ../run/out/bc/predictionsCF/UserKNN-F1.out");
Task UKNN_F2 = new Task("UserKNN-F2.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F2/UserKNN.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1345.txt --test-file=BD/Sample2.txt --recommender=UserKNN --recommender-options=\"k=103 correlation=Pearson weighted_binary=True\" > ../run/out/bc/predictionsCF/UserKNN-F2.out");
Task UKNN_F3 = new Task("UserKNN-F3.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F3/UserKNN.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1245.txt --test-file=BD/Sample3.txt --recommender=UserKNN --recommender-options=\"k=103 correlation=Pearson weighted_binary=True\" > ../run/out/bc/predictionsCF/UserKNN-F3.out");
Task UKNN_F4 = new Task("UserKNN-F4.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F4/UserKNN.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1235.txt --test-file=BD/Sample4.txt --recommender=UserKNN --recommender-options=\"k=103 correlation=Pearson weighted_binary=True\" > ../run/out/bc/predictionsCF/UserKNN-F4.out");
Task UKNN_F5 = new Task("UserKNN-F5.out", "../../MyMediaLite-3.10/bin/rating_prediction --prediction-file=Predictions/F5/UserKNN.txt --prediction-line={0},{1},{2},0 --training-file=BD/Sample1234.txt --test-file=BD/Sample5.txt --recommender=UserKNN --recommender-options=\"k=103 correlation=Pearson weighted_binary=True\" > ../run/out/bc/predictionsCF/UserKNN-F5.out");

ArrayList<Task> filteringTasks = new ArrayList<>(Arrays.asList(
        BMF_F1, BMF_F2, BMF_F3, BMF_F4, BMF_F5,
        FWMF_F1, FWMF_F2, FWMF_F3, FWMF_F4, FWMF_F5,
        LFLLM_F1, LFLLM_F2, LFLLM_F3, LFLLM_F4, LFLLM_F5,
        MF_F1, MF_F2, MF_F3, MF_F4, MF_F5,
        SCAFM_F1, SCAFM_F2, SCAFM_F3, SCAFM_F4, SCAFM_F5,
        SCDPP_F1, SCDPP_F2, SCDPP_F3, SCDPP_F4, SCDPP_F5,
        UKNN_F1, UKNN_F2, UKNN_F3, UKNN_F4, UKNN_F5));

// -------------------------------------------------------------------------------------------------------------
// Preparação dos resultados para o SciKitLearn
Task createFiles_HR_F1 = new Task("createFiles-HR-F1.out", "./run/createScikitFiles.exe Bookcrossing ratings.txt F1 Predictions Scikit > ./run/out/bc/scikit/createFiles-HR-F1.out");
Task createFiles_HR_F2 = new Task("createFiles-HR-F2.out", "./run/createScikitFiles.exe Bookcrossing ratings.txt F2 Predictions Scikit > ./run/out/bc/scikit/createFiles-HR-F2.out");
Task createFiles_HR_F3 = new Task("createFiles-HR-F3.out", "./run/createScikitFiles.exe Bookcrossing ratings.txt F3 Predictions Scikit > ./run/out/bc/scikit/createFiles-HR-F3.out");
Task createFiles_HR_F4 = new Task("createFiles-HR-F4.out", "./run/createScikitFiles.exe Bookcrossing ratings.txt F4 Predictions Scikit > ./run/out/bc/scikit/createFiles-HR-F4.out");
Task createFiles_HR_F5 = new Task("createFiles-HR-F5.out", "./run/createScikitFiles.exe Bookcrossing ratings.txt F5 Predictions Scikit > ./run/out/bc/scikit/createFiles-HR-F5.out");

createFiles_HR_F1.withDependencies(filteringTasks);
createFiles_HR_F2.withDependencies(filteringTasks);
createFiles_HR_F3.withDependencies(filteringTasks);
createFiles_HR_F4.withDependencies(filteringTasks);
createFiles_HR_F5.withDependencies(filteringTasks);

ArrayList<Task> preparingTasks = new ArrayList<>(Arrays.asList(
        createFiles_HR_F1,
        createFiles_HR_F2,
        createFiles_HR_F3,
        createFiles_HR_F4,
        createFiles_HR_F5));

// -------------------------------------------------------------------------------------------------------------
// Execução dos métodos híbridos
Task scikitTP_F1_01_LSVR = new Task("scikitTunedPredictions-F1-01-LinearSVR.out", "python -u -W ignore run/scikitPrediction.py Bookcrossing/ Scikit/ F1 LinearSVR PredictionsSkl > run/out/bc/prediction/scikitTunedPredictions-F1-01-LinearSVR.out");
Task scikitTP_F2_01_LSVR = new Task("scikitTunedPredictions-F2-01-LinearSVR.out", "python -u -W ignore run/scikitPrediction.py Bookcrossing/ Scikit/ F2 LinearSVR PredictionsSkl > run/out/bc/prediction/scikitTunedPredictions-F2-01-LinearSVR.out");
Task scikitTP_F3_01_LSVR = new Task("scikitTunedPredictions-F3-01-LinearSVR.out", "python -u -W ignore run/scikitPrediction.py Bookcrossing/ Scikit/ F3 LinearSVR PredictionsSkl > run/out/bc/prediction/scikitTunedPredictions-F3-01-LinearSVR.out");
Task scikitTP_F4_01_LSVR = new Task("scikitTunedPredictions-F4-01-LinearSVR.out", "python -u -W ignore run/scikitPrediction.py Bookcrossing/ Scikit/ F4 LinearSVR PredictionsSkl > run/out/bc/prediction/scikitTunedPredictions-F4-01-LinearSVR.out");
Task scikitTP_F5_01_LSVR = new Task("scikitTunedPredictions-F5-01-LinearSVR.out", "python -u -W ignore run/scikitPrediction.py Bookcrossing/ Scikit/ F5 LinearSVR PredictionsSkl > run/out/bc/prediction/scikitTunedPredictions-F5-01-LinearSVR.out");

Task scikitTP_F1_01_Ridge = new Task("scikitTunedPredictions-F1-01-Ridge.out", "python -u -W ignore run/scikitPrediction.py Bookcrossing/ Scikit/ F1 Ridge PredictionsSkl > run/out/bc/prediction/scikitTunedPredictions-F1-01-Ridge.out");
Task scikitTP_F2_01_Ridge = new Task("scikitTunedPredictions-F2-01-Ridge.out", "python -u -W ignore run/scikitPrediction.py Bookcrossing/ Scikit/ F2 Ridge PredictionsSkl > run/out/bc/prediction/scikitTunedPredictions-F2-01-Ridge.out");
Task scikitTP_F3_01_Ridge = new Task("scikitTunedPredictions-F3-01-Ridge.out", "python -u -W ignore run/scikitPrediction.py Bookcrossing/ Scikit/ F3 Ridge PredictionsSkl > run/out/bc/prediction/scikitTunedPredictions-F3-01-Ridge.out");
Task scikitTP_F4_01_Ridge = new Task("scikitTunedPredictions-F4-01-Ridge.out", "python -u -W ignore run/scikitPrediction.py Bookcrossing/ Scikit/ F4 Ridge PredictionsSkl > run/out/bc/prediction/scikitTunedPredictions-F4-01-Ridge.out");
Task scikitTP_F5_01_Ridge = new Task("scikitTunedPredictions-F5-01-Ridge.out", "python -u -W ignore run/scikitPrediction.py Bookcrossing/ Scikit/ F5 Ridge PredictionsSkl > run/out/bc/prediction/scikitTunedPredictions-F5-01-Ridge.out");

scikitTP_F1_01_LSVR.withDependencies(preparingTasks);
scikitTP_F2_01_LSVR.withDependencies(preparingTasks);
scikitTP_F3_01_LSVR.withDependencies(preparingTasks);
scikitTP_F4_01_LSVR.withDependencies(preparingTasks);
scikitTP_F5_01_LSVR.withDependencies(preparingTasks);

scikitTP_F1_01_Ridge.withDependencies(preparingTasks);
scikitTP_F2_01_Ridge.withDependencies(preparingTasks);
scikitTP_F3_01_Ridge.withDependencies(preparingTasks);
scikitTP_F4_01_Ridge.withDependencies(preparingTasks);
scikitTP_F5_01_Ridge.withDependencies(preparingTasks);

ArrayList<Task> scikitTasks = new ArrayList<>(Arrays.asList(
        scikitTP_F1_01_LSVR, scikitTP_F2_01_LSVR, scikitTP_F3_01_LSVR, scikitTP_F4_01_LSVR, scikitTP_F5_01_LSVR,
        scikitTP_F1_01_Ridge, scikitTP_F2_01_Ridge, scikitTP_F3_01_Ridge, scikitTP_F4_01_Ridge, scikitTP_F5_01_Ridge));

// -------------------------------------------------------------------------------------------------------------
// Avaliação
Task executeEMC_F1 = new Task("executeEvaluationMetricsCalculator-R1-<MO_FOLD>-F1<GROUP_OUT>.out", "java -Xms4G -Xmx6G -jar run/EvaluationRunner.jar <BD> F1 Predictions PredictionsSkl Measures > run/out/<OUT>/eval/executeEvaluationMetricsCalculator-R1-<MO_FOLD>-F1<GROUP_OUT>.out");
Task executeEMC_F2 = new Task("executeEvaluationMetricsCalculator-R1-<MO_FOLD>-F2<GROUP_OUT>.out", "java -Xms4G -Xmx6G -jar run/EvaluationRunner.jar <BD> F2 Predictions PredictionsSkl Measures > run/out/<OUT>/eval/executeEvaluationMetricsCalculator-R1-<MO_FOLD>-F2<GROUP_OUT>.out");
Task executeEMC_F3 = new Task("executeEvaluationMetricsCalculator-R1-<MO_FOLD>-F3<GROUP_OUT>.out", "java -Xms4G -Xmx6G -jar run/EvaluationRunner.jar <BD> F3 Predictions PredictionsSkl Measures > run/out/<OUT>/eval/executeEvaluationMetricsCalculator-R1-<MO_FOLD>-F3<GROUP_OUT>.out");
Task executeEMC_F4 = new Task("executeEvaluationMetricsCalculator-R1-<MO_FOLD>-F4<GROUP_OUT>.out", "java -Xms4G -Xmx6G -jar run/EvaluationRunner.jar <BD> F4 Predictions PredictionsSkl Measures > run/out/<OUT>/eval/executeEvaluationMetricsCalculator-R1-<MO_FOLD>-F4<GROUP_OUT>.out");
Task executeEMC_F5 = new Task("executeEvaluationMetricsCalculator-R1-<MO_FOLD>-F5<GROUP_OUT>.out", "java -Xms4G -Xmx6G -jar run/EvaluationRunner.jar <BD> F5 Predictions PredictionsSkl Measures > run/out/<OUT>/eval/executeEvaluationMetricsCalculator-R1-<MO_FOLD>-F5<GROUP_OUT>.out");

executeEMC_F1.withDependencies(scikitTasks);
executeEMC_F2.withDependencies(scikitTasks);
executeEMC_F3.withDependencies(scikitTasks);
executeEMC_F4.withDependencies(scikitTasks);
executeEMC_F5.withDependencies(scikitTasks);

ArrayList<Task> evalueatingTasks = new ArrayList<>(Arrays.asList(
        executeEMC_F1,
        executeEMC_F2,
        executeEMC_F3,
        executeEMC_F4,
        executeEMC_F5));

// -------------------------------------------------------------------------------------------------------------
// Execução

ArrayList<Task> allTasks = new ArrayList<>();
allTasks.addAll(filteringTasks);
allTasks.addAll(preparingTasks);
allTasks.addAll(scikitTasks);
allTasks.addAll(evalueatingTasks);

Experiment experiment = new Experiment("Recomendation System", allTasks);
experiment.execute();