Examples with EvalBinaryClassBatchOp com.alibaba.alink.operator.batch.evaluation.EvalBinaryClassBatchOp used on opensource projects

Example 16 with EvalBinaryClassBatchOp

use of com.alibaba.alink.operator.batch.evaluation.EvalBinaryClassBatchOp in project Alink by alibaba.

the class Chap10 method c_3_2.

static void c_3_2() throws Exception {
    BatchOperator<?> train_data = new AkSourceBatchOp().setFilePath(DATA_DIR + TRAIN_FILE);
    BatchOperator<?> test_data = new AkSourceBatchOp().setFilePath(DATA_DIR + TEST_FILE);
    Pipeline pipeline = new Pipeline().add(new FeatureHasher().setSelectedCols(FEATURE_COL_NAMES).setCategoricalCols(CATEGORY_FEATURE_COL_NAMES).setOutputCol(VEC_COL_NAME)).add(new LogisticRegression().setVectorCol(VEC_COL_NAME).setLabelCol(LABEL_COL_NAME).setPredictionCol(PREDICTION_COL_NAME).setPredictionDetailCol(PRED_DETAIL_COL_NAME));
    pipeline.fit(train_data).transform(test_data).link(new EvalBinaryClassBatchOp().setPositiveLabelValueString("2").setLabelCol(LABEL_COL_NAME).setPredictionDetailCol(PRED_DETAIL_COL_NAME).lazyPrintMetrics());
    BatchOperator.execute();
}

Example 17 with EvalBinaryClassBatchOp

use of com.alibaba.alink.operator.batch.evaluation.EvalBinaryClassBatchOp in project Alink by alibaba.

the class Chap10 method c_1.

static void c_1() throws Exception {
    BatchOperator<?> train_data = new AkSourceBatchOp().setFilePath(DATA_DIR + TRAIN_FILE).select(CLAUSE_CREATE_FEATURES);
    BatchOperator<?> test_data = new AkSourceBatchOp().setFilePath(DATA_DIR + TEST_FILE).select(CLAUSE_CREATE_FEATURES);
    String[] new_features = ArrayUtils.removeElement(train_data.getColNames(), LABEL_COL_NAME);
    train_data.lazyPrint(5, "< new features >");
    LogisticRegressionTrainBatchOp trainer = new LogisticRegressionTrainBatchOp().setFeatureCols(new_features).setLabelCol(LABEL_COL_NAME);
    LogisticRegressionPredictBatchOp predictor = new LogisticRegressionPredictBatchOp().setPredictionCol(PREDICTION_COL_NAME).setPredictionDetailCol(PRED_DETAIL_COL_NAME);
    train_data.link(trainer);
    predictor.linkFrom(trainer, test_data);
    trainer.lazyPrintTrainInfo().lazyCollectTrainInfo(new Consumer<LinearModelTrainInfo>() {

        @Override
        public void accept(LinearModelTrainInfo linearModelTrainInfo) {
            printImportance(linearModelTrainInfo.getColNames(), linearModelTrainInfo.getImportance());
        }
    });
    predictor.link(new EvalBinaryClassBatchOp().setPositiveLabelValueString("2").setLabelCol(LABEL_COL_NAME).setPredictionDetailCol(PRED_DETAIL_COL_NAME).lazyPrintMetrics());
    BatchOperator.execute();
}

Example 18 with EvalBinaryClassBatchOp

use of com.alibaba.alink.operator.batch.evaluation.EvalBinaryClassBatchOp in project Alink by alibaba.

the class Chap10 method c_2.

static void c_2() throws Exception {
    BatchOperator<?> train_data = new AkSourceBatchOp().setFilePath(DATA_DIR + TRAIN_FILE).select(CLAUSE_CREATE_FEATURES);
    BatchOperator<?> test_data = new AkSourceBatchOp().setFilePath(DATA_DIR + TEST_FILE).select(CLAUSE_CREATE_FEATURES);
    String[] new_features = ArrayUtils.removeElement(train_data.getColNames(), LABEL_COL_NAME);
    train_data.lazyPrint(5, "< new features >");
    LogisticRegressionTrainBatchOp trainer = new LogisticRegressionTrainBatchOp().setFeatureCols(new_features).setLabelCol(LABEL_COL_NAME).setL1(0.01);
    LogisticRegressionPredictBatchOp predictor = new LogisticRegressionPredictBatchOp().setPredictionCol(PREDICTION_COL_NAME).setPredictionDetailCol(PRED_DETAIL_COL_NAME);
    train_data.link(trainer);
    predictor.linkFrom(trainer, test_data);
    trainer.lazyPrintTrainInfo().lazyCollectTrainInfo(new Consumer<LinearModelTrainInfo>() {

        @Override
        public void accept(LinearModelTrainInfo linearModelTrainInfo) {
            printImportance(linearModelTrainInfo.getColNames(), linearModelTrainInfo.getImportance());
        }
    });
    predictor.link(new EvalBinaryClassBatchOp().setPositiveLabelValueString("2").setLabelCol(LABEL_COL_NAME).setPredictionDetailCol(PRED_DETAIL_COL_NAME).lazyPrintMetrics());
    BatchOperator.execute();
}

Example 19 with EvalBinaryClassBatchOp

use of com.alibaba.alink.operator.batch.evaluation.EvalBinaryClassBatchOp in project Alink by alibaba.

the class Chap10 method c_3_1.

static void c_3_1() throws Exception {
    BatchOperator<?> train_data = new AkSourceBatchOp().setFilePath(DATA_DIR + TRAIN_FILE);
    BatchOperator<?> test_data = new AkSourceBatchOp().setFilePath(DATA_DIR + TEST_FILE);
    Pipeline pipeline = new Pipeline().add(new OneHotEncoder().setSelectedCols(CATEGORY_FEATURE_COL_NAMES).setEncode(Encode.VECTOR)).add(new VectorAssembler().setSelectedCols(FEATURE_COL_NAMES).setOutputCol(VEC_COL_NAME)).add(new LogisticRegression().setVectorCol(VEC_COL_NAME).setLabelCol(LABEL_COL_NAME).setPredictionCol(PREDICTION_COL_NAME).setPredictionDetailCol(PRED_DETAIL_COL_NAME));
    pipeline.fit(train_data).transform(test_data).link(new EvalBinaryClassBatchOp().setPositiveLabelValueString("2").setLabelCol(LABEL_COL_NAME).setPredictionDetailCol(PRED_DETAIL_COL_NAME).lazyPrintMetrics());
    BatchOperator.execute();
}

Example 20 with EvalBinaryClassBatchOp

use of com.alibaba.alink.operator.batch.evaluation.EvalBinaryClassBatchOp in project Alink by alibaba.

the class Chap11 method c_8.

static void c_8() throws Exception {
    AkSourceBatchOp test_data = new AkSourceBatchOp().setFilePath(DATA_DIR + TEST_FILE);
    AkSourceBatchOp train_sample = new AkSourceBatchOp().setFilePath(DATA_DIR + TRAIN_SAMPLE_FILE);
    String[] featureColNames = ArrayUtils.removeElement(test_data.getColNames(), LABEL_COL_NAME);
    new GbdtClassifier().setNumTrees(100).setMaxDepth(5).setMaxBins(256).setFeatureCols(featureColNames).setLabelCol(LABEL_COL_NAME).setPredictionCol(PREDICTION_COL_NAME).setPredictionDetailCol(PRED_DETAIL_COL_NAME).fit(train_sample).transform(test_data).link(new EvalBinaryClassBatchOp().setLabelCol(LABEL_COL_NAME).setPredictionDetailCol(PRED_DETAIL_COL_NAME).lazyPrintMetrics("GBDT with Stratified Sample"));
    BatchOperator.execute();
}