MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator().setMetricName("precision"); if (verbose) { System.out.println("N = " + trainingData.count()); System.out.println("K = " + numLabels); System.out.println("H = " + numHiddenUnits); System.out.println("training precision = " + evaluator.evaluate(predictionAndLabel));
@Test public void crossValidationWithLogisticRegression() { LogisticRegression lr = new LogisticRegression(); ParamMap[] lrParamMaps = new ParamGridBuilder() .addGrid(lr.regParam(), new double[]{0.001, 1000.0}) .addGrid(lr.maxIter(), new int[]{0, 10}) .build(); BinaryClassificationEvaluator eval = new BinaryClassificationEvaluator(); CrossValidator cv = new CrossValidator() .setEstimator(lr) .setEstimatorParamMaps(lrParamMaps) .setEvaluator(eval) .setNumFolds(3); CrossValidatorModel cvModel = cv.fit(dataset); LogisticRegression parent = (LogisticRegression) cvModel.bestModel().parent(); Assert.assertEquals(0.001, parent.getRegParam(), 0.0); Assert.assertEquals(10, parent.getMaxIter()); } }
@Test public void crossValidationWithLogisticRegression() { LogisticRegression lr = new LogisticRegression(); ParamMap[] lrParamMaps = new ParamGridBuilder() .addGrid(lr.regParam(), new double[]{0.001, 1000.0}) .addGrid(lr.maxIter(), new int[]{0, 10}) .build(); BinaryClassificationEvaluator eval = new BinaryClassificationEvaluator(); CrossValidator cv = new CrossValidator() .setEstimator(lr) .setEstimatorParamMaps(lrParamMaps) .setEvaluator(eval) .setNumFolds(3); CrossValidatorModel cvModel = cv.fit(dataset); LogisticRegression parent = (LogisticRegression) cvModel.bestModel().parent(); Assert.assertEquals(0.001, parent.getRegParam(), 0.0); Assert.assertEquals(10, parent.getMaxIter()); } }
MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator().setMetricName("precision"); double accuracy = evaluator.evaluate(predictions); System.out.println("training accuracy = " + accuracy);
@Test public void crossValidationWithLogisticRegression() { LogisticRegression lr = new LogisticRegression(); ParamMap[] lrParamMaps = new ParamGridBuilder() .addGrid(lr.regParam(), new double[]{0.001, 1000.0}) .addGrid(lr.maxIter(), new int[]{0, 10}) .build(); BinaryClassificationEvaluator eval = new BinaryClassificationEvaluator(); CrossValidator cv = new CrossValidator() .setEstimator(lr) .setEstimatorParamMaps(lrParamMaps) .setEvaluator(eval) .setNumFolds(3); CrossValidatorModel cvModel = cv.fit(dataset); LogisticRegression parent = (LogisticRegression) cvModel.bestModel().parent(); Assert.assertEquals(0.001, parent.getRegParam(), 0.0); Assert.assertEquals(10, parent.getMaxIter()); } }