Java Code Examples for org.nd4j.evaluation.classification.Evaluation#stats()
The following examples show how to use
org.nd4j.evaluation.classification.Evaluation#stats() .
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Example 1
| Source File: EvalTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testConfusionMatrixStats() {
Evaluation e = new Evaluation();
INDArray c0 = Nd4j.create(new double[] {1, 0, 0}, new long[]{1, 3});
INDArray c1 = Nd4j.create(new double[] {0, 1, 0}, new long[]{1, 3});
INDArray c2 = Nd4j.create(new double[] {0, 0, 1}, new long[]{1, 3});
apply(e, 3, c2, c0); //Predicted class 2 when actually class 0, 3 times
apply(e, 2, c0, c1); //Predicted class 0 when actually class 1, 2 times
String s1 = " 0 0 3 | 0 = 0"; //First row: predicted 2, actual 0 - 3 times
String s2 = " 2 0 0 | 1 = 1"; //Second row: predicted 0, actual 1 - 2 times
String stats = e.stats();
assertTrue(stats, stats.contains(s1));
assertTrue(stats, stats.contains(s2));
}
Example 2
| Source File: EvalTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSingleClassBinaryClassification() {
Evaluation eval = new Evaluation(1);
for (int xe = 0; xe < 3; xe++) {
INDArray zero = Nd4j.create(1,1);
INDArray one = Nd4j.ones(1,1);
//One incorrect, three correct
eval.eval(one, zero);
eval.eval(one, one);
eval.eval(one, one);
eval.eval(zero, zero);
// System.out.println(eval.stats());
eval.stats();
assertEquals(0.75, eval.accuracy(), 1e-6);
assertEquals(4, eval.getNumRowCounter());
assertEquals(1, (int) eval.truePositives().get(0));
assertEquals(2, (int) eval.truePositives().get(1));
assertEquals(1, (int) eval.falseNegatives().get(1));
eval.reset();
}
}
Example 3
| Source File: EvalTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testEvalInvalid() {
Evaluation e = new Evaluation(5);
e.eval(0, 1);
e.eval(1, 0);
e.eval(1, 1);
// System.out.println(e.stats());
e.stats();
assertFalse(e.stats().contains("\uFFFD"));
}
Example 4
| Source File: EvalTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testLabelReset(){
Map<Integer,String> m = new HashMap<>();
m.put(0, "False");
m.put(1, "True");
Evaluation e1 = new Evaluation(m);
INDArray zero = Nd4j.create(new double[]{1,0}).reshape(1,2);
INDArray one = Nd4j.create(new double[]{0,1}).reshape(1,2);
e1.eval(zero, zero);
e1.eval(zero, zero);
e1.eval(one, zero);
e1.eval(one, one);
e1.eval(one, one);
e1.eval(one, one);
String s1 = e1.stats();
// System.out.println(s1);
e1.reset();
e1.eval(zero, zero);
e1.eval(zero, zero);
e1.eval(one, zero);
e1.eval(one, one);
e1.eval(one, one);
e1.eval(one, one);
String s2 = e1.stats();
assertEquals(s1, s2);
}
Example 5
| Source File: EvalTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testEval2() {
DataType dtypeBefore = Nd4j.defaultFloatingPointType();
Evaluation first = null;
String sFirst = null;
try {
for (DataType globalDtype : new DataType[]{DataType.DOUBLE, DataType.FLOAT, DataType.HALF, DataType.INT}) {
Nd4j.setDefaultDataTypes(globalDtype, globalDtype.isFPType() ? globalDtype : DataType.DOUBLE);
for (DataType lpDtype : new DataType[]{DataType.DOUBLE, DataType.FLOAT, DataType.HALF}) {
//Confusion matrix:
//actual 0 20 3
//actual 1 10 5
Evaluation evaluation = new Evaluation(Arrays.asList("class0", "class1"));
INDArray predicted0 = Nd4j.create(new double[]{1, 0}, new long[]{1, 2}).castTo(lpDtype);
INDArray predicted1 = Nd4j.create(new double[]{0, 1}, new long[]{1, 2}).castTo(lpDtype);
INDArray actual0 = Nd4j.create(new double[]{1, 0}, new long[]{1, 2}).castTo(lpDtype);
INDArray actual1 = Nd4j.create(new double[]{0, 1}, new long[]{1, 2}).castTo(lpDtype);
for (int i = 0; i < 20; i++) {
evaluation.eval(actual0, predicted0);
}
for (int i = 0; i < 3; i++) {
evaluation.eval(actual0, predicted1);
}
for (int i = 0; i < 10; i++) {
evaluation.eval(actual1, predicted0);
}
for (int i = 0; i < 5; i++) {
evaluation.eval(actual1, predicted1);
}
assertEquals(20, evaluation.truePositives().get(0), 0);
assertEquals(3, evaluation.falseNegatives().get(0), 0);
assertEquals(10, evaluation.falsePositives().get(0), 0);
assertEquals(5, evaluation.trueNegatives().get(0), 0);
assertEquals((20.0 + 5) / (20 + 3 + 10 + 5), evaluation.accuracy(), 1e-6);
String s = evaluation.stats();
if(first == null) {
first = evaluation;
sFirst = s;
} else {
assertEquals(first, evaluation);
assertEquals(sFirst, s);
}
}
}
} finally {
Nd4j.setDefaultDataTypes(dtypeBefore, dtypeBefore);
}
}
Example 6
| Source File: EvalTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testEvalMasking() {
int miniBatch = 5;
int nOut = 3;
int tsLength = 6;
INDArray labels = Nd4j.zeros(miniBatch, nOut, tsLength);
INDArray predicted = Nd4j.zeros(miniBatch, nOut, tsLength);
Nd4j.getRandom().setSeed(12345);
Random r = new Random(12345);
for (int i = 0; i < miniBatch; i++) {
for (int j = 0; j < tsLength; j++) {
INDArray rand = Nd4j.rand(1, nOut);
rand.divi(rand.sumNumber());
predicted.put(new INDArrayIndex[] {NDArrayIndex.point(i), all(), NDArrayIndex.point(j)},
rand);
int idx = r.nextInt(nOut);
labels.putScalar(new int[] {i, idx, j}, 1.0);
}
}
//Create a longer labels/predicted with mask for first and last time step
//Expect masked evaluation to be identical to original evaluation
INDArray labels2 = Nd4j.zeros(miniBatch, nOut, tsLength + 2);
labels2.put(new INDArrayIndex[] {all(), all(),
interval(1, tsLength + 1)}, labels);
INDArray predicted2 = Nd4j.zeros(miniBatch, nOut, tsLength + 2);
predicted2.put(new INDArrayIndex[] {all(), all(),
interval(1, tsLength + 1)}, predicted);
INDArray labelsMask = Nd4j.ones(miniBatch, tsLength + 2);
for (int i = 0; i < miniBatch; i++) {
labelsMask.putScalar(new int[] {i, 0}, 0.0);
labelsMask.putScalar(new int[] {i, tsLength + 1}, 0.0);
}
Evaluation evaluation = new Evaluation();
evaluation.evalTimeSeries(labels, predicted);
Evaluation evaluation2 = new Evaluation();
evaluation2.evalTimeSeries(labels2, predicted2, labelsMask);
// System.out.println(evaluation.stats());
// System.out.println(evaluation2.stats());
evaluation.stats();
evaluation2.stats();
assertEquals(evaluation.accuracy(), evaluation2.accuracy(), 1e-12);
assertEquals(evaluation.f1(), evaluation2.f1(), 1e-12);
assertMapEquals(evaluation.falsePositives(), evaluation2.falsePositives());
assertMapEquals(evaluation.falseNegatives(), evaluation2.falseNegatives());
assertMapEquals(evaluation.truePositives(), evaluation2.truePositives());
assertMapEquals(evaluation.trueNegatives(), evaluation2.trueNegatives());
for (int i = 0; i < nOut; i++)
assertEquals(evaluation.classCount(i), evaluation2.classCount(i));
}
Example 7
| Source File: EvalTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testEvalMethods() {
//Check eval(int,int) vs. eval(INDArray,INDArray)
Evaluation e1 = new Evaluation(4);
Evaluation e2 = new Evaluation(4);
INDArray i0 = Nd4j.create(new double[] {1, 0, 0, 0}, new long[]{1, 4});
INDArray i1 = Nd4j.create(new double[] {0, 1, 0, 0}, new long[]{1, 4});
INDArray i2 = Nd4j.create(new double[] {0, 0, 1, 0}, new long[]{1, 4});
INDArray i3 = Nd4j.create(new double[] {0, 0, 0, 1}, new long[]{1, 4});
e1.eval(i0, i0); //order: actual, predicted
e2.eval(0, 0); //order: predicted, actual
e1.eval(i0, i2);
e2.eval(2, 0);
e1.eval(i0, i2);
e2.eval(2, 0);
e1.eval(i1, i2);
e2.eval(2, 1);
e1.eval(i3, i3);
e2.eval(3, 3);
e1.eval(i3, i0);
e2.eval(0, 3);
e1.eval(i3, i0);
e2.eval(0, 3);
org.nd4j.evaluation.classification.ConfusionMatrix<Integer> cm = e1.getConfusionMatrix();
assertEquals(1, cm.getCount(0, 0)); //Order: actual, predicted
assertEquals(2, cm.getCount(0, 2));
assertEquals(1, cm.getCount(1, 2));
assertEquals(1, cm.getCount(3, 3));
assertEquals(2, cm.getCount(3, 0));
// System.out.println(e1.stats());
// System.out.println(e2.stats());
e1.stats();
e2.stats();
assertEquals(e1.stats(), e2.stats());
}
Example 8
| Source File: EvalTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testTopNAccuracy() {
Evaluation e = new Evaluation(null, 3);
INDArray i0 = Nd4j.create(new double[] {1, 0, 0, 0, 0}, new long[]{1, 5});
INDArray i1 = Nd4j.create(new double[] {0, 1, 0, 0, 0}, new long[]{1, 5});
INDArray p0_0 = Nd4j.create(new double[] {0.8, 0.05, 0.05, 0.05, 0.05}, new long[]{1, 5}); //class 0: highest prob
INDArray p0_1 = Nd4j.create(new double[] {0.4, 0.45, 0.05, 0.05, 0.05}, new long[]{1, 5}); //class 0: 2nd highest prob
INDArray p0_2 = Nd4j.create(new double[] {0.1, 0.45, 0.35, 0.05, 0.05}, new long[]{1, 5}); //class 0: 3rd highest prob
INDArray p0_3 = Nd4j.create(new double[] {0.1, 0.40, 0.30, 0.15, 0.05}, new long[]{1, 5}); //class 0: 4th highest prob
INDArray p1_0 = Nd4j.create(new double[] {0.05, 0.80, 0.05, 0.05, 0.05}, new long[]{1, 5}); //class 1: highest prob
INDArray p1_1 = Nd4j.create(new double[] {0.45, 0.40, 0.05, 0.05, 0.05}, new long[]{1, 5}); //class 1: 2nd highest prob
INDArray p1_2 = Nd4j.create(new double[] {0.35, 0.10, 0.45, 0.05, 0.05}, new long[]{1, 5}); //class 1: 3rd highest prob
INDArray p1_3 = Nd4j.create(new double[] {0.40, 0.10, 0.30, 0.15, 0.05}, new long[]{1, 5}); //class 1: 4th highest prob
// Correct TopNCorrect Total
e.eval(i0, p0_0); // 1 1 1
assertEquals(1.0, e.accuracy(), 1e-6);
assertEquals(1.0, e.topNAccuracy(), 1e-6);
assertEquals(1, e.getTopNCorrectCount());
assertEquals(1, e.getTopNTotalCount());
e.eval(i0, p0_1); // 1 2 2
assertEquals(0.5, e.accuracy(), 1e-6);
assertEquals(1.0, e.topNAccuracy(), 1e-6);
assertEquals(2, e.getTopNCorrectCount());
assertEquals(2, e.getTopNTotalCount());
e.eval(i0, p0_2); // 1 3 3
assertEquals(1.0 / 3, e.accuracy(), 1e-6);
assertEquals(1.0, e.topNAccuracy(), 1e-6);
assertEquals(3, e.getTopNCorrectCount());
assertEquals(3, e.getTopNTotalCount());
e.eval(i0, p0_3); // 1 3 4
assertEquals(0.25, e.accuracy(), 1e-6);
assertEquals(0.75, e.topNAccuracy(), 1e-6);
assertEquals(3, e.getTopNCorrectCount());
assertEquals(4, e.getTopNTotalCount());
e.eval(i1, p1_0); // 2 4 5
assertEquals(2.0 / 5, e.accuracy(), 1e-6);
assertEquals(4.0 / 5, e.topNAccuracy(), 1e-6);
e.eval(i1, p1_1); // 2 5 6
assertEquals(2.0 / 6, e.accuracy(), 1e-6);
assertEquals(5.0 / 6, e.topNAccuracy(), 1e-6);
e.eval(i1, p1_2); // 2 6 7
assertEquals(2.0 / 7, e.accuracy(), 1e-6);
assertEquals(6.0 / 7, e.topNAccuracy(), 1e-6);
e.eval(i1, p1_3); // 2 6 8
assertEquals(2.0 / 8, e.accuracy(), 1e-6);
assertEquals(6.0 / 8, e.topNAccuracy(), 1e-6);
assertEquals(6, e.getTopNCorrectCount());
assertEquals(8, e.getTopNTotalCount());
// System.out.println(e.stats());
e.stats();
}
Example 9
| Source File: EvalTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testEvalStatsBinaryCase(){
//Make sure we report class 1 precision/recall/f1 not macro averaged, for binary case
Evaluation e = new Evaluation();
INDArray l0 = Nd4j.createFromArray(new double[]{1,0}).reshape(1,2);
INDArray l1 = Nd4j.createFromArray(new double[]{0,1}).reshape(1,2);
e.eval(l1, l1);
e.eval(l1, l1);
e.eval(l1, l1);
e.eval(l0, l0);
e.eval(l1, l0);
e.eval(l1, l0);
e.eval(l0, l1);
double tp = 3;
double fp = 1;
double fn = 2;
double prec = tp / (tp + fp);
double rec = tp / (tp + fn);
double f1 = 2 * prec * rec / (prec + rec);
assertEquals(prec, e.precision(), 1e-6);
assertEquals(rec, e.recall(), 1e-6);
DecimalFormat df = new DecimalFormat("0.0000");
String stats = e.stats();
//System.out.println(stats);
String stats2 = stats.replaceAll("( )+", " ");
String recS = " Recall: " + df.format(rec);
String preS = " Precision: " + df.format(prec);
String f1S = "F1 Score: " + df.format(f1);
assertTrue(stats2, stats2.contains(recS));
assertTrue(stats2, stats2.contains(preS));
assertTrue(stats2, stats2.contains(f1S));
}
