/
knn_classifier_impl_test.dart
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/
knn_classifier_impl_test.dart
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import 'package:ml_algo/src/classifier/knn_classifier/knn_classifier_impl.dart';
import 'package:ml_algo/src/knn_solver/neigbour.dart';
import 'package:ml_dataframe/ml_dataframe.dart';
import 'package:ml_linalg/linalg.dart';
import 'package:ml_tech/unit_testing/matchers/iterable_2d_almost_equal_to.dart';
import 'package:mockito/mockito.dart';
import 'package:test/test.dart';
import '../../mocks.dart';
void main() {
group('KnnClassifierImpl', () {
group('constructor', () {
final solverMock = KnnSolverMock();
final kernelMock = KernelMock();
tearDown(() {
reset(solverMock);
reset(kernelMock);
});
test('should throw an exception if no class labels are provided', () {
final classLabels = <num>[];
final actual = () => KnnClassifierImpl(
'target',
classLabels,
kernelMock,
solverMock,
DType.float32,
);
expect(actual, throwsException);
});
});
group('predict method', () {
final solverMock = KnnSolverMock();
final kernelMock = KernelMock();
setUp(() => when(kernelMock.getWeightByDistance(any, any)).thenReturn(1));
tearDown(() {
reset(solverMock);
reset(kernelMock);
});
test('should throw an exception if no features are provided', () {
final classifier = KnnClassifierImpl(
'target',
[1],
kernelMock,
solverMock,
DType.float32,
);
final features = DataFrame.fromMatrix(Matrix.empty());
expect(() => classifier.predict(features), throwsException);
});
test('should return a dataframe with just one column, consisting of '
'weighted majority-based outcomes of closest observations of provided '
'features', () {
final classLabels = [1, 2, 3];
final classifier = KnnClassifierImpl(
'target',
classLabels,
kernelMock,
solverMock,
DType.float32,
);
final testFeatureMatrix = Matrix.fromList(
[
[10, 10, 10, 10],
[20, 20, 20, 20],
[30, 30, 30, 30],
],
);
final testFeatures = DataFrame.fromMatrix(testFeatureMatrix);
final mockedNeighbours = [
[
Neighbour(1, Vector.fromList([1])),
Neighbour(20, Vector.fromList([2])),
Neighbour(21, Vector.fromList([1])),
],
[
Neighbour(33, Vector.fromList([1])),
Neighbour(44, Vector.fromList([3])),
Neighbour(93, Vector.fromList([3])),
],
[
Neighbour(-1, Vector.fromList([2])),
Neighbour(-30, Vector.fromList([2])),
Neighbour(-40, Vector.fromList([1])),
],
];
when(kernelMock.getWeightByDistance(1)).thenReturn(10);
when(kernelMock.getWeightByDistance(20)).thenReturn(15);
when(kernelMock.getWeightByDistance(21)).thenReturn(10);
when(kernelMock.getWeightByDistance(33)).thenReturn(11);
when(kernelMock.getWeightByDistance(44)).thenReturn(15);
when(kernelMock.getWeightByDistance(93)).thenReturn(15);
when(kernelMock.getWeightByDistance(-1)).thenReturn(5);
when(kernelMock.getWeightByDistance(-30)).thenReturn(5);
when(kernelMock.getWeightByDistance(-40)).thenReturn(1);
when(solverMock.findKNeighbours(testFeatureMatrix))
.thenReturn(mockedNeighbours);
final actual = classifier.predict(testFeatures);
final expectedOutcomes = [
[1],
[3],
[2],
];
expect(actual.rows, equals(expectedOutcomes));
});
test('should return a dataframe, consisting of just one column with '
'a proper name', () {
final classLabels = [1, 2];
final classifier = KnnClassifierImpl(
'target',
classLabels,
kernelMock,
solverMock,
DType.float32,
);
final testFeatureMatrix = Matrix.fromList(
[
[10, 10, 10, 10],
],
);
final testFeatures = DataFrame.fromMatrix(testFeatureMatrix);
final mockedNeighbours = [
[
Neighbour(1, Vector.fromList([1])),
Neighbour(20, Vector.fromList([2])),
Neighbour(21, Vector.fromList([1])),
],
];
when(solverMock.findKNeighbours(testFeatureMatrix))
.thenReturn(mockedNeighbours);
final actual = classifier.predict(testFeatures);
expect(actual.header, equals(['target']));
});
test('should return a label of first neighbour among found k neighbours '
'if there is no major class', () {
final classLabels = [1, 2, 3];
final classifier = KnnClassifierImpl(
'target',
classLabels,
kernelMock,
solverMock,
DType.float32,
);
final testFeatureMatrix = Matrix.fromList(
[
[10, 10, 10, 10],
],
);
final testFeatures = DataFrame.fromMatrix(testFeatureMatrix);
final mockedNeighbours = [
[
Neighbour(-1, Vector.fromList([3])),
Neighbour(-30, Vector.fromList([2])),
Neighbour(-40, Vector.fromList([1])),
],
];
when(solverMock.findKNeighbours(testFeatureMatrix))
.thenReturn(mockedNeighbours);
final actual = classifier.predict(testFeatures);
final expectedOutcomes = [
[3],
];
expect(actual.rows, equals(expectedOutcomes));
});
test('should return a label of neighbours with bigger weights even if '
'they are not the majority', () {
final classLabels = [1, 2, 3];
final classifier = KnnClassifierImpl(
'target',
classLabels,
kernelMock,
solverMock,
DType.float32,
);
final testFeatureMatrix = Matrix.fromList(
[
[10, 10, 10, 10],
],
);
final testFeatures = DataFrame.fromMatrix(testFeatureMatrix);
final mockedNeighbours = [
[
Neighbour(0, Vector.fromList([1])),
Neighbour(2, Vector.fromList([2])),
Neighbour(3, Vector.fromList([1])),
],
];
when(kernelMock.getWeightByDistance(0)).thenReturn(1);
when(kernelMock.getWeightByDistance(2)).thenReturn(100);
when(kernelMock.getWeightByDistance(3)).thenReturn(5);
when(solverMock.findKNeighbours(testFeatureMatrix))
.thenReturn(mockedNeighbours);
final actual = classifier.predict(testFeatures);
final expectedOutcomes = [
[2],
];
expect(actual.rows, equals(expectedOutcomes));
});
});
group('predictProbability', () {
final solverMock = KnnSolverMock();
final kernelMock = KernelMock();
setUp(() => when(kernelMock.getWeightByDistance(any, any)).thenReturn(1));
tearDown(() {
reset(solverMock);
reset(kernelMock);
});
test('should return probability distribution of classes for each feature '
'row', () {
final classLabels = [1, 2, 3];
final classifier = KnnClassifierImpl(
'target',
classLabels,
kernelMock,
solverMock,
DType.float32,
);
final testFeatureMatrix = Matrix.fromList(
[
[10, 10, 10, 10],
[20, 20, 20, 20],
[30, 30, 30, 30],
],
);
final testFeatures = DataFrame.fromMatrix(testFeatureMatrix);
final mockedNeighbours = [
[
Neighbour(1, Vector.fromList([1])),
Neighbour(20, Vector.fromList([2])),
Neighbour(21, Vector.fromList([3])),
],
[
Neighbour(33, Vector.fromList([1])),
Neighbour(44, Vector.fromList([3])),
Neighbour(93, Vector.fromList([2])),
],
[
Neighbour(-1, Vector.fromList([2])),
Neighbour(-30, Vector.fromList([1])),
Neighbour(-40, Vector.fromList([3])),
],
];
when(kernelMock.getWeightByDistance(1)).thenReturn(10);
when(kernelMock.getWeightByDistance(20)).thenReturn(15);
when(kernelMock.getWeightByDistance(21)).thenReturn(10);
when(kernelMock.getWeightByDistance(33)).thenReturn(11);
when(kernelMock.getWeightByDistance(44)).thenReturn(15);
when(kernelMock.getWeightByDistance(93)).thenReturn(15);
when(kernelMock.getWeightByDistance(-1)).thenReturn(5);
when(kernelMock.getWeightByDistance(-30)).thenReturn(5);
when(kernelMock.getWeightByDistance(-40)).thenReturn(1);
when(solverMock.findKNeighbours(testFeatureMatrix))
.thenReturn(mockedNeighbours);
final actual = classifier.predictProbabilities(testFeatures);
final expectedProbabilities = [
[ 10 / 35, 15 / 35, 10 / 35 ],
[ 11 / 41, 15 / 41, 15 / 41 ],
[ 5 / 11, 5 / 11, 1 / 11 ],
];
expect(actual.rows, iterable2dAlmostEqualTo(expectedProbabilities));
});
test('should return probability distribution of classes where '
'probabilities of absent class labels are 0.0', () {
final classLabels = [1, 2, 3];
final classifier = KnnClassifierImpl(
'target',
classLabels,
kernelMock,
solverMock,
DType.float32,
);
final testFeatureMatrix = Matrix.fromList(
[
[10, 10, 10, 10],
[20, 20, 20, 20],
[30, 30, 30, 30],
],
);
final testFeatures = DataFrame.fromMatrix(testFeatureMatrix);
final mockedNeighbours = [
[
Neighbour(1, Vector.fromList([2])),
Neighbour(20, Vector.fromList([2])),
Neighbour(21, Vector.fromList([1])),
],
[
Neighbour(1, Vector.fromList([3])),
Neighbour(20, Vector.fromList([3])),
Neighbour(21, Vector.fromList([3])),
],
];
when(kernelMock.getWeightByDistance(1)).thenReturn(10);
when(kernelMock.getWeightByDistance(20)).thenReturn(15);
when(kernelMock.getWeightByDistance(21)).thenReturn(10);
when(solverMock.findKNeighbours(testFeatureMatrix))
.thenReturn(mockedNeighbours);
final actual = classifier.predictProbabilities(testFeatures);
final expectedProbabilities = [
[ 10 / 35, 25 / 35, 0.0 ],
[ 0.0, 0.0, 1.0 ],
];
expect(actual.rows, iterable2dAlmostEqualTo(expectedProbabilities));
});
test('should return a dataframe with a header, containing proper column '
'names', () {
final classLabels = [1, 2, 3];
final classifier = KnnClassifierImpl(
'target',
classLabels,
kernelMock,
solverMock,
DType.float32,
);
final testFeatureMatrix = Matrix.fromList(
[
[10, 10, 10, 10],
],
);
final testFeatures = DataFrame.fromMatrix(testFeatureMatrix);
final mockedNeighbours = [
[
Neighbour(1, Vector.fromList([1])),
],
];
when(solverMock.findKNeighbours(testFeatureMatrix))
.thenReturn(mockedNeighbours);
final actual = classifier.predictProbabilities(testFeatures);
expect(actual.header,
equals(['Class label 1', 'Class label 2', 'Class label 3']));
});
test('should consider initial order of column labels', () {
final firstClassLabel = 1;
final secondClassLabel = 2;
final thirdClassLabel = 3;
final classLabels = [thirdClassLabel, firstClassLabel, secondClassLabel];
final classifier = KnnClassifierImpl(
'target',
classLabels,
kernelMock,
solverMock,
DType.float32,
);
final testFeatureMatrix = Matrix.fromList(
[
[10, 10, 10, 10],
],
);
final testFeatures = DataFrame.fromMatrix(testFeatureMatrix);
final mockedNeighbours = [
[
Neighbour(1, Vector.fromList([firstClassLabel])),
Neighbour(10, Vector.fromList([secondClassLabel])),
Neighbour(20, Vector.fromList([thirdClassLabel])),
],
];
when(solverMock.findKNeighbours(testFeatureMatrix))
.thenReturn(mockedNeighbours);
final firstClassWeight = 100;
final secondClassWeight = 90;
final thirdClassWeight = 70;
when(kernelMock.getWeightByDistance(1)).thenReturn(firstClassWeight);
when(kernelMock.getWeightByDistance(10)).thenReturn(secondClassWeight);
when(kernelMock.getWeightByDistance(20)).thenReturn(thirdClassWeight);
final actual = classifier.predictProbabilities(testFeatures);
final predictedProbabilities = actual.rows;
expect(actual.header,
equals(['Class label 3', 'Class label 1', 'Class label 2']));
expect(predictedProbabilities, iterable2dAlmostEqualTo([
[thirdClassWeight / 260, firstClassWeight / 260, secondClassWeight / 260],
]));
});
test('should throw an exception if provided knn solver learned on wrong '
'class labels', () {
final firstClassLabel = 1;
final secondClassLabel = 2;
final thirdClassLabel = 3;
final unexpectedClassLabel = 100;
final classLabels = [thirdClassLabel, firstClassLabel, secondClassLabel];
final classifier = KnnClassifierImpl(
'target',
classLabels,
kernelMock,
solverMock,
DType.float32,
);
final testFeatureMatrix = Matrix.fromList(
[
[10, 10, 10, 10],
],
);
final testFeatures = DataFrame.fromMatrix(testFeatureMatrix);
final mockedNeighbours = [
[
Neighbour(20, Vector.fromList([unexpectedClassLabel])),
],
];
when(solverMock.findKNeighbours(testFeatureMatrix))
.thenReturn(mockedNeighbours);
final actual = () => classifier.predictProbabilities(testFeatures);
expect(actual, throwsException);
});
});
});
}