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| /**
| * @license
| * Copyright 2018 Google LLC. All Rights Reserved.
| * Licensed under the Apache License, Version 2.0 (the "License");
| * you may not use this file except in compliance with the License.
| * You may obtain a copy of the License at
| *
| * http://www.apache.org/licenses/LICENSE-2.0
| *
| * Unless required by applicable law or agreed to in writing, software
| * distributed under the License is distributed on an "AS IS" BASIS,
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
| * See the License for the specific language governing permissions and
| * limitations under the License.
| * =============================================================================
| */
| import * as tf from '../index';
| import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
| import { expectArraysEqual } from '../test_util';
| /**
| * Unit tests for confusionMatrix().
| */
| describeWithFlags('confusionMatrix', ALL_ENVS, () => {
| // Reference (Python) TensorFlow code:
| //
| // ```py
| // import tensorflow as tf
| //
| // tf.enable_eager_execution()
| //
| // labels = tf.constant([0, 1, 2, 1, 0])
| // predictions = tf.constant([0, 2, 2, 1, 0])
| // out = tf.confusion_matrix(labels, predictions, 3)
| //
| // print(out)
| // ```
| it('3x3 all cases present in both labels and predictions', async () => {
| const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');
| const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');
| const numClasses = 3;
| const out = tf.math.confusionMatrix(labels, predictions, numClasses);
| expectArraysEqual(await out.data(), [2, 0, 0, 0, 1, 1, 0, 0, 1]);
| expect(out.dtype).toBe('int32');
| expect(out.shape).toEqual([3, 3]);
| });
| it('float32 arguments are accepted', async () => {
| const labels = tf.tensor1d([0, 1, 2, 1, 0], 'float32');
| const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'float32');
| const numClasses = 3;
| const out = tf.math.confusionMatrix(labels, predictions, numClasses);
| expectArraysEqual(await out.data(), [2, 0, 0, 0, 1, 1, 0, 0, 1]);
| expect(out.dtype).toBe('int32');
| expect(out.shape).toEqual([3, 3]);
| });
| // Reference (Python) TensorFlow code:
| //
| // ```py
| // import tensorflow as tf
| //
| // tf.enable_eager_execution()
| //
| // labels = tf.constant([3, 3, 2, 2, 1, 1, 0, 0])
| // predictions = tf.constant([2, 2, 2, 2, 0, 0, 0, 0])
| // out = tf.confusion_matrix(labels, predictions, 4)
| //
| // print(out)
| // ```
| it('4x4 all cases present in labels, but not predictions', async () => {
| const labels = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');
| const predictions = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');
| const numClasses = 4;
| const out = tf.math.confusionMatrix(labels, predictions, numClasses);
| expectArraysEqual(await out.data(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]);
| expect(out.dtype).toBe('int32');
| expect(out.shape).toEqual([4, 4]);
| });
| it('4x4 all cases present in predictions, but not labels', async () => {
| const labels = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');
| const predictions = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');
| const numClasses = 4;
| const out = tf.math.confusionMatrix(labels, predictions, numClasses);
| expectArraysEqual(await out.data(), [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0]);
| expect(out.dtype).toBe('int32');
| expect(out.shape).toEqual([4, 4]);
| });
| it('Plain arrays as inputs', async () => {
| const labels = [3, 3, 2, 2, 1, 1, 0, 0];
| const predictions = [2, 2, 2, 2, 0, 0, 0, 0];
| const numClasses = 4;
| const out = tf.math.confusionMatrix(labels, predictions, numClasses);
| expectArraysEqual(await out.data(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]);
| expect(out.dtype).toBe('int32');
| expect(out.shape).toEqual([4, 4]);
| });
| it('Int32Arrays as inputs', async () => {
| const labels = new Int32Array([3, 3, 2, 2, 1, 1, 0, 0]);
| const predictions = new Int32Array([2, 2, 2, 2, 0, 0, 0, 0]);
| const numClasses = 4;
| const out = tf.math.confusionMatrix(labels, predictions, numClasses);
| expectArraysEqual(await out.data(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]);
| expect(out.dtype).toBe('int32');
| expect(out.shape).toEqual([4, 4]);
| });
| // Reference (Python) TensorFlow code:
| //
| // ```py
| // import tensorflow as tf
| //
| // tf.enable_eager_execution()
| //
| // labels = tf.constant([0, 4])
| // predictions = tf.constant([4, 0])
| // out = tf.confusion_matrix(labels, predictions, 5)
| //
| // print(out)
| // ```
| it('5x5 predictions and labels both missing some cases', async () => {
| const labels = tf.tensor1d([0, 4], 'int32');
| const predictions = tf.tensor1d([4, 0], 'int32');
| const numClasses = 5;
| const out = tf.math.confusionMatrix(labels, predictions, numClasses);
| expectArraysEqual(await out.data(), [
| 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
| 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
| ]);
| expect(out.dtype).toBe('int32');
| expect(out.shape).toEqual([5, 5]);
| });
| it('Invalid numClasses leads to Error', () => {
| expect(() => tf.math.confusionMatrix(tf.tensor1d([0, 1]), tf.tensor1d([1, 0]), 2.5))
| .toThrowError(/numClasses .* positive integer.* got 2\.5/);
| });
| it('Incorrect tensor rank leads to Error', () => {
| expect(() => tf.math.confusionMatrix(
| // tslint:disable-next-line:no-any
| tf.scalar(0), tf.scalar(0), 1))
| .toThrowError(/rank .* 1.*got 0/);
| expect(() =>
| // tslint:disable-next-line:no-any
| tf.math.confusionMatrix(tf.zeros([3, 3]), tf.zeros([9]), 2))
| .toThrowError(/rank .* 1.*got 2/);
| expect(() =>
| // tslint:disable-next-line:no-any
| tf.math.confusionMatrix(tf.zeros([9]), tf.zeros([3, 3]), 2))
| .toThrowError(/rank .* 1.*got 2/);
| });
| it('Mismatch in lengths leads to Error', () => {
| expect(
| // tslint:disable-next-line:no-any
| () => tf.math.confusionMatrix(tf.zeros([3]), tf.zeros([9]), 2))
| .toThrowError(/Mismatch .* 3 vs.* 9/);
| });
| });
| //# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysEqual} from '../test_util';\n\n/**\n * Unit tests for confusionMatrix().\n */\n\ndescribeWithFlags('confusionMatrix', ALL_ENVS, () => {\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // labels = tf.constant([0, 1, 2, 1, 0])\n  // predictions = tf.constant([0, 2, 2, 1, 0])\n  // out = tf.confusion_matrix(labels, predictions, 3)\n  //\n  // print(out)\n  // ```\n  it('3x3 all cases present in both labels and predictions', async () => {\n    const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n    const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n    const numClasses = 3;\n    const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n    expectArraysEqual(await out.data(), [2, 0, 0, 0, 1, 1, 0, 0, 1]);\n    expect(out.dtype).toBe('int32');\n    expect(out.shape).toEqual([3, 3]);\n  });\n\n  it('float32 arguments are accepted', async () => {\n    const labels = tf.tensor1d([0, 1, 2, 1, 0], 'float32');\n    const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'float32');\n    const numClasses = 3;\n    const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n    expectArraysEqual(await out.data(), [2, 0, 0, 0, 1, 1, 0, 0, 1]);\n    expect(out.dtype).toBe('int32');\n    expect(out.shape).toEqual([3, 3]);\n  });\n\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // labels = tf.constant([3, 3, 2, 2, 1, 1, 0, 0])\n  // predictions = tf.constant([2, 2, 2, 2, 0, 0, 0, 0])\n  // out = tf.confusion_matrix(labels, predictions, 4)\n  //\n  // print(out)\n  // ```\n  it('4x4 all cases present in labels, but not predictions', async () => {\n    const labels = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');\n    const predictions = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');\n    const numClasses = 4;\n    const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n    expectArraysEqual(\n        await out.data(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]);\n    expect(out.dtype).toBe('int32');\n    expect(out.shape).toEqual([4, 4]);\n  });\n\n  it('4x4 all cases present in predictions, but not labels', async () => {\n    const labels = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');\n    const predictions = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');\n    const numClasses = 4;\n    const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n    expectArraysEqual(\n        await out.data(), [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0]);\n    expect(out.dtype).toBe('int32');\n    expect(out.shape).toEqual([4, 4]);\n  });\n\n  it('Plain arrays as inputs', async () => {\n    const labels: number[] = [3, 3, 2, 2, 1, 1, 0, 0];\n    const predictions: number[] = [2, 2, 2, 2, 0, 0, 0, 0];\n    const numClasses = 4;\n    const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n    expectArraysEqual(\n        await out.data(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]);\n    expect(out.dtype).toBe('int32');\n    expect(out.shape).toEqual([4, 4]);\n  });\n\n  it('Int32Arrays as inputs', async () => {\n    const labels = new Int32Array([3, 3, 2, 2, 1, 1, 0, 0]);\n    const predictions = new Int32Array([2, 2, 2, 2, 0, 0, 0, 0]);\n    const numClasses = 4;\n    const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n    expectArraysEqual(\n        await out.data(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]);\n    expect(out.dtype).toBe('int32');\n    expect(out.shape).toEqual([4, 4]);\n  });\n\n  // Reference (Python) TensorFlow code:\n  //\n  // ```py\n  // import tensorflow as tf\n  //\n  // tf.enable_eager_execution()\n  //\n  // labels = tf.constant([0, 4])\n  // predictions = tf.constant([4, 0])\n  // out = tf.confusion_matrix(labels, predictions, 5)\n  //\n  // print(out)\n  // ```\n  it('5x5 predictions and labels both missing some cases', async () => {\n    const labels = tf.tensor1d([0, 4], 'int32');\n    const predictions = tf.tensor1d([4, 0], 'int32');\n    const numClasses = 5;\n    const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n    expectArraysEqual(await out.data(), [\n      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n      0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0\n    ]);\n    expect(out.dtype).toBe('int32');\n    expect(out.shape).toEqual([5, 5]);\n  });\n\n  it('Invalid numClasses leads to Error', () => {\n    expect(\n        () => tf.math.confusionMatrix(\n            tf.tensor1d([0, 1]), tf.tensor1d([1, 0]), 2.5))\n        .toThrowError(/numClasses .* positive integer.* got 2\\.5/);\n  });\n\n  it('Incorrect tensor rank leads to Error', () => {\n    expect(\n        () => tf.math.confusionMatrix(\n            // tslint:disable-next-line:no-any\n            tf.scalar(0) as any, tf.scalar(0) as any, 1))\n        .toThrowError(/rank .* 1.*got 0/);\n    expect(\n        () =>\n            // tslint:disable-next-line:no-any\n        tf.math.confusionMatrix(tf.zeros([3, 3]) as any, tf.zeros([9]), 2))\n        .toThrowError(/rank .* 1.*got 2/);\n    expect(\n        () =>\n            // tslint:disable-next-line:no-any\n        tf.math.confusionMatrix(tf.zeros([9]), tf.zeros([3, 3]) as any, 2))\n        .toThrowError(/rank .* 1.*got 2/);\n  });\n\n  it('Mismatch in lengths leads to Error', () => {\n    expect(\n        // tslint:disable-next-line:no-any\n        () => tf.math.confusionMatrix(tf.zeros([3]) as any, tf.zeros([9]), 2))\n        .toThrowError(/Mismatch .* 3 vs.* 9/);\n  });\n});\n"]}
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