@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2021 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';
async function expectResult(result, nGrams, nGramsSplits) {
expectArraysEqual(await result.nGrams.data(), nGrams);
expectArraysEqual(await result.nGramsSplits.data(), nGramsSplits);
expect(result.nGrams.shape).toEqual([nGrams.length]);
expect(result.nGramsSplits.shape).toEqual([nGramsSplits.length]);
expect(result.nGrams.dtype).toEqual('string');
expect(result.nGramsSplits.dtype).toEqual('int32');
}
describeWithFlags('stringNGrams', ALL_ENVS, () => {
it('padded trigrams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [3], 'LP', 'RP', -1, false);
const nGrams = [
'LP|LP|a', 'LP|a|b', 'a|b|c', 'b|c|d', 'c|d|RP', 'd|RP|RP',
'LP|LP|e', 'LP|e|f', 'e|f|RP', 'f|RP|RP' // 1
];
const nGramsSplits = [0, 6, 10];
await expectResult(result, nGrams, nGramsSplits);
});
it('padded bigrams and trigrams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [2, 3], 'LP', 'RP', -1, false);
const nGrams = [
'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP', 'LP|LP|a', 'LP|a|b', 'a|b|c',
'b|c|d', 'c|d|RP', 'd|RP|RP',
'LP|e', 'e|f', 'f|RP', 'LP|LP|e', 'LP|e|f', 'e|f|RP', 'f|RP|RP' // 1
];
const nGramsSplits = [0, 11, 18];
await expectResult(result, nGrams, nGramsSplits);
});
it('padded bigrams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [2], 'LP', 'RP', -1, false);
const nGrams = [
'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP',
'LP|e', 'e|f', 'f|RP' // 1
];
const nGramsSplits = [0, 5, 8];
await expectResult(result, nGrams, nGramsSplits);
});
it('padding is at most nGramSize - 1', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [2], 'LP', 'RP', 4, false);
const nGrams = [
'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP',
'LP|e', 'e|f', 'f|RP' // 1
];
const nGramsSplits = [0, 5, 8];
await expectResult(result, nGrams, nGramsSplits);
});
it('padded unigram and bigrams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [1, 2], 'LP', 'RP', -1, false);
const nGrams = [
'a', 'b', 'c', 'd', 'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP',
'e', 'f', 'LP|e', 'e|f', 'f|RP' // 1
];
const nGramsSplits = [0, 9, 14];
await expectResult(result, nGrams, nGramsSplits);
});
it('overlapping padded nGrams', async () => {
// This test validates that n-grams with both left and right padding in a
// single ngram token are created correctly.
// Batch items are:
// 0: "a"
// 1: "b", "c", "d"
// 2: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|', [3], 'LP', 'RP', -1, false);
const nGrams = [
'LP|LP|a', 'LP|a|RP', 'a|RP|RP',
'LP|LP|b', 'LP|b|c', 'b|c|d', 'c|d|RP', 'd|RP|RP',
'LP|LP|e', 'LP|e|f', 'e|f|RP', 'f|RP|RP' // 2
];
const nGramsSplits = [0, 3, 8, 12];
await expectResult(result, nGrams, nGramsSplits);
});
it('overlapping padded multi char nGrams', async () => {
// Batch items are:
// 0: "a"
// 1: "b", "c", "d"
// 2: "e", "f"
const result = tf.string.stringNGrams(['aa', 'bb', 'cc', 'dd', 'ee', 'ff'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|', [3], 'LP', 'RP', -1, false);
const nGrams = [
'LP|LP|aa', 'LP|aa|RP', 'aa|RP|RP',
'LP|LP|bb', 'LP|bb|cc', 'bb|cc|dd', 'cc|dd|RP', 'dd|RP|RP',
'LP|LP|ee', 'LP|ee|ff', 'ee|ff|RP', 'ff|RP|RP' // 2
];
const nGramsSplits = [0, 3, 8, 12];
await expectResult(result, nGrams, nGramsSplits);
});
it('multi overlapping padded nGrams', async () => {
// This test validates that n-grams with more than 1 padding value on each
// side are created correctly.
// Batch items are:
// 0: "a"
const result = tf.string.stringNGrams(['a'], tf.tensor1d([0, 1], 'int32'), '|', [5], 'LP', 'RP', -1, false);
const nGrams = [
'LP|LP|LP|LP|a', 'LP|LP|LP|a|RP', 'LP|LP|a|RP|RP', 'LP|a|RP|RP|RP',
'a|RP|RP|RP|RP'
];
const nGramsSplits = [0, 5];
await expectResult(result, nGrams, nGramsSplits);
});
it('unpadded trigrams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [3], '', '', 0, false);
const nGrams = ['a|b|c', 'b|c|d'];
const nGramsSplits = [0, 2, 2];
await expectResult(result, nGrams, nGramsSplits);
});
it('unpadded trigrams with empty sequence', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 4, 6], 'int32'), '|', [3], '', '', 0, false);
const nGrams = ['a|b|c', 'b|c|d'];
const nGramsSplits = [0, 2, 2, 2];
await expectResult(result, nGrams, nGramsSplits);
});
it('unpadded trigrams with preserve short', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [3], '', '', 0, true);
const nGrams = ['a|b|c', 'b|c|d', 'e|f'];
const nGramsSplits = [0, 2, 3];
await expectResult(result, nGrams, nGramsSplits);
});
it('unpadded trigrams with preserve short and empty sequence', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 4, 6], 'int32'), '|', [3], '', '', 0, true);
const nGrams = ['a|b|c', 'b|c|d', 'e|f'];
const nGramsSplits = [0, 2, 2, 3];
await expectResult(result, nGrams, nGramsSplits);
});
it('unpadded trigrams and quad grams with preserve short', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [4, 3], '', '', 0, true);
const nGrams = ['a|b|c|d', 'a|b|c', 'b|c|d', 'e|f'];
const nGramsSplits = [0, 3, 4];
await expectResult(result, nGrams, nGramsSplits);
});
it('unpadded bigrams and trigrams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [2, 3], '', '', 0, false);
const nGrams = ['a|b', 'b|c', 'c|d', 'a|b|c', 'b|c|d', 'e|f'];
const nGramsSplits = [0, 5, 6];
await expectResult(result, nGrams, nGramsSplits);
});
it('unpadded bigrams and trigrams with preserve short', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [2, 3], '', '', 0, true);
// Note that in this case, because the bigram 'e|f' was already generated,
// the op will not generate a special preserveShort bigram.
const nGrams = ['a|b', 'b|c', 'c|d', 'a|b|c', 'b|c|d', 'e|f'];
const nGramsSplits = [0, 5, 6];
await expectResult(result, nGrams, nGramsSplits);
});
it('unpadded trigrams and bigrams with preserve short', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [3, 2], '', '', 0, true);
// Note that in this case, because the bigram 'e|f' was already generated,
// the op will not generate a special preserveShort bigram.
const nGrams = ['a|b|c', 'b|c|d', 'a|b', 'b|c', 'c|d', 'e|f'];
const nGramsSplits = [0, 5, 6];
await expectResult(result, nGrams, nGramsSplits);
});
it('unpadded bigrams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [2], '', '', 0, false);
const nGrams = ['a|b', 'b|c', 'c|d', 'e|f'];
const nGramsSplits = [0, 3, 4];
await expectResult(result, nGrams, nGramsSplits);
});
it('overlapping unpadded nGrams', async () => {
// Batch items are:
// 0: "a"
// 1: "b", "c", "d"
// 2: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|', [3], '', '', 0, false);
const nGrams = ['b|c|d'];
const nGramsSplits = [0, 0, 1, 1];
await expectResult(result, nGrams, nGramsSplits);
});
it('overlapping unpadded nGrams no output', async () => {
// Batch items are:
// 0: "a"
// 1: "b", "c", "d"
// 2: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|', [5], '', '', 0, false);
const nGrams = [];
const nGramsSplits = [0, 0, 0, 0];
await expectResult(result, nGrams, nGramsSplits);
});
it('singly padded trigrams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [3], 'LP', 'RP', 1, false);
const nGrams = [
'LP|a|b', 'a|b|c', 'b|c|d', 'c|d|RP',
'LP|e|f', 'e|f|RP'
];
const nGramsSplits = [0, 4, 6];
await expectResult(result, nGrams, nGramsSplits);
});
it('singly padded bigrams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [2], 'LP', 'RP', 1, false);
const nGrams = [
'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP',
'LP|e', 'e|f', 'f|RP'
];
const nGramsSplits = [0, 5, 8];
await expectResult(result, nGrams, nGramsSplits);
});
it('singly padded bigrams and 5grams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [2, 5], 'LP', 'RP', 1, false);
const nGrams = [
'LP|a', 'a|b', 'b|c', 'c|d', 'd|RP', 'LP|a|b|c|d', 'a|b|c|d|RP',
'LP|e', 'e|f', 'f|RP'
];
const nGramsSplits = [0, 7, 10];
await expectResult(result, nGrams, nGramsSplits);
});
it('singly padded 5grams with preserve short', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [5], 'LP', 'RP', 1, true);
const nGrams = [
'LP|a|b|c|d', 'a|b|c|d|RP',
'LP|e|f|RP'
];
const nGramsSplits = [0, 2, 3];
await expectResult(result, nGrams, nGramsSplits);
});
it('overlapping singly padded nGrams', async () => {
// Batch items are:
// 0: "a"
// 1: "b", "c", "d"
// 2: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|', [3], 'LP', 'RP', 1, false);
const nGrams = [
'LP|a|RP',
'LP|b|c', 'b|c|d', 'c|d|RP',
'LP|e|f', 'e|f|RP'
];
const nGramsSplits = [0, 1, 4, 6];
await expectResult(result, nGrams, nGramsSplits);
});
it('overlapping singly padded nGrams no output', async () => {
// Batch items are:
// 0: "a"
// 1: "b", "c", "d"
// 2: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 1, 4, 6], 'int32'), '|', [5], 'LP', 'RP', 1, false);
const nGrams = ['LP|b|c|d|RP'];
const nGramsSplits = [0, 0, 1, 1];
await expectResult(result, nGrams, nGramsSplits);
});
it('singly padded unigrams', async () => {
// Batch items are:
// 0: "a", "b", "c", "d"
// 1: "e", "f"
const result = tf.string.stringNGrams(['a', 'b', 'c', 'd', 'e', 'f'], tf.tensor1d([0, 4, 6], 'int32'), '|', [1], 'LP', 'RP', 1, false);
const nGrams = ['a', 'b', 'c', 'd', 'e', 'f'];
const nGramsSplits = [0, 4, 6];
await expectResult(result, nGrams, nGramsSplits);
});
it('empty input', async () => {
const result = tf.string.stringNGrams(tf.tensor1d([], 'string'), tf.tensor1d([], 'int32'), '|', [1], 'LP', 'RP', 3, false);
const nGrams = [];
const nGramsSplits = [];
await expectResult(result, nGrams, nGramsSplits);
});
it('no tokens', async () => {
// Batch items are:
// 0:
// 1: "a"
const result = tf.string.stringNGrams(['a'], tf.tensor1d([0, 0, 1], 'int32'), '|', [3], 'L', 'R', -1, false);
const nGrams = [
'L|L|R', 'L|R|R',
'L|L|a', 'L|a|R', 'a|R|R' // second split
];
const nGramsSplits = [0, 2, 5];
await expectResult(result, nGrams, nGramsSplits);
});
it('no tokens no pad', async () => {
// Batch items are:
// 0:
// 1: "a"
const result = tf.string.stringNGrams(['a'], tf.tensor1d([0, 0, 1], 'int32'), '|', [3], '', '', 0, false);
const nGrams = [];
const nGramsSplits = [0, 0, 0];
await expectResult(result, nGrams, nGramsSplits);
});
it('throw error if first partition index is not 0', async () => {
expect(() => tf.string.stringNGrams(['a'], tf.tensor1d([1, 1, 1], 'int32'), '|', [3], '', '', 0, false))
.toThrowError(/First split value must be 0/);
});
it('throw error if partition indices are decreasing', async () => {
expect(() => tf.string.stringNGrams(['a'], tf.tensor1d([0, 1, 0], 'int32'), '|', [3], '', '', 0, false))
.toThrowError(/must be in \[1, 1\]/);
});
it('throw error if partition index is >= input size', async () => {
expect(() => tf.string.stringNGrams(['a'], tf.tensor1d([0, 2, 1], 'int32'), '|', [3], '', '', 0, false))
.toThrowError(/must be in \[0, 1\]/);
});
it('throw error if last partition index is !== input size', async () => {
expect(() => tf.string.stringNGrams(['a'], tf.tensor1d([0, 0], 'int32'), '|', [3], '', '', 0, false))
.toThrowError(/Last split value must be data size/);
});
});
//# 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