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data.js
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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 '@tensorflow/tfjs';
export const IRIS_CLASSES =
['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'];
export const IRIS_NUM_CLASSES = IRIS_CLASSES.length;
// Iris flowers data. Source:
// https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
const IRIS_DATA = [
[5.1, 3.5, 1.4, 0.2, 0], [4.9, 3.0, 1.4, 0.2, 0], [4.7, 3.2, 1.3, 0.2, 0],
[4.6, 3.1, 1.5, 0.2, 0], [5.0, 3.6, 1.4, 0.2, 0], [5.4, 3.9, 1.7, 0.4, 0],
[4.6, 3.4, 1.4, 0.3, 0], [5.0, 3.4, 1.5, 0.2, 0], [4.4, 2.9, 1.4, 0.2, 0],
[4.9, 3.1, 1.5, 0.1, 0], [5.4, 3.7, 1.5, 0.2, 0], [4.8, 3.4, 1.6, 0.2, 0],
[4.8, 3.0, 1.4, 0.1, 0], [4.3, 3.0, 1.1, 0.1, 0], [5.8, 4.0, 1.2, 0.2, 0],
[5.7, 4.4, 1.5, 0.4, 0], [5.4, 3.9, 1.3, 0.4, 0], [5.1, 3.5, 1.4, 0.3, 0],
[5.7, 3.8, 1.7, 0.3, 0], [5.1, 3.8, 1.5, 0.3, 0], [5.4, 3.4, 1.7, 0.2, 0],
[5.1, 3.7, 1.5, 0.4, 0], [4.6, 3.6, 1.0, 0.2, 0], [5.1, 3.3, 1.7, 0.5, 0],
[4.8, 3.4, 1.9, 0.2, 0], [5.0, 3.0, 1.6, 0.2, 0], [5.0, 3.4, 1.6, 0.4, 0],
[5.2, 3.5, 1.5, 0.2, 0], [5.2, 3.4, 1.4, 0.2, 0], [4.7, 3.2, 1.6, 0.2, 0],
[4.8, 3.1, 1.6, 0.2, 0], [5.4, 3.4, 1.5, 0.4, 0], [5.2, 4.1, 1.5, 0.1, 0],
[5.5, 4.2, 1.4, 0.2, 0], [4.9, 3.1, 1.5, 0.1, 0], [5.0, 3.2, 1.2, 0.2, 0],
[5.5, 3.5, 1.3, 0.2, 0], [4.9, 3.1, 1.5, 0.1, 0], [4.4, 3.0, 1.3, 0.2, 0],
[5.1, 3.4, 1.5, 0.2, 0], [5.0, 3.5, 1.3, 0.3, 0], [4.5, 2.3, 1.3, 0.3, 0],
[4.4, 3.2, 1.3, 0.2, 0], [5.0, 3.5, 1.6, 0.6, 0], [5.1, 3.8, 1.9, 0.4, 0],
[4.8, 3.0, 1.4, 0.3, 0], [5.1, 3.8, 1.6, 0.2, 0], [4.6, 3.2, 1.4, 0.2, 0],
[5.3, 3.7, 1.5, 0.2, 0], [5.0, 3.3, 1.4, 0.2, 0], [7.0, 3.2, 4.7, 1.4, 1],
[6.4, 3.2, 4.5, 1.5, 1], [6.9, 3.1, 4.9, 1.5, 1], [5.5, 2.3, 4.0, 1.3, 1],
[6.5, 2.8, 4.6, 1.5, 1], [5.7, 2.8, 4.5, 1.3, 1], [6.3, 3.3, 4.7, 1.6, 1],
[4.9, 2.4, 3.3, 1.0, 1], [6.6, 2.9, 4.6, 1.3, 1], [5.2, 2.7, 3.9, 1.4, 1],
[5.0, 2.0, 3.5, 1.0, 1], [5.9, 3.0, 4.2, 1.5, 1], [6.0, 2.2, 4.0, 1.0, 1],
[6.1, 2.9, 4.7, 1.4, 1], [5.6, 2.9, 3.6, 1.3, 1], [6.7, 3.1, 4.4, 1.4, 1],
[5.6, 3.0, 4.5, 1.5, 1], [5.8, 2.7, 4.1, 1.0, 1], [6.2, 2.2, 4.5, 1.5, 1],
[5.6, 2.5, 3.9, 1.1, 1], [5.9, 3.2, 4.8, 1.8, 1], [6.1, 2.8, 4.0, 1.3, 1],
[6.3, 2.5, 4.9, 1.5, 1], [6.1, 2.8, 4.7, 1.2, 1], [6.4, 2.9, 4.3, 1.3, 1],
[6.6, 3.0, 4.4, 1.4, 1], [6.8, 2.8, 4.8, 1.4, 1], [6.7, 3.0, 5.0, 1.7, 1],
[6.0, 2.9, 4.5, 1.5, 1], [5.7, 2.6, 3.5, 1.0, 1], [5.5, 2.4, 3.8, 1.1, 1],
[5.5, 2.4, 3.7, 1.0, 1], [5.8, 2.7, 3.9, 1.2, 1], [6.0, 2.7, 5.1, 1.6, 1],
[5.4, 3.0, 4.5, 1.5, 1], [6.0, 3.4, 4.5, 1.6, 1], [6.7, 3.1, 4.7, 1.5, 1],
[6.3, 2.3, 4.4, 1.3, 1], [5.6, 3.0, 4.1, 1.3, 1], [5.5, 2.5, 4.0, 1.3, 1],
[5.5, 2.6, 4.4, 1.2, 1], [6.1, 3.0, 4.6, 1.4, 1], [5.8, 2.6, 4.0, 1.2, 1],
[5.0, 2.3, 3.3, 1.0, 1], [5.6, 2.7, 4.2, 1.3, 1], [5.7, 3.0, 4.2, 1.2, 1],
[5.7, 2.9, 4.2, 1.3, 1], [6.2, 2.9, 4.3, 1.3, 1], [5.1, 2.5, 3.0, 1.1, 1],
[5.7, 2.8, 4.1, 1.3, 1], [6.3, 3.3, 6.0, 2.5, 2], [5.8, 2.7, 5.1, 1.9, 2],
[7.1, 3.0, 5.9, 2.1, 2], [6.3, 2.9, 5.6, 1.8, 2], [6.5, 3.0, 5.8, 2.2, 2],
[7.6, 3.0, 6.6, 2.1, 2], [4.9, 2.5, 4.5, 1.7, 2], [7.3, 2.9, 6.3, 1.8, 2],
[6.7, 2.5, 5.8, 1.8, 2], [7.2, 3.6, 6.1, 2.5, 2], [6.5, 3.2, 5.1, 2.0, 2],
[6.4, 2.7, 5.3, 1.9, 2], [6.8, 3.0, 5.5, 2.1, 2], [5.7, 2.5, 5.0, 2.0, 2],
[5.8, 2.8, 5.1, 2.4, 2], [6.4, 3.2, 5.3, 2.3, 2], [6.5, 3.0, 5.5, 1.8, 2],
[7.7, 3.8, 6.7, 2.2, 2], [7.7, 2.6, 6.9, 2.3, 2], [6.0, 2.2, 5.0, 1.5, 2],
[6.9, 3.2, 5.7, 2.3, 2], [5.6, 2.8, 4.9, 2.0, 2], [7.7, 2.8, 6.7, 2.0, 2],
[6.3, 2.7, 4.9, 1.8, 2], [6.7, 3.3, 5.7, 2.1, 2], [7.2, 3.2, 6.0, 1.8, 2],
[6.2, 2.8, 4.8, 1.8, 2], [6.1, 3.0, 4.9, 1.8, 2], [6.4, 2.8, 5.6, 2.1, 2],
[7.2, 3.0, 5.8, 1.6, 2], [7.4, 2.8, 6.1, 1.9, 2], [7.9, 3.8, 6.4, 2.0, 2],
[6.4, 2.8, 5.6, 2.2, 2], [6.3, 2.8, 5.1, 1.5, 2], [6.1, 2.6, 5.6, 1.4, 2],
[7.7, 3.0, 6.1, 2.3, 2], [6.3, 3.4, 5.6, 2.4, 2], [6.4, 3.1, 5.5, 1.8, 2],
[6.0, 3.0, 4.8, 1.8, 2], [6.9, 3.1, 5.4, 2.1, 2], [6.7, 3.1, 5.6, 2.4, 2],
[6.9, 3.1, 5.1, 2.3, 2], [5.8, 2.7, 5.1, 1.9, 2], [6.8, 3.2, 5.9, 2.3, 2],
[6.7, 3.3, 5.7, 2.5, 2], [6.7, 3.0, 5.2, 2.3, 2], [6.3, 2.5, 5.0, 1.9, 2],
[6.5, 3.0, 5.2, 2.0, 2], [6.2, 3.4, 5.4, 2.3, 2], [5.9, 3.0, 5.1, 1.8, 2],
];
/**
* Convert Iris data arrays to `tf.Tensor`s.
*
* @param data The Iris input feature data, an `Array` of `Array`s, each element
* of which is assumed to be a length-4 `Array` (for petal length, petal
* width, sepal length, sepal width).
* @param targets An `Array` of numbers, with values from the set {0, 1, 2}:
* representing the true category of the Iris flower. Assumed to have the same
* array length as `data`.
* @param testSplit Fraction of the data at the end to split as test data: a
* number between 0 and 1.
* @return A length-4 `Array`, with
* - training data as `tf.Tensor` of shape [numTrainExapmles, 4].
* - training one-hot labels as a `tf.Tensor` of shape [numTrainExamples, 3]
* - test data as `tf.Tensor` of shape [numTestExamples, 4].
* - test one-hot labels as a `tf.Tensor` of shape [numTestExamples, 3]
*/
function convertToTensors(data, targets, testSplit) {
const numExamples = data.length;
if (numExamples !== targets.length) {
throw new Error('data and split have different numbers of examples');
}
// Randomly shuffle `data` and `targets`.
const indices = [];
for (let i = 0; i < numExamples; ++i) {
indices.push(i);
}
tf.util.shuffle(indices);
const shuffledData = [];
const shuffledTargets = [];
for (let i = 0; i < numExamples; ++i) {
shuffledData.push(data[indices[i]]);
shuffledTargets.push(targets[indices[i]]);
}
// Split the data into a training set and a tet set, based on `testSplit`.
const numTestExamples = Math.round(numExamples * testSplit);
const numTrainExamples = numExamples - numTestExamples;
const xDims = shuffledData[0].length;
// Create a 2D `tf.Tensor` to hold the feature data.
const xs = tf.tensor2d(shuffledData, [numExamples, xDims]);
// Create a 1D `tf.Tensor` to hold the labels, and convert the number label
// from the set {0, 1, 2} into one-hot encoding (.e.g., 0 --> [1, 0, 0]).
const ys = tf.oneHot(tf.tensor1d(shuffledTargets).toInt(), IRIS_NUM_CLASSES);
// Split the data into training and test sets, using `slice`.
const xTrain = xs.slice([0, 0], [numTrainExamples, xDims]);
const xTest = xs.slice([numTrainExamples, 0], [numTestExamples, xDims]);
const yTrain = ys.slice([0, 0], [numTrainExamples, IRIS_NUM_CLASSES]);
const yTest = ys.slice([0, 0], [numTestExamples, IRIS_NUM_CLASSES]);
return [xTrain, yTrain, xTest, yTest];
}
/**
* Obtains Iris data, split into training and test sets.
*
* @param testSplit Fraction of the data at the end to split as test data: a
* number between 0 and 1.
*
* @param return A length-4 `Array`, with
* - training data as an `Array` of length-4 `Array` of numbers.
* - training labels as an `Array` of numbers, with the same length as the
* return training data above. Each element of the `Array` is from the set
* {0, 1, 2}.
* - test data as an `Array` of length-4 `Array` of numbers.
* - test labels as an `Array` of numbers, with the same length as the
* return test data above. Each element of the `Array` is from the set
* {0, 1, 2}.
*/
export function getIrisData(testSplit) {
return tf.tidy(() => {
const dataByClass = [];
const targetsByClass = [];
for (let i = 0; i < IRIS_CLASSES.length; ++i) {
dataByClass.push([]);
targetsByClass.push([]);
}
for (const example of IRIS_DATA) {
const target = example[example.length - 1];
const data = example.slice(0, example.length - 1);
dataByClass[target].push(data);
targetsByClass[target].push(target);
}
const xTrains = [];
const yTrains = [];
const xTests = [];
const yTests = [];
for (let i = 0; i < IRIS_CLASSES.length; ++i) {
const [xTrain, yTrain, xTest, yTest] =
convertToTensors(dataByClass[i], targetsByClass[i], testSplit);
xTrains.push(xTrain);
yTrains.push(yTrain);
xTests.push(xTest);
yTests.push(yTest);
}
const concatAxis = 0;
return [
tf.concat(xTrains, concatAxis), tf.concat(yTrains, concatAxis),
tf.concat(xTests, concatAxis), tf.concat(yTests, concatAxis)
];
});
}