forked from sheetjs/docs.sheetjs.com
332 lines
9.0 KiB
Plaintext
332 lines
9.0 KiB
Plaintext
---
|
|
title: Typed Arrays and ML
|
|
pagination_prev: demos/extensions/index
|
|
pagination_next: demos/engines/index
|
|
sidebar_custom_props:
|
|
summary: Parse and serialize Uint8Array data from TensorFlow
|
|
---
|
|
|
|
<head>
|
|
<script src="https://docs.sheetjs.com/tfjs/tf.min.js"></script>
|
|
</head>
|
|
|
|
Machine learning libraries in JS typically use "Typed Arrays". Typed Arrays are
|
|
not JS Arrays! With some data wrangling, translating between SheetJS worksheets
|
|
and typed arrays is straightforward.
|
|
|
|
This demo covers conversions between worksheets and Typed Arrays for use with
|
|
TensorFlow.js and other ML libraries.
|
|
|
|
:::note
|
|
|
|
Live code blocks in this page load the standalone build from version `3.18.0`.
|
|
|
|
For use in web frameworks, the `@tensorflow/tfjs` module should be used.
|
|
|
|
For use in NodeJS, the native bindings module is `@tensorflow/tfjs-node`.
|
|
|
|
:::
|
|
|
|
## CSV Data Interchange
|
|
|
|
`tf.data.csv` generates a Dataset from CSV data. The function expects a URL.
|
|
Fortunately blob URLs are supported, making data import straightforward:
|
|
|
|
```js
|
|
function worksheet_to_csv_url(worksheet) {
|
|
/* generate CSV */
|
|
const csv = XLSX.utils.sheet_to_csv(worksheet);
|
|
|
|
/* CSV -> Uint8Array -> Blob */
|
|
const u8 = new TextEncoder().encode(csv);
|
|
const blob = new Blob([u8], { type: "text/csv" });
|
|
|
|
/* generate a blob URL */
|
|
return URL.createObjectURL(blob);
|
|
}
|
|
```
|
|
|
|
[This demo mirrors `TFjs` docs](https://js.tensorflow.org/api/latest/#data.csv),
|
|
fetching [an XLSX export of the example dataset](https://sheetjs.com/data/bht.xlsx).
|
|
|
|
<details><summary><b>TF CSV Demo using XLSX files</b> (click to show)</summary>
|
|
|
|
:::caution
|
|
|
|
If the live demo shows a message
|
|
|
|
```
|
|
ReferenceError: tf is not defined
|
|
```
|
|
|
|
please refresh the page. This is a known bug in the documentation generator.
|
|
|
|
:::
|
|
|
|
```jsx live
|
|
function SheetJSToTFJSCSV() {
|
|
const [output, setOutput] = React.useState("");
|
|
const doit = React.useCallback(async () => {
|
|
/* fetch file */
|
|
const f = await fetch("https://sheetjs.com/data/bht.xlsx");
|
|
const ab = await f.arrayBuffer();
|
|
/* parse file and get first worksheet */
|
|
const wb = XLSX.read(ab);
|
|
const ws = wb.Sheets[wb.SheetNames[0]];
|
|
|
|
/* generate CSV */
|
|
const csv = XLSX.utils.sheet_to_csv(ws);
|
|
|
|
/* generate blob URL */
|
|
const u8 = new TextEncoder().encode(csv);
|
|
const blob = new Blob([u8], {type: "text/csv"});
|
|
const url = URL.createObjectURL(blob);
|
|
|
|
/* feed to tfjs */
|
|
const dataset = tf.data.csv(url, {columnConfigs:{"medv":{isLabel:true}}});
|
|
|
|
/* this part mirrors the tf.data.csv docs */
|
|
const flat = dataset.map(({xs,ys}) => ({xs: Object.values(xs), ys: Object.values(ys)})).batch(10);
|
|
const model = tf.sequential();
|
|
model.add(tf.layers.dense({inputShape: [(await dataset.columnNames()).length - 1], units: 1}));
|
|
model.compile({ optimizer: tf.train.sgd(0.000001), loss: 'meanSquaredError' });
|
|
let base = output;
|
|
await model.fitDataset(flat, { epochs: 10, callbacks: { onEpochEnd: async (epoch, logs) => {
|
|
setOutput(base += "\n" + epoch + ":" + logs.loss);
|
|
}}});
|
|
model.summary();
|
|
});
|
|
return ( <pre><b><a href="https://js.tensorflow.org/api/latest/#data.csv">Original CSV demo</a></b><br/><br/>
|
|
<button onClick={doit}>Click to run</button>
|
|
{output}
|
|
</pre> );
|
|
}
|
|
```
|
|
|
|
</details>
|
|
|
|
In the other direction, `XLSX.read` will readily parse CSV exports.
|
|
|
|
## JS Array Interchange
|
|
|
|
[The official Linear Regression tutorial](https://www.tensorflow.org/js/tutorials/training/linear_regression)
|
|
loads data from a JSON file:
|
|
|
|
```json
|
|
[
|
|
{
|
|
"Name": "chevrolet chevelle malibu",
|
|
"Miles_per_Gallon": 18,
|
|
"Cylinders": 8,
|
|
"Displacement": 307,
|
|
"Horsepower": 130,
|
|
"Weight_in_lbs": 3504,
|
|
"Acceleration": 12,
|
|
"Year": "1970-01-01",
|
|
"Origin": "USA"
|
|
},
|
|
{
|
|
"Name": "buick skylark 320",
|
|
"Miles_per_Gallon": 15,
|
|
"Cylinders": 8,
|
|
"Displacement": 350,
|
|
"Horsepower": 165,
|
|
"Weight_in_lbs": 3693,
|
|
"Acceleration": 11.5,
|
|
"Year": "1970-01-01",
|
|
"Origin": "USA"
|
|
},
|
|
// ...
|
|
]
|
|
```
|
|
|
|
In real use cases, data is stored in [spreadsheets](https://sheetjs.com/data/cd.xls)
|
|
|
|
|
|
|
|
Following the tutorial, the data fetching method is easily adapted. Differences
|
|
from the official example are highlighted below:
|
|
|
|
```js
|
|
/**
|
|
* Get the car data reduced to just the variables we are interested
|
|
* and cleaned of missing data.
|
|
*/
|
|
async function getData() {
|
|
// highlight-start
|
|
/* fetch file */
|
|
const carsDataResponse = await fetch('https://sheetjs.com/data/cd.xls');
|
|
/* get file data (ArrayBuffer) */
|
|
const carsDataAB = await carsDataResponse.arrayBuffer();
|
|
/* parse */
|
|
const carsDataWB = XLSX.read(carsDataAB);
|
|
/* get first worksheet */
|
|
const carsDataWS = carsDataWB.Sheets[carsDataWB.SheetNames[0]];
|
|
/* generate array of JS objects */
|
|
const carsData = XLSX.utils.sheet_to_json(carsDataWS);
|
|
// highlight-end
|
|
const cleaned = carsData.map(car => ({
|
|
mpg: car.Miles_per_Gallon,
|
|
horsepower: car.Horsepower,
|
|
}))
|
|
.filter(car => (car.mpg != null && car.horsepower != null));
|
|
|
|
return cleaned;
|
|
}
|
|
```
|
|
|
|
## Low-Level Operations
|
|
|
|
:::caution
|
|
|
|
While it is more efficient to use low-level operations, JS or CSV interchange
|
|
is strongly recommended when possible.
|
|
|
|
:::
|
|
|
|
### Data Transposition
|
|
|
|
A typical dataset in a spreadsheet will start with one header row and represent
|
|
each data record in its own row. For example, the Iris dataset might look like
|
|
|
|
|
|
|
|
`XLSX.utils.sheet_to_json` will translate this into an array of row objects:
|
|
|
|
```js
|
|
var aoo = [
|
|
{"sepal length": 5.1, "sepal width": 3.5, ...},
|
|
{"sepal length": 4.9, "sepal width": 3, ...},
|
|
...
|
|
];
|
|
```
|
|
|
|
TF.js and other libraries tend to operate on individual columns, equivalent to:
|
|
|
|
```js
|
|
var sepal_lengths = [5.1, 4.9, ...];
|
|
var sepal_widths = [3.5, 3, ...];
|
|
```
|
|
|
|
When a `tensor2d` can be exported, it will look different from the spreadsheet:
|
|
|
|
```js
|
|
var data_set_2d = [
|
|
[5.1, 4.9, ...],
|
|
[3.5, 3, ...],
|
|
...
|
|
]
|
|
```
|
|
|
|
This is the transpose of how people use spreadsheets!
|
|
|
|
#### Typed Arrays and Columns
|
|
|
|
A single typed array can be converted to a pure JS array with `Array.from`:
|
|
|
|
```js
|
|
var column = Array.from(dataset_typedarray);
|
|
```
|
|
|
|
Similarly, `Float32Array.from` generates a typed array from a normal array:
|
|
|
|
```js
|
|
var dataset = Float32Array.from(column);
|
|
```
|
|
|
|
### Exporting Datasets to a Worksheet
|
|
|
|
`XLSX.utils.aoa_to_sheet` can generate a worksheet from an array of arrays.
|
|
ML libraries typically provide APIs to pull an array of arrays, but it will
|
|
be transposed. To export multiple data sets, manually "transpose" the data:
|
|
|
|
```js
|
|
/* assuming data is an array of typed arrays */
|
|
var aoa = [];
|
|
for(var i = 0; i < data.length; ++i) {
|
|
for(var j = 0; j < data[i].length; ++j) {
|
|
if(!aoa[j]) aoa[j] = [];
|
|
aoa[j][i] = data[i][j];
|
|
}
|
|
}
|
|
/* aoa can be directly converted to a worksheet object */
|
|
var ws = XLSX.utils.aoa_to_sheet(aoa);
|
|
```
|
|
|
|
### Importing Data from a Spreadsheet
|
|
|
|
`sheet_to_json` with the option `header:1` will generate a row-major array of
|
|
arrays that can be transposed. However, it is more efficient to walk the sheet
|
|
manually:
|
|
|
|
```js
|
|
/* find worksheet range */
|
|
var range = XLSX.utils.decode_range(ws['!ref']);
|
|
var out = []
|
|
/* walk the columns */
|
|
for(var C = range.s.c; C <= range.e.c; ++C) {
|
|
/* create the typed array */
|
|
var ta = new Float32Array(range.e.r - range.s.r + 1);
|
|
/* walk the rows */
|
|
for(var R = range.s.r; R <= range.e.r; ++R) {
|
|
/* find the cell, skip it if the cell isn't numeric or boolean */
|
|
var cell = ws[XLSX.utils.encode_cell({r:R, c:C})];
|
|
if(!cell || cell.t != 'n' && cell.t != 'b') continue;
|
|
/* assign to the typed array */
|
|
ta[R - range.s.r] = cell.v;
|
|
}
|
|
out.push(ta);
|
|
}
|
|
```
|
|
|
|
If the data set has a header row, the loop can be adjusted to skip those rows.
|
|
|
|
### TF.js Tensors
|
|
|
|
A single `Array#map` can pull individual named fields from the result, which
|
|
can be used to construct TensorFlow.js tensor objects:
|
|
|
|
```js
|
|
const aoo = XLSX.utils.sheet_to_json(worksheet);
|
|
const lengths = aoo.map(row => row["sepal length"]);
|
|
const tensor = tf.tensor1d(lengths);
|
|
```
|
|
|
|
`tf.Tensor` objects can be directly transposed using `transpose`:
|
|
|
|
```js
|
|
var aoo = XLSX.utils.sheet_to_json(worksheet);
|
|
// "x" and "y" are the fields we want to pull from the data
|
|
var data = aoo.map(row => ([row["x"], row["y"]]));
|
|
|
|
// create a tensor representing two column datasets
|
|
var tensor = tf.tensor2d(data).transpose();
|
|
|
|
// individual columns can be accessed
|
|
var col1 = tensor.slice([0,0], [1,tensor.shape[1]]).flatten();
|
|
var col2 = tensor.slice([1,0], [1,tensor.shape[1]]).flatten();
|
|
```
|
|
|
|
For exporting, `stack` can be used to collapse the columns into a linear array:
|
|
|
|
```js
|
|
/* pull data into a Float32Array */
|
|
var result = tf.stack([col1, col2]).transpose();
|
|
var shape = tensor.shape;
|
|
var f32 = tensor.dataSync();
|
|
|
|
/* construct an array of arrays of the data in spreadsheet order */
|
|
var aoa = [];
|
|
for(var j = 0; j < shape[0]; ++j) {
|
|
aoa[j] = [];
|
|
for(var i = 0; i < shape[1]; ++i) aoa[j][i] = f32[j * shape[1] + i];
|
|
}
|
|
|
|
/* add headers to the top */
|
|
aoa.unshift(["x", "y"]);
|
|
|
|
/* generate worksheet */
|
|
var worksheet = XLSX.utils.aoa_to_sheet(aoa);
|
|
```
|
|
|